diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 8df0619..995a422 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -16,8 +16,8 @@ env: JULIA_PKG_PRECOMPILE_AUTO: "0" jobs: - base: - name: base Julia ${{ matrix.version }} / ${{ matrix.dependency-set }} + integration: + name: Julia ${{ matrix.version }} / ${{ matrix.dependency-set }} runs-on: ubuntu-latest strategy: fail-fast: false @@ -37,9 +37,8 @@ jobs: with: version: ${{ matrix.version }} - uses: julia-actions/cache@v2 - - name: Run base tests and optional-extension fallback checks + - name: Run integration tests env: - RISKLABAI_TEST_DEEP_BSDE: "0" RISKLABAI_DEPENDENCY_SET: ${{ matrix.dependency-set }} run: | julia --startup-file=no -e ' @@ -48,91 +47,42 @@ jobs: Pkg.develop(path = pwd()) if ENV["RISKLABAI_DEPENDENCY_SET"] == "minimum" Pkg.add([ + Pkg.PackageSpec(name = "Clarabel", version = v"0.11.1"), Pkg.PackageSpec(name = "Clustering", version = v"0.15.8"), Pkg.PackageSpec(name = "Combinatorics", version = v"1.1.0"), + Pkg.PackageSpec(name = "CovarianceEstimation", version = v"0.2.13"), Pkg.PackageSpec(name = "DataFrames", version = v"1.8.2"), Pkg.PackageSpec(name = "DecisionTree", version = v"0.12.4"), Pkg.PackageSpec(name = "Distributions", version = v"0.25.128"), Pkg.PackageSpec(name = "HypothesisTests", version = v"0.11.8"), - Pkg.PackageSpec(name = "QuadGK", version = v"2.11.3"), - Pkg.PackageSpec(name = "SpecialFunctions", version = v"2.8.0"), - ]) - else - Pkg.add([ - Pkg.PackageSpec(name = "Clustering"), - Pkg.PackageSpec(name = "Combinatorics"), - Pkg.PackageSpec(name = "DataFrames"), - Pkg.PackageSpec(name = "DecisionTree"), - Pkg.PackageSpec(name = "Distributions"), - Pkg.PackageSpec(name = "HypothesisTests"), - Pkg.PackageSpec(name = "QuadGK"), - Pkg.PackageSpec(name = "SpecialFunctions"), - ]) - end - using RiskLabAI - include(joinpath(pwd(), "test", "runtests.jl")) - ' - - deep-bsde: - name: Deep-BSDE Julia ${{ matrix.version }} / ${{ matrix.dependency-set }} - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - include: - - version: "1.10.12" - dependency-set: minimum - - version: "1.10.12" - dependency-set: current - - version: "1.12.7" - dependency-set: minimum - - version: "1.12.7" - dependency-set: current - steps: - - uses: actions/checkout@v4 - - uses: julia-actions/setup-julia@v2 - with: - version: ${{ matrix.version }} - - uses: julia-actions/cache@v2 - - name: Run tests with the weak dependencies loaded - env: - RISKLABAI_TEST_DEEP_BSDE: "1" - RISKLABAI_DEPENDENCY_SET: ${{ matrix.dependency-set }} - run: | - julia --startup-file=no -e ' - using Pkg - Pkg.activate(; temp = true) - Pkg.develop(path = pwd()) - if ENV["RISKLABAI_DEPENDENCY_SET"] == "minimum" - Pkg.add([ - Pkg.PackageSpec(name = "Clustering", version = v"0.15.8"), - Pkg.PackageSpec(name = "Combinatorics", version = v"1.1.0"), - Pkg.PackageSpec(name = "DataFrames", version = v"1.8.2"), - Pkg.PackageSpec(name = "DecisionTree", version = v"0.12.4"), - Pkg.PackageSpec(name = "Distributions", version = v"0.25.128"), - Pkg.PackageSpec(name = "HypothesisTests", version = v"0.11.8"), - Pkg.PackageSpec(name = "QuadGK", version = v"2.11.3"), - Pkg.PackageSpec(name = "SpecialFunctions", version = v"2.8.0"), + Pkg.PackageSpec(name = "JuMP", version = v"1.32.1"), Pkg.PackageSpec(name = "Lux", version = v"1.31.4"), + Pkg.PackageSpec(name = "Optim", version = v"2.3.2"), Pkg.PackageSpec(name = "Optimisers", version = v"0.4.7"), + Pkg.PackageSpec(name = "QuadGK", version = v"2.11.3"), + Pkg.PackageSpec(name = "SpecialFunctions", version = v"2.8.0"), Pkg.PackageSpec(name = "Zygote", version = v"0.7.11"), ]) else Pkg.add([ + Pkg.PackageSpec(name = "Clarabel"), Pkg.PackageSpec(name = "Clustering"), Pkg.PackageSpec(name = "Combinatorics"), + Pkg.PackageSpec(name = "CovarianceEstimation"), Pkg.PackageSpec(name = "DataFrames"), Pkg.PackageSpec(name = "DecisionTree"), Pkg.PackageSpec(name = "Distributions"), Pkg.PackageSpec(name = "HypothesisTests"), - Pkg.PackageSpec(name = "QuadGK"), - Pkg.PackageSpec(name = "SpecialFunctions"), + Pkg.PackageSpec(name = "JuMP"), Pkg.PackageSpec(name = "Lux"), + Pkg.PackageSpec(name = "Optim"), Pkg.PackageSpec(name = "Optimisers"), + Pkg.PackageSpec(name = "QuadGK"), + Pkg.PackageSpec(name = "SpecialFunctions"), Pkg.PackageSpec(name = "Zygote"), ]) end - using Lux, Optimisers, Zygote, RiskLabAI + using RiskLabAI include(joinpath(pwd(), "test", "runtests.jl")) ' diff --git a/PARITY.md b/PARITY.md index cd56c60..ddd7946 100644 --- a/PARITY.md +++ b/PARITY.md @@ -61,16 +61,16 @@ Python's `plot_weights` (matplotlib) is omitted (no plotting dependency). | Concept | Python | Julia | Notes | |---|---|---|---| -| Label concurrency | `expand_label_for_meta_labeling` | `Data.expand_label_for_meta_labeling` | events as `(event_start, event_end)` vectors; returns `(index, concurrency)` | +| Label concurrency | `expand_label_for_meta_labeling` | `Data.expand_label_for_meta_labeling` | counts all overlapping events, including those outside the molecule; inclusive endpoints; returns `(index, concurrency)` | | Average uniqueness | `calculate_average_uniqueness` | `Data.calculate_average_uniqueness` | T×N indicator matrix → per-event uniqueness; exact parity | +| Sequential bootstrap | `sequential_bootstrap` | `Data.sequential_bootstrap` | samples with replacement using average added uniqueness; one-based Julia event positions; accepts a seed or `AbstractRNG` | | Return-attribution weights | `sample_weight_absolute_return_meta_labeling` | `Data.sample_weight_absolute_return_meta_labeling` | `Σ|r_t|/c_t`, normalised to N; NaN first-return skipped (matches pandas `.sum`) | | Time decay | `calculate_time_decay` | `Data.calculate_time_decay` | linear decay; oldest→`clf_last_weight ∈ [0,1]`; exact parity | **Deliberate divergence:** Python carries event start times in the pandas Series *index*; the Julia port passes `event_start`/`event_end` (and the price index) as parallel sorted vectors. `calculate_time_decay` assumes the weights -are already in chronological order (Python sorts by index). All four reference -outputs are asserted against the Python implementation. +are already in chronological order (Python sorts by index). Shared Python 3.2.0 fixtures cover 44 concurrency/return-weight cases, including outside-molecule overlaps and zero-duration events. Four sequential-bootstrap fixtures supply identical uniforms and convert Python's zero-based positions to Julia's one-based positions; equal seeds alone do not establish cross-language equivalence. The fixture generator checks the Python results against independent event enumeration and the added-uniqueness formula. Domain checks cover invalid indicators, inactive events, invalid draw counts/seeds and zero-draw RNG preservation. ## Data.Denoise — PR 9 (wired) @@ -81,13 +81,27 @@ outputs are asserted against the Python implementation. | cov ↔ corr | `cov_to_corr` / `corr_to_cov` | `Data.cov_to_corr` / `Data.corr_to_cov` | exact parity | | Denoised correlation | `denoised_corr` | `Data.denoised_corr` | exact parity (sign-invariant reconstruction) | | Optimal portfolio | `optimal_portfolio` | `Data.optimal_portfolio` | GMV / mean-variance; exact parity | -| MP fit / denoise cov | `find_max_eval`, `denoise_cov`, `optimal_portfolio_denoised` | `Data.{find_max_eval,denoise_cov,optimal_portfolio_denoised}` | **behavioural** parity: Gaussian KDE + golden-section minimiser replace scikit-learn KDE + SciPy `minimize` | - -**Deliberate divergence:** the KDE-fit step uses a hand-written Gaussian KDE and -a golden-section search instead of scikit-learn/SciPy, so `find_max_eval` / -`denoise_cov` are validated behaviourally (symmetric output, variances -preserved), not bit-for-bit. No new dependencies were added (the legacy file's -`KernelDensity`/`Optim`/`MultivariateStats`/`BlockArrays` usage is dropped). +| MP fit / denoise cov | `find_max_eval`, `denoise_cov`, `optimal_portfolio_denoised` | `Data.{find_max_eval,denoise_cov,optimal_portfolio_denoised}` | Bounded shared-fixture parity for a native Gaussian KDE and scalar local fit; numerical convergence/fallback differences remain possible | + +The native local fit uses the public initial variance, bounds, More–Thuente +interpolation and stopping settings. The public `find_max_eval` path supplies +the analytic derivative of the same MP-density loss, including its moving grid. +Forward finite differences remain the internal fallback when no derivative is +supplied. Extrapolated trial intervals are retained before applying the absolute +step bound. The 243 original fit fixtures and three additional line-search +regressions pass; twenty-four covariance/portfolio fixtures retain independent +spectral checks. + +Near a stationary point, floating-point changes can alter convergence status. +The unchanged variance-one fallback can then change the signal count and the +resulting portfolio. Chapter 10 exposed such a case even when the two latent +optimizer variances agreed within `4e-12`. Its technical hold is closed following +the approved corrected normal-import and final-location validation in the tested +Windows environment. The original failure remains historical evidence; all +documented operating-system, numerical-backend, runtime, cross-platform, +historical-baseline and release limitations remain unchanged. Covariance +reconstruction conditional on a recorded cutoff is distinct from unconditional +optimizer parity; neither bitwise identity nor a global minimum is promised. ## Data.Labeling — PR 10 (wired) @@ -336,15 +350,16 @@ Clustered Optimisation (`nco`) can follow. ## Validation — cross-validators — PR (wired) -Port of the `backtest.validation` sub-package: the train/test index-generating -logic. Estimator-driven `backtest_predictions` is deferred to the -cross-validation-scoring slice (needs the ML backend). +Port of the `backtest.validation` sub-package: train/test indices, chronological +paths and estimator-driven `backtest_predictions` through explicit caller +callbacks. Supplied-feature adaptive boundaries are included. | Concept | Python | Julia | Notes | |---|---|---|---| | Standard K-Fold | `KFold` | `Validation.KFoldCV` | contiguous folds, optional shuffle; `cv_split(cv, n)` | | Purged K-Fold + embargo | `PurgedKFold` | `Validation.PurgedKFoldCV` | **exact** purge + embargo index logic (verified vs pandas reference); `cv_split(cv)` | | Combinatorial Purged CV | `CombinatorialPurged` | `Validation.CombinatorialPurgedCV` | **exact**; `C(n,k)` splits, `backtest_paths` assembles the `k·C/n` paths | +| Adaptive combinatorial CV | `AdaptiveCombinatorialPurged` | `Validation.AdaptiveCombinatorialPurgedCV` | supplied feature, linear quantiles and public fine-split boundary adjustments | | Walk-Forward | `WalkForward` | `Validation.WalkForwardCV` | **exact**; growing train window, `gap` / `max_train_size` | | Number of splits | `get_n_splits` | `Validation.get_n_splits` | exact | @@ -379,19 +394,19 @@ generators return a plain price vector (Python: a business-day-indexed Series). ## Features.feature_importance — orthogonal & weighted-τ — PR (wired) -Backend-independent pieces of the `features.feature_importance` sub-package. The -classifier-driven importances (MDI/MDA/SFI and clustered variants) follow with -the `DecisionTree.jl` backend. +Backend-independent pieces of the `features.feature_importance` sub-package. +Caller-estimator MDI/MDA/SFI and clustered variants complement the existing +DecisionTree.jl convenience methods. | Concept | Python | Julia | Notes | |---|---|---|---| -| PCA orthogonalisation | `orthogonal_features` | `Features.orthogonal_features` | **exact** eigenvalues / cumulative variance / retained count (eigenvectors sign-free); returns transformed features + spectrum | -| Weighted Kendall-τ | `calculate_weighted_tau` | `Features.calculate_weighted_tau` | Vigna additive-hyperbolic weighted-τ (the estimator `scipy.stats.weightedtau` computes); validated against its definition (±1 extremes + a hand-computed case) | +| PCA orthogonalisation | `orthogonal_features` | `Features.orthogonal_features` | shared spectra and invariant reconstruction after incomplete/constant-column removal; nullspace count can depend on floating-point rounding at 100% variance | +| Weighted Kendall-τ | `calculate_weighted_tau` | `Features.calculate_weighted_tau` | shared SciPy fixtures and independent exact pair sums; lexicographic ties and NaNs ordered lowest | -**Deliberate divergence:** DataFrames → `Matrix`; `orthogonal_features` does not -auto-drop constant columns (Python `dropna`s them). `calculate_weighted_tau` is -validated against the weighted-τ definition rather than bit-checked against scipy -(scipy unavailable in CI). +**Representation:** DataFrames become matrices. PCA eigenvector rows correspond +to usable columns in their original order. A dataset without a usable column +fails explicitly. Repeated eigenvalues permit different bases of the same +eigenspace; shared tests compare invariant quantities. ### Tree-based importances (DecisionTree.jl backend) — wired @@ -405,9 +420,11 @@ validated against the weighted-τ definition rather than bit-checked against sci **Deliberate divergence:** the tree backend is `DecisionTree.jl` (added dependency), which is not bit-identical to scikit-learn — these importances are **behavioural** and validated structurally (informative features rank above -noise). Sample weights are not supported by the backend and are dropped. The -`controller`/`factory`/`strategy` scaffolding is replaced by plain functions; -clustered MDI/MDA follow in a small follow-up. +noise). These existing two-argument convenience functions do not expose sample +weights. New caller-estimator overloads preserve training/scoring weights and +do not choose an alternative estimator. Clustered MDI/MDA are plain functions; +the Python controller/factory/strategy scaffolding is unnecessary for these +native calculation contracts. ## Ensemble & cross-validation scoring — PR (wired) @@ -423,10 +440,10 @@ Port of the `ensemble` sub-package plus the estimator-driven scoring half of **Deliberate divergence:** the sklearn `BaggingClassifierAccuracy` class becomes a functional API; `class_weight="balanced"` and the Matplotlib plotting helper are -dropped. `cross_val_score` is the practical Julia equivalent of sklearn's -`cross_val_score` over the purged/combinatorial validators; the full path-level -`backtest_predictions` (per-CPCV-path OOS prediction assembly) remains a possible -elaboration. +dropped. `cross_val_score` retains its existing random-forest scoring contract. +Full path-level `backtest_predictions` now accepts caller-selected estimators +through explicit clone, weighted-fit and prediction callbacks. The random-forest +scorer is not a replacement for another specified estimator or scoring rule. ## Hyper-parameter tuning — PR (wired) @@ -496,13 +513,13 @@ behavioural (validated structurally). Each docstring carries the documented usag | Path-level Adaptive CPCV | `validation.path_adaptive_cpcv.{adaptive_probability_of_backtest_overfitting,estimate_volatility_regimes}` | `Validation.*` (new `PathAdaptiveCPCV.jl`) | **exact** (fully deterministic; regimes, weighted PBO and selected config match) | | Leakage-aware HPO | `validation.leakage_aware_hpo.{leakage_aware_hpo,deflated_sharpe_gate}` | `Validation.*` (`HyperParameterTuning.jl`) | `deflated_sharpe_gate` **exact**; `leakage_aware_hpo` purged-CV random search (Optuna TPE/CMA-ES is an optional analogue, not bundled) | | KSG MI + distance correlation | `distance.codependence.{ksg_mutual_information,distance_correlation}` | `Data.{ksg_mutual_information,distance_correlation}` | distance correlation **exact**; KSG **exact** via the jitter-invariant integer neighbour counts (brute-force O(N²) kNN, Chebyshev) | -| Adaptive Fractional Differencing | `differentiation.adaptive_differentiation.{adaptive_fractional_difference,rescaled_range_hurst,adaptive_differencing_order}` | `Data.*` (`Differentiation.jl`) | R/S Hurst + `d_hat` **exact**; order/threshold/p-value **behavioural** (ADF impl ≠ statsmodels). Wavelet Hurst (`pywt`) is an optional component → `NaN` (R/S-only blend, Python's pywt-absent path) | +| Adaptive Fractional Differencing | `differentiation.adaptive_differentiation.{adaptive_fractional_difference,rescaled_range_hurst,adaptive_differencing_order}` | `Data.*` (`Differentiation.jl`, `WaveletHurst.jl`) | Default db2 wavelet/R/S blend, missing-value alignment, thresholds, orders and ADF probabilities pass shared Python 3.2.0 fixtures; see the current bounded validation below | **Deliberate divergences (this wave):** `SpecialFunctions` added as a direct -dependency (digamma/loggamma/erfi/erfc). Two optional Python dependencies are not -bundled and fall back to the always-available path, matching the -dependency-absent behaviour: AFD's wavelet Hurst (`pywt`) → R/S estimate alone, and -leakage-aware HPO's Optuna sampler (`optuna`) → random sampling. The OU optimizer +dependency (digamma/loggamma/erfi/erfc). Leakage-aware HPO's optional Optuna sampler +(`optuna`) uses random sampling in this historical implementation. The default +AFD wavelet component is now implemented natively without a new dependency. +The OU optimizer uses a coarse-to-fine grid search in place of SciPy L-BFGS-B. CSCV-path order is the lexicographic `Combinatorics.combinations` order (matches `itertools.combinations`). @@ -525,6 +542,165 @@ is deterministic and reproduces `ruptures` exactly; the GPL-3 `exuber` reference not used (the Python side uses the permissive BSD-2 `ruptures`, and the Julia side is a clean-room reimplementation of the published Killick et al. 2012 algorithm). +## CausalFactorAnalysis: bounded estimators + +`RiskLabAI.CausalFactorAnalysis` provides native treatment-effect arithmetic and the approved bounded estimators below. Inputs are already identified means/probabilities or caller-supplied observations and design probabilities. Numerical checks do not establish graph validity, exchangeability, parallel trends, transportability, exposure-mapping validity or nuisance-model training history. + +| Concept | Python 3.2.0 | Julia | Validation | +|---|---|---|---| +| Mean effects and decomposition | `average_treatment_effect`, `treatment_effect_decomposition`, `randomized_mean_difference`, `difference_in_differences` | Same names under `CausalFactorAnalysis`; immutable decomposition types | Shared scalar/vector cases; decomposition identities; finite-domain checks | +| Adjustment arithmetic and IV ratio | `backdoor_adjusted_expectation`, `backdoor_adjusted_average_treatment_effect`, `frontdoor_adjusted_probability`, `linear_instrumental_variable_effect` | Same names under `CausalFactorAnalysis` | Shared probabilities/covariances; independent front-door sum; support and domain failures | +| Bounded outcomes and transport | `manski_ate_bounds`, `transported_ate` | Same names under `CausalFactorAnalysis` | Exhaustive missing-potential-outcome extrema; supported target standardization; zero-target and unsupported-stratum checks | +| Randomized exposure means | `exposure_mean_contrast` | Same name under `CausalFactorAnalysis` | Average over every independent binary randomized assignment in the finite fixture; positivity and omitted-category checks | +| One-step policy evaluation and selection | `evaluate_policy`, `select_policy` | Same names under `CausalFactorAnalysis` | Shared IPW/DR scores; independent finite-action sums; held-out reward perturbation; ties, invalid probabilities, zero weights and invalid chronological splits | + +The 168 shared cases in `test/fixtures/causal_estimators_python_320.toml` cover 13 functions. Deterministic floating comparisons use `rtol=1e-10`, `atol=1e-12`. Julia returns named tuples for Python dictionary results. Actions, exposure columns, split rows and selected-policy indices are one-based in Julia. The two decomposition types retain the Python field names. Exposure contrasts do not report independent-unit standard errors, and policy scores use neither clipping nor self-normalization. Graph identification, protocol diagnostics and the separate OLS sensitivity supplier remain outside this completed subset. + +## Constrained and ellipsoid-robust portfolios + +`Optimization.constrained_minimum_variance` and `Optimization.robust_mean_variance` +match the approved Python 3.2.0 convex problems: fully invested, nonnegative, +capped weights with an optional nominal return floor. Covariance must be PSD +within the stated eigensolver tolerance and is never repaired. The robust mean +set is the caller-supplied ellipsoid `mu + L*u`, `norm(u) <= radius`; covariance +is fixed. JuMP/Clarabel status and primal feasibility are checked before returning +weights, variance, nominal/worst means and the objective in a named tuple. + +The 21 shared fixtures independently minimize each two-asset problem by scalar +convex calculus. Objective comparisons retain `rtol=1e-8` and scale-aware +`atol=1e-10`; weight comparisons use componentwise `rtol=atol=1e-6`, matching +the Python fixture check. A conic solver's objective tolerance does not imply +equal relative accuracy for weights or each derived statistic: the Python +reference itself differs from the analytic weights by up to `8.63e-7` and from +the corresponding variance by `1.87e-8`. Cross-solver variance and mean bounds +therefore propagate the declared weight error through their quadratic, linear +and norm formulas. Returned diagnostics are also recomputed from returned weights +at `rtol=1e-12`. The optimal-objective and analytical-weight requirements are +unchanged. Nonunique optima are checked for objective and feasibility, without +requiring arbitrary equality of optimal weights. Additional cases cover singular +and zero covariance, very small/large scales, infeasibility and invalid domains. + +Fourteen additional public-book cases use sixteen industries, fixed block +resamples, three risk-aversion values and four uncertainty radii, including a +zero loading and a nominal return floor. The conic representation lists the +budget, nonnegativity and cap constraints in the public reference order. The +second-order cone already implies a nonnegative norm epigraph, so no redundant +scalar bound is added. These equivalent representation choices preserve the +objective, feasible set, solver settings and tolerances. All fourteen cases +match the public weights and diagnostics at `rtol=1e-9`, `atol=1e-11`; +the maximum observed weight difference is `3.4e-11`. + +## Clearing, execution, majorization and SPO+ + +The native `Optimization` functions `eisenberg_noe_clearing`, +`almgren_chriss_execution`, `is_majorized`, `is_doubly_stochastic`, +`majorization_matrix`, `birkhoff_von_neumann_decomposition` and `spo_plus_loss` +implement the preserved Python 3.2.0 contracts. `spo_plus` is the native Zygote +counterpart of `spo_plus_torch`, supporting vector and row-batched predictions +while treating truth, vertices and first-index oracle choices as constants. +Vertex and permutation indices are one-based. Dictionary results become named +tuples; no general optimization oracle or nonlinear feasible region is implied. + +The shared fixtures contain 39 clearing networks/scales, 20 execution cases, +40 SPO+ cases, 15 majorization comparisons and nine stochastic matrices. +Independent checks use monotone descent to the greatest clearing fixed point, +the execution inventory first-order system, finite-hull support values and +subgradient inequalities, and stochastic-witness/permutation reconstruction. +PyTorch gradients and native Zygote gradients agree on the supplied SPO+ cases, +including ties and batches. Clearing residuals use the caller's scaled `1e-8` +default tolerance. Execution comparisons use `rtol=1e-10`, `atol=1e-12`. +Majorization witnesses and decompositions use absolute residual `1e-10` and +are compared by their defining constraints, since their representations are +not unique. Majorization partial sums use higher-precision accumulation. +The Birkhoff implementation uses native augmenting-path matching on positive +residual support; no equality of decomposition order is required. + +Nineteen additional public-book clearing cases cover baseline networks, near-zero +unpaid balances, repeated shocks and monetary scales from `1e-12` to `1e12`. +The internal Clarabel accuracy cap is `min(1e-14, atol/10)`; the caller's +feasibility tolerance, linear objective and greatest-fixed-point contract are +unchanged. Rescaled payments are bounded again by zero and nominal liabilities +to remove last-bit rescaling overshoots. Across 2,089 public-book scenarios, +payments and unpaid balances match the preserved Python results at +`rtol=1e-9`, `atol=1e-11`, with identical default decisions and independent +monotone-iteration checks. The largest absolute gap occurs at monetary scale +`1e12` and is below `7e-15` after normalization by that scale. + +## Ledoit--Wolf simulation covariance + +`Data.simulates_cov_mu(...; shrink=true)` now uses `CovarianceEstimation` with +the centered maximum-likelihood covariance, Ledoit--Wolf shrinkage and a +scaled-identity target. Constant observations, one feature and exact target +degeneracy return the empirical covariance; they do not switch estimators. +Centering relative to the first observation and rescaling internally avoid +constant-data cancellation and fourth-moment overflow. The default unshrunk +path retains sample-covariance normalization. + +Eight shared observation matrices run through the preserved Python simulation +adapter and the Julia adapter with only their random draws supplied explicitly. +They cover `p > n`, rank deficiency, one feature, zero and constant observations; +the supplier fixtures independently verify the outer-product shrinkage formula. +Comparisons use `rtol=1e-10`, `atol=1e-12`, with additional native scale-equivariance +checks at `1e-100` and `1e100`. Equal NumPy and Julia seeds are not treated as +equivalent streams. Julia returns a mean vector and covariance matrix, including +the one-feature case where NumPy's unshrunk covariance is scalar. + +## Supplied-prior ambiguity and exponential Hawkes/VPIN + +`Features.probability_ambiguity` computes the prior-weighted population +variance of exhaustive bin probabilities, weighted by their prior means. +`normal_prior_bin_probabilities` supplies stable normal-tail bin probabilities. +The scope is the supplied-prior measure; raw intraday probability estimation +and return-variance ambiguity proxies are not introduced. Eighteen ambiguity +cases and twelve binning cases are checked against pairwise population-variance +identities and an independent complementary-error-function calculation. + +The six `Features` order-flow functions preserve the public empty-history, +univariate exponential Hawkes contract and VPIN's completed, equally sized, +already classified volume buckets. Intensity is predictable and excludes events +at the query time. Fifteen deterministic cases compare intensity, integrated +intensity, likelihood and the analytic score; quadrature and explicit event-pair +sums independently validate the reference. Six simulations share supplied +exponential and uniform draws, with no equal-seed cross-language assumption. +VPIN is checked against explicit rolling sums. + +`fit_hawkes` uses Optim's bounded L-BFGS method, the analytic score, the same +three fixed starts and the same scaled parameter bounds as Python, or a single +caller-supplied start. Three reference fits and a time-unit transformation check +use an absolute likelihood gap of `1e-4`. Deterministic comparisons use +`rtol=1e-10`, `atol=1e-12`; finite-difference score checks use `rtol=1e-5`, +`atol=1e-7`. Fits are numerical local solutions, with no identification or global +optimality claim. Optimizer messages and convergence counts may differ, and +decay is unidentified in the zero-excitation limit. + +## Accepted-DAG identification evidence and structural mechanisms + +`CausalFactorAnalysis.CausalDAG` stores immutable canonical node/edge tuples and +explicit observed nodes. Latent nodes remain in all graphical calculations. +The native d-separation, ordinary back-door, front-door, minimal back-door set, +instrument and path-role functions reproduce the public Python criteria and +complete path evidence. Pearl's conditional instrument profile and the stricter +unconditional CFI profile remain distinct. Front-door eligibility requires a +nonempty observed mediator set and a directed causal path; positivity remains +an external premise. Search limits raise errors and never yield partial evidence. + +Evidence records use named tuples. `classify_treatment_outcome_role` returns the +published role string instead of a Python enum; its eight permitted one-hop +signatures and rejection of reverse mediation are unchanged. +`evaluate_structural_causal_model` evaluates caller-supplied deterministic +mechanisms in lexical topological order with lexical parent columns. Inputs and +result values use immutable tuple-backed abstract arrays. A mechanism should +accept Julia abstract matrices/vectors; writes to snapshots are rejected. + +Validation covers all 64 ordered four-node DAGs: 1,536 d-separation queries, +back-door/front-door subsets, minimal sets, instruments and path roles, plus +latent-confounding examples and all eight one-hop roles. NetworkX's independent +d-separator and directed reachability algorithms check the Python reference. +Julia compares complete normalized evidence fields exactly. Twelve structural +examples match a separately solved simultaneous linear system at `rtol=1e-10`, +`atol=1e-12`; additional checks verify immutability and domain/search boundaries. +These methods neither discover a graph nor establish its causal assumptions. + _(further submodules appended as they are wired)_ ## Additive causal-factor completion @@ -562,3 +738,970 @@ multiple dispatch, and directly declared QuadGK. Quadrature error estimates are backend-specific. The shared numerical fixture supplements independent native mathematical and graph oracles; neither implementation is treated as the sole correctness authority. + +## Fitted density, targeted shrinkage and tail risk + +`Data.fit_kde` returns an immutable native `KernelDensityEstimate` and +`Data.score_samples` evaluates its log density on a vector or one-column +matrix. The supplied positive numeric bandwidth and all six public kernels +are supported. Compact kernels exclude points exactly one bandwidth away; +log-sum-exp evaluation preserves far-tail Gaussian log densities. The public +targeted eigenvector shrinkage construction `denoised_corr2` retains the +supplied signal/noise partition, the noise diagonal and the `1e-12` absolute +diagonality tolerance, without additional correlation normalization. + +`Backtest.expected_shortfall` preserves the Python signed lower-tail return +mean, type-7 quantile interpolation and inclusive ties. Its risk-free argument +is unused. `generalized_pareto_tail_risk` instead reports upper-tail loss +quantiles and expectations for a supplied threshold/exceedance rate. It uses +the exponential limit at shape zero and infinite shortfall for shape at least +one. `quantile_exception_tests` preserves the Kupiec and Christoffersen +likelihood conventions: coverage uses all observations, transitions condition +on the first, and absent previous-state rows yield `nothing` for unidentified +independence and conditional coverage results. P-values remain asymptotic. + +`fit_pot_tail_risk` fits a zero-location GPD to strictly positive excesses above +the caller's threshold. Native Optim Nelder–Mead rescales excesses by their +mean, starts at shape/normalized scale `(1,1)`, allows unrestricted shape, +and checks numerical convergence, support and finite likelihood. This is +local numerical likelihood fitting, with no global-optimality or finite-MLE +existence guarantee. Threshold selection and held-out separation remain the +caller's responsibility. + +The shared Python 3.2.0 fixtures contain 25 density cases, 63 shrinkage cases, +20 empirical shortfall cases, 40 Pareto risk cases, 387 exception-test cases +and ten fits. Independent evidence includes normalized kernel integrals, +outer-product reconstruction, 70-digit Pareto formulas, every binary sequence +through length six, direct transition likelihoods and chi-square survival +identities, and tightly converged likelihood/finite-difference score checks. +Deterministic comparisons use `rtol=1e-10`, `atol=1e-12`; numerical fit parameter +comparisons use `rtol=atol=5e-4` against the default SciPy fit, with a stricter +`1e-5` comparison against the tightly converged reference and objective error +below `1e-7` per excess. Native tests also check density snapshot immutability, +invalid domains and Pareto unit scaling by `1e-50` and `1e50`. + +## Public factor, specification and allocation diagnostics + +`CausalFactorAnalysis` implements the released `factor_mirage` coefficients, +conditional factor returns, integrated forecast returns and collider model +diagnostics, with immutable `StrategyPerformance`, `ColliderCoefficients` +and `ColliderDiagnostics` records. Exact rational intermediates preserve the +strict comparison rules and avoid intermediate overflow/cancellation; square +root signals use 320-bit arithmetic before checked Float64 conversion. The +optional realized confounder link follows the public regime-change contract. +Collider factor returns consume the supplied pre-outcome proxy. Diagnostic +t-statistic fields remain expected signals rather than p-values. + +The three public `specification_diagnostics` systems have native population +and experiment functions and four immutable result types. Experiments retain +the fixed structural equations, independent standard-normal disturbances, +classical OLS variances, degrees of freedom, coefficient labels and sample-size +ceiling. Native seeds select a local Julia RNG; the optional `rng` permits +identical primitive draws. Shared tests compare both specifications against +the preserved Python execution and an independent pivoted-QR regression. +Tuples preserve coefficient/result snapshot immutability. + +`minimum_variance_factor_weights` and `allocation_misspecification_diagnostics` +implement the supplied positive-definite covariance/full-rank equality +constraint problem with unrestricted weights. Cholesky whitening and reduced +QR preserve the public unit normalization, rank and feasibility checks; +square systems use the direct exposure solve. No covariance repair or +additional constraints are applied. The two exposure systems can have different +factor counts. Diagnostic vectors are independent immutable snapshots and +sign reversals use nonzero opposite signs, avoiding product underflow. + +The factor/specification fixtures cover 50 coefficient/return cases, 150 +diagnostic cases, five extreme-scale cases, three exact population systems and +12 shared-draw experiments. Independent covariance normal equations verify +coefficients, forecast products, variances and strict comparison rules. The +allocation fixtures add 61 weight cases and 15 comparisons checked by full +block KKT systems, an exact three-asset solution and covariance/exposure-unit +scaling. Deterministic numerical comparisons use `rtol=1e-10`, `atol=1e-12`, +with zero absolute tolerance for tiny extreme-scale coefficient results; +labels, counts and comparison flags are exact. These methods expose the +released synthetic/model calculations without new empirical conclusions or +paper-specific collider allocation appendices. + +## Search-adjusted false-discovery evidence + +`CausalFactorAnalysis` exposes the 13 released search-adjustment functions and +six immutable evidence/model types. Supplied trial errors, mixture parameters +and independent search counts retain their public domains. Log-domain +evaluation preserves posterior boundaries and Gaussian tail calculations. +Family FDR conditions on the event that every searched candidate is null; +`max_selection_null_probability` concerns the selected winner's null status. +The equal-component nonidentification witness remains limited to the +unrestricted model class. No empirical parameter-fitting procedure is added. + +The native selected-winner integrator uses adaptive 15/7-point Gauss–Kronrod +quadrature after transforming the infinite interval. It validates callback +values, positive selection probability, convergence tolerances, subdivision +limits and the final probability bound. Its reported absolute error estimate +is implementation dependent and is not expected to equal SciPy's estimate. +The mathematical rule constants were generated locally with the already +pinned QuadGK supplier and checked against 37 polynomial moments; QuadGK is +not added as a runtime dependency. + +Shared fixtures cover 54 single-trial posteriors, 216 family posteriors, 192 +family-error cases, 40 comparisons, 81 mixture maxima, 12 conditional-tail +ratios, 18 Gaussian distributions/likelihoods, 12 aggregate threshold records, +16 nonidentification witnesses and 27 winner-probability integrals. Independent +checks use exact rational arithmetic, 100-digit probabilities and Gaussian +tails, 2048-point Gauss–Legendre integration in null-probability coordinates, +and analytic equal-component/one-trial identities. Deterministic and shared +probability comparisons use `rtol=1e-10`, `atol=1e-12`; the independent fixed-rule +integrals use `rtol=1e-8`, `atol=1e-10`. Native tests also verify immutable +threshold snapshots and rejection of undefined posteriors, inconsistent +callbacks and exhausted integration limits. + +## Kelly sizing and probability calibration + +The four public `advanced_bet_sizing` interfaces preserve binary Kelly payout +conventions, long-only clipping, the pessimistic standard-error probability +box and right-closed calibration-error bins. Native scalar and array sizing +retain their shapes. `PlattCalibrator` uses `fit!` and `transform`; an unfit +model or one-class calibration block is the identity. Fitted predictions use +the public row-major flattening order. Calibration-block separation remains +the caller's responsibility. + +The Platt objective retains the clipped logit, fixed `C=1e6` slope penalty and +unpenalized intercept. Native L-BFGS uses tighter numerical stopping criteria +than the default Python solver. Eight shared fits are checked against an +independent Newton solution refined by the analytic score equations, using +`rtol=atol=1e-5` for parameters and predictions and an explicit objective check. +The default Python prediction differs by up to 0.028725 on the separable +synthetic fixture; differences are bounded using its residual score, Hessian +curvature and the sigmoid Lipschitz constant. This is a stopping-accuracy +difference for the same objective, with no calibration-efficacy claim. + +Nine Kelly cases, 108 robust-sizing cases and four calibration-bin cases also +use shared Python 3.2.0 outputs and independent growth/bin-membership checks. +Native tests cover invalid inputs, floating-point bin edges, one-class refits +and matrix prediction order. + +## Seven-stage causal-factor protocol records + +The 11 public immutable protocol record types and +`validate_causal_factor_protocol` preserve the released structural evidence +contract. Constructors accept positional or keyword fields, freeze valid +vector collections into tuples and retain malformed values for aggregate +validation. The report exposes the fixed source metadata and published stage +order. Julia raises `ArgumentError` with the same unique stage-specific +diagnostic set; diagnostic order is not contractual because Python iterates +sets. The protocol's numbered stages do not change disclosure classification +or approval state. + +Validation retains half-open event intervals, complete training membership, +prediction-before-refit evidence, purging and post-test embargoes, complete +and distinct CPCV paths, resolved graph roles and identification premises, +purpose-compatible separate performance evidence, implemented portfolio +considerations, and complete trial families with dependence-aware correction +inputs. Successful validation checks structural consistency of the supplied +record. It neither runs the recorded algorithms nor proves causal premises +or the truth of empirical evidence. + +The shared synthetic fixtures contain 777 records: 77 accepted and 700 +rejected, covering 332 distinct diagnostics. Independent NetworkX graph +criteria, pairwise interval arithmetic, fold/path multiplicities and trial +partition counts supplement Python 3.2.0 execution. Native tests also check +tuple snapshot immutability, constructor defaults, fixed report properties, +and touching versus overlapping half-open intervals. The initial native +empty-conditioning set error and its corrected validation run are retained +in the local program evidence. + +## Predict-before-update adaptation + +`prequential_adaptation` preserves the caller's estimator through explicit +native `clone_estimator`, `fit_estimator` and `predict_estimator` callbacks. +Each fit starts from an independent unfit clone; the caller supplies model +parameters and random state. Warm-up forecasts/losses are NaN. Alarm and refit +indices are one-based, and the alarm row enters its rolling training window +only after that row has been predicted and scored. Callback input slices are +independent copies. + +The internal ADWIN2 implementation keeps chronological exponential summaries, +with the fixed public settings of five summaries per size, checks every 32 +losses, minimum subwindow size five and grace period ten. It preserves the +installed River 0.26.1 detector's next-observation reset and cut arithmetic. +Losses are finite values in `[0,1]`. The adaptive detector window and the +caller-selected rolling estimator window remain separate. + +Shared fixtures validate 24 detector streams with 36,864 updates and 16 +mean/ridge adaptation scenarios. Detector alarms and widths match exactly; +totals, centered sums of squares, predictions and losses use `rtol=1e-10`, +`atol=1e-12`. Independent retained-window moments, augmented least-squares +regression, recorded fit intervals and 64 prefix comparisons validate the +reference. Native tests additionally check summary reconstruction, isolation +from callback input mutation, invalid domains and reset timing. No predictive +improvement or general adaptation guarantee is asserted. + +## Single-asset entropic deep hedging + +The six public deep-hedging interfaces use native arrays, Zygote and the +already pinned Optimisers Adam. `DeepHedge` retains the shared three-layer +dense/tanh architecture, remaining-time/current-log-price/previous-holding +inputs and unconstrained positions. Explicit parameter arrays are copied on +construction; seeded initialization and GBM simulation use local Julia RNGs. +Supplied primitive normal draws and supplied network weights provide direct +cross-language comparisons without equating language-specific seed streams. + +`hedging_pnl` preserves the self-financing short-call cash flow, premium and +proportional entry/rebalancing/liquidation costs. `entropic_risk` preserves the +detached minimum anchor and exponential-weight gradient. Call deltas use +zero rates, and GBM drift remains a supplied simulation-measure parameter. +Training mutates only model parameters using supplied training paths, +full-batch entropic loss and Adam with the public decays and epsilon. +Each recorded loss precedes its parameter update; independent evaluation +paths enter only subsequent evaluation. + +The 76 shared fixtures cover 16 GBM cases, 24 Float64/Float32 cash-flow cases, +16 entropic risks, 12 delta grids and eight shared-weight training cases. +Independent checks include 80-digit cash ledgers and risks, analytic holding +and risk gradients, a NumPy recurrent network, directional finite differences +and explicit Adam moment/bias-correction replay. Float64 deterministic and +network checks use `rtol=1e-10`, `atol=1e-12`. Float32 cash-flow roundoff is +bounded by `32*eps` times the absolute operation scale; tiny-aversion risk +uses `16*eps*max(1,abs(anchor),log(n)/aversion)`. Native tests verify global RNG +preservation, immutable input parameter snapshots, adapted positions and +unchanged weights during held-out evaluation. The initial overly strict +decimal-equality assertion is retained in the local test evidence. No +optimality, pricing-measure or performance-superiority claim is added. + +## Temporal-only TimeGAN + +`chronological_timegan_data`, `TimeGAN`, `fit_timegan` and +`timegan_quality_report` retain the public fixed-window temporal model. +Data split before windowing; scaling uses training minima/ranges, constant +training features have scale one and validation values are never clipped. +Arrays have `(windows,time,features)` order. The Float64 native GRUs use +reset/update/new gates with reset applied after the hidden projection. +Embedding and recovery are causal, the generator is autoregressive, and the +discriminator is bidirectional. `latent_sequence`, `timegan_losses` and +`sample_timegan` provide the native counterparts of Python instance methods. + +Training preserves reconstruction, teacher-forced supervision including the +initial latent, and the minimax fake-data logarithm. The three fixed phases +use separate Adam states with the public decays and epsilon. Joint training +updates E/R, then G, then D, recomputing losses between updates. Histories +contain losses before each step's final group update. Optional shared weights, +one-based minibatch indices and normal draws separate numerical comparisons +from language-specific RNG streams. Seeded operations preserve the global RNG. + +Twenty shared cases cover four chronological splits, four shared-weight +models with all training phases, and twelve held-out quality scenarios. +Independent NumPy GRU recurrences, stable loss formulas, 112 finite-difference +parameter directions and explicit Adam moment/bias-correction replay check +the reference. Quality checks use direct lag-pair enumeration, augmented +least-squares ridge with alpha one and Newton score/Hessian checks for +balanced logistic regression with C one and an unpenalized intercept. +The native logistic optimizer solves the same objective more tightly than +the default Python stopping rule. Fixture predictions agree; default Python +parameter distances from the independently refined solution reach 0.000713. +Deterministic outputs, gradients and trained parameters use `rtol=1e-10`, +`atol=1e-12`; finite-difference checks use `rtol=2e-5`, `atol=2e-7`. + +Native validation also checks causal prefixes, teacher timing, constructor +snapshots, invalid domains and fresh-noise evaluation after training. Matrix +sequence accumulation avoids a lazy tuple-gradient incompatibility in the +pinned differentiation packages; the failed runs remain in local evidence. +No separate supervisor or moment-matching network is added. Synthetic windows +are not an unlimited market path. Descriptive quality scores neither select +parameters nor certify realistic dynamics or uncertainty for overlapping +windows; chronological real validation and fresh synthetic noise remain +caller-supplied requirements. + +## Fixed-coordinate constrained neural SDEs + +The ten public neural-SDE interfaces preserve the supplied finite-grid and +price-basis contract. Call-grid inequalities retain intrinsic/upper bounds, +vertical-spread slopes, strike convexity including the known zero-strike +price, and calendar ordering. A separate static probability witness rejects +positive flat tails and verifies total mass, mean one and quote reconstruction. +Scalar affine state bounds reject unbounded, empty and singleton intervals. +The HJM diagnostic solves only the supplied finite-grid linear equation by +minimum-norm SVD with dimension-scaled machine-epsilon rank tolerance. + +`IntervalNeuralSDE` and `PolytopeNeuralSDE` use copied explicit Float64 weights +or local seeded dense/tanh initialization. They preserve inward drift +corrections and diffusion that vanishes linearly at a boundary. The polytope +uses the corrected fixed-coordinate transformation `Q*diag(g(distance))*Q'*L`, +with two-pass orthogonalization of stably sorted nearest independent normals. +This retains the public correction to the paper's one-sided square-root +transformation. Approved JuMP/Clarabel checks boundedness after normal-row +normalization. Negative distances within relative `1e-12` roundoff tolerance +are evaluated as zero; positive distances are not snapped to a face. + +`negative_log_likelihood` is the native instance-method counterpart and +preserves the approximate Gaussian Euler objective. Boundary starts and +singular transition covariance fail without variance jitter. Fitting uses +fixed iterations of Adam on supplied training transitions; histories record +pre-update losses. Simulation uses caller-supplied Brownian increments under +the fitted measure. Any Euler exit invalidates the entire run and identifies +its one-based step. No clipping, projection, reflection or path removal occurs. + +Thirty-seven shared cases cover four call grids, eight affine intervals, +twelve HJM systems, eight scalar models and five polytope models including +rotated boxes and a simplex. Independent slope inequalities, payoff sums, +rank-revealing QR, dense networks, sums of rank-one diffusion shrinkers, +Gaussian likelihood formulas, parameter/state finite differences, explicit +Adam replay and Euler updates check the reference. Deterministic values, +gradients and trained parameters use `rtol=1e-10`, `atol=1e-12`; directional +finite differences use `rtol=2e-5`, `atol=2e-7`. Native checks include inward +face drift, zero normal diffusion, row rescaling/permutation, positive +near-boundary diffusion, held-out evaluation and explicit exit failures. +The corrected standalone zero-tangent assertion is retained in local evidence. + +Static witnesses, boundary conditions and even a zero HJM residual do not +certify a dynamic martingale measure, off-grid prices, global calibration, +parameter recovery or invariant discrete Euler steps. + +## PDE network components + +Twelve native network types and the dense initialization callback preserve +the public linear, sine, bias-free ReLU, time-modulated and set-attention +architectures. Float64 parameter trees accept copied explicit weights; +local seeded initialization leaves the global random stream unchanged. +Inactive Python constructor modules are omitted. Attention retains the +public square-root-of-total-value-dimension scaling and population layer +normalization with epsilon `1e-5`. Set permutation identities are checked. + +The time-dependent sine network retains active batch normalization with +epsilon `1e-6`, momentum `0.01`, population training variance and unbiased +running variance. `model.training=false` uses fixed running moments. The +Monte Carlo network requires the caller's `y_mc` input and implements the +public control formula without inferring a gradient estimator. +`initialize_weights` mutates one dense parameter leaf using supplied or +local normal draws with standard deviation `0.1` and zero bias. + +Sixteen network cases and three initializer cases compare shared forward +values, complete input gradients, parameter gradient norms, projections and +selected coordinates against preserved Python 3.2.0. Independent NumPy +equations and 278 finite-difference directions check the reference. +Deterministic values and gradients use `rtol=1e-10`, `atol=1e-12`; +finite differences use `rtol=2e-5`, `atol=2e-7`. Sequential layer evaluation +and a distinct residual-branch input copy preserve gradients in the pinned +differentiation runtime. Failed gradient runs remain in local evidence. + +This coverage validates network components. Full PDE solver training parity +and convergence are separate requirements. Reproduced Python solver +validation-mode and missing-Monte-Carlo-input errors remain recorded locally. + +## PDE equations and solver training + +Twelve shared equation cases validate all four public rates, Hamiltonians, +volatilities, terminal functions and supplied-normal forward paths against +independent cumulative-sum, product and exact geometric-step calculations. +`Equation` remains a native abstract interface. Dimensions and step counts +must be positive; horizons must be finite and nonnegative. The public +`PricingDiffRate` multi-sample terminal helper returns only the strike-120 +call, while its ordinary terminal returns the strike-120/150 call spread. +Both contracts remain distinct and are explicitly checked. + +`FBSDESolver`, `FBSNNSolver`, `compute_pde_loss` and `solve_pde` provide native +loss evaluation and fixed-step training. The deprecated Python misspelling +`FBSNNolver` maps to the canonical native `FBSNNSolver`. DeepBSDE retains the +terminal MSE, bias-free ReLU networks, network Adam learning rate and separate +initial-value/gradient learning rates `0.1` and `0.001`. All three use betas +`(0.9,0.99)` and epsilon `1e-8`. The unused first Python DeepBSDE network is +omitted. The existing `solve_deep_bsde` convenience wrapper now delegates to +this contract, replacing its earlier single-optimizer approximation. + +FBSNN retains the sum of one-step residual MSEs plus terminal MSE and Adam +betas `(0.99,0.999)`. Analytic sine-network state derivatives remain +differentiable in the parameters and available during validation. Backward +solvers other than DeepBSDE keep the supplied initial value during training +and report the public coefficient-based estimate after each update. DTNN +retains training-mode batch moments for validation. Network optimizer state +persists across repeated training calls; DeepBSDE initial parameters restart. + +Three unmodified Python execution failures are preserved in local evidence: +FBSNN disables derivatives during validation, the Monte Carlo call omits its +required gradient input, and the set-network call omits a required array +axis. Native Monte Carlo training requires caller-supplied gradient arrays; +the set route inserts a singleton set axis within the public stated +one-dimensional equation scope. No estimator or higher-dimensional set +control is inferred. Explicit batch inputs are validated and copied before +training; ordinary sampling uses a local RNG, one fixed validation batch of +128 and fresh training batches. + +Sixteen shared solver cases validate losses, coefficient recurrences, +complete path/increment/initial-parameter gradients, parameter-gradient +summaries, trained parameter summaries, normalization state and two-step +optimizer replay. Independent NumPy recurrences and analytic derivatives +check values; 286 parameter directions check gradients; direct moment and +bias-correction calculations check every Python Adam update. Deterministic +comparisons use `rtol=1e-10`, `atol=1e-12`. Finite differences of the larger +PDE objectives use `rtol=1e-4`, `atol=5e-5`. Shared comparisons use Float64 +inputs, weights and temporary reference buffer defaults. They do not assert +equal random streams, PDE convergence, approximation accuracy or an error +rate. The corrected continuation-test naming error remains in local evidence. + +## Fractional filters and default-wavelet adaptive differencing + +The default adaptive algorithm now includes the native db2 wavelet-variance +component and its equal-weight blend with R/S Hurst. Closed-form db2 and Haar +filters use half-sample symmetric reflection, the public level rule, mean +squared detail coefficients and the same octave exclusion. `db1` aliases Haar; +other named filters are explicitly unsupported. The forced three-level rule +on short inputs is preserved; such boundary-dominated estimates are not given +an additional statistical interpretation. + +Fixed and expanding filters forward-fill internal missing observations while +retaining leading missing values and original output positions. Adaptive +memory estimation removes missing levels before forming increments, as in +Python, then filters the original aligned series. Returned Julia vectors +retain their NaN warm-up and original length. Threshold ranking and upward +order selection retain the public ADF significance and lag settings. The +existing ADF supplier uses the same response-surface probability convention; +shared probabilities and selection outcomes are checked directly. + +There are 295 shared cases: 49 weight cases, 35 filters, 180 wavelet/R/S +combinations, 15 ADF checks and 16 complete adaptive selections. Independent +high-precision generalized binomial weights, direct sums, explicit reflection, +block R/S ranges, pivoted-QR ADF regressions and candidate enumeration check +the reference. Default numerical tolerances are `rtol=1e-10`, `atol=1e-12`; +ADF probabilities and complete aligned adaptive outputs use `rtol=1e-9`, +`atol=1e-11`. A finite positive FFD threshold is required; integration weights +that never cross the supplied threshold fail explicitly. The former R/S-only +regression anchors are updated to the preserved wavelet-enabled reference. +This validation makes no new empirical accuracy or predictive-lift claim. + +## Estimator-driven validation paths and adaptive boundaries + +Native K-fold, purged K-fold, combinatorial and walk-forward validators now +provide complete paths through `backtest_predictions`. The caller supplies +`clone_estimator`, `fit_estimator`, `predict` and, when requested, +`predict_proba`. Each fit receives copied training rows and the aligned +observation weights; omitted weights become ones. The fitting callback returns +the fitted model. Failures propagate rather than triggering the Python wrapper's +unweighted retry. An unweighted estimator can be used through an explicit +caller-written callback when that is the intended contract. + +Combinatorial validators fit once per combination and reuse each fitted model +across its path segments. Integer path keys replace `"Path N"` strings. Shuffled +K-fold restores observation order and now sorts training positions as the +reference does. Supplied one-based permutations provide exact shared-draw +comparisons; Julia and NumPy seeds do not imply the same random stream. Standard +lexicographic CPCV paths retain their existing order. The path-location helper +now also returns group order when combinations arrive in a different order. +Walk-forward folds without training observations retain their NaN predictions. +Keyed dictionaries of validators and estimator/data inputs support separate +datasets without inferring a shared model. + +`AdaptiveCombinatorialPurgedCV` copies the supplied external feature and uses +the public full-vector linear quantiles, fine contiguous subsplits and signed +boundary adjustments. No feature or regime estimator is inferred. The default +three fine subsplits preserve contiguous complete group coverage. Small +subsplit counts can yield empty or overlapping groups under the source rule; +the native implementation preserves these outputs explicitly. Inputs require +finite features, aligned increasing event starts, ordered quantiles and at +least one observation per fine subsplit. Public continuous-range purging uses +the last test event's end, while disjoint ranges retain inclusive overlap and +observation-count embargo rules. + +There are 1,718 shared cases: 1,536 exhaustive purge/embargo cases, 104 complete +combinatorial/adaptive geometries, 54 basic fold designs and 24 prediction +scenarios. Independent interval intersections, group occurrence lists, linear +quantiles, split arithmetic and deterministic test-estimator formulas check +the reference. Row and weight traces verify clone/reset behavior and one fit +per combination. Index comparisons are exact; probability comparisons use +`rtol=1e-12`, `atol=1e-13`. The diagnostic estimator is used only in tests and +does not stand in for a book experiment's estimator. Bagged estimator fitting, +scientific model selection and empirical comparative findings are separate +contracts. + +## Bagged combinatorial validation + +`BaggedCombinatorialPurgedCV` fits one bagged caller estimator per combinatorial +split and assembles the same chronological paths. Its validated supplier +contract is scikit-learn 1.9.1: observation weights determine sampling +probabilities, floating `max_samples` multiplies the sum of training weights, +and the base estimator receives sampled-row multiplicities when it supports +weights. With explicit `fit_sample_weights=false`, it instead receives sampled +rows and no weight argument. Integer sizes remain counts. Feature draws follow +the requested feature fraction/count and replacement rule. + +Classifier targets use the wrapper's zero-based class codes internally; +returned labels retain their original sorted classes. Member probability +columns are aligned before averaging. Without a probability callback, the +ensemble averages hard votes. Regressors average predictions and reject +probability requests. All fits are serial and failures propagate. Native +random streams are local when `random_seed` is set; supplied training-relative +row positions, feature positions and estimator seeds enable exact replay +without a cross-language seed-equivalence assumption. + +Thirty-six shared cases cover probability classifiers, hard-vote classifiers +and regressors, weighted and explicitly unweighted members, omitted and unequal +observation weights, and three row/feature sampling designs. Each case has six +combinations and three members per combination. Independent arithmetic verifies +all fitted-input traces, weighted sample counts, class alignment and averaged +path outputs at `rtol=1e-12`, `atol=1e-13`. Test estimators remain diagnostic +fixtures; the public book's selected estimator and scientific scoring rule +must still be supplied by its caller. + +## Weighted caller-estimator feature importance + +MDI, MDA and SFI now have overloads taking an explicit estimator plus fit and +prediction callbacks. Clustered MDI/MDA have matching native functions. +MDI can also aggregate a supplied tree-by-feature matrix. These interfaces +preserve the caller's model and observation weights; the existing two-argument +DecisionTree.jl convenience methods retain their separately documented behavior. + +MDI replaces zero tree importances with NaN, skips these entries in feature +means and sample deviations, scales deviations by the square root of the total +tree count, and normalizes both columns by the sum of means. Clustered MDI first +sums each cluster within each tree, including zero for an empty/all-missing +cluster. Overlapping clusters retain their supplied memberships. + +MDA uses separate training and scoring weights, the public negative-log-loss +difference, and joint row permutations within each supplied cluster. Empty +clusters have zero decrease. SFI uses weighted accuracy or negative log loss +and advances in feature-major, fold-minor order. Both methods retain sequential +estimator fitting state, including a caller's warm-start behavior. Returned +results include the final fitted estimator. Supplied folds and permutations +permit exact replay; native random streams do not imply NumPy seed equivalence. +Log loss clips at the prediction floating type's machine epsilon and preserves +the supplier's weighted-average convention. + +PCA now removes constant or incomplete columns before normalization, matching +the public column filter. Weighted Kendall tau now uses both decreasing +lexicographic rankings to resolve ties and orders NaNs below other scores. +An empty or completely tied comparison remains undefined. + +The 141 shared cases include 24 PCA configurations, 91 weighted-rank cases, +eight impurity matrices, sixteen weighted importance scenarios and two loss +boundaries. Independent SVD, exact rational pair sums, NaN-aware aggregation, +weighted probability/loss arithmetic and fitted-input traces check the +reference. Importance tolerances are `rtol=1e-11`, `atol=1e-13`; PCA uses +`atol=1e-10` for spectral and invariant comparisons. + +At a 100% variance threshold on one centered five-by-eight fixture, Python +retains a fifth eigenvalue of `1.81e-15`, while Julia retains four components. +Independent SVD identifies four nonzero directions; transformed observation +Gram matrices differ by at most `2.39e-15`. The threshold algorithm is unchanged. +Tests require the same positive spectrum and reconstruction, while allowing +numerically zero component-count differences and arbitrary repeated-eigenspace +bases. These checks validate calculation contracts and do not establish a +feature's predictive or causal relevance. + +## Weighted native pipeline and classifier search + +`SampleWeightedPipeline` composes caller-selected native estimators through +explicit clone, fit, transform, parameter-setting, prediction and class-label +callbacks. Global fitting weights go only to the final estimator, overriding +an explicitly supplied final-step weight. Intermediate transformers receive +weights only when their own `step__sample_weight` option is supplied. Fitting +arrays are sliced with each training fold. Callers provide a fresh-estimator +clone operation; fitted state is never reused between candidates or folds. + +`clf_hyper_fit` preserves the public scoring decision: labels exactly `{0,1}` +use F1 with positive class 1; other labels use negative log loss. The validated +scikit-learn 1.9.1 supplier also routes top-level `sample_weight` to scoring. +Step-prefixed weights affect fitting only. Shared fixtures expose a maximum +weighted-versus-unweighted score difference of `0.3919975566370453`, so these +routes are tested separately. Fold scores use an arithmetic mean, first ties +win, and the selected estimator is fitted again on all observations. Default +individual fit/scoring failures become NaN; all failed fits raise an error. + +Finite grids use sorted keys with the final key varying fastest. Finite random +search samples without replacement; distribution-valued grids use supplied +draws or native distribution sampling with replacement. One-based supplied +candidate positions, explicit parameter draws and bagging row/feature/seed +plans permit replay across languages. Native seeds alone do not reproduce +NumPy streams. Execution is serial. Optional post-search bagging preserves +the validated supplier's weight-proportional sampling and multiplicity fits, +with original class labels restored in predictions. As in the supplier's +default configuration, step-prefixed fit options are rejected at the outer +bagging fit when metadata routing is unavailable. + +The 44 shared fixtures cover six direct weight-routing cases, 24 purged +grid/random searches, eight post-search bags, five failure/tie cases and one +continuous-distribution search. Independent standardization, class-probability +arithmetic, F1/log-loss sums, interval purging, candidate enumeration and every +estimator's fitted-input trace check the reference at `rtol=1e-11`, +`atol=1e-13`. Diagnostic classifiers in the fixtures test these contracts only; +they do not replace the books' estimators or establish predictive relevance. +The existing random-forest search convenience functions retain their earlier +behavioral scope. + +## Backtest statistics and Sharpe inference + +Shared fixtures check fourteen public statistics and inference functions across +1,204 synthetic cases. Exhaustive four-position tracks are checked by an +independent pro-rata entry-lot ledger. Calendar concentration includes every +month between the first and last observation, including empty months, and +counts nonmissing observations. HHI and drawdown grouping preserve the public +missing-value behavior. Date and DateTime holding durations use the same day +units; drawdown durations retain the public 365.25-day year convention. + +The preserved Python concentration function uses pandas' removed `M` frequency +alias and fails on pandas 3.0.6. Its failure is recorded. A bounded in-memory +replay maps only `M` to the equivalent month-end alias `ME`; separate calendar +enumeration checks the resulting arithmetic. The preserved source is unchanged. + +Conditional drawdown fixtures independently integrate fractional empirical +tail mass. Sharpe-difference tests accept supplied one-based circular-block +resamples, checking the full studentized bootstrap probability against direct +moment arithmetic and dense Bartlett covariance kernels. Default native random +seeds remain language-specific. The public 5% rejection rule is unchanged. + +Probabilistic Sharpe, benchmark maxima, influence functions and HAC intervals +retain their existing population/sample variance conventions. Oversized HAC +lags contribute zero beyond the sample length while preserving the requested +Bartlett denominator. Standard-library normal quantiles and complementary error +functions independently check probabilities and intervals. Comparisons use +`rtol=1e-11`, `atol=1e-13`, including explicit NaN/infinity outcomes where the +public contract is undefined or unbounded. These checks establish arithmetic +and replay consistency; they do not establish empirical test size, coverage or +predictive performance. + +## Multiple-testing, test-set and binary strategy formulas + +Another 634 shared synthetic cases cover fourteen existing functions: Holm and +BHY p-value corrections, Sharpe haircuts, expected maximum Sharpe, test-set +error formulas, binary-outcome risk and Gaussian mixtures. Exact rational rank +envelopes check all 125 length-three combinations of five fixed p-values. +Independent normal probabilities and inverse probabilities use sufficient +precision to resolve the public `1e-300` haircut floor. The public truncated +Euler constant in expected maxima is preserved. + +Binary moments are checked by enumerating the two outcomes. Implied precision +also matches 70-digit quadratic roots and defining residuals. Boundary values, +including the public zero-dispersion and infinite-frequency conventions, remain +explicit. Gaussian mixtures now accept a pair of supplied standard-normal +vectors and a one-based output permutation. Component counts, zero deviations, +empty/singleton samples and input preservation are tested without equating +native and NumPy seeds. + +Comparisons use `rtol=1e-11`, `atol=1e-13`; inverse-relationship checks account for +floating-point propagation. The significance thresholds and statistical targets +are unchanged. This arithmetic validation does not establish empirical +error-control or strategy performance. + +## Probability and sigmoid bet sizing + +The eleven existing public bet-sizing functions are checked with 347 shared +cases and independent interval enumeration. Inclusive bet intervals retain +their closing observation; active signals use half-open intervals and missing +end times remain active indefinitely. Zero-duration intervals, reordered starts, +signed meta-labels, class-count transforms, ties-to-even discretization and +signal saturation are covered. Native inputs remain already-aligned vectors; +pandas index alignment is a caller responsibility under the documented adapter. + +Sigmoid positions use truncation toward zero. Inverse-price and width boundary +conventions, and the public limit-price range over absolute position indices, +are preserved exactly, including empty position ranges. Checks use +`rtol=1e-11`, `atol=1e-13` and separately verify valid sigmoid inverse identities. +No sizing rule or profitability interpretation is changed. + +## OU first-passage metrics and fitted persistence + +Earlier validation checked seven public interfaces through 250 shared cases. +Adaptive scale integrals independently verified hit probabilities; scalar +outcome enumeration checked gain moments. Eighteen differential boundary +problems checked the earlier 400-node first-exit quadrature, with maximum +relative error `6.12e-6`. The native AR(1) fit uses minimum-norm least squares +for rank-deficient inputs and centers residuals when computing the released +two-degree-of-freedom variance. Finite-row filtering, persistence clipping and +pre-clipping residuals are retained. + +The approved Chapter 22.02 correction splits the mean-exit integral at entry and +uses 64-point Gauss–Legendre quadrature on each side. The existing quadrature +fallback is retained. The search also checks edge endpoints and refines +stationary maxima on all four box edges. Its approved boundary-count assertion +requires four contacts. Normal-import and final-location validation passed in +the recorded tested Windows environment; no validation beyond that environment +is claimed, and all documented limitations remain unchanged. + +The existing coarse-to-fine Julia optimizer remains distinct from SciPy L-BFGS-B. +Six earlier shared objective-and-bound checks gave maximum objective difference +below `7e-12` and threshold difference below `3.5e-6`; these checks do not certify +identical optimizer iterates or universal global optimality. Metrics use +`rtol=1e-9`, `atol=2e-11`; fitted values use `rtol=1e-8`, `atol=2e-9`. + +## Discrete-message entropy + +Eight public entropy interfaces and the longest-match helper are checked with +3,854 shared cases. Exhaustive binary strings through seven symbols and Unicode +code points test overlapping word counts, Shannon and bias-corrected entropy, +Lempel-Ziv parsing and Kontoyiannis windows. Independent trie parsing and brute +substring matching retain the public overlapping-match convention. + +The NSB check uses 45-digit concentration inversion and rising-factorial +evidence independently of the released floating log-gamma computation. It +validates the same finite trapezoidal grid, with `rtol=1e-9`, `atol=2e-10`; +other entropy checks use `rtol` between `1e-12` and `1e-11`. No estimator or +alphabet convention is changed. + +## OHLC spread and volatility calculations + +Seven public interfaces are checked through 197 shared cases. Native rolling +calculations now treat infinity as missing, matching pandas alongside NaN +warm-up and window behavior. Scalar sums, extrema and a hyperbolic-tangent +identity independently check spread calculations. The released +Bekker-Parkinson formula is preserved. + +Row-wise EDGE indicators and moment calculations cover short and constant +series, missing OHLC components, sparse trading and seven negative signed +estimates. Inputs remain already-aligned vectors; values and scale invariance +are checked at `rtol=1e-9`, `atol=2e-12` without an empirical spread-accuracy claim. + +## Information distances and KSG jitter + +Seven public interfaces are checked with 301 shared cases. Histogram edges now +match NumPy's floating edge construction, right inclusion and equality of +signed zeros. Automatic bin counts preserve NumPy's near-one tolerance and +avoid native integer overflow at large observation counts. Explicit empty +variation-of-information input is rejected, as in the public reference. + +KSG accepts supplied standard-normal jitter for each input, then applies the +public population-deviation scale internally. Exhaustive Chebyshev neighbor +counts and harmonic-number identities verify tied, constant and short samples +without claiming equal native and NumPy seeds. Distance correlation also +matches an independent uncentered-moment identity. Shared values use +`rtol=1e-10`, `atol=1e-12`; histogram cells and neighbor counts are exact. + +## Trend windows and missing sample weights + +Four public interfaces are checked with 866 shared cases. Sixty-digit OLS +calculations independently verify the slope t-values; installed SciPy 1.18.1 +behavior is retained for short and degenerate regressions. Trend scanning keeps +the first maximum after the public nonfinite-to-zero ranking transform, reports +the maximum-window end, and now removes undefined trend rows. + +Exhaustive binary concurrency matrices and nonnegative weighted examples check +average uniqueness. Missing entries are excluded from row concurrency, matching +pandas. Time-decay accumulation resumes after missing weights while retaining +NaN at the missing positions. Native vectors must already be chronological; +reference fixtures explicitly check the sorting adapter. Numerical comparisons +use `rtol=2e-8`, `atol=2e-9`, including regression error propagation. + +## CUSUM and barrier labels + +Six public labeling interfaces are checked through 480 shared cases. CUSUM +now skips missing differences without resetting accumulated moves. A pairwise +weighted-variance identity verifies pandas' debiased EWM with absolute-time +decay and missing observations. Daily lookbacks, vertical dates, strict +horizontal-barrier comparisons, and first-touch times are checked separately. + +Triple-barrier inputs may omit the side column, retaining the public default +of one. Meta-labeling uses the next available price for between-observation +timestamps and produces NaN when no later price exists. Missing side mappings +in the native event builder remain missing rather than becoming long positions. +Times, row selection and label signs are exact; floating values use +`rtol=1e-10`, `atol=1e-12`. Caller-aligned vector representations and the +existing labeling decision rules are preserved. + +## ADF, PSY and structural-break simulation + +Eleven public references are checked through 1,077 shared cases. The ADF design +now preserves pandas' order of operations around missing observations: discard +missing first differences, shift that compressed difference sequence, join it +to the original lagged levels, then remove incomplete rows. Independent QR +regressions and explicit enumeration check forward/backward windows and original +observation positions. Exhaustive short sequences check episode duration and +the first collapse timestamp. + +PSY critical values accept supplied standard-normal innovations. The volatility +robust routine accepts supplied Rademacher signs. Independent path construction, +window enumeration and quantiles check the resulting statistics, p-values and +unchanged strict 5% decisions without equating runtime seeds. Statistical values +use `rtol=5e-8`, `atol=5e-9`; shared p-values, decisions and observation indices +are exact. + +The existing normal/l2 PELT interface now computes centered segment variances, +avoiding cancellation for large price levels while preserving ruptures 1.1.10 +pruning and returned boundaries. **Reference compatibility does not establish +global optimality:** seven of the 102 shared PELT cases differ from unpruned +dynamic programming on the same candidate grid. Exhaustive partition enumeration +confirms the first counterexample. For its 15 observations, normal cost, +`min_size=2`, `jump=1`, and penalty multiplier `0.5`, the reference returns +`[2,4,6,8,10,12]` at objective `-21.2006061218`; `[2,4,6,8,10]` attains +`-21.8979243821`. Both outputs, objectives and full synthetic inputs are retained +in the fixture. The maximum observed objective gap is `0.8161520392`. +The reference pruning rule has not been replaced with a new decision procedure. + +## Synthetic covariances and regime paths + +Seven public references are checked through 156 cases with scalar Gram sums, +block membership, drift profiles, parameter padding and independently computed +variance/return ledgers. Covariance generators accept supplied normal, uniform +and permutation draws. The one-point drift profile now starts at time zero. + +Heston–Merton simulations accept already-correlated Gaussian outcomes and +Poisson counts. Native sampling supports the positive-semidefinite boundaries +`rho = ±1` and zero jump dispersion. Exact mean/covariance checks cover the +native Gaussian factor. The public jump compensator, variance reset and +nonnegative diffusion variance are unchanged; equal NumPy and Julia seeds do +not imply equal streams. + +Regime simulations accept an explicit regime order, initial state and +transition uniforms. These preserve Python dictionary insertion order and +right-sided categorical CDF boundaries. Regime-specific parameter lengths +expand the selected states using the public rule. Missing log returns are +forward-filled before accumulation; leading missing prices remain missing. +The native price vector omits Python's business-day index as documented. +Shared indices, names and counts are exact; floating outputs use `rtol=1e-11`, +`atol=1e-12`. No empirical simulation-fidelity claim is inferred from parity. + +## Covariance algebra, NERCOME and hierarchical allocation + +Shared cases check covariance normalization, Marcenko–Pastur grids, spectral +reconstruction and noise eigenvalue averaging, normalized covariance weights, +and PCA risk allocation. One-point MP grids now return the lower support point. +PCA checks compare eigenspace risk and total variance, allowing arbitrary signs +and bases within repeated-eigenvalue subspaces. They do not equate raw signed +hedging weights across eigensolvers. + +NERCOME accepts supplied row permutations. Thirty-six cases compare held-out +projected variances with independent centered Gram calculations, including +constant columns, zero splits and the public positive-definite eigenvalue floor. +These checks do not validate the historical empirical performance claims in +the existing documentation. Native seeds and degenerate eigenvector bases can +differ across implementations. + +The native HRP wrapper now preserves the reference single-linkage traversal, +stable sorting of equal-height edges and ascending child identifiers. The +previous supplier order changed weights in six of sixty cases, by up to +`0.0496904218`. All sixty complete portfolios now agree. Sixty-four exhaustive +four-node binary-distance patterns check tie ordering; independent graph +connected components check every tree-cut threshold. Another 120 permutations +check recursive allocation. NCO's intra/inter-cluster arithmetic is checked +through eighteen supplied partitions; its default stochastic k-means output +is not equated with scikit-learn. + +The previous native golden search and reference local optimizer chose different +factor counts in two of eighteen diagnostic cases. Those historical results are +preserved. The native fit now uses projected scalar BFGS with the reference +initial point, bounds, More–Thuente settings and outer stopping rules. The +public fit supplies the analytic derivative of the same objective; forward +finite differences remain the internal fallback when no derivative is supplied. +All 243 current shared fits select the same factor counts; +variance differences are below `5.6e-6` under the declared `1e-5` absolute +tolerance. Twenty-four covariance and portfolio cases independently check the +downstream spectral arithmetic. This is a local fit, not a global-minimum +guarantee; eigenvalues near a cutoff remain sensitive to roundoff. + +All 31 canonical bar-state constants are checked exactly. The three cumulative +imbalance values now use the public Greek theta character while their Julia +identifiers remain ASCII. + +### Clustering selection and streamed bars + +Clustering selection now computes silhouette scores from Euclidean distances +between rows of the correlation-distance embedding, matching the reference. +A supplied `label_provider(features, cluster_count, rng)` replays candidate +labels through base selection and recursive refinement. Thirty-nine shared +base/top cases include nine recursive calls, eight accepted refinements and one +rejection. Native seeded calls preserve the caller's global RNG state. Default +native and sklearn k-means partitions remain distinct stochastic outputs; +within-cluster matrix ordering is stable in Julia and may differ from NumPy's +unspecified argsort tie order. Thirty-six recorded block partitions and Gaussian +primitive cases independently check random block correlations. + +The six bar constructors are checked against 224 reference cases with scalar +accounting, explicit EWMA weight sums and batch replay. Standard, imbalance and +run bars now retain global tick counts and closing prices across batches; +imbalance histories persist. Information-driven bars expose the public +pre-emission threshold snapshots, including shared history-list references. +Expected-count windows of zero or `nothing` use the reference unbounded mean +fallback; fixed bars ignore that window. The run buy-proportion update uses the +imbalance window when the expected-count window is absent or zero. Twenty time +cases check exact boundaries, skipped empty periods and negative Unix epochs. +These contracts use finite prices, nonnegative volumes and chronological +millisecond timestamps; callers provide the required timezone conversion. + +### Caller-estimator debiased importance and ensemble accuracy + +The three-argument MDI+ overload accepts a caller-fitted ensemble through +`fit_estimator` and `tree_records`. Records supply one-based split features, +thresholds and bootstrap rows. Twenty-four cases independently check augmented +QR ridge fits, unique in-bag observations, out-of-bag population variances and +the public tree-use denominator. This is the released simplified stump-basis +variant; the full paper algorithm and existing empirical claims are outside +this validation. + +The caller-estimator CPI overload accepts independent clone, fit, probability +and class callbacks plus supplied folds and Gaussian draws. Thirty-two cases +check every fitted and predicted array, conditional ridge knockoffs and pooled +clipped loss differences. The near-zero gate now uses population standard +deviation, matching the public implementation; the paired Student statistic +still uses sample deviation. Five direct cases include a counterexample at that +population/sample boundary. Native and sklearn forest training outputs are not +declared equivalent by these conditional arithmetic checks. + +The caller-estimator bagging evaluator recomputes fresh training accuracies and +three member-weight schemes, preserving direct comparison of member outputs to +the external labels and assigning zero votes to the first class. Twelve fitted +reference cases and three exact rational cases validate this aggregation. The +released encoded-label mismatch for nonstandard labels and its feature-subset +prediction failure are retained explicitly. The default native tree convenience +does not replace the public entropy/balanced estimator in this contract, and +mutable Python cache reuse is not emulated by the fresh functional evaluator. + +Fitted-predictor bootstrap accepts supplied one-based row plans, including +expanded plans for duplicated pandas index labels. Twenty-four cases cover +zero/one/multiple replicates and sample deviation. Forty-five theoretical +majority-vote cases agree with high-precision finite binomial sums within the +declared floating tolerance; the released `1-cdf` cancellation is preserved. + +### PBO ranks, regimes and circular resampling + +The five PBO interfaces are checked against 378 shared cases with explicit +partition enumeration, first-maximizing strategy selection, scalar dispersion, +linear quantiles, modal path weights and supplied circular block starts. First +maxima now preserve the public signed-zero behavior. Regime labels preserve the +observed NumPy behavior for missing values, including all-missing cutoffs. + +Ordinal ranks accept `rank_order(scores)` for exact reference replay. Default +Julia stable sorting and NumPy's default sorting can rank tied scores differently; +one ten-strategy counterexample reverses the overfit decision. The default native +tie rule is preserved and this difference remains explicit. No tie-invariant +decision claim is made. Bagged PBO accepts one-based `bootstrap_rows` plans; +native and NumPy random streams are not declared seed-equivalent. + +The volatility feature uses trailing observations, while the released regime +quantile cutoffs use the entire supplied sample. These checks do not certify +prefix-invariant regime labels or historical empirical selection claims. + +### Leakage-aware caller-estimator search + +The four-argument HPO overload accepts a caller sampler through `ask_trial` and +`tell_trial!`, the public suggestion and estimator-factory functions, and fit, +prediction or custom-score callbacks. It preserves all three public fold-skip +conditions, fresh unweighted fits, first completed maxima, failed NaN trials, +completed infinite scores, and finite-trial population moments. Reported best +parameters contain sampled settings; fixed factory settings remain separate. + +Shared checks cover 144 objective cases, twelve recorded actual Optuna 4.9.0 TPE +studies with up to eighteen trials, six trial-state boundary cases and 32 DSR +gates. Proposal replay validates orchestration. It does not supply fresh native +TPE/CMA-ES proposals, so callers requesting those families must provide the +corresponding sampler. The earlier random-forest/random-search convenience +remains a separate interface. No search-efficiency or empirical performance +claim follows from these checks. + +### Native component discovery and work ordering + +`Components` provides native factories, canonical names, aliases, metadata and +lazy loader callbacks. Explicit accepted-keyword lists replace Python signature +introspection. The six public family catalogues retain their canonical names; +labeling, bet-sizing and portfolio registries start empty. `AbstractLabeler` and +`label` provide multiple-dispatch extension points. Registered feature-importance +components configure the already validated functional interfaces. + +`ParallelWork` preserves the scalar partition equations and submitted-order +parallel mapping. General jobs may be collected in completion order; explicit +`ordered=true` restores submission order. Indexed table work requires an index +column and sorts the combined table. Concurrency is bounded by the Julia thread +count, and job definitions are preserved. Completion order and speedups are +properties of the current schedule and machine, not numerical guarantees. + +The bar controller exposes all sixteen public routes with the public defaults +and the twelve-column schema. It processes caller-loaded tables and returns +completion status, fully processed row count and an error alongside the bars. +Thus empty or partial output does not certify successful input completion. CSV +loading and output writing remain explicit caller operations. Shared checks +cover 119 partition, mapping, registry, labeler and synthetic controller cases, +including all sixteen routes under two batch sizes. These native interfaces +preserve teaching contracts without recreating Python class/import scaffolding. diff --git a/Project.toml b/Project.toml index 9a93797..cec2c68 100644 --- a/Project.toml +++ b/Project.toml @@ -4,37 +4,40 @@ version = "1.1.0" authors = ["Hamid Arian "] [deps] +Clarabel = "61c947e1-3e6d-4ee4-985a-eec8c727bd6e" Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" +CovarianceEstimation = "587fd27a-f159-11e8-2dae-1979310e6154" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" +JuMP = "4076af6c-e467-56ae-b986-b466b2749572" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Lux = "b2108857-7c20-44ae-9111-449ecde12c47" +Optim = "429524aa-4258-5aef-a3af-852621145aeb" +Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" - -[weakdeps] -Lux = "b2108857-7c20-44ae-9111-449ecde12c47" -Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2" Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" -[extensions] -RiskLabAIDeepBSDEExt = ["Lux", "Optimisers", "Zygote"] - [compat] +Clarabel = "=0.11.1" Clustering = "0.15.8" Combinatorics = "1.1" +CovarianceEstimation = "=0.2.13" DataFrames = "1.8" Dates = "1" DecisionTree = "0.12.4" Distributions = "0.25.128" HypothesisTests = "0.11.8" +JuMP = "=1.32.1" LinearAlgebra = "1" Lux = "1.31.4" +Optim = "=2.3.2" Optimisers = "0.4.7" QuadGK = "2.11.3" Random = "1" diff --git a/README.md b/README.md index 5ecca02..c5a7b86 100644 --- a/README.md +++ b/README.md @@ -34,8 +34,7 @@ not to every Julia module. Ornstein-Uhlenbeck trading rules - **Validation** - K-fold, purged, combinatorial-purged, and walk-forward validation, plus grid and random search -- **Optional capability** - a Deep-BSDE PDE solver using Lux, Optimisers, and - Zygote through a Julia package extension +- **PDE methods** - a native Deep-BSDE solver using Lux, Optimisers, and Zygote ## Compatibility @@ -59,15 +58,8 @@ using Pkg Pkg.add("RiskLabAI") ``` -To enable the optional Deep-BSDE solver, add its three weak dependencies to -the active environment: - -```julia -using Pkg -Pkg.add(["Lux", "Optimisers", "Zygote"]) -``` - -The base package does not require this automatic-differentiation stack. +The Deep-BSDE solver and its numerical dependencies are installed with the +package. ## Causal-factor quick start diff --git a/docs/compatibility.md b/docs/compatibility.md index 1ded093..3fc88b3 100644 --- a/docs/compatibility.md +++ b/docs/compatibility.md @@ -1,9 +1,9 @@ # Julia runtime and dependency support -Status: **local 1.1.0 candidate; release blocked**. +Status: **published 1.1.0 baseline with unreleased local additions**. -This policy is inherited from the approved RiskLabAI `1.0.0` baseline and -applies to the additive `1.1.0` candidate. Local compatibility and package +This policy records the published RiskLabAI `1.1.0` baseline and distinguishes +it from the current local integration. Local compatibility and package inspection do not authorize version control, registration, publication, or release. @@ -22,65 +22,60 @@ replace 1.12 in the matrix and pass again. Both official runtime archives were verified against their published SHA-256 checksums before testing. -## Required and optional dependencies +## Required dependencies The required external dependency floors are: +- Clarabel 0.11.1; - Clustering 0.15.8; - Combinatorics 1.1.0; +- CovarianceEstimation 0.2.13; - DataFrames 1.8.2; - DecisionTree 0.12.4; - Distributions 0.25.128; - HypothesisTests 0.11.8; +- JuMP 1.32.1; +- Lux 1.31.4; +- Optim 2.3.2; +- Optimisers 0.4.7; - QuadGK 2.11.3; -- SpecialFunctions 2.8.0. +- SpecialFunctions 2.8.0; +- Zygote 0.7.11. The current-compatible lanes resolved Distributions 0.25.131 and SpecialFunctions 2.9.0; the other required packages remained at their stated floors. Dates, LinearAlgebra, Random, and Statistics are required standard libraries. -Lux, Optimisers, and Zygote are isolated behind the optional `deep_bsde` -capability. The tested minimums are Lux 1.31.4, Optimisers 0.4.7, and Zygote -0.7.11. Current lanes used Lux 1.31.4, Optimisers 0.4.9, and Zygote 0.7.12. -The base source loads without these packages. Calling `solve_deep_bsde` without -the extension raises a direct dependency error. - -The governed base test target contains only `Test`; it does not promote weak -dependencies into required dependencies. Base lanes verify four explicit -extension-absence and fallback assertions. Separate `deep_bsde` lanes install -and load Lux, Optimisers, and Zygote before running the four numerical solver -assertions. Every CI lane creates a temporary test environment and adds all -eight required external packages as direct dependencies at either their exact -floors or current compatible versions. Extension lanes additionally add the -three weak dependencies. This keeps direct imports in the preserved tests -independent of incidental manifest state. +Lux, Optimisers, and Zygote are direct dependencies because the native +Deep-BSDE solver is part of the package. Each test lane constructs a temporary +environment and checks all external packages at either their stated floors or +current compatible versions. TimeSeries is not a required dependency. No preserved source behavior uses it, and the complete base library passes without it. ## Matrix evidence -Four runtime/dependency combinations were exercised: Julia 1.10.12 and 1.12.7, -each with the minimum and current-compatible required dependency sets. Every -combination was run once as a base lane and once as a `deep_bsde` extension -lane, for eight required executions. - -Every lane passed 12,926 assertions, including 12,414 causal-factor -assertions. That is 103,408 passing assertions in the eight executions, -including 99,312 causal-factor assertions. Base lanes loaded without Lux, -Optimisers, or Zygote and passed all four extension-absence and fallback -assertions. Extension lanes loaded the three weak dependencies and passed all -four numerical deep-BSDE assertions. There were no failures, errors, broken -tests, or skips. The causal suite includes independent analytical and -exhaustive graph oracles; it does not use Python-Julia agreement as its sole -correctness test. +The published 1.1.0 baseline was exercised on Julia 1.10.12 and 1.12.7 with +minimum and current-compatible dependency sets. That earlier matrix separated +the base package from the optional Deep-BSDE extension. + +Those published-baseline runs passed 12,926 assertions per lane, including +12,414 causal-factor assertions. The causal suite includes independent +analytical and exhaustive graph oracles; it does not use Python-Julia +agreement as its sole correctness test. + +The current merged source has also passed the released causal suite, the new +regression files, and the native Deep-BSDE checks in the local Windows +environment. This local result does not replace the two-runtime compatibility +matrix. Both supported Julia lines and both dependency sets must pass again +before a later release. The CI workflow keeps Julia 1.10.12 LTS and Julia 1.12.7 stable as explicit -base and extension jobs. Both minimum and current jobs construct their -temporary test environments explicitly. JuliaFormatter 2.9.0 checks only the -independently maintained causal source, causal tests, and test entry point; -preserved source remains governed by the complete analytical test suite. +minimum and current jobs. JuliaFormatter 2.9.0 checks the independently +maintained causal source, causal tests, and test entry point; preserved source +remains governed by the complete analytical test suite. The deep solver now converts Lux parameters and state to Float64, matching the PDE state and eliminating a mixed-precision fallback present in every original @@ -93,8 +88,7 @@ a correctness authority. ## Human-controlled release gates -`Project.toml`, the public surface, package-scoped tests, documentation, source -allowlists, and governed CI workflow are complete. The local 1.1.0 candidate is -bound to the approved merged 1.0.0 baseline commit and tree. Package-inspection -evidence is retained outside the distributable tree. Version-control, -registration, publication, and release actions require separate human action. +The current integration must pass its full compatibility matrix before a +release decision. Package-inspection evidence is retained outside the +distributable tree. Version-control, registration, publication, and release +actions require separate human action. diff --git a/src/Backtest/Adaptation.jl b/src/Backtest/Adaptation.jl new file mode 100644 index 0000000..2d8eae1 --- /dev/null +++ b/src/Backtest/Adaptation.jl @@ -0,0 +1,170 @@ +struct _ADWINSummary + count::Int + total::Float64 + centered_squares::Float64 +end + +mutable struct _ADWINWindow + delta::Float64 + summaries::Vector{_ADWINSummary} + width::Int + total::Float64 + centered_squares::Float64 + clock::Int + drift_detected::Bool +end + +function _ADWINWindow(delta) + probability = _adaptation_scalar(delta, "delta") + 0 < probability < 1 || throw(ArgumentError("delta must lie strictly between zero and one")) + return _ADWINWindow(probability, _ADWINSummary[], 0, 0.0, 0.0, 0, false) +end + +function _adwin_compress!(window) + size = 1 + while true + positions = findall(summary -> summary.count == size, window.summaries) + length(positions) <= 5 && return window + first, second = positions[1:2] + older, newer = window.summaries[first], window.summaries[second] + mean_difference = older.total / size - newer.total / size + combined_squares = older.centered_squares + newer.centered_squares + size * 0.5 * mean_difference * mean_difference + window.summaries[first] = _ADWINSummary(2size, older.total + newer.total, combined_squares) + deleteat!(window.summaries, second) + size *= 2 + end +end + +function _adwin_has_cut(window) + older_count, newer_count = 0, window.width + older_total, newer_total = 0.0, window.total + for index in 1:(length(window.summaries)-1) + summary = window.summaries[index] + older_count += summary.count + newer_count -= summary.count + older_total += summary.total + newer_total -= summary.total + min(older_count, newer_count) >= 5 || continue + mean_difference = older_total / older_count - newer_total / newer_count + confidence_term = log(2.0 * log(Float64(window.width)) / window.delta) + inverse_count = 1.0 / (older_count - 4) + 1.0 / (newer_count - 4) + variance_term = 2.0 * inverse_count * (window.centered_squares / window.width) * confidence_term + threshold = sqrt(variance_term >= 0 ? variance_term : NaN) + 2.0 / 3.0 * confidence_term * inverse_count + abs(mean_difference) > threshold && return true + end + return false +end + +function _adwin_remove_oldest!(window) + oldest = popfirst!(window.summaries) + window.width -= oldest.count + window.total -= oldest.total + difference = oldest.total / oldest.count - window.total / window.width + removed_squares = oldest.centered_squares + Float64(oldest.count) * window.width * difference * difference / (oldest.count + window.width) + window.centered_squares -= removed_squares + return window +end + +function _adwin_update!(window::_ADWINWindow, value) + loss = _adaptation_scalar(value, "loss") + 0 <= loss <= 1 || throw(ArgumentError("ADWIN losses must be in [0,1]")) + if window.drift_detected + empty!(window.summaries) + window.width, window.total, window.centered_squares, window.clock = 0, 0.0, 0.0, 0 + end + window.drift_detected = false + push!(window.summaries, _ADWINSummary(1, loss, 0.0)) + window.width += 1 + if window.width > 1 + difference = loss - window.total / (window.width - 1) + window.centered_squares += (window.width - 1) * difference * difference / window.width + end + window.total += loss + _adwin_compress!(window) + window.clock += 1 + if window.clock % 32 == 0 && window.width > 10 + while _adwin_has_cut(window) + window.drift_detected = true + _adwin_remove_oldest!(window) + end + end + return window.drift_detected +end + +function _adaptation_scalar(value, name) + value isa Real && !(value isa Bool) && isfinite(value) || throw(ArgumentError("$name must be a finite real scalar")) + result = Float64(value) + isfinite(result) || throw(ArgumentError("$name is outside the Float64 range")) + return result +end + +function _adaptation_array(value, name, dimension) + value isa AbstractArray && ndims(value) == dimension && !isempty(value) || throw(ArgumentError("$name must be a nonempty real array of dimension $dimension")) + return map(item -> _adaptation_scalar(item, name), value) +end + +""" + prequential_adaptation(features, targets, estimator; fit_estimator, + predict_estimator, clone_estimator, loss_function, window_size, + min_samples, delta) + +Predict each observation before revealing its target and then update the +public ADWIN2 bounded-loss detector. Warm-up predictions/losses are `NaN`. +The first fit uses the first `min_samples` observations. Each alarm triggers a +fresh fit on at most `window_size` latest observed rows, including the alarm +row; this fit can affect only later predictions. Returned alarm/refit indices +are one-based, including the initial refit index `min_samples`. + +Native estimator callbacks preserve the caller's model family: +`clone_estimator(estimator)` returns an independent unfit estimator with the +same parameters, `fit_estimator(clone, x, y)` returns the fitted model, and +`predict_estimator(model, x_row)` returns a one-element vector. A fresh clone +is made for every fit. The caller controls estimator random state. Training +and prediction callbacks receive independent data slices. + +The detector follows the public fixed defaults: checks every 32 losses, +five buckets per size, minimum subwindow length five, grace period ten and +reset on the observation after an alarm. `loss_function(target,prediction)` +must return a finite scalar in `[0,1]`. The detector's window and the caller's +rolling training window have distinct roles. +""" +function prequential_adaptation(features, targets, estimator; + fit_estimator, predict_estimator, clone_estimator, loss_function, + window_size, min_samples, delta) + x = _adaptation_array(features, "features", 2) + y = _adaptation_array(targets, "targets", 1) + for (value, name) in ((window_size, "window_size"), (min_samples, "min_samples")) + value isa Integer && !(value isa Bool) && value > 0 || throw(ArgumentError("$name must be a positive integer")) + end + size(x, 1) == length(y) && min_samples <= window_size && min_samples < length(y) || throw(ArgumentError("Require matching rows, min_samples <= window_size, and enough data")) + applicable(loss_function, first(y), 0.0) || throw(ArgumentError("loss_function must accept a target and prediction")) + applicable(clone_estimator, estimator) || throw(ArgumentError("clone_estimator must accept the supplied estimator")) + detector = _ADWINWindow(delta) + predictions, losses = fill(NaN, length(y)), fill(NaN, length(y)) + alarms, refits = Int[], [Int(min_samples)] + function fit_observed(indices) + fresh = clone_estimator(estimator) + training_x, training_y = x[indices, :], y[indices] + applicable(fit_estimator, fresh, training_x, training_y) || throw(ArgumentError("fit_estimator must accept a clone and training arrays")) + return fit_estimator(fresh, training_x, training_y) + end + model = fit_observed(1:Int(min_samples)) + for index in (Int(min_samples)+1):length(y) + row = x[index:index, :] + applicable(predict_estimator, model, row) || throw(ArgumentError("predict_estimator must accept the fitted model and a row matrix")) + raw_prediction = predict_estimator(model, row) + raw_prediction isa AbstractVector && length(raw_prediction) == 1 || throw(ArgumentError("Estimator must return one scalar prediction per row")) + prediction = _adaptation_scalar(only(raw_prediction), "prediction") + applicable(loss_function, y[index], prediction) || throw(ArgumentError("loss_function must accept a target and prediction")) + loss = _adaptation_scalar(loss_function(y[index], prediction), "loss") + 0 <= loss <= 1 || throw(ArgumentError("ADWIN losses must be in [0,1]")) + predictions[index], losses[index] = prediction, loss + if _adwin_update!(detector, loss) + push!(alarms, index) + beginning = Int(max(1, index + 1 - window_size)) + model = fit_observed(beginning:index) + push!(refits, index) + end + end + return (predictions = predictions, losses = losses, alarm_indices = alarms, refit_indices = refits) +end diff --git a/src/Backtest/AdvancedBetSizing.jl b/src/Backtest/AdvancedBetSizing.jl new file mode 100644 index 0000000..c7fc514 --- /dev/null +++ b/src/Backtest/AdvancedBetSizing.jl @@ -0,0 +1,150 @@ +import DataFrames: transform + +function _sizing_values(values, name) + if values isa Real + result = Float64(values) + isfinite(result) || throw(ArgumentError("$name must be finite")) + return result + end + values isa AbstractArray && all(value -> value isa Real && isfinite(value), values) || throw(ArgumentError("$name must contain finite real values")) + result = Float64.(values) + all(isfinite, result) || throw(ArgumentError("$name is outside the Float64 range")) + return result +end + +""" + kelly_bet_fraction(probability; payout_ratio=1.0, max_fraction=1.0) + +Return the binary-bet fraction `(p*(b+1)-1)/b`, clipped to `[0,max_fraction]`, +where `b` is the positive net win payout and a loss costs one unit staked. +Scalar and array shapes are retained. The cap must be finite and nonnegative. +""" +function kelly_bet_fraction(probability; payout_ratio = 1.0, max_fraction = 1.0) + values = _sizing_values(probability, "probability") + payout = _tail_real_scalar(payout_ratio, "payout_ratio") + cap = _tail_real_scalar(max_fraction, "max_fraction") + payout > 0 && cap >= 0 || throw(ArgumentError("payout_ratio must be positive and max_fraction nonnegative")) + return clamp.((values .* (payout + 1) .- 1) ./ payout, 0.0, cap) +end + +kelly_bet_fraction(probability, payout_ratio, max_fraction = 1.0) = kelly_bet_fraction(probability; payout_ratio, max_fraction) + +""" + distributionally_robust_kelly_fraction(probability_estimate, n_observations; + payout_ratio=1.0, max_fraction=1.0, confidence_z=1.0) + +Evaluate long-only Kelly at the pessimistic edge of the supplied standard-error +box. Preserve the public clipping of estimates and lower bounds to +`[1e-4,1-1e-4]`, and the denominator floor of one observation. The confidence +multiplier is finite and nonnegative. This is the public box-ambiguity model; +no general ambiguity-set optimizer or interval-coverage guarantee is implied. +""" +function distributionally_robust_kelly_fraction(probability_estimate, n_observations; + payout_ratio = 1.0, max_fraction = 1.0, confidence_z = 1.0) + values = clamp.(_sizing_values(probability_estimate, "probability_estimate"), 1e-4, 1 - 1e-4) + n_observations isa Integer && !(n_observations isa Bool) || throw(ArgumentError("n_observations must be an integer")) + count = max(_tail_real_scalar(n_observations, "n_observations"), 1.0) + multiplier = _tail_real_scalar(confidence_z, "confidence_z") + multiplier >= 0 || throw(ArgumentError("confidence_z must be nonnegative")) + standard_error = sqrt.(values .* (1 .- values) ./ count) + lower = clamp.(values .- multiplier .* standard_error, 1e-4, 1 - 1e-4) + return kelly_bet_fraction(lower; payout_ratio, max_fraction) +end + +distributionally_robust_kelly_fraction(probability_estimate, n_observations, payout_ratio, max_fraction = 1.0, confidence_z = 1.0) = distributionally_robust_kelly_fraction(probability_estimate, n_observations; payout_ratio, max_fraction, confidence_z) + +""" + expected_calibration_error(probabilities, outcomes; n_bins=10) + +Return the bin-mass-weighted absolute confidence/accuracy gap. Equal-width bins +are right-closed, with zero included in the first bin, matching the public +floating-point edge construction. Probabilities and binary outcomes are +nonempty vectors of equal length. +""" +function expected_calibration_error(probabilities, outcomes; n_bins = 10) + probabilities isa AbstractVector && outcomes isa AbstractVector && !isempty(probabilities) && length(probabilities) == length(outcomes) || throw(ArgumentError("probabilities and outcomes must be nonempty equal-length vectors")) + values = _sizing_values(probabilities, "probabilities") + all(value -> 0 <= value <= 1, values) && all(value -> value isa Real && value in (0, 1), outcomes) || throw(ArgumentError("require probabilities in [0,1] and binary outcomes")) + n_bins isa Integer && !(n_bins isa Bool) && n_bins > 0 || throw(ArgumentError("n_bins must be a positive integer")) + edges = [index * (1.0 / n_bins) for index in 0:n_bins] + edges[end] = 1.0 + error = 0.0 + for index in 1:n_bins + selected = (index == 1 ? values .>= edges[index] : values .> edges[index]) .& (values .<= edges[index+1]) + count = sum(selected) + count == 0 && continue + error += count / length(values) * abs(mean(outcomes[selected]) - mean(values[selected])) + end + return error +end + +expected_calibration_error(probabilities, outcomes, n_bins) = expected_calibration_error(probabilities, outcomes; n_bins) + +""" + PlattCalibrator() + +Native logistic calibration of supplied probabilities on a separate calibration +block. Fit with `fit!` and evaluate with `transform`. The predictor is the +logit clipped at probabilities `1e-4` and `1-1e-4`; slope regularization matches +the public logistic model's `C=1e6`, with an unpenalized intercept. An unfit +calibrator or one-class calibration block uses the identity transformation. +Training/validation separation is the caller's responsibility. +""" +mutable struct PlattCalibrator + coefficients::Union{Nothing, NTuple{2, Float64}} +end + +PlattCalibrator() = PlattCalibrator(nothing) + +_platt_sigmoid(value) = value >= 0 ? 1 / (1 + exp(-value)) : exp(value) / (1 + exp(value)) + +function _platt_logits(values) + clipped = clamp.(_sizing_values(values, "predicted_probabilities"), 1e-4, 1 - 1e-4) + logarithms = log.(clipped ./ (1 .- clipped)) + return logarithms isa AbstractVector ? logarithms : vec(permutedims(logarithms, Tuple(ndims(logarithms):-1:1))) +end + +function _platt_loss_gradient!(gradient, parameters, logits, outcomes) + slope, intercept = parameters + loss, slope_score, intercept_score = 0.0, 0.0, 0.0 + for (value, outcome) in zip(logits, outcomes) + score = slope * value + intercept + signed_score = (1 - 2outcome) * score + loss += max(signed_score, 0) + log1p(exp(-abs(signed_score))) + residual = _platt_sigmoid(score) - outcome + slope_score += residual * value + intercept_score += residual + end + count = length(logits) + penalty = 1e-6 / count + gradient .= (slope_score / count + penalty * slope, intercept_score / count) + return loss / count + penalty * slope^2 / 2 +end + +"""Fit the public two-parameter penalized logistic map and return the same calibrator.""" +function fit!(calibrator::PlattCalibrator, predicted_probabilities, outcomes) + predicted_probabilities isa AbstractArray && !isempty(predicted_probabilities) && outcomes isa AbstractVector && length(predicted_probabilities) == length(outcomes) || throw(ArgumentError("calibration probabilities and outcomes must have matching nonempty observations")) + probabilities = _sizing_values(predicted_probabilities, "predicted_probabilities") + all(value -> value isa Real && value in (0, 1), outcomes) || throw(ArgumentError("calibration outcomes must be binary")) + if all(==(first(outcomes)), outcomes) + calibrator.coefficients = nothing + return calibrator + end + logits = _platt_logits(probabilities) + objective(parameters) = _platt_loss_gradient!(zeros(2), parameters, logits, outcomes) + gradient!(gradient, parameters) = (_platt_loss_gradient!(gradient, parameters, logits, outcomes); gradient) + result = Optim.optimize(objective, gradient!, zeros(2), Optim.LBFGS(), Optim.Options(g_tol = 1e-10, f_reltol = 1e-14, iterations = 1000)) + Optim.converged(result) && isfinite(Optim.minimum(result)) && all(isfinite, Optim.minimizer(result)) || error("Platt calibration failed to converge") + calibrator.coefficients = Tuple(Optim.minimizer(result)) + return calibrator +end + +"""Map supplied probabilities to a row-major flattened prediction vector, or retain their shape under identity.""" +function transform(calibrator::PlattCalibrator, predicted_probabilities) + values = _sizing_values(predicted_probabilities, "predicted_probabilities") + isnothing(calibrator.coefficients) && return values + all(isfinite, calibrator.coefficients) || throw(ArgumentError("calibrator coefficients must be finite")) + inputs = values isa Number ? [values] : values + slope, intercept = calibrator.coefficients + return _platt_sigmoid.(slope .* _platt_logits(inputs) .+ intercept) +end diff --git a/src/Backtest/Backtest.jl b/src/Backtest/Backtest.jl index 0b1f773..6d08716 100644 --- a/src/Backtest/Backtest.jl +++ b/src/Backtest/Backtest.jl @@ -38,6 +38,14 @@ include("MultipleTesting.jl") # Closed-form optimal Ornstein–Uhlenbeck trading rules (Lipton–López de Prado 2020). include("OUTradingRules.jl") +include("TailRisk.jl") +include("AdvancedBetSizing.jl") +include("Adaptation.jl") +include("DeepHedging.jl") + +export prequential_adaptation +export gbm_hedging_paths, hedging_pnl, entropic_risk, black_scholes_call_delta, + DeepHedge, train_deep_hedge export # backtest statistics @@ -103,6 +111,16 @@ export mean_exit_time, ou_rule_metrics, optimal_ou_trading_rule, - fit_ornstein_uhlenbeck + fit_ornstein_uhlenbeck, + expected_shortfall, + generalized_pareto_tail_risk, + fit_pot_tail_risk, + quantile_exception_tests, + kelly_bet_fraction, + distributionally_robust_kelly_fraction, + expected_calibration_error, + PlattCalibrator, + fit!, + transform end # module Backtest diff --git a/src/Backtest/BacktestStatistics.jl b/src/Backtest/BacktestStatistics.jl index e38d0d1..e8a8eab 100644 --- a/src/Backtest/BacktestStatistics.jl +++ b/src/Backtest/BacktestStatistics.jl @@ -74,7 +74,7 @@ function calculate_holding_period( holding = DataFrame(index = eltype(index)[], dT = Float64[], w = Float64[]) time_entry = 0.0 day_ms = 86_400_000.0 - time_diff = [Dates.value(index[i] - index[1]) / day_ms for i = 1:n] + time_diff = [Dates.value(Dates.Millisecond(index[i] - index[1])) / day_ms for i = 1:n] for i = 2:n current = target_positions[i] @@ -110,9 +110,10 @@ Mirrors Python's `calculate_hhi`. function calculate_hhi(bet_returns::AbstractVector{<:Real}) n = length(bet_returns) n <= 2 && return NaN - total = sum(bet_returns) + valid = filter(value -> !isnan(value), bet_returns) + total = sum(valid) total == 0 && return NaN - weights = bet_returns ./ total + weights = valid ./ total hhi = sum(weights .^ 2) return (hhi - 1.0 / n) / (1.0 - 1.0 / n) end @@ -126,8 +127,8 @@ monthly observation counts. Mirrors Python's `calculate_hhi_concentration`. function calculate_hhi_concentration(index::AbstractVector, returns::AbstractVector{<:Real}) positive = calculate_hhi(returns[returns .>= 0]) negative = calculate_hhi(returns[returns .< 0]) - months = [(year(t), month(t)) for t in index] - counts = Float64[count(==(key), months) for key in unique(months)] + months = [12year(t)+month(t) for t in index] + counts = isempty(months) ? Float64[] : Float64[count(position -> months[position] == key && !isnan(returns[position]), eachindex(months)) for key in minimum(months):maximum(months)] time = calculate_hhi(counts) return (positive = positive, negative = negative, time = time) end @@ -149,7 +150,7 @@ function compute_drawdowns_time_under_water( high_water_mark = Vector{Float64}(undef, n) running = -Inf for i = 1:n - running = max(running, pnl[i]) + isnan(pnl[i]) || (running = max(running, pnl[i])) high_water_mark[i] = running end @@ -163,7 +164,12 @@ function compute_drawdowns_time_under_water( while j < n && high_water_mark[j+1] == high_water_mark[i] j += 1 end - group_min = minimum(@view pnl[i:j]) + valid = filter(value -> !isnan(value), @view pnl[i:j]) + if isempty(valid) + i = j + 1 + continue + end + group_min = minimum(valid) if (j - i + 1) > 1 && high_water_mark[i] != group_min push!(starts, index[i]) push!( @@ -171,7 +177,7 @@ function compute_drawdowns_time_under_water( dollars ? high_water_mark[i] - group_min : 1.0 - group_min / high_water_mark[i], ) - years = (Dates.value(index[j] - index[i]) / day_ms) / 365.25 + years = (Dates.value(Dates.Millisecond(index[j] - index[i])) / day_ms) / 365.25 push!(time_under_water, years) end i = j + 1 @@ -312,7 +318,8 @@ end """ sharpe_difference_test(returns_a, returns_b; method="ledoit_wolf", - block_length=nothing, n_boot=1000, bandwidth=nothing, random_state=0) + block_length=nothing, n_boot=1000, bandwidth=nothing, random_state=0, + bootstrap_indices=nothing) Test H0: SR_A = SR_B. `method="naive"` is the influence-function delta-method z-test using the i.i.d. variance (ignores serial dependence, over-rejects under @@ -326,9 +333,11 @@ prefer the Ledoit-Wolf bootstrap Sharpe-difference test when comparing two Sharp under serial dependence/heavy tails (holds nominal size where the naive test inflates ~3×; converges on i.i.d.). -Deliberate divergence: the LW bootstrap uses Julia's `rng`, so its p-value is -reproducible under `random_state` but not bit-identical to Python (delta, se and -stat are deterministic and parity-matched). Mirrors Python's +The default bootstrap uses Julia's random stream. Supply `bootstrap_indices` +as one vector of one-based positions per resample to replay shared circular-block +draws exactly. Every sample must contain the full track length and preserve +consecutive circular positions within each block. Equal language-specific seeds +alone do not imply equal p-values. Mirrors Python's `sharpe_difference_test`. Reference: Ledoit & Wolf (2008), Journal of Empirical Finance 15(5). """ @@ -340,6 +349,7 @@ function sharpe_difference_test( n_boot::Integer = 1000, bandwidth::Union{Integer,Nothing} = nothing, random_state::Integer = 0, + bootstrap_indices = nothing, ) (method in ("ledoit_wolf", "naive")) || throw(ArgumentError("method must be 'ledoit_wolf' or 'naive'")) @@ -359,12 +369,20 @@ function sharpe_difference_test( t = length(a) bl = block_length === nothing ? max(1, ceil(Int, t^(1.0 / 3.0))) : block_length bw = bandwidth === nothing ? bl : bandwidth + bl >= 1 && n_boot >= 0 && t >= 1 || throw(ArgumentError("bootstrap needs positive block length, nonnegative count and a nonempty track")) + if bootstrap_indices !== nothing + length(bootstrap_indices) == n_boot || throw(DimensionMismatch("bootstrap indices must provide one vector per resample")) + for indices in bootstrap_indices + length(indices) == t && all(index -> index isa Integer && 1 <= index <= t, indices) || throw(ArgumentError("bootstrap indices must index a full observation track")) + all((position-1)%bl == 0 || indices[position] == mod1(indices[position-1]+1, t) for position in 2:t) || throw(ArgumentError("bootstrap positions must follow consecutive circular blocks")) + end + end se_hat = sqrt(_bartlett_hac_var(influence, bw)) z_hat = se_hat > 0 ? abs(delta / se_hat) : 0.0 rng = MersenneTwister(random_state) count_ge = 0 - for _ = 1:n_boot - idx = _circular_block_indices(t, bl, rng) + for sample = 1:n_boot + idx = bootstrap_indices === nothing ? _circular_block_indices(t, bl, rng) : Int.(bootstrap_indices[sample]) delta_b, influence_b = _sharpe_influence(a[idx], b[idx]) se_b = sqrt(_bartlett_hac_var(influence_b, bw)) se_b <= 0 && continue diff --git a/src/Backtest/DeepHedging.jl b/src/Backtest/DeepHedging.jl new file mode 100644 index 0000000..b75cc23 --- /dev/null +++ b/src/Backtest/DeepHedging.jl @@ -0,0 +1,204 @@ +import Zygote +import Optimisers + +function _hedging_array(value, name, dimension) + value isa AbstractArray && ndims(value) == dimension && eltype(value) <: AbstractFloat && !isempty(value) && all(isfinite, value) || throw(ArgumentError("$name must be a nonempty finite floating array with $dimension dimensions")) + return value +end + +function _hedging_count(value, name) + value isa Integer && !(value isa Bool) && 0 < value <= typemax(Int) || throw(ArgumentError("$name must be a positive supported integer")) + return Int(value) +end + +function _hedging_rng(seed) + seed isa Integer && !(seed isa Bool) && typemin(Int64) <= seed <= typemax(UInt64) || throw(ArgumentError("seed must be an integer in the supported 64-bit range")) + return MersenneTwister(UInt64(mod(BigInt(seed), BigInt(1) << 64))) +end + +function _hedging_positive(value, name; allow_zero = false) + result = _tail_real_scalar(value, name) + (allow_zero ? result >= 0 : result > 0) || throw(ArgumentError("$name is below its allowed bound")) + return result +end + +""" + gbm_hedging_paths(; spot, maturity, volatility, drift, steps, paths, seed, + normal_draws=nothing) + +Return Float64 GBM observations with shape `(paths,steps+1)`. The local Julia +RNG preserves global state. Supplied standard-normal primitive draws with +shape `(paths,steps)` permit exact cross-language comparisons; equal seeds +do not identify equal random streams. Drift specifies the simulation measure. +Zero interest and dividend rates remain the benchmark's accounting convention. +""" +function gbm_hedging_paths(; spot, maturity, volatility, drift, steps, paths, seed, normal_draws = nothing) + initial = _hedging_positive(spot, "spot") + horizon = _hedging_positive(maturity, "maturity") + sigma = _hedging_positive(volatility, "volatility"; allow_zero = true) + mu = _tail_real_scalar(drift, "drift") + count, samples = _hedging_count(steps, "steps"), _hedging_count(paths, "paths") + rng = _hedging_rng(seed) + draws = if isnothing(normal_draws) + randn(rng, samples, count) + else + _hedging_array(normal_draws, "normal_draws", 2) + size(normal_draws) == (samples, count) || throw(ArgumentError("normal_draws must match paths and steps")) + Float64.(normal_draws) + end + dt = horizon / count + increments = (mu - sigma^2 / 2) * dt .+ sigma * sqrt(dt) .* draws + result = initial .* exp.(hcat(zeros(samples, 1), cumsum(increments; dims = 2))) + all(value -> isfinite(value) && value > 0, result) || throw(ArgumentError("GBM paths exceeded the supported numerical range")) + return result +end + +""" + hedging_pnl(prices, positions; strike, cost_rate, premium=0.0) + +Return terminal cash flow of a short European call and self-financing hedge. +Prices have shape `(paths,steps+1)` and positions `(paths,steps)`, with the same +floating element type. Proportional costs include initial buying and terminal +liquidation. Positions may be unconstrained; their adaptation to available +information is the caller's responsibility. Native Zygote gradients are supported. +""" +function hedging_pnl(prices, positions; strike, cost_rate, premium = 0.0) + strike_value, cost, initial_cash = Zygote.ignore() do + _hedging_array(prices, "prices", 2) + _hedging_array(positions, "positions", 2) + size(prices) == (size(positions, 1), size(positions, 2) + 1) && all(>(0), prices) && eltype(prices) == eltype(positions) || throw(ArgumentError("Positive prices and matching positions must share a floating element type")) + type = eltype(prices) + return type(_hedging_positive(strike, "strike")), type(_hedging_positive(cost_rate, "cost_rate"; allow_zero = true)), type(_tail_real_scalar(premium, "premium")) + end + trades = hcat(positions[:, 1:1], positions[:, 2:end] .- positions[:, 1:end-1], -positions[:, end:end]) + gains = vec(sum(positions .* (prices[:, 2:end] .- prices[:, 1:end-1]); dims = 2)) + costs = cost .* vec(sum(prices .* abs.(trades); dims = 2)) + excess = prices[:, end] .- strike_value + payoff = ifelse.(excess .> 0, excess, zero(eltype(prices))) + result = initial_cash .+ gains .- costs .- payoff + Zygote.ignore() do + all(isfinite, result) || throw(ArgumentError("Hedging cash flow exceeded the supported numerical range")) + end + return result +end + +"""Stable entropic risk of supplied PnL with positive risk aversion and native gradients.""" +function entropic_risk(pnl, risk_aversion) + aversion, anchor = Zygote.ignore() do + _hedging_array(pnl, "pnl", 1) + return eltype(pnl)(_hedging_positive(risk_aversion, "risk_aversion")), minimum(pnl) + end + result = -anchor + (log(sum(exp.(-aversion .* (pnl .- anchor)))) - eltype(pnl)(log(length(pnl)))) / aversion + Zygote.ignore() do + isfinite(result) || throw(ArgumentError("Entropic risk exceeded the supported numerical range")) + end + return result +end + +"""Zero-rate European call deltas at the preterminal equally spaced observation times.""" +function black_scholes_call_delta(prices; strike, maturity, volatility) + _hedging_array(prices, "prices", 2) + size(prices, 2) >= 2 && all(>(0), prices) || throw(ArgumentError("At least two positive price observations are required")) + type = eltype(prices) + strike_value = type(_hedging_positive(strike, "strike")) + horizon = type(_hedging_positive(maturity, "maturity")) + sigma = type(_hedging_positive(volatility, "volatility")) + count = size(prices, 2) - 1 + remaining = horizon .* type.(count:-1:1) ./ type(count) + d1 = (log.(prices[:, 1:end-1] ./ strike_value) .+ sigma^2 .* remaining' ./ type(2)) ./ (sigma .* sqrt.(remaining')) + return type.(cdf.(Normal(), d1)) +end + +function _hedging_parameters(parameters, hidden_size) + parameters isa NamedTuple && keys(parameters) == (:weight1, :bias1, :weight2, :bias2, :weight3, :bias3) || throw(ArgumentError("parameters must name the three dense weights and biases")) + shapes = ((hidden_size, 3), (hidden_size,), (hidden_size, hidden_size), (hidden_size,), (1, hidden_size), (1,)) + for (value, shape) in zip(values(parameters), shapes) + value isa AbstractArray{Float64} && size(value) == shape && all(isfinite, value) || throw(ArgumentError("hedge parameters must be finite Float64 arrays with the specified layer shapes")) + end + return parameters +end + +""" + DeepHedge(hidden_size=16; seed=0, parameters=nothing) + +Shared three-layer dense hedge with two tanh hidden activations. Inputs are +remaining time, current log-price relative to strike and the previous holding; +output positions are unconstrained. The local seeded initializer uses the +public fan-in uniform bounds in Float32 before conversion to Float64. +Explicit Float64 parameter arrays allow shared-weight validation. Evaluate +with `model(prices; strike,maturity)` and fit with `train_deep_hedge`. +""" +mutable struct DeepHedge + hidden_size::Int + parameters::NamedTuple + function DeepHedge(hidden_size = 16; seed = 0, parameters = nothing) + width = _hedging_count(hidden_size, "hidden_size") + rng = _hedging_rng(seed) + if isnothing(parameters) + initialize(shape, inputs) = Float64.((rand(rng, Float32, shape) .* 2f0 .- 1f0) .* Float32(1 / sqrt(inputs))) + parameters = (weight1 = initialize((width, 3), 3), bias1 = initialize((width,), 3), + weight2 = initialize((width, width), width), bias2 = initialize((width,), width), + weight3 = initialize((1, width), width), bias3 = initialize((1,), width)) + end + checked = _hedging_parameters(parameters, width) + new(width, map(copy, checked)) + end +end + +function _deep_hedge_positions(parameters, prices, strike, maturity) + count = size(prices, 2) - 1 + initial = (previous = zeros(eltype(prices), size(prices, 1)), + positions = zeros(eltype(prices), size(prices, 1), 0)) + result = foldl(1:count; init = initial) do state, index + features = vcat(fill(maturity * (count - index + 1) / count, 1, size(prices, 1)), + permutedims(log.(prices[:, index] ./ strike)), permutedims(state.previous)) + hidden1 = tanh.(parameters.weight1 * features .+ parameters.bias1) + hidden2 = tanh.(parameters.weight2 * hidden1 .+ parameters.bias2) + holding = vec(parameters.weight3 * hidden2 .+ parameters.bias3) + return (previous = holding, positions = hcat(state.positions, holding)) + end + return result.positions +end + +function (model::DeepHedge)(prices; strike, maturity) + strike_value, horizon = Zygote.ignore() do + _hedging_array(prices, "prices", 2) + eltype(prices) == Float64 && size(prices, 2) >= 2 && all(>(0), prices) || throw(ArgumentError("Hedge prices must be positive Float64 observations with at least two times")) + _hedging_parameters(model.parameters, model.hidden_size) + return _hedging_positive(strike, "strike"), _hedging_positive(maturity, "maturity") + end + return _deep_hedge_positions(model.parameters, prices, strike_value, horizon) +end + +""" + train_deep_hedge(model, training_prices; strike, maturity, cost_rate, + risk_aversion, iterations, learning_rate=0.001) + +Mutate the hedge parameters with full-batch Adam and return each pre-update +entropic loss. Only supplied training paths enter optimization. Adam retains +the public moment decays `(0.9,0.999)` and epsilon `1e-8`. Evaluate independent +paths afterwards; no evaluation outcomes choose weights or stopping here. +Finite gradients/parameters are required; no optimality or superiority is asserted. +""" +function train_deep_hedge(model::DeepHedge, training_prices; strike, maturity, cost_rate, + risk_aversion, iterations, learning_rate = 0.001) + count = _hedging_count(iterations, "iterations") + rate = _hedging_positive(learning_rate, "learning_rate") + model(training_prices; strike, maturity) + strike_value = _hedging_positive(strike, "strike") + horizon = _hedging_positive(maturity, "maturity") + objective(parameters) = entropic_risk(hedging_pnl(training_prices, + _deep_hedge_positions(parameters, training_prices, strike_value, horizon); strike, cost_rate), risk_aversion) + state = Optimisers.setup(Optimisers.Adam(rate, (0.9, 0.999), 1e-8), model.parameters) + history = Float64[] + for _ in 1:count + loss, pullback = Zygote.pullback(objective, model.parameters) + gradient = only(pullback(one(loss))) + all(value -> value isa AbstractArray && all(isfinite, value), values(gradient)) || error("Nonfinite hedging gradients") + state, parameters = Optimisers.update(state, model.parameters, gradient) + all(value -> all(isfinite, value), values(parameters)) || error("Nonfinite fitted hedge parameters") + model.parameters = parameters + push!(history, loss) + end + return history +end diff --git a/src/Backtest/OUTradingRules.jl b/src/Backtest/OUTradingRules.jl index 562ea10..4d501d5 100644 --- a/src/Backtest/OUTradingRules.jl +++ b/src/Backtest/OUTradingRules.jl @@ -23,6 +23,7 @@ optimal mean-reverting trading strategies; López de Prado (2018), AFML ch.13. """ using SpecialFunctions: erfi +using LinearAlgebra: pinv, eigen, SymTridiagonal # Trapezoidal integral ∫ y dx (matches numpy.trapz, including descending x). function _ou_trapz(y, x) @@ -94,33 +95,103 @@ function _mean_exit_time_quadrature(entry_gap, profit_take, stop_loss, theta, si return _ou_trapz(green .* speed, ys) end +const _OU_QUADRATURE_NODES, _OU_QUADRATURE_WEIGHTS = let + off_diagonal = [i / sqrt(4.0 * i * i - 1.0) for i in 1:63] + decomposition = eigen(SymTridiagonal(zeros(64), off_diagonal)) + decomposition.values, 2.0 .* decomposition.vectors[1, :].^2 +end + +"""Return split-integral time, probabilities and analytic barrier derivatives.""" +function _ou_exit_geometry(entry_gap, profit_take, stop_loss, theta, sigma) + c = theta / (sigma * sigma) + y0 = -float(entry_gap) + a, b = y0 - stop_loss, y0 + profit_take + rc = sqrt(c) + pref = sqrt(pi) / (2.0 * rc) + ea, eb, ey = erfi(rc * a), erfi(rc * b), erfi(rc * y0) + all(isfinite, (ea, eb, ey)) || return nothing + total = pref * (eb - ea) + upper, lower = pref * (ey - ea) / total, pref * (eb - ey) / total + integrals = Tuple{Float64, Float64}[] + for (left, right) in [(a, y0), (y0, b)] + half = (right - left) / 2.0 + nodes = (left + right) / 2.0 .+ half .* _OU_QUADRATURE_NODES + values = erfi.(rc .* nodes) + speed = (2.0 / (sigma * sigma)) .* exp.(-c .* nodes .* nodes) + from_lower = pref .* (values .- ea) .* speed + from_upper = pref .* (eb .- values) .* speed + push!(integrals, (half * sum(_OU_QUADRATURE_WEIGHTS .* from_lower), half * sum(_OU_QUADRATURE_WEIGHTS .* from_upper))) + end + time = lower * integrals[1][1] + upper * integrals[2][2] + ratio_upper, ratio_lower = exp(c * b * b) / total, exp(c * a * a) / total + time_profit = upper * ratio_upper * (integrals[1][1] + integrals[2][1]) + time_stop = lower * ratio_lower * (integrals[1][2] + integrals[2][2]) + result = (time, upper, lower, time_profit, time_stop, -upper * ratio_upper, lower * ratio_lower) + return all(isfinite, result) ? result : nothing +end + +"""Differentiate the same first-passage return-rate objective at finite barriers.""" +function _ou_rate_gradient(profit_take, stop_loss, theta, sigma, entry_gap, cost) + geometry = _ou_exit_geometry(entry_gap, profit_take, stop_loss, theta, sigma) + (isnothing(geometry) || geometry[1] <= 0) && return (NaN, NaN) + time, upper, lower, time_profit, time_stop, upper_profit, upper_stop = geometry + gain = profit_take * upper - stop_loss * lower - cost + gain_profit = upper + (profit_take + stop_loss) * upper_profit + gain_stop = -lower + (profit_take + stop_loss) * upper_stop + return ((gain_profit * time - gain * time_profit) / (time * time), (gain_stop * time - gain * time_stop) / (time * time)) +end + +"""Retain edge endpoints and refine every sampled stationary maximum bracket.""" +function _ou_boundary_candidates(theta, sigma, entry_gap, cost, lo, hi) + candidates = Tuple{Float64, Float64}[] + for axis in 1:2 + for fixed in [lo, hi] + point(value) = axis == 1 ? (value, fixed) : (fixed, value) + grid = collect(range(lo, hi; length=41)) + derivatives = Float64[] + for value in grid + pair = point(value) + push!(candidates, pair) + push!(derivatives, _ou_rate_gradient(pair..., theta, sigma, entry_gap, cost)[axis]) + end + for index in 1:length(grid)-1 + dl, dr = derivatives[index], derivatives[index+1] + (isfinite(dl) && isfinite(dr) && dl > 0 && dr < 0) || continue + left, right = grid[index], grid[index+1] + for _ in 1:52 + middle = (left + right) / 2.0 + (middle == left || middle == right) && break + derivative = _ou_rate_gradient(point(middle)..., theta, sigma, entry_gap, cost)[axis] + isfinite(derivative) || break + if derivative > 0 + left = middle + elseif derivative < 0 + right = middle + else + left = right = middle + break + end + end + push!(candidates, point((left + right) / 2.0)) + end + end + end + return candidates +end + """ mean_exit_time(entry_gap, profit_take, stop_loss, theta, sigma) -> Float64 Closed-form OU mean first-exit time `E[τ]` from the two-barrier interval via the -Karlin–Taylor Green's function. Mirrors Python's `mean_exit_time`. +Karlin–Taylor Green's function, integrated separately on each side of entry. +Mirrors Python's `mean_exit_time`. """ function mean_exit_time(entry_gap, profit_take, stop_loss, theta, sigma) - c = theta / (sigma * sigma) y0 = -float(entry_gap) - a, b = y0 - stop_loss, y0 + profit_take - b <= a && return NaN - rc = sqrt(c) - pref = sqrt(pi) / (2.0 * rc) - ys = collect(range(a, b; length = 400)) - erfi_y = erfi.(rc .* ys) - erfi_a, erfi_b, erfi_y0 = erfi(rc * a), erfi(rc * b), erfi(rc * y0) - (all(isfinite, erfi_y) && isfinite(erfi_b)) || - return _mean_exit_time_quadrature(entry_gap, profit_take, stop_loss, theta, sigma) - s_ya = pref .* (erfi_y .- erfi_a) - s_by = pref .* (erfi_b .- erfi_y) - s_ba = pref * (erfi_b - erfi_a) - s_y0a = pref * (erfi_y0 - erfi_a) - s_by0 = pref * (erfi_b - erfi_y0) - green = - [(ys[i] <= y0 ? s_ya[i] * s_by0 : s_y0a * s_by[i]) / s_ba for i in eachindex(ys)] - speed = (2.0 / (sigma * sigma)) .* exp.(-c .* ys .^ 2) - return _ou_trapz(green .* speed, ys) + y0 + profit_take <= y0 - stop_loss && return NaN + geometry = _ou_exit_geometry(entry_gap, profit_take, stop_loss, theta, sigma) + isnothing(geometry) && return _mean_exit_time_quadrature(entry_gap, profit_take, stop_loss, theta, sigma) + return geometry[1] end """ @@ -143,7 +214,7 @@ function ou_rule_metrics(profit_take, stop_loss, theta, sigma, entry_gap, cost = (profit_take <= 0 || stop_loss <= 0) && return bad u = hit_upper_probability(entry_gap, profit_take, stop_loss, theta, sigma) isfinite(u) || return bad - q = 1.0 - u + q = hit_upper_probability(-entry_gap, stop_loss, profit_take, theta, sigma) expected_gain = profit_take * u - stop_loss * q - cost std_gain = (profit_take + stop_loss) * sqrt(max(u * q, 0.0)) e_tau = mean_exit_time(entry_gap, profit_take, stop_loss, theta, sigma) @@ -173,7 +244,8 @@ misspecification), so check the OU fit at decision time. Objective: expected net return per unit time. Deliberate divergence: a coarse-to-fine grid search replaces SciPy's L-BFGS-B -multi-start (no Optim dependency). Mirrors Python's `optimal_ou_trading_rule`. +multi-start (no Optim dependency). Both searches additionally check endpoints and +refine derivative roots on all four box edges. Mirrors Python's `optimal_ou_trading_rule`. """ function optimal_ou_trading_rule(theta, sigma, entry_gap; cost = 0.0, bounds = (0.25, 4.0)) lo, hi = bounds @@ -198,6 +270,12 @@ function optimal_ou_trading_rule(theta, sigma, entry_gap; cost = 0.0, bounds = ( blo_pt, bhi_pt = max(lo, best_pt - dpt), min(hi, best_pt + dpt) blo_sl, bhi_sl = max(lo, best_sl - dsl), min(hi, best_sl + dsl) end + for (pt, sl) in _ou_boundary_candidates(theta, sigma, entry_gap, cost, lo, hi) + value = objective(pt, sl) + if value > best_v + best_v, best_pt, best_sl = value, pt, sl + end + end metrics = ou_rule_metrics(best_pt, best_sl, theta, sigma, entry_gap, cost) return merge((profit_take = best_pt, stop_loss = best_sl), metrics) end @@ -214,15 +292,14 @@ function fit_ornstein_uhlenbeck(series::AbstractVector{<:Real}; dt::Real = 1.0) x = float.(filter(isfinite, series)) x0 = @view x[1:(end-1)] x1 = @view x[2:end] - mx0, mx1 = mean(x0), mean(x1) - b = sum((x0 .- mx0) .* (x1 .- mx1)) / sum((x0 .- mx0) .^ 2) - a = mx1 - b * mx0 + mx1 = mean(x1) + a, b = pinv(hcat(ones(length(x0)), x0)) * x1 residual = x1 .- (a .+ b .* x0) b = min(max(b, 1e-6), 0.999999) theta = -log(b) / dt mu = a / (1.0 - b) n1 = length(x1) - var_eps = sum(residual .^ 2) / (n1 - 2) # ddof = 2 + var_eps = sum((residual .- mean(residual)) .^ 2) / (n1 - 2) sigma = sqrt(var_eps * 2.0 * theta / (1.0 - b * b)) ss_tot = sum((x1 .- mx1) .^ 2) r2 = ss_tot > 0 ? 1.0 - sum(residual .^ 2) / ss_tot : 0.0 diff --git a/src/Backtest/ProbabilisticSharpeRatio.jl b/src/Backtest/ProbabilisticSharpeRatio.jl index ecd3cb6..35192a8 100644 --- a/src/Backtest/ProbabilisticSharpeRatio.jl +++ b/src/Backtest/ProbabilisticSharpeRatio.jl @@ -103,7 +103,8 @@ end Newey–West (Bartlett-kernel) long-run variance `Ω̂ = γ₀ + 2 Σₖ₌₁ᴸ (1 - k/(L+1)) γₖ`, with `γₖ` the lag-`k` autocovariance; `lag=0` -gives the sample variance. Floored at `1e-12`. Mirrors Python's +gives the variance with denominator `T`. Lags beyond the track contribute zero +while the original Bartlett denominator is retained. Floored at `1e-12`. Mirrors Python's `newey_west_long_run_variance`. """ function newey_west_long_run_variance(series::AbstractVector{<:Real}, lag::Integer) @@ -112,7 +113,7 @@ function newey_west_long_run_variance(series::AbstractVector{<:Real}, lag::Integ t = length(x) t == 0 && return 1e-12 total = (x' * x) / t - for k = 1:lag + for k = 1:min(lag, t-1) weight = 1.0 - k / (lag + 1.0) total += 2.0 * weight * (x[(k+1):end]' * x[1:(end-k)]) / t end diff --git a/src/Backtest/ProbabilityOfBacktestOverfitting.jl b/src/Backtest/ProbabilityOfBacktestOverfitting.jl index ef45916..4f187b7 100644 --- a/src/Backtest/ProbabilityOfBacktestOverfitting.jl +++ b/src/Backtest/ProbabilityOfBacktestOverfitting.jl @@ -5,7 +5,9 @@ AFML Ch. 11–12): the combinatorially-symmetric cross-validation (CSCV) estimat of PBO. The metric defaults to the population-std Sharpe ratio (`Backtest.sharpe_ratio`); -results match the Python implementation exactly (verified in `test/runtests.jl`). +shared checks cover distinct scores and supplied reference rank orders. Native +stable sorting can assign different ordinal ranks to tied scores than NumPy's +default sort, including different overfit decisions. The combinatorial train/test splits use `Combinatorics.combinations`, mirroring Python's `itertools.combinations`, and the row partitioning mirrors `numpy.array_split` (the first `T mod S` partitions take one extra row). @@ -13,28 +15,46 @@ Python's `itertools.combinations`, and the row partitioning mirrors Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 11. """ +function _first_metric_maximum(values) + isempty(values) && throw(ArgumentError("At least one strategy is required.")) + selected = firstindex(values) + for index in (selected+1):lastindex(values) + if !isnan(values[selected]) && (isnan(values[index]) || values[index] > values[selected]) + selected = index + end + end + return selected +end + """ performance_evaluation(train_partition, test_partition, n_strategies, metric, risk_free_return) -> (is_overfit, logit_value) Pick the best strategy in-sample (by `metric`), find its out-of-sample rank, and return whether it landed in the bottom half (`logit ≤ 0`) together with the logit -of its relative rank. Mirrors Python's `performance_evaluation`. +of its relative rank. Optional `rank_order(scores)` supplies the one-based +ascending strategy permutation, including exact tie order. Without a provider, +native stable sorting is retained and is not equivalent to NumPy tie ordering. """ function performance_evaluation( train_partition::AbstractMatrix{<:Real}, test_partition::AbstractMatrix{<:Real}, n_strategies::Integer, metric, - risk_free_return::Real, + risk_free_return::Real; + rank_order = nothing, ) evaluate_train = [metric(view(train_partition, :, i), risk_free_return) for i = 1:n_strategies] - best_strategy_idx = argmax(evaluate_train) + best_strategy_idx = _first_metric_maximum(evaluate_train) evaluate_test = [metric(view(test_partition, :, i), risk_free_return) for i = 1:n_strategies] # 1-based ordinal ranks (argsort∘argsort); equals Python's rank + 1. - ranks = sortperm(sortperm(evaluate_test)) + order = rank_order === nothing ? sortperm(evaluate_test) : Vector{Int}(rank_order(copy(evaluate_test))) + sort(order) == collect(1:n_strategies) || throw(ArgumentError("Rank order must be a permutation of all strategies.")) + ordered = evaluate_test[order] + any((isnan(ordered[index]) && !isnan(ordered[index+1])) || (!isnan(ordered[index+1]) && ordered[index] > ordered[index+1]) for index in 1:(length(ordered)-1)) && throw(ArgumentError("Rank order must be nondecreasing with NaNs last.")) + ranks = invperm(order) rank_of_best = ranks[best_strategy_idx] w_bar = rank_of_best / (n_strategies + 1) logit_value = log(w_bar / (1 - w_bar)) @@ -50,13 +70,15 @@ train/test splits of the `T×N` `performances` matrix (`S = n_partitions`, must even). `pbo` is the share of splits whose best in-sample strategy underperforms out-of-sample; `logit_values` are the per-split logits. `metric` defaults to the population-std Sharpe ratio. Mirrors Python's -`probability_of_backtest_overfitting`. +`probability_of_backtest_overfitting`. Optional `rank_order` is forwarded to +each path's performance evaluation for exact tied-score replay. """ function probability_of_backtest_overfitting( performances::AbstractMatrix{<:Real}; n_partitions::Integer = 16, risk_free_return::Real = 0.0, metric = nothing, + rank_order = nothing, ) isodd(n_partitions) && throw(ArgumentError("Number of partitions must be even.")) chosen_metric = @@ -85,7 +107,8 @@ function probability_of_backtest_overfitting( test, n_strategies, chosen_metric, - risk_free_return, + risk_free_return; + rank_order = rank_order, ) push!(overfits, is_overfit) push!(logit_values, logit_value) diff --git a/src/Backtest/StrategyRisk.jl b/src/Backtest/StrategyRisk.jl index dc33bb4..43bcca1 100644 --- a/src/Backtest/StrategyRisk.jl +++ b/src/Backtest/StrategyRisk.jl @@ -113,10 +113,14 @@ end """ mix_gaussians(mu1, mu2, sigma1, sigma2, probability, n_obs; - rng=Random.default_rng()) -> Vector{Float64} + rng=Random.default_rng(), normal_draws=nothing, permutation=nothing) + -> Vector{Float64} Mixture of two Gaussian bet outcomes: `⌊n_obs·probability⌋` draws from -`N(mu1, sigma1)` and the rest from `N(mu2, sigma2)`, shuffled. Stochastic. +`N(mu1, sigma1)` and the rest from `N(mu2, sigma2)`, shuffled. Supply a pair of +standard-normal draw vectors and a one-based output permutation for exact replay. +Neither supplied draws nor the permutation are mutated. Default draws use the +native random stream, without implying NumPy seed equivalence. Mirrors Python's `mix_gaussians`. """ function mix_gaussians( @@ -127,14 +131,28 @@ function mix_gaussians( probability::Real, n_obs::Integer; rng::AbstractRNG = Random.default_rng(), + normal_draws = nothing, + permutation = nothing, ) + n_obs >= 0 && 0 <= probability <= 1 && sigma1 >= 0 && sigma2 >= 0 || throw(ArgumentError("mixture sizes, probabilities and deviations must be valid")) n_obs1 = trunc(Int, n_obs * probability) n_obs2 = n_obs - n_obs1 - returns1 = mu1 .+ sigma1 .* randn(rng, n_obs1) - returns2 = mu2 .+ sigma2 .* randn(rng, n_obs2) + if normal_draws !== nothing + length(normal_draws) == 2 && length(normal_draws[1]) == n_obs1 && length(normal_draws[2]) == n_obs2 || throw(DimensionMismatch("normal draws must match both mixture component counts")) + all(isfinite, normal_draws[1]) && all(isfinite, normal_draws[2]) || throw(ArgumentError("normal draws must be finite")) + end + draws1 = normal_draws === nothing ? randn(rng, n_obs1) : Float64[normal_draws[1]...] + draws2 = normal_draws === nothing ? randn(rng, n_obs2) : Float64[normal_draws[2]...] + returns1 = mu1 .+ sigma1 .* draws1 + returns2 = mu2 .+ sigma2 .* draws2 returns = vcat(returns1, returns2) - shuffle!(rng, returns) - return returns + if permutation === nothing + shuffle!(rng, returns) + return returns + end + indices = Int[permutation...] + sort(indices) == collect(1:n_obs) || throw(ArgumentError("permutation must index every mixture observation exactly once")) + return returns[indices] end """ diff --git a/src/Backtest/TailRisk.jl b/src/Backtest/TailRisk.jl new file mode 100644 index 0000000..e8c02a9 --- /dev/null +++ b/src/Backtest/TailRisk.jl @@ -0,0 +1,141 @@ +using Distributions: Chisq, ccdf +import Optim + +function _tail_real_scalar(value, name) + value isa Real && !(value isa Bool) && isfinite(value) || throw(ArgumentError("$name must be a finite real scalar")) + result = Float64(value) + isfinite(result) || throw(ArgumentError("$name is outside the Float64 range")) + return result +end + +function _tail_real_vector(values, name) + values isa AbstractVector && !isempty(values) && all(value -> value isa Real && !(value isa Bool) && isfinite(value), values) || throw(ArgumentError("$name must be a nonempty finite real vector")) + result = Float64.(values) + all(isfinite, result) || throw(ArgumentError("$name is outside the Float64 range")) + return result +end + +""" + expected_shortfall(returns, step_risk_free_rate; confidence_level=0.05) + +Return the mean of signed returns at or below their linearly interpolated +empirical quantile. The supplied level lies in `[0,1]`, and ties are included. +The risk-free argument is retained for public API compatibility and is unused, +as in Python 3.2.0. No sign reversal or risk-free subtraction is performed. +""" +function expected_shortfall(returns, step_risk_free_rate; confidence_level = 0.05) + values = _tail_real_vector(returns, "returns") + level = _tail_real_scalar(confidence_level, "confidence_level") + 0 <= level <= 1 || throw(ArgumentError("confidence_level must lie in [0, 1]")) + cutoff = quantile(values, level) + return mean(values[values .<= cutoff]) +end + +expected_shortfall(returns, step_risk_free_rate, confidence_level) = expected_shortfall(returns, step_risk_free_rate; confidence_level) + +""" + generalized_pareto_tail_risk(threshold, exceedance_probability, shape, scale, probability) + +Return the unconditional loss quantile and upper-tail mean for the supplied +zero-location excess model. Require `0 < rate <= 1`, positive scale and +`1-rate < probability < 1`. Shape zero uses the exponential limit; shape at +least one gives infinite expected shortfall. All loss quantities share units. +No threshold-selection rule or time-series filter is implied. +""" +function generalized_pareto_tail_risk(threshold, exceedance_probability, shape, scale, probability) + threshold = _tail_real_scalar(threshold, "threshold") + rate = _tail_real_scalar(exceedance_probability, "exceedance_probability") + shape = _tail_real_scalar(shape, "shape") + scale = _tail_real_scalar(scale, "scale") + probability = _tail_real_scalar(probability, "probability") + scale > 0 && 0 < rate <= 1 && 1 - rate < probability < 1 || throw(ArgumentError("require positive scale, 0 < rate <= 1 and 1-rate < probability < 1")) + log_survival = log((1 - probability) / rate) + excess = scale * (shape == 0 ? -log_survival : expm1(-shape * log_survival) / shape) + value_at_risk = threshold + excess + expected_shortfall = shape >= 1 ? Inf : value_at_risk + (scale + shape * excess) / (1 - shape) + isfinite(value_at_risk) && (shape >= 1 || isfinite(expected_shortfall)) || throw(ArgumentError("tail risk is outside the supported floating-point range")) + return (; value_at_risk, expected_shortfall) +end + +function _pareto_negative_log_likelihood(parameters, excesses) + shape, scale = parameters + isfinite(shape) && isfinite(scale) && scale > 0 || return Inf + value = length(excesses) * log(scale) + for excess in excesses + standardized = excess / scale + support = 1 + shape * standardized + support > 0 || return Inf + value += shape == 0 ? standardized : (1 + shape) * log1p(shape * standardized) / shape + end + return isfinite(value) ? value : Inf +end + +""" + fit_pot_tail_risk(losses; threshold, probability) + +Fit a zero-location generalized Pareto likelihood to strictly positive +threshold excesses, requiring at least two distinct excesses. The threshold +is caller-supplied and must be chosen without held-out outcomes. Native +Nelder–Mead fitting rescales excesses by their mean and checks convergence, +support and finite likelihood. Shape is unrestricted. Numerical convergence +does not guarantee a global optimum or existence of a finite global MLE. +""" +function fit_pot_tail_risk(losses; threshold, probability) + losses = _tail_real_vector(losses, "losses") + threshold = _tail_real_scalar(threshold, "threshold") + excesses = losses[losses .> threshold] .- threshold + length(excesses) >= 2 && all(isfinite, excesses) && maximum(excesses) > minimum(excesses) || throw(ArgumentError("at least two distinct finite positive excesses are required")) + unit = maximum(excesses) + unit *= mean(excesses / unit) + scaled = excesses / unit + objective(parameters) = _pareto_negative_log_likelihood(parameters, scaled) + method = Optim.NelderMead(initial_simplex = Optim.AffineSimplexer(a = 0.0, b = 0.05), parameters = Optim.FixedParameters()) + result = Optim.optimize(objective, [1.0, 1.0], method, Optim.Options(g_tol = 1e-12, iterations = 4000)) + Optim.converged(result) && isfinite(Optim.minimum(result)) || error("Pareto likelihood optimization failed to converge") + shape, normalized_scale = Optim.minimizer(result) + scale = normalized_scale * unit + isfinite(_pareto_negative_log_likelihood([shape, scale], excesses)) || error("fitted Pareto model fails its support/likelihood check") + rate = length(excesses) / length(losses) + risk = generalized_pareto_tail_risk(threshold, rate, shape, scale, probability) + return (; risk..., shape, scale, threshold, exceedance_count = length(excesses), sample_count = length(losses), exceedance_probability = rate) +end + +function _tail_bernoulli_likelihood(zeros, ones, probability) + return (ones == 0 ? 0.0 : ones * log(probability)) + (zeros == 0 ? 0.0 : zeros * log1p(-probability)) +end + +""" + quantile_exception_tests(exceptions; exception_probability) + +Return Kupiec coverage and Christoffersen independence and conditional coverage +statistics with asymptotic chi-square p-values. Coverage uses all observations; +transition likelihoods condition on the first. When a previous-state row is +absent, independence and conditional coverage fields are `nothing`. The input +is a nonempty binary vector with one for a loss above its forecast quantile. +These tests concern quantile forecasts, not expected-shortfall forecasts. +""" +function quantile_exception_tests(exceptions; exception_probability) + exceptions isa AbstractVector && !isempty(exceptions) && all(value -> value isa Real && value in (0, 1), exceptions) || throw(ArgumentError("exceptions must be a nonempty binary vector")) + probability = _tail_real_scalar(exception_probability, "exception_probability") + 0 < probability < 1 || throw(ArgumentError("exception_probability must lie in (0, 1)")) + hits = Int.(exceptions) + total, ones = length(hits), sum(hits) + coverage = max(0.0, 2 * (_tail_bernoulli_likelihood(total - ones, ones, ones / total) - _tail_bernoulli_likelihood(total - ones, ones, probability))) + transitions = zeros(Int, 2, 2) + for index in 2:total + transitions[hits[index-1]+1, hits[index]+1] += 1 + end + rows = vec(sum(transitions; dims = 2)) + independence = nothing + if all(>(0), rows) + conditional = sum(_tail_bernoulli_likelihood(transitions[index, 1], transitions[index, 2], transitions[index, 2] / rows[index]) for index in 1:2) + transition_ones = sum(transitions[:, 2]) + pooled = _tail_bernoulli_likelihood(sum(transitions[:, 1]), transition_ones, transition_ones / (total - 1)) + independence = max(0.0, 2 * (conditional - pooled)) + end + combined = isnothing(independence) ? nothing : coverage + independence + return (; coverage_statistic = coverage, coverage_pvalue = ccdf(Chisq(1), coverage), + independence_statistic = independence, independence_pvalue = isnothing(independence) ? nothing : ccdf(Chisq(1), independence), + conditional_coverage_statistic = combined, conditional_coverage_pvalue = isnothing(combined) ? nothing : ccdf(Chisq(2), combined), + transitions, exception_count = ones, sample_count = total) +end diff --git a/src/CausalFactorAnalysis/BoundsTransport.jl b/src/CausalFactorAnalysis/BoundsTransport.jl new file mode 100644 index 0000000..3485be6 --- /dev/null +++ b/src/CausalFactorAnalysis/BoundsTransport.jl @@ -0,0 +1,71 @@ +""" + manski_ate_bounds(outcomes, treatment; lower, upper) + +Return worst-case finite-population ATE bounds under consistency and supplied +finite potential-outcome limits. No exchangeability or sampling confidence +interval is assumed. All-treated and all-control populations are allowed. +""" +function manski_ate_bounds(outcomes, treatment; lower, upper) + outcomes = _real_array(outcomes, "outcomes", 1) + lower = _finite_real("lower", lower) + upper = _finite_real("upper", upper) + treatment isa AbstractVector && + eltype(treatment) <: Real && + size(treatment) == size(outcomes) && + all(value -> value == 0 || value == 1, treatment) && + lower <= upper && + all(value -> lower <= value <= upper, outcomes) || throw( + ArgumentError( + "require binary matching treatment and outcomes within ordered bounds", + ), + ) + lower_effect = [ + treatment[i] == 1 ? outcomes[i] - upper : lower - outcomes[i] for + i in eachindex(outcomes) + ] + upper_effect = [ + treatment[i] == 1 ? outcomes[i] - lower : upper - outcomes[i] for + i in eachindex(outcomes) + ] + return ( + lower = mean(lower_effect), + upper = mean(upper_effect), + treated_fraction = mean(treatment), + sample_count = length(outcomes), + ) +end + +""" + transported_ate(treated_means, control_means, target_probabilities, + source_treated_counts, source_control_counts) + +Standardize supported stratum effects to a supplied target population. Every +target-positive stratum needs source observations of both treatments. Counts +may be nonnegative real weights. Zero-target strata contribute nothing, but +all supplied means must be finite. Source exchangeability, consistency, shared +treatment definitions and transportability of conditional means are caller +assumptions and are not certified by these numerical checks. +""" +function transported_ate( + treated_means, + control_means, + target_probabilities, + source_treated_counts, + source_control_counts, +) + treated = _real_array(treated_means, "treated_means", 1) + control = _real_array(control_means, "control_means", 1) + probabilities = _probabilities(target_probabilities, "target_probabilities", 1) + count1 = _real_array(source_treated_counts, "source_treated_counts", 1) + count0 = _real_array(source_control_counts, "source_control_counts", 1) + all( + values -> size(values) == size(treated), + (control, probabilities, count1, count0), + ) && + all(>=(0), count1) && + all(>=(0), count0) || + throw(ArgumentError("stratum arrays must match with nonnegative source counts")) + any((probabilities .> 0) .& ((count1 .== 0) .| (count0 .== 0))) && + throw(ArgumentError("target-positive strata lack source treatment support")) + return backdoor_adjusted_average_treatment_effect(treated, control, probabilities) +end diff --git a/src/CausalFactorAnalysis/CausalFactorAnalysis.jl b/src/CausalFactorAnalysis/CausalFactorAnalysis.jl index 405e46f..34b1c40 100644 --- a/src/CausalFactorAnalysis/CausalFactorAnalysis.jl +++ b/src/CausalFactorAnalysis/CausalFactorAnalysis.jl @@ -5,6 +5,9 @@ using Random using Statistics include("TreatmentEffects.jl") +include("BoundsTransport.jl") +include("Interference.jl") +include("PolicyEvaluation.jl") include("SpecificationDiagnostics.jl") include("FactorMirage.jl") include("Optimizer.jl") @@ -101,6 +104,11 @@ export BackdoorAdjustmentEvidence, max_selection_family_errors, max_selection_null_probability, maximum_mixture_cdf, - single_trial_false_discovery_rate + single_trial_false_discovery_rate, + manski_ate_bounds, + transported_ate, + exposure_mean_contrast, + evaluate_policy, + select_policy end diff --git a/src/CausalFactorAnalysis/FalseDiscoveryRates.jl b/src/CausalFactorAnalysis/FalseDiscoveryRates.jl index f5e6b42..0022539 100644 --- a/src/CausalFactorAnalysis/FalseDiscoveryRates.jl +++ b/src/CausalFactorAnalysis/FalseDiscoveryRates.jl @@ -539,6 +539,10 @@ function max_selection_null_probability( absolute = _fdr_positive("absolute_tolerance", absolute_tolerance) relative = _fdr_positive("relative_tolerance", relative_tolerance) subdivisions = _fdr_positive_integer("max_subintervals", max_subintervals) + applicable(null_density, threshold_value) || + throw(ArgumentError("null_density must be callable with a real value")) + applicable(mixture_cdf, threshold_value) || + throw(ArgumentError("mixture_cdf must be callable with a real value")) checked_cdf(value) = _fdr_probability("mixture_cdf return value", mixture_cdf(value)) mixture_at_threshold = checked_cdf(threshold_value) diff --git a/src/CausalFactorAnalysis/Interference.jl b/src/CausalFactorAnalysis/Interference.jl new file mode 100644 index 0000000..88678d8 --- /dev/null +++ b/src/CausalFactorAnalysis/Interference.jl @@ -0,0 +1,53 @@ +function _column_indices(values, row_count, column_count, name) + values isa AbstractVector && + eltype(values) <: Integer && + !(eltype(values) <: Bool) && + length(values) == row_count && + all(value -> 1 <= value <= column_count, values) || + throw(ArgumentError("$name must be matching one-based integer column indices")) + return Int.(values) +end + +""" + exposure_mean_contrast(outcomes, exposures, exposure_probabilities; first, second) + +Return the Horvitz-Thompson mean contrast `first - second` under the supplied +randomized exposure design. Labels and contrast columns use one-based indices. +Every unit needs positive probability for both contrasted exposures and its +observed exposure. Row probability sums may be below one for omitted categories. +The exposure mapping and potential-outcome interpretation are caller premises. +No standard error or confidence interval is returned without joint exposure +probabilities. +""" +function exposure_mean_contrast(outcomes, exposures, exposure_probabilities; first, second) + outcomes = _real_array(outcomes, "outcomes", 1) + probabilities = _real_array(exposure_probabilities, "exposure_probabilities", 2) + size(probabilities, 1) == length(outcomes) || + throw(ArgumentError("probability rows must match outcomes")) + labels = + _column_indices(exposures, length(outcomes), size(probabilities, 2), "exposures") + for value in (first, second) + value isa Integer && !(value isa Bool) && 1 <= value <= size(probabilities, 2) || + throw(ArgumentError("contrast indices must name probability columns")) + end + first != second && + all(>=(0), probabilities) && + all(<=(1 + 1e-12), sum(probabilities; dims = 2)) || + throw(ArgumentError("require distinct contrasts and valid probability mass")) + all(>(0), probabilities[:, [first, second]]) && + all(probabilities[i, labels[i]] > 0 for i in eachindex(outcomes)) || + throw(ArgumentError("the requested exposure contrast lacks positivity")) + first_scores = (labels .== first) .* outcomes ./ probabilities[:, first] + second_scores = (labels .== second) .* outcomes ./ probabilities[:, second] + contributions = first_scores - second_scores + all(isfinite, first_scores) && + all(isfinite, second_scores) && + all(isfinite, contributions) || + throw(ArgumentError("exposure scores exceed floating-point range")) + return ( + first_mean = mean(first_scores), + second_mean = mean(second_scores), + contrast = mean(contributions), + unit_contributions = contributions, + ) +end diff --git a/src/CausalFactorAnalysis/Optimizer.jl b/src/CausalFactorAnalysis/Optimizer.jl index 374670a..d903bee 100644 --- a/src/CausalFactorAnalysis/Optimizer.jl +++ b/src/CausalFactorAnalysis/Optimizer.jl @@ -5,7 +5,7 @@ const _FEASIBILITY_ATOL = 1.0e-12 function _real_matrix(name::AbstractString, values) values isa AbstractMatrix || throw(ArgumentError("$name must be a numeric matrix")) - all(value -> value isa Real, values) || + all(value -> value isa Real && !(value isa Bool), values) || throw(ArgumentError("$name must be real-valued")) result = try Matrix{Float64}(values) @@ -18,7 +18,7 @@ end function _real_vector(name::AbstractString, values) values isa AbstractVector || throw(ArgumentError("$name must be a numeric vector")) - all(value -> value isa Real, values) || + all(value -> value isa Real && !(value isa Bool), values) || throw(ArgumentError("$name must be real-valued")) result = try Vector{Float64}(values) @@ -82,28 +82,54 @@ function minimum_variance_factor_weights(covariance, factor_exposures, target_ex whitened_exposures = lower \ scaled_exposures all(isfinite, whitened_exposures) || throw(ArgumentError("factor_exposures produce non-finite whitened values")) - rank(whitened_exposures) == n_factors || - throw(ArgumentError("factor_exposures must have full column rank")) + singular_values = svdvals(whitened_exposures) + count( + >(maximum(singular_values) * max(size(whitened_exposures)...) * eps(Float64)), + singular_values, + ) == n_factors || throw(ArgumentError("factor_exposures must have full column rank")) if n_factors == n_assets weights = transpose(scaled_exposures) \ scaled_targets else factorization = qr(whitened_exposures) - orthonormal_basis = Matrix(factorization.Q)[:, 1:n_factors] - triangular_factor = Matrix(factorization.R)[1:n_factors, 1:n_factors] - transformed_targets = transpose(triangular_factor) \ scaled_targets - whitened_weights = orthonormal_basis * transformed_targets + transformed_targets = lu(Matrix(transpose(factorization.R))) \ scaled_targets + whitened_weights = + factorization.Q * vcat(transformed_targets, zeros(n_assets - n_factors)) + if !all(isfinite, whitened_weights) + orthonormal_basis = Matrix(factorization.Q)[:, 1:n_factors] + whitened_weights = orthonormal_basis * transformed_targets + end weights = transpose(lower) \ whitened_weights end all(isfinite, weights) || throw(ArgumentError("the allocation produced non-finite weights")) - achieved_exposures = transpose(scaled_exposures) * weights - residuals = abs.(achieved_exposures - scaled_targets) tolerances = map(scaled_targets) do target target != 0.0 ? _FEASIBILITY_RTOL * abs(target) : _FEASIBILITY_ATOL end - all(isfinite, achieved_exposures) && all(residuals .<= tolerances) || throw( + resolved = setprecision(BigFloat, 256) do + achieved_exposures = transpose(BigFloat.(scaled_exposures)) * BigFloat.(weights) + residuals = abs.(achieved_exposures - BigFloat.(scaled_targets)) + all(isfinite, achieved_exposures) && all(residuals .<= BigFloat.(tolerances)) + end + if !resolved + weights = setprecision(BigFloat, 256) do + covariance_big = BigFloat.(scaled_covariance) + exposures_big = BigFloat.(scaled_exposures) + targets_big = BigFloat.(scaled_targets) + solved_exposures = covariance_big \ exposures_big + constraint_matrix = transpose(exposures_big) * solved_exposures + weights_big = solved_exposures * (constraint_matrix \ targets_big) + Float64.(weights_big) + end + resolved = setprecision(BigFloat, 256) do + achieved_exposures = transpose(BigFloat.(scaled_exposures)) * BigFloat.(weights) + residuals = abs.(achieved_exposures - BigFloat.(scaled_targets)) + all(isfinite, achieved_exposures) && + all(residuals .<= BigFloat.(tolerances)) + end + end + resolved || throw( ArgumentError("factor constraints could not be resolved to numerical precision"), ) - return Vector{Float64}(weights) + return copy(weights) end diff --git a/src/CausalFactorAnalysis/PolicyEvaluation.jl b/src/CausalFactorAnalysis/PolicyEvaluation.jl new file mode 100644 index 0000000..0b73e67 --- /dev/null +++ b/src/CausalFactorAnalysis/PolicyEvaluation.jl @@ -0,0 +1,113 @@ +""" + evaluate_policy(actions, rewards, behavior_probabilities, policy_probabilities, + outcome_predictions) + +Return one-step IPW and doubly robust policy values, observation scores and +importance-weight diagnostics. Matrices have shape `(rows, actions)`; actions +are one-based columns. Predictions must be out of fold or independently trained. +The caller justifies consistency and conditional exchangeability. All policy +support must have behavior support. Probabilities are neither clipped nor +renormalized, and no sequential reinforcement-learning claim is made. +""" +function evaluate_policy( + actions, + rewards, + behavior_probabilities, + policy_probabilities, + outcome_predictions, +) + rewards = _real_array(rewards, "rewards", 1) + behavior = _probabilities(behavior_probabilities, "behavior_probabilities", 2) + policy = _probabilities(policy_probabilities, "policy_probabilities", 2) + predictions = _real_array(outcome_predictions, "outcome_predictions", 2) + size(behavior) == size(policy) == size(predictions) && + size(behavior, 1) == length(rewards) || + throw(ArgumentError("outcome and probability shapes are incompatible")) + actions = _column_indices(actions, length(rewards), size(behavior, 2), "actions") + observed = [behavior[i, actions[i]] for i in eachindex(rewards)] + if any((policy .> 0) .& (behavior .== 0)) || any(iszero, observed) + throw(ArgumentError("policy evaluation requires behavior-policy overlap")) + end + weights = [policy[i, actions[i]] / observed[i] for i in eachindex(rewards)] + ipw = weights .* rewards + selected_predictions = [predictions[i, actions[i]] for i in eachindex(rewards)] + dr = + vec(sum(policy .* predictions; dims = 2)) + + weights .* (rewards - selected_predictions) + all(isfinite, ipw) && all(isfinite, dr) && all(isfinite, weights) || + throw(ArgumentError("policy scores exceed floating-point range")) + maximum_weight = maximum(weights) + normalized = iszero(maximum_weight) ? weights : weights ./ maximum_weight + effective = + all(iszero, normalized) ? 0.0 : sum(normalized)^2 / dot(normalized, normalized) + return ( + ipw_value = mean(ipw), + dr_value = mean(dr), + ipw_scores = ipw, + dr_scores = dr, + importance_weights = weights, + effective_sample_size = effective, + max_weight = maximum_weight, + mean_weight = mean(weights), + ) +end + +function _split_indices(values, row_count) + values isa AbstractVector || throw(ArgumentError("split indices must be vectors")) + indices = _column_indices(values, length(values), row_count, "split indices") + !isempty(indices) && length(unique(indices)) == length(indices) || + throw(ArgumentError("split indices must be nonempty and distinct")) + return indices +end + +""" + select_policy(actions, rewards, behavior_probabilities, candidate_probabilities, + outcome_predictions; training_indices, evaluation_indices) + +Select the largest training DR value and evaluate the chosen policy on later +held-out rows. Candidates have shape `(policies, rows, actions)` and must be +fixed before evaluation. The first candidate wins ties. Split rows and the +returned policy index are one-based. Training must precede evaluation, with no +duplicate rows. The caller remains responsible for nuisance-model training +history and independent evaluation; evaluation rewards never choose the policy. +""" +function select_policy( + actions, + rewards, + behavior_probabilities, + candidate_probabilities, + outcome_predictions; + training_indices, + evaluation_indices, +) + rewards = _real_array(rewards, "rewards", 1) + candidates = _real_array(candidate_probabilities, "candidate_probabilities", 3) + size(candidates, 2) == length(rewards) && + size(actions) == size(rewards) && + size(behavior_probabilities) == + size(outcome_predictions) == + (size(candidates, 2), size(candidates, 3)) || + throw(ArgumentError("candidate and observation arrays must match")) + train = _split_indices(training_indices, length(rewards)) + evaluation = _split_indices(evaluation_indices, length(rewards)) + maximum(train) < minimum(evaluation) || + throw(ArgumentError("training must precede held-out evaluation")) + values = [ + evaluate_policy( + actions[train], + rewards[train], + behavior_probabilities[train, :], + candidates[policy, train, :], + outcome_predictions[train, :], + ).dr_value for policy in axes(candidates, 1) + ] + selected = argmax(values) + result = evaluate_policy( + actions[evaluation], + rewards[evaluation], + behavior_probabilities[evaluation, :], + candidates[selected, evaluation, :], + outcome_predictions[evaluation, :], + ) + return (selected_policy = selected, training_values = values, evaluation = result) +end diff --git a/src/CausalFactorAnalysis/Protocol.jl b/src/CausalFactorAnalysis/Protocol.jl index 33cfc54..aea86b1 100644 --- a/src/CausalFactorAnalysis/Protocol.jl +++ b/src/CausalFactorAnalysis/Protocol.jl @@ -81,6 +81,28 @@ Base.@kwdef struct FoldEvidence refit_components::Tuple{Vararg{String}} end +function FoldEvidence( + fold_id, + path_id, + train_ids::AbstractVector, + test_ids::AbstractVector, + fit_ids::AbstractVector, + prediction_ids::AbstractVector, + refit_id, + refit_components::AbstractVector, +) + return FoldEvidence( + String(fold_id), + String(path_id), + Tuple(String.(train_ids)), + Tuple(String.(test_ids)), + Tuple(String.(fit_ids)), + Tuple(String.(prediction_ids)), + String(refit_id), + Tuple(String.(refit_components)), + ) +end + Base.@kwdef struct ValidationEvidence method_labels::Tuple{Vararg{String}} event_horizons::Tuple{Vararg{EventHorizon}} @@ -88,6 +110,20 @@ Base.@kwdef struct ValidationEvidence embargo::Real = 0.0 end +function ValidationEvidence( + method_labels::AbstractVector, + event_horizons::AbstractVector, + folds::AbstractVector, + embargo::Real = 0.0, +) + return ValidationEvidence( + Tuple(String.(method_labels)), + Tuple(event_horizons), + Tuple(folds), + embargo, + ) +end + Base.@kwdef struct VariableSelectionStage purpose::String selected_variables::Tuple{Vararg{String}} @@ -107,6 +143,26 @@ Base.@kwdef struct CausalDiscoveryStage assumptions::Tuple{Vararg{String}} end +function CausalDiscoveryStage( + method_labels::AbstractVector, + graph_id, + graph_nodes::AbstractVector, + directed_edges::AbstractVector, + graph_kind, + ambiguous_edges::AbstractVector, + assumptions::AbstractVector, +) + return CausalDiscoveryStage( + Tuple(String.(method_labels)), + String(graph_id), + Tuple(String.(graph_nodes)), + Tuple((String(edge[1]), String(edge[2])) for edge in directed_edges), + String(graph_kind), + Tuple((String(edge[1]), String(edge[2])) for edge in ambiguous_edges), + Tuple(String.(assumptions)), + ) +end + Base.@kwdef struct CausalAdjustmentSetStage treatment::String outcome::String @@ -177,6 +233,28 @@ Base.@kwdef struct MultipleTestingAdjustmentsStage selection_bias_evidence_id::Union{Nothing,String} = nothing end +function MultipleTestingAdjustmentsStage( + method_labels::AbstractVector, + trial_family_id, + declared_trial_ids::AbstractVector, + family_partitions::AbstractVector, + alpha::Real, + backtests_independent::Bool, +) + partitions = Tuple( + (String(partition[1]), Tuple(String.(partition[2]))) for + partition in family_partitions + ) + return MultipleTestingAdjustmentsStage( + method_labels = Tuple(String.(method_labels)), + trial_family_id = String(trial_family_id), + declared_trial_ids = Tuple(String.(declared_trial_ids)), + family_partitions = partitions, + alpha = alpha, + backtests_independent = backtests_independent, + ) +end + struct CausalFactorProtocolReport trial_family_id::String declared_trial_ids::Tuple{Vararg{String}} @@ -674,7 +752,7 @@ function _protocol_d_separated(edges, left, right, conditioned) if left in conditioned || right in conditioned return true end - visited = Set(conditioned) + visited = Set{String}(conditioned) pending = String[left] while !isempty(pending) node = pop!(pending) @@ -1543,6 +1621,8 @@ function validate_causal_factor_protocol(report::CausalFactorProtocolReport) return report end +validate_causal_factor_protocol(report) = + throw(ArgumentError("Protocol: report type is invalid.")) function _protocol_backdoor_d_separated(edges, exposure, outcome, conditioned) diff --git a/src/CausalFactorAnalysis/SpecificationDiagnostics.jl b/src/CausalFactorAnalysis/SpecificationDiagnostics.jl index bcd168b..6f3075d 100644 --- a/src/CausalFactorAnalysis/SpecificationDiagnostics.jl +++ b/src/CausalFactorAnalysis/SpecificationDiagnostics.jl @@ -116,6 +116,24 @@ function _ols_diagnostics(outcome::Vector{Float64}, regressors) ) end +function _specification_ols(outcome, regressors) + outcome_values = _finite_vector("outcome", outcome) + regressor_values = Tuple( + (String(name), _finite_vector("regressor", values)) for + (name, values) in regressors + ) + return _ols_diagnostics(outcome_values, regressor_values) +end + +function _specification_rng(sample_size, seed, rng) + n_observations = _sample_size(sample_size) + random_seed = _random_seed(seed) + isnothing(rng) || + rng isa AbstractRNG || + throw(ArgumentError("rng must be an AbstractRNG or nothing")) + return n_observations, isnothing(rng) ? MersenneTwister(random_seed) : rng +end + function fork_population_diagnostics() return SpecificationPopulationResult( "fork", @@ -143,9 +161,8 @@ function confounded_mediator_population_diagnostics() ) end -function fork_specification_experiment(sample_size = 5_000, seed = 0) - n_observations = _sample_size(sample_size) - random_state = MersenneTwister(_random_seed(seed)) +function fork_specification_experiment(sample_size = 5_000, seed = 0; rng = nothing) + n_observations, random_state = _specification_rng(sample_size, seed, rng) z = randn(random_state, n_observations) x = z + randn(random_state, n_observations) y = z + randn(random_state, n_observations) @@ -157,9 +174,8 @@ function fork_specification_experiment(sample_size = 5_000, seed = 0) ) end -function collider_specification_experiment(sample_size = 5_000, seed = 0) - n_observations = _sample_size(sample_size) - random_state = MersenneTwister(_random_seed(seed)) +function collider_specification_experiment(sample_size = 5_000, seed = 0; rng = nothing) + n_observations, random_state = _specification_rng(sample_size, seed, rng) x = randn(random_state, n_observations) y = randn(random_state, n_observations) z = x + y + randn(random_state, n_observations) @@ -171,9 +187,12 @@ function collider_specification_experiment(sample_size = 5_000, seed = 0) ) end -function confounded_mediator_specification_experiment(sample_size = 5_000, seed = 0) - n_observations = _sample_size(sample_size) - random_state = MersenneTwister(_random_seed(seed)) +function confounded_mediator_specification_experiment( + sample_size = 5_000, + seed = 0; + rng = nothing, +) + n_observations, random_state = _specification_rng(sample_size, seed, rng) x = randn(random_state, n_observations) w = randn(random_state, n_observations) z = x + w + randn(random_state, n_observations) diff --git a/src/CausalFactorAnalysis/TreatmentEffects.jl b/src/CausalFactorAnalysis/TreatmentEffects.jl index 93b98ff..f868778 100644 --- a/src/CausalFactorAnalysis/TreatmentEffects.jl +++ b/src/CausalFactorAnalysis/TreatmentEffects.jl @@ -86,6 +86,30 @@ function _finite_matrix(name::AbstractString, values) return Matrix{Float64}(result) end +function _real_array(values, name, dimensions) + values isa AbstractArray && + ndims(values) == dimensions && + all(value -> value isa Real && !(value isa Bool), values) || + throw(ArgumentError("$name must be a real numeric array of dimension $dimensions")) + isempty(values) && throw(ArgumentError("$name cannot be empty")) + result = Float64.(values) + all(isfinite, result) || throw(ArgumentError("$name must contain finite values")) + return result +end + +function _probabilities(values, name, dimensions; strict_vector = false) + result = _real_array(values, name, dimensions) + all(value -> 0 <= value <= 1, result) || + throw(ArgumentError("$name must contain probabilities in [0, 1]")) + totals = sum(result; dims = dimensions) + valid = + strict_vector ? + all(total -> abs(total - 1) <= max(1e-12, 1e-12 * max(abs(total), 1.0)), totals) : + all(total -> abs(total - 1) <= 2e-12, totals) + valid || throw(ArgumentError("$name must sum to one along its last dimension")) + return result +end + function average_treatment_effect(treated_interventional_mean, control_interventional_mean) treated = _finite_real("treated_interventional_mean", treated_interventional_mean) control = _finite_real("control_interventional_mean", control_interventional_mean) diff --git a/src/Cluster/Clustering.jl b/src/Cluster/Clustering.jl index eda3b0f..ed2addd 100644 --- a/src/Cluster/Clustering.jl +++ b/src/Cluster/Clustering.jl @@ -4,10 +4,10 @@ API (López de Prado, AFML Ch. 4): the Optimized Nested Clustering (ONC) algorithm and its k-means base step, plus random block-correlation generators. Parity note: k-means is **stochastic** and not bit-identical across -implementations (`Clustering.jl` here vs scikit-learn in Python), so -`cluster_k_means_base`/`cluster_k_means_top` and the random generators are -**behavioural** ports — the tests check structural properties (valid partitions, -shapes), not exact values. `silhouette_samples` is deterministic and matches the +implementations (`Clustering.jl` here vs scikit-learn in Python). Supplied label +providers validate selection and refinement independently of those generators; +supplied block cuts and Gaussian primitives replay random correlations. +The default fitted partitions remain behavioural. `silhouette_samples` matches the canonical (scikit-learn) definition exactly. `covariance_to_correlation` delegates to `Data.cov_to_corr` (the single source of truth). @@ -18,7 +18,7 @@ Reference: De Prado, M. (2020), Machine Learning for Asset Managers, Ch. 4. """ using Statistics: mean, std, cov -using Random: AbstractRNG, MersenneTwister, default_rng, shuffle, randn, seed! +using Random: AbstractRNG, MersenneTwister, default_rng, shuffle, randn using Clustering: kmeans, assignments using ..Data: cov_to_corr @@ -48,7 +48,9 @@ function silhouette_samples( labels::AbstractVector{<:Integer}, ) n = length(labels) + size(distance) == (n, n) || throw(DimensionMismatch("Distance and label dimensions differ.")) unique_labels = unique(labels) + 2 <= length(unique_labels) < n || throw(ArgumentError("Silhouettes require between two and n-1 distinct labels.")) scores = zeros(Float64, n) for i = 1:n same = findall(==(labels[i]), labels) @@ -74,36 +76,56 @@ end _correlation_distance(correlation) = sqrt.((1 .- map(x -> isnan(x) ? 0.0 : x, correlation)) ./ 2.0) +function _embedding_distances(features) + n = size(features, 1) + distance = zeros(Float64, n, n) + for right in 2:n, left in 1:(right-1) + value = sqrt(sum(abs2(features[left, column] - features[right, column]) for column in axes(features, 2))) + distance[left, right] = distance[right, left] = value + end + return distance +end + """ cluster_k_means_base(correlation; max_clusters=10, iterations=10, random_state=nothing) -> (correlation_sorted, clusters, silhouette) K-means base step: over `2:max_clusters` clusters and `iterations` initialisations, keep the clustering with the highest silhouette t-statistic (mean/std, population -std). Returns the cluster-sorted correlation matrix, a `Dict(label => item -indices)`, and the per-item silhouette vector (original order). Behavioural -(k-means is stochastic). Mirrors Python's `cluster_k_means_base`. +std) using Euclidean distances between rows of the correlation-distance +embedding. Returns the cluster-sorted correlation matrix, a `Dict(label => item +indices)`, and the per-item silhouette vector (original order). Optional +`label_provider(features, cluster_count, rng)` supplies integer candidate labels. +Labels retain provider values; item positions are one-based. Ordering within +equal labels is stable. Default native k-means remains stochastic. """ function cluster_k_means_base( correlation::AbstractMatrix{<:Real}; max_clusters::Integer = 10, iterations::Integer = 10, random_state = nothing, + label_provider = nothing, ) + n = size(correlation, 1) + size(correlation, 2) == n || throw(DimensionMismatch("Correlation must be square.")) + 2 <= max_clusters < n || throw(ArgumentError("max_clusters must be between two and n-1.")) + iterations > 0 || throw(ArgumentError("iterations must be positive.")) distance = _correlation_distance(correlation) - random_state !== nothing && seed!(random_state) + silhouette_distance = _embedding_distances(distance) + rng = _rng(random_state) best_labels = nothing best_silhouette = nothing best_score = -Inf for _ = 1:iterations for n_clusters = 2:max_clusters - labels = assignments(kmeans(Matrix(distance), n_clusters)) - silhouette = silhouette_samples(distance, labels) + labels = label_provider === nothing ? assignments(kmeans(permutedims(distance), n_clusters; rng = rng)) : Int.(label_provider(copy(distance), n_clusters, rng)) + length(labels) == n || throw(DimensionMismatch("Candidate labels must cover every item.")) + silhouette = silhouette_samples(silhouette_distance, labels) stat_mean = mean(silhouette) stat_std = std(silhouette; corrected = false) score = stat_std == 0 ? sign(stat_mean) * Inf : stat_mean / stat_std - if best_labels === nothing || score > best_score + if score > best_score best_score = score best_labels = labels best_silhouette = silhouette @@ -111,6 +133,7 @@ function cluster_k_means_base( end end + best_labels === nothing && throw(ArgumentError("Clustering failed to find a valid solution.")) index_sorted = sortperm(best_labels) correlation_sorted = correlation[index_sorted, index_sorted] clusters = Dict(c => findall(==(c), best_labels) for c in unique(best_labels)) @@ -148,14 +171,15 @@ function make_new_outputs( for (key, items) in clusters_new labels[items] .= key end - silhouette_new = silhouette_samples(distance, labels) + silhouette_new = silhouette_samples(_embedding_distances(distance), labels) return (correlation_new, clusters_new, silhouette_new) end # cluster silhouette t-statistic (pandas .std() -> sample std, ddof = 1) function _cluster_t_stats(silhouette, clusters) t_stats = Dict{Int,Float64}() - for (i, items) in clusters + for i in sort(collect(keys(clusters))) + items = clusters[i] s = std(silhouette[items]; corrected = true) s > 0 && (t_stats[i] = mean(silhouette[items]) / s) end @@ -169,13 +193,15 @@ end Optimized Nested Clustering (ONC): run the base k-means, then recursively re-cluster the clusters whose silhouette t-statistic is below average, keeping the re-clustering only if it improves the mean t-statistic. Behavioural (built on -stochastic k-means). Mirrors Python's `cluster_k_means_top`. +stochastic k-means). A supplied `label_provider` is forwarded through every +recursive candidate fit, preserving the strict public refinement decision. """ function cluster_k_means_top( correlation::AbstractMatrix{<:Real}; max_clusters = nothing, iterations::Integer = 10, random_state = nothing, + label_provider = nothing, ) n = size(correlation, 2) mc = max_clusters === nothing ? n - 1 : min(max_clusters, n - 1) @@ -188,12 +214,14 @@ function cluster_k_means_top( max_clusters = mc, iterations = iterations, random_state = random_state, + label_provider = label_provider, ) cluster_t_stats = _cluster_t_stats(silhouette, clusters) isempty(cluster_t_stats) && return (correlation_sorted, clusters, silhouette) - t_stat_mean = mean(values(cluster_t_stats)) - redo = [i for (i, t) in cluster_t_stats if t < t_stat_mean] + ordered_keys = sort(collect(keys(cluster_t_stats))) + t_stat_mean = mean(cluster_t_stats[key] for key in ordered_keys) + redo = [key for key in ordered_keys if cluster_t_stats[key] < t_stat_mean] length(redo) <= 1 && return (correlation_sorted, clusters, silhouette) keys_redo = reduce(vcat, (clusters[i] for i in redo)) @@ -207,13 +235,15 @@ function cluster_k_means_top( max_clusters = min(remained, size(correlation_temp, 2) - 1), iterations = iterations, random_state = random_state, + label_provider = label_provider, ) clusters_2 = Dict(k => keys_redo[v] for (k, v) in clusters_2_local) clusters_1 = Dict(i => clusters[i] for i in keys(clusters) if !(i in redo)) correlation_new, clusters_new, silhouette_new = make_new_outputs(correlation, clusters_1, clusters_2) - new_t_stats = collect(values(_cluster_t_stats(silhouette_new, clusters_new))) + new_stats = _cluster_t_stats(silhouette_new, clusters_new) + new_t_stats = [new_stats[key] for key in sort(collect(keys(new_stats)))] isempty(new_t_stats) && return (correlation_sorted, clusters, silhouette) if mean(new_t_stats) <= t_stat_mean_redo @@ -226,26 +256,34 @@ end random_covariance_sub(n_observations, n_columns, sigma; random_state=nothing) -> Matrix Random covariance of one block: a shared factor plus idiosyncratic noise. -Stochastic. Mirrors Python's `random_covariance_sub`. +Optional `common_draws` and `idiosyncratic_draws` are standard Gaussian primitives +with shapes `(n_observations,)` and `(n_observations, n_columns)`. A singleton +block retains the public unit covariance without consuming Gaussian draws. """ function random_covariance_sub( n_observations::Integer, n_columns::Integer, sigma::Real; random_state = nothing, + common_draws = nothing, + idiosyncratic_draws = nothing, ) n_columns == 1 && return ones(1, 1) rng = _rng(random_state) - data = repeat(randn(rng, n_observations, 1), 1, n_columns) - data = data .+ sigma .* randn(rng, n_observations, n_columns) + common = common_draws === nothing ? randn(rng, n_observations) : Vector{Float64}(common_draws) + noise = idiosyncratic_draws === nothing ? randn(rng, n_observations, n_columns) : Matrix{Float64}(idiosyncratic_draws) + length(common) == n_observations || throw(DimensionMismatch("Common draws must match n_observations.")) + size(noise) == (n_observations, n_columns) || throw(DimensionMismatch("Idiosyncratic draws have the wrong shape.")) + data = reshape(common, :, 1) .+ sigma .* noise return cov(data) end """ random_block_covariance(n_columns, n_blocks; block_size_min=1, sigma=1.0, random_state=nothing) -> Matrix -Random block-diagonal covariance matrix. Stochastic. Mirrors Python's -`random_block_covariance`. +Random block-diagonal covariance matrix. Optional distinct integer `cuts` replay +the block partition and `block_draws` supplies one `(common, noise)` record per +block. The sample covariance and observation-count rules match the reference. """ function random_block_covariance( n_columns::Integer, @@ -253,17 +291,24 @@ function random_block_covariance( block_size_min::Integer = 1, sigma::Real = 1.0, random_state = nothing, + cuts = nothing, + block_draws = nothing, ) + n_blocks > 0 && block_size_min > 0 && n_columns >= n_blocks * block_size_min || throw(ArgumentError("Requested blocks cannot fit the available columns.")) rng = _rng(random_state) limit = n_columns - (block_size_min - 1) * n_blocks - chosen = sort(shuffle(rng, collect(1:(limit-1)))[1:(n_blocks-1)]) + chosen = cuts === nothing ? sort(shuffle(rng, collect(1:(limit-1)))[1:(n_blocks-1)]) : sort(Vector{Int}(cuts)) + length(chosen) == n_blocks - 1 && length(unique(chosen)) == length(chosen) && all(cut -> 1 <= cut < limit, chosen) || throw(ArgumentError("Block cuts must be distinct interior positions.")) + block_draws === nothing || length(block_draws) == n_blocks || throw(DimensionMismatch("Supply one primitive record per block.")) parts = vcat(chosen, limit) parts = vcat(parts[1], diff(parts)) .- 1 .+ block_size_min blocks = Matrix{Float64}[] - for col_size in parts + for (index, col_size) in enumerate(parts) n_obs = Int(max(col_size * (col_size + 1) / 2.0, 100)) - push!(blocks, random_covariance_sub(n_obs, col_size, sigma; random_state = rng)) + common = block_draws === nothing ? nothing : block_draws[index].common + noise = block_draws === nothing ? nothing : block_draws[index].noise + push!(blocks, random_covariance_sub(n_obs, col_size, sigma; random_state = rng, common_draws = common, idiosyncratic_draws = noise)) end total = sum(size(b, 1) for b in blocks) @@ -281,13 +326,16 @@ end random_block_correlation(n_columns, n_blocks; random_state=nothing, block_size_min=1) -> Matrix Random block-diagonal correlation matrix with an added market component. -Stochastic. Mirrors Python's `random_block_correlation`. +Optional `draws=(cuts, blocks, market)` supplies block cuts, per-block +`(common, noise)` primitives and the corresponding market primitives. Equal +seeds across Julia and NumPy are not equivalent random streams. """ function random_block_correlation( n_columns::Integer, n_blocks::Integer; random_state = nothing, block_size_min::Integer = 1, + draws = nothing, ) rng = _rng(random_state) covariance_1 = random_block_covariance( @@ -296,6 +344,8 @@ function random_block_correlation( block_size_min = block_size_min, sigma = 0.5, random_state = rng, + cuts = draws === nothing ? nothing : draws.cuts, + block_draws = draws === nothing ? nothing : draws.blocks, ) covariance_2 = random_block_covariance( n_columns, @@ -303,6 +353,7 @@ function random_block_correlation( block_size_min = n_columns, sigma = 1.0, random_state = rng, + block_draws = draws === nothing ? nothing : [draws.market], ) return covariance_to_correlation(covariance_1 .+ covariance_2) end diff --git a/src/Components/BarController.jl b/src/Components/BarController.jl new file mode 100644 index 0000000..c6d73cd --- /dev/null +++ b/src/Components/BarController.jl @@ -0,0 +1,87 @@ +const BAR_COLUMNS = [Utils.DATE_TIME, Utils.TICK_NUMBER, Utils.OPEN_PRICE, + Utils.HIGH_PRICE, Utils.LOW_PRICE, Utils.CLOSE_PRICE, Utils.CUMULATIVE_VOLUME, + Utils.CUMULATIVE_BUY_VOLUME, Utils.CUMULATIVE_SELL_VOLUME, + Utils.CUMULATIVE_TICKS, Utils.CUMULATIVE_DOLLAR, Utils.THRESHOLD] + +"""Native bar dispatch; caller-loaded tables keep filesystem operations explicit.""" +struct BarController end + +function bar_methods() + information = [expectation*"_"*metric*"_"*family*"_bars" for family in ("imbalance", "run") for expectation in ("expected", "fixed") for metric in ("dollar", "volume", "tick")] + return vcat(information, [metric*"_standard_bars" for metric in ("dollar", "volume", "tick")], ["time_bars"]) +end + +""" +Initialize any of the sixteen public bar routes with their controller defaults. +Public keyword names are translated to the native metric-dispatched constructors; +extra settings are ignored as in the public initializer. +""" +function initialize_bars(method::AbstractString; + threshold = nothing, + window_size_for_expected_n_ticks_estimation = 10000, + window_size_for_expected_imbalance_estimation::Integer = 10000, + initial_estimate_of_expected_n_ticks_in_bar::Real = 20000, + expected_ticks_number_bounds = nothing, analyse_thresholds::Bool = false, + resolution_type::AbstractString = "D", resolution_units::Integer = 1, kwargs...) + method in bar_methods() || throw(KeyError("unknown bar method: $method")) + method == "time_bars" && return Data.TimeBars(resolution_type = String(resolution_type), resolution_units = Int(resolution_units)) + metric_name = occursin("dollar", method) ? "dollar" : occursin("volume", method) ? "volume" : "tick" + metric = metric_name == "dollar" ? Data.Dollar : metric_name == "volume" ? Data.Volume : Data.Tick + if occursin("standard", method) + default = metric_name == "dollar" ? 70000000. : metric_name == "volume" ? 30000. : 6000. + bar_type = metric_name == "dollar" ? Utils.CUMULATIVE_DOLLAR : metric_name == "volume" ? Utils.CUMULATIVE_VOLUME : Utils.CUMULATIVE_TICKS + return Data.StandardBars{metric}(bar_type = bar_type, threshold = Float64(threshold === nothing ? default : threshold)) + end + imbalance = occursin("imbalance", method) + expected = startswith(method, "expected") + type = expected ? (imbalance ? Data.ExpectedImbalanceBars : Data.ExpectedRunBars) : (imbalance ? Data.FixedImbalanceBars : Data.FixedRunBars) + common = (bar_type = metric_name*(imbalance ? "_imbalance" : "_run"), + expected_imbalance_window = Int(window_size_for_expected_imbalance_estimation), + initial_estimate_of_expected_n_ticks_in_bar = Float64(initial_estimate_of_expected_n_ticks_in_bar), + does_analyse_thresholds = analyse_thresholds) + if expected + bounds = expected_ticks_number_bounds === nothing ? nothing : Tuple(Float64.(expected_ticks_number_bounds)) + return type{metric}(; common..., + window_size_for_expected_n_ticks_estimation = window_size_for_expected_n_ticks_estimation, + expected_ticks_number_bounds = bounds) + end + return type{metric}(; common...) +end + +function _bar_dataframe(rows) + isempty(rows) && return DataFrame([Any[] for _ in BAR_COLUMNS], BAR_COLUMNS) + return DataFrame([getindex.(rows, column) for column in eachindex(BAR_COLUMNS)], BAR_COLUMNS) +end + +""" + process_bars(BarController(), method, arguments, data; batch_size=1000000) + +Process chronological `(time, price, volume)` rows in batches. Return +`(bars, completed_input, processed_rows, error)` so an empty or partial result +does not imply successful input completion. Unknown routes and construction +errors return the standard bar columns and explicit failure status. CSV loading +and output writing belong to the caller; no implicit IO is performed. +""" +function process_bars(::BarController, method::AbstractString, arguments, data; + batch_size::Integer = 1000000) + batch_size > 0 || throw(ArgumentError("batch_size must be positive")) + rows = Any[] + processed = 0 + error_message = nothing + try + settings = (; (Symbol(key) => value for (key, value) in pairs(arguments))...) + generator = initialize_bars(method; settings...) + values = data isa AbstractDataFrame ? Matrix(data) : data + size(values, 2) == 3 || throw(DimensionMismatch("tick input must have time, price and volume columns")) + for first_row in 1:batch_size:size(values, 1) + last_row = min(first_row+batch_size-1, size(values, 1)) + append!(rows, Data.construct_bars_from_data(generator; data = values[first_row:last_row, :])) + processed = last_row + end + catch failure + failure isa InterruptException && rethrow() + error_message = sprint(showerror, failure) + end + return (bars = _bar_dataframe(rows), completed_input = error_message === nothing, + processed_rows = processed, error = error_message) +end diff --git a/src/Components/Components.jl b/src/Components/Components.jl new file mode 100644 index 0000000..1f48a5c --- /dev/null +++ b/src/Components/Components.jl @@ -0,0 +1,164 @@ +module Components + +using ..Data +using ..Validation +using ..Features +using ..Utils +using DataFrames + +"""Optional dispatch interface for caller-defined labelers; define `label(model, data)` methods.""" +abstract type AbstractLabeler end + +function label end + +mutable struct ComponentEntry + factory::Any + loader::Any + metadata::Dict{String,Any} + accepted_keywords::Union{Nothing,Set{Symbol}} +end + +"""Case-insensitive component discovery with canonical names, aliases and native factories.""" +struct ComponentRegistry + name::String + interface::Any + entries::Dict{String,ComponentEntry} + index::Dict{String,String} +end + +ComponentRegistry(name::AbstractString; interface = nothing) = + ComponentRegistry(String(name), interface, Dict{String,ComponentEntry}(), Dict{String,String}()) + +function _component_entry(registry::ComponentRegistry, key::AbstractString) + normalized = lowercase(key) + haskey(registry.index, normalized) || throw(KeyError("$(repr(key)) is not registered in $(registry.name); available: $(join(available_components(registry), ", "))")) + return registry.entries[registry.index[normalized]] +end + +"""Register a native factory, optionally replacing the canonical entry reached through an alias.""" +function register_component!(registry::ComponentRegistry, key::AbstractString, factory; + aliases = String[], metadata = Dict(), override::Bool = false, + accepted_keywords = nothing, loader = nothing) + isempty(key) && throw(ArgumentError("component keys must be nonempty")) + all(alias -> alias isa AbstractString && !isempty(alias), aliases) || throw(ArgumentError("aliases must be nonempty strings")) + normalized = lowercase(key) + haskey(registry.index, normalized) && !override && throw(KeyError("component $(repr(key)) is already registered")) + haskey(registry.index, normalized) && unregister_component!(registry, key) + accepted = accepted_keywords === nothing ? nothing : Set(Symbol.(accepted_keywords)) + registry.entries[String(key)] = ComponentEntry(factory, loader, Dict{String,Any}(String(k) => v for (k, v) in pairs(metadata)), accepted) + registry.index[normalized] = String(key) + for alias in aliases + registry.index[lowercase(alias)] = String(key) + end + return factory +end + +"""Register a loader callback returning a native factory on its first use.""" +function register_lazy!(registry::ComponentRegistry, key::AbstractString, loader; kwargs...) + register_component!(registry, key, nothing; loader = loader, kwargs...) + return registry +end + +"""Remove one registry entry and every alias pointing to its canonical name.""" +function unregister_component!(registry::ComponentRegistry, key::AbstractString) + _component_entry(registry, key) + canonical = registry.index[lowercase(key)] + delete!(registry.entries, canonical) + filter!(pair -> last(pair) != canonical, registry.index) + return registry +end + +function component_factory(registry::ComponentRegistry, key::AbstractString) + entry = _component_entry(registry, key) + if entry.factory === nothing + entry.loader === nothing && throw(ArgumentError("component has no factory or loader")) + entry.factory = entry.loader() + entry.factory === nothing && throw(ArgumentError("component loader returned no factory")) + end + return entry.factory +end + +""" +Create a component with native positional/keyword arguments. Optional keyword +filtering uses `accepted_keywords` recorded at registration; `nothing` means +forward every keyword. This replaces Python signature introspection explicitly. +""" +function create_component(registry::ComponentRegistry, key::AbstractString, args...; + filter_unknown_kwargs::Bool = false, kwargs...) + factory = component_factory(registry, key) + accepted = _component_entry(registry, key).accepted_keywords + selected = filter_unknown_kwargs && accepted !== nothing ? (; (name => value for (name, value) in pairs(kwargs) if name in accepted)...) : kwargs + return factory(args...; selected...) +end + +component_metadata(registry::ComponentRegistry, key::AbstractString) = copy(_component_entry(registry, key).metadata) +available_components(registry::ComponentRegistry) = sort!(collect(keys(registry.entries))) +component_aliases(registry::ComponentRegistry) = Dict(key => value for (key, value) in registry.index if key != lowercase(value)) +is_lazy(registry::ComponentRegistry, key::AbstractString) = _component_entry(registry, key).factory === nothing +Base.in(key, registry::ComponentRegistry) = key isa AbstractString && haskey(registry.index, lowercase(key)) +Base.length(registry::ComponentRegistry) = length(registry.entries) +Base.keys(registry::ComponentRegistry) = available_components(registry) +Base.getindex(registry::ComponentRegistry, key::AbstractString) = component_factory(registry, key) + +include("BarController.jl") + +const BARS = ComponentRegistry("bars") +const CROSS_VALIDATORS = ComponentRegistry("cross_validators") +const FEATURE_IMPORTANCE = ComponentRegistry("feature_importance") +const LABELERS = ComponentRegistry("labelers"; interface = AbstractLabeler) +const BET_SIZERS = ComponentRegistry("bet_sizers") +const PORTFOLIO_OPTIMIZERS = ComponentRegistry("portfolio_optimizers") +const REGISTRIES = Dict("bars" => BARS, "cross_validators" => CROSS_VALIDATORS, + "feature_importance" => FEATURE_IMPORTANCE, "labelers" => LABELERS, + "bet_sizers" => BET_SIZERS, "portfolio_optimizers" => PORTFOLIO_OPTIMIZERS) + +function _builtin_bar_factory(family) + return function (; metric = "dollar", kwargs...) + metric_name = lowercase(String(metric)) + method = family == "standard_bars" ? metric_name*"_standard_bars" : replace(family, "_bars" => "", count = 1) + if family != "standard_bars" + parts = split(method, "_") + method = parts[1]*"_"*metric_name*"_"*parts[2]*"_bars" + end + return initialize_bars(method; kwargs...) + end +end + +for family in ("standard_bars", "expected_imbalance_bars", "fixed_imbalance_bars", "expected_run_bars", "fixed_run_bars") + register_component!(BARS, family, _builtin_bar_factory(family); metadata = Dict("family" => "bars", "afml_chapter" => 2)) +end +register_component!(BARS, "time_bars", (; kwargs...) -> initialize_bars("time_bars"; kwargs...); metadata = Dict("family" => "bars", "afml_chapter" => 2)) +for (name, factory) in (("kfold", Validation.KFoldCV), ("walkforward", Validation.WalkForwardCV), ("purgedkfold", Validation.PurgedKFoldCV), ("combinatorialpurged", Validation.CombinatorialPurgedCV), ("baggedcombinatorialpurged", Validation.BaggedCombinatorialPurgedCV), ("adaptivecombinatorialpurged", Validation.AdaptiveCombinatorialPurgedCV)) + register_component!(CROSS_VALIDATORS, name, factory; metadata = Dict("family" => "cross_validator")) +end + +struct ConfiguredComponent{F,A,K} + function_value::F + arguments::A + options::K +end + +(component::ConfiguredComponent)(args...; kwargs...) = component.function_value(component.arguments..., args...; merge(component.options, (; kwargs...))...) + +function _configured_factory(method) + return (args...; kwargs...) -> ConfiguredComponent(method, args, (; kwargs...)) +end + +for (name, method) in (("MDI", Features.feature_importance_mdi), ("MDA", Features.feature_importance_mda), ("SFI", Features.feature_importance_sfi), ("ClusteredMDI", Features.clustered_feature_importance_mdi), ("ClusteredMDA", Features.clustered_feature_importance_mda)) + register_component!(FEATURE_IMPORTANCE, name, _configured_factory(method); metadata = Dict("family" => "feature_importance")) +end + +"""Return a native component family by its canonical family name.""" +get_registry(family::AbstractString) = REGISTRIES[String(family)] + +"""Return sorted canonical component names for each public family.""" +list_components() = Dict(family => available_components(registry) for (family, registry) in REGISTRIES) + +export ComponentRegistry, register_component!, register_lazy!, unregister_component!, + component_factory, create_component, component_metadata, available_components, + component_aliases, is_lazy, AbstractLabeler, label, + BARS, CROSS_VALIDATORS, FEATURE_IMPORTANCE, LABELERS, BET_SIZERS, + PORTFOLIO_OPTIMIZERS, get_registry, list_components, + BAR_COLUMNS, BarController, initialize_bars, bar_methods, process_bars + +end diff --git a/src/Data/Data.jl b/src/Data/Data.jl index 43f9fdd..1604da1 100644 --- a/src/Data/Data.jl +++ b/src/Data/Data.jl @@ -26,12 +26,15 @@ include("Structures/run_bars.jl") # Fractional differentiation (AFML Ch. 5). include("Differentiation/Differentiation.jl") +include("Differentiation/WaveletHurst.jl") # Sample weighting: uniqueness, return attribution, time decay (AFML Ch. 4). include("Weights/SampleWeight.jl") # Covariance denoising via Random Matrix Theory (AFML Ch. 2). include("Denoise/Denoising.jl") +include("Denoise/MPVarianceFit.jl") +include("Denoise/DensityEstimation.jl") # Labeling: CUSUM filters, volatility, triple barrier, meta-labeling (AFML Ch. 3). include("Labeling/Labeling.jl") @@ -43,6 +46,9 @@ include("Distance/DistanceMetric.jl") # Synthetic data generators (MLAM Ch. 2; Heston–Merton regime simulator). include("SyntheticData/SyntheticData.jl") +include("TimeGAN.jl") +include("TimeGANTraining.jl") +include("TimeGANQuality.jl") export # metric taxonomy @@ -75,6 +81,7 @@ export # sample weights expand_label_for_meta_labeling, calculate_average_uniqueness, + sequential_bootstrap, sample_weight_absolute_return_meta_labeling, calculate_time_decay, # denoising @@ -83,6 +90,10 @@ export cov_to_corr, corr_to_cov, denoised_corr, + denoised_corr2, + KernelDensityEstimate, + fit_kde, + score_samples, find_max_eval, denoise_cov, optimal_portfolio, @@ -119,6 +130,13 @@ export align_params_length, heston_merton_log_returns, generate_prices_from_regimes, - parallel_generate_prices + parallel_generate_prices, + chronological_timegan_data, + TimeGAN, + latent_sequence, + timegan_losses, + sample_timegan, + fit_timegan, + timegan_quality_report end # module Data diff --git a/src/Data/Denoise/Denoising.jl b/src/Data/Denoise/Denoising.jl index 9d66fae..99477b5 100644 --- a/src/Data/Denoise/Denoising.jl +++ b/src/Data/Denoise/Denoising.jl @@ -6,8 +6,10 @@ AFML Ch. 2): Marcenko–Pastur PDF, eigenvalue denoising, and cov↔corr helpers The closed-form pieces (`marcenko_pastur_pdf`, `pca`, `cov_to_corr`, `corr_to_cov`, `denoised_corr`, `optimal_portfolio`) match Python to numerical precision. The Marcenko–Pastur *fit* (`find_max_eval` / `denoise_cov`) uses a -Gaussian KDE and a 1-D minimiser; these are implementation-defined (Python uses -scikit-learn's KDE + SciPy's optimiser), so they are validated behaviourally. +Gaussian KDE and a projected scalar local optimizer with the reference initial +point, bounds, finite differences, line search and stopping rules. Shared cases +check fitted cutoffs and downstream factor counts; roundoff remains relevant +when an eigenvalue is close to the cutoff. Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 2. """ @@ -27,7 +29,7 @@ function marcenko_pastur_pdf(variance::Real, q::Real; num_points::Integer = 1000 lambda_min = variance * (1 - sqrt(1 / q))^2 lambda_max = variance * (1 + sqrt(1 / q))^2 e_min = max(lambda_min, 1e-10) - eigenvalues = collect(range(e_min, lambda_max; length = num_points)) + eigenvalues = num_points == 1 ? [Float64(e_min)] : collect(range(e_min, lambda_max; length = num_points)) pdf = similar(eigenvalues) for i in eachindex(eigenvalues) e = eigenvalues[i] @@ -122,6 +124,41 @@ function _gaussian_kde_density( return density end +"""Differentiate the existing MP-density loss, including its moving grid.""" +function _mp_pdf_fit_gradient(variance::Real, q::Real, eigenvalues::AbstractVector{<:Real}, bandwidth::Real) + grid, density = marcenko_pastur_pdf(variance, q; num_points=length(eigenvalues)) + lower_rate = (1-sqrt(1/q))^2 + upper_rate = (1+sqrt(1/q))^2 + lower, upper = variance*lower_rate, variance*upper_rate + normalization = 1/(length(eigenvalues)*bandwidth*sqrt(2pi)) + gradient = 0.0 + for index in eachindex(grid) + position = length(grid)==1 ? 0.0 : (index-1)/(length(grid)-1) + if lower >= 1e-10 + grid_gradient = lower_rate + position*(upper_rate-lower_rate) + density_gradient = -density[index]/variance + else + grid_gradient = position*upper_rate + density_gradient = 0.0 + if upper > grid[index] > lower + density_gradient = density[index]*(-1/variance-grid_gradient/grid[index]+0.5*((upper_rate-grid_gradient)/(upper-grid[index])+(grid_gradient-lower_rate)/(grid[index]-lower))) + end + end + empirical, empirical_gradient = 0.0, 0.0 + for value in eigenvalues + distance = grid[index]-value + kernel = exp(-0.5*(distance/bandwidth)^2) + empirical += kernel + empirical_gradient -= kernel*distance + end + empirical *= normalization + empirical_gradient *= normalization*grid_gradient/bandwidth^2 + gradient += 2*(empirical-density[index])*(empirical_gradient-density_gradient) + end + return gradient +end + + function _mp_pdf_fit_error( variance::Real, q::Real, @@ -160,26 +197,31 @@ end find_max_eval(eigenvalues, q, bandwidth) -> (lambda_max, variance) Fit the Marcenko–Pastur distribution to the observed eigenvalues to find the -noise cutoff `lambda_max` and the implied variance. Mirrors Python's -`find_max_eval`; the KDE + minimiser are implementation-defined (behavioural -parity only). +noise cutoff `lambda_max` and the implied variance. The scalar local fit follows +the reference bounded optimization contract, including variance one on failure. +It does not guarantee a global minimum. Matrix inputs use their diagonal. """ function find_max_eval(eigenvalues::AbstractVector{<:Real}, q::Real, bandwidth::Real) - variance = _golden_section_min( - v -> _mp_pdf_fit_error(v, q, eigenvalues, bandwidth), - 1e-5, - 1 - 1e-5, - ) + isempty(eigenvalues) && throw(ArgumentError("at least one eigenvalue is required")) + all(isfinite, eigenvalues) || throw(ArgumentError("eigenvalues must be finite")) + isfinite(q) && q > 0 || throw(ArgumentError("q must be positive and finite")) + isfinite(bandwidth) && bandwidth > 0 || throw(ArgumentError("bandwidth must be positive and finite")) + fitted = _mp_bounded_local_min(v -> _mp_pdf_fit_error(v, q, eigenvalues, bandwidth); derivative = v -> _mp_pdf_fit_gradient(v, q, eigenvalues, bandwidth)) + variance = fitted.converged ? fitted.variance : 1. lambda_max = variance * (1 + sqrt(1 / q))^2 return lambda_max, variance end +find_max_eval(eigenvalues::AbstractMatrix{<:Real}, q::Real, bandwidth::Real) = + find_max_eval(diag(eigenvalues), q, bandwidth) + """ denoise_cov(cov0, q; bandwidth=0.01) -> Matrix De-noise a covariance matrix: convert to correlation, fit Marcenko–Pastur to locate the signal eigenvalues, rebuild the denoised correlation, and convert -back. Mirrors Python's `denoise_cov` (KDE-fit step is behavioural parity). +back. Mirrors Python's `denoise_cov` with local MP fitting and strict cutoff +comparison. Eigensolver roundoff near the cutoff can affect factor selection. """ function denoise_cov(cov0::AbstractMatrix{<:Real}, q::Real; bandwidth::Real = 0.01) corr0 = cov_to_corr(cov0) @@ -278,6 +320,11 @@ return matrix. `random_state` seeds Julia's RNG (see the divergence note above; the Python reference uses NumPy's PCG64). Mirrors Python's `nercome_denoised_covariance`. +Optional `permutations` supplies an `n_splits × T` matrix of one-based row +permutations for numerical replay. Repeated eigenvalues permit different +orthonormal bases across linear-algebra implementations; exact eigenvectors +inside such eigenspaces are not promised. + Reference: Lam, C. (2016). Nonparametric eigenvalue-regularized precision or covariance matrix estimator. The Annals of Statistics, 44(3), 928–953. """ @@ -286,10 +333,15 @@ function nercome_denoised_covariance( n_splits::Integer = 50, split_fraction::Real = 2.0 / 3.0, random_state::Union{Integer,Nothing} = nothing, + permutations = nothing, ) data = Float64.(returns) n, p = size(data) n >= 4 || throw(ArgumentError("NERCOME needs at least 4 observations")) + if permutations !== nothing + size(permutations) == (n_splits, n) || throw(ArgumentError("permutations must have shape n_splits by observations")) + all(row -> sort(collect(row)) == collect(1:n), eachrow(permutations)) || throw(ArgumentError("each supplied row must permute all observation indices")) + end sample_std = vec(std(data; dims = 1)) # ddof=1 (corrected), per column sample_std = [s <= 0 ? 1.0 : s for s in sample_std] z = data ./ sample_std' @@ -300,8 +352,8 @@ function nercome_denoised_covariance( random_state === nothing ? default_rng() : MersenneTwister(random_state) accumulated = zeros(p, p) used = 0 - for _ = 1:n_splits - order = randperm(rng, n) + for split = 1:n_splits + order = permutations === nothing ? randperm(rng, n) : Int.(vec(permutations[split, :])) first, second = order[1:m], order[(m+1):end] length(second) < 2 && continue cov_first = cov(view(z, first, :)) # columns = variables (dims=1) diff --git a/src/Data/Denoise/DensityEstimation.jl b/src/Data/Denoise/DensityEstimation.jl new file mode 100644 index 0000000..202bce5 --- /dev/null +++ b/src/Data/Denoise/DensityEstimation.jl @@ -0,0 +1,102 @@ +""" + KernelDensityEstimate + +An immutable one-dimensional fitted kernel density with equal observation +weights. Evaluate log densities with [`score_samples`](@ref). The fitted +observations are copied into a tuple, so later input mutations have no effect. +""" +struct KernelDensityEstimate + observations::Tuple{Vararg{Float64}} + bandwidth::Float64 + kernel::String + function KernelDensityEstimate(observations, bandwidth, kernel) + values = _density_values(observations, "observations") + bandwidth isa Real && isfinite(bandwidth) && bandwidth > 0 || throw(ArgumentError("bandwidth must be finite and positive")) + width = Float64(bandwidth) + isfinite(width) && width > 0 || throw(ArgumentError("bandwidth must be representable as a positive Float64")) + kernel isa AbstractString && kernel in ("gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine") || throw(ArgumentError("unsupported one-dimensional kernel")) + return new(Tuple(values), width, String(kernel)) + end +end + +function _density_values(values, name) + values isa AbstractArray && !isempty(values) && all(value -> value isa Real && isfinite(value), values) || throw(ArgumentError("$name must contain finite real observations")) + result = vec(Float64.(values)) + all(isfinite, result) || throw(ArgumentError("$name is outside the Float64 range")) + return result +end + +""" + fit_kde(observations; bandwidth=0.01, kernel="gaussian") + +Fit the public univariate, equally weighted density contract. Supported kernels +are Gaussian, tophat, Epanechnikov, exponential, linear and cosine. Arrays are +flattened to univariate observations, and bandwidth is supplied in data units. +Use `score_samples(estimate, query)` for log densities on a vector or one-column +matrix. Finite-support kernels exclude points exactly one bandwidth away. +""" +fit_kde(observations; bandwidth = 0.01, kernel = "gaussian") = KernelDensityEstimate(observations, bandwidth, kernel) +fit_kde(observations, bandwidth, kernel = "gaussian") = fit_kde(observations; bandwidth, kernel) + +function _density_log_kernel(distance, kernel) + kernel == "gaussian" && return -0.5 * distance^2 - 0.5 * log(2pi) + kernel == "exponential" && return -distance - log(2) + distance < 1 || return -Inf + kernel == "tophat" && return -log(2) + kernel == "epanechnikov" && return log(0.75) + log1p(-distance^2) + kernel == "linear" && return log1p(-distance) + return log(pi / 4) + log(cos(pi * distance / 2)) +end + +""" + score_samples(estimate::KernelDensityEstimate, query) + +Return the log density at each query using a stable log-sum-exp mixture. +Queries are a nonempty finite vector or one-column matrix; values outside all +compact kernel supports have log density `-Inf`. +""" +function score_samples(estimate::KernelDensityEstimate, query) + query isa AbstractVector || query isa AbstractMatrix && size(query, 2) == 1 || throw(ArgumentError("query must be a vector or one-column matrix")) + points = _density_values(query, "query") + result = similar(points) + normalizer = log(length(estimate.observations)) + log(estimate.bandwidth) + terms = Vector{Float64}(undef, length(estimate.observations)) + for (index, point) in enumerate(points) + for (observation, value) in enumerate(estimate.observations) + distance = abs(point - value) + standardized = isfinite(distance) ? distance / estimate.bandwidth : abs(point / estimate.bandwidth - value / estimate.bandwidth) + terms[observation] = _density_log_kernel(standardized, estimate.kernel) + end + largest = maximum(terms) + result[index] = largest == -Inf ? -Inf : largest + log(sum(exp(value - largest) for value in terms)) - normalizer + end + return result +end + +""" + denoised_corr2(eigenvalues, eigenvectors, num_factors; alpha=0.0) + +Retain the leading supplied eigenpairs and shrink only the off-diagonal noise +contribution by `alpha`. `eigenvalues` is a square diagonal matrix, with entries +off its diagonal allowed only within absolute tolerance `1e-12`. The caller +supplies the eigenpair order and signal count. This construction preserves the +diagonal contribution and does not normalize the result to unit diagonal. +""" +function denoised_corr2(eigenvalues, eigenvectors, num_factors; alpha = 0.0) + eigenvalues isa AbstractMatrix && size(eigenvalues, 1) == size(eigenvalues, 2) && !isempty(eigenvalues) || throw(ArgumentError("eigenvalues must be a nonempty square diagonal matrix")) + eigenvectors isa AbstractMatrix && size(eigenvectors) == size(eigenvalues) || throw(ArgumentError("eigenvectors must be square and match eigenvalues")) + for array in (eigenvalues, eigenvectors) + all(value -> value isa Real && !(value isa Bool) && isfinite(value), array) || throw(ArgumentError("eigenpairs must contain finite real numbers")) + end + values, vectors = Float64.(eigenvalues), Float64.(eigenvectors) + all(isfinite, values) && all(isfinite, vectors) || throw(ArgumentError("eigenpairs are outside the Float64 range")) + all(abs.(values - Diagonal(diag(values))) .<= 1e-12) || throw(ArgumentError("eigenvalues must be diagonal")) + num_factors isa Integer && !(num_factors isa Bool) && 0 <= num_factors <= size(values, 1) || throw(ArgumentError("num_factors must be an integer between zero and the dimension")) + alpha isa Real && !(alpha isa Bool) && isfinite(alpha) && 0 <= alpha <= 1 || throw(ArgumentError("alpha must be finite and lie in [0, 1]")) + signal_vectors, noise_vectors = vectors[:, 1:num_factors], vectors[:, num_factors+1:end] + signal = signal_vectors * values[1:num_factors, 1:num_factors] * signal_vectors' + noise = noise_vectors * values[num_factors+1:end, num_factors+1:end] * noise_vectors' + return signal + Float64(alpha) * noise + (1 - Float64(alpha)) * Diagonal(diag(noise)) +end + +denoised_corr2(eigenvalues, eigenvectors, num_factors, alpha) = denoised_corr2(eigenvalues, eigenvectors, num_factors; alpha) diff --git a/src/Data/Denoise/MPVarianceFit.jl b/src/Data/Denoise/MPVarianceFit.jl new file mode 100644 index 0000000..117b552 --- /dev/null +++ b/src/Data/Denoise/MPVarianceFit.jl @@ -0,0 +1,161 @@ +""" + _mp_bounded_local_min(objective; derivative=nothing, max_iter=15000, max_evals=15000) + +Fit the scalar MP variance from 0.5 on [1e-5, 1-1e-5] with projected BFGS steps, +an optional analytic derivative or forward differences and the reference line-search and stopping +settings. In one dimension the secant curvature defines the BFGS approximation. +More–Thuente trial intervals retain their extrapolated limits before applying +the absolute step bound. Warning steps are accepted before testing the outer +stopping rules. This is a local fit; nonconvergence is returned explicitly for +the public variance-one fallback. Floating-point differences near stationary +points can change line-search termination and therefore the fallback outcome. +""" +function _mp_bounded_local_min(objective; derivative = nothing, max_iter::Integer = 15000, max_evals::Integer = 15000) + low, high, x = 1e-5, 1-1e-5, .5 + evaluations = Ref(0) + evaluate = value -> begin + evaluations[] += 1 + Float64(objective(clamp(value, low, high))) + end + gradient = (value, result) -> begin + derivative !== nothing && return Float64(derivative(value)) + next = value+1e-8 <= high ? value+1e-8 : value-1e-8 + (evaluate(next)-result)/(next-value) + end + f = evaluate(x) + g = gradient(x, f) + curvature = 1. + result = (value, objective_value, converged, iteration) -> + (variance = value, objective = objective_value, converged = converged, + iterations = iteration, evaluations = evaluations[]) + for iteration in 1:max_iter + (isfinite(f) && isfinite(g)) || return result(x, f, false, iteration-1) + abs(x-clamp(x-g, low, high)) <= 1e-5 && return result(x, f, true, iteration-1) + direction = clamp(x-g/curvature, low, high)-x + maximum_step = direction > 0 ? (high-x)/direction : (low-x)/direction + objective_line = step -> begin + trial = clamp(x+step*direction, low, high) + value = evaluate(trial) + return value, gradient(trial, value)*direction + end + search = _mp_line_search(objective_line, f, g*direction, + iteration == 1 ? 1. : min(maximum_step, 1e10)) + if !search.converged + if curvature != 1. && evaluations[] < max_evals + curvature = 1. + continue + else + return result(x, f, false, iteration-1) + end + end + next_x = clamp(x+search.step*direction, low, high) + next_f = evaluate(next_x) + next_g = gradient(next_x, next_f) + evaluations[] > max_evals && return result(next_x, next_f, false, iteration) + if abs(next_x-clamp(next_x-next_g, low, high)) <= 1e-5 || f-next_f <= 2.220446049250313e-9*max(abs(f), abs(next_f), 1.) + return result(next_x, next_f, true, iteration) + end + displacement = next_x-x + change = next_g-g + change*displacement > eps(Float64)*(-g*displacement) && (curvature = change/displacement) + x, f, g = next_x, next_f, next_g + end + return result(x, f, false, max_iter) +end + +"""Safeguarded cubic and secant interpolation for the scalar MP line search.""" +function _mp_interpolate(best, opposite, trial, bracketed, limits) + x, fx, gx = best + y, fy, gy = opposite + t, ft, gt = trial + lower, upper = limits + sign_change = sign(gt)*sign(gx)<0 + if ft>fx + theta = 3*(fx-ft)/(t-x)+gx+gt + scale = max(abs(theta),abs(gx),abs(gt)) + gamma = scale*sqrt(max(0.,(theta/scale)^2-(gx/scale)*(gt/scale))) + tx && (gamma=-gamma) + cubic = t+(gamma-gt+theta)/(2gamma-gt+gx)*(x-t) + secant = t+gt/(gt-gx)*(x-t) + next = abs(cubic-t)>abs(secant-t) ? cubic : secant + bracketed = true + elseif abs(gt)x && (gamma=-gamma) + ratio = (gamma-gt+theta)/(gamma+(gx-gt)+gamma) + cubic = ratio<0 && gamma!=0 ? t+ratio*(x-t) : (t>x ? upper : lower) + secant = t+gt/(gt-gx)*(x-t) + if bracketed + next = abs(cubic-t)x ? min(t+.66*(y-t),next) : max(t+.66*(y-t),next) + else + next = abs(cubic-t)>abs(secant-t) ? cubic : secant + next = clamp(next,lower,upper) + end + elseif bracketed + theta = 3*(ft-fy)/(y-t)+gy+gt + scale = max(abs(theta),abs(gy),abs(gt)) + gamma = scale*sqrt(max(0.,(theta/scale)^2-(gy/scale)*(gt/scale))) + t>y && (gamma=-gamma) + next = t+(gamma-gt+theta)/(2gamma-gt+gy)*(y-t) + else + next = t>x ? upper : lower + end + if ft>fx + opposite = trial + else + sign_change && (opposite=best) + best = trial + end + return best,opposite,next,bracketed +end + +"""Use the public bounded fit's sufficient decrease, curvature and warning rules.""" +function _mp_line_search(evaluate, initial_value, initial_gradient, maximum_step) + best = (0.,initial_value,initial_gradient) + opposite = best + step, lower, upper = 1.,0.,5. + width, previous_width = maximum_step,2maximum_step + bracketed, modified = false,true + decrease_gradient = 1e-3*initial_gradient + for evaluation in 1:20 + value, gradient = evaluate(step) + isfinite(value) && isfinite(gradient) || return (step=step,converged=false) + sufficient = initial_value+step*decrease_gradient + modified && value<=sufficient && gradient>=0 && (modified=false) + warning = bracketed && (step<=lower || step>=upper || upper-lower<=.1upper) + warning |= step==maximum_step && value<=sufficient && gradient<=decrease_gradient + warning |= step==0 && (value>sufficient || gradient>=decrease_gradient) + wolfe = value<=sufficient && abs(gradient)<=-.9initial_gradient + (warning || wolfe) && return (step=step,converged=true) + if modified && value<=best[2] && value>sufficient + transformed(point) = (point[1],point[2]-point[1]*decrease_gradient,point[3]-decrease_gradient) + recover(point) = (point[1],point[2]+point[1]*decrease_gradient,point[3]+decrease_gradient) + best,opposite,step,bracketed = _mp_interpolate(transformed(best),transformed(opposite),transformed((step,value,gradient)),bracketed,(lower,upper)) + best,opposite = recover(best),recover(opposite) + else + best,opposite,step,bracketed = _mp_interpolate(best,opposite,(step,value,gradient),bracketed,(lower,upper)) + end + if bracketed + abs(opposite[1]-best[1])>=.66previous_width && (step=best[1]+.5*(opposite[1]-best[1])) + previous_width,width = width,abs(opposite[1]-best[1]) + lower,upper = minmax(best[1],opposite[1]) + else + lower,upper = step+1.1*(step-best[1]),step+4*(step-best[1]) + end + step = clamp(step,0.,maximum_step) + bracketed && (step<=lower || step>=upper || upper-lower<=.1upper) && (step=best[1]) + end + return (step=step,converged=false) +end diff --git a/src/Data/Differentiation/Differentiation.jl b/src/Data/Differentiation/Differentiation.jl index 33e3509..104fcc8 100644 --- a/src/Data/Differentiation/Differentiation.jl +++ b/src/Data/Differentiation/Differentiation.jl @@ -12,6 +12,19 @@ Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 5. using Statistics: cor, mean, std using HypothesisTests: ADFTest, pvalue +function _differencing_forward_fill(series) + result = Float64.(series) + previous = NaN + for index in eachindex(result) + if isnan(result[index]) + result[index] = previous + else + previous = result[index] + end + end + return result +end + """ calculate_weights_std(degree, size) -> Vector{Float64} @@ -35,6 +48,8 @@ Weights for the fixed-width-window (FFD) method, generated until the magnitude drops below `threshold`; ordered with `w₀` at the end (Snippet 5.3). """ function calculate_weights_ffd(degree::Real, threshold::Real = 1e-5) + isfinite(degree) && isfinite(threshold) && threshold > 0 || throw(ArgumentError("FFD requires a finite order and positive finite threshold")) + degree > -1 || threshold > abs(degree) || throw(ArgumentError("These integration weights never cross the supplied truncation threshold")) weights = Float64[1.0] k = 1 while true @@ -52,7 +67,9 @@ end Standard (expanding-window) fractionally differentiated series. Returns a vector the same length as `series`; the warm-up region (where the cumulative relative weight loss is below `threshold`) is filled with `NaN`. Mirrors Python's -`fractional_difference_std` for a single series. +`fractional_difference_std` for a single series. Internal missing observations +are forward-filled; leading missing observations and output positions are +retained. The warm-up threshold is calculated from the original series length. """ function fractional_difference_std( series::AbstractVector{<:Real}, @@ -62,6 +79,9 @@ function fractional_difference_std( n = length(series) result = fill(NaN, n) n == 0 && return result + filled = _differencing_forward_fill(series) + first_valid = findfirst(!isnan, filled) + isnothing(first_valid) && return result weights = calculate_weights_std(degree, n) cumulative = cumsum(abs.(weights)) @@ -69,10 +89,10 @@ function fractional_difference_std( skip = count(<(threshold), cumulative) weights_natural = reverse(weights) # [w0, w1, ...] - for k = (skip+1):n + for k = (first_valid+skip):n acc = 0.0 - for m = 0:(k-1) - acc += weights_natural[m+1] * series[k-m] + for m = 0:(k-first_valid) + acc += weights_natural[m+1] * filled[k-m] end result[k] = acc end @@ -85,6 +105,8 @@ end Fixed-width-window (FFD) fractionally differentiated series. Returns a vector the same length as `series`, with the first `width-1` entries (the warm-up) filled with `NaN`. Mirrors Python's `fractional_difference_fixed_single`. +Internal missing observations are forward-filled before filtering; leading +missing observations and original observation positions are retained. """ function fractional_difference_fixed( series::AbstractVector{<:Real}, @@ -96,11 +118,14 @@ function fractional_difference_fixed( n = length(series) result = fill(NaN, n) n < width && return result + filled = _differencing_forward_fill(series) + first_valid = findfirst(!isnan, filled) + isnothing(first_valid) && return result - for k = width:n + for k = (first_valid+width-1):n acc = 0.0 for m = 0:(width-1) - acc += weights[m+1] * series[k-m] + acc += weights[m+1] * filled[k-m] end result[k] = acc end @@ -198,12 +223,13 @@ _polyfit_slope(x, y) = """ wavelet_variance_hurst(series; wavelet="db2", skip_fine=1) -> Float64 -Wavelet-variance Hurst estimate. Optional component (no wavelet dependency in the -Julia port): always returns `NaN`, so `adaptive_differencing_order` falls back to -the R/S estimate alone (Python's pywt-absent path). Mirrors Python's -`wavelet_variance_hurst` when `pywt` is unavailable. +Wavelet-variance Hurst estimate with the public default `db2` filter and +half-sample symmetric extension. Native closed-form `db2`, `haar` and `db1` +filters need no additional dependency. Other named filters are rejected. +The public level rule and mean-square detail powers are retained, including +boundary-dominated short inputs. Fewer than two retained octaves returns NaN. """ -wavelet_variance_hurst(series; wavelet = "db2", skip_fine::Integer = 1) = NaN +wavelet_variance_hurst(series; wavelet = "db2", skip_fine::Integer = 1) = _differencing_wavelet_hurst(series, wavelet, skip_fine) """ rescaled_range_hurst(series) -> Float64 @@ -246,7 +272,8 @@ end Bias-corrected memory order `d̂` of a series of increments: a ridge-style blend of the wavelet-variance and R/S Hurst estimates (`d̂ = H̄ - 0.5`, clipped to -`[0, 0.99]`). With no wavelet dependency the blend is the R/S estimate alone. +`[0, 0.99]`). The default db2 component is available natively. Only finite +component estimates enter the equal-weight blend. Mirrors Python's `adaptive_differencing_order`. """ function adaptive_differencing_order(increments::AbstractVector{<:Real}) @@ -288,7 +315,9 @@ chooses the fixed-width-FFD weight-truncation threshold by cross-validation (the threshold retaining the most memory subject to ADF stationarity), and raises the order along `delta_grid` only as far as the ADF stationarity boundary if needed. Returns a `NamedTuple` `(order, d_hat, adf_pvalue, memory_retained, threshold, -series)`. +series)`. The returned vector retains the original length and NaN warm-up. +Memory-order estimation removes missing levels before forming increments; +filtering forward-fills the original series while preserving its positions. Preferred-when / avoid-when guidance: prefer AFD over fixed-width FFD when the differencing order itself must be right — @@ -307,6 +336,7 @@ function adaptive_fractional_difference( adf_maxlag::Integer = 1, ) grid = delta_grid === nothing ? DEFAULT_DELTA_GRID : collect(delta_grid) + original = Float64.(series) level = float.(filter(!isnan, series)) increments = diff(level) d_hat = adaptive_differencing_order(increments) @@ -325,10 +355,10 @@ function adaptive_fractional_difference( best_threshold = cv_thresholds[2] best = nothing for threshold in cv_thresholds - diffv = fractional_difference_fixed(level, order_anchor; threshold = threshold) + diffv = fractional_difference_fixed(original, order_anchor; threshold = threshold) count(!isnan, diffv) < 20 && continue pv = _adf_pvalue(diffv, adf_maxlag) - mem = _memory_retained(diffv, level) + mem = _memory_retained(diffv, original) candidate = (pv < adf_significance, isfinite(mem) ? mem : -1.0, threshold, pv) if best === nothing || (candidate[1], candidate[2]) > (best[1], best[2]) best = candidate @@ -337,14 +367,14 @@ function adaptive_fractional_difference( best !== nothing && (best_threshold = best[3]) order = order_anchor - diffv = fractional_difference_fixed(level, order; threshold = best_threshold) + diffv = fractional_difference_fixed(original, order; threshold = best_threshold) pv = count(!isnan, diffv) >= 20 ? _adf_pvalue(diffv, adf_maxlag) : 1.0 grid_up = [g for g in grid if g > order_anchor] gi = 1 while pv >= adf_significance && gi <= length(grid_up) order = grid_up[gi] gi += 1 - diffv = fractional_difference_fixed(level, order; threshold = best_threshold) + diffv = fractional_difference_fixed(original, order; threshold = best_threshold) pv = count(!isnan, diffv) >= 20 ? _adf_pvalue(diffv, adf_maxlag) : 1.0 end @@ -352,7 +382,7 @@ function adaptive_fractional_difference( order = order, d_hat = d_hat, adf_pvalue = pv, - memory_retained = _memory_retained(diffv, level), + memory_retained = _memory_retained(diffv, original), threshold = best_threshold, series = diffv, ) diff --git a/src/Data/Differentiation/WaveletHurst.jl b/src/Data/Differentiation/WaveletHurst.jl new file mode 100644 index 0000000..40fb9ca --- /dev/null +++ b/src/Data/Differentiation/WaveletHurst.jl @@ -0,0 +1,54 @@ +function _differencing_wavelet_filters(wavelet) + wavelet isa AbstractString || throw(ArgumentError("Supply a supported wavelet name")) + if wavelet in ("haar", "db1") + scale = inv(sqrt(2.0)) + return [scale, scale], [-scale, scale] + end + wavelet == "db2" || throw(ArgumentError("Native wavelet Hurst supports db2, haar and db1")) + root, denominator = sqrt(3.0), 4 * sqrt(2.0) + low = [1 - root, 3 - root, 3 + root, 1 + root] ./ denominator + return low, reverse(low) .* [-1.0, 1.0, -1.0, 1.0] +end + +function _differencing_symmetric_index(position, count) + folded = mod(position, 2count) + return folded < count ? folded + 1 : 2count - folded +end + +function _differencing_wavelet_step(values, low, high) + count = fld(length(values) + length(low) - 1, 2) + approximation, detail = zeros(count), zeros(count) + for index in 0:count-1 + for offset in 0:length(low)-1 + value = values[_differencing_symmetric_index(2index + 1 - offset, length(values))] + approximation[index+1] += low[offset+1] * value + detail[index+1] += high[offset+1] * value + end + end + return approximation, detail +end + +function _differencing_wavelet_details(series, wavelet) + series isa AbstractVector{<:Real} && !isempty(series) || throw(ArgumentError("Wavelet Hurst requires a nonempty real vector")) + low, high = _differencing_wavelet_filters(wavelet) + count = length(series) + maximum_level = max(0, floor(Int, log2(count / (length(low) - 1)))) + level = max(3, min(maximum_level, floor(Int, log2(count)) - 2)) + values = Float64.(series) .- mean(series) + details = Vector{Float64}[] + for _ in 1:level + values, detail = _differencing_wavelet_step(values, low, high) + push!(details, detail) + end + return reverse(details) +end + +function _differencing_wavelet_hurst(series, wavelet, skip_fine) + details = _differencing_wavelet_details(series, wavelet) + octaves = collect(length(details):-1:1) + keep = octaves .>= 1 + skip_fine + count(keep) >= 2 || return NaN + powers = [mean(detail.^2) for detail in details][keep] + adjusted = ifelse.(powers .<= 0, 1e-12, powers) + return (_polyfit_slope(Float64.(octaves[keep]), log2.(adjusted)) + 1) / 2 +end diff --git a/src/Data/Distance/DistanceMetric.jl b/src/Data/Distance/DistanceMetric.jl index 3db1779..61e0bb1 100644 --- a/src/Data/Distance/DistanceMetric.jl +++ b/src/Data/Distance/DistanceMetric.jl @@ -16,14 +16,23 @@ using Statistics: cor # numpy.histogram2d with an integer bin count (equal-width; last bin closed). function _histogram2d(x::AbstractVector{<:Real}, y::AbstractVector{<:Real}, bins::Integer) + bins > 0 || throw(ArgumentError("bins must be positive")) + length(x) == length(y) || throw(DimensionMismatch("x and y must have the same length")) + all(isfinite, x) && all(isfinite, y) || throw(ArgumentError("histogram inputs must be finite")) counts = zeros(Float64, bins, bins) - bin_index(v, lo, hi) = - lo == hi ? 1 : - (v == hi ? bins : clamp(floor(Int, (v - lo) / (hi - lo) * bins) + 1, 1, bins)) - xlo, xhi = minimum(x), maximum(x) - ylo, yhi = minimum(y), maximum(y) + isempty(x) && return counts + function bin_edges(values) + lower, upper = extrema(values) + if lower == upper + lower, upper = lower - .5, upper + .5 + end + edges = lower .+ collect(0:bins) .* ((upper - lower) / bins) + edges[end] = upper + return edges + end + xedges, yedges = bin_edges(x), bin_edges(y) for k in eachindex(x) - counts[bin_index(x[k], xlo, xhi), bin_index(y[k], ylo, yhi)] += 1.0 + counts[min(searchsortedlast(xedges, x[k]; lt = <), bins), min(searchsortedlast(yedges, y[k]; lt = <), bins)] += 1.0 end return counts end @@ -71,6 +80,7 @@ function calculate_variation_of_information( bins::Integer; norm::Bool = false, ) + isempty(x) && throw(ArgumentError("variation of information requires observations")) hist = _histogram2d(x, y, bins) mutual_information = _mutual_info(hist) marginal_x = _entropy(vec(sum(hist; dims = 2))) @@ -90,22 +100,23 @@ Optimal number of histogram bins (univariate when `correlation` is `nothing`, otherwise bivariate). Mirrors Python's `calculate_number_of_bins`. """ function calculate_number_of_bins(num_observations::Integer; correlation = nothing) + observations = float(num_observations) if correlation === nothing z = ( 8 + - 324 * num_observations + - 12 * sqrt(36 * num_observations + 729 * num_observations^2) + 324 * observations + + 12 * sqrt(36 * observations + 729 * observations^2) )^(1 / 3) return round(Int, z / 6 + 2 / (3z) + 1 / 3) end - if isapprox(correlation, 1.0) || isapprox(correlation, -1.0) + if abs(correlation - 1.0) <= 1e-8 + 1e-5 || abs(correlation + 1.0) <= 1e-8 + 1e-5 correlation = sign(correlation) * (1 - 1e-10) end (1 - correlation^2) == 0 && return calculate_number_of_bins(num_observations) return round( Int, - 2^-0.5 * sqrt(1 + sqrt(1 + 24 * num_observations / (1 - correlation^2))), + 2^-0.5 * sqrt(1 + sqrt(1 + 24 * observations / (1 - correlation^2))), ) end @@ -224,15 +235,18 @@ using Statistics: mean, std using Random: MersenneTwister, randn # Break exact ties with negligible data-scaled noise (numpy population std). -function _jitter(values, rng) +function _jitter(values, rng, draws = nothing) v = float.(vec(values)) scale = std(v; corrected = false) scale == 0.0 && (scale = 1.0) - return v .+ randn(rng, length(v)) .* scale .* 1e-10 + noise = draws === nothing ? randn(rng, length(v)) : Float64.(collect(draws)) + length(noise) == length(v) || throw(DimensionMismatch("jitter draws must match the observations")) + all(isfinite, noise) || throw(ArgumentError("jitter draws must be finite")) + return v .+ noise .* scale .* 1e-10 end """ - ksg_mutual_information(x, y; k=4, random_state=0) -> Float64 + ksg_mutual_information(x, y; k=4, random_state=0, jitter_draws=nothing) -> Float64 Kraskov–Stögbauer–Grassberger mutual information (algorithm 1), in nats, using the Chebyshev (max) norm in the joint space: @@ -250,16 +264,24 @@ data (no free lunch there). Use raw KSG (the surrogate de-bias adds nothing). Brute-force O(N²) kNN (no kd-tree dependency). Mirrors Python's `ksg_mutual_information`. Reference: Kraskov, Stögbauer & Grassberger (2004), Physical Review E 69(6). + +Supply `jitter_draws=(x_noise, y_noise)` to replay standardized normal draws +across runtimes. The public population-standard-deviation scaling is applied +inside the estimator; native and NumPy seeds are not equivalent. """ function ksg_mutual_information( x::AbstractVector{<:Real}, y::AbstractVector{<:Real}; k::Integer = 4, random_state::Union{Integer,Nothing} = 0, + jitter_draws = nothing, ) + length(x) == length(y) || throw(DimensionMismatch("x and y must have the same length")) + k >= 1 || throw(ArgumentError("k must be positive")) + jitter_draws === nothing || length(jitter_draws) == 2 || throw(ArgumentError("jitter_draws must contain two vectors")) rng = MersenneTwister(random_state === nothing ? 0 : random_state) - xj = _jitter(x, rng) - yj = _jitter(y, rng) + xj = _jitter(x, rng, jitter_draws === nothing ? nothing : jitter_draws[1]) + yj = _jitter(y, rng, jitter_draws === nothing ? nothing : jitter_draws[2]) n = length(xj) n < 2 && return 0.0 k = min(k, n - 1) diff --git a/src/Data/Labeling/FinancialLabels.jl b/src/Data/Labeling/FinancialLabels.jl index a4ab6ac..f7a3886 100644 --- a/src/Data/Labeling/FinancialLabels.jl +++ b/src/Data/Labeling/FinancialLabels.jl @@ -81,7 +81,7 @@ function find_trend_using_trend_scanning( best_t = t end end - push!(results, (idx, vertical_time, best_t, sign(best_t))) + isnan(best_t) || push!(results, (idx, vertical_time, best_t, sign(best_t))) end return results end diff --git a/src/Data/Labeling/Labeling.jl b/src/Data/Labeling/Labeling.jl index 62b4cca..42e4d82 100644 --- a/src/Data/Labeling/Labeling.jl +++ b/src/Data/Labeling/Labeling.jl @@ -20,6 +20,7 @@ using Statistics: mean # pandas-compatible debiased exponentially-weighted std (adjust=true). function _ewm_std(x::AbstractVector{<:Real}, span::Integer) + span >= 1 || throw(ArgumentError("span must be at least one")) alpha = 2 / (span + 1) n = length(x) out = fill(NaN, n) @@ -28,6 +29,7 @@ function _ewm_std(x::AbstractVector{<:Real}, span::Integer) sum_w2 = 0.0 weighted_mean = 0.0 for i = 1:t + isfinite(x[i]) || continue w = (1 - alpha)^(t - i) weighted_mean += w * x[i] sum_w += w @@ -36,6 +38,7 @@ function _ewm_std(x::AbstractVector{<:Real}, span::Integer) m = weighted_mean / sum_w ssd = 0.0 for i = 1:t + isfinite(x[i]) || continue w = (1 - alpha)^(t - i) ssd += w * (x[i] - m)^2 end @@ -62,6 +65,7 @@ function symmetric_cusum_filter( shift_neg = 0.0 for i = 2:length(close) delta = close[i] - close[i-1] + isnan(delta) && continue shift_pos = max(0.0, shift_pos + delta) shift_neg = min(0.0, shift_neg + delta) if shift_neg < -threshold @@ -92,6 +96,7 @@ function cusum_filter_events_dynamic_threshold( shift_neg = 0.0 for i = 2:length(close) delta = close[i] - close[i-1] + isnan(delta) && continue thr = threshold[i] shift_pos = max(0.0, shift_pos + delta) shift_neg = min(0.0, shift_neg + delta) @@ -179,7 +184,7 @@ function triple_barrier( vfill = ismissing(vbar) ? close_index[end] : vbar end_pos = searchsortedlast(close_index, vfill) segment = close[start_pos:end_pos] - side = events.side[r] + side = "side" in names(events) ? events.side[r] : 1.0 width = events.base_width[r] profit_taking = ptsl[1] > 0 ? ptsl[1] * width : Inf stop_loss = ptsl[2] > 0 ? -ptsl[2] * width : -Inf @@ -235,7 +240,7 @@ function meta_events( sides = fill(1.0, length(starts)) ptsl_final = (ptsl[1], ptsl[1]) else - sides = [get(side, t, 1.0) for t in starts] + sides = [get(side, t, NaN) for t in starts] ptsl_final = (ptsl[1], ptsl[2]) end @@ -263,12 +268,15 @@ function meta_labeling( close_index::AbstractVector, close::AbstractVector{<:Real}, ) - price = Dict(t => p for (t, p) in zip(close_index, close)) + function price(timestamp) + position = searchsortedfirst(close_index, timestamp) + return position <= length(close) ? close[position] : NaN + end valid = .!ismissing.(events.end_time) sub = events[valid, :] starts = sub.event_start ends = sub.end_time - returns = [log(price[ends[i]]) - log(price[starts[i]]) for i in eachindex(starts)] + returns = [log(price(ends[i])) - log(price(starts[i])) for i in eachindex(starts)] has_side = "side" in names(sub) if has_side diff --git a/src/Data/Structures/abstract_imbalance_bars.jl b/src/Data/Structures/abstract_imbalance_bars.jl index 38f1883..3b890a3 100644 --- a/src/Data/Structures/abstract_imbalance_bars.jl +++ b/src/Data/Structures/abstract_imbalance_bars.jl @@ -43,32 +43,42 @@ function construct_bars_from_data( bars_list = Vector{Union{DateTime,Int,Float64}}[] tick_counter = imbalance_bars.tick_counter - n_row = size(data, 1) - imbalance_bars.previous_tick_imbalances = zeros(Float64, n_row) - - for (k, tick_data) in enumerate(eachrow(data)) + for tick_data in eachrow(data) tick_counter += 1 + imbalance_bars.tick_counter = tick_counter (date_time, price, volume) = tick_data price = Float64(price) volume = Float64(volume) signed_tick = tick_rule(imbalance_bars, price) update_base_fields(imbalance_bars, price, signed_tick, volume) + imbalance_bars.close_price = price imbalance = imbalance_at_tick(T, price, signed_tick, volume) - imbalance_bars.previous_tick_imbalances[k] = imbalance + push!(imbalance_bars.previous_tick_imbalances, imbalance) imbalance_bars.cumulative_theta += imbalance # First-time warm-up of E[b]. if isnan(imbalance_bars.expected_imbalance) imbalance_bars.expected_imbalance = ewma_expected_imbalance( imbalance_bars, - view(imbalance_bars.previous_tick_imbalances, 1:k), + imbalance_bars.previous_tick_imbalances, imbalance_bars.expected_imbalance_window; warm_up = true, ) end + if imbalance_bars.analyse_thresholds !== nothing + snapshot = _information_bar_snapshot(imbalance_bars, date_time) + merge!(snapshot, Dict( + CUMULATIVE_THETA => imbalance_bars.cumulative_theta, + EXPECTED_IMBALANCE => imbalance_bars.expected_imbalance, + PREVIOUS_BARS_N_TICKS_LIST => imbalance_bars.previous_bars_number_of_ticks, + PREVIOUS_TICK_IMBALANCES_LIST => imbalance_bars.previous_tick_imbalances, + )) + push!(imbalance_bars.analyse_thresholds, snapshot) + end + expected_ticks = imbalance_bars.expected_ticks_number expected_imbalance = imbalance_bars.expected_imbalance threshold = @@ -95,7 +105,7 @@ function construct_bars_from_data( imbalance_bars.expected_ticks_number = expected_number_of_ticks(imbalance_bars) imbalance_bars.expected_imbalance = ewma_expected_imbalance( imbalance_bars, - view(imbalance_bars.previous_tick_imbalances, 1:k), + imbalance_bars.previous_tick_imbalances, imbalance_bars.expected_imbalance_window; warm_up = false, ) diff --git a/src/Data/Structures/abstract_information_driven_bars.jl b/src/Data/Structures/abstract_information_driven_bars.jl index b22c4a5..21dd261 100644 --- a/src/Data/Structures/abstract_information_driven_bars.jl +++ b/src/Data/Structures/abstract_information_driven_bars.jl @@ -48,6 +48,20 @@ function ewma_expected_imbalance( return ewma(collect(Float64, last(array, ewma_window)), ewma_window)[end] end +function _information_bar_snapshot(bars::AbstractInformationDrivenBarsType, timestamp) + return Dict{String,Any}( + PREVIOUS_TICK_RULE => bars.previous_tick_rule, + CUMULATIVE_TICKS => bars.cumulative_ticks, + CUMULATIVE_DOLLAR => bars.cumulative_dollar, + CUMULATIVE_VOLUME => bars.cumulative_volume, + CUMULATIVE_BUY_VOLUME => bars.cumulative_buy_volume, + N_TICKS_ON_BAR_FORMATION => bars.n_ticks_on_bar_formation, + EXPECTED_TICKS_NUMBER => bars.expected_ticks_number, + EXPECTED_IMBALANCE_WINDOW => bars.expected_imbalance_window, + "timestamp" => timestamp, + ) +end + # Per-tick imbalance θ_t, dispatched on the metric (AFML p.29). imbalance_at_tick( ::Type{Tick}, diff --git a/src/Data/Structures/abstract_run_bars.jl b/src/Data/Structures/abstract_run_bars.jl index 89298b1..3a16482 100644 --- a/src/Data/Structures/abstract_run_bars.jl +++ b/src/Data/Structures/abstract_run_bars.jl @@ -54,12 +54,14 @@ function construct_bars_from_data(run_bars::AbstractRunBarsType{T}; data) where for tick_data in eachrow(data) tick_counter += 1 + run_bars.tick_counter = tick_counter (date_time, price, volume) = tick_data price = Float64(price) volume = Float64(volume) signed_tick = tick_rule(run_bars, price) update_base_fields(run_bars, price, signed_tick, volume) + run_bars.close_price = price imbalance = imbalance_at_tick(T, price, signed_tick, volume) if imbalance > 0 @@ -93,6 +95,23 @@ function construct_bars_from_data(run_bars::AbstractRunBarsType{T}; data) where end end + if run_bars.analyse_thresholds !== nothing + snapshot = _information_bar_snapshot(run_bars, date_time) + merge!(snapshot, Dict( + CUMULATIVE_BUY_THETA => run_bars.cumulative_buy_theta, + CUMULATIVE_SELL_THETA => run_bars.cumulative_sell_theta, + EXPECTED_BUY_IMBALANCE => run_bars.expected_buy_imbalance, + EXPECTED_SELL_IMBALANCE => run_bars.expected_sell_imbalance, + EXPECTED_BUY_TICKS_PROPORTION => run_bars.expected_buy_ticks_proportion, + BUY_TICKS_NUMBER => run_bars.buy_ticks_number, + PREVIOUS_BARS_N_TICKS_LIST => run_bars.previous_bars_number_of_ticks, + PREVIOUS_TICK_IMBALANCES_BUY_LIST => run_bars.previous_tick_imbalances_buy, + PREVIOUS_TICK_IMBALANCES_SELL_LIST => run_bars.previous_tick_imbalances_sell, + PREVIOUS_BARS_BUY_TICKS_PROPORTIONS_LIST => run_bars.previous_bars_buy_ticks_proportions, + )) + push!(run_bars.analyse_thresholds, snapshot) + end + threshold = calculate_run_threshold(run_bars) if bar_construction_condition(run_bars, threshold) @@ -116,7 +135,7 @@ function construct_bars_from_data(run_bars::AbstractRunBarsType{T}; data) where run_bars.expected_ticks_number = expected_number_of_ticks(run_bars) window = - isnothing(run_bars.window_size_for_expected_n_ticks_estimation) ? + (isnothing(run_bars.window_size_for_expected_n_ticks_estimation) || iszero(run_bars.window_size_for_expected_n_ticks_estimation)) ? run_bars.expected_imbalance_window : run_bars.window_size_for_expected_n_ticks_estimation proportions = run_bars.previous_bars_buy_ticks_proportions diff --git a/src/Data/Structures/imbalance_bars.jl b/src/Data/Structures/imbalance_bars.jl index 2002013..46281e2 100644 --- a/src/Data/Structures/imbalance_bars.jl +++ b/src/Data/Structures/imbalance_bars.jl @@ -17,7 +17,7 @@ clamped to `expected_ticks_number_bounds`. """ function ExpectedImbalanceBars{T}(; bar_type::String, - window_size_for_expected_n_ticks_estimation::Int, + window_size_for_expected_n_ticks_estimation::Union{Int,Nothing}, expected_imbalance_window::Int, initial_estimate_of_expected_n_ticks_in_bar::Float64, expected_ticks_number_bounds::Union{Tuple{Float64,Float64},Nothing} = nothing, @@ -40,9 +40,12 @@ end function expected_number_of_ticks(bars::ExpectedImbalanceBarsType)::Float64 previous = bars.previous_bars_number_of_ticks + window = bars.window_size_for_expected_n_ticks_estimation + if isnothing(window) || window <= 0 + return isempty(previous) ? NaN : mean(previous) + end isempty(previous) && return bars.expected_ticks_number # no bars yet - window = bars.window_size_for_expected_n_ticks_estimation expected = ewma(collect(Float64, last(previous, window)), window)[end] return min( max(expected, bars.expected_ticks_number_lower_bound), @@ -68,7 +71,7 @@ function FixedImbalanceBars{T}(; ) where {T<:Metric} base = AbstractImbalanceBars{T}( bar_type = bar_type, - window_size_for_expected_n_ticks_estimation = window_size_for_expected_n_ticks_estimation, + window_size_for_expected_n_ticks_estimation = nothing, expected_imbalance_window = expected_imbalance_window, initial_estimate_of_expected_n_ticks_in_bar = initial_estimate_of_expected_n_ticks_in_bar, does_analyse_thresholds = does_analyse_thresholds, diff --git a/src/Data/Structures/run_bars.jl b/src/Data/Structures/run_bars.jl index 2f49179..6e59639 100644 --- a/src/Data/Structures/run_bars.jl +++ b/src/Data/Structures/run_bars.jl @@ -17,7 +17,7 @@ clamped to `expected_ticks_number_bounds`. """ function ExpectedRunBars{T}(; bar_type::String, - window_size_for_expected_n_ticks_estimation::Int, + window_size_for_expected_n_ticks_estimation::Union{Int,Nothing}, expected_imbalance_window::Int, initial_estimate_of_expected_n_ticks_in_bar::Float64, expected_ticks_number_bounds::Union{Tuple{Float64,Float64},Nothing} = nothing, @@ -40,9 +40,12 @@ end function expected_number_of_ticks(bars::ExpectedRunBarsType)::Float64 previous = bars.previous_bars_number_of_ticks + window = bars.window_size_for_expected_n_ticks_estimation + if isnothing(window) || window <= 0 + return isempty(previous) ? NaN : mean(previous) + end isempty(previous) && return bars.expected_ticks_number - window = bars.window_size_for_expected_n_ticks_estimation expected = ewma(collect(Float64, last(previous, window)), window)[end] return min( max(expected, bars.expected_ticks_number_lower_bound), @@ -68,7 +71,7 @@ function FixedRunBars{T}(; ) where {T<:Metric} base = AbstractRunBars{T}( bar_type = bar_type, - window_size_for_expected_n_ticks_estimation = window_size_for_expected_n_ticks_estimation, + window_size_for_expected_n_ticks_estimation = nothing, expected_imbalance_window = expected_imbalance_window, initial_estimate_of_expected_n_ticks_in_bar = initial_estimate_of_expected_n_ticks_in_bar, does_analyse_thresholds = does_analyse_thresholds, diff --git a/src/Data/Structures/standard_bars.jl b/src/Data/Structures/standard_bars.jl index 8c027ae..1a24fa6 100644 --- a/src/Data/Structures/standard_bars.jl +++ b/src/Data/Structures/standard_bars.jl @@ -27,10 +27,12 @@ function construct_bars_from_data(standard_bars::StandardBars{T}; data) where {T tick_counter = standard_bars.tick_counter for row in eachrow(data) tick_counter += 1 + standard_bars.tick_counter = tick_counter (date_time, price, volume) = row signed_tick = tick_rule(standard_bars, Float64(price)) update_base_fields(standard_bars, Float64(price), signed_tick, Float64(volume)) + standard_bars.close_price = Float64(price) threshold = standard_bars.threshold if bar_construction_condition(standard_bars, threshold) diff --git a/src/Data/SyntheticData/SyntheticData.jl b/src/Data/SyntheticData/SyntheticData.jl index 1b95722..0d0c38e 100644 --- a/src/Data/SyntheticData/SyntheticData.jl +++ b/src/Data/SyntheticData/SyntheticData.jl @@ -11,12 +11,13 @@ Parity notes: * `random_cov`, `form_true_matrix`, `simulates_cov_mu`, `heston_merton_log_returns`, `generate_prices_from_regimes` and `parallel_generate_prices` are **stochastic** (Monte-Carlo); they take an - optional `random_state`/`rng` and are validated structurally. + optional `random_state`/`rng`. Supplied draws allow numerical replay without + assuming equal random streams across languages. Deliberate divergence: numba/joblib/quantecon are replaced by native Julia -(plain loops, a native Markov-chain simulate, `Distributions`). The optional -Ledoit–Wolf shrinkage in `simulates_cov_mu` (`shrink=true`) is deferred to the -covariance-estimation backend; the default `shrink=false` (`np.cov`) is exact. +(plain loops, a native Markov-chain simulate, `Distributions`). Ledoit–Wolf +shrinkage in `simulates_cov_mu` uses the centered maximum-likelihood covariance +and scaled-identity target. The default `shrink=false` uses sample covariance. Price generators return a plain price vector (Python returns a business-day indexed Series). @@ -25,8 +26,9 @@ Reference: De Prado, M. (2020), Machine Learning for Asset Managers, Ch. 2. using Random: AbstractRNG, MersenneTwister, default_rng, shuffle using Statistics: mean, cov -using LinearAlgebra: Symmetric, Diagonal +using LinearAlgebra: Symmetric, Diagonal, diag using Distributions: MvNormal, Poisson +using CovarianceEstimation: LinearShrinkage, DiagonalCommonVariance _synth_rng(random_state) = random_state === nothing ? default_rng() : @@ -59,11 +61,16 @@ end Random positive-definite covariance `W Wᵀ + diag(u)` with `W` a `num_columns × num_factors` standard-normal matrix and `u` uniform. Stochastic. Mirrors Python's `random_cov`. +Optional `normal_draws` and `uniform_draws` provide the matrix and diagonal +draws directly, with shapes `(num_columns, num_factors)` and `num_columns`. """ -function random_cov(num_columns::Integer, num_factors::Integer; rng = default_rng()) - w = randn(rng, num_columns, num_factors) +function random_cov(num_columns::Integer, num_factors::Integer; rng = default_rng(), normal_draws = nothing, uniform_draws = nothing) + w = normal_draws === nothing ? randn(rng, num_columns, num_factors) : Matrix{Float64}(normal_draws) + u = uniform_draws === nothing ? rand(rng, num_columns) : Float64.(uniform_draws) + size(w) == (num_columns, num_factors) && all(isfinite, w) || throw(ArgumentError("normal draws must have the requested finite matrix shape")) + length(u) == num_columns && all(x -> isfinite(x) && 0 <= x <= 1, u) || throw(ArgumentError("uniform draws must have num_columns entries in [0, 1]")) cov = w * w' - cov += Diagonal(rand(rng, num_columns)) + cov += Diagonal(u) return cov end @@ -73,19 +80,29 @@ end Shuffled block-diagonal correlation converted to a covariance matrix with random volatilities, plus a random mean vector. Stochastic. Mirrors Python's `form_true_matrix`. +Optional `permutation`, `standard_deviation_draws` and standard `normal_draws` +replay the public draws. The permutation uses Julia's one-based indices. """ function form_true_matrix( n_blocks::Integer, block_size::Integer, block_correlation::Real; rng = default_rng(), + permutation = nothing, + standard_deviation_draws = nothing, + normal_draws = nothing, ) correlation = form_block_matrix(n_blocks, block_size, block_correlation) - order = shuffle(rng, collect(1:size(correlation, 1))) + n = size(correlation, 1) + order = permutation === nothing ? shuffle(rng, collect(1:n)) : Int.(permutation) + sort(order) == collect(1:n) || throw(ArgumentError("permutation must contain every matrix index exactly once")) correlation = correlation[order, order] - standard_deviations = rand(rng, size(correlation, 1)) .* 0.15 .+ 0.05 # U(0.05, 0.2) + standard_deviations = standard_deviation_draws === nothing ? rand(rng, n) .* 0.15 .+ 0.05 : Float64.(standard_deviation_draws) + length(standard_deviations) == n && all(x -> isfinite(x) && .05 <= x <= .2, standard_deviations) || throw(ArgumentError("standard deviation draws must have n entries in [0.05, 0.2]")) + normal = normal_draws === nothing ? randn(rng, n) : Float64.(normal_draws) + length(normal) == n && all(isfinite, normal) || throw(ArgumentError("normal draws must have n finite entries")) covariance = corr_to_cov(correlation, standard_deviations) - mu = randn(rng, size(covariance, 1)) .* standard_deviations .+ standard_deviations + mu = normal .* standard_deviations .+ standard_deviations return mu, covariance end @@ -94,8 +111,10 @@ end Draw `n_observations` multivariate-normal samples from `(mu, covariance)` and return their sample mean and covariance. Stochastic. Mirrors Python's -`simulates_cov_mu` for the default `shrink=false` path; `shrink=true` -(Ledoit–Wolf) is deferred to the covariance-estimation backend. +`simulates_cov_mu`. The `shrink=false` path uses unbiased sample covariance; +`shrink=true` uses centered Ledoit–Wolf scaled-identity shrinkage with +maximum-likelihood normalization. Degenerate targets retain their empirical +covariance. Native random streams need not match NumPy for equal seeds. """ function simulates_cov_mu( mu::AbstractVecOrMat{<:Real}, @@ -104,20 +123,35 @@ function simulates_cov_mu( shrink::Bool = false, rng = default_rng(), ) - shrink && throw( - ArgumentError( - "Ledoit–Wolf shrinkage (shrink=true) is deferred to the " * - "covariance-estimation backend; use shrink=false", - ), - ) samples = permutedims( rand(rng, MvNormal(vec(mu), Symmetric(Matrix(covariance))), n_observations), ) mu1 = vec(mean(samples; dims = 1)) - cov1 = cov(samples) + cov1 = shrink ? _ledoit_wolf_covariance(samples) : cov(samples) return mu1, cov1 end +function _ledoit_wolf_covariance(samples::AbstractMatrix{<:Real}) + n, p = size(samples) + n > 0 && p > 0 && all(isfinite, samples) || throw(ArgumentError("samples must be nonempty and finite")) + shifted = Float64.(samples) .- samples[1:1, :] + centered = shifted .- mean(shifted; dims = 1) + all(isfinite, centered) || throw(ArgumentError("centered samples exceed floating-point range")) + scale = maximum(abs, centered) + scale == 0 && return zeros(p, p) + normalized = centered / scale + empirical = normalized' * normalized / n + target = Diagonal(fill(sum(diag(empirical)) / p, p)) + covariance = if p == 1 || all(iszero, empirical - target) + empirical + else + Matrix(cov(LinearShrinkage(DiagonalCommonVariance(), :lw; corrected = false), normalized; dims = 1)) + end + result = covariance .* scale^2 + all(isfinite, result) || throw(ArgumentError("covariance exceeds floating-point range")) + return result +end + """ drift_volatility_burst(bubble_length, a_before, a_after, b_before, b_after, alpha, beta; explosion_filter_width=0.1) -> (drifts, volatilities) @@ -136,7 +170,7 @@ function drift_volatility_burst( explosion_filter_width::Real = 0.1, ) w = explosion_filter_width - steps = collect(range(0.0, 1.0; length = bubble_length)) + steps = bubble_length == 1 ? [0.0] : collect(range(0.0, 1.0; length = bubble_length)) filtered = copy(steps) for k in eachindex(steps) s = steps[k] @@ -245,6 +279,10 @@ end Draw correlated Wiener increments and Poisson jumps, then evaluate `compute_log_returns`. Stochastic. Mirrors Python's `heston_merton_log_returns`. +`gaussian_draws` supplies an `n_steps × 3` matrix of already-correlated stock, +volatility and jump-size outcomes; `poisson_draws` supplies nonnegative integer +counts. Native sampling supports positive-semidefinite covariance, including +`rho = ±1` and zero jump dispersion. Equal language seeds need not agree. """ function heston_merton_log_returns( total_time::Real, @@ -259,9 +297,17 @@ function heston_merton_log_returns( v::AbstractVector, regime_change::AbstractVector{Bool}; random_state = nothing, + gaussian_draws = nothing, + poisson_draws = nothing, ) - all(length(p) == n_steps for p in (mu, kappa, theta, xi, rho, lambda, m, v)) || + n_steps > 0 && isfinite(total_time) && total_time >= 0 || throw(ArgumentError("n_steps must be positive and total_time finite and nonnegative")) + all(length(p) == n_steps for p in (mu, kappa, theta, xi, rho, lambda, m, v, regime_change)) || throw(ArgumentError("all parameter vectors must have length n_steps")) + all(x -> isfinite(x) && abs(x) <= 1, rho) || throw(ArgumentError("correlations must be finite and in [-1, 1]")) + all(x -> isfinite(x) && x >= 0, lambda) || throw(ArgumentError("jump intensities must be finite and nonnegative")) + all(isfinite, m) && all(isfinite, v) || throw(ArgumentError("jump means and dispersions must be finite")) + gaussian_draws === nothing || (size(gaussian_draws) == (n_steps, 3) && all(isfinite, gaussian_draws)) || throw(ArgumentError("gaussian_draws must be a finite n_steps by 3 matrix")) + poisson_draws === nothing || (length(poisson_draws) == n_steps && all(x -> isfinite(x) && x >= 0 && isinteger(x), poisson_draws)) || throw(ArgumentError("poisson_draws must contain n_steps nonnegative integer counts")) rng = _synth_rng(random_state) dt = total_time / n_steps @@ -272,12 +318,11 @@ function heston_merton_log_returns( jump_comp = Vector{Float64}(undef, n_steps) poisson_var = Vector{Float64}(undef, n_steps) for i = 1:n_steps - block = [1.0 rho[i] 0.0; rho[i] 1.0 0.0; 0.0 0.0 v[i]^2] - z = rand(rng, MvNormal([0.0, 0.0, m[i]], Symmetric(block))) + z = gaussian_draws === nothing ? _heston_merton_gaussian(rho[i], m[i], v[i], randn(rng, 3)) : view(gaussian_draws, i, :) dw_stock[i] = z[1] dw_vol[i] = z[2] jump_comp[i] = z[3] - poisson_var[i] = rand(rng, Poisson(lambda[i] * dt)) + poisson_var[i] = poisson_draws === nothing ? rand(rng, Poisson(lambda[i] * dt)) : poisson_draws[i] end return compute_log_returns( @@ -299,22 +344,33 @@ function heston_merton_log_returns( ) end +function _heston_merton_gaussian(rho, jump_mean, jump_dispersion, normal) + return (normal[1], rho * normal[1] + sqrt(max(1 - rho^2, 0)) * normal[2], jump_mean + abs(jump_dispersion) * normal[3]) +end + # Native categorical sampler + Markov-chain simulate (replaces quantecon). -function _sample_categorical(rng, probabilities) - u = rand(rng) +function _sample_categorical(rng, probabilities; uniform = nothing) + u = uniform === nothing ? rand(rng) : uniform + isfinite(u) && 0 <= u < 1 || throw(ArgumentError("transition uniforms must be in [0, 1)")) cumulative = 0.0 + last_positive = 0 for i in eachindex(probabilities) cumulative += probabilities[i] - u <= cumulative && return i + probabilities[i] > 0 && (last_positive = i) + u < cumulative && return i end - return length(probabilities) + return last_positive end -function _markov_simulate(transition_matrix, n_steps, n_states; rng) +function _markov_simulate(transition_matrix, n_steps, n_states; rng, initial_state = nothing, transition_uniforms = nothing) + n_steps > 0 && n_states > 0 || throw(ArgumentError("state and step counts must be positive")) + size(transition_matrix) == (n_states, n_states) && all(x -> isfinite(x) && x >= 0, transition_matrix) && all(x -> isapprox(x, 1; rtol = 1e-12, atol = 1e-12), sum(transition_matrix; dims = 2)) || throw(ArgumentError("transition rows must be finite nonnegative probability vectors")) + transition_uniforms === nothing || length(transition_uniforms) == n_steps - 1 || throw(ArgumentError("transition_uniforms must have n_steps - 1 entries")) states = Vector{Int}(undef, n_steps) - states[1] = rand(rng, 1:n_states) + states[1] = initial_state === nothing ? rand(rng, 1:n_states) : initial_state + 1 <= states[1] <= n_states || throw(ArgumentError("initial_state must index a regime")) for t = 2:n_steps - states[t] = _sample_categorical(rng, view(transition_matrix, states[t-1], :)) + states[t] = _sample_categorical(rng, view(transition_matrix, states[t-1], :); uniform = transition_uniforms === nothing ? nothing : transition_uniforms[t-1]) end return states end @@ -326,6 +382,11 @@ Simulate a Markov-switching Heston–Merton price path. `regimes` maps a regime name to a parameter dictionary (keys `"mu","kappa","theta","xi","rho","lam","m","v"`). Returns the price vector and the per-step regime names. Stochastic. Mirrors Python's `generate_prices_from_regimes` (without the business-day index). +`regime_order` supplies the ordering of transition-matrix rows and columns; +its default is dictionary iteration order. For cross-language replay, provide +Python's insertion order, one-based `initial_state`, `transition_uniforms`, +and the `gaussian_draws`/`poisson_draws` accepted by `heston_merton_log_returns`. +Missing log returns are forward-filled before cumulative price construction. """ function generate_prices_from_regimes( regimes::AbstractDict, @@ -333,11 +394,17 @@ function generate_prices_from_regimes( total_time::Real, n_steps::Integer; random_state = nothing, + regime_order = nothing, + initial_state = nothing, + transition_uniforms = nothing, + gaussian_draws = nothing, + poisson_draws = nothing, ) rng = _synth_rng(random_state) - regime_names = collect(keys(regimes)) + regime_names = regime_order === nothing ? collect(keys(regimes)) : collect(regime_order) + length(regime_names) == length(regimes) && Set(regime_names) == Set(keys(regimes)) || throw(ArgumentError("regime_order must contain each regime name exactly once")) simulated = - _markov_simulate(transition_matrix, n_steps, length(regime_names); rng = rng) + _markov_simulate(transition_matrix, n_steps, length(regime_names); rng = rng, initial_state = initial_state, transition_uniforms = transition_uniforms) simulated_regimes = [regime_names[s] for s in simulated] parameter_keys = ["mu", "kappa", "theta", "xi", "rho", "lam", "m", "v"] @@ -348,6 +415,7 @@ function generate_prices_from_regimes( while step <= n_steps name = simulated_regimes[step] params, regime_length = align_params_length(regimes[name]) + regime_length > 0 || throw(ArgumentError("each regime must supply at least one parameter step")) take = min(regime_length, n_steps - step + 1) for k in parameter_keys append!(parameter_lists[k], params[k][1:take]) @@ -370,9 +438,21 @@ function generate_prices_from_regimes( parameter_lists["v"], regime_change; random_state = rand(rng, 0:999_999), + gaussian_draws = gaussian_draws, + poisson_draws = poisson_draws, ) - prices = 100.0 .* exp.(cumsum(log_returns)) + prices = Vector{Float64}(undef, n_steps) + previous, cumulative = NaN, 0.0 + for i in eachindex(log_returns) + isnan(log_returns[i]) || (previous = log_returns[i]) + if isnan(previous) + prices[i] = NaN + else + cumulative += previous + prices[i] = 100 * exp(cumulative) + end + end return prices, regime_path end diff --git a/src/Data/TimeGAN.jl b/src/Data/TimeGAN.jl new file mode 100644 index 0000000..7a0f852 --- /dev/null +++ b/src/Data/TimeGAN.jl @@ -0,0 +1,222 @@ +import Random +import Zygote +import Optimisers +import Optim +import LinearAlgebra + +function _timegan_real(value, name; minimum = nothing, strict = false) + value isa Real && !(value isa Bool) && isfinite(value) || throw(ArgumentError("$name must be a finite real scalar")) + result = Float64(value) + isfinite(result) || throw(ArgumentError("$name is outside the Float64 range")) + if !isnothing(minimum) && (strict ? result <= minimum : result < minimum) + throw(ArgumentError("$name is below its allowed bound")) + end + return result +end + +function _timegan_count(value, name) + value isa Integer && !(value isa Bool) && 0 < value <= typemax(Int) || throw(ArgumentError("$name must be a positive supported integer")) + return Int(value) +end + +function _timegan_array(value, name, dimension) + value isa AbstractArray && ndims(value) == dimension && !isempty(value) || throw(ArgumentError("$name must be a nonempty real array with $dimension dimensions")) + return map(item -> _timegan_real(item, name), value) +end + +function _timegan_rng(seed) + seed isa Integer && !(seed isa Bool) && typemin(Int64) <= seed <= typemax(UInt64) || throw(ArgumentError("seed must be an integer in the supported 64-bit range")) + return Random.MersenneTwister(UInt64(mod(BigInt(seed), BigInt(1) << 64))) +end + +function _timegan_windows(segment, length) + return cat((reshape(segment[index:index+length-1, :], 1, length, size(segment, 2)) for index in 1:(size(segment, 1)-length+1))...; dims = 1) +end + +""" + chronological_timegan_data(series; training_end, window_length) + +Split before windowing and scale only with training-segment feature minima +and ranges. Constant training features have scale one; validation is never +clipped. Returned arrays have `(windows,time,features)` dimensions, and no +window crosses the split. `training_end` is the training row count. +""" +function chronological_timegan_data(series; training_end, window_length) + values = _timegan_array(series, "series", 2) + split = _timegan_count(training_end, "training_end") + length = _timegan_count(window_length, "window_length") + length >= 2 && split >= length && size(values, 1) - split >= length || throw(ArgumentError("Both chronological segments need a complete window of length >= 2")) + training = values[1:split, :] + minimum_values = vec(minimum(training; dims = 1)) + scale = vec(maximum(training; dims = 1)) .- minimum_values + scale[scale .== 0] .= 1.0 + scaled = (values .- minimum_values') ./ scale' + return (training = _timegan_windows(scaled[1:split, :], length), validation = _timegan_windows(scaled[split+1:end, :], length), + minimum = minimum_values, scale = scale, training_end = split) +end + +function _timegan_shapes(features, hidden) + recurrent(inputs) = (weight_ih = (3hidden, inputs), weight_hh = (3hidden, hidden), bias_ih = (3hidden,), bias_hh = (3hidden,)) + output(inputs, outputs) = (weight = (outputs, inputs), bias = (outputs,)) + return (embedding = (forward = recurrent(features), output = output(hidden, hidden)), + recovery = (forward = recurrent(hidden), output = output(hidden, features)), + generator_cell = recurrent(2hidden), generator_output = output(hidden, hidden), + discriminator = (forward = recurrent(hidden), backward = recurrent(hidden), output = output(2hidden, 1))) +end + +function _timegan_validate_parameters(parameters, shapes) + if shapes isa NamedTuple + parameters isa NamedTuple && keys(parameters) == keys(shapes) || throw(ArgumentError("TimeGAN parameters must match the named public architecture")) + for name in keys(shapes) + _timegan_validate_parameters(getproperty(parameters, name), getproperty(shapes, name)) + end + else + parameters isa AbstractArray{Float64} && size(parameters) == shapes && all(isfinite, parameters) || throw(ArgumentError("TimeGAN parameter arrays must have the required Float64 shapes and finite values")) + end + return parameters +end + +function _timegan_initial_parameters(rng, features, hidden) + uniform(shape, inputs) = Float64.((rand(rng, Float32, shape) .* 2f0 .- 1f0) .* Float32(1 / sqrt(inputs))) + recurrent(inputs) = (weight_ih = uniform((3hidden, inputs), hidden), weight_hh = uniform((3hidden, hidden), hidden), + bias_ih = uniform((3hidden,), hidden), bias_hh = uniform((3hidden,), hidden)) + output(inputs, outputs) = (weight = uniform((outputs, inputs), inputs), bias = uniform((outputs,), inputs)) + return (embedding = (forward = recurrent(features), output = output(hidden, hidden)), + recovery = (forward = recurrent(hidden), output = output(hidden, features)), + generator_cell = recurrent(2hidden), generator_output = output(hidden, hidden), + discriminator = (forward = recurrent(hidden), backward = recurrent(hidden), output = output(2hidden, 1))) +end + +""" + TimeGAN(features, hidden_size=12; seed=0, parameters=nothing) + +Temporal-only GRU TimeGAN with causal embedding/recovery, an autoregressive +generator and a bidirectional discriminator. The public reset/update/new GRU +gate order and reset-after hidden projection are retained. Parameters and +model inputs use Float64. Local initialization preserves global RNG state; +supplied parameter arrays are copied for shared-weight comparisons. +Use `latent_sequence`, `timegan_losses`, `sample_timegan` and `fit_timegan`. +Synthetic fixed-length windows do not define an unlimited market path. +""" +mutable struct TimeGAN + features::Int + hidden_size::Int + parameters::NamedTuple + function TimeGAN(features, hidden_size = 12; seed = 0, parameters = nothing) + dimensions = _timegan_count(features, "features") + hidden = _timegan_count(hidden_size, "hidden_size") + rng = _timegan_rng(seed) + supplied = isnothing(parameters) ? _timegan_initial_parameters(rng, dimensions, hidden) : parameters + _timegan_validate_parameters(supplied, _timegan_shapes(dimensions, hidden)) + new(dimensions, hidden, deepcopy(supplied)) + end +end + +_timegan_sigmoid(value) = value >= 0 ? 1 / (1 + exp(-value)) : exp(value) / (1 + exp(value)) + +function _timegan_check(model, value, name, features) + value isa AbstractArray{Float64, 3} && size(value, 1) > 0 && size(value, 2) >= 2 && size(value, 3) == features && all(isfinite, value) || throw(ArgumentError("$name must be finite Float64 (windows,time>=2,$features) data matching the model")) + _timegan_validate_parameters(model.parameters, _timegan_shapes(model.features, model.hidden_size)) + return nothing +end + +function _timegan_cell(parameters, input, state) + hidden = size(state, 1) + observed = parameters.weight_ih * input .+ parameters.bias_ih + previous = parameters.weight_hh * state .+ parameters.bias_hh + reset = _timegan_sigmoid.(observed[1:hidden, :] .+ previous[1:hidden, :]) + update = _timegan_sigmoid.(observed[hidden+1:2hidden, :] .+ previous[hidden+1:2hidden, :]) + candidate = tanh.(observed[2hidden+1:3hidden, :] .+ reset .* previous[2hidden+1:3hidden, :]) + return candidate .+ (state .- candidate) .* update +end + +function _timegan_recurrent(parameters, inputs; backward = false) + hidden = size(parameters.weight_hh, 2) + times = backward ? (size(inputs, 2):-1:1) : (1:size(inputs, 2)) + initial = (hidden = zeros(hidden, size(inputs, 1)), sequence = zeros(hidden * size(inputs, 1), 0)) + result = foldl(times; init = initial) do state, time + next_hidden = _timegan_cell(parameters, permutedims(inputs[:, time, :]), state.hidden) + return (hidden = next_hidden, sequence = hcat(state.sequence, vec(next_hidden))) + end + sequence = backward ? reverse(result.sequence; dims = 2) : result.sequence + return permutedims(reshape(sequence, hidden, size(inputs, 1), size(inputs, 2)), (2, 3, 1)) +end + +function _timegan_sequence_map(parameters, inputs; sigmoid = true) + forward = _timegan_recurrent(parameters.forward, inputs) + hidden = haskey(parameters, :backward) ? cat(forward, _timegan_recurrent(parameters.backward, inputs; backward = true); dims = 3) : forward + flattened = reshape(permutedims(hidden, (3, 1, 2)), size(hidden, 3), :) + projected = parameters.output.weight * flattened .+ parameters.output.bias + output = permutedims(reshape(projected, size(projected, 1), size(inputs, 1), size(inputs, 2)), (2, 3, 1)) + return sigmoid ? _timegan_sigmoid.(output) : output +end + +function _timegan_latent(parameters, noise, teacher) + hidden, windows = size(noise, 3), size(noise, 1) + initial = (state = zeros(hidden, windows), previous = zeros(hidden, windows), sequence = zeros(hidden * windows, 0)) + result = foldl(1:size(noise, 2); init = initial) do prior, time + previous = !isnothing(teacher) && time > 1 ? permutedims(teacher[:, time-1, :]) : prior.previous + state = _timegan_cell(parameters.generator_cell, vcat(previous, permutedims(noise[:, time, :])), prior.state) + output = _timegan_sigmoid.(parameters.generator_output.weight * state .+ parameters.generator_output.bias) + return (state = state, previous = output, sequence = hcat(prior.sequence, vec(output))) + end + return permutedims(reshape(result.sequence, hidden, windows, size(noise, 2)), (2, 3, 1)) +end + +"""Generate latent windows freely or condition time t on the supplied teacher latent at t-1.""" +function latent_sequence(model::TimeGAN, noise; teacher = nothing) + Zygote.ignore() do + _timegan_check(model, noise, "noise", model.hidden_size) + if !isnothing(teacher) + _timegan_check(model, teacher, "teacher", model.hidden_size) + size(teacher) == size(noise) || throw(ArgumentError("Teacher and noise shapes must match")) + end + end + return _timegan_latent(model.parameters, noise, teacher) +end + +_timegan_logsigmoid(value) = value >= 0 ? -log1p(exp(-value)) : value - log1p(exp(value)) + +function _timegan_losses(parameters, observations, noise) + latent = _timegan_sequence_map(parameters.embedding, observations) + reconstructed = _timegan_sequence_map(parameters.recovery, latent) + synthetic = _timegan_latent(parameters, noise, nothing) + teacher = _timegan_latent(parameters, noise, latent) + real_logits = _timegan_sequence_map(parameters.discriminator, latent; sigmoid = false) + fake_logits = _timegan_sequence_map(parameters.discriminator, synthetic; sigmoid = false) + count = size(observations, 1) + reconstruction = sum(abs2, observations .- reconstructed) / count + supervised = sum(abs2, latent .- teacher) / count + real_log = sum(_timegan_logsigmoid.(real_logits)) / count + fake_log = sum(_timegan_logsigmoid.(-fake_logits)) / count + return (reconstruction = reconstruction, supervised = supervised, discriminator = -real_log - fake_log, generator_adversarial = fake_log) +end + +""" +Return the public reconstruction, supervision, discriminator and minimax +generator losses, averaged over windows and summed over time/features. +Supervision includes the initial latent. No separate supervisor network or +moment-matching extension is introduced. +""" +function timegan_losses(model::TimeGAN, observations, noise) + Zygote.ignore() do + _timegan_check(model, observations, "observations", model.features) + _timegan_check(model, noise, "noise", model.hidden_size) + all(value -> 0 <= value <= 1, observations) || throw(ArgumentError("Training observations must be scaled to [0,1]")) + size(observations)[1:2] == size(noise)[1:2] || throw(ArgumentError("Observations and noise need matching windows/times")) + end + return _timegan_losses(model.parameters, observations, noise) +end + +"""Return detached scaled windows from fresh local Gaussian noise or supplied primitive draws.""" +function sample_timegan(model::TimeGAN, windows, length; seed, normal_draws = nothing) + return Zygote.ignore() do + count = _timegan_count(windows, "windows") + times = _timegan_count(length, "length") + rng = _timegan_rng(seed) + noise = isnothing(normal_draws) ? randn(rng, count, times, model.hidden_size) : normal_draws + size(noise) == (count, times, model.hidden_size) || throw(ArgumentError("Supplied normal draws must match the requested window shape")) + _timegan_check(model, noise, "noise", model.hidden_size) + _timegan_sequence_map(model.parameters.recovery, _timegan_latent(model.parameters, noise, nothing)) + end +end diff --git a/src/Data/TimeGANQuality.jl b/src/Data/TimeGANQuality.jl new file mode 100644 index 0000000..77d14a1 --- /dev/null +++ b/src/Data/TimeGANQuality.jl @@ -0,0 +1,94 @@ +function _timegan_lag(array) + features = size(array, 3) + left = reshape(permutedims(array[:, 1:end-1, :], (2, 1, 3)), :, features) + right = reshape(permutedims(array[:, 2:end, :], (2, 1, 3)), :, features) + centered_left = left .- mean(left; dims = 1) + centered_right = right .- mean(right; dims = 1) + numerator = vec(sum(centered_left .* centered_right; dims = 1)) + denominator = sqrt.(vec(sum(abs2, centered_left; dims = 1)) .* vec(sum(abs2, centered_right; dims = 1))) + return [denominator[index] > 0 ? numerator[index] / denominator[index] : NaN for index in eachindex(denominator)] +end + +function _timegan_flatten(array) + return permutedims(reshape(permutedims(array, (3, 2, 1)), :, size(array, 1))) +end + +function _timegan_scaler(features) + center = vec(mean(features; dims = 1)) + centered = features .- center' + variance = vec(sum(abs2, centered; dims = 1)) ./ size(features, 1) + bound = size(features, 1) .* eps(Float64) .* variance .+ (size(features, 1) .* center .* eps(Float64)).^2 + scale = sqrt.(variance) + scale[variance .<= bound] .= 1.0 + return center, scale +end + +function _timegan_predictive(train, validation) + features = _timegan_flatten(train[:, 1:end-1, :]) + outcomes = train[:, end, :] + center, scale = _timegan_scaler(features) + standardized = (features .- center') ./ scale' + input_center = vec(mean(standardized; dims = 1)) + outcome_center = vec(mean(outcomes; dims = 1)) + design = standardized .- input_center' + weights = (design' * design + LinearAlgebra.I) \ (design' * (outcomes .- outcome_center')) + test = (_timegan_flatten(validation[:, 1:end-1, :]) .- center') ./ scale' + prediction = (test .- input_center') * weights .+ outcome_center' + return mean(abs2, prediction .- validation[:, end, :]) +end + +function _timegan_logistic_loss!(gradient, parameters, features, outcomes, weights) + coefficients, intercept = parameters[1:end-1], parameters[end] + scores = features * coefficients .+ intercept + signed = (1 .- 2 .* outcomes) .* scores + loss = sum(weights .* (max.(signed, 0.0) .+ log1p.(exp.(-abs.(signed))))) + residuals = weights .* (_timegan_sigmoid.(scores) .- outcomes) + total_weight = sum(weights) + gradient[1:end-1] .= (features' * residuals .+ coefficients) ./ total_weight + gradient[end] = sum(residuals) / total_weight + return (loss + sum(abs2, coefficients) / 2) / total_weight +end + +function _timegan_discrimination(real_train, real_validation, synthetic_train, synthetic_validation) + train = vcat(_timegan_flatten(real_train), _timegan_flatten(synthetic_train)) + real_count, fake_count = size(real_train, 1), size(synthetic_train, 1) + labels = [ones(real_count); zeros(fake_count)] + weights = [fill(length(labels) / (2real_count), real_count); fill(length(labels) / (2fake_count), fake_count)] + center, scale = _timegan_scaler(train) + features = (train .- center') ./ scale' + objective(parameters) = _timegan_logistic_loss!(zeros(length(parameters)), parameters, features, labels, weights) + gradient!(gradient, parameters) = (_timegan_logistic_loss!(gradient, parameters, features, labels, weights); gradient) + fit = Optim.optimize(objective, gradient!, zeros(size(features, 2) + 1), Optim.LBFGS(), Optim.Options(g_tol = 1e-10, f_reltol = 1e-14, iterations = 1000)) + Optim.converged(fit) && isfinite(Optim.minimum(fit)) && all(isfinite, Optim.minimizer(fit)) || error("TimeGAN descriptive discriminator failed to converge") + parameters = Optim.minimizer(fit) + test = (vcat(_timegan_flatten(real_validation), _timegan_flatten(synthetic_validation)) .- center') ./ scale' + predictions = test * parameters[1:end-1] .+ parameters[end] .> 0 + split = size(real_validation, 1) + return (mean(predictions[1:split]) + mean(.!predictions[split+1:end])) / 2 +end + +""" + timegan_quality_report(real_training, real_validation, + synthetic_training, synthetic_validation) + +Return descriptive lag-one correlation gaps, train-synthetic/test-real and +real-trained next-step MSE, and held-out balanced real/fake accuracy. Ridge +uses alpha one after training-only standardization; logistic discrimination +uses balanced class weights, fixed C one and an unpenalized intercept. +The native logistic optimizer uses tighter stopping criteria for the same +objective. All arrays use the same units and window/time/feature shapes. + +Real validation must come from a disjoint chronological segment and synthetic +validation from fresh noise. The caller supplies this separation. These are +light descriptive diagnostics, not learned GRU evaluation, uncertainty for +overlapping windows, certification of realistic dynamics or parameter selection. +""" +function timegan_quality_report(real_training, real_validation, synthetic_training, synthetic_validation) + arrays = map(value -> _timegan_array(value, "windows", 3), (real_training, real_validation, synthetic_training, synthetic_validation)) + all(array -> size(array)[2:3] == size(first(arrays))[2:3] && size(array, 1) >= 2, arrays) && size(first(arrays), 2) >= 2 || throw(ArgumentError("All window shapes must agree and need at least two samples/times")) + rt, rv, st, sv = arrays + real_lag, synthetic_lag = _timegan_lag(rv), _timegan_lag(sv) + return (real_lag_one_correlation = real_lag, synthetic_lag_one_correlation = synthetic_lag, + lag_one_absolute_gap = abs.(real_lag .- synthetic_lag), synthetic_trained_prediction_mse = _timegan_predictive(st, rv), + real_trained_prediction_mse = _timegan_predictive(rt, rv), balanced_discriminator_accuracy = _timegan_discrimination(rt, rv, st, sv)) +end diff --git a/src/Data/TimeGANTraining.jl b/src/Data/TimeGANTraining.jl new file mode 100644 index 0000000..0855647 --- /dev/null +++ b/src/Data/TimeGANTraining.jl @@ -0,0 +1,86 @@ +_timegan_allfinite(value::NamedTuple) = all(_timegan_allfinite, values(value)) +_timegan_allfinite(value) = value isa AbstractArray && all(isfinite, value) + +function _timegan_groups(parameters) + return ((embedding = parameters.embedding, recovery = parameters.recovery), + (generator_cell = parameters.generator_cell, generator_output = parameters.generator_output), + (discriminator = parameters.discriminator,)) +end + +function _timegan_training_draw(draw, data, batch_size, hidden) + draw isa NamedTuple && keys(draw) == (:indices, :noise) || throw(ArgumentError("Each supplied training draw must name indices and noise")) + indices, noise = draw.indices, draw.noise + indices isa AbstractVector && length(indices) == batch_size && all(index -> index isa Integer && !(index isa Bool) && 1 <= index <= size(data, 1), indices) || throw(ArgumentError("Supplied minibatch indices must be valid one-based observation positions")) + noise isa AbstractArray{Float64, 3} && size(noise) == (batch_size, size(data, 2), hidden) && all(isfinite, noise) || throw(ArgumentError("Supplied Gaussian training draws must match batch/time/hidden dimensions")) + return Int.(indices), noise +end + +""" + fit_timegan(model, training_windows; embedding_steps, supervised_steps, + joint_steps, batch_size, learning_rate=0.001, embedding_weight=1.0, + generator_weight=10.0, seed=0, training_draws=nothing) + +Mutate the temporal-only model through reconstruction, teacher-forced generator +training and joint training. Positive step counts are fixed in advance. +Joint updates occur in E/R, G, D order, recomputing losses after each update. +The generator uses the public minimax fake-data term plus supervision. Adam +states persist across phases. Histories record losses before the last group +update of each step, matching the public contract. + +Only scaled training windows enter optimization. Optional per-step named +tuples `(indices,noise)` supply one-based minibatch positions and Gaussian +primitive draws for cross-language validation; otherwise a local RNG is used. +Training losses do not establish generation quality or choose stopping time. +""" +function fit_timegan(model::TimeGAN, training_windows; embedding_steps, supervised_steps, + joint_steps, batch_size, learning_rate = 0.001, embedding_weight = 1.0, + generator_weight = 10.0, seed = 0, training_draws = nothing) + counts = (_timegan_count(embedding_steps, "embedding_steps"), _timegan_count(supervised_steps, "supervised_steps"), _timegan_count(joint_steps, "joint_steps")) + batch = _timegan_count(batch_size, "batch_size") + rate = _timegan_real(learning_rate, "learning_rate"; minimum = 0, strict = true) + embedding_scale = _timegan_real(embedding_weight, "embedding_weight"; minimum = 0) + generator_scale = _timegan_real(generator_weight, "generator_weight"; minimum = 0) + data = _timegan_array(training_windows, "training_windows", 3) + _timegan_check(model, data, "training_windows", model.features) + all(value -> 0 <= value <= 1, data) || throw(ArgumentError("Training observations must be scaled to [0,1]")) + rng = _timegan_rng(seed) + if !isnothing(training_draws) + training_draws isa Union{Tuple, AbstractVector} && length(training_draws) == sum(counts) || throw(ArgumentError("Supply exactly one training draw for each declared step")) + foreach(draw -> _timegan_training_draw(draw, data, batch, model.hidden_size), training_draws) + end + groups = _timegan_groups(model.parameters) + states = Any[Optimisers.setup(Optimisers.Adam(rate, (0.9, 0.999), 1e-8), group) for group in groups] + history = NamedTuple{(:phase, :reconstruction, :supervised, :discriminator, :generator_adversarial), Tuple{String, Float64, Float64, Float64, Float64}}[] + draw_index = 0 + for (phase, count) in enumerate(counts) + for _ in 1:count + draw_index += 1 + indices, noise = isnothing(training_draws) ? (rand(rng, 1:size(data, 1), batch), randn(rng, batch, size(data, 2), model.hidden_size)) : _timegan_training_draw(training_draws[draw_index], data, batch, model.hidden_size) + observed = data[indices, :, :] + updates = phase == 1 ? (1,) : phase == 2 ? (2,) : (1, 2, 3) + final_losses = nothing + for group in updates + parameters = model.parameters + selected = _timegan_groups(parameters)[group] + function objective(candidate) + losses = _timegan_losses(merge(parameters, candidate), observed, noise) + if group == 1 + return losses.reconstruction + (phase == 3 ? embedding_scale * losses.supervised : 0.0) + elseif group == 2 + return generator_scale * losses.supervised + (phase == 3 ? losses.generator_adversarial : 0.0) + end + return losses.discriminator + end + final_losses = _timegan_losses(parameters, observed, noise) + loss, pullback = Zygote.pullback(objective, selected) + gradient = only(pullback(one(loss))) + isfinite(loss) && _timegan_allfinite(gradient) || error("TimeGAN objective or gradients became nonfinite") + states[group], fitted = Optimisers.update(states[group], selected, gradient) + _timegan_allfinite(fitted) || error("TimeGAN parameters became nonfinite") + model.parameters = merge(parameters, fitted) + end + push!(history, merge((phase = ("embedding", "supervised", "joint")[phase],), final_losses)) + end + end + return history +end diff --git a/src/Data/Weights/SampleWeight.jl b/src/Data/Weights/SampleWeight.jl index 472873e..2943fce 100644 --- a/src/Data/Weights/SampleWeight.jl +++ b/src/Data/Weights/SampleWeight.jl @@ -1,14 +1,16 @@ +using Random + """ Sample-weighting techniques — native Julia port mirroring the Python `RiskLabAI.data.weights.sample_weights` API (López de Prado, AFML Ch. 4): -label concurrency, average uniqueness, absolute-return sample weights, and -time decay. +label concurrency, average uniqueness, sequential bootstrap, absolute-return +sample weights, and time decay. Representation note (deliberate divergence): the Python API uses pandas Series whose *index* carries event start times. The Julia port passes event starts and ends as parallel vectors (`event_start`, `event_end`) and the bar/price index as -a sorted vector. The numerics match the Python implementation exactly (verified -against reference values in `test/runtests.jl`). +a sorted vector. Shared Python 3.2.0 fixtures check concurrency, return weights +and sequential-bootstrap choices in `test/sample_weight_regressions.jl`. Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 4. """ @@ -19,7 +21,9 @@ Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 4. Count concurrent events at each timestamp over the affected span. Mirrors Python's `expand_label_for_meta_labeling`. `event_start[i] → event_end[i]` define -event `i`; `molecule` is the subset of start times to process. +event `i`; `molecule` is the subset of start times to process. All events that +overlap the selected span contribute, including events outside `molecule`. +Endpoints are inclusive; missing or NaN ends use the last close timestamp. """ function expand_label_for_meta_labeling( close_index::AbstractVector, @@ -29,25 +33,29 @@ function expand_label_for_meta_labeling( ) last_label = close_index[end] m_first = molecule[1] + length(event_start) == length(event_end) || + throw(DimensionMismatch("event starts and ends must have equal lengths")) + filled_end = [ + ismissing(value) || (value isa AbstractFloat && isnan(value)) ? last_label : value for + value in event_end + ] + end_of = Dict(zip(event_start, filled_end)) + max_end = maximum(end_of[start] for start in molecule) keep = [ - (event_start[i] in molecule) && (event_end[i] > m_first) for + (filled_end[i] >= m_first) && (event_start[i] <= max_end) for i in eachindex(event_start) ] starts = event_start[keep] - ends = event_end[keep] + ends = filled_end[keep] isempty(starts) && return (index = empty(close_index), concurrency = Float64[]) - first_start = starts[1] - max_end = maximum(ends) - span = [t for t in close_index if t >= first_start && t <= max_end] + span = [t for t in close_index if t >= m_first && t <= max_end] concurrency = zeros(Float64, length(span)) for (s, e) in zip(starts, ends) - capped = min(e, span[end]) - concurrency[(span .>= s) .& (span .<= capped)] .+= 1.0 + concurrency[(span .>= s) .& (span .<= e)] .+= 1.0 end - keepmask = (span .>= m_first) .& (span .<= max_end) - return (index = span[keepmask], concurrency = concurrency[keepmask]) + return (index = span, concurrency = concurrency) end """ @@ -59,7 +67,7 @@ Average uniqueness of each event (column) given a T×N indicator matrix: """ function calculate_average_uniqueness(index_matrix::AbstractMatrix{<:Real}) n_periods, n_events = size(index_matrix) - concurrency = vec(sum(index_matrix; dims = 2)) + concurrency = vec(sum(value -> isnan(value) ? zero(value) : value, index_matrix; dims = 2)) result = zeros(Float64, n_events) for j = 1:n_events total = 0.0 @@ -77,6 +85,81 @@ function calculate_average_uniqueness(index_matrix::AbstractMatrix{<:Real}) return result end +""" + sequential_bootstrap(indicator_matrix; sample_length=nothing, random_state=nothing) + +Draw one-based event-column positions with replacement using average added +uniqueness. Rows are observation intervals and columns are events. The matrix +must contain finite real zeros and ones, and every event must be active in at +least one row. The default draw count is the number of event columns. + +For candidate event `j`, average `1 / (c_t + 1)` over its active rows, where +`c_t` counts all previous selections, including repeats. Normalize these scores +to obtain the next draw probabilities. The first draw is uniform. + +`random_state` accepts a nonnegative integer seed, an `AbstractRNG`, or `nothing` +for a fresh `Xoshiro` generator. A supplied generator advances in place. Zero +draws consume no random numbers. Julia and NumPy seeds do not imply identical +streams; parity fixtures supply identical uniform draws explicitly. +""" +function sequential_bootstrap( + indicator_matrix::AbstractMatrix; + sample_length = nothing, + random_state = nothing, +) + eltype(indicator_matrix) <: Real || + throw(ArgumentError("indicator_matrix must contain real binary values")) + all(value -> isfinite(value) && (value == 0 || value == 1), indicator_matrix) || + throw(ArgumentError("indicator_matrix must contain only finite zeros and ones")) + event_count = size(indicator_matrix, 2) + durations = vec(sum(indicator_matrix; dims = 1)) + any(iszero, durations) && + throw(ArgumentError("each event must contain at least one active interval")) + draw_count = isnothing(sample_length) ? event_count : sample_length + draw_count isa Integer && !(draw_count isa Bool) && draw_count >= 0 || + throw(ArgumentError("sample_length must be a nonnegative integer")) + event_count == 0 && draw_count > 0 && + throw(ArgumentError("cannot draw from an indicator matrix with no events")) + generator = if isnothing(random_state) + Xoshiro() + elseif random_state isa AbstractRNG + random_state + elseif random_state isa Integer && !(random_state isa Bool) && random_state >= 0 + Xoshiro(random_state) + else + throw(ArgumentError("random_state must be a nonnegative seed or AbstractRNG")) + end + selected = Vector{Int}(undef, draw_count) + concurrency = zeros(Int, size(indicator_matrix, 1)) + scores = zeros(Float64, event_count) + for draw in eachindex(selected) + for event in 1:event_count + total = 0.0 + for time in axes(indicator_matrix, 1) + if indicator_matrix[time, event] == 1 + total += 1.0 / (concurrency[time] + 1) + end + end + scores[event] = total / durations[event] + end + threshold = rand(generator) * sum(scores) + cumulative = 0.0 + chosen = event_count + for event in 1:event_count + cumulative += scores[event] + if threshold < cumulative + chosen = event + break + end + end + selected[draw] = chosen + for time in axes(indicator_matrix, 1) + concurrency[time] += indicator_matrix[time, chosen] == 1 + end + end + return selected +end + """ sample_weight_absolute_return_meta_labeling(event_start, event_end, price_index, price, molecule) -> Vector{Float64} @@ -140,7 +223,16 @@ recent observation keeps weight 1, the oldest gets `clf_last_weight` function calculate_time_decay(weight::AbstractVector{<:Real}; clf_last_weight::Real = 1.0) (clf_last_weight < 0 || clf_last_weight > 1) && throw(ArgumentError("clf_last_weight must be between 0 and 1")) - cumulative = cumsum(weight) + cumulative = Vector{Float64}(undef, length(weight)) + running = 0.0 + for (index, value) in enumerate(weight) + if isnan(value) + cumulative[index] = NaN + else + running += value + cumulative[index] = running + end + end if clf_last_weight == 1.0 || isempty(cumulative) || cumulative[end] == 0 slope = 0.0 const_term = 1.0 diff --git a/src/Ensemble/BaggingContracts.jl b/src/Ensemble/BaggingContracts.jl new file mode 100644 index 0000000..afcf1ab --- /dev/null +++ b/src/Ensemble/BaggingContracts.jl @@ -0,0 +1,84 @@ +function _ensemble_accuracy(predictions, labels) + length(predictions) == length(labels) || throw(DimensionMismatch("Predictions must align with labels.")) + isempty(labels) && throw(ArgumentError("Accuracy requires observations.")) + return sum(predictions .== labels) / length(labels) +end + +function _bagging_member_weights(predictions::AbstractMatrix, labels::AbstractVector) + size(predictions, 1) == length(labels) || throw(DimensionMismatch("Member predictions must align with training labels.")) + count = size(predictions, 2) + count > 0 || throw(ArgumentError("At least one fitted member is required.")) + accuracy = [_ensemble_accuracy(view(predictions, :, member), labels) for member in 1:count] + uniform = fill(1.0 / count, count) + total = sum(accuracy) + variability = 1 .- accuracy .^ 2 + variance_total = sum(variability) + return Dict("uniform" => uniform, "c_i" => (total == 0 ? copy(uniform) : accuracy ./ total), "variance" => (variance_total == 0 ? copy(uniform) : variability ./ variance_total)) +end + +function _bagging_member_prediction(predictions::AbstractMatrix, weights, classes) + length(classes) == 2 || throw(ArgumentError("Bagging accuracy supports two external classes.")) + size(predictions, 2) == length(weights) || throw(DimensionMismatch("Weights must align with fitted members.")) + signed = ifelse.(predictions .== classes[2], 1.0, -1.0) + votes = signed * weights + tolerance = eps(Float64) * max(1.0, sum(abs, weights)) * max(1, length(weights)) + return [value > tolerance ? classes[2] : classes[1] for value in votes] +end + +""" + bagging_evaluate_schemes(estimator, x_train, y_train, x_test, y_test; + fit_estimator, tree_predictions, estimator_classes) + +Fit the caller-selected ensemble once through `fit_estimator(model, x, y, +nothing)`. `tree_predictions(fitted_model, x)` returns an observation-by-member +matrix; `estimator_classes(fitted_model)` returns the two ordered external +labels. Recompute fresh training accuracies and the public uniform, accuracy +and variance weights, then evaluate signed votes with zero assigned to the +first class. Member outputs are compared directly with external labels, as in +the released wrapper; encoded-label mismatches are not silently remapped. +The supplied ensemble retains responsibility for the public entropy criterion, +balanced class weights and fitted feature subsets. Existing native training +conveniences and mutable Python evaluator cache reuse are separate contracts. +""" +function bagging_evaluate_schemes(estimator, x_train::AbstractMatrix, y_train::AbstractVector, x_test::AbstractMatrix, y_test::AbstractVector; fit_estimator, tree_predictions, estimator_classes) + size(x_train, 1) == length(y_train) && size(x_test, 1) == length(y_test) || throw(DimensionMismatch("Feature rows must align with labels.")) + fitted = fit_estimator(estimator, copy(x_train), copy(y_train), nothing) + classes = collect(estimator_classes(fitted)) + train_predictions = tree_predictions(fitted, copy(x_train)) + schemes = _bagging_member_weights(train_predictions, y_train) + accuracies = Dict{String,Float64}() + for name in ("uniform", "c_i", "variance") + predictions = tree_predictions(fitted, copy(x_test)) + accuracies[name] = _ensemble_accuracy(_bagging_member_prediction(predictions, schemes[name], classes), y_test) + end + return accuracies +end + +""" + calculate_bootstrap_accuracy(estimator, x, y; predict, + n_bootstraps=1000, random_state=nothing, bootstrap_rows=nothing) + +Evaluate a fitted caller predictor through `predict(model, sampled_x)` on each +resampled row plan. Optional `bootstrap_rows` is a vector of one-based row +vectors; variable lengths can replay explicit expansion of duplicated pandas +index labels. Inputs must already be aligned by position. Return all accuracies, +their mean and sample deviation; zero draws produce NaN summaries and one draw +produces a NaN sample deviation. Native random seeds are not NumPy streams. +""" +function calculate_bootstrap_accuracy(estimator, x::AbstractMatrix, y::AbstractVector; predict, n_bootstraps::Integer = 1000, random_state = nothing, bootstrap_rows = nothing) + n = length(y) + size(x, 1) == n || throw(DimensionMismatch("Feature rows must align with labels.")) + n > 0 && n_bootstraps >= 0 || throw(ArgumentError("Require observations and a nonnegative bootstrap count.")) + rng = _ensemble_rng(random_state) + plans = bootstrap_rows === nothing ? [rand(rng, 1:n, n) for _ in 1:n_bootstraps] : [Vector{Int}(rows) for rows in bootstrap_rows] + length(plans) == n_bootstraps || throw(DimensionMismatch("Bootstrap plans must match n_bootstraps.")) + values = Float64[] + for rows in plans + !isempty(rows) && all(index -> 1 <= index <= n, rows) || throw(ArgumentError("Bootstrap rows must be nonempty valid positions.")) + predictions = predict(estimator, copy(x[rows, :])) + push!(values, _ensemble_accuracy(predictions, y[rows])) + end + average = isempty(values) ? NaN : sum(values) / length(values) + deviation = length(values) < 2 ? NaN : std(values; corrected = true) + return values, average, deviation +end diff --git a/src/Ensemble/Ensemble.jl b/src/Ensemble/Ensemble.jl index 058e3c4..c2710d8 100644 --- a/src/Ensemble/Ensemble.jl +++ b/src/Ensemble/Ensemble.jl @@ -9,6 +9,7 @@ majority-vote bagging classifier and an empirical weighted-bagging evaluator module Ensemble include("BaggingAccuracy.jl") +include("BaggingContracts.jl") export bagging_classifier_accuracy, diff --git a/src/Features/DebiasedImportanceContracts.jl b/src/Features/DebiasedImportanceContracts.jl new file mode 100644 index 0000000..4cf48ff --- /dev/null +++ b/src/Features/DebiasedImportanceContracts.jl @@ -0,0 +1,118 @@ +""" + mdi_plus_importance(estimator, x, y; fit_estimator, tree_records, n_trees=60) + +Fit the caller ensemble with `fit_estimator(model, x, y, nothing)` and obtain +ordered tree records from `tree_records(fitted_model)`. Each record contains +one-based `features`, matching `thresholds`, and one-based `bootstrap_indices`. +The released simplified MDI+ calculation uses globally centered stump bases, +unique in-bag rows, intercept ridge with penalty one, and out-of-bag partial +population variances. Trees with no splits or fewer than five out-of-bag rows +are skipped. The estimator family and bootstrap draws remain caller choices. +""" +function mdi_plus_importance(estimator, x::AbstractMatrix, y::AbstractVector; fit_estimator, tree_records, n_trees::Integer = 60) + _importance_input_shape(x, y) + n_trees >= 0 || throw(ArgumentError("n_trees must be nonnegative.")) + xv, yv = Matrix{Float64}(x), Vector{Float64}(y) + n, p = size(xv) + fitted = fit_estimator(estimator, copy(x), copy(y), nothing) + records = collect(tree_records(fitted)) + accumulated = zeros(p) + used = 0 + for row in Iterators.take(records, n_trees) + features = Vector{Int}(row.features) + thresholds = Vector{Float64}(row.thresholds) + length(features) == length(thresholds) || throw(DimensionMismatch("Each split needs one threshold.")) + all(feature -> 1 <= feature <= p, features) || throw(ArgumentError("Split features must index the data columns.")) + isempty(features) && continue + in_bag = sort(unique(Vector{Int}(row.bootstrap_indices))) + !isempty(in_bag) && all(index -> 1 <= index <= n, in_bag) || throw(ArgumentError("Bootstrap positions must index the data rows.")) + out_of_bag = setdiff(1:n, in_bag) + length(out_of_bag) < 5 && continue + basis = Float64[xv[index, features[column]] <= thresholds[column] for index in 1:n, column in eachindex(features)] + basis .-= mean(basis; dims = 1) + coefficients, _, _ = _ridge_coefficients(basis[in_bag, :], yv[in_bag]) + for feature in unique(features) + selected = features .== feature + partial = basis[out_of_bag, selected] * coefficients[selected] + accumulated[feature] += _var_pop(partial) + end + used += 1 + end + return accumulated ./ max(used, 1) +end + +function _cpi_summary(differences) + importance = zeros(length(differences)) + p_value = ones(length(differences)) + for (feature, values) in enumerate(differences) + length(values) >= 2 || throw(ArgumentError("CPI requires at least two held-out loss differences.")) + importance[feature] = mean(values) + if _std_pop(values) >= 1e-12 + statistic = importance[feature] / (std(values; corrected = true) / sqrt(length(values))) + p_value[feature] = ccdf(TDist(length(values) - 1), statistic) + end + end + return (importance = importance, p_value = p_value) +end + +function _cpi_splits(n, n_splits, random_state, supplied) + if supplied === nothing + 2 <= n_splits <= n || throw(ArgumentError("n_splits must be between two and the observation count.")) + return [(sort(train), sort(test)) for (train, test) in _kfold(n, n_splits; shuffle = true, rng = MersenneTwister(random_state))] + end + splits = [(Vector{Int}(first(pair)), Vector{Int}(last(pair))) for pair in supplied] + for (train, test) in splits + !isempty(train) && !isempty(test) || throw(ArgumentError("Every CPI fold needs training and test observations.")) + sort(vcat(train, test)) == collect(1:n) || throw(ArgumentError("Each CPI fold must partition all observations without overlap.")) + end + sort(reduce(vcat, (test for (_, test) in splits); init = Int[])) == collect(1:n) || throw(ArgumentError("CPI test folds must cover each observation exactly once.")) + return splits +end + +""" + conditional_predictive_impact(estimator, x, y; clone_estimator, + fit_estimator, predict_proba, estimator_classes, random_state=0, + n_splits=4, splits=nothing, gaussian_draws=nothing) + +Clone and fit the caller classifier once per fold, retaining its class-column +order. Callbacks are `clone_estimator(model)`, `fit_estimator(model, x, y, nothing)`, +`predict_proba(model, x)`, and `estimator_classes(model)`. Optional one-based +`(train, test)` splits and per-fold test-row by feature Gaussian matrices replay +the public computation. Conditional ridge uses penalty one and an intercept; +residual population deviation receives the public additive `1e-9`. Losses are +clipped to `[1e-15, 1-1e-15]` without renormalization. Pooled loss increases use +the public population-deviation gate and one-sided paired Student test. +This interface validates arithmetic conditional on the caller model and draws; +it does not certify empirical calibration or default forest equivalence. +""" +function conditional_predictive_impact(estimator, x::AbstractMatrix, y::AbstractVector; clone_estimator, fit_estimator, predict_proba, estimator_classes, random_state::Integer = 0, n_splits::Integer = 4, splits = nothing, gaussian_draws = nothing) + _importance_input_shape(x, y) + xv = Matrix{Float64}(x) + n, p = size(xv) + p >= 2 || throw(ArgumentError("Conditional ridge requires at least two feature columns.")) + folds = _cpi_splits(n, n_splits, random_state, splits) + gaussian_draws === nothing || length(gaussian_draws) == length(folds) || throw(DimensionMismatch("Supply one Gaussian matrix per fold.")) + differences = [Float64[] for _ in 1:p] + for (fold, (train, test)) in enumerate(folds) + model = fit_estimator(clone_estimator(estimator), copy(xv[train, :]), copy(y[train]), nothing) + classes = collect(estimator_classes(model)) + original = Matrix{Float64}(predict_proba(model, copy(xv[test, :]))) + size(original) == (length(test), length(classes)) || throw(DimensionMismatch("Probability columns must match the supplied classes.")) + loss_true = _per_sample_log_loss(y[test], original, classes) + rng = MersenneTwister(random_state + 100 + fold - 1) + draws = gaussian_draws === nothing ? randn(rng, length(test), p) : Matrix{Float64}(gaussian_draws[fold]) + size(draws) == (length(test), p) || throw(DimensionMismatch("Gaussian draws must match test observations and features.")) + for feature in 1:p + others = [column for column in 1:p if column != feature] + coefficients, xbar, ybar = _ridge_coefficients(xv[train, others], xv[train, feature]) + residual = xv[train, feature] .- _ridge_predict(coefficients, xbar, ybar, xv[train, others]) + sigma = _std_pop(residual) + 1e-9 + knockoff = copy(xv[test, :]) + knockoff[:, feature] = _ridge_predict(coefficients, xbar, ybar, xv[test, others]) .+ sigma .* draws[:, feature] + probabilities = Matrix{Float64}(predict_proba(model, knockoff)) + size(probabilities) == size(original) || throw(DimensionMismatch("Knockoff probability dimensions changed.")) + append!(differences[feature], _per_sample_log_loss(y[test], probabilities, classes) .- loss_true) + end + end + return _cpi_summary(differences) +end diff --git a/src/Features/FeatureImportance.jl b/src/Features/FeatureImportance.jl index 9d8c2aa..9e25208 100644 --- a/src/Features/FeatureImportance.jl +++ b/src/Features/FeatureImportance.jl @@ -3,15 +3,16 @@ Feature importance — native Julia port mirroring the Python `RiskLabAI.features.feature_importance` sub-package (López de Prado, AFML Ch. 8). This file wires the **backend-independent** pieces: PCA-based feature -orthogonalisation and the weighted Kendall-τ. The classifier-driven importances -(MDI, MDA, SFI and their clustered variants) build on a tree ensemble and are -wired with the `DecisionTree.jl` backend in a follow-up. +orthogonalisation and the weighted Kendall-τ. The existing classifier-driven +convenience methods use DecisionTree.jl. Caller-estimator overloads and clustered +aggregation are provided by FeatureImportanceContracts.jl. Parity notes: * `orthogonal_features` is **exact** up to the unavoidable sign ambiguity of - eigenvectors (eigenvalues, cumulative variance and the retained component - count match Python exactly; the transformed columns are sign-free - orthogonal). Verified in `test/runtests.jl`. + eigenvectors and bases of repeated eigenspaces. Eigenvalues and cumulative + variance match within floating-point tolerance; a 100% variance threshold + can retain different numbers of numerically zero components. Invariant + reconstruction and weighted-rank fixtures are checked in the test suite. * `calculate_weighted_tau` implements Vigna's additive-hyperbolic weighted Kendall-τ (the estimator `scipy.stats.weightedtau` computes); validated against its mathematical definition (perfect concordance → 1, perfect @@ -30,7 +31,10 @@ using LinearAlgebra: eigen, Symmetric PCA orthogonalisation of a `observations × features` matrix: z-score normalise, eigendecompose `Xᵀ X` (eigenvalues descending), retain the leading components whose cumulative explained variance first reaches `variance_threshold`, and -project. Returns the transformed (orthogonal) features and the retained +project. Columns with undefined normalized observations, including constant or +missing columns, are removed as in the public reference. At least one usable +column is required. Returned eigenvector rows correspond to retained columns +in their original order. Returns the transformed features and the retained eigenvalues/eigenvectors/cumulative variance. Mirrors Python's `orthogonal_features`. """ @@ -38,9 +42,12 @@ function orthogonal_features( features::AbstractMatrix{<:Real}; variance_threshold::Real = 0.95, ) - means = mean(features; dims = 1) - stds = std(features; dims = 1) # sample std (ddof = 1) - normalized = (features .- means) ./ stds + retained = [column for column in axes(features, 2) if all(isfinite, features[:, column]) && size(features, 1) >= 2 && std(features[:, column]) > 0] + isempty(retained) && throw(ArgumentError("PCA requires a nonconstant complete feature column")) + selected = features[:, retained] + means = mean(selected; dims = 1) + stds = std(selected; dims = 1) + normalized = (selected .- means) ./ stds dot_product = normalized' * normalized factorization = eigen(Symmetric(Matrix(dot_product))) @@ -72,6 +79,7 @@ function calculate_weighted_tau( feature_importances::AbstractVector{<:Real}, principal_component_ranks::AbstractVector{<:Real}, ) + length(feature_importances) == length(principal_component_ranks) || throw(DimensionMismatch("importance and rank vectors must align")) weights = 1.0 ./ principal_component_ranks return _weighted_tau(collect(float(feature_importances)), collect(weights)) end @@ -91,8 +99,8 @@ function _weighted_tau_one(x, y, key) denominator_y = 0.0 for i = 1:n, j = (i+1):n weight = w[i] + w[j] - sign_x = sign(x[i] - x[j]) - sign_y = sign(y[i] - y[j]) + sign_x = cmp(x[i], x[j]) + sign_y = cmp(y[i], y[j]) numerator += weight * sign_x * sign_y denominator_x += weight * abs(sign_x) denominator_y += weight * abs(sign_y) @@ -101,7 +109,14 @@ function _weighted_tau_one(x, y, key) end # Symmetric weighted-τ (scipy's rank=True): average over both orderings. -_weighted_tau(x, y) = 0.5 * (_weighted_tau_one(x, y, x) + _weighted_tau_one(x, y, y)) +_tau_score(value) = isnan(value) ? (0, 0.0) : (1, value == 0 ? 0.0 : Float64(value)) + +function _weighted_tau(x, y) + left, right = _tau_score.(x), _tau_score.(y) + forward = collect(zip(left, right)) + backward = collect(zip(right, left)) + return 0.5 * (_weighted_tau_one(left, right, forward) + _weighted_tau_one(left, right, backward)) +end # --------------------------------------------------------------------------- # # Classifier-driven importances (DecisionTree.jl backend). @@ -511,7 +526,7 @@ function conditional_predictive_impact( d = differences[j] importance[j] = sum(d) / length(d) s = std(d) # sample std (ddof = 1) - if s < 1e-12 + if _std_pop(d) < 1e-12 p_value[j] = 1.0 else t_stat = importance[j] / (s / sqrt(length(d))) diff --git a/src/Features/FeatureImportanceContracts.jl b/src/Features/FeatureImportanceContracts.jl new file mode 100644 index 0000000..7f42391 --- /dev/null +++ b/src/Features/FeatureImportanceContracts.jl @@ -0,0 +1,234 @@ +using Random: randperm + +function _importance_summary(values) + size(values, 1) >= 1 || throw(ArgumentError("at least one importance observation is required")) + means = [_nan_mean(view(values, :, column)) for column in axes(values, 2)] + errors = [_nan_std(view(values, :, column)) / sqrt(size(values, 1)) for column in axes(values, 2)] + return (mean = means, std = errors) +end + +function _importance_clusters(clusters, n_features) + named = clusters isa AbstractDict || clusters isa AbstractVector{<:Pair} + entries = named ? collect(clusters) : [index => group for (index, group) in enumerate(clusters)] + groups = [Int.(collect(last(entry))) for entry in entries] + all(all(index -> 1 <= index <= n_features, group) for group in groups) || throw(ArgumentError("cluster feature positions must index the data columns")) + return groups, ["C_" * string(first(entry)) for entry in entries] +end + +""" + feature_importance_mdi(per_tree_importances) -> (; mean, std) + +Aggregate a tree-by-feature importance matrix using the released zero-to-NaN +rule, sample standard deviation divided by the square root of all trees, and +normalization by the sum of feature means. Undefined columns remain NaN. +""" +function feature_importance_mdi(per_tree_importances::AbstractMatrix{<:Real}) + values = Float64.(per_tree_importances) + values[values .== 0] .= NaN + result = _importance_summary(values) + total = sum(filter(!isnan, result.mean)) + return (mean = result.mean ./ total, std = result.std ./ total) +end + +""" + clustered_feature_importance_mdi(per_tree_importances, clusters) + +Sum member-feature importances within each tree before computing normalized +cluster means and standard errors. NaNs and zero importances are skipped; +an all-missing or empty cluster contributes zero for that tree. Clusters may +overlap. Provide ordered vectors of one-based feature positions, ordered +`name => positions` pairs, or a dictionary. Returned `names` identify the +output order with the public `C_` prefix. +""" +function clustered_feature_importance_mdi(per_tree_importances::AbstractMatrix{<:Real}, clusters) + values = Float64.(per_tree_importances) + values[values .== 0] .= NaN + groups, names = _importance_clusters(clusters, size(values, 2)) + grouped = [sum((values[row, column] for column in group if !isnan(values[row, column])); init = 0.0) for row in axes(values, 1), group in groups] + result = _importance_summary(grouped) + total = sum(result.mean) + return (mean = result.mean ./ total, std = result.std ./ total, names = names) +end + +function _importance_weights(weights, count) + values = weights === nothing ? ones(count) : Float64.(weights) + length(values) == count || throw(DimensionMismatch("weights must align with observations")) + all(value -> isfinite(value) && value >= 0, values) || throw(ArgumentError("weights must be finite and nonnegative")) + return values +end + +function _importance_input_shape(x, y) + length(y) == size(x, 1) || throw(DimensionMismatch("labels must align with observations")) + size(x, 1) > 0 && size(x, 2) > 0 || throw(ArgumentError("feature data must be nonempty")) + return nothing +end + +""" + feature_importance_mdi(estimator, x, y; fit_estimator, tree_importances, + sample_weights=nothing) + +Fit the caller's ensemble through `fit_estimator(model, x, y, weights)` and +aggregate `tree_importances(fitted_model)`. The callback receives `nothing` +when no weights were supplied. Return mean, standard error and the fitted +`estimator`; no tree backend or alternative weighting rule is selected. +""" +function feature_importance_mdi(estimator, x::AbstractMatrix, y::AbstractVector; fit_estimator, tree_importances, sample_weights = nothing) + _importance_input_shape(x, y) + weights = sample_weights === nothing ? nothing : _importance_weights(sample_weights, length(y)) + fitted = fit_estimator(estimator, copy(x), copy(y), weights) + values = tree_importances(fitted) + size(values, 2) == size(x, 2) || throw(DimensionMismatch("tree importances must align with features")) + return merge(feature_importance_mdi(values), (estimator = fitted,)) +end + +""" + clustered_feature_importance_mdi(estimator, x, y, clusters; + fit_estimator, tree_importances, sample_weights=nothing) + +Fit the caller ensemble with aligned observation weights, then aggregate the +supplied feature clusters. Return cluster means, standard errors, names and +the fitted estimator. +""" +function clustered_feature_importance_mdi(estimator, x::AbstractMatrix, y::AbstractVector, clusters; fit_estimator, tree_importances, sample_weights = nothing) + _importance_input_shape(x, y) + _importance_clusters(clusters, size(x, 2)) + weights = sample_weights === nothing ? nothing : _importance_weights(sample_weights, length(y)) + fitted = fit_estimator(estimator, copy(x), copy(y), weights) + values = tree_importances(fitted) + size(values, 2) == size(x, 2) || throw(DimensionMismatch("tree importances must align with features")) + return merge(clustered_feature_importance_mdi(values, clusters), (estimator = fitted,)) +end + +function _importance_folds(n_samples, n_splits, folds, shuffle, random_state) + if folds === nothing + 2 <= n_splits <= n_samples || throw(ArgumentError("n_splits must be between two and the sample count")) + result = [(sort(train), sort(test)) for (train, test) in _kfold(n_samples, n_splits; shuffle, rng = MersenneTwister(random_state))] + else + result = [(Int.(collect(train)), Int.(collect(test))) for (train, test) in folds] + length(result) >= 2 || throw(ArgumentError("at least two folds are required")) + end + for (train, test) in result + !isempty(train) && !isempty(test) && allunique(train) && allunique(test) || throw(ArgumentError("folds must have nonempty distinct positions")) + all(index -> 1 <= index <= n_samples, [train; test]) || throw(ArgumentError("fold positions must index the observations")) + sort(train) == setdiff(1:n_samples, test) || throw(ArgumentError("training positions must complement each test fold")) + end + sort(reduce(vcat, [test for (_, test) in result])) == collect(1:n_samples) || throw(ArgumentError("test folds must partition all observations")) + return result +end + +function _importance_log_loss(labels, probabilities, weights, classes) + ordered_classes = sort(unique(classes)) + length(ordered_classes) >= 2 || throw(ArgumentError("log loss requires at least two classes")) + values = float.(probabilities) + ndims(values) == 1 && (values = reshape(values, :, 1)) + size(values, 2) == 1 && length(ordered_classes) == 2 && (values = hcat(1 .- values, values)) + size(values) == (length(labels), length(ordered_classes)) || throw(DimensionMismatch("probabilities must align with observations and classes")) + all(value -> isfinite(value) && 0 <= value <= 1, values) || throw(ArgumentError("probabilities must lie in [0, 1]")) + sum(weights) > 0 || throw(ArgumentError("scoring weights must have positive sum")) + epsilon = eps(eltype(values)) + columns = Dict(label => index for (index, label) in enumerate(ordered_classes)) + all(haskey(columns, label) for label in labels) || throw(ArgumentError("observed labels must belong to the declared classes")) + losses = [-log(clamp(values[row, columns[label]], epsilon, one(epsilon)-epsilon)) for (row, label) in enumerate(labels)] + return sum(weights .* losses) / sum(weights) +end + +function _importance_permutations(permutations, folds, groups) + permutations === nothing && return nothing + length(permutations) == length(folds) || throw(DimensionMismatch("permutations must align with folds")) + for (fold, (_, test)) in enumerate(folds) + length(permutations[fold]) == length(groups) || throw(DimensionMismatch("permutations must align with feature groups")) + all(sort(Int.(permutation)) == collect(1:length(test)) for permutation in permutations[fold]) || throw(ArgumentError("each permutation must index every test observation once")) + end + return permutations +end + +""" + clustered_feature_importance_mda(estimator, x, y, clusters; + fit_estimator, predict_proba, estimator_classes, train_sample_weights=nothing, + score_sample_weights=nothing, n_splits=10, random_state=42, + folds=nothing, permutations=nothing) + +Compute the released weighted decrease in negative log loss after jointly +permuting each cluster's test rows. Empty clusters have exactly zero decrease. +Callbacks fit and update the same estimator sequentially across folds, retaining +the source warm-start semantics. Training and scoring weights are separate. +Supplied one-based folds and `[fold][cluster]` row permutations enable exact +cross-language replay; native random streams are not NumPy seed equivalents. +Return means, standard errors, cluster names and the final fitted estimator. +""" +function clustered_feature_importance_mda(estimator, x::AbstractMatrix, y::AbstractVector, clusters; fit_estimator, predict_proba, estimator_classes, train_sample_weights = nothing, score_sample_weights = nothing, n_splits::Integer = 10, random_state::Integer = 42, folds = nothing, permutations = nothing) + _importance_input_shape(x, y) + groups, names = _importance_clusters(clusters, size(x, 2)) + training_weights = _importance_weights(train_sample_weights, length(y)) + scoring_weights = _importance_weights(score_sample_weights, length(y)) + splits = _importance_folds(length(y), n_splits, folds, true, random_state) + _importance_permutations(permutations, splits, groups) + decreases = zeros(length(splits), length(groups)) + fitted = estimator + for (fold, (train, test)) in enumerate(splits) + fitted = fit_estimator(fitted, x[train, :], y[train], training_weights[train]) + classes = estimator_classes(fitted) + baseline = -_importance_log_loss(y[test], predict_proba(fitted, x[test, :]), scoring_weights[test], classes) + rng = MersenneTwister(random_state + fold - 1) + for (column, group) in enumerate(groups) + isempty(group) && continue + permutation = permutations === nothing ? randperm(rng, length(test)) : Int.(permutations[fold][column]) + shuffled = x[test, :] + shuffled[:, group] = shuffled[permutation, group] + loss = _importance_log_loss(y[test], predict_proba(fitted, shuffled), scoring_weights[test], classes) + decreases[fold, column] = baseline + loss + end + end + return merge(_importance_summary(decreases), (names = names, estimator = fitted)) +end + +""" + feature_importance_mda(estimator, x, y; fit_estimator, predict_proba, + estimator_classes, train_sample_weights=nothing, score_sample_weights=nothing, + n_splits=10, random_state=42, folds=nothing, permutations=nothing) + +Caller-estimator single-column permutation importance. Uses the same weighted +loss, sequential fit state and supplied-draw contract as clustered MDA. +""" +function feature_importance_mda(estimator, x::AbstractMatrix, y::AbstractVector; kwargs...) + result = clustered_feature_importance_mda(estimator, x, y, [[column] for column in axes(x, 2)]; kwargs...) + return (mean = result.mean, std = result.std, estimator = result.estimator) +end + +""" + feature_importance_sfi(estimator, x, y; fit_estimator, predict=nothing, + predict_proba=nothing, estimator_classes=nothing, scoring=:log_loss, + train_sample_weights=nothing, score_sample_weights=nothing, + n_splits=10, folds=nothing) + +Fit the caller estimator to each single feature over contiguous K-fold splits +and aggregate weighted accuracy or negative log loss. Fit state advances in +feature-major, fold-minor order as in the reference. Supplied folds can replay +the reference partition. Return mean scores, standard errors and the final +fitted estimator; no forest or alternative score is selected implicitly. +""" +function feature_importance_sfi(estimator, x::AbstractMatrix, y::AbstractVector; fit_estimator, predict = nothing, predict_proba = nothing, estimator_classes = nothing, scoring::Symbol = :log_loss, train_sample_weights = nothing, score_sample_weights = nothing, n_splits::Integer = 10, folds = nothing) + _importance_input_shape(x, y) + scoring in (:log_loss, :accuracy) || throw(ArgumentError("scoring must be :log_loss or :accuracy")) + scoring == :accuracy && predict === nothing && throw(ArgumentError("accuracy requires a prediction callback")) + scoring == :log_loss && (predict_proba === nothing || estimator_classes === nothing) && throw(ArgumentError("log loss requires probability and class callbacks")) + training_weights = _importance_weights(train_sample_weights, length(y)) + scoring_weights = _importance_weights(score_sample_weights, length(y)) + splits = _importance_folds(length(y), n_splits, folds, false, 0) + scores = zeros(length(splits), size(x, 2)) + fitted = estimator + for column in axes(x, 2) + for (fold, (train, test)) in enumerate(splits) + fitted = fit_estimator(fitted, x[train, column:column], y[train], training_weights[train]) + if scoring == :log_loss + scores[fold, column] = -_importance_log_loss(y[test], predict_proba(fitted, x[test, column:column]), scoring_weights[test], estimator_classes(fitted)) + else + predictions = predict(fitted, x[test, column:column]) + predictions isa AbstractVector && length(predictions) == length(test) || throw(DimensionMismatch("predictions must align with test observations")) + sum(scoring_weights[test]) > 0 || throw(ArgumentError("scoring weights must have positive sum")) + scores[fold, column] = sum(scoring_weights[test] .* (predictions .== y[test])) / sum(scoring_weights[test]) + end + end + end + return merge(_importance_summary(scores), (estimator = fitted,)) +end diff --git a/src/Features/FeatureInputs.jl b/src/Features/FeatureInputs.jl new file mode 100644 index 0000000..df4f31b --- /dev/null +++ b/src/Features/FeatureInputs.jl @@ -0,0 +1,18 @@ +function _feature_real_array(values, name, dimensions; empty = false) + values isa AbstractArray && ndims(values) == dimensions && eltype(values) <: Real && !(eltype(values) <: Bool) || throw(ArgumentError("$name must be a real array of dimension $dimensions")) + converted = Float64.(values) + (empty || !isempty(converted)) && all(isfinite, converted) || throw(ArgumentError("$name must contain finite values and cannot be empty")) + return converted +end + +function _feature_real_scalar(value, name; minimum = -Inf, strict = false) + value isa Real && !(value isa Bool) || throw(ArgumentError("$name must be a real scalar")) + converted = Float64(value) + isfinite(converted) && (strict ? converted > minimum : converted >= minimum) || throw(ArgumentError("$name is outside its allowed domain")) + return converted +end + +function _feature_positive_integer(value, name) + value isa Integer && !(value isa Bool) && value > 0 || throw(ArgumentError("$name must be a positive integer")) + return Int(value) +end diff --git a/src/Features/Features.jl b/src/Features/Features.jl index 1c2f92b..d4e0871 100644 --- a/src/Features/Features.jl +++ b/src/Features/Features.jl @@ -4,13 +4,20 @@ Feature-engineering submodule, mirroring the Python `RiskLabAI.features` sub-package. -This PR wires the **entropy features** (Shannon, plug-in, Lempel–Ziv and -Kontoyiannis entropy). Microstructural features and structural-break tests are -wired in subsequent PRs; classifier-driven feature importance is deferred -pending a Julia ML-backend decision. +Includes entropy, microstructure, structural breaks, probability/order-flow +features and feature importance. Importance supports both the existing native +forest conveniences and explicit caller-estimator weighted contracts. """ module Features +include("FeatureInputs.jl") +include("ProbabilityAmbiguity.jl") +include("OrderFlow.jl") + +export probability_ambiguity, normal_prior_bin_probabilities, + hawkes_intensity, hawkes_integrated_intensity, hawkes_log_likelihood, + simulate_hawkes, fit_hawkes, volume_synchronized_pin + # Entropy estimators (AFML Ch. 18). include("EntropyFeatures.jl") @@ -23,6 +30,8 @@ include("StructuralBreaks.jl") # Feature importance — backend-independent pieces (PCA orthogonalisation, weighted-τ). # Classifier-driven importances (MDI/MDA/SFI) follow with the DecisionTree.jl backend. include("FeatureImportance.jl") +include("FeatureImportanceContracts.jl") +include("DebiasedImportanceContracts.jl") export # entropy features @@ -64,6 +73,8 @@ export feature_importance_mdi, feature_importance_mda, feature_importance_sfi, + clustered_feature_importance_mdi, + clustered_feature_importance_mda, get_test_dataset, mdi_plus_importance, conditional_predictive_impact diff --git a/src/Features/MicrostructuralFeatures.jl b/src/Features/MicrostructuralFeatures.jl index 3ffaa41..00adf65 100644 --- a/src/Features/MicrostructuralFeatures.jl +++ b/src/Features/MicrostructuralFeatures.jl @@ -7,7 +7,7 @@ volatility estimator. Representation note (deliberate divergence): pandas Series become plain `Vector`s; the rolling statistics replicate pandas' `rolling(window=w)` exactly — a result is `NaN` for the first `w-1` points and whenever the window still -contains a `NaN` — so the warm-up `NaN` pattern and values match the Python +contains a nonfinite value — so the warm-up `NaN` pattern and values match the Python implementation exactly (verified in `test/runtests.jl`). Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 19; @@ -26,7 +26,7 @@ function _rolling(x::AbstractVector{<:Real}, w::Integer, agg) out = fill(NaN, n) for i = w:n window = @view x[(i-w+1):i] - any(isnan, window) || (out[i] = agg(window)) + all(isfinite, window) && (out[i] = agg(window)) end return out end diff --git a/src/Features/OrderFlow.jl b/src/Features/OrderFlow.jl new file mode 100644 index 0000000..bebc1b6 --- /dev/null +++ b/src/Features/OrderFlow.jl @@ -0,0 +1,170 @@ +using Random: AbstractRNG, MersenneTwister, default_rng, randexp +using Optim + +function _hawkes_parameters(mu, alpha, beta) + mu = _feature_real_scalar(mu, "mu"; minimum = 0, strict = true) + alpha = _feature_real_scalar(alpha, "alpha"; minimum = 0) + beta = _feature_real_scalar(beta, "beta"; minimum = 0, strict = true) + alpha < beta || throw(ArgumentError("stationarity requires alpha < beta")) + return mu, alpha, beta +end + +function _hawkes_events(events, horizon) + values = _feature_real_array(events, "events", 1; empty = true) + all(0 .<= values .<= horizon) && all(>(0), diff(values)) || throw(ArgumentError("events must be strictly increasing in [0, horizon]")) + return values +end + +"""Return empty-prehistory predictable exponential Hawkes intensity using only events strictly before each query time.""" +function hawkes_intensity(times, events, mu, alpha, beta) + mu, alpha, beta = _hawkes_parameters(mu, alpha, beta) + times = _feature_real_array(times, "times", 1; empty = true) + events = _hawkes_events(events, Inf) + all(>=(0), times) || throw(ArgumentError("times must be nonnegative")) + return [mu + alpha * sum(exp(-beta * (time - event)) for event in events if event < time; init = 0.0) for time in times] +end + +"""Integrate the empty-history exponential Hawkes intensity over a positive horizon.""" +function hawkes_integrated_intensity(events, horizon, mu, alpha, beta) + mu, alpha, beta = _hawkes_parameters(mu, alpha, beta) + horizon = _feature_real_scalar(horizon, "horizon"; minimum = 0, strict = true) + events = _hawkes_events(events, horizon) + result = mu * horizon + alpha / beta * sum(-expm1.(-beta .* (horizon .- events))) + isfinite(result) || throw(ArgumentError("integrated intensity exceeds floating-point range")) + return result +end + +"""Evaluate the exact Hawkes event log likelihood with an O(n) exponential recurrence and empty prehistory.""" +function hawkes_log_likelihood(events, horizon, mu, alpha, beta) + mu, alpha, beta = _hawkes_parameters(mu, alpha, beta) + horizon = _feature_real_scalar(horizon, "horizon"; minimum = 0, strict = true) + events = _hawkes_events(events, horizon) + excitation, total, previous = 0.0, 0.0, 0.0 + for (i, event) in enumerate(events) + excitation = exp(-beta * (event - previous)) * (excitation + (i > 1)) + total += log(mu + alpha * excitation) + previous = event + end + return total - hawkes_integrated_intensity(events, horizon, mu, alpha, beta) +end + +""" + simulate_hawkes(horizon, mu, alpha, beta; random_state=nothing, max_events=100000) + +Simulate the stationary-parameter exponential Hawkes model with empty +prehistory using adaptive thinning. Accept a local seed, an `AbstractRNG` or +the default RNG. Reaching `max_events` raises an error instead of returning +a truncated history. Equal seeds do not imply NumPy/Julia stream equality. +""" +function simulate_hawkes(horizon, mu, alpha, beta; random_state = nothing, max_events = 100000) + mu, alpha, beta = _hawkes_parameters(mu, alpha, beta) + horizon = _feature_real_scalar(horizon, "horizon"; minimum = 0, strict = true) + limit = _feature_positive_integer(max_events, "max_events") + rng = if isnothing(random_state) + default_rng() + elseif random_state isa AbstractRNG + random_state + elseif random_state isa Integer && !(random_state isa Bool) && random_state >= 0 + MersenneTwister(random_state) + else + throw(ArgumentError("random_state must be a nonnegative seed or an AbstractRNG")) + end + events = Float64[] + time, excitation = 0.0, 0.0 + while true + bound = mu + excitation + wait = randexp(rng) / bound + next_time = time + wait + next_time > horizon && break + next_time > time || error("time increments are below floating-point resolution") + excitation *= exp(-beta * wait) + time = next_time + if rand(rng) * bound <= mu + excitation + length(events) < limit || error("max_events reached before simulation completed") + push!(events, time) + excitation += alpha + end + end + return events +end + +function _hawkes_scaled_objective_gradient!(gradient, parameters, events) + baseline, branching, decay = parameters + remaining = 1 .- events + value = baseline + branching * sum(-expm1.(-decay .* remaining)) + gradient .= (1.0, sum(-expm1.(-decay .* remaining)), branching * sum(remaining .* exp.(-decay .* remaining))) + excitation, derivative, previous = 0.0, 0.0, 0.0 + for (i, event) in enumerate(events) + gap = event - previous + multiplier = exp(-decay * gap) + derivative = multiplier * derivative - gap * multiplier * (excitation + (i > 1)) + excitation = multiplier * (excitation + (i > 1)) + intensity = baseline + branching * decay * excitation + value -= log(intensity) + gradient[1] -= 1 / intensity + gradient[2] -= decay * excitation / intensity + gradient[3] -= branching * (excitation + decay * derivative) / intensity + previous = event + end + return value +end + +""" + fit_hawkes(events, horizon; initial=nothing) + +Fit the empty-history exponential Hawkes likelihood under positive baseline +and decay and branching ratio in `[0,1)`. Use the same three fixed starts as +the public Python reference, or one caller-supplied start. Time is rescaled +to the unit interval; Optim's bounded solver uses the analytic score. +Return parameters, original-unit likelihood and convergence diagnostics. +A numerical fit does not establish parameter identification or global +likelihood optimality; at least two events are required. +""" +function fit_hawkes(events, horizon; initial = nothing) + horizon = _feature_real_scalar(horizon, "horizon"; minimum = 0, strict = true) + events = _hawkes_events(events, horizon) + n = length(events) + n >= 2 || throw(ArgumentError("at least two events are required to fit three parameters")) + scaled = events / horizon + starts = [[n * (1 - ratio), ratio, Float64(n)] for ratio in (0.1, 0.5, 0.8)] + if !isnothing(initial) + supplied = _feature_real_array(initial, "initial", 1) + length(supplied) == 3 || throw(ArgumentError("initial must contain mu, alpha and beta")) + mu, alpha, beta = _hawkes_parameters(supplied...) + starts = [[mu * horizon, alpha / beta, beta * horizon]] + end + lower, upper = [1e-9, 0.0, 1e-9], [Inf, 1 - 1e-8, Inf] + objective(x) = _hawkes_scaled_objective_gradient!(zeros(3), x, scaled) + gradient!(g, x) = (_hawkes_scaled_objective_gradient!(g, x, scaled); g) + results = Any[] + for start in starts + start = max.(start, lower .+ 1e-8) + start[2] = min(start[2], upper[2] - 1e-8) + result = Optim.optimize(objective, gradient!, lower, upper, start, Fminbox(LBFGS()), Optim.Options(g_tol = 1e-8, iterations = 1000, outer_iterations = 20)) + Optim.converged(result) && isfinite(Optim.minimum(result)) && push!(results, result) + end + isempty(results) && error("Hawkes likelihood optimization failed") + best = results[argmin(Optim.minimum.(results))] + baseline, branching, decay = Optim.minimizer(best) + mu, alpha, beta = baseline / horizon, branching * decay / horizon, decay / horizon + return (; mu, alpha, beta, log_likelihood = hawkes_log_likelihood(events, horizon, mu, alpha, beta), + branching_ratio = branching, converged_starts = length(results), message = "Optim bounded likelihood convergence") +end + +""" + volume_synchronized_pin(buy_volume, sell_volume; bucket_volume, window) + +Compute rolling VPIN for completed, equally sized, preclassified volume +buckets. Return one value aligned to the last bucket of each full window. +Partial buckets and implicit trade classification are not accepted. +""" +function volume_synchronized_pin(buy_volume, sell_volume; bucket_volume, window) + buy = _feature_real_array(buy_volume, "buy_volume", 1) + sell = _feature_real_array(sell_volume, "sell_volume", 1) + volume = _feature_real_scalar(bucket_volume, "bucket_volume"; minimum = 0, strict = true) + window = _feature_positive_integer(window, "window") + length(buy) == length(sell) && all(>=(0), buy) && all(>=(0), sell) && all(abs.((buy + sell) / volume .- 1) .<= 2e-12) || throw(ArgumentError("volumes must be nonnegative completed equal-volume buckets")) + window <= length(buy) || throw(ArgumentError("window exceeds the completed bucket count")) + imbalance = abs.(buy - sell) / volume + return [sum(@view imbalance[i:i+window-1]) / window for i in 1:length(buy)-window+1] +end diff --git a/src/Features/ProbabilityAmbiguity.jl b/src/Features/ProbabilityAmbiguity.jl new file mode 100644 index 0000000..eb75cc8 --- /dev/null +++ b/src/Features/ProbabilityAmbiguity.jl @@ -0,0 +1,58 @@ +using Distributions: Normal, cdf + +function _feature_probability_rows(values, name, dimensions) + probabilities = _feature_real_array(values, name, dimensions) + all(0 .<= probabilities .<= 1) && all(abs.(sum(probabilities; dims = dimensions) .- 1) .<= 2e-12) || throw(ArgumentError("$name must contain nonnegative probability rows summing to one")) + return probabilities +end + +""" + probability_ambiguity(probabilities; prior_weights=nothing, bin_width=nothing) + +Compute the sum of prior-mean bin probabilities times their population +variances over the supplied finite prior distribution. Rows are priors and +columns are exhaustive mutually exclusive bins. Weights are explicit or +uniform and are never normalized automatically. Optional `bin_width` in +`(0,1)` divides the measure by `bin_width*(1-bin_width)`. This is the approved +supplied-prior measure, separate from return variance and raw intraday +probability estimation. +""" +function probability_ambiguity(probabilities; prior_weights = nothing, bin_width = nothing) + probabilities = _feature_probability_rows(probabilities, "probabilities", 2) + n = size(probabilities, 1) + weights = isnothing(prior_weights) ? fill(1 / n, n) : _feature_probability_rows(prior_weights, "prior_weights", 1) + length(weights) == n || throw(ArgumentError("there must be one weight per prior")) + means = vec(weights' * probabilities) + variances = vec(weights' * (probabilities .- permutedims(means)).^2) + value = sum(means .* variances) + width = isnothing(bin_width) ? nothing : _feature_real_scalar(bin_width, "bin_width"; minimum = 0, strict = true) + isnothing(width) || width < 1 || throw(ArgumentError("bin_width must be less than one")) + return (unscaled = value, scaled = isnothing(width) ? nothing : value / (width * (1 - width)), + mean_probabilities = means, probability_variances = variances, prior_weights = weights, bin_width = width) +end + +""" + normal_prior_bin_probabilities(means, standard_deviations; edges=nothing) + +Convert finite normal priors with positive standard deviations to exhaustive +bin probabilities, including both tails. Means, scales and edges share return +units. Default finite edges range from -0.06 to 0.06 in steps of 0.002. +Positive-tail intervals use survival probabilities to avoid cancellation. +""" +function normal_prior_bin_probabilities(means, standard_deviations; edges = nothing) + means = _feature_real_array(means, "means", 1) + scales = _feature_real_array(standard_deviations, "standard_deviations", 1) + edges = isnothing(edges) ? collect(range(-0.06, 0.06; length = 61)) : _feature_real_array(edges, "edges", 1) + length(scales) == length(means) && all(>(0), scales) && all(>(0), diff(edges)) || throw(ArgumentError("scales must match means and be positive; edges must increase")) + result = Matrix{Float64}(undef, length(means), length(edges) + 1) + normal = Normal() + for i in eachindex(means) + z = (edges .- means[i]) / scales[i] + result[i, 1] = cdf(normal, z[1]) + for j in 1:length(z)-1 + result[i, j + 1] = z[j] >= 0 ? cdf(normal, -z[j]) - cdf(normal, -z[j + 1]) : cdf(normal, z[j + 1]) - cdf(normal, z[j]) + end + result[i, end] = cdf(normal, -z[end]) + end + return result +end diff --git a/src/Features/StructuralBreaks.jl b/src/Features/StructuralBreaks.jl index 1f91147..097ac04 100644 --- a/src/Features/StructuralBreaks.jl +++ b/src/Features/StructuralBreaks.jl @@ -15,7 +15,7 @@ Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 17. """ using LinearAlgebra: inv, SingularException -using Statistics: quantile, mean +using Statistics: quantile, mean, var using Random: AbstractRNG, default_rng, randn, MersenneTwister """ @@ -49,9 +49,12 @@ the 1-based position in `log_price` of each retained row. Mirrors Python's function prepare_data(log_price::AbstractVector{<:Real}, constant::AbstractString, lags::Integer) n = length(log_price) delta(k) = log_price[k] - log_price[k-1] # d[k] = p[k] - p[k-1], k ≥ 2 - kept = collect((lags+2):n) + lags >= 0 || throw(ArgumentError("lags must be nonnegative")) + difference_positions = [k for k in 2:n if !isnan(delta(k))] + difference_ordinal = Dict(k => ordinal for (ordinal, k) in enumerate(difference_positions)) + kept = [k for (ordinal, k) in enumerate(difference_positions) if ordinal > lags && !isnan(log_price[k-1])] m = length(kept) - n_constant = constant == "nc" ? 0 : constant == "c" ? 1 : constant == "ct" ? 2 : 3 + n_constant = constant == "c" ? 1 : constant == "ct" ? 2 : constant == "ctt" ? 3 : 0 n_cols = 1 + lags + n_constant y = Vector{Float64}(undef, m) x = Matrix{Float64}(undef, m, n_cols) @@ -61,10 +64,10 @@ function prepare_data(log_price::AbstractVector{<:Real}, constant::AbstractStrin x[r, col] = log_price[k-1] # level_l1 col += 1 for i = 1:lags - x[r, col] = delta(k - i) # delta_l{i} + x[r, col] = delta(difference_positions[difference_ordinal[k]-i]) col += 1 end - if constant != "nc" + if constant in ("c", "ct", "ctt") x[r, col] = 1.0 col += 1 # constant end @@ -385,6 +388,9 @@ rather than NumPy's PCG64 stream, so the critical values are reproducible under given `rng` but not bit-identical to the Python reference (the ADF statistics are pivotal under the null, so the values depend only on `T`, the window and the specification). Mirrors Python's `simulate_psy_critical_values`. + +`normal_draws` may supply an `n_simulations × sample_length` matrix of standard +normal innovations to replay the same null paths across runtimes. """ function simulate_psy_critical_values( sample_length::Integer, @@ -394,14 +400,19 @@ function simulate_psy_critical_values( n_simulations::Integer = 2000, level::Real = 0.95, rng::AbstractRNG = default_rng(), + normal_draws = nothing, ) + n_simulations >= 1 || throw(ArgumentError("n_simulations must be positive")) + draws = normal_draws === nothing ? nothing : Matrix{Float64}(normal_draws) + draws === nothing || size(draws) == (n_simulations, sample_length) || throw(DimensionMismatch("normal_draws must have one row per simulation")) + draws === nothing || all(isfinite, draws) || throw(ArgumentError("normal_draws must be finite")) nmin = min_sample_length === nothing ? psy_minimum_window(sample_length) : min_sample_length fast = constant == "c" && lags == 0 sadf_paths = fill(NaN, n_simulations, sample_length) bsadf_paths = fill(NaN, n_simulations, sample_length) for s = 1:n_simulations - y = cumsum(randn(rng, sample_length)) # random-walk null + y = cumsum(draws === nothing ? randn(rng, sample_length) : vec(draws[s, :])) if fast sadf, bsadf = _psy_sadf_bsadf_sequences(y, nmin) else @@ -496,6 +507,9 @@ GSADF/BSADF. `random_state` seeds Julia's RNG (see the divergence note above; the Python reference uses NumPy's PCG64). Mirrors Python's `volatility_robust_sadf`. +`sign_draws` may supply an `n_bootstrap × (length(log_price)-1)` matrix whose +entries are exactly minus or plus one, replaying the public wild-bootstrap paths. + Reference: Harvey, D. I., Leybourne, S. J., Sollis, R. & Taylor, A. M. R. (2016). Tests for explosive financial bubbles in the presence of non-stationary volatility. Journal of Empirical Finance, 38, 548–574. @@ -505,9 +519,14 @@ function volatility_robust_sadf( min_length::Union{Integer,Nothing} = nothing, n_bootstrap::Integer = 99, random_state::Union{Integer,Nothing} = nothing, + sign_draws = nothing, ) y = Float64.(log_price) length(y) >= 8 || throw(ArgumentError("log_price must be a 1-D series of length >= 8")) + n_bootstrap >= 0 || throw(ArgumentError("n_bootstrap must be nonnegative")) + draws = sign_draws === nothing ? nothing : Matrix{Float64}(sign_draws) + draws === nothing || size(draws) == (n_bootstrap, length(y)-1) || throw(DimensionMismatch("sign_draws must have one row per bootstrap path")) + draws === nothing || all(value -> value == -1 || value == 1, draws) || throw(ArgumentError("sign_draws must contain only minus or plus one")) nmin = min_length === nothing ? psy_minimum_window(length(y)) : Int(min_length) sadf_obs, gsadf_obs = _sup_adf_statistics(y, nmin) @@ -518,8 +537,8 @@ function volatility_robust_sadf( ge_sadf = 0 ge_gsadf = 0 path = similar(y) - for _ = 1:n_bootstrap - signs = rand(rng, (-1.0, 1.0), length(residual)) # Rademacher ±1 + for simulation = 1:n_bootstrap + signs = draws === nothing ? rand(rng, (-1.0, 1.0), length(residual)) : vec(draws[simulation, :]) path[1] = y0 path[2:end] = y0 .+ cumsum(signs .* residual) sadf_b, gsadf_b = _sup_adf_statistics(path, nmin) @@ -559,12 +578,13 @@ end # and the same 1e-6 ridge `ruptures` adds for truly constant segments. `model="l2"` # uses the sum-of-squared-errors cost (b-a)·var. `cum1`/`cum2` are leading-zero # prefix sums of `y` and `y.^2`, so each segment cost is O(1). -function _pelt_segment_cost(model::AbstractString, cum1, cum2, a::Integer, b::Integer) +function _pelt_segment_cost(model::AbstractString, cum1, cum2, a::Integer, b::Integer, observations = nothing) nseg = b - a s1 = cum1[b+1] - cum1[a+1] s2 = cum2[b+1] - cum2[a+1] mu = s1 / nseg variance = max(s2 / nseg - mu * mu, 0.0) # population variance, guarded + observations === nothing || (variance = var(view(observations, (a+1):b); corrected = false)) if model == "normal" return nseg * log(variance + 1e-6) elseif model == "l2" @@ -586,6 +606,7 @@ function _pelt_predict( penalty::Real, min_size::Integer, jump::Integer, + observations = nothing, ) best_cost = Dict{Int,Float64}(0 => 0.0) backptr = Dict{Int,Int}() @@ -609,7 +630,7 @@ function _pelt_predict( empty!(sub_t) for t in admissible haskey(best_cost, t) || continue - push!(sub_costs, best_cost[t] + _pelt_segment_cost(model, cum1, cum2, t, bkp) + penalty) + push!(sub_costs, best_cost[t] + (_pelt_segment_cost(model, cum1, cum2, t, bkp, observations) + penalty)) push!(sub_t, t) end @@ -677,6 +698,6 @@ function pelt_change_points( cum1 = [0.0; cumsum(x)] cum2 = [0.0; cumsum(x .^ 2)] penalty = float(penalty_multiplier) * log(n) - bkps = _pelt_predict(model, cum1, cum2, n, penalty, eff_min, Int(jump)) + bkps = _pelt_predict(model, cum1, cum2, n, penalty, eff_min, Int(jump), x) return [b for b in bkps if 0 < b < n] end diff --git a/src/Optimization/Clearing.jl b/src/Optimization/Clearing.jl new file mode 100644 index 0000000..5754f5b --- /dev/null +++ b/src/Optimization/Clearing.jl @@ -0,0 +1,42 @@ +""" + eisenberg_noe_clearing(external_assets, nominal_liabilities, + relative_liabilities; atol=1e-8) + +Return the greatest proportional clearing payment vector with an outside +creditor. Row `i` of `relative_liabilities` gives debtor `i`'s fractions owed +to network creditors; missing row mass is owed outside. Assets, liabilities +and fractions are finite and nonnegative. There are no bankruptcy costs or +fire sales. A linear program maximizes total feasible payments, including +networks with multiple fixed points. Bounds and the fixed-point residual +are checked after scaling by the greatest nominal liability. +""" +function eisenberg_noe_clearing(external_assets, nominal_liabilities, relative_liabilities; atol = 1e-8) + assets = _portfolio_array(external_assets, "external_assets", 1) + liabilities = _portfolio_array(nominal_liabilities, "nominal_liabilities", 1) + relative = _portfolio_array(relative_liabilities, "relative_liabilities", 2) + tolerance = _portfolio_scalar(atol, "atol") + 0 < tolerance < 1 || throw(ArgumentError("atol must lie in (0, 1)")) + n = length(assets) + length(liabilities) == n && size(relative) == (n, n) || throw(ArgumentError("liability dimensions must match assets")) + all(>=(0), assets) && all(>=(0), liabilities) && all(>=(0), relative) || throw(ArgumentError("inputs must be nonnegative")) + all(sum(relative; dims = 2) .<= 1) || throw(ArgumentError("relative-liability rows must sum to at most one")) + scale = maximum(liabilities) + scale == 0 && return zeros(n) + upper = liabilities / scale + resources = min.(assets, liabilities) / scale + model = Model(Clarabel.Optimizer) + set_silent(model) + for name in ("tol_gap_abs", "tol_gap_rel", "tol_feas") + set_optimizer_attribute(model, name, min(1e-14, tolerance / 10)) + end + @variable(model, 0 <= payment[i = 1:n] <= upper[i]) + @constraint(model, [i = 1:n], payment[i] - sum(relative[j, i] * payment[j] for j in 1:n) <= resources[i]) + @objective(model, Max, sum(payment)) + optimize!(model) + termination_status(model) == JuMP.MOI.OPTIMAL && has_values(model) || error("Clearing optimization is not verified: $(termination_status(model))") + result = value.(payment) + all(isfinite, result) && all(-tolerance .<= result .<= upper .+ tolerance) || error("Clearing returned invalid payment bounds") + result = clamp.(result, 0, upper) + maximum(abs.(result - min.(upper, resources + relative' * result))) <= tolerance || error("Clearing failed the fixed-point check") + return clamp.(result * scale, 0, liabilities) +end diff --git a/src/Optimization/Constrained.jl b/src/Optimization/Constrained.jl new file mode 100644 index 0000000..8f7291f --- /dev/null +++ b/src/Optimization/Constrained.jl @@ -0,0 +1,131 @@ +using JuMP +using Clarabel +using LinearAlgebra + +function _portfolio_array(values, name, dimensions) + values isa AbstractArray && ndims(values) == dimensions && + eltype(values) <: Real && !(eltype(values) <: Bool) && !isempty(values) || + throw(ArgumentError("$name must be a nonempty real array of dimension $dimensions")) + converted = Float64.(values) + all(isfinite, converted) || throw(ArgumentError("$name must contain finite values")) + return converted +end + +function _portfolio_scalar(value, name; minimum = -Inf) + value isa Real && !(value isa Bool) || throw(ArgumentError("$name must be a real scalar")) + converted = Float64(value) + isfinite(converted) && converted >= minimum || + throw(ArgumentError("$name must be finite and at least $minimum")) + return converted +end + +function _portfolio_inputs(covariance, expected_returns, caps, return_floor) + covariance = _portfolio_array(covariance, "covariance", 2) + n = size(covariance, 1) + size(covariance, 2) == n || throw(ArgumentError("covariance must be square")) + magnitude = max(maximum(abs, covariance), floatmin(Float64)) + normalized = covariance / magnitude + all(abs.(normalized - normalized') .<= 1e-12 .+ 1e-12 .* abs.(normalized')) || + throw(ArgumentError("covariance must be symmetric")) + eigmin(Symmetric(normalized, :L)) >= -1e-12 * opnorm(normalized, 2) || + throw(ArgumentError("covariance must be positive semidefinite")) + expected = isnothing(expected_returns) ? zeros(n) : _portfolio_array(expected_returns, "expected_returns", 1) + cap = isnothing(caps) ? ones(n) : _portfolio_array(caps, "caps", 1) + length(expected) == length(cap) == n && all(>=(0), cap) && sum(cap) >= 1 || + throw(ArgumentError("means and caps must match covariance and allow full investment")) + floor = isnothing(return_floor) ? nothing : _portfolio_scalar(return_floor, "return_floor") + !isnothing(floor) && isnothing(expected_returns) && + throw(ArgumentError("a return floor requires expected_returns")) + return covariance, expected, cap, floor +end + +function _solve_portfolio(covariance, expected, cap, floor; loading = nothing, radius = 0.0, risk_aversion = 1.0, minimum_variance = false) + n = length(expected) + scale = minimum_variance ? max(maximum(abs, covariance), floatmin(Float64)) : + max(maximum(abs, expected), risk_aversion * maximum(abs, covariance), radius * norm(loading), floatmin(Float64)) + isfinite(scale) || throw(ArgumentError("objective scaling exceeds floating-point range")) + model = Model(Clarabel.Optimizer) + set_silent(model) + for name in ("tol_gap_abs", "tol_gap_rel", "tol_feas") + set_optimizer_attribute(model, name, 1e-10) + end + @variable(model, weights[1:n]) + @constraint(model, sum(weights) == 1) + @constraint(model, weights .>= 0) + @constraint(model, weights .<= cap) + if !isnothing(floor) + @constraint(model, sum(expected[i] * weights[i] for i in 1:n) >= floor) + end + variance = @expression(model, sum(covariance[i, j] * weights[i] * weights[j] for i in 1:n, j in 1:n)) + if minimum_variance + @objective(model, Min, variance / scale) + else + penalty = 0.0 + if radius > 0 + @variable(model, uncertainty_norm) + @constraint(model, [uncertainty_norm; loading' * weights] in SecondOrderCone()) + penalty = radius * uncertainty_norm + end + @objective(model, Min, (risk_aversion * variance / 2 - sum(expected[i] * weights[i] for i in 1:n) + penalty) / scale) + end + try + optimize!(model) + catch exception + exception isa InterruptException && rethrow() + error("portfolio solver failed: $(sprint(showerror, exception))") + end + status = termination_status(model) + status in (JuMP.MOI.INFEASIBLE, JuMP.MOI.ALMOST_INFEASIBLE) && + throw(ArgumentError("portfolio constraints are infeasible")) + status == JuMP.MOI.OPTIMAL && has_values(model) || + error("portfolio optimization is not verified: $status") + result = value.(weights) + nominal = dot(expected, result) + if !all(isfinite, result) || abs(sum(result) - 1) > 1e-7 || + any(result .< -1e-7) || any(result .> cap .+ 1e-7) || + (!isnothing(floor) && nominal < floor - 1e-7 * max(1, abs(floor))) + error("portfolio solver returned an infeasible result") + end + risk = dot(result, covariance * result) + worst = isnothing(loading) ? nominal : nominal - radius * norm(loading' * result) + objective = minimum_variance ? risk : worst - risk_aversion * risk / 2 + all(isfinite, (risk, nominal, worst, objective)) || error("portfolio diagnostics exceed floating-point range") + return (weights = result, variance = risk, expected_return = nominal, + worst_case_expected_return = worst, objective = objective, solver_status = "optimal") +end + +""" + constrained_minimum_variance(covariance; expected_returns=nothing, + return_floor=nothing, caps=nothing) + +Minimize variance over fully invested nonnegative capped weights. An optional +return floor uses supplied expected returns. Covariance must be positive +semidefinite within the numerical eigensolver tolerance; it is not repaired. +JuMP and Clarabel solve the scaled quadratic objective. Solver status and +primal constraints are checked before returning weights and diagnostics. +""" +function constrained_minimum_variance(covariance; expected_returns = nothing, return_floor = nothing, caps = nothing) + covariance, expected, cap, floor = _portfolio_inputs(covariance, expected_returns, caps, return_floor) + return _solve_portfolio(covariance, expected, cap, floor; minimum_variance = true) +end + +""" + robust_mean_variance(expected_returns, covariance, uncertainty_loading; + radius, risk_aversion, caps=nothing, return_floor=nothing) + +Maximize the worst mean minus half `risk_aversion` times variance for means in +`expected_returns + uncertainty_loading * u`, with `norm(u) <= radius`. +Covariance is fixed and every uncertainty input is supplied by the caller. +The optional return floor constrains nominal return. Radius and risk aversion +may be zero. Returns weights, nominal and worst-case means, variance, the +maximized objective and verified solver status. +""" +function robust_mean_variance(expected_returns, covariance, uncertainty_loading; radius, risk_aversion, caps = nothing, return_floor = nothing) + covariance, expected, cap, floor = _portfolio_inputs(covariance, expected_returns, caps, return_floor) + loading = _portfolio_array(uncertainty_loading, "uncertainty_loading", 2) + size(loading, 1) == length(expected) || + throw(ArgumentError("uncertainty_loading must have one row per asset")) + radius = _portfolio_scalar(radius, "radius"; minimum = 0.0) + risk_aversion = _portfolio_scalar(risk_aversion, "risk_aversion"; minimum = 0.0) + return _solve_portfolio(covariance, expected, cap, floor; loading, radius, risk_aversion) +end diff --git a/src/Optimization/DecisionFocused.jl b/src/Optimization/DecisionFocused.jl new file mode 100644 index 0000000..10ffecd --- /dev/null +++ b/src/Optimization/DecisionFocused.jl @@ -0,0 +1,65 @@ +using Zygote + +""" + spo_plus_loss(predicted_cost, true_cost, vertices) + +Return SPO+ loss, a subgradient and one-based oracle vertex indices for +linear cost minimization over the convex hull of supplied vertex rows. +Complete enumeration is the exact oracle for this fixed finite hull. Ties +choose the first supplied vertex; vertices are never inferred or generated. +""" +function spo_plus_loss(predicted_cost, true_cost, vertices) + predicted = _portfolio_array(predicted_cost, "predicted_cost", 1) + truth = _portfolio_array(true_cost, "true_cost", 1) + choices = _portfolio_array(vertices, "vertices", 2) + length(predicted) == length(truth) == size(choices, 2) || throw(ArgumentError("cost and vertex dimensions must match")) + true_objectives = choices * truth + transformed_objectives = choices * (2 * predicted - truth) + all(isfinite, true_objectives) && all(isfinite, transformed_objectives) || throw(ArgumentError("oracle objectives exceed floating-point range")) + true_index, transformed_index = argmin(true_objectives), argmin(transformed_objectives) + true_decision, transformed = choices[true_index, :], choices[transformed_index, :] + loss = dot(truth - 2 * predicted, transformed) + dot(2 * predicted - truth, true_decision) + subgradient = 2 * (true_decision - transformed) + isfinite(loss) && all(isfinite, subgradient) || throw(ArgumentError("loss or subgradient exceeds floating-point range")) + return (; loss, subgradient, true_vertex = true_index, transformed_vertex = transformed_index) +end + +""" + spo_plus(predicted_cost, true_cost, vertices) + +Return differentiable SPO+ loss for a floating vector or a matrix with one +cost vector per row. All arrays share an element type. Zygote treats truth, +vertices and first-index oracle selections as constants; the gradient with +respect to predictions is the selected SPO+ subgradient. Batched losses are +returned without reduction. This is the native Julia counterpart of the +Python tensor adapter. +""" +function spo_plus(predicted_cost, true_cost, vertices) + truth, selected_true, selected_transformed = Zygote.ignore() do + all(x -> x isa AbstractArray && eltype(x) <: AbstractFloat && all(isfinite, x), (predicted_cost, true_cost, vertices)) || throw(ArgumentError("all inputs must be finite floating arrays")) + eltype(predicted_cost) == eltype(true_cost) == eltype(vertices) || throw(ArgumentError("inputs must share an element type")) + ndims(predicted_cost) in (1, 2) && size(predicted_cost) == size(true_cost) && !isempty(predicted_cost) && + ndims(vertices) == 2 && size(vertices, 1) > 0 && size(vertices, 2) == size(predicted_cost, ndims(predicted_cost)) || throw(ArgumentError("cost and vertex shapes are incompatible")) + truth = copy(true_cost) + predicted_rows = ndims(predicted_cost) == 1 ? permutedims(predicted_cost) : predicted_cost + truth_rows = ndims(truth) == 1 ? permutedims(truth) : truth + true_objectives = truth_rows * vertices' + transformed_objectives = (2 * predicted_rows - truth_rows) * vertices' + all(isfinite, true_objectives) && all(isfinite, transformed_objectives) || throw(ArgumentError("oracle objectives exceed floating-point range")) + first_indices = [argmin(row) for row in eachrow(true_objectives)] + transformed_indices = [argmin(row) for row in eachrow(transformed_objectives)] + selected_true = vertices[first_indices, :] + selected_transformed = vertices[transformed_indices, :] + all(isfinite, 2 * (selected_true - selected_transformed)) || throw(ArgumentError("subgradient exceeds floating-point range")) + if ndims(predicted_cost) == 1 + selected_true, selected_transformed = vec(selected_true), vec(selected_transformed) + end + return truth, selected_true, selected_transformed + end + terms = (truth - 2 * predicted_cost) .* selected_transformed + (2 * predicted_cost - truth) .* selected_true + loss = ndims(predicted_cost) == 1 ? sum(terms) : vec(sum(terms; dims = 2)) + Zygote.ignore() do + all(isfinite, loss isa Number ? (loss,) : loss) || throw(ArgumentError("loss exceeds floating-point range")) + end + return loss +end diff --git a/src/Optimization/Execution.jl b/src/Optimization/Execution.jl new file mode 100644 index 0000000..af6ba80 --- /dev/null +++ b/src/Optimization/Execution.jl @@ -0,0 +1,42 @@ +""" + almgren_chriss_execution(inventory, horizon, intervals; volatility, + temporary_impact, permanent_impact=0.0, + risk_aversion=0.0) + +Compute the discrete, single-asset, zero-drift Almgren--Chriss sell schedule +with constant linear impact and no spread. All quantities are finite and +nonnegative, with positive horizon, temporary impact and integer intervals. +The effective temporary impact `temporary_impact - permanent_impact*tau/2` +must be positive. Cost variance uses post-trade inventory, so liquidation +in one interval has zero modeled variance. Return times, inventories, trades, +expected cost and cost variance. Reject nonfinite derived costs. +""" +function almgren_chriss_execution(inventory, horizon, intervals; volatility, temporary_impact, permanent_impact = 0.0, risk_aversion = 0.0) + quantity = _portfolio_scalar(inventory, "inventory"; minimum = 0) + duration = _portfolio_scalar(horizon, "horizon"; minimum = 0) + sigma = _portfolio_scalar(volatility, "volatility"; minimum = 0) + eta = _portfolio_scalar(temporary_impact, "temporary_impact"; minimum = 0) + gamma = _portfolio_scalar(permanent_impact, "permanent_impact"; minimum = 0) + risk = _portfolio_scalar(risk_aversion, "risk_aversion"; minimum = 0) + intervals isa Integer && !(intervals isa Bool) && intervals > 0 || throw(ArgumentError("intervals must be a positive integer")) + duration > 0 && eta > 0 || throw(ArgumentError("horizon and temporary_impact must be positive")) + tau = duration / intervals + effective_eta = eta - gamma * tau / 2 + tau > 0 && isfinite(effective_eta) && effective_eta > 0 || throw(ArgumentError("time step and effective temporary impact must be positive")) + index = Float64.(0:intervals) + remaining = if quantity == 0 || risk == 0 || sigma == 0 + quantity .* (1 .- index / intervals) + else + log_z = log(tau) + log(sigma) + (log(risk) - log(effective_eta)) / 2 - log(2.0) + step = log_z < 350 ? 2 * asinh(exp(log_z)) : 2 * (log_z + log(2.0)) + step == 0 ? quantity .* (1 .- index / intervals) : + quantity .* exp.(-step .* index) .* (-expm1.(-2 * step .* (intervals .- index))) ./ (-expm1(-2 * step * intervals)) + end + remaining[1], remaining[end] = quantity, 0.0 + trades = -diff(remaining) + cost = gamma * quantity^2 / 2 + effective_eta / tau * dot(trades, trades) + variance = sigma^2 * tau * sum(abs2, remaining[2:end]) + isfinite(cost) && isfinite(variance) || throw(ArgumentError("execution costs exceed floating-point range")) + return (times = collect(range(0, duration; length = intervals + 1)), inventory = remaining, + trades, expected_cost = cost, cost_variance = variance) +end diff --git a/src/Optimization/HierarchicalRiskParity.jl b/src/Optimization/HierarchicalRiskParity.jl index 753dd27..b57b698 100644 --- a/src/Optimization/HierarchicalRiskParity.jl +++ b/src/Optimization/HierarchicalRiskParity.jl @@ -6,18 +6,16 @@ weights, cluster variance, quasi-diagonalisation, and recursive bisection. Representation note (deliberate divergence): pandas DataFrames become `Matrix`es and asset labels become 1-based integer indices. These deterministic building blocks match the Python implementation exactly (verified in `test/runtests.jl`). -The top-level `hrp(cov, corr)` wrapper uses single-linkage hierarchical -clustering (`Clustering.hclust`); the resulting leaf order is a **behavioural** -match to SciPy's (dendrogram leaf order is not bit-identical across -implementations), so `hrp` weights are validated structurally (sum to one, -positive). The deterministic building blocks below match Python exactly. +The top-level `hrp(cov, corr)` wrapper uses native single-linkage minimum-spanning +tree construction with stable edge ordering and ascending cluster identifiers. +These preserve the reference leaf order, including equal-distance ties that +can affect subsequent recursive allocation. `quasi_diagonal` accepts a SciPy-format linkage matrix. Reference: De Prado, M. (2018), Advances in Financial Machine Learning, Ch. 16. """ using LinearAlgebra: diag -using Clustering: hclust """ inverse_variance_weights(covariance_matrix) -> Vector{Float64} @@ -122,9 +120,9 @@ distance_corr(correlation_matrix::AbstractMatrix{<:Real}) = Hierarchical Risk Parity portfolio weights (in original asset order): correlation distance → single-linkage clustering → quasi-diagonal leaf order → recursive -bisection. Mirrors Python's `hrp`. Behavioural: the single-linkage leaf order -(`Clustering.hclust`) is not bit-identical to SciPy's, but the weights remain a -valid long-only allocation (positive, summing to one). +bisection. Mirrors Python's `hrp`. The single-linkage leaf order +uses the reference minimum-spanning-tree traversal, stable edge sorting and +ascending child identifiers, including ties. """ function hrp( covariance::AbstractMatrix{<:Real}, @@ -132,9 +130,52 @@ function hrp( ) distance = distance_corr(correlation) distance = (distance .+ distance') ./ 2 # enforce exact symmetry for hclust - order = hclust(distance; linkage = :single).order + distance[diagind(distance)] .= 0.0 + order = _single_linkage_order(distance) bisection = recursive_bisection(covariance, order) weights = zeros(Float64, length(order)) weights[order] = bisection return weights end + +function _single_linkage_order(distance::AbstractMatrix{<:Real}) + n = size(distance, 1) + size(distance, 2) == n && all(isfinite, distance) && all(>=(0), distance) || throw(ArgumentError("distance must be a finite nonnegative square matrix")) + n <= 1 && return collect(1:n) + visited = falses(n) + nearest = fill(Inf, n) + edge_start, edge_end, heights = Int[], Int[], Float64[] + current = 1 + for step in 1:(n-1) + visited[current] = true + candidate, height = 0, Inf + for item in 1:n + visited[item] && continue + nearest[item] = min(nearest[item], distance[current, item]) + if nearest[item] < height + candidate, height = item, nearest[item] + end + end + push!(edge_start, current) + push!(edge_end, candidate) + push!(heights, height) + current = candidate + end + parents = collect(1:(2n-1)) + counts = ones(Int, 2n-1) + linkage = zeros(n-1, 4) + for (step, edge) in enumerate(sortperm(heights; alg = Base.Sort.MergeSort)) + left, right = edge_start[edge], edge_end[edge] + while parents[left] != left + left = parents[left] + end + while parents[right] != right + right = parents[right] + end + combined = n + step + counts[combined] = counts[left] + counts[right] + linkage[step, :] = [min(left, right)-1, max(left, right)-1, heights[edge], counts[combined]] + parents[left] = parents[right] = combined + end + return quasi_diagonal(linkage) +end diff --git a/src/Optimization/Majorization.jl b/src/Optimization/Majorization.jl new file mode 100644 index 0000000..90dd4f3 --- /dev/null +++ b/src/Optimization/Majorization.jl @@ -0,0 +1,132 @@ +function _majorization_tolerance(atol) + value = _portfolio_scalar(atol, "atol") + 0 < value < 1 || throw(ArgumentError("atol must lie in (0, 1)")) + return value +end + +function _majorization_array(values) + values isa AbstractArray && eltype(values) <: Real || throw(ArgumentError("inputs must contain finite real numbers")) + result = Float64.(values) + all(isfinite, result) || throw(ArgumentError("inputs must be finite")) + return result +end + +function _majorization_vectors(target, source) + left, right = _majorization_array(target), _majorization_array(source) + ndims(left) == 1 && size(left) == size(right) && !isempty(left) || throw(ArgumentError("target and source must be nonempty vectors of equal length")) + return left, right +end + +"""Check finite nonnegative entries and unit row/column sums within `atol`, without normalization.""" +function is_doubly_stochastic(matrix; atol = 1e-10) + tolerance = _majorization_tolerance(atol) + values = try + _majorization_array(matrix) + catch exception + exception isa ArgumentError || rethrow() + return false + end + ndims(values) == 2 && size(values, 1) > 0 && size(values, 1) == size(values, 2) || return false + return all(values .>= -tolerance) && all(abs.(sum(values; dims = 1) .- 1) .<= tolerance) && all(abs.(sum(values; dims = 2) .- 1) .<= tolerance) +end + +"""Test equal-total finite vector majorization using descending partial sums and absolute tolerance.""" +function is_majorized(target, source; atol = 1e-10) + tolerance = _majorization_tolerance(atol) + left, right = _majorization_vectors(target, source) + left_sums = cumsum(BigFloat.(sort(left; rev = true))) + right_sums = cumsum(BigFloat.(sort(right; rev = true))) + return abs(left_sums[end] - right_sums[end]) <= tolerance && all(left_sums[1:end-1] .<= right_sums[1:end-1] .+ tolerance) +end + +""" + majorization_matrix(target, source; atol=1e-10) + +Construct a checked doubly stochastic witness `D * source == target` with a +linear feasibility problem. The witness is generally nonunique. Row sums, +column sums, nonnegativity and the unscaled mapping residual must satisfy +the requested absolute tolerance. Inputs are not normalized or modified. +""" +function majorization_matrix(target, source; atol = 1e-10) + tolerance = _majorization_tolerance(atol) + left, right = _majorization_vectors(target, source) + is_majorized(left, right; atol = tolerance) || throw(ArgumentError("target is not majorized by source")) + n = length(left) + all(abs.(left - right) .<= tolerance) && return Matrix{Float64}(I, n, n) + scale = max(1.0, maximum(abs, left), maximum(abs, right)) + model = Model(Clarabel.Optimizer) + set_silent(model) + for name in ("tol_gap_abs", "tol_gap_rel", "tol_feas") + set_optimizer_attribute(model, name, min(1e-12, tolerance / (10 * scale))) + end + @variable(model, 0 <= witness[1:n, 1:n] <= 1) + @constraint(model, [i = 1:n], sum(witness[i, :]) == 1) + @constraint(model, [j = 1:n], sum(witness[:, j]) == 1) + @constraint(model, [i = 1:n], sum(witness[i, j] * (right[j] / scale) for j in 1:n) == left[i] / scale) + @objective(model, Min, 0) + optimize!(model) + termination_status(model) == JuMP.MOI.OPTIMAL && has_values(model) || error("No verified majorization witness: $(termination_status(model))") + result = value.(witness) + is_doubly_stochastic(result; atol = tolerance) && all(abs.(result * right - left) .<= tolerance) || error("Majorization witness exceeds the requested residual tolerance") + return result +end + +function _positive_support_matching(residual) + n = size(residual, 1) + column_rows = zeros(Int, n) + function augment(row, seen) + for column in 1:n + if residual[row, column] > 0 && !seen[column] + seen[column] = true + if column_rows[column] == 0 || augment(column_rows[column], seen) + column_rows[column] = row + return true + end + end + end + return false + end + for row in 1:n + augment(row, falses(n)) || error("Residual support has no complete positive matching") + end + permutation = zeros(Int, n) + for column in 1:n + permutation[column_rows[column]] = column + end + return permutation +end + +""" + birkhoff_von_neumann_decomposition(matrix; atol=1e-10) + +Return positive weights and a matrix of one-based permutations. Row `k` +selects column `permutations[k,i]` in matrix row `i`. Repeated complete +matchings on positive residual support construct a nonunique decomposition; +the weighted permutation sum and total mass are checked against the original +input. Tiny negative entries within tolerance are treated as roundoff. +""" +function birkhoff_von_neumann_decomposition(matrix; atol = 1e-10) + tolerance = _majorization_tolerance(atol) + is_doubly_stochastic(matrix; atol = tolerance) || throw(ArgumentError("matrix must be doubly stochastic")) + original = _majorization_array(matrix) + n = size(original, 1) + residual = max.(original, 0) + reconstruction = zeros(n, n) + weights, permutations = Float64[], Vector{Int}[] + mass = 0.0 + for _ in 1:length(original) + abs(mass - 1) <= tolerance && maximum(abs, residual) <= tolerance && break + permutation = _positive_support_matching(residual) + weight = minimum(residual[i, permutation[i]] for i in 1:n) + weight > 0 || error("Decomposition did not make positive progress") + push!(weights, weight) + push!(permutations, permutation) + for i in 1:n + residual[i, permutation[i]] -= weight + reconstruction[i, permutation[i]] += weight + end + mass += weight + end + abs(mass - 1) <= tolerance && maximum(abs, reconstruction - original) <= tolerance || error("Decomposition exceeds the requested residual tolerance") + return weights, reduce(vcat, permutedims.(permutations)) +end diff --git a/src/Optimization/NestedClusteredOptimization.jl b/src/Optimization/NestedClusteredOptimization.jl index 03272a7..d76f127 100644 --- a/src/Optimization/NestedClusteredOptimization.jl +++ b/src/Optimization/NestedClusteredOptimization.jl @@ -46,30 +46,40 @@ optimal weights within each cluster, compute optimal weights between clusters (each cluster treated as one asset), then recombine. `number_clusters` defaults to `N ÷ 2`. Behavioural (built on stochastic k-means). Mirrors Python's `get_optimal_portfolio_weights_nco`. +Optional `clusters` supplies a partition of one-based asset indices, allowing +the allocation arithmetic to be replayed independently of the k-means supplier. """ function get_optimal_portfolio_weights_nco( covariance::AbstractMatrix{<:Real}; - mu::Union{Nothing,AbstractVector{<:Real}} = nothing, + mu::Union{Nothing,AbstractVecOrMat{<:Real}} = nothing, number_clusters::Union{Nothing,Integer} = nothing, + clusters = nothing, ) n = size(covariance, 2) correlation = covariance_to_correlation(covariance) nc = number_clusters === nothing ? max(n ÷ 2, 2) : number_clusters - _, clusters, _ = cluster_k_means_base(correlation; max_clusters = nc, iterations = 10) - labels = collect(keys(clusters)) + partition = if clusters === nothing + cluster_k_means_base(correlation; max_clusters = nc, iterations = 10)[2] + else + Dict(key => Int.(collect(items)) for (key, items) in pairs(clusters)) + end + !isempty(partition) && all(!isempty, values(partition)) && sort(reduce(vcat, values(partition))) == collect(1:n) || throw(ArgumentError("clusters must partition every asset index exactly once")) + expected_mean = mu === nothing ? nothing : vec(mu) + expected_mean === nothing || length(expected_mean) == n || throw(ArgumentError("mu must have one value per asset")) + labels = collect(keys(partition)) weights_intra = zeros(Float64, n, length(labels)) for (column, label) in enumerate(labels) - assets = clusters[label] + assets = partition[label] cov_intra = covariance[assets, assets] - mu_intra = mu === nothing ? nothing : mu[assets] + mu_intra = expected_mean === nothing ? nothing : expected_mean[assets] weights_intra[assets, column] = get_optimal_portfolio_weights(cov_intra; mu = mu_intra) end covariance_inter = weights_intra' * covariance * weights_intra - mu_inter = mu === nothing ? nothing : weights_intra' * mu + mu_inter = expected_mean === nothing ? nothing : weights_intra' * expected_mean weights_inter = get_optimal_portfolio_weights(covariance_inter; mu = mu_inter) return weights_intra * weights_inter diff --git a/src/Optimization/Optimization.jl b/src/Optimization/Optimization.jl index 21b764c..be1fa88 100644 --- a/src/Optimization/Optimization.jl +++ b/src/Optimization/Optimization.jl @@ -7,7 +7,8 @@ sub-package. Wires **Hierarchical Risk Parity** (building blocks + the `hrp` wrapper), **PCA hedging** weights, and **Nested Clustered Optimisation** (`nco`, built on the `Cluster` k-means backend). The sklearn-based hyper-parameter tuning awaits the -ML-backend decision. +ML-backend decision. Constrained quadratic and ellipsoid-robust portfolios use +JuMP and Clarabel with caller-supplied covariance, means and constraints. """ module Optimization @@ -20,6 +21,12 @@ include("Hedging.jl") # Nested Clustered Optimisation, built on the Cluster k-means backend (AFML Ch. 16). include("NestedClusteredOptimization.jl") +include("Constrained.jl") +include("Clearing.jl") +include("Execution.jl") +include("Majorization.jl") +include("DecisionFocused.jl") + export # hierarchical risk parity inverse_variance_weights, @@ -32,6 +39,16 @@ export pca_weights, # nested clustered optimisation get_optimal_portfolio_weights, - get_optimal_portfolio_weights_nco + get_optimal_portfolio_weights_nco, + constrained_minimum_variance, + robust_mean_variance, + eisenberg_noe_clearing, + almgren_chriss_execution, + is_doubly_stochastic, + is_majorized, + majorization_matrix, + birkhoff_von_neumann_decomposition, + spo_plus_loss, + spo_plus end # module Optimization diff --git a/src/ParallelWork.jl b/src/ParallelWork.jl new file mode 100644 index 0000000..c650655 --- /dev/null +++ b/src/ParallelWork.jl @@ -0,0 +1,122 @@ +module ParallelWork + +using DataFrames + +"""Zero-based boundary counts for contiguous groups with at most `num_threads` parts.""" +function linear_partitions(num_atoms::Integer, num_threads::Integer) + num_atoms >= 0 || throw(ArgumentError("num_atoms must be nonnegative")) + num_threads > 0 || throw(ArgumentError("num_threads must be positive")) + parts = min(num_atoms, num_threads) + return parts == 0 ? [0] : [ceil(Int, index*num_atoms/parts) for index in 0:parts] +end + +"""Triangular-work boundaries, optionally reversing group sizes for an upper triangle.""" +function nested_partitions(num_atoms::Integer, num_threads::Integer; upper_triangle::Bool = false) + num_atoms >= 0 || throw(ArgumentError("num_atoms must be nonnegative")) + num_threads > 0 || throw(ArgumentError("num_threads must be positive")) + parts = min(num_atoms, num_threads) + points = [0.] + for _ in 1:parts + previous = last(points) + push!(points, (sqrt(1+4*(previous^2+previous+num_atoms*(num_atoms+1.)/parts))-1)/2) + end + boundaries = round.(Int, points) + return upper_triangle ? vcat(0, cumsum(reverse(diff(boundaries)))) : boundaries +end + +function _workers(count) + count == -1 && return Threads.nthreads() + count > 0 || throw(ArgumentError("worker count must be positive or -1")) + return min(Int(count), Threads.nthreads()) +end + +function _work_results(func, items; workers, ordered::Bool) + values = collect(items) + isempty(values) && return Any[] + workers == 1 && return map(func, values) + next_index = Threads.Atomic{Int}(1) + completed = Channel{Tuple{Int,Any}}(length(values)) + @sync for _ in 1:min(workers, length(values)) + Threads.@spawn begin + while true + index = Threads.atomic_add!(next_index, 1) + index > length(values) && break + result = func(values[index]) + put!(completed, (index, result)) + end + end + end + results = [take!(completed) for _ in eachindex(values)] + ordered && sort!(results; by = first) + return last.(results) +end + +""" +Run independent calls with bounded Julia threads and return submitted order. +With `lin_partition=true`, the callback receives one-based position groups, +including empty groups when requested; returned groups are flattened. Threads +share memory, so callbacks must avoid conflicting writes and shared mutable RNGs. +""" +function parallel_run(func, iterable; num_cpus::Integer = -1, + lin_partition::Bool = false, kwargs...) + workers = _workers(num_cpus) + values = collect(iterable) + if !lin_partition + return _work_results(value -> func(value; kwargs...), values; workers = workers, ordered = true) + end + parts = num_cpus == -1 ? Threads.nthreads() : Int(num_cpus) + quotient, remainder = divrem(length(values), parts) + sizes = [quotient+(index <= remainder) for index in 1:parts] + boundaries = vcat(0, cumsum(sizes)) + groups = [collect((boundaries[index]+1):boundaries[index+1]) for index in 1:parts] + results = _work_results(group -> func(group; kwargs...), groups; workers = workers, ordered = true) + return reduce(vcat, results; init = Any[]) +end + +function _expand_job(job) + options = (; (Symbol(key) => value for (key, value) in pairs(job) if Symbol(key) != :func)...) + function_value = job isa NamedTuple ? job.func : haskey(job, :func) ? job[:func] : job["func"] + return function_value(; options...) +end + +"""Evaluate native function/keyword jobs in input order without mutating the jobs.""" +process_jobs_sequential(jobs) = map(_expand_job, jobs) + +""" +Evaluate jobs with bounded threads. The default collects completion order for +parallel execution; `ordered=true` returns input order. Sequential execution +always follows input order. Failures propagate instead of certifying partial work. +""" +function process_jobs(jobs; num_threads::Integer = -1, ordered::Bool = false) + return _work_results(_expand_job, jobs; workers = _workers(num_threads), ordered = ordered) +end + +""" +Partition caller indices, invoke `func(; molecule=group, ...)`, and combine +DataFrame results sorted by the explicit `index_column`. Non-table results stay +grouped in input order. This native interface replaces implicit pandas indices. +""" +function parallel_indexed(func, indices; argument::Symbol = :molecule, + num_threads::Integer = -1, mp_batches::Integer = 1, + linear_partition::Bool = true, index_column::Symbol = :index, kwargs...) + workers = _workers(num_threads) + mp_batches > 0 || throw(ArgumentError("mp_batches must be positive")) + values = collect(indices) + parts = (num_threads == -1 ? Threads.nthreads() : Int(num_threads))*mp_batches + boundaries = linear_partition ? linear_partitions(length(values), parts) : nested_partitions(length(values), parts) + groups = [values[(boundaries[index]+1):boundaries[index+1]] for index in 1:length(boundaries)-1] + call = group -> func(; merge((; kwargs...), NamedTuple{(argument,)}((group,)))...) + results = _work_results(call, groups; workers = workers, ordered = true) + isempty(results) && return DataFrame(index_column => Any[]) + if all(result -> result isa AbstractDataFrame, results) + combined = vcat(results...; cols = :setequal) + index_column in propertynames(combined) || throw(ArgumentError("table result is missing the explicit index column")) + return sort!(combined, index_column) + end + return results +end + +export linear_partitions, nested_partitions, parallel_run, process_jobs, + process_jobs_sequential, parallel_indexed + +end diff --git a/src/Pde/DeepBSDESolver.jl b/src/Pde/DeepBSDESolver.jl index d2e71ab..f4313fd 100644 --- a/src/Pde/DeepBSDESolver.jl +++ b/src/Pde/DeepBSDESolver.jl @@ -1,18 +1,11 @@ """ -Deep BSDE solver — native Julia port mirroring the Python -`RiskLabAI.pde.solver.FBSDESolver` (`"DeepBSDE"` method) on the **Lux.jl** -backend (Han, Jentzen & E, 2018). A separate feed-forward subnetwork per time -step predicts the BSDE control `Z_t`; the initial value `Y_0` and gradient `Z_0` -are trainable scalars/vectors. Training minimises the terminal-condition MSE by -Adam (`Optimisers.jl`) with reverse-mode gradients (`Zygote.jl`). - -Parity note: this is a **behavioural** port — a trained network is not -numerically identical to the PyTorch one. It is validated structurally (the -training loss is finite and decreases). The per-step subnetwork is the plain MLP -of the Python `DeepBSDE` module (ReLU between bias-free linear layers; the -commented-out batch-norm is omitted). The PyTorch set-transformer architectures -(`ISAB`/`MAB`/`SAB`/`PMA`/`DeepTimeSetTransformer`) are research scaffolding and -are not ported. +Deep BSDE convenience interface. The public wrapper delegates to the native +`FBSDESolver` and `solve_pde` contract, retaining the public terminal MSE, +bias-free ReLU networks and separate Adam schedules for network weights, +initial value and initial gradient. Shared weights and supplied paths validate +the deterministic objective, gradients and finite-step updates. Julia and +Python random seeds do not imply the same initial weights or Brownian paths. +The earlier internal Lux helpers remain available for compatibility. Reference: Han, J., Jentzen, A., E, W. (2018), PNAS. """ @@ -22,9 +15,6 @@ using Lux using Zygote using Optimisers -import RiskLabAI.Pde: Equation, pde_driver, pde_hamiltonian, pde_sample, - pde_terminal, solve_deep_bsde - _act(x) = max(x, zero(x)) # Per-time-step MLP: ReLU after each hidden bias-free linear layer, raw output. @@ -65,8 +55,10 @@ end -> (losses, y0_estimates) Train the Deep BSDE solver for equation `eq` (any `Pde.Equation`). Returns the -per-iteration validation loss and the running `Y_0` estimate. Behavioural. -Mirrors Python's `FBSDESolver(..., "DeepBSDE").solve`. +per-iteration validation loss and running `Y_0` estimate. The network learning +rate is supplied; initial value and gradient rates remain 0.1 and 0.001, with +public Adam betas `(0.9,0.99)`. Use `FBSDESolver` and `solve_pde` directly for +explicit parameter trees and supplied training/validation batches. """ function solve_deep_bsde( eq::Equation; @@ -79,31 +71,6 @@ function solve_deep_bsde( ) dim = eq.dim sizes = vcat(dim, collect(hidden_sizes), dim) - n_nets = eq.num_time_interval - 1 - - models = [_build_subnet(sizes) for _ = 1:n_nets] - net_params = [] - net_states = [] - for model in models - ps, st = Lux.setup(rng, model) - push!(net_params, Lux.f64(ps)) - push!(net_states, Lux.f64(st)) - end - states = Tuple(net_states) - - θ = (y0 = [Float64(init_y)], z0 = zeros(1, dim), nets = Tuple(net_params)) - - dw_val, x_val = pde_sample(eq, 128; rng = rng) - opt_state = Optimisers.setup(Optimisers.Adam(learning_rate), θ) - - losses = Float64[] - y0_estimates = Float64[] - for _ = 1:iterations - dw_train, x_train = pde_sample(eq, batch_size; rng = rng) - gradient = Zygote.gradient(p -> _bsde_loss(p, eq, x_train, dw_train, models, states), θ)[1] - opt_state, θ = Optimisers.update(opt_state, θ, gradient) - push!(losses, _bsde_loss(θ, eq, x_val, dw_val, models, states)) - push!(y0_estimates, θ.y0[1]) - end - return losses, y0_estimates + model = FBSDESolver(eq, sizes, learning_rate, "DeepBSDE"; seed = rand(rng, UInt32)) + return solve_pde(model; iterations, batch_size, init_y, rng) end diff --git a/src/Pde/EquationContracts.jl b/src/Pde/EquationContracts.jl new file mode 100644 index 0000000..0769204 --- /dev/null +++ b/src/Pde/EquationContracts.jl @@ -0,0 +1,20 @@ +""" + pde_terminal_for_sample(eq, states) + +Evaluate the released multi-sample terminal helper on a batch-by-sample-by- +dimension Float64 array, retaining the final singleton axis. The public +`PricingDiffRate` helper returns only the strike-120 call, whereas +`pde_terminal` returns the strike-120/150 call spread. These are distinct +released contracts and must not be interchanged. +""" +function pde_terminal_for_sample(eq::Equation, states) + Zygote.ignore() do + states isa AbstractArray{Float64, 3} && !isempty(states) && size(states, 3) == eq.dim && all(isfinite, states) || throw(ArgumentError("Sample states must be finite nonempty batch-by-sample-by-dimension Float64 arrays")) + end + return _pde_sample_terminal(eq, states) +end + +_pde_sample_terminal(eq::HJBLQ, states) = log.(0.5 .* (1 .+ sum(states.^2; dims = 3))) +_pde_sample_terminal(eq::BlackScholesBarenblatt, states) = sum(states.^2; dims = 3) +_pde_sample_terminal(eq::PricingDefaultRisk, states) = _relu(minimum(states; dims = 3)) +_pde_sample_terminal(eq::PricingDiffRate, states) = _relu(maximum(states; dims = 3) .- 120) diff --git a/src/Pde/Equations.jl b/src/Pde/Equations.jl index 9da4806..bad759c 100644 --- a/src/Pde/Equations.jl +++ b/src/Pde/Equations.jl @@ -5,10 +5,11 @@ Hamiltonian `H(z)`, terminal payoff) for four financial PDEs solved by the Deep BSDE method (Han, Jentzen & E, 2018). The neural Deep-BSDE solver itself is wired separately (it needs a deep-learning backend). -Parity notes: the generator functions (`pde_driver`, `pde_hamiltonian`, -`pde_terminal`, `pde_sigma`) are **deterministic** and match Python exactly -(verified in `test/runtests.jl`). `pde_sample` is **stochastic** (Euler–Maruyama -forward simulation) and validated structurally. +The generator functions (`pde_driver`, `pde_hamiltonian`, `pde_terminal`, +`pde_sigma`) retain the public deterministic formulas. `pde_sample` accepts +supplied primitive normal draws for cross-language path comparisons. The +differential-rate equation uses the public exact geometric step; the other +equations retain their public additive or Euler steps. Representation note (deliberate divergence): torch tensors become `Matrix`es with shape `batch × dim`; `pde_sample` returns dense `num_sample × dim × steps` arrays. @@ -21,6 +22,13 @@ using Random: default_rng abstract type Equation end +function _pde_equation_configuration(dim, total_time, num_time_interval) + dim isa Integer && !(dim isa Bool) && dim > 0 || throw(ArgumentError("Equation dimension must be a positive integer")) + num_time_interval isa Integer && !(num_time_interval isa Bool) && num_time_interval > 0 || throw(ArgumentError("The number of time intervals must be a positive integer")) + total_time isa Real && !(total_time isa Bool) && isfinite(total_time) && total_time >= 0 || throw(ArgumentError("The time horizon must be finite and nonnegative")) + return Float64(total_time) / num_time_interval +end + _relu(v) = max.(v, 0.0) # Common config accessors. @@ -44,7 +52,7 @@ struct HJBLQ <: Equation end function HJBLQ(dim::Integer, total_time::Real, num_time_interval::Integer) - dt = total_time / num_time_interval + dt = _pde_equation_configuration(dim, total_time, num_time_interval) return HJBLQ(dim, total_time, num_time_interval, dt, sqrt(dt), zeros(dim), sqrt(2.0), 1.0) end @@ -71,7 +79,7 @@ struct BlackScholesBarenblatt <: Equation end function BlackScholesBarenblatt(dim::Integer, total_time::Real, num_time_interval::Integer) - dt = total_time / num_time_interval + dt = _pde_equation_configuration(dim, total_time, num_time_interval) x_init = [1.0 / (1.0 + (i - 1) % 2) for i = 1:dim] return BlackScholesBarenblatt( dim, total_time, num_time_interval, dt, sqrt(dt), x_init, 0.4, 0.05, 0.0, @@ -108,7 +116,7 @@ struct PricingDefaultRisk <: Equation end function PricingDefaultRisk(dim::Integer, total_time::Real, num_time_interval::Integer) - dt = total_time / num_time_interval + dt = _pde_equation_configuration(dim, total_time, num_time_interval) gammah, gammal, vh, vl = 0.2, 0.02, 50.0, 70.0 return PricingDefaultRisk( dim, total_time, num_time_interval, dt, sqrt(dt), ones(dim) .* 100.0, @@ -144,7 +152,7 @@ struct PricingDiffRate <: Equation end function PricingDiffRate(dim::Integer, total_time::Real, num_time_interval::Integer) - dt = total_time / num_time_interval + dt = _pde_equation_configuration(dim, total_time, num_time_interval) return PricingDiffRate( dim, total_time, num_time_interval, dt, sqrt(dt), ones(dim) .* 100.0, 0.2, 0.06, 0.04, 0.06, 1.0 / dim, @@ -171,15 +179,20 @@ function pde_terminal(eq::PricingDiffRate, t, x) end """ - pde_sample(eq, num_sample; rng=default_rng()) -> (dw, x) + pde_sample(eq, num_sample; rng=default_rng(), normal_draws=nothing) -> (dw, x) -Euler–Maruyama simulation of the forward SDE: returns Wiener increments +Simulation of the public forward SDE discretization: returns Wiener increments `dw` (`num_sample × dim × num_time_interval`) and the asset paths `x` -(`num_sample × dim × num_time_interval+1`). Stochastic. Mirrors Python's -`Equation.sample`. +(`num_sample × dim × num_time_interval+1`). Supplied finite Float64 primitive +normal draws have the same shape as `dw` and bypass random sampling. """ -function pde_sample(eq::Equation, num_sample::Integer; rng = default_rng()) - dw = randn(rng, num_sample, eq.dim, eq.num_time_interval) .* eq.sqrt_delta_t +function pde_sample(eq::Equation, num_sample::Integer; rng = default_rng(), normal_draws = nothing) + num_sample > 0 && !(num_sample isa Bool) || throw(ArgumentError("The sample count must be a positive integer")) + _pde_equation_configuration(eq.dim, eq.total_time, eq.num_time_interval) + shape = (num_sample, eq.dim, eq.num_time_interval) + draws = isnothing(normal_draws) ? randn(rng, shape) : normal_draws + draws isa AbstractArray{Float64, 3} && size(draws) == shape && all(isfinite, draws) || throw(ArgumentError("Primitive normal draws must be finite Float64 arrays matching the requested sample shape")) + dw = draws .* eq.sqrt_delta_t x = zeros(num_sample, eq.dim, eq.num_time_interval + 1) x[:, :, 1] .= reshape(eq.x_init, 1, eq.dim) for i = 1:eq.num_time_interval diff --git a/src/Pde/NetworkPrimitives.jl b/src/Pde/NetworkPrimitives.jl new file mode 100644 index 0000000..7b186b7 --- /dev/null +++ b/src/Pde/NetworkPrimitives.jl @@ -0,0 +1,159 @@ +abstract type AbstractPDENetwork end + +function _pde_dense_parameters(rng, inputs, outputs; bias = true) + bound = Float32(1 / sqrt(inputs)) + weight = Float64.((2f0 .* rand(rng, Float32, outputs, inputs) .- 1f0) .* bound) + return bias ? (weight = weight, bias = Float64.((2f0 .* rand(rng, Float32, outputs) .- 1f0) .* bound)) : (weight = weight,) +end + +function _pde_parameter_copy(supplied, template) + isnothing(supplied) && return template + if template isa NamedTuple + supplied isa NamedTuple && keys(supplied) == keys(template) || throw(ArgumentError("Network parameters must match the named architecture")) + return NamedTuple{keys(template)}(Tuple(_pde_parameter_copy(getproperty(supplied, name), getproperty(template, name)) for name in keys(template))) + elseif template isa Tuple + supplied isa Tuple && length(supplied) == length(template) || throw(ArgumentError("Network layer counts must match the architecture")) + return map(_pde_parameter_copy, supplied, template) + end + supplied isa AbstractArray{Float64} && size(supplied) == size(template) && all(isfinite, supplied) || throw(ArgumentError("Network parameters must be finite Float64 arrays with the expected shapes")) + return copy(supplied) +end + +function _pde_check_array(value, dimensions, features) + value isa AbstractArray{Float64} && ndims(value) == dimensions && !isempty(value) && size(value, dimensions) == features && all(isfinite, value) || throw(ArgumentError("Inputs must be finite nonempty Float64 arrays with $dimensions dimensions and $features features")) + return nothing +end + +function _pde_dense(parameters, inputs::AbstractMatrix) + result = inputs * parameters.weight' + return haskey(parameters, :bias) ? result .+ parameters.bias' : result +end + +function _pde_dense(parameters, inputs::AbstractArray{<:Real, 3}) + flattened = reshape(permutedims(inputs, (3, 1, 2)), size(inputs, 3), :) + projected = parameters.weight * flattened + projected = haskey(parameters, :bias) ? projected .+ parameters.bias : projected + return permutedims(reshape(projected, size(projected, 1), size(inputs, 1), size(inputs, 2)), (2, 3, 1)) +end + +_pde_relu(value) = value > 0 ? value : zero(value) + +function _pde_layer_sizes(sizes) + sizes isa Union{Tuple, AbstractVector} && length(sizes) >= 2 || throw(ArgumentError("Supply at least input and output layer sizes")) + return Tuple(_nse_count(value, "layer size") for value in sizes) +end + +function _pde_feedforward_parameters(rng, sizes; bias = true) + return (layers = Tuple(_pde_dense_parameters(rng, sizes[index], sizes[index+1]; bias) for index in 1:length(sizes)-1),) +end + +"""Public linear-only PDE component; the unused Python batch-normalization module does not enter the native computation.""" +mutable struct Net1 <: AbstractPDENetwork + input_dim::Int + output_dim::Int + parameters::NamedTuple + function Net1(input_dim, output_dim; seed = 0, parameters = nothing) + inputs, outputs = _nse_count(input_dim, "input_dim"), _nse_count(output_dim, "output_dim") + template = (layer = _pde_dense_parameters(_nse_rng(seed), inputs, outputs),) + new(inputs, outputs, _pde_parameter_copy(parameters, template)) + end +end + +function (model::Net1)(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 2, model.input_dim) + end + return _pde_dense(model.parameters.layer, inputs) +end + +"""Six-layer public time network with widths 4,100,150,200,300,200,output_dim and tanh after every layer.""" +mutable struct TimeNet <: AbstractPDENetwork + output_dim::Int + parameters::NamedTuple + function TimeNet(output_dim; seed = 0, parameters = nothing) + outputs = _nse_count(output_dim, "output_dim") + template = _pde_feedforward_parameters(_nse_rng(seed), (4, 100, 150, 200, 300, 200, outputs)) + new(outputs, _pde_parameter_copy(parameters, template)) + end +end + +function (model::TimeNet)(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 2, 4) + end + return _pde_time_forward(model.parameters, inputs) +end + +function _pde_time_forward(parameters, inputs) + output = inputs + for layer in parameters.layers + output = tanh.(_pde_dense(layer, output)) + end + return output +end + +"""Feed-forward FBSNN with sine after every hidden dense layer and a linear final layer.""" +mutable struct FBSNNNetwork <: AbstractPDENetwork + layer_sizes::Tuple + parameters::NamedTuple + function FBSNNNetwork(layer_sizes; seed = 0, parameters = nothing) + sizes = _pde_layer_sizes(layer_sizes) + new(sizes, _pde_parameter_copy(parameters, _pde_feedforward_parameters(_nse_rng(seed), sizes))) + end +end + +"""Public bias-free Deep BSDE network with ReLU hidden layers and linear output; inactive batch normalization is omitted.""" +mutable struct DeepBSDE <: AbstractPDENetwork + layer_sizes::Tuple + parameters::NamedTuple + function DeepBSDE(layer_sizes; seed = 0, parameters = nothing) + sizes = _pde_layer_sizes(layer_sizes) + new(sizes, _pde_parameter_copy(parameters, _pde_feedforward_parameters(_nse_rng(seed), sizes; bias = false))) + end +end + +function _pde_hidden_forward(parameters, inputs, activation) + count = length(parameters.layers) + output = inputs + for (index, layer) in enumerate(parameters.layers) + output = _pde_dense(layer, output) + output = index < count ? activation.(output) : output + end + return output +end + +function (model::Union{FBSNNNetwork, DeepBSDE})(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 2, first(model.layer_sizes)) + end + return _pde_hidden_forward(model.parameters, inputs, model isa FBSNNNetwork ? sin : _pde_relu) +end + +function _pde_time_set_parameters(rng, inputs, outputs) + return (feature = _pde_dense_parameters(rng, inputs, outputs), time = _pde_feedforward_parameters(rng, (1, 10, 10, 10, outputs))) +end + +function _pde_time_set_forward(parameters, time, inputs) + time_features = _pde_hidden_forward(parameters.time, time, _pde_relu) + return exp.(time_features) .* _pde_dense(parameters.feature, inputs) +end + +"""Public time-conditioned feature map with a 1,10,10,10,output_dim ReLU time network and exponential multiplicative modulation.""" +mutable struct TimeNetForSet <: AbstractPDENetwork + input_dim::Int + output_dim::Int + parameters::NamedTuple + function TimeNetForSet(input_dim = 1, output_dim = 64; seed = 0, parameters = nothing) + inputs, outputs = _nse_count(input_dim, "input_dim"), _nse_count(output_dim, "output_dim") + new(inputs, outputs, _pde_parameter_copy(parameters, _pde_time_set_parameters(_nse_rng(seed), inputs, outputs))) + end +end + +function (model::TimeNetForSet)(time, inputs) + Zygote.ignore() do + _pde_check_array(time, 2, 1) + _pde_check_array(inputs, 2, model.input_dim) + size(time, 1) == size(inputs, 1) || throw(ArgumentError("Time and feature batch sizes must agree")) + end + return _pde_time_set_forward(model.parameters, time, inputs) +end diff --git a/src/Pde/NeuralPDESolvers.jl b/src/Pde/NeuralPDESolvers.jl new file mode 100644 index 0000000..f15681e --- /dev/null +++ b/src/Pde/NeuralPDESolvers.jl @@ -0,0 +1,181 @@ +""" + FBSDESolver(equation, layer_sizes, learning_rate, method; seed=0, parameters=nothing) + +Native public backward-equation solver. `DeepBSDE` takes full input/hidden/ +output widths; `DTNN` and `Monte-Carlo` take hidden widths. Network Adam uses +betas `(0.9,0.99)`. The unused first Python DeepBSDE network is omitted. +The set-transformer route retains the stated one-dimensional equation scope +and inserts its required singleton set axis. Monte Carlo controls require +supplied gradient arrays; no estimator is inferred. Parameters use Float64. +""" +mutable struct FBSDESolver + pde::Equation + layer_sizes::Tuple + learning_rate::Float64 + method::String + solver::Union{AbstractPDENetwork, Tuple} + parameters::Union{NamedTuple, Tuple} + optimizer_state::Any + function FBSDESolver(equation::Equation, layer_sizes, learning_rate, method; seed = 0, parameters = nothing) + _pde_equation_configuration(equation.dim, equation.total_time, equation.num_time_interval) + rate = _nse_real(learning_rate, "learning_rate") + rate > 0 || throw(ArgumentError("The learning rate must be positive")) + method isa AbstractString && method in ("DeepBSDE", "DTNN", "Monte-Carlo", "Deep-Time-SetTransformer") || throw(ArgumentError("Unknown public PDE solving method")) + rng = _nse_rng(seed) + if method == "DeepBSDE" + sizes = _pde_layer_sizes(layer_sizes) + first(sizes) == last(sizes) == equation.dim || throw(ArgumentError("DeepBSDE input and output widths must match the equation dimension")) + models = Tuple(DeepBSDE(sizes; seed = rand(rng, UInt32)) for _ in 2:equation.num_time_interval) + template = Tuple(model.parameters for model in models) + else + sizes = _pde_hidden_sizes(layer_sizes) + if method == "Deep-Time-SetTransformer" + equation.dim == 1 || throw(ArgumentError("The public set-transformer solver declares a one-dimensional equation")) + models = DeepTimeSetTransformer(1; seed) + elseif method == "DTNN" + models = TimeDependentNetwork(equation.dim, sizes, equation.dim; seed) + else + models = TimeDependentNetworkMonteCarlo(equation.dim, sizes, equation.dim, equation.sigma; seed) + end + template = models.parameters + end + supplied = _pde_parameter_copy(parameters, template) + state = method == "DeepBSDE" && isempty(supplied) ? () : Optimisers.setup(Optimisers.Adam(rate, (0.9, 0.99)), supplied) + model = new(equation, sizes, rate, String(method), models, supplied, state) + _pde_sync_parameters!(model) + return model + end +end + +""" + FBSNNSolver(equation, layer_sizes, learning_rate; seed=0, parameters=nothing) + +Public sine-network FBSNN objective with full widths starting at `dim+1` +and ending at one. Analytic sine-network state derivatives remain +differentiable in the parameters, including during validation. Adam retains +the public `(0.99,0.999)` betas. The unused public initial-value argument is +accepted by `solve_pde` without entering the objective. +""" +mutable struct FBSNNSolver + pde::Equation + layer_sizes::Tuple + learning_rate::Float64 + solver::FBSNNNetwork + parameters::NamedTuple + optimizer_state::Any + function FBSNNSolver(equation::Equation, layer_sizes, learning_rate; seed = 0, parameters = nothing) + _pde_equation_configuration(equation.dim, equation.total_time, equation.num_time_interval) + sizes = _pde_layer_sizes(layer_sizes) + first(sizes) == equation.dim + 1 && last(sizes) == 1 || throw(ArgumentError("FBSNN widths must start with time plus state and end with one solution value")) + rate = _nse_real(learning_rate, "learning_rate") + rate > 0 || throw(ArgumentError("The learning rate must be positive")) + model = FBSNNNetwork(sizes; seed, parameters) + state = Optimisers.setup(Optimisers.Adam(rate, (0.99, 0.999)), model.parameters) + new(equation, sizes, rate, model, model.parameters, state) + end +end + +function _pde_sync_parameters!(model) + if model.solver isa Tuple + for (network, parameters) in zip(model.solver, model.parameters) + network.parameters = parameters + end + else + model.solver.parameters = model.parameters + end + return nothing +end + +function _pde_solver_batch(equation, paths, increments, time) + paths isa AbstractArray{Float64, 3} && !isempty(paths) && size(paths, 2) == equation.dim && size(paths, 3) == equation.num_time_interval + 1 && all(isfinite, paths) || throw(ArgumentError("Paths must be finite Float64 batch-by-dimension-by-time arrays")) + increments isa AbstractArray{Float64, 3} && size(increments) == (size(paths, 1), equation.dim, equation.num_time_interval) && all(isfinite, increments) || throw(ArgumentError("Brownian increments must match the equation and path batch")) + time isa AbstractMatrix{Float64} && size(time) == (size(paths, 1), 1) && all(isfinite, time) || throw(ArgumentError("The time multiplier must be a finite Float64 batch-by-one matrix")) + return nothing +end + +function _pde_control_gradients(model, paths, gradients) + if model.method == "Monte-Carlo" + gradients isa AbstractArray{Float64, 3} && size(gradients) == (size(paths, 1), model.pde.dim, model.pde.num_time_interval) && all(isfinite, gradients) || throw(ArgumentError("Monte Carlo controls require supplied finite Float64 batch-by-dimension-by-step gradient estimates")) + else + isnothing(gradients) || throw(ArgumentError("Monte Carlo gradient inputs apply only to the Monte-Carlo method")) + end + return nothing +end + +function _pde_control(model, parameters, time, states, index, initial_gradient, monte_carlo_gradients) + if model.method == "DeepBSDE" + return index == 1 ? ones(size(states, 1), 1) * initial_gradient : _pde_hidden_forward(parameters[index-1], states, _pde_relu) + elseif model.method == "DTNN" + return _pde_dtnn_forward(model.solver, parameters, time, states) + elseif model.method == "Monte-Carlo" + return _pde_monte_carlo_forward(model.solver, parameters, time, states, monte_carlo_gradients[:, :, index]) + end + return _pde_deep_set_forward(parameters, time, reshape(states, size(states, 1), 1, 1)) +end + +function _pde_bsde_loss(model, parameters, paths, increments, time, initial_value, initial_gradient, monte_carlo_gradients) + equation = model.pde + y = ones(size(paths, 1), 1) .* only(initial_value) + coefficient, stochastic = ones(size(y)), zeros(size(y)) + for index in 1:equation.num_time_interval + states = paths[:, :, index] + current_time = time .* (equation.delta_t * (index - 1)) + control = _pde_control(model, parameters, current_time, states, index, initial_gradient, monte_carlo_gradients) + rate = pde_driver(equation, current_time, states, y, control) + hamiltonian = pde_hamiltonian(equation, current_time, states, y, control) + increment = sum(control .* increments[:, :, index]; dims = 2) .+ hamiltonian .* equation.delta_t + factor = 1 .+ rate .* equation.delta_t + y = y .* factor .+ increment + stochastic = stochastic .* factor .+ increment + coefficient = coefficient .* factor + end + payoff = pde_terminal(equation, time .* equation.total_time, paths[:, :, end]) + return Statistics.mean((payoff .- y).^2), coefficient, stochastic, payoff +end + +"""Evaluate the public backward-equation terminal MSE and its coefficient, stochastic and payoff arrays on supplied paths.""" +function compute_pde_loss(model::FBSDESolver, paths, increments, time, initial_value, initial_gradient; monte_carlo_gradients = nothing) + Zygote.ignore() do + _pde_solver_batch(model.pde, paths, increments, time) + initial_value isa AbstractVector{Float64} && length(initial_value) == 1 && all(isfinite, initial_value) || throw(ArgumentError("The initial value must be one finite Float64 parameter")) + initial_gradient isa AbstractMatrix{Float64} && size(initial_gradient) == (1, model.pde.dim) && all(isfinite, initial_gradient) || throw(ArgumentError("The initial gradient must be a finite Float64 one-by-dimension matrix")) + _pde_control_gradients(model, paths, monte_carlo_gradients) + end + return _pde_bsde_loss(model, model.parameters, paths, increments, time, initial_value, initial_gradient, monte_carlo_gradients) +end + +function _pde_sine_value_gradient(layers, inputs) + projected = _pde_dense(first(layers), inputs) + if length(layers) == 1 + return projected, ones(size(projected)) * first(layers).weight + end + output, gradient = _pde_sine_value_gradient(Base.tail(layers), sin.(projected)) + return output, (gradient .* cos.(projected)) * first(layers).weight +end + +function _pde_fbsnn_loss(model, parameters, paths, increments, time) + equation, loss = model.pde, 0.0 + for index in 1:equation.num_time_interval + states, next_states = paths[:, :, index], paths[:, :, index+1] + current_time, next_time = time .* (equation.delta_t * (index - 1)), time .* (equation.delta_t * index) + value, derivative = _pde_sine_value_gradient(parameters.layers, hcat(current_time, states)) + next_value = _pde_hidden_forward(parameters, hcat(next_time, next_states), sin) + control = derivative[:, 2:end] .* pde_sigma(equation, states) + rate = pde_driver(equation, current_time, states, value, control) + hamiltonian = pde_hamiltonian(equation, current_time, states, value, control) + predicted = value .* (1 .+ rate .* equation.delta_t) .+ hamiltonian .* equation.delta_t .+ sum(control .* increments[:, :, index]; dims = 2) + loss += Statistics.mean((predicted .- next_value).^2) + end + terminal_time, terminal_states = time .* equation.total_time, paths[:, :, end] + payoff = pde_terminal(equation, terminal_time, terminal_states) + terminal_value = _pde_hidden_forward(parameters, hcat(terminal_time, terminal_states), sin) + return loss + Statistics.mean((payoff .- terminal_value).^2) +end + +"""Evaluate the public FBSNN sum of one-step residual MSEs and terminal MSE, retaining state derivatives during evaluation.""" +function compute_pde_loss(model::FBSNNSolver, paths, increments, time, initial_value = nothing) + Zygote.ignore() do + _pde_solver_batch(model.pde, paths, increments, time) + end + return _pde_fbsnn_loss(model, model.parameters, paths, increments, time) +end diff --git a/src/Pde/NeuralPDETraining.jl b/src/Pde/NeuralPDETraining.jl new file mode 100644 index 0000000..5bbf629 --- /dev/null +++ b/src/Pde/NeuralPDETraining.jl @@ -0,0 +1,115 @@ +function _pde_training_batch(model, batch, expected_count = nothing) + batch isa NamedTuple && haskey(batch, :paths) && haskey(batch, :increments) || throw(ArgumentError("A supplied batch must contain paths and increments")) + time = ones(size(batch.paths, 1), 1) + _pde_solver_batch(model.pde, batch.paths, batch.increments, time) + isnothing(expected_count) || size(batch.paths, 1) == expected_count || throw(ArgumentError("Training batches must match batch_size")) + gradients = get(batch, :monte_carlo_gradients, nothing) + model isa FBSDESolver && _pde_control_gradients(model, batch.paths, gradients) + model isa FBSNNSolver && !isnothing(gradients) && throw(ArgumentError("FBSNN obtains controls from the solution network derivative")) + model isa FBSDESolver && model.method == "DTNN" && size(batch.paths, 1) < 2 && throw(ArgumentError("DTNN requires at least two observations for public training-mode batch normalization")) + return (paths = copy(batch.paths), increments = copy(batch.increments), time = time, + monte_carlo_gradients = isnothing(gradients) ? nothing : copy(gradients)) +end + +function _pde_draw_training_batch(model, count, rng) + increments, paths = pde_sample(model.pde, count; rng) + return _pde_training_batch(model, (paths = paths, increments = increments), count) +end + +function _pde_training_inputs(model, iterations, batch_size, init_y, seed, rng, training_batches, validation_batch) + count, batch = _nse_count(iterations, "iterations"), _nse_count(batch_size, "batch_size") + initial = _nse_real(init_y, "init_y") + random = isnothing(rng) ? _nse_rng(seed) : rng + random isa AbstractRNG || throw(ArgumentError("rng must be a native random-number generator")) + if model isa FBSDESolver && model.method == "Monte-Carlo" + !isnothing(training_batches) && !isnothing(validation_batch) || throw(ArgumentError("Monte-Carlo training requires explicit training and validation batches with supplied gradient estimates")) + end + if isnothing(training_batches) + prepared = nothing + model isa FBSDESolver && model.method == "DTNN" && batch < 2 && throw(ArgumentError("DTNN batch_size must exceed one")) + else + training_batches isa Union{Tuple, AbstractVector} && length(training_batches) == count || throw(ArgumentError("Supply exactly one training batch per iteration")) + prepared = Tuple(_pde_training_batch(model, item, batch) for item in training_batches) + end + validation = isnothing(validation_batch) ? _pde_draw_training_batch(model, 128, random) : _pde_training_batch(model, validation_batch) + return count, batch, initial, random, prepared, validation +end + +_pde_optimizer_step(state, parameters, gradient) = isnothing(gradient) ? (state, parameters) : Optimisers.update(state, parameters, gradient) + +""" + solve_pde(model; iterations=20, batch_size=64, init_y=0.0, seed=0, + rng=nothing, training_batches=nothing, validation_batch=nothing) + +Train for a fixed number of iterations and return validation losses and +initial-value estimates. Supplied batches contain `paths` and `increments`; +Monte Carlo batches also contain `monte_carlo_gradients`. Supplied batches +are checked and copied before training. Otherwise one fixed validation batch +of 128 and fresh training batches are sampled using the local RNG. + +DeepBSDE initial value and gradient use separate public Adam learning rates +0.1 and 0.001, both with betas `(0.9,0.99)`. Other backward solvers retain +the supplied initial value during training and report the public coefficient +estimate after each update. DTNN preserves public training-mode moments for +validation. Network optimizer state persists across calls; DeepBSDE initial +parameters and their optimizers restart on each call, as in Python. +""" +function solve_pde(model::FBSDESolver; iterations = 20, batch_size = 64, init_y = 0.0, + seed = 0, rng = nothing, training_batches = nothing, validation_batch = nothing) + count, batch_size, initial, random, batches, validation = _pde_training_inputs(model, iterations, batch_size, init_y, seed, rng, training_batches, validation_batch) + model.method == "DTNN" && (model.solver.training = true) + y0, z0 = [initial], zeros(1, model.pde.dim) + value_state = Optimisers.setup(Optimisers.Adam(0.1, (0.9, 0.99)), y0) + gradient_state = Optimisers.setup(Optimisers.Adam(0.001, (0.9, 0.99)), z0) + losses, estimates = Float64[], Float64[] + for index in 1:count + batch = isnothing(batches) ? _pde_draw_training_batch(model, batch_size, random) : batches[index] + if model.method == "DeepBSDE" + parameters = (networks = model.parameters, value = y0, gradient = z0) + value, derivative = Zygote.withgradient(parameters) do supplied + first(_pde_bsde_loss(model, supplied.networks, batch.paths, batch.increments, batch.time, supplied.value, supplied.gradient, nothing)) + end + isfinite(value) || throw(DomainError(value, "Nonfinite PDE training loss")) + derivatives = only(derivative) + model.optimizer_state, model.parameters = _pde_optimizer_step(model.optimizer_state, model.parameters, derivatives.networks) + value_state, y0 = _pde_optimizer_step(value_state, y0, derivatives.value) + gradient_state, z0 = _pde_optimizer_step(gradient_state, z0, derivatives.gradient) + else + value, derivative = Zygote.withgradient(model.parameters) do supplied + first(_pde_bsde_loss(model, supplied, batch.paths, batch.increments, batch.time, y0, z0, batch.monte_carlo_gradients)) + end + isfinite(value) || throw(DomainError(value, "Nonfinite PDE training loss")) + model.optimizer_state, model.parameters = _pde_optimizer_step(model.optimizer_state, model.parameters, only(derivative)) + end + _pde_sync_parameters!(model) + loss, coefficient, stochastic, payoff = _pde_bsde_loss(model, model.parameters, validation.paths, validation.increments, validation.time, y0, z0, validation.monte_carlo_gradients) + estimate = model.method == "DeepBSDE" ? only(y0) : Statistics.mean((payoff .- stochastic) ./ coefficient) + isfinite(loss) && isfinite(estimate) || throw(DomainError((loss, estimate), "Nonfinite PDE validation result")) + push!(losses, loss) + push!(estimates, estimate) + end + return losses, estimates +end + +"""Train the public FBSNN objective with persistent Adam state and derivative-enabled held-out evaluation; return validation losses and network initial values.""" +function solve_pde(model::FBSNNSolver; iterations = 20, batch_size = 64, init_y = 0.0, + seed = 0, rng = nothing, training_batches = nothing, validation_batch = nothing) + count, batch_size, _, random, batches, validation = _pde_training_inputs(model, iterations, batch_size, init_y, seed, rng, training_batches, validation_batch) + losses, estimates = Float64[], Float64[] + for index in 1:count + batch = isnothing(batches) ? _pde_draw_training_batch(model, batch_size, random) : batches[index] + value, derivative = Zygote.withgradient(model.parameters) do supplied + _pde_fbsnn_loss(model, supplied, batch.paths, batch.increments, batch.time) + end + isfinite(value) || throw(DomainError(value, "Nonfinite FBSNN training loss")) + model.optimizer_state, model.parameters = _pde_optimizer_step(model.optimizer_state, model.parameters, only(derivative)) + _pde_sync_parameters!(model) + loss = _pde_fbsnn_loss(model, model.parameters, validation.paths, validation.increments, validation.time) + initial_inputs = hcat(zeros(size(validation.paths, 1), 1), validation.paths[:, :, 1]) + estimate = Statistics.mean(_pde_hidden_forward(model.parameters, initial_inputs, sin)) + isfinite(loss) && isfinite(estimate) || throw(DomainError((loss, estimate), "Nonfinite FBSNN validation result")) + push!(losses, loss) + push!(estimates, estimate) + end + return losses, estimates +end diff --git a/src/Pde/NeuralSDEGeometry.jl b/src/Pde/NeuralSDEGeometry.jl new file mode 100644 index 0000000..2b65110 --- /dev/null +++ b/src/Pde/NeuralSDEGeometry.jl @@ -0,0 +1,170 @@ +import LinearAlgebra +import Statistics +import Random +import Zygote +import Optimisers +import JuMP +import Clarabel + +function _nse_real(value, name; positive = false) + value isa Real && !(value isa Bool) && isfinite(value) || throw(ArgumentError("$name must be finite and real")) + result = Float64(value) + isfinite(result) && (!positive || result > 0) || throw(ArgumentError("$name is outside its allowed range")) + return result +end + +function _nse_array(value, name, dimensions) + value isa AbstractArray && ndims(value) == dimensions && !isempty(value) || throw(ArgumentError("$name must be a nonempty real array of dimension $dimensions")) + return map(item -> _nse_real(item, name), value) +end + +function _nse_count(value, name) + value isa Integer && !(value isa Bool) && 0 < value <= typemax(Int) || throw(ArgumentError("$name must be a positive supported integer")) + return Int(value) +end + +function _nse_rng(seed) + seed isa Integer && !(seed isa Bool) && typemin(Int64) <= seed <= typemax(UInt64) || throw(ArgumentError("seed must be in the supported integer range")) + return Random.MersenneTwister(UInt64(mod(BigInt(seed), BigInt(1) << 64))) +end + +""" + normalized_call_grid_constraints(strikes, maturities) + +Return `(matrix,bounds)` for weak normalized-call inequalities `matrix*c >= +bounds`. Flatten prices in maturity-major order. Conditions retain intrinsic +and upper bounds, vertical slopes in [-1,0], convexity including the known +strike-zero call value one, and calendar monotonicity. These finite-grid +inequalities alone do not certify attainable marginals or dynamic arbitrage +freedom, particularly at degenerate positive flat tails. +""" +function normalized_call_grid_constraints(strikes, maturities) + strikes = _nse_array(strikes, "strikes", 1) + maturities = _nse_array(maturities, "maturities", 1) + length(strikes) >= 2 && all(>(0), strikes) && all(>(0), maturities) && all(>(0), diff(strikes)) && all(>(0), diff(maturities)) || throw(ArgumentError("Require increasing positive strikes and maturities, with at least two strikes")) + n, m = length(strikes), length(maturities) + identity = Matrix{Float64}(LinearAlgebra.I, n * m, n * m) + rows, bounds = Vector{Float64}[], Float64[] + for time in 1:m + for strike in 1:n + row = identity[(time-1)*n+strike, :] + append!(rows, (row, -row)) + append!(bounds, (max(1 - strikes[strike], 0), -1)) + end + slopes = [identity[(time-1)*n+1, :] / strikes[1]] + constants = [-1 / strikes[1]] + for strike in 1:n-1 + push!(slopes, (identity[(time-1)*n+strike+1, :] - identity[(time-1)*n+strike, :]) / (strikes[strike+1] - strikes[strike])) + push!(constants, 0) + end + for (slope, constant) in zip(slopes, constants) + append!(rows, (slope, -slope)) + append!(bounds, (-1 - constant, constant)) + end + for strike in 1:n-1 + push!(rows, slopes[strike+1] - slopes[strike]) + push!(bounds, constants[strike] - constants[strike+1]) + end + if time > 1 + for strike in 1:n + push!(rows, identity[(time-1)*n+strike, :] - identity[(time-2)*n+strike, :]) + push!(bounds, 0) + end + end + end + return permutedims(hcat(rows...)), bounds +end + +""" + normalized_call_grid_witness(strikes, maturities, prices) + +Construct finite mean-one marginals reproducing the supplied call grid. +Piecewise-linear curves join (0,1), the quotes and a common zero-price tail. +Reject positive flat tails. Round only tiny negative masses and check mass, +mean and repricing residuals at the public tolerances. This static witness +does not identify a unique interpolation or a dynamic pricing measure. +""" +function normalized_call_grid_witness(strikes, maturities, prices) + matrix, bounds = normalized_call_grid_constraints(strikes, maturities) + strikes = _nse_array(strikes, "strikes", 1) + maturities = _nse_array(maturities, "maturities", 1) + prices = _nse_array(prices, "prices", 2) + size(prices) == (length(maturities), length(strikes)) || throw(ArgumentError("Prices must have shape (maturities,strikes)")) + minimum(matrix * vec(permutedims(prices)) - bounds) >= -1e-12 || throw(ArgumentError("Prices violate normalized call-grid inequalities")) + final_slope = (prices[:, end] - prices[:, end-1]) / (strikes[end] - strikes[end-1]) + positive_tail = prices[:, end] .> 0 + any(positive_tail .& (final_slope .>= 0)) && throw(ArgumentError("A positive flat tail has no attainable call-price witness")) + tail_length = zeros(length(maturities)) + tail_length[positive_tail] .= prices[positive_tail, end] ./ -final_slope[positive_tail] + tail = strikes[end] + max(1.0, 2maximum(tail_length)) + isfinite(tail) && tail > strikes[end] || throw(ArgumentError("Tail support is not numerically representable")) + support = [0.0; strikes; tail] + values = hcat(ones(length(maturities)), prices, zeros(length(maturities))) + slopes = diff(values; dims = 2) ./ permutedims(diff(support)) + probabilities = hcat(1 .+ slopes[:, 1], diff(slopes; dims = 2), -slopes[:, end]) + minimum(probabilities) >= -1e-12 || throw(ArgumentError("Call slopes do not yield nonnegative probability masses")) + probabilities = max.(probabilities, 0) + probabilities ./= sum(probabilities; dims = 2) + reconstructed = probabilities * max.(support .- strikes', 0) + mean_error = maximum(abs, probabilities * support .- 1) + price_error = maximum(abs, reconstructed - prices) + isfinite(mean_error) && isfinite(price_error) && max(mean_error, price_error) <= 1e-10 || throw(ArgumentError("Probability witness fails mean or repricing validation")) + return (support = support, probabilities = probabilities, maximum_mean_error = mean_error, maximum_price_error = price_error) +end + +"""Solve the supplied affine price inequalities exactly for a bounded scalar state interval with nonempty interior.""" +function affine_price_state_interval(offset, loading, matrix, bounds) + offset, loading = _nse_array(offset, "offset", 1), _nse_array(loading, "loading", 1) + matrix, bounds = _nse_array(matrix, "matrix", 2), _nse_array(bounds, "bounds", 1) + length(offset) == length(loading) && size(matrix) == (length(bounds), length(offset)) || throw(ArgumentError("Price basis and inequality shapes must agree")) + slopes, right = matrix * loading, bounds - matrix * offset + any((slopes .== 0) .& (right .> 0)) && throw(ArgumentError("Price constraints are infeasible for this basis")) + lower = maximum(right[slopes .> 0] ./ slopes[slopes .> 0]; init = -Inf) + upper = minimum(right[slopes .< 0] ./ slopes[slopes .< 0]; init = Inf) + isfinite(lower) && isfinite(upper) && lower < upper || throw(ArgumentError("The affine price state must have a bounded nonempty interior")) + return lower, upper +end + +function _nse_bounded_polytope(normals, bounds) + dimension = size(normals, 2) + for coordinate in 1:dimension, direction in (-1.0, 1.0) + model = JuMP.Model(Clarabel.Optimizer) + JuMP.set_silent(model) + for name in ("tol_gap_abs", "tol_gap_rel", "tol_feas") + JuMP.set_optimizer_attribute(model, name, 1e-10) + end + JuMP.@variable(model, state[1:dimension]) + JuMP.@constraint(model, normals * state .>= bounds) + JuMP.@objective(model, Min, direction * state[coordinate]) + JuMP.optimize!(model) + JuMP.termination_status(model) == JuMP.MOI.OPTIMAL && JuMP.has_values(model) || throw(ArgumentError("The supplied polytope must be bounded and numerically feasible")) + optimum = JuMP.value.(state) + tolerance = 1e-8 * max(1, maximum(abs, bounds), maximum(abs, optimum)) + all(isfinite, optimum) && minimum(normals * optimum - bounds) >= -tolerance || throw(ArgumentError("The polytope bounding solution is not verified")) + end + return nothing +end + +""" + hjm_drift_residual(price_basis, drift, diffusion, required_price_drift) + +Solve the supplied finite-grid HJM linear equation by minimum-norm least +squares. Return risk prices, residuals, squared residual, numerical rank and +singular values. Required price drift is independently supplied by the caller; +even zero residual does not verify derivatives or an equivalent martingale +measure. SVD rank uses the public dimension-scaled machine-epsilon threshold. +""" +function hjm_drift_residual(price_basis, drift, diffusion, required_price_drift) + basis, diffusion = _nse_array(price_basis, "price_basis", 2), _nse_array(diffusion, "diffusion", 2) + drift, required = _nse_array(drift, "drift", 1), _nse_array(required_price_drift, "required_price_drift", 1) + size(basis) == (length(required), length(drift)) && size(diffusion) == (length(drift), length(drift)) || throw(ArgumentError("Price and state drift/diffusion dimensions must agree")) + operator, rhs = basis * diffusion, basis * drift - required + decomposition = LinearAlgebra.svd(operator) + threshold = maximum(size(operator)) * eps(Float64) * maximum(decomposition.S) + active = decomposition.S .> threshold + coefficients = decomposition.U' * rhs + coefficients = [active[index] ? coefficients[index] / decomposition.S[index] : 0.0 for index in eachindex(coefficients)] + risk_price = decomposition.V * coefficients + residual = rhs - operator * risk_price + return (market_price_of_risk = risk_price, residual = residual, squared_residual = sum(abs2, residual), rank = count(active), singular_values = decomposition.S) +end diff --git a/src/Pde/NeuralSDEModels.jl b/src/Pde/NeuralSDEModels.jl new file mode 100644 index 0000000..cb0a007 --- /dev/null +++ b/src/Pde/NeuralSDEModels.jl @@ -0,0 +1,232 @@ +abstract type AbstractConstrainedNeuralSDE end + +function _nse_parameters(parameters, rng, inputs, hidden, outputs) + shapes = (weight1 = (hidden, inputs), bias1 = (hidden,), weight2 = (outputs, hidden), bias2 = (outputs,)) + uniform(shape, fan_in) = Float64.((2f0 .* rand(rng, Float32, shape) .- 1f0) .* Float32(1 / sqrt(fan_in))) + supplied = isnothing(parameters) ? (weight1 = uniform(shapes.weight1, inputs), bias1 = uniform(shapes.bias1, inputs), + weight2 = uniform(shapes.weight2, hidden), bias2 = uniform(shapes.bias2, hidden)) : parameters + supplied isa NamedTuple && keys(supplied) == keys(shapes) || throw(ArgumentError("Parameters must name weight1,bias1,weight2,bias2")) + for name in keys(shapes) + value = getproperty(supplied, name) + value isa AbstractArray{Float64} && size(value) == getproperty(shapes, name) && all(isfinite, value) || throw(ArgumentError("Network parameters must have finite Float64 values and matching shapes")) + end + return deepcopy(supplied) +end + +_nse_network(parameters, inputs) = tanh.(inputs * parameters.weight1' .+ parameters.bias1') * parameters.weight2' .+ parameters.bias2' +_nse_relu(value) = value > 0 ? value : zero(value) +_nse_nonnegative(value) = value < 0 ? zero(value) : value +_nse_softplus(value) = value > 20 ? value : value >= 0 ? value + log1p(exp(-value)) : log1p(exp(value)) + +""" + IntervalNeuralSDE(lower, upper; drift_bound, diffusion_scale, + hidden_size=12, seed=0, parameters=nothing) + +Scalar-factor neural SDE on a supplied interval, with the public inward drift +correction and linear boundary-vanishing diffusion. The Float64 dense/tanh +network uses a local initializer; explicit shared weights are copied. +Diffusion is zero and drift points inward at the endpoints. Continuous-time +boundary conditions do not certify invariant Euler steps or a pricing measure. +""" +mutable struct IntervalNeuralSDE <: AbstractConstrainedNeuralSDE + lower::Float64 + upper::Float64 + drift_bound::Float64 + diffusion_scale::Float64 + parameters::NamedTuple + function IntervalNeuralSDE(lower, upper; drift_bound, diffusion_scale, hidden_size = 12, seed = 0, parameters = nothing) + left, right = _nse_real(lower, "lower"), _nse_real(upper, "upper") + left < right && isfinite(right - left) || throw(ArgumentError("Interval width must be finite and positive")) + bound = _nse_real(drift_bound, "drift_bound"; positive = true) + scale = _nse_real(diffusion_scale, "diffusion_scale"; positive = true) + hidden = _nse_count(hidden_size, "hidden_size") + new(left, right, bound, scale, _nse_parameters(parameters, _nse_rng(seed), 1, hidden, 2)) + end +end + +function _nse_states(model::IntervalNeuralSDE, states) + states isa AbstractVector{Float64} && !isempty(states) && all(isfinite, states) && all(value -> model.lower <= value <= model.upper, states) || throw(ArgumentError("States must be a finite admissible Float64 vector")) + return nothing +end + +function _nse_forward(model::IntervalNeuralSDE, parameters, states) + width = model.upper - model.lower + center = model.lower + width / 2 + raw = _nse_network(parameters, reshape((states .- center) ./ width, :, 1)) + raw_drift = model.drift_bound .* tanh.(raw[:, 1]) + raw_diffusion = _nse_softplus.(raw[:, 2]) + left, right = states .- model.lower, model.upper .- states + rho = width / 4 + drift = raw_drift .+ _nse_relu.(-raw_drift .- model.drift_bound .* left ./ rho) .- _nse_relu.(raw_drift .- model.drift_bound .* right ./ rho) + distance = min.(left, right) + diffusion = raw_diffusion .* distance ./ (model.diffusion_scale .+ distance) + return drift, diffusion +end + +"""Return native drift and diffusion for a finite vector of states within the supplied interval.""" +function (model::IntervalNeuralSDE)(states) + Zygote.ignore() do + _nse_states(model, states) + end + return _nse_forward(model, model.parameters, states) +end + +""" + PolytopeNeuralSDE(normals, bounds, interior; drift_bound, + diffusion_scale, hidden_size=16, seed=0, parameters=nothing) + +Hard-constrained neural dynamics on the supplied bounded polytope +`normals*state >= bounds`. Rows are normalized without changing feasibility. +A strictly interior point fixes the drift-correction geometry. Diffusion uses +the corrected fixed-coordinate transformation `Q*diag(g(distance))*Q'*L`, +with linear `g(d)=d/(diffusion_scale+d)` and nearest independent normals. +This differs from the paper's literal one-sided square-root transformation. +The constructor checks boundedness using the approved JuMP/Clarabel supplier. +It does not identify factors, calibrate prices or establish dynamic no-arbitrage. +""" +mutable struct PolytopeNeuralSDE <: AbstractConstrainedNeuralSDE + dimension::Int + normals::Matrix{Float64} + bounds::Vector{Float64} + interior::Vector{Float64} + drift_bound::Float64 + diffusion_scale::Float64 + rho::Float64 + parameters::NamedTuple + function PolytopeNeuralSDE(normals, bounds, interior; drift_bound, diffusion_scale, hidden_size = 16, seed = 0, parameters = nothing) + normals, bounds, interior = _nse_array(normals, "normals", 2), _nse_array(bounds, "bounds", 1), _nse_array(interior, "interior", 1) + dimension = length(interior) + size(normals) == (length(bounds), dimension) || throw(ArgumentError("Normals, bounds and interior dimensions must agree")) + lengths = [LinearAlgebra.norm(row) for row in eachrow(normals)] + all(value -> isfinite(value) && value > 0, lengths) && LinearAlgebra.rank(normals) == dimension || throw(ArgumentError("Normals must be nonzero and span the state dimension")) + normals, bounds = normals ./ lengths, bounds ./ lengths + slack = normals * interior - bounds + all(>(0), slack) || throw(ArgumentError("The interior must satisfy every inequality strictly")) + _nse_bounded_polytope(normals, bounds) + bound = _nse_real(drift_bound, "drift_bound"; positive = true) + scale = _nse_real(diffusion_scale, "diffusion_scale"; positive = true) + hidden = _nse_count(hidden_size, "hidden_size") + outputs = dimension + dimension * (dimension + 1) ÷ 2 + new(dimension, normals, bounds, interior, bound, scale, minimum(slack) / 2, + _nse_parameters(parameters, _nse_rng(seed), dimension, hidden, outputs)) + end +end + +function _nse_states(model::PolytopeNeuralSDE, states) + states isa AbstractMatrix{Float64} && size(states, 1) > 0 && size(states, 2) == model.dimension && all(isfinite, states) || throw(ArgumentError("States must be finite Float64 rows matching the polytope dimension")) + distances = states * model.normals' .- model.bounds' + tolerance = 1e-12 * max(1.0, maximum(abs, model.bounds), maximum(abs, states)) + minimum(distances) >= -tolerance || throw(ArgumentError("State lies outside the supplied polytope")) + return nothing +end + +function _nse_distances(model::PolytopeNeuralSDE, states) + return _nse_nonnegative.(states * model.normals' .- model.bounds') +end + +function _nse_frame(model, distances) + return Zygote.ignore() do + directions, faces = Vector{Float64}[], Int[] + for face in sortperm(distances; alg = Base.Sort.MergeSort) + residual = copy(model.normals[face, :]) + for _ in 1:2 + for direction in directions + residual -= LinearAlgebra.dot(residual, direction) * direction + end + end + magnitude = LinearAlgebra.norm(residual) + if magnitude > 1e-10 + push!(directions, residual / magnitude) + push!(faces, face) + end + length(directions) == model.dimension && break + end + length(directions) == model.dimension || error("Boundary normals are numerically rank deficient") + return hcat(directions...), faces + end +end + +function _nse_forward(model::PolytopeNeuralSDE, parameters, states) + distances = _nse_distances(model, states) + raw = _nse_network(parameters, (states .- model.interior') ./ model.rho) + dimension = model.dimension + drift = model.drift_bound .* tanh.(raw[:, 1:dimension]) + transformed = map(1:size(states, 1)) do index + base_diffusion = [column > row ? 0.0 : row == column ? _nse_softplus(raw[index, dimension+row*(row-1)÷2+column]) : raw[index, dimension+row*(row-1)÷2+column] for row in 1:dimension, column in 1:dimension] + basis, faces = _nse_frame(model, distances[index, :]) + selected = distances[index, faces] + scales = selected ./ (model.diffusion_scale .+ selected) + return (basis .* scales') * basis' * base_diffusion + end + flattened = foldl((accumulated, value) -> hcat(accumulated, vec(value)), transformed; init = zeros(dimension^2, 0)) + diffusion = permutedims(reshape(flattened, dimension, dimension, size(states, 1)), (3, 1, 2)) + corrections = model.interior' .- states + normal_drift = drift * model.normals' + inward_product = corrections * model.normals' + active = distances .< model.rho + denominator = ifelse.(active, inward_product, 1.0) + tolerance = model.drift_bound * sqrt(dimension) .* distances ./ model.rho + weights = ifelse.(active, _nse_relu.(-normal_drift .- tolerance) ./ denominator, 0.0) + return drift .+ sum(weights; dims = 2) .* corrections, diffusion +end + +""" +Return drift `(rows,factors)` and diffusion `(rows,factors,factors)`. Negative +distances within the public relative 1e-12 tolerance are evaluated as zero; +positive distances are never snapped to a face. Nearest-normal ties retain +input order, while the two-sided coordinate transformation preserves equal +shrinking scales under frame permutations. +""" +function (model::PolytopeNeuralSDE)(states) + Zygote.ignore() do + _nse_states(model, states) + end + return _nse_forward(model, model.parameters, states) +end + +function _nse_likelihood(model::IntervalNeuralSDE, parameters, states, next_states, dt) + drift, diffusion = _nse_forward(model, parameters, states) + Zygote.ignore() do + all(>(0), diffusion) || throw(ArgumentError("Likelihood requires strictly interior starts with nonsingular diffusion")) + end + variance = diffusion.^2 .* dt + return Statistics.mean(log(2pi) .+ log.(variance) .+ (next_states .- states .- drift .* dt).^2 ./ variance) / 2 +end + +function _nse_likelihood(model::PolytopeNeuralSDE, parameters, states, next_states, dt) + Zygote.ignore() do + minimum(_nse_distances(model, states)) > 0 || throw(ArgumentError("Likelihood starts must be strictly interior")) + end + drift, diffusion = _nse_forward(model, parameters, states) + losses = map(1:size(states, 1)) do index + covariance = dt .* (diffusion[index, :, :] * diffusion[index, :, :]') + factor = LinearAlgebra.cholesky(LinearAlgebra.Symmetric(covariance, :L)) + residual = next_states[index, :] - states[index, :] - dt .* drift[index, :] + standardized = factor.L \ residual + log_determinant = 2sum(log, LinearAlgebra.diag(factor.L)) + return (model.dimension * log(2pi) + log_determinant + sum(abs2, standardized)) / 2 + end + return Statistics.mean(losses) +end + +""" + negative_log_likelihood(model, states, next_states, dt) + +Mean approximate Gaussian Euler transition NLL for supplied admissible +transitions with strictly interior starts. No variance jitter is added; +singular boundary starts and numerically nonpositive covariance fail. This +likelihood is not the exact constrained transition density. +""" +function negative_log_likelihood(model::AbstractConstrainedNeuralSDE, states, next_states, dt) + timestep = Zygote.ignore() do + _nse_states(model, states) + _nse_states(model, next_states) + size(states) == size(next_states) || throw(ArgumentError("Transition shapes must match")) + _nse_real(dt, "dt"; positive = true) + end + loss = _nse_likelihood(model, model.parameters, states, next_states, timestep) + Zygote.ignore() do + isfinite(loss) || throw(ArgumentError("Transition likelihood is nonfinite")) + end + return loss +end diff --git a/src/Pde/NeuralSDETraining.jl b/src/Pde/NeuralSDETraining.jl new file mode 100644 index 0000000..a55d1d0 --- /dev/null +++ b/src/Pde/NeuralSDETraining.jl @@ -0,0 +1,90 @@ +function _fit_neural_sde(model, states, next_states; dt, iterations, learning_rate) + count = _nse_count(iterations, "iterations") + rate = _nse_real(learning_rate, "learning_rate"; positive = true) + timestep = _nse_real(dt, "dt"; positive = true) + negative_log_likelihood(model, states, next_states, timestep) + optimizer = Optimisers.setup(Optimisers.Adam(rate, (0.9, 0.999), 1e-8), model.parameters) + history = Float64[] + for _ in 1:count + objective = parameters -> _nse_likelihood(model, parameters, states, next_states, timestep) + loss, pullback = Zygote.pullback(objective, model.parameters) + isfinite(loss) || throw(ArgumentError("Transition likelihood is nonfinite")) + gradients = only(pullback(one(loss))) + all(value -> !isnothing(value) && all(isfinite, value), values(gradients)) || error("Nonfinite neural-SDE gradients") + optimizer, parameters = Optimisers.update(optimizer, model.parameters, gradients) + all(value -> all(isfinite, value), values(parameters)) || error("Nonfinite neural-SDE parameters") + model.parameters = parameters + push!(history, loss) + end + return history +end + +"""Fit the scalar Euler likelihood on supplied training transitions using fixed iterations and native Adam; return pre-update losses.""" +function fit_interval_neural_sde(model::IntervalNeuralSDE, states, next_states; dt, iterations, learning_rate = 0.001) + return _fit_neural_sde(model, states, next_states; dt, iterations, learning_rate) +end + +""" +Fit the supplied polytope interior transitions by approximate Euler likelihood. +Only training transitions enter fixed-step Adam. Finite optimization does not +certify parameter recovery, HJM consistency or global optimality. +""" +function fit_polytope_neural_sde(model::PolytopeNeuralSDE, states, next_states; dt, iterations, learning_rate = 0.001) + return _fit_neural_sde(model, states, next_states; dt, iterations, learning_rate) +end + +""" +Apply ordinary scalar Euler steps to supplied Brownian increments, with shape +`(paths,steps)`. Return detached `(paths,steps+1)` states. An exit invalidates +the entire run and reports its one-based step; no clipping or reflection is +performed. Use coupled finer increments to assess discretization sensitivity. +Simulation remains under the fitted measure. +""" +function simulate_interval_neural_sde(model::IntervalNeuralSDE, initial, brownian_increments; dt) + return Zygote.ignore() do + timestep = _nse_real(dt, "dt"; positive = true) + _nse_states(model, initial) + increments = brownian_increments + increments isa AbstractMatrix{Float64} && size(increments, 1) == length(initial) && size(increments, 2) > 0 && all(isfinite, increments) || throw(ArgumentError("Brownian increments must match initial states and Float64 model precision")) + states = reshape(copy(initial), :, 1) + for time in axes(increments, 2) + drift, diffusion = model(states[:, end]) + proposal = states[:, end] + timestep .* drift + diffusion .* increments[:, time] + all(isfinite, proposal) && all(value -> model.lower <= value <= model.upper, proposal) || error("Euler step $time exits the admissible price state; reduce the step and reassess") + states = hcat(states, proposal) + end + return states + end +end + +""" +Ordinary polytope Euler simulation with explicit whole-run exit failure. +Initial states are `(paths,factors)` and Brownian increments are +`(paths,steps,factors)` with variance dt supplied by the caller. Return +detached `(paths,steps+1,factors)` states. No projection, clipping, reflection +or discarded path is introduced. +""" +function simulate_polytope_neural_sde(model::PolytopeNeuralSDE, initial, brownian_increments; dt) + return Zygote.ignore() do + timestep = _nse_real(dt, "dt"; positive = true) + _nse_states(model, initial) + increments = brownian_increments + increments isa AbstractArray{Float64, 3} && size(increments, 1) == size(initial, 1) && size(increments, 2) > 0 && size(increments, 3) == model.dimension && all(isfinite, increments) || throw(ArgumentError("Brownian increments must match state dimensions and Float64 model precision")) + current = copy(initial) + states = reshape(copy(initial), size(initial, 1), 1, model.dimension) + for time in axes(increments, 2) + drift, diffusion = model(current) + noise = permutedims(hcat((diffusion[index, :, :] * increments[index, time, :] for index in axes(current, 1))...)) + proposal = current + timestep .* drift + noise + try + _nse_states(model, proposal) + catch exception + exception isa ArgumentError || rethrow() + error("Euler step $time exits the polytope; reduce the step and reassess") + end + states = cat(states, reshape(proposal, size(initial, 1), 1, model.dimension); dims = 2) + current = proposal + end + return states + end +end diff --git a/src/Pde/Pde.jl b/src/Pde/Pde.jl index 09d55ae..4279c36 100644 --- a/src/Pde/Pde.jl +++ b/src/Pde/Pde.jl @@ -4,29 +4,23 @@ PDE submodule, mirroring the Python `RiskLabAI.pde` sub-package: financial PDEs solved by the Deep BSDE method (Han, Jentzen & E, 2018). -The base module wires the **equations** — the forward-SDE sampler and the BSDE -generator functions. The neural Deep-BSDE solver is supplied only by the -optional extension because it requires a deep-learning backend. +The module provides forward-SDE samplers, generator and terminal functions, +native neural components and fixed-coordinate constrained neural SDEs. """ module Pde include("Equations.jl") +include("EquationContracts.jl") -""" - solve_deep_bsde(args...; kwargs...) - -Solve a PDE with the optional neural Deep-BSDE backend. The base package keeps -the PDE equations available without loading a deep-learning stack. Installing -and loading the `deep_bsde` extension adds the equation-specific method. -""" -function solve_deep_bsde(args...; kwargs...) - throw( - ArgumentError( - "solve_deep_bsde requires the optional deep_bsde extension " * - "(Lux, Optimisers, and Zygote).", - ), - ) -end +include("DeepBSDESolver.jl") +include("NeuralSDEGeometry.jl") +include("NeuralSDEModels.jl") +include("NeuralSDETraining.jl") +include("NetworkPrimitives.jl") +include("SetNetworks.jl") +include("TimeDependentNetworks.jl") +include("NeuralPDESolvers.jl") +include("NeuralPDETraining.jl") export Equation, @@ -38,7 +32,36 @@ export pde_driver, pde_hamiltonian, pde_terminal, + pde_terminal_for_sample, pde_sigma, - solve_deep_bsde + solve_deep_bsde, + normalized_call_grid_constraints, + normalized_call_grid_witness, + affine_price_state_interval, + IntervalNeuralSDE, + PolytopeNeuralSDE, + negative_log_likelihood, + fit_interval_neural_sde, + fit_polytope_neural_sde, + simulate_interval_neural_sde, + simulate_polytope_neural_sde, + hjm_drift_residual, + Net1, + TimeNet, + FBSNNNetwork, + DeepBSDE, + TimeNetForSet, + MAB, + SAB, + ISAB, + PMA, + DeepTimeSetTransformer, + TimeDependentNetwork, + TimeDependentNetworkMonteCarlo, + initialize_weights, + FBSDESolver, + FBSNNSolver, + compute_pde_loss, + solve_pde end # module Pde diff --git a/src/Pde/SetNetworks.jl b/src/Pde/SetNetworks.jl new file mode 100644 index 0000000..944a56a --- /dev/null +++ b/src/Pde/SetNetworks.jl @@ -0,0 +1,191 @@ +function _pde_attention_dimensions(dim_q, dim_k, dim_v, num_heads, ln) + dimensions = Tuple(_nse_count(value, "attention dimension") for value in (dim_q, dim_k, dim_v, num_heads)) + dimensions[3] % dimensions[4] == 0 && ln isa Bool || throw(ArgumentError("Value dimensions must divide into whole heads, and ln must be Boolean")) + return dimensions +end + +function _pde_mab_parameters(rng, query, key, value, ln) + norm = () -> (scale = ones(value), offset = zeros(value)) + return (fc_q = _pde_dense_parameters(rng, query, value), fc_k = _pde_dense_parameters(rng, key, value), + fc_v = _pde_dense_parameters(rng, key, value), fc_o = _pde_dense_parameters(rng, value, value), + norm0 = ln ? norm() : (;), norm1 = ln ? norm() : (;)) +end + +function _pde_layernorm(parameters, inputs) + centered = inputs .- Statistics.mean(inputs; dims = 3) + variance = Statistics.mean(centered.^2; dims = 3) + return centered ./ sqrt.(variance .+ 1e-5) .* reshape(parameters.scale, 1, 1, :) .+ reshape(parameters.offset, 1, 1, :) +end + +function _pde_attention(parameters, queries, keys, heads, ln) + q, k, v = _pde_dense(parameters.fc_q, queries), _pde_dense(parameters.fc_k, keys), _pde_dense(parameters.fc_v, keys) + dimension, query_count = size(q, 3), size(q, 2) + width = dimension ÷ heads + batches = map(1:size(q, 1)) do batch + foldl(1:heads; init = zeros(query_count, 0)) do prior, head + indices = (head-1)*width+1:head*width + observed = q[batch, :, indices] + scores = observed * k[batch, :, indices]' ./ sqrt(dimension) + anchor = Zygote.ignore() do + maximum(scores; dims = 2) + end + weights = exp.(scores .- anchor) + attention = weights ./ sum(weights; dims = 2) + hcat(prior, observed + attention * v[batch, :, indices]) + end + end + flattened = foldl((prior, value) -> hcat(prior, vec(value)), batches; init = zeros(query_count * dimension, 0)) + output = permutedims(reshape(flattened, query_count, dimension, size(q, 1)), (3, 1, 2)) + output = ln ? _pde_layernorm(parameters.norm0, output) : output + output = output .+ _pde_relu.(_pde_dense(parameters.fc_o, output)) + return ln ? _pde_layernorm(parameters.norm1, output) : output +end + +""" +Multi-head attention with the public `sqrt(dim_v)` score scaling, projected +query residual, ReLU output residual and optional two layer-normalization +operations. Inputs have `(batch,set,features)` dimensions. Native dimensions +must divide into complete heads. Shared Float64 parameter arrays are copied. +""" +mutable struct MAB <: AbstractPDENetwork + dim_q::Int + dim_k::Int + dim_v::Int + num_heads::Int + ln::Bool + parameters::NamedTuple + function MAB(dim_q, dim_k, dim_v, num_heads; ln = false, seed = 0, parameters = nothing) + query, key, value, heads = _pde_attention_dimensions(dim_q, dim_k, dim_v, num_heads, ln) + new(query, key, value, heads, ln, _pde_parameter_copy(parameters, _pde_mab_parameters(_nse_rng(seed), query, key, value, ln))) + end +end + +function (model::MAB)(queries, keys) + Zygote.ignore() do + _pde_check_array(queries, 3, model.dim_q) + _pde_check_array(keys, 3, model.dim_k) + size(queries, 1) == size(keys, 1) || throw(ArgumentError("Attention query and key batch sizes must agree")) + end + return _pde_attention(model.parameters, queries, keys, model.num_heads, model.ln) +end + +"""Public self-attention block, using the same set as attention queries and keys.""" +mutable struct SAB <: AbstractPDENetwork + dim_in::Int + dim_out::Int + num_heads::Int + ln::Bool + parameters::NamedTuple + function SAB(dim_in, dim_out, num_heads; ln = false, seed = 0, parameters = nothing) + inputs, _, outputs, heads = _pde_attention_dimensions(dim_in, dim_in, dim_out, num_heads, ln) + new(inputs, outputs, heads, ln, _pde_parameter_copy(parameters, (mab = _pde_mab_parameters(_nse_rng(seed), inputs, inputs, outputs, ln),))) + end +end + +function (model::SAB)(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 3, model.dim_in) + end + return _pde_attention(model.parameters.mab, inputs, inputs, model.num_heads, model.ln) +end + +function _pde_inducing(rng, count, dimension) + bound = Float32(sqrt(6 / ((count + 1) * dimension))) + return Float64.((2f0 .* rand(rng, Float32, 1, count, dimension) .- 1f0) .* bound) +end + +"""Public induced self-attention with trainable Xavier-uniform inducing vectors and two attention blocks.""" +mutable struct ISAB <: AbstractPDENetwork + dim_in::Int + dim_out::Int + num_heads::Int + num_inds::Int + ln::Bool + parameters::NamedTuple + function ISAB(dim_in, dim_out, num_heads, num_inds; ln = false, seed = 0, parameters = nothing) + inputs, _, outputs, heads = _pde_attention_dimensions(dim_in, dim_in, dim_out, num_heads, ln) + count = _nse_count(num_inds, "num_inds") + rng = _nse_rng(seed) + template = (inducing = _pde_inducing(rng, count, outputs), mab0 = _pde_mab_parameters(rng, outputs, inputs, outputs, ln), mab1 = _pde_mab_parameters(rng, inputs, outputs, outputs, ln)) + new(inputs, outputs, heads, count, ln, _pde_parameter_copy(parameters, template)) + end +end + +function _pde_isab_forward(parameters, inputs, heads, ln) + inducing = repeat(parameters.inducing; outer = (size(inputs, 1), 1, 1)) + hidden = _pde_attention(parameters.mab0, inducing, inputs, heads, ln) + return _pde_attention(parameters.mab1, inputs, hidden, heads, ln) +end + +function (model::ISAB)(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 3, model.dim_in) + end + return _pde_isab_forward(model.parameters, inputs, model.num_heads, model.ln) +end + +function _pde_pma_parameters(rng, dimension, seeds, ln) + return (seeds = _pde_inducing(rng, seeds, dimension), mab = _pde_mab_parameters(rng, dimension, dimension, dimension, ln)) +end + +function _pde_pma_forward(parameters, inputs, heads, ln) + seeds = repeat(parameters.seeds; outer = (size(inputs, 1), 1, 1)) + return _pde_attention(parameters.mab, seeds, inputs, heads, ln) +end + +"""Public pooling attention with trainable Xavier-uniform seed vectors; output shape is `(batch,num_seeds,dim)`.""" +mutable struct PMA <: AbstractPDENetwork + dim::Int + num_heads::Int + num_seeds::Int + ln::Bool + parameters::NamedTuple + function PMA(dim, num_heads, num_seeds; ln = false, seed = 0, parameters = nothing) + inputs, _, _, heads = _pde_attention_dimensions(dim, dim, dim, num_heads, ln) + count = _nse_count(num_seeds, "num_seeds") + new(inputs, heads, count, ln, _pde_parameter_copy(parameters, _pde_pma_parameters(_nse_rng(seed), inputs, count, ln))) + end +end + +function (model::PMA)(inputs) + Zygote.ignore() do + _pde_check_array(inputs, 3, model.dim) + end + return _pde_pma_forward(model.parameters, inputs, model.num_heads, model.ln) +end + +"""Public five-layer feature-centered set network, one-seed attention pool and four learned time-conditioned output layers.""" +mutable struct DeepTimeSetTransformer <: AbstractPDENetwork + input_dim::Int + parameters::NamedTuple + function DeepTimeSetTransformer(input_dim; seed = 0, parameters = nothing) + inputs = _nse_count(input_dim, "input_dim") + rng = _nse_rng(seed) + template = (layers = _pde_feedforward_parameters(rng, (inputs, 32, 32, 32, 32, 32)), regressor = _pde_pma_parameters(rng, 32, 1, false), + time_layers = Tuple(_pde_time_set_parameters(rng, 32, outputs) for outputs in (32, 32, 32, 1))) + new(inputs, _pde_parameter_copy(parameters, template)) + end +end + +function _pde_deep_set_forward(parameters, time, inputs) + features = inputs + for (index, layer) in enumerate(parameters.layers.layers) + projected = _pde_relu.(_pde_dense(layer, features)) + features = index < 5 ? projected .- Statistics.mean(projected; dims = 3) : projected + end + output = dropdims(_pde_pma_forward(parameters.regressor, features, 4, false); dims = 2) + for (index, layer) in enumerate(parameters.time_layers) + projected = _pde_time_set_forward(layer, time, output) + output = index < 4 ? _pde_relu.(projected) : projected + end + return output +end + +function (model::DeepTimeSetTransformer)(time, inputs) + Zygote.ignore() do + _pde_check_array(time, 2, 1) + _pde_check_array(inputs, 3, model.input_dim) + size(time, 1) == size(inputs, 1) || throw(ArgumentError("Time and set batch sizes must agree")) + end + return _pde_deep_set_forward(model.parameters, time, inputs) +end diff --git a/src/Pde/TimeDependentNetworks.jl b/src/Pde/TimeDependentNetworks.jl new file mode 100644 index 0000000..e128867 --- /dev/null +++ b/src/Pde/TimeDependentNetworks.jl @@ -0,0 +1,147 @@ +function _pde_time_dependent_parameters(rng, inputs, hidden, outputs; monte_carlo = false) + widths = (inputs, hidden...) + return (layers = Tuple(_pde_dense_parameters(rng, widths[index], widths[index+1]) for index in 1:length(hidden)), + time_layers = Tuple(_pde_feedforward_parameters(rng, (4, 100, 150, 200, 300, 200, width)) for width in (monte_carlo ? (widths[1:end-1]..., outputs) : widths[1:end-1])), + linear = _pde_dense_parameters(rng, last(hidden), outputs), + normalization = monte_carlo ? () : Tuple((scale = ones(width), offset = zeros(width)) for width in widths[1:end-1])) +end + +function _pde_hidden_sizes(values) + values isa Union{Tuple, AbstractVector} && !isempty(values) || throw(ArgumentError("Supply at least one hidden layer")) + return Tuple(_nse_count(value, "hidden layer size") for value in values) +end + +function _pde_batchnorm(parameters, running, inputs, training) + if training + Zygote.ignore() do + size(inputs, 1) > 1 || throw(ArgumentError("Training batch normalization requires more than one observation")) + end + center = Statistics.mean(inputs; dims = 1) + variance = Statistics.mean((inputs .- center).^2; dims = 1) + next_state = Zygote.ignore() do + n = size(inputs, 1) + (mean = 0.99 .* running.mean .+ 0.01 .* vec(center), + variance = 0.99 .* running.variance .+ 0.01 .* vec(variance) .* (n / (n - 1)), batches = running.batches + 1) + end + else + center, variance = running.mean', running.variance' + next_state = running + end + normalized = (inputs .- center) ./ sqrt.(variance .+ 1e-6) + return normalized .* parameters.scale' .+ parameters.offset', next_state +end + +""" +Time-dependent sine network with the public active batch normalization, +polynomial/exponential time features and multiplicative time weights. +`training=true` uses batch moments and updates running mean/unbiased variance +with momentum 0.01 and epsilon 1e-6. Set `model.training=false` for fixed +running-statistic evaluation. Unused normalization and output-time modules +from the Python constructor are omitted from the native parameter tree. +""" +mutable struct TimeDependentNetwork <: AbstractPDENetwork + input_dim::Int + hidden_sizes::Tuple + output_dim::Int + training::Bool + parameters::NamedTuple + state::Tuple + function TimeDependentNetwork(input_dim, hidden_sizes, output_dim; seed = 0, parameters = nothing) + inputs, outputs = _nse_count(input_dim, "input_dim"), _nse_count(output_dim, "output_dim") + hidden = _pde_hidden_sizes(hidden_sizes) + template = _pde_time_dependent_parameters(_nse_rng(seed), inputs, hidden, outputs) + state = Tuple((mean = zeros(width), variance = ones(width), batches = 0) for width in (inputs, hidden[1:end-1]...)) + new(inputs, hidden, outputs, true, _pde_parameter_copy(parameters, template), state) + end +end + +function _pde_dtnn_forward(model, parameters, time, inputs; update_state = true) + time_features = hcat(time, time.^2, time.^3, exp.(time)) + features, next_states = inputs, () + for index in eachindex(parameters.layers) + normalized, next_state = _pde_batchnorm(parameters.normalization[index], model.state[index], features, model.training) + time_weights = _pde_time_forward(parameters.time_layers[index], time_features) + projected = _pde_dense(parameters.layers[index], normalized .* (1 .+ time_weights)) + features = sin.(projected) + next_states = (next_states..., next_state) + end + if update_state + Zygote.ignore() do + model.state = next_states + end + end + return _pde_dense(parameters.linear, features) +end + +function (model::TimeDependentNetwork)(time, inputs) + Zygote.ignore() do + _pde_check_array(time, 2, 1) + _pde_check_array(inputs, 2, model.input_dim) + size(time, 1) == size(inputs, 1) || throw(ArgumentError("Time and feature batches must agree")) + end + return _pde_dtnn_forward(model, model.parameters, time, inputs) +end + +""" +Public time-dependent Monte Carlo control network with ReLU hidden layers. +The supplied `y_mc` gradient estimate enters exactly as +`sigma*x*network_output + (1-time_weight)*y_mc`. No gradient estimator or +pricing measure is inferred. Inactive Python batch-normalization modules are +omitted. Input/output dimensions must support the public elementwise product. +""" +mutable struct TimeDependentNetworkMonteCarlo <: AbstractPDENetwork + input_dim::Int + hidden_sizes::Tuple + output_dim::Int + sigma::Float64 + parameters::NamedTuple + function TimeDependentNetworkMonteCarlo(input_dim, hidden_sizes, output_dim, sigma; seed = 0, parameters = nothing) + inputs, outputs = _nse_count(input_dim, "input_dim"), _nse_count(output_dim, "output_dim") + inputs == outputs || min(inputs, outputs) == 1 || throw(ArgumentError("Input and output dimensions do not support the public elementwise control formula")) + hidden, volatility = _pde_hidden_sizes(hidden_sizes), _nse_real(sigma, "sigma") + template = _pde_time_dependent_parameters(_nse_rng(seed), inputs, hidden, outputs; monte_carlo = true) + new(inputs, hidden, outputs, volatility, _pde_parameter_copy(parameters, template)) + end +end + +function _pde_monte_carlo_forward(model, parameters, time, inputs, y_mc) + time_features = hcat(time, time.^2, time.^3, exp.(time)) + features = copy(inputs) + for index in eachindex(parameters.layers) + time_weights = _pde_time_forward(parameters.time_layers[index], time_features) + features = _pde_relu.(_pde_dense(parameters.layers[index], features .* (1 .+ time_weights))) + end + output_weight = _pde_time_forward(parameters.time_layers[end], time_features) + return model.sigma .* inputs .* _pde_dense(parameters.linear, features) .+ (1 .- output_weight) .* y_mc +end + +function (model::TimeDependentNetworkMonteCarlo)(time, inputs, y_mc) + Zygote.ignore() do + _pde_check_array(time, 2, 1) + _pde_check_array(inputs, 2, model.input_dim) + _pde_check_array(y_mc, 2, max(model.input_dim, model.output_dim)) + size(time, 1) == size(inputs, 1) == size(y_mc, 1) || throw(ArgumentError("Time, features and Monte Carlo gradients need matching batch sizes")) + end + return _pde_monte_carlo_forward(model, model.parameters, time, inputs, y_mc) +end + +""" + initialize_weights(layer_parameters; seed=0, normal_draws=nothing) + +Mutate one native dense parameter leaf `(weight,bias)` to public normal +weights with standard deviation 0.1 and zero bias. Supplied primitive normal +draws support exact comparisons. Other parameter containers are unchanged, +matching the Python callback's restriction to a Linear module. A bias-free +leaf is rejected, preserving the public callback's unsupported case. +""" +function initialize_weights(parameters; seed = 0, normal_draws = nothing) + parameters isa NamedTuple && haskey(parameters, :weight) || return nothing + keys(parameters) == (:weight, :bias) || throw(ArgumentError("The public initializer requires a dense layer with bias")) + parameters.weight isa AbstractMatrix{Float64} && parameters.bias isa AbstractVector{Float64} && size(parameters.weight, 1) == length(parameters.bias) || throw(ArgumentError("Dense weight and bias shapes must agree and use Float64")) + rng = _nse_rng(seed) + draws = isnothing(normal_draws) ? randn(rng, size(parameters.weight)) : normal_draws + draws isa AbstractMatrix{Float64} && size(draws) == size(parameters.weight) && all(isfinite, draws) || throw(ArgumentError("Primitive normal draws must match the dense weight shape")) + parameters.weight .= 0.1 .* draws + parameters.bias .= 0 + return nothing +end diff --git a/src/RiskLabAI.jl b/src/RiskLabAI.jl index 27467a4..eba90ef 100644 --- a/src/RiskLabAI.jl +++ b/src/RiskLabAI.jl @@ -30,6 +30,7 @@ using .Features: shannon_entropy, probability_mass_function, plug_in_entropy_est volatility_robust_sadf, pelt_change_points, orthogonal_features, calculate_weighted_tau, feature_importance_mdi, feature_importance_mda, feature_importance_sfi, + clustered_feature_importance_mdi, clustered_feature_importance_mda, get_test_dataset, mdi_plus_importance, conditional_predictive_impact include("Cluster/Cluster.jl") @@ -66,9 +67,14 @@ using .Backtest: sharpe_ratio, bet_timing, calculate_holding_period, optimal_ou_trading_rule, fit_ornstein_uhlenbeck include("Validation/Validation.jl") +include("Components/Components.jl") +include("ParallelWork.jl") using .Validation: KFoldCV, PurgedKFoldCV, CombinatorialPurgedCV, WalkForwardCV, + AdaptiveCombinatorialPurgedCV, BaggedCombinatorialPurgedCV, backtest_predictions, filtered_training_indices_with_embargo, cv_split, backtest_paths, get_n_splits, cross_val_score, grid_search_cv, random_search_cv, leakage_aware_hpo, deflated_sharpe_gate, + SampleWeightedPipeline, clone_pipeline, configure_pipeline!, fit_pipeline!, + predict_pipeline, predict_pipeline_proba, pipeline_classes, pipeline_parameter_grid, clf_hyper_fit, moving_block_bootstrap_indices, bagged_probability_of_backtest_overfitting, estimate_volatility_regimes, adaptive_probability_of_backtest_overfitting @@ -108,6 +114,7 @@ export # Features — feature importance orthogonal_features, calculate_weighted_tau, feature_importance_mdi, feature_importance_mda, feature_importance_sfi, + clustered_feature_importance_mdi, clustered_feature_importance_mda, get_test_dataset, mdi_plus_importance, conditional_predictive_impact, # Optimization — HRP, hedging & NCO inverse_variance_weights, cluster_variance, quasi_diagonal, recursive_bisection, @@ -146,8 +153,11 @@ export optimal_ou_trading_rule, fit_ornstein_uhlenbeck, # Validation — cross-validators, scoring & tuning KFoldCV, PurgedKFoldCV, CombinatorialPurgedCV, WalkForwardCV, + AdaptiveCombinatorialPurgedCV, BaggedCombinatorialPurgedCV, backtest_predictions, filtered_training_indices_with_embargo, cv_split, backtest_paths, get_n_splits, cross_val_score, grid_search_cv, random_search_cv, + SampleWeightedPipeline, clone_pipeline, configure_pipeline!, fit_pipeline!, + predict_pipeline, predict_pipeline_proba, pipeline_classes, pipeline_parameter_grid, clf_hyper_fit, # Validation — path-level Bagged / Adaptive CPCV moving_block_bootstrap_indices, bagged_probability_of_backtest_overfitting, estimate_volatility_regimes, adaptive_probability_of_backtest_overfitting, diff --git a/src/Utils/constants.jl b/src/Utils/constants.jl index 19d142a..d2908bc 100644 --- a/src/Utils/constants.jl +++ b/src/Utils/constants.jl @@ -20,9 +20,9 @@ const CUMULATIVE_VOLUME = "Cumulative Volume" const CUMULATIVE_BUY_VOLUME = "Cumulative Buy Volume" const CUMULATIVE_SELL_VOLUME = "Cumulative Sell Volume" -const CUMULATIVE_THETA = "Cumulative theta" -const CUMULATIVE_BUY_THETA = "Cumulative Buy theta" -const CUMULATIVE_SELL_THETA = "Cumulative Sell theta" +const CUMULATIVE_THETA = "Cumulative θ" +const CUMULATIVE_BUY_THETA = "Cumulative Buy θ" +const CUMULATIVE_SELL_THETA = "Cumulative Sell θ" const EXPECTED_IMBALANCE = "expected_imbalance" const EXPECTED_TICKS_NUMBER = "exp_num_ticks" diff --git a/src/Validation/AdaptiveCombinatorialCV.jl b/src/Validation/AdaptiveCombinatorialCV.jl new file mode 100644 index 0000000..b9e1db1 --- /dev/null +++ b/src/Validation/AdaptiveCombinatorialCV.jl @@ -0,0 +1,92 @@ +using Statistics: quantile + +function _validate_event_ranges(event_starts, event_ends; embargo = 0.0) + length(event_starts) == length(event_ends) || throw(DimensionMismatch("event starts and ends must align")) + issorted(event_starts) && allunique(event_starts) || throw(ArgumentError("event starts must be strictly increasing")) + all(start <= stop for (start, stop) in zip(event_starts, event_ends)) || throw(ArgumentError("event ends must not precede their starts")) + isfinite(embargo) && 0 <= embargo <= 1 || throw(ArgumentError("embargo must lie in [0, 1]")) + return nothing +end + +""" + filtered_training_indices_with_embargo(event_starts, event_ends, test_positions; + embargo=0.0, continuous=false) + +Return one-based training positions after inclusive overlap removal and the +released observation-count embargo. Events must have increasing distinct starts +and aligned ends. For `continuous=true`, the interval ends at the last test +event's end, preserving the public continuous-range convention. +""" +function filtered_training_indices_with_embargo(event_starts::AbstractVector, event_ends::AbstractVector, test_positions::AbstractVector{<:Integer}; embargo::Real = 0.0, continuous::Bool = false) + _validate_event_ranges(event_starts, event_ends; embargo) + all(position -> 1 <= position <= length(event_starts), test_positions) || throw(ArgumentError("test positions must index the event vectors")) + return _purged_train_indices(event_starts, event_ends, test_positions, embargo, continuous) +end + +""" + AdaptiveCombinatorialPurgedCV(n_splits, n_test_groups, event_starts, event_ends; + external_feature, embargo=0.0, n_subsplits=3, lower_quantile=0.25, + upper_quantile=0.75, subtract_border_adjustments=true) + +Combinatorial purged validation with the released supplied-feature boundary +adjustments. Quantiles use the full supplied feature vector and linear +interpolation. A value below the lower quantile gives adjustment -1; a value +above the upper quantile gives +1. The adjustment is subtracted by default. +Feature values are copied and must align with events. At least +`n_splits*n_subsplits` observations are required. Very small subsplit counts can +produce empty or overlapping segments under the public boundary rule; these +segments are retained without inventing a different partitioning rule. +""" +struct AdaptiveCombinatorialPurgedCV + n_splits::Int + n_test_groups::Int + event_starts::Vector + event_ends::Vector + embargo::Float64 + external_feature::Vector{Float64} + n_subsplits::Int + lower_quantile::Float64 + upper_quantile::Float64 + subtract_border_adjustments::Bool +end + +function AdaptiveCombinatorialPurgedCV(n_splits::Integer, n_test_groups::Integer, event_starts::AbstractVector, event_ends::AbstractVector; external_feature::AbstractVector, embargo::Real = 0.0, n_subsplits::Integer = 3, lower_quantile::Real = 0.25, upper_quantile::Real = 0.75, subtract_border_adjustments::Bool = true) + _validate_event_ranges(event_starts, event_ends; embargo) + 1 <= n_test_groups < n_splits || throw(ArgumentError("test groups must be positive and less than all groups")) + n_subsplits >= 1 || throw(ArgumentError("n_subsplits must be positive")) + length(event_starts) >= n_splits * n_subsplits || throw(ArgumentError("each fine subsplit requires an observation")) + length(external_feature) == length(event_starts) || throw(DimensionMismatch("external feature must align with events")) + all(isfinite, external_feature) || throw(ArgumentError("external feature must be finite")) + 0 <= lower_quantile <= upper_quantile <= 1 || throw(ArgumentError("quantiles must be ordered within [0, 1]")) + return AdaptiveCombinatorialPurgedCV(Int(n_splits), Int(n_test_groups), collect(event_starts), collect(event_ends), Float64(embargo), Float64.(external_feature), Int(n_subsplits), Float64(lower_quantile), Float64(upper_quantile), subtract_border_adjustments) +end + +function _validation_segments(cv::AdaptiveCombinatorialPurgedCV) + count = length(cv.event_starts) + fine = _array_split(count, cv.n_splits * cv.n_subsplits) + starts = first.(fine) + lower, upper = quantile(cv.external_feature, [cv.lower_quantile, cv.upper_quantile]) + boundaries = Int[] + for border in cv.n_subsplits:cv.n_subsplits:(length(fine)-1) + value = cv.external_feature[starts[border+1]] + adjustment = value < lower ? -1 : value > upper ? 1 : 0 + shifted = border + (cv.subtract_border_adjustments ? -adjustment : adjustment) + push!(boundaries, starts[clamp(shifted, 1, length(starts)-1)+1]) + end + edges = [1; boundaries; count+1] + return [collect(edges[index]:(edges[index+1]-1)) for index in 1:cv.n_splits] +end + +_validation_segments(cv::CombinatorialPurgedCV) = _array_split(length(cv.event_starts), cv.n_splits) +get_n_splits(cv::AdaptiveCombinatorialPurgedCV) = binomial(cv.n_splits, cv.n_test_groups) +cv_split(cv::AdaptiveCombinatorialPurgedCV) = _combinatorial_cv_split(cv, _validation_segments(cv)) +backtest_paths(cv::AdaptiveCombinatorialPurgedCV) = _combinatorial_backtest_paths(cv, _validation_segments(cv)) +backtest_paths(cv::PurgedKFoldCV) = Dict(1 => cv_split(cv)) +backtest_paths(cv::KFoldCV, n_samples::Integer; permutation = nothing) = Dict(1 => cv_split(cv, n_samples; permutation)) +backtest_paths(cv::WalkForwardCV, n_samples::Integer) = Dict(1 => cv_split(cv, n_samples)) +backtest_paths(validators::AbstractDict) = Dict(key => backtest_paths(cv) for (key, cv) in validators) + +function backtest_paths(validators::AbstractDict, data::AbstractDict) + Set(keys(validators)) == Set(keys(data)) || throw(ArgumentError("validator and data keys must match")) + return Dict(key => cv isa Union{KFoldCV,WalkForwardCV} ? backtest_paths(cv, size(data[key], 1)) : backtest_paths(cv) for (key, cv) in validators) +end diff --git a/src/Validation/BacktestPredictions.jl b/src/Validation/BacktestPredictions.jl new file mode 100644 index 0000000..ed6b18e --- /dev/null +++ b/src/Validation/BacktestPredictions.jl @@ -0,0 +1,96 @@ +function _prediction_layout(cv::Union{CombinatorialPurgedCV,AdaptiveCombinatorialPurgedCV}, n_samples; permutation = nothing) + permutation === nothing || throw(ArgumentError("permutation is only supported for shuffled K-fold")) + length(cv.event_starts) == n_samples || throw(DimensionMismatch("events must align with observations")) + _validate_event_ranges(cv.event_starts, cv.event_ends; embargo = cv.embargo) + 1 <= cv.n_test_groups < cv.n_splits <= n_samples || throw(ArgumentError("invalid combinatorial group counts")) + segments = _validation_segments(cv) + splits = _combinatorial_cv_split(cv, segments) + locations = _path_locations(cv.n_splits, collect(combinations(1:cv.n_splits, cv.n_test_groups))) + paths = Dict(path => [(collect(segments[group]), split) for (group, split) in coordinates] for (path, coordinates) in locations) + return splits, paths +end + +function _prediction_layout(cv::Union{KFoldCV,PurgedKFoldCV,WalkForwardCV}, n_samples; permutation = nothing) + if cv isa KFoldCV + splits = cv_split(cv, n_samples; permutation) + elseif cv isa WalkForwardCV + permutation === nothing || throw(ArgumentError("permutation is only supported for shuffled K-fold")) + splits = cv_split(cv, n_samples) + else + permutation === nothing || throw(ArgumentError("permutation is only supported for shuffled K-fold")) + length(cv.event_starts) == n_samples || throw(DimensionMismatch("events must align with observations")) + _validate_event_ranges(cv.event_starts, cv.event_ends; embargo = cv.embargo) + 1 <= cv.n_splits <= n_samples || throw(ArgumentError("invalid fold count")) + splits = cv_split(cv) + end + return splits, Dict(1 => [(test, split) for (split, (_, test)) in enumerate(splits)]) +end + +function _checked_path_prediction(model, data, predict_function, probability) + result = predict_function(model, data) + expected_dimensions = probability ? 2 : 1 + result isa AbstractArray && ndims(result) == expected_dimensions || throw(DimensionMismatch("prediction callback returned the wrong rank")) + size(result, 1) == size(data, 1) || throw(DimensionMismatch("predictions must align with test observations")) + return copy(result) +end + +""" + backtest_predictions(cv, estimator, x, y; clone_estimator, fit_estimator, + predict, sample_weights=nothing, predict_probability=false, + predict_proba=nothing, permutation=nothing) + +Fit caller-selected estimators and assemble the released validation paths. +`clone_estimator(estimator)` must return an independent estimator with the desired +reset semantics. `fit_estimator(model, x_train, y_train, weights)` returns its +fitted model. `predict(model, x_test)` returns a vector; `predict_proba` returns a +matrix with a consistent class-column order. Each fit receives the aligned +observation weights, or ones when omitted; callback failures propagate. + +Combinatorial validators fit once per combination and reuse that model for all +of its path segments. Paths have integer keys and chronological group order. +Shuffled K-fold restores original observation order and accepts a supplied +one-based permutation for cross-language comparisons. Walk-forward folds with +no training observations produce NaNs. Execution is serial. This interface does +not choose an estimator, replace observation weights, or silently retry an +unweighted fit. A dictionary of validators, estimators, matrices and label +vectors supports independent datasets under identical keys. +""" +function backtest_predictions(cv, estimator, x::AbstractMatrix, y::AbstractVector; clone_estimator, fit_estimator, predict, sample_weights = nothing, predict_probability::Bool = false, predict_proba = nothing, permutation = nothing) + n_samples = size(x, 1) + length(y) == n_samples || throw(DimensionMismatch("labels must align with observations")) + weights = sample_weights === nothing ? ones(n_samples) : Float64.(sample_weights) + length(weights) == n_samples || throw(DimensionMismatch("weights must align with observations")) + all(value -> isfinite(value) && value >= 0, weights) || throw(ArgumentError("sample weights must be finite and nonnegative")) + probability_function = predict_probability ? predict_proba : predict + probability_function === nothing && throw(ArgumentError("probability predictions require predict_proba")) + splits, paths = _prediction_layout(cv, n_samples; permutation) + models = map(splits) do (train, _) + cv isa WalkForwardCV && isempty(train) && return nothing + model = clone_estimator(estimator) + return fit_estimator(model, x[train, :], y[train], weights[train]) + end + predictions = Dict{Int,Any}() + for (path, coordinates) in paths + parts = map(coordinates) do (test, split) + if cv isa WalkForwardCV && isempty(splits[split][1]) + return predict_probability ? fill(NaN, length(test), length(unique(y))) : fill(NaN, length(test)) + end + return _checked_path_prediction(models[split], x[test, :], probability_function, predict_probability) + end + values = reduce(vcat, parts) + if cv isa KFoldCV && cv.shuffle + positions = reduce(vcat, [test for (test, _) in coordinates]) + ordering = sortperm(positions) + values = predict_probability ? values[ordering, :] : values[ordering] + end + predictions[path] = values + end + return predictions +end + +function backtest_predictions(validators::AbstractDict, estimators::AbstractDict, x::AbstractDict, y::AbstractDict; sample_weights = nothing, kwargs...) + keyset = Set(keys(x)) + all(Set(keys(value)) == keyset for value in (validators, estimators, y)) || throw(ArgumentError("dataset keys must match")) + sample_weights === nothing || sample_weights isa AbstractDict && Set(keys(sample_weights)) == keyset || throw(ArgumentError("sample-weight keys must match")) + return Dict(key => backtest_predictions(validators[key], estimators[key], x[key], y[key]; sample_weights = sample_weights === nothing ? nothing : sample_weights[key], kwargs...) for key in keys(x)) +end diff --git a/src/Validation/BaggedCombinatorialCV.jl b/src/Validation/BaggedCombinatorialCV.jl new file mode 100644 index 0000000..4246f69 --- /dev/null +++ b/src/Validation/BaggedCombinatorialCV.jl @@ -0,0 +1,198 @@ +using Random: randperm + +""" + BaggedCombinatorialPurgedCV(n_splits, n_test_groups, event_starts, event_ends; + embargo=0.0, classifier=true, n_estimators=10, max_samples=1.0, + max_features=1.0, bootstrap=true, bootstrap_features=false, + random_seed=nothing) + +Fit a caller-selected bagged estimator for each combinatorial purged split. +Integer sample/feature sizes are counts; floating sizes are fractions. Under +the validated scikit-learn 1.9.1 contract, fractional sample counts use the sum +of training weights. Sample weights determine row-draw probabilities. A +weight-aware base estimator receives draw multiplicities, while an explicitly +unweighted base estimator receives resampled rows. The wrapped index validator +is available as `base`. +""" +struct BaggedCombinatorialPurgedCV + base::CombinatorialPurgedCV + classifier::Bool + n_estimators::Int + max_samples::Union{Int,Float64} + max_features::Union{Int,Float64} + bootstrap::Bool + bootstrap_features::Bool + random_seed::Union{Nothing,Int} +end + +function BaggedCombinatorialPurgedCV(n_splits::Integer, n_test_groups::Integer, event_starts::AbstractVector, event_ends::AbstractVector; embargo::Real = 0.0, classifier::Bool = true, n_estimators::Integer = 10, max_samples::Real = 1.0, max_features::Real = 1.0, bootstrap::Bool = true, bootstrap_features::Bool = false, random_seed::Union{Nothing,Integer} = nothing) + _validate_event_ranges(event_starts, event_ends; embargo) + 1 <= n_test_groups < n_splits <= length(event_starts) || throw(ArgumentError("invalid combinatorial group counts")) + n_estimators >= 1 || throw(ArgumentError("n_estimators must be positive")) + for (name, value) in (("max_samples", max_samples), ("max_features", max_features)) + (value isa Integer ? value >= 1 : isfinite(value) && 0 < value <= 1) || throw(ArgumentError("$name must be a positive count or a fraction in (0, 1]")) + end + base = CombinatorialPurgedCV(n_splits, n_test_groups, event_starts, event_ends; embargo) + return BaggedCombinatorialPurgedCV(base, classifier, Int(n_estimators), max_samples isa Integer ? Int(max_samples) : Float64(max_samples), max_features isa Integer ? Int(max_features) : Float64(max_features), bootstrap, bootstrap_features, random_seed === nothing ? nothing : Int(random_seed)) +end + +cv_split(cv::BaggedCombinatorialPurgedCV) = cv_split(cv.base) +backtest_paths(cv::BaggedCombinatorialPurgedCV) = backtest_paths(cv.base) +get_n_splits(cv::BaggedCombinatorialPurgedCV) = get_n_splits(cv.base) +_cv_splits(cv::BaggedCombinatorialPurgedCV, _) = cv_split(cv) + +function _bagging_sizes(cv, weights, n_features) + isempty(weights) && throw(ArgumentError("bagging requires nonempty training data")) + all(value -> isfinite(value) && value >= 0, weights) && sum(weights) > 0 || throw(ArgumentError("training weights must be finite, nonnegative and have positive sum")) + sample_count = cv.max_samples isa Integer ? cv.max_samples : max(1, floor(Int, cv.max_samples * sum(weights))) + feature_count = cv.max_features isa Integer ? cv.max_features : max(1, floor(Int, cv.max_features * n_features)) + feature_count <= n_features || throw(ArgumentError("max_features exceeds the available features")) + cv.bootstrap || sample_count <= count(>(0), weights) || throw(ArgumentError("sampling without replacement needs enough positive-weight observations")) + return sample_count, feature_count +end + +function _bagging_weighted_draws(rng, weights, count, replace) + available = copy(weights) + indices = Int[] + for _ in 1:count + cumulative = cumsum(available) + total = cumulative[end] + threshold = min(rand(rng) * total, prevfloat(total)) + position = searchsortedlast(cumulative, threshold) + 1 + push!(indices, position) + replace || (available[position] = 0.0) + end + return indices +end + +function _bagging_supplied_plan(values, n_combinations, n_estimators, name) + values === nothing && return nothing + length(values) == n_combinations && all(length(value) == n_estimators for value in values) || throw(DimensionMismatch("$name must provide one entry per estimator in every combination")) + return values +end + +function _bagging_draw_plan(cv, splits, weights, n_features; sample_indices, feature_indices, estimator_seeds, rng) + sample_indices = _bagging_supplied_plan(sample_indices, length(splits), cv.n_estimators, "sample_indices") + feature_indices = _bagging_supplied_plan(feature_indices, length(splits), cv.n_estimators, "feature_indices") + estimator_seeds = _bagging_supplied_plan(estimator_seeds, length(splits), cv.n_estimators, "estimator_seeds") + return map(enumerate(splits)) do (split, (train, _)) + local_rng = cv.random_seed === nothing ? rng : MersenneTwister(cv.random_seed) + train_weights = weights[train] + sample_count, feature_count = _bagging_sizes(cv, train_weights, n_features) + map(1:cv.n_estimators) do member + seed = estimator_seeds === nothing ? rand(local_rng, 0:(typemax(Int32)-1)) : Int(estimator_seeds[split][member]) + features = if feature_indices !== nothing + Int.(feature_indices[split][member]) + elseif cv.bootstrap_features + rand(local_rng, 1:n_features, feature_count) + elseif feature_count == n_features + collect(1:n_features) + else + randperm(local_rng, n_features)[1:feature_count] + end + samples = sample_indices === nothing ? _bagging_weighted_draws(local_rng, train_weights, sample_count, cv.bootstrap) : Int.(sample_indices[split][member]) + length(features) == feature_count && all(index -> 1 <= index <= n_features, features) || throw(ArgumentError("invalid supplied feature indices")) + length(samples) == sample_count && all(index -> 1 <= index <= length(train), samples) || throw(ArgumentError("invalid supplied sample indices")) + cv.bootstrap_features || allunique(features) || throw(ArgumentError("feature draws without replacement must be distinct")) + cv.bootstrap || allunique(samples) || throw(ArgumentError("row draws without replacement must be distinct")) + all(train_weights[index] > 0 for index in samples) || throw(ArgumentError("a zero-weight observation cannot be sampled")) + return (samples = samples, features = features, seed = seed) + end + end +end + +function _fit_bagged_path(cv, prototype, x, y, plan, clone_estimator, fit_estimator, fit_sample_weights, estimator_classes) + classes = cv.classifier ? sort(unique(y)) : [] + encoded = cv.classifier ? [searchsortedfirst(classes, label)-1 for label in y] : y + members = map(plan) do draw + model = applicable(clone_estimator, prototype, draw.seed) ? clone_estimator(prototype, draw.seed) : clone_estimator(prototype) + if fit_sample_weights + multiplicities = zeros(Float64, length(y)) + for position in draw.samples + multiplicities[position] += 1 + end + train_x, train_y, train_weights = x[:, draw.features], copy(encoded), multiplicities + else + train_x, train_y, train_weights = x[draw.samples, draw.features], encoded[draw.samples], nothing + end + fitted = fit_estimator(model, train_x, train_y, train_weights) + codes = cv.classifier ? (estimator_classes === nothing ? sort(unique(train_y)) : collect(estimator_classes(fitted))) : [] + if cv.classifier + all(code -> code isa Integer && 0 <= code < length(classes), codes) && allunique(codes) || throw(ArgumentError("estimator class codes must index the zero-based encoded labels")) + end + return (model = fitted, features = copy(draw.features), codes = Int.(codes)) + end + return (members = members, classes = classes) +end + +function _bagged_probability(fitted, x, predict_function, predict_proba) + probability = zeros(size(x, 1), length(fitted.classes)) + for member in fitted.members + data = x[:, member.features] + if predict_proba === nothing + predicted = _checked_path_prediction(member.model, data, predict_function, false) + for (row, code) in enumerate(predicted) + code isa Integer && 0 <= code < length(fitted.classes) || throw(ArgumentError("classifier predictions must be zero-based encoded labels")) + probability[row, code+1] += 1 + end + else + values = _checked_path_prediction(member.model, data, predict_proba, true) + size(values, 2) == length(member.codes) || throw(DimensionMismatch("probability columns must align with estimator class codes")) + probability[:, member.codes .+ 1] .+= values + end + end + return probability ./ length(fitted.members) +end + +function _bagged_regression(fitted, x, predict_function) + values = zeros(size(x, 1)) + for member in fitted.members + values .+= _checked_path_prediction(member.model, x[:, member.features], predict_function, false) + end + return values ./ length(fitted.members) +end + +""" + backtest_predictions(cv::BaggedCombinatorialPurgedCV, estimator, x, y; + clone_estimator, fit_estimator, predict, predict_proba=nothing, + estimator_classes=nothing, fit_sample_weights=true, sample_weights=nothing, + predict_probability=false, sample_indices=nothing, feature_indices=nothing, + estimator_seeds=nothing, rng=default_rng()) + +Assemble serial bagged CPCV paths using explicit caller-estimator callbacks. +`clone_estimator(prototype, seed)` can receive a fresh native seed; a one-argument +callback is also accepted for deterministic estimators. Shared draws and seeds +are nested as `[combination][estimator]`, with one-based positions relative to +the combination's training rows and the original feature columns. Supplied +draws must follow the requested replacement rules and avoid zero-weight rows. +Cross-language seeds alone do not specify identical random streams. + +Classifier targets are encoded as `0:k-1`, matching the public bagging wrapper; +returned labels use the original sorted classes. `predict_proba` columns default +to sorted encoded classes present in each member's fit; `estimator_classes` +can declare a different order. When `predict_proba` is absent, hard class votes +are averaged. Regressors average their predictions and reject probability +requests. Set `fit_sample_weights=false` only for an intentionally unweighted +estimator: the fitting callback then receives sampled rows and `nothing` as its +weight argument. All fitting failures propagate without an unweighted retry. +""" +function backtest_predictions(cv::BaggedCombinatorialPurgedCV, estimator, x::AbstractMatrix, y::AbstractVector; clone_estimator, fit_estimator, predict, predict_proba = nothing, estimator_classes = nothing, fit_sample_weights::Bool = true, sample_weights = nothing, predict_probability::Bool = false, sample_indices = nothing, feature_indices = nothing, estimator_seeds = nothing, rng::AbstractRNG = default_rng()) + !cv.classifier && predict_probability && throw(ArgumentError("regressors do not produce class probabilities")) + length(y) == size(x, 1) || throw(DimensionMismatch("labels must align with observations")) + weights = sample_weights === nothing ? ones(size(x, 1)) : Float64.(sample_weights) + length(weights) == size(x, 1) || throw(DimensionMismatch("weights must align with observations")) + splits, _ = _prediction_layout(cv.base, size(x, 1)) + plans = _bagging_draw_plan(cv, splits, weights, size(x, 2); sample_indices, feature_indices, estimator_seeds, rng) + split = Ref(0) + fit_bag = function(prototype, train_x, train_y, train_weights) + split[] += 1 + return _fit_bagged_path(cv, prototype, train_x, train_y, plans[split[]], clone_estimator, fit_estimator, fit_sample_weights, estimator_classes) + end + probability = (fitted, data) -> _bagged_probability(fitted, data, predict, predict_proba) + prediction = if cv.classifier + (fitted, data) -> [fitted.classes[argmax(row)] for row in eachrow(probability(fitted, data))] + else + (fitted, data) -> _bagged_regression(fitted, data, predict) + end + return backtest_predictions(cv.base, estimator, x, y; clone_estimator = identity, fit_estimator = fit_bag, predict = prediction, predict_proba = probability, predict_probability, sample_weights = weights) +end diff --git a/src/Validation/CrossValScore.jl b/src/Validation/CrossValScore.jl index ec859c8..6d9adac 100644 --- a/src/Validation/CrossValScore.jl +++ b/src/Validation/CrossValScore.jl @@ -19,6 +19,7 @@ _cv_splits(cv::KFoldCV, n_samples) = cv_split(cv, n_samples) _cv_splits(cv::WalkForwardCV, n_samples) = cv_split(cv, n_samples) _cv_splits(cv::PurgedKFoldCV, _) = cv_split(cv) _cv_splits(cv::CombinatorialPurgedCV, _) = cv_split(cv) +_cv_splits(cv::AdaptiveCombinatorialPurgedCV, _) = cv_split(cv) function _accuracy(forest, x_test, y_test) return sum(apply_forest(forest, x_test) .== y_test) / length(y_test) diff --git a/src/Validation/CrossValidators.jl b/src/Validation/CrossValidators.jl index 2df4917..f5c0ff9 100644 --- a/src/Validation/CrossValidators.jl +++ b/src/Validation/CrossValidators.jl @@ -3,8 +3,8 @@ Cross-validators — native Julia port mirroring the Python `RiskLabAI.backtest.validation` sub-package (López de Prado, AFML Ch. 7): standard K-Fold, Purged K-Fold with embargo, Combinatorial Purged Cross-Validation (CPCV), and Walk-Forward. These produce **train/test index -splits**; the estimator-driven `backtest_predictions` (which needs an ML backend) -is wired with the cross-validation-scoring slice. +splits** and chronological backtest paths. Estimator-driven predictions use +explicit cloning, fitting and prediction callbacks. Representation note (deliberate divergence): pandas `times` (a Series indexed by observation-start, valued by observation-end) becomes two parallel vectors @@ -63,7 +63,7 @@ function _purged_train_indices( current_start = sorted_starts[1] current_end = sorted_ends[1] for k = 2:length(sorted_starts) - if sorted_starts[k] > current_end + if sorted_starts[k] > sorted_ends[k-1] push!(block_starts, current_start) push!(block_ends, current_end) current_start = sorted_starts[k] @@ -127,13 +127,16 @@ get_n_splits(cv::KFoldCV) = cv.n_splits Train/test index splits (1-based) for `n_samples` observations. """ -function cv_split(cv::KFoldCV, n_samples::Integer) - indices = collect(1:n_samples) - cv.shuffle && shuffle!(cv.rng, indices) +function cv_split(cv::KFoldCV, n_samples::Integer; permutation = nothing) + 1 <= cv.n_splits <= n_samples || throw(ArgumentError("n_splits must be between one and the sample count")) + indices = permutation === nothing ? collect(1:n_samples) : Int.(permutation) + sort(indices) == collect(1:n_samples) || throw(ArgumentError("permutation must contain every observation exactly once")) + permutation !== nothing && !cv.shuffle && throw(ArgumentError("a supplied permutation requires shuffle=true")) + cv.shuffle && permutation === nothing && shuffle!(cv.rng, indices) splits = Tuple{Vector{Int},Vector{Int}}[] for fold in _array_split(n_samples, cv.n_splits) test = indices[fold] - train = setdiff(indices, test) + train = sort!(setdiff(indices, test)) push!(splits, (train, test)) end return splits @@ -223,6 +226,10 @@ _combination_test(segments, combination) = function cv_split(cv::CombinatorialPurgedCV) n = length(cv.event_starts) segments = _array_split(n, cv.n_splits) + return _combinatorial_cv_split(cv, segments) +end + +function _combinatorial_cv_split(cv, segments) splits = Tuple{Vector{Int},Vector{Int}}[] for combination in combinations(1:cv.n_splits, cv.n_test_groups) test = _combination_test(segments, combination) @@ -250,6 +257,9 @@ function _path_locations(n_splits::Integer, combination_list) push!(get!(locations, path_id, Tuple{Int,Int}[]), (group, j)) end end + for coordinates in values(locations) + sort!(coordinates; by = first) + end return locations end @@ -264,6 +274,10 @@ path. Mirrors Python's `_single_backtest_paths`. function backtest_paths(cv::CombinatorialPurgedCV) n = length(cv.event_starts) segments = _array_split(n, cv.n_splits) + return _combinatorial_backtest_paths(cv, segments) +end + +function _combinatorial_backtest_paths(cv, segments) combination_list = collect(combinations(1:cv.n_splits, cv.n_test_groups)) locations = _path_locations(cv.n_splits, combination_list) @@ -308,6 +322,9 @@ WalkForwardCV(n_splits::Integer; max_train_size = nothing, gap::Integer = 0) = get_n_splits(cv::WalkForwardCV) = cv.n_splits function cv_split(cv::WalkForwardCV, n_samples::Integer) + 1 <= cv.n_splits <= n_samples || throw(ArgumentError("n_splits must be between one and the sample count")) + cv.gap >= 0 || throw(ArgumentError("gap must be nonnegative")) + cv.max_train_size === nothing || cv.max_train_size > 0 || throw(ArgumentError("max_train_size must be positive")) splits = Tuple{Vector{Int},Vector{Int}}[] for fold in _array_split(n_samples, cv.n_splits) test = collect(fold) diff --git a/src/Validation/HyperParameterTuning.jl b/src/Validation/HyperParameterTuning.jl index f6bfc3b..727a5e2 100644 --- a/src/Validation/HyperParameterTuning.jl +++ b/src/Validation/HyperParameterTuning.jl @@ -184,7 +184,7 @@ function leakage_aware_hpo( n_trials = n_trials, trial_scores = scores, mean_trial_score = isempty(scores) ? NaN : mean(scores), - std_trial_score = length(scores) < 2 ? NaN : std(scores; corrected = false), + std_trial_score = isempty(scores) ? NaN : std(scores; corrected = false), ) end diff --git a/src/Validation/LeakageAwareSearch.jl b/src/Validation/LeakageAwareSearch.jl new file mode 100644 index 0000000..556d172 --- /dev/null +++ b/src/Validation/LeakageAwareSearch.jl @@ -0,0 +1,92 @@ +""" + _purged_cv_score(estimator_factory, params, x, y, cv; fit_estimator, + predict=nothing, scoring=nothing) + +Score a fresh caller estimator on each eligible purged fold. The public contract +skips folds with fewer than 25 training rows, fewer than five testing rows or +fewer than two training classes. Fits receive no observation weights. A custom +`scoring(model, x_test, y_test)` replaces accuracy; an empty score list returns +negative infinity. Exceptions propagate. +""" +function _purged_cv_score(estimator_factory, params, x, y, cv::PurgedKFoldCV; + fit_estimator, predict = nothing, scoring = nothing) + size(x, 1) == length(y) == length(cv.event_starts) == length(cv.event_ends) || throw(DimensionMismatch("features, labels and event intervals must align")) + scoring === nothing && predict === nothing && throw(ArgumentError("provide predict or a custom scoring callback")) + scores = Float64[] + for (train, test) in cv_split(cv) + (length(train) < 25 || length(test) < 5 || length(unique(y[train])) < 2) && continue + model = estimator_factory(params) + fitted = fit_estimator(model, x[train, :], y[train]) + fitted !== nothing && (model = fitted) + value = if scoring === nothing + predicted = predict(model, x[test, :]) + length(predicted) == length(test) || throw(DimensionMismatch("prediction count must match the testing fold")) + mean(predicted .== y[test]) + else + scoring(model, x[test, :], y[test]) + end + push!(scores, Float64(value)) + end + return isempty(scores) ? -Inf : mean(scores) +end + +function _hpo_trial_summary(history, n_trials) + completed = filter(trial -> trial.state == :complete, history) + isempty(completed) && throw(ArgumentError("no completed trial is available")) + best = first(completed) + for trial in Iterators.drop(completed, 1) + trial.score > best.score && (best = trial) + end + scores = Float64[trial.score for trial in history if isfinite(trial.score)] + return (best_params = deepcopy(best.params), best_score = best.score, + n_trials = n_trials, trial_scores = scores, + mean_trial_score = isempty(scores) ? NaN : mean(scores), + std_trial_score = isempty(scores) ? NaN : std(scores; corrected = false)) +end + +""" + leakage_aware_hpo(suggest_params, estimator_factory, x, y; event_starts, + event_ends, ask_trial, fit_estimator, predict=nothing, scoring=nothing, + trial_parameters=trial->trial.params, tell_trial! = (trial,score)->nothing, + n_trials=50, n_splits=5, embargo=0.0, random_state=0) + +Run leakage-controlled optimization with an explicit caller-supplied sampler. +`ask_trial(number, history, rng)` returns a trial consumed by `suggest_params`. +History contains completed/failed scores and sampled parameter dictionaries; +`tell_trial!` can update a stateful sampler. The sampled `trial_parameters` define +`best_params`, while the dictionary returned by `suggest_params` configures the +fresh estimator and may also contain fixed settings. Nonfinite scores are absent +from finite-score summaries; NaN trials fail, while infinities remain completed. +Ties retain the first completed maximum and finite-score deviation is population +deviation, including zero for a singleton. + +This overload implements the public optimization orchestration and purged +objective. It requires an actual TPE/CMA-ES provider when that search family is +requested; it does not substitute random search or claim native sampler parity. +Recorded proposals support exact validation but are not fresh optimization. +""" +function leakage_aware_hpo(suggest_params, estimator_factory, + x::AbstractMatrix, y::AbstractVector; + event_starts::AbstractVector, event_ends::AbstractVector, + ask_trial, fit_estimator, predict = nothing, scoring = nothing, + trial_parameters = trial -> trial.params, + tell_trial! = (trial, score) -> nothing, + n_trials::Integer = 50, n_splits::Integer = 5, + embargo::Real = 0., random_state::Integer = 0) + n_trials >= 0 || throw(ArgumentError("n_trials must be nonnegative")) + 1 <= n_splits <= size(x, 1) || throw(ArgumentError("n_splits must be between one and the sample count")) + cv = PurgedKFoldCV(n_splits, event_starts, event_ends; embargo = embargo) + rng = MersenneTwister(random_state) + history = NamedTuple[] + for number in 1:n_trials + trial = ask_trial(number, deepcopy(history), rng) + configuration = suggest_params(trial) + score = _purged_cv_score(estimator_factory, configuration, x, y, cv; + fit_estimator = fit_estimator, predict = predict, scoring = scoring) + params = deepcopy(trial_parameters(trial)) + tell_trial!(trial, score) + push!(history, (number = number, params = params, score = score, + state = isnan(score) ? :failed : :complete)) + end + return _hpo_trial_summary(history, n_trials) +end diff --git a/src/Validation/PathAdaptiveCPCV.jl b/src/Validation/PathAdaptiveCPCV.jl index 4b1f177..8dc0e78 100644 --- a/src/Validation/PathAdaptiveCPCV.jl +++ b/src/Validation/PathAdaptiveCPCV.jl @@ -20,7 +20,7 @@ Combinatorial Purged Cross-Validation. Bailey, Borwein, López de Prado & Zhu (2017), Journal of Computational Finance 20(4). """ -using ..Backtest: performance_evaluation, sharpe_ratio +using ..Backtest: performance_evaluation, sharpe_ratio, _first_metric_maximum using Statistics: mean, median, quantile using Combinatorics: combinations @@ -53,8 +53,9 @@ end Observable regime label per period from the cross-configuration volatility: the per-period dispersion across configurations (a market-stress proxy), smoothed by a trailing (causal) moving average, then split at the observed median (`n_regimes ≤ 2`) -or the interior quantiles (`n_regimes > 2`). Uses only returns up to each period, so -it carries no look-ahead. `window` defaults to `max(T ÷ 20, 3)`. Mirrors Python's +or the interior quantiles (`n_regimes > 2`). The smoothed feature uses each +period's prefix, while cutoff estimation uses the full supplied sample. +`window` defaults to `max(T ÷ 20, 3)`. Mirrors Python's `estimate_volatility_regimes`. """ function estimate_volatility_regimes( @@ -72,6 +73,9 @@ function estimate_volatility_regimes( cumulative[k+1] = cumulative[k] + dispersion[k] end smooth = [(cumulative[j+1] - cumulative[max(j - w, 0)+1]) / min(j, w) for j = 1:t_len] + if any(isnan, smooth) + return n_regimes <= 2 ? zeros(Int, t_len) : [isnan(value) ? 0 : n_regimes - 1 for value in smooth] + end if n_regimes <= 2 med = median(smooth) return Int.(smooth .> med) @@ -92,7 +96,8 @@ function _cscv_path_stats( P::AbstractMatrix{<:Real}, ranges::Vector{UnitRange{Int}}, metric, - risk_free_return::Real, + risk_free_return::Real; + rank_order = nothing, ) n_strategies = size(P, 2) all_ids = collect(1:length(ranges)) @@ -102,7 +107,7 @@ function _cscv_path_stats( train = reduce(vcat, (P[ranges[i], :] for i in train_ids)) test = reduce(vcat, (P[ranges[i], :] for i in test_ids)) is_overfit, _ = - performance_evaluation(train, test, n_strategies, metric, risk_free_return) + performance_evaluation(train, test, n_strategies, metric, risk_free_return; rank_order = rank_order) test_metrics = [metric(view(test, :, j), risk_free_return) for j = 1:n_strategies] push!(stats, (is_overfit ? 1.0 : 0.0, test_metrics, test_ids)) end @@ -127,7 +132,9 @@ and there is adequate data for selection (it does not help where selection is noise-dominated by too little data); it converges to plain CPCV in a stationary regime with no over-adaptation. -Mirrors Python's `adaptive_probability_of_backtest_overfitting`. +Mirrors Python's `adaptive_probability_of_backtest_overfitting` conditional on +the same ranks. Optional `rank_order(scores)` supplies each path's ascending +strategy permutation; native default ordering of tied scores can differ. """ function adaptive_probability_of_backtest_overfitting( performances::AbstractMatrix{<:Real}; @@ -136,6 +143,7 @@ function adaptive_probability_of_backtest_overfitting( risk_free_return::Real = 0.0, metric = nothing, n_regimes::Integer = 2, + rank_order = nothing, ) P = float.(performances) isodd(n_partitions) && throw(ArgumentError("Number of partitions must be even.")) @@ -153,7 +161,7 @@ function adaptive_probability_of_backtest_overfitting( ranges = _array_split_ranges(t_len, n_partitions) regime_partitions = [regimes[r] for r in ranges] - stats = _cscv_path_stats(P, ranges, chosen_metric, risk_free_return) + stats = _cscv_path_stats(P, ranges, chosen_metric, risk_free_return; rank_order = rank_order) overfit = [s[1] for s in stats] test_metrics = [s[2] for s in stats] @@ -168,6 +176,6 @@ function adaptive_probability_of_backtest_overfitting( pbo = sum(weights .* overfit) / wsum col_means = [sum(weights[k] * test_metrics[k][j] for k = 1:n_paths) / wsum for j = 1:n_configs] - selected = argmax(col_means) + selected = _first_metric_maximum(col_means) return (pbo, selected) end diff --git a/src/Validation/PathBaggedCPCV.jl b/src/Validation/PathBaggedCPCV.jl index 86454e5..c0640f0 100644 --- a/src/Validation/PathBaggedCPCV.jl +++ b/src/Validation/PathBaggedCPCV.jl @@ -33,17 +33,20 @@ Moving-block bootstrap row indices (1-based) for a length-`n_observations` serie random block start points each contribute `block_size` consecutive wrap-around rows, truncated to `n_observations`. Blocks preserve the serial structure. Mirrors Python's `moving_block_bootstrap_indices` (which returns 0-based indices). +Optional one-based `starts` permits exact primitive-draw replay. """ function moving_block_bootstrap_indices( n_observations::Integer, block_size::Integer, - rng::AbstractRNG, + rng::AbstractRNG; + starts = nothing, ) block_size = max(Int(block_size), 1) n_blocks = cld(n_observations, block_size) - starts = rand(rng, 0:(n_observations-1), n_blocks) + block_starts = starts === nothing ? rand(rng, 0:(n_observations-1), n_blocks) : Vector{Int}(starts) .- 1 + length(block_starts) == n_blocks && all(start -> 0 <= start < n_observations, block_starts) || throw(ArgumentError("Supply one valid start per circular block.")) out = Int[] - for s in starts, o = 0:(block_size-1) + for s in block_starts, o = 0:(block_size-1) push!(out, (s + o) % n_observations) end return out[1:n_observations] .+ 1 @@ -64,7 +67,9 @@ overfitting (PBO) estimate whenever the CPCV path set is small or noisy; it converges to plain CPCV in the data-rich limit and is neutral on which model is selected. -Mirrors Python's `bagged_probability_of_backtest_overfitting`. +Optional `bootstrap_rows` supplies one complete one-based row vector per bag, +and `rank_order` forwards a tied-score ordering provider through every CSCV +path. Equal native and NumPy seeds do not imply equal draws. """ function bagged_probability_of_backtest_overfitting( performances::AbstractMatrix{<:Real}; @@ -74,20 +79,26 @@ function bagged_probability_of_backtest_overfitting( risk_free_return::Real = 0.0, metric = nothing, rng::AbstractRNG = MersenneTwister(0), + bootstrap_rows = nothing, + rank_order = nothing, ) P = float.(performances) t_len = size(P, 1) + n_bag >= 0 || throw(ArgumentError("n_bag must be nonnegative.")) + bootstrap_rows === nothing || length(bootstrap_rows) == n_bag || throw(DimensionMismatch("Supply one row vector per bag.")) bs = block_size === nothing ? max(t_len ÷ 20, 5) : block_size pbos = Float64[] - for _ = 1:n_bag - rows = moving_block_bootstrap_indices(t_len, bs, rng) + for bag = 1:n_bag + rows = bootstrap_rows === nothing ? moving_block_bootstrap_indices(t_len, bs, rng) : Vector{Int}(bootstrap_rows[bag]) + length(rows) == t_len && all(index -> 1 <= index <= t_len, rows) || throw(ArgumentError("Each bag must contain one valid row position per observation.")) pbo, _ = probability_of_backtest_overfitting( P[rows, :]; n_partitions = n_partitions, risk_free_return = risk_free_return, metric = metric, + rank_order = rank_order, ) push!(pbos, pbo) end - return (mean(pbos), pbos) + return (isempty(pbos) ? NaN : mean(pbos), pbos) end diff --git a/src/Validation/PipelineHyperparameterSearch.jl b/src/Validation/PipelineHyperparameterSearch.jl new file mode 100644 index 0000000..cbebdcd --- /dev/null +++ b/src/Validation/PipelineHyperparameterSearch.jl @@ -0,0 +1,180 @@ +using ..Features: _importance_log_loss + +function _pipeline_grids(grid) + grids = grid isa AbstractDict || grid isa NamedTuple ? [grid] : collect(grid) + !isempty(grids) || throw(ArgumentError("at least one parameter grid is required")) + return [_pipeline_options(part) for part in grids] +end + +_pipeline_parameter_list(value) = value isa AbstractVector || value isa Tuple + +"""Enumerate finite parameter grids with sorted keys and the last key varying fastest.""" +function pipeline_parameter_grid(grid) + candidates = Dict{String,Any}[] + for part in _pipeline_grids(grid) + all(_pipeline_parameter_list(value) && !isempty(value) for value in values(part)) || throw(ArgumentError("grid parameters must be nonempty vectors or tuples")) + rows = [Dict{String,Any}()] + for key in sort(collect(keys(part))) + rows = [merge(row, Dict{String,Any}(key => deepcopy(value))) for row in rows for value in part[key]] + end + append!(candidates, rows) + end + return candidates +end + +function _pipeline_candidates(grid, iterations, indices, draws, rng) + iterations >= 0 || throw(ArgumentError("rnd_search_iter must be nonnegative")) + indices !== nothing && draws !== nothing && throw(ArgumentError("supply candidate indices or draws, not both")) + if iterations == 0 + indices === nothing && draws === nothing || throw(ArgumentError("supplied random candidates require rnd_search_iter > 0")) + return pipeline_parameter_grid(grid) + end + grids = _pipeline_grids(grid) + finite = all(_pipeline_parameter_list(value) for part in grids for value in values(part)) + if draws !== nothing + length(draws) == iterations || throw(DimensionMismatch("candidate_draws must contain rnd_search_iter parameter dictionaries")) + return _pipeline_options.(draws) + elseif finite + candidates = pipeline_parameter_grid(grid) + count = min(iterations, length(candidates)) + positions = indices === nothing ? randperm(rng, length(candidates))[1:count] : Int.(indices) + length(positions) == count && allunique(positions) && all(position -> 1 <= position <= length(candidates), positions) || throw(ArgumentError("candidate_indices must be distinct valid positions in the finite grid")) + return candidates[positions] + end + indices === nothing || throw(ArgumentError("candidate_indices require a finite parameter grid")) + return map(1:iterations) do _ + part = rand(rng, grids) + Dict{String,Any}(key => deepcopy(rand(rng, part[key])) for key in sort(collect(keys(part)))) + end +end + +function _pipeline_slice_options(options, rows, count) + return Dict{String,Any}(key => value isa AbstractArray && size(value, 1) == count ? (ndims(value) == 1 ? value[rows] : value[rows, ntuple(_ -> Colon(), ndims(value)-1)...]) : deepcopy(value) for (key, value) in options) +end + +function _pipeline_search_score(pipeline, x, y, weights, scoring) + if scoring == :f1 + predicted = predict_pipeline(pipeline, x) + length(predicted) == length(y) && all(value -> value in (0, 1), predicted) || throw(ArgumentError("binary F1 requires one binary prediction per observation")) + true_positive = sum(weights[(y .== 1) .& (predicted .== 1)]) + false_positive = sum(weights[(y .!= 1) .& (predicted .== 1)]) + false_negative = sum(weights[(y .== 1) .& (predicted .!= 1)]) + denominator = 2true_positive + false_positive + false_negative + return denominator == 0 ? 0.0 : 2true_positive / denominator + end + classes = pipeline_classes(pipeline) + probability = predict_pipeline_proba(pipeline, x) + length(classes) >= 2 && classes == sort(unique(classes)) || throw(ArgumentError("log-loss estimators must expose at least two sorted distinct classes")) + size(probability) == (length(y), length(classes)) || throw(DimensionMismatch("probabilities must align with estimator classes")) + response = length(classes) == 2 ? probability[:, 2] : probability + return -_importance_log_loss(y, response, weights, sort(unique(y))) +end + +function _pipeline_bagging(best, bagging, x, y, weights; sample_indices, feature_indices, estimator_seeds, rng) + length(bagging) == 3 || throw(ArgumentError("bagging must contain estimator count, sample fraction and feature fraction")) + count = trunc(Int, bagging[1]) + sample_fraction = bagging[2] > 0 ? Float64(bagging[2]) : 1.0 + feature_fraction = Float64(bagging[3]) + count >= 1 && 0 < sample_fraction <= 1 && 0 < feature_fraction <= 1 || throw(ArgumentError("invalid bagging count or fractions")) + options = (classifier = true, n_estimators = count, max_samples = sample_fraction, max_features = feature_fraction, bootstrap = true, bootstrap_features = false, random_seed = nothing) + prototype = clone_pipeline(best) + fit_bag = function(name, _, data, labels, params) + all(key == "sample_weight" for key in keys(params)) || throw(ArgumentError("post-search bagging accepts only global sample_weight without supplier metadata routing")) + fitted_weights = get(params, "sample_weight", ones(length(labels))) + splits = [(collect(1:length(labels)), Int[])] + plans = _bagging_draw_plan(options, splits, fitted_weights, size(data, 2); sample_indices = sample_indices === nothing ? nothing : [sample_indices], feature_indices = feature_indices === nothing ? nothing : [feature_indices], estimator_seeds = estimator_seeds === nothing ? nothing : [estimator_seeds], rng) + return _fit_bagged_path(options, prototype, data, labels, only(plans), (model, seed) -> clone_pipeline(model; seed), (model, tx, ty, tw) -> fit_pipeline!(model, tx, ty; sample_weight = tw), true, pipeline_classes) + end + probability_callback = best.callbacks.predict_proba_step === nothing ? nothing : predict_pipeline_proba + probabilities = (_, fitted, data) -> _bagged_probability(fitted, data, predict_pipeline, probability_callback) + predictions = (_, fitted, data) -> [fitted.classes[argmax(row)] for row in eachrow(probabilities(nothing, fitted, data))] + bag = SampleWeightedPipeline(["bag" => nothing]; clone_step = (args...) -> nothing, fit_step = fit_bag, transform_step = (_, _, data) -> data, predict_step = predictions, predict_proba_step = probabilities, classes_step = (_, fitted) -> fitted.classes) + return fit_pipeline!(bag, x, y; sample_weight = weights) +end + +""" + clf_hyper_fit(cv, x, y, pipeline, param_grid; sample_weight=nothing, + fit_params=Dict(), rnd_search_iter=0, bagging=nothing, + candidate_indices=nothing, candidate_draws=nothing, folds=nothing, + return_search=false, error_score=NaN, rng=default_rng(), + bagging_sample_indices=nothing, bagging_feature_indices=nothing, + bagging_estimator_seeds=nothing) + +Search a caller-selected native pipeline with explicit cross-validation. Binary +labels exactly `{0,1}` use F1 with positive label 1; other labels use negative log +loss. As in the validated scikit-learn 1.9.1 supplier, top-level sample weights +route to both fitting and scoring. Step-prefixed weights affect fitting only. +Every observation-aligned fitting array is sliced to each training fold. +Candidates and folds have independent unfitted pipeline clones. Fold means are +unweighted arithmetic means; ties select the first candidate. The winner is +refitted on every observation. Individual fit/scoring failures default to NaN; +all failed fits raise an error. `error_score=:raise` propagates the first error. + +Finite randomized grids sample without replacement. Native distribution values +use `rand(rng, distribution)` and sample with replacement. One-based candidate +indices or explicit drawn parameter dictionaries replay external random draws; +equal seeds across languages do not imply identical draws. Native execution is +serial. Optional `[count, sample_fraction, feature_fraction]` bagging returns a +one-step pipeline fitted to the full data under the validated weighted bagging +contract. Supplied bagging draws are nested by member, with one-based original +row/feature positions. Extra step-prefixed fit options with bagging are rejected, +matching the supplier's default metadata-routing configuration. + +The default return value is the fitted pipeline, matching the public function's +actual return. `return_search=true` additionally exposes candidate parameters, +fold scores, mean scores, first-best position and failure diagnostics. +""" +function clf_hyper_fit(cv, x::AbstractMatrix, y::AbstractVector, pipeline::SampleWeightedPipeline, param_grid; sample_weight = nothing, fit_params = Dict{String,Any}(), rnd_search_iter::Integer = 0, bagging = nothing, candidate_indices = nothing, candidate_draws = nothing, folds = nothing, return_search::Bool = false, error_score = NaN, rng::AbstractRNG = default_rng(), bagging_sample_indices = nothing, bagging_feature_indices = nothing, bagging_estimator_seeds = nothing) + count = length(y) + count == size(x, 1) && count >= 2 || throw(DimensionMismatch("search requires aligned observations and labels")) + error_score === :raise || error_score isa Real || throw(ArgumentError("error_score must be numeric or :raise")) + options = _pipeline_options(fit_params) + haskey(options, "sample_weight") && (sample_weight === nothing && (sample_weight = options["sample_weight"]); delete!(options, "sample_weight")) + pop!(options, "groups", nothing) + weights = sample_weight === nothing ? ones(count) : Float64.(sample_weight) + length(weights) == count && all(value -> isfinite(value) && value >= 0, weights) && sum(weights) > 0 || throw(ArgumentError("search weights must be finite, nonnegative and observation-aligned with positive sum")) + sample_weight === nothing || (options["sample_weight"] = weights) + splits = folds === nothing ? collect(_cv_splits(cv, count)) : [(Int.(first(fold)), Int.(last(fold))) for fold in folds] + !isempty(splits) || throw(ArgumentError("search requires at least one split")) + all(allunique(train) && allunique(test) && isempty(intersect(train, test)) && !isempty(test) && all(index -> 1 <= index <= count, [train; test]) for (train, test) in splits) || throw(ArgumentError("folds must contain disjoint valid observation positions and nonempty test sets")) + candidates = _pipeline_candidates(param_grid, rnd_search_iter, candidate_indices, candidate_draws, rng) + scoring = Set(y) == Set([0, 1]) ? :f1 : :neg_log_loss + scores = fill(NaN, length(candidates), length(splits)) + failures = NamedTuple[] + successful_fits = 0 + for (candidate, parameters) in enumerate(candidates), (fold, (train, test)) in enumerate(splits) + model = configure_pipeline!(clone_pipeline(pipeline), parameters) + phase = :fit + try + fit_pipeline!(model, x[train, :], y[train]; fit_params = _pipeline_slice_options(options, train, count)) + successful_fits += 1 + phase = :score + scores[candidate, fold] = _pipeline_search_score(model, x[test, :], y[test], weights[test], scoring) + catch error + error isa InterruptException && rethrow() + error_score === :raise && rethrow() + scores[candidate, fold] = Float64(error_score) + push!(failures, (candidate = candidate, fold = fold, phase = phase, message = sprint(showerror, error))) + end + end + successful_fits > 0 || throw(ArgumentError("all $(length(candidates)*length(splits)) candidate fits failed")) + means = vec(mean(scores; dims = 2)) + ranked = [isnan(value) ? -Inf : value for value in means] + best_index = argmax(ranked) + best = configure_pipeline!(clone_pipeline(pipeline), candidates[best_index]) + fit_pipeline!(best, x, y; fit_params = options) + estimator = if bagging !== nothing && first(bagging) > 0 + all(key == "sample_weight" for key in keys(options)) || throw(ArgumentError("post-search bagging accepts only global sample_weight without supplier metadata routing")) + _pipeline_bagging(best, bagging, x, y, sample_weight === nothing ? nothing : weights; sample_indices = bagging_sample_indices, feature_indices = bagging_feature_indices, estimator_seeds = bagging_estimator_seeds, rng) + else + best + end + evidence = (estimator = estimator, best_estimator = best, best_index = best_index, best_params = deepcopy(candidates[best_index]), candidates = candidates, fold_scores = scores, mean_scores = means, scoring = scoring, failures = failures) + return return_search ? evidence : estimator +end + +"""Use the public default five-fold purged search with one-percent embargo.""" +function clf_hyper_fit(x::AbstractMatrix, y::AbstractVector, event_starts::AbstractVector, event_ends::AbstractVector, pipeline::SampleWeightedPipeline, param_grid; n_splits::Integer = 5, embargo::Real = 0.01, kwargs...) + cv = PurgedKFoldCV(n_splits, event_starts, event_ends; embargo) + return clf_hyper_fit(cv, x, y, pipeline, param_grid; kwargs...) +end diff --git a/src/Validation/SampleWeightedPipeline.jl b/src/Validation/SampleWeightedPipeline.jl new file mode 100644 index 0000000..7eb2317 --- /dev/null +++ b/src/Validation/SampleWeightedPipeline.jl @@ -0,0 +1,128 @@ +""" + SampleWeightedPipeline(steps; clone_step, fit_step, transform_step, + predict_step, predict_proba_step=nothing, classes_step, + configure_step, fit_transform_step=nothing) + +Compose named native estimators without selecting their model family. Callbacks +receive `(name, step, ...)`. `fit_step` receives training features, labels and a +string-keyed options dictionary and returns the fitted step. Transformer steps +use `transform_step`; an optional `fit_transform_step` can instead return the +fitted step and training transformation together. Prediction callbacks receive +the fitted final step and transformed features; `classes_step` returns its +ordered class labels. `clone_step` must return a fresh unfitted estimator and +may accept a third seed argument. `configure_step` applies a parameter dictionary +to one step. Intermediate `nothing` or `:passthrough` steps are supported. + +Top-level fitting weights route only to the final estimator. Explicit +`name__option` fitting arguments route to the named step. Global weights override +an explicit final-step weight, preserving the public SampleWeightedPipeline +contract. No estimator is silently replaced or retried without weights. +""" +mutable struct SampleWeightedPipeline + steps::Vector{Pair{String,Any}} + callbacks::NamedTuple + fitted::Bool +end + +function SampleWeightedPipeline(steps; clone_step, fit_step, transform_step, predict_step, predict_proba_step = nothing, classes_step, configure_step = (name, step, options) -> isempty(options) ? step : throw(ArgumentError("configure_step is required for nonempty parameters")), fit_transform_step = nothing) + named = Pair{String,Any}[string(first(step)) => last(step) for step in steps] + !isempty(named) && allunique(first.(named)) && all(!isempty(first(step)) && !occursin("__", first(step)) for step in named) || throw(ArgumentError("pipeline step names must be nonempty, unique and exclude __")) + callbacks = (; clone_step, fit_step, transform_step, predict_step, predict_proba_step, classes_step, configure_step, fit_transform_step) + return SampleWeightedPipeline(named, callbacks, false) +end + +_pipeline_passthrough(step) = step === nothing || step === :passthrough +_pipeline_options(options) = Dict{String,Any}(string(key) => deepcopy(value) for (key, value) in pairs(options)) + +"""Clone every pipeline step through its caller-supplied unfitted-estimator contract.""" +function clone_pipeline(pipeline::SampleWeightedPipeline; seed = nothing) + callback = pipeline.callbacks.clone_step + steps = map(pipeline.steps) do (name, step) + cloned = _pipeline_passthrough(step) ? step : seed !== nothing && applicable(callback, name, step, seed) ? callback(name, step, seed) : callback(name, step) + return name => cloned + end + return SampleWeightedPipeline(steps; pipeline.callbacks...) +end + +"""Apply `step__parameter` options to native steps, or replace a named step.""" +function configure_pipeline!(pipeline::SampleWeightedPipeline, parameters) + options = _pipeline_options(parameters) + names = first.(pipeline.steps) + for key in keys(options) + first(split(key, "__"; limit = 2)) in names || throw(ArgumentError("unknown pipeline step in parameter $key")) + end + for (position, (name, step)) in enumerate(pipeline.steps) + replacement = get(options, name, step) + nested = Dict{String,Any}(chopprefix(key, name * "__") => value for (key, value) in options if startswith(key, name * "__")) + replacement = pipeline.callbacks.configure_step(name, replacement, nested) + pipeline.steps[position] = name => replacement + end + pipeline.fitted = false + return pipeline +end + +"""Fit each transformation on training rows, routing global weights only to the final step.""" +function fit_pipeline!(pipeline::SampleWeightedPipeline, x::AbstractMatrix, y::AbstractVector; sample_weight = nothing, fit_params = Dict{String,Any}()) + size(x, 1) == length(y) || throw(DimensionMismatch("pipeline features and labels must align")) + options = _pipeline_options(fit_params) + haskey(options, "sample_weight") && (sample_weight === nothing && (sample_weight = options["sample_weight"]); delete!(options, "sample_weight")) + if sample_weight !== nothing + length(sample_weight) == length(y) || throw(DimensionMismatch("pipeline weights must align with labels")) + options[first(pipeline.steps[end]) * "__sample_weight"] = copy(sample_weight) + end + routed = Dict(name => Dict{String,Any}() for (name, _) in pipeline.steps) + for (key, value) in options + parts = split(key, "__"; limit = 2) + length(parts) == 2 && haskey(routed, parts[1]) && !isempty(parts[2]) || throw(ArgumentError("fit options must use a known step__option name")) + routed[parts[1]][parts[2]] = value + end + pipeline.fitted = false + data, labels = copy(x), copy(y) + for position in eachindex(pipeline.steps) + name, step = pipeline.steps[position] + final = position == length(pipeline.steps) + if !final && _pipeline_passthrough(step) + continue + elseif !final && pipeline.callbacks.fit_transform_step !== nothing + fitted, data = pipeline.callbacks.fit_transform_step(name, step, data, labels, routed[name]) + else + fitted = pipeline.callbacks.fit_step(name, step, data, labels, routed[name]) + final || (data = pipeline.callbacks.transform_step(name, fitted, data)) + end + pipeline.steps[position] = name => fitted + size(data, 1) == length(y) || throw(DimensionMismatch("pipeline transformations must preserve observation count")) + end + pipeline.fitted = true + return pipeline +end + +function _pipeline_transform(pipeline, x) + pipeline.fitted || throw(ArgumentError("pipeline must be fitted before prediction")) + data = copy(x) + for (name, step) in pipeline.steps[1:end-1] + _pipeline_passthrough(step) || (data = pipeline.callbacks.transform_step(name, step, data)) + size(data, 1) == size(x, 1) || throw(DimensionMismatch("pipeline transformations must preserve observation count")) + end + return data +end + +"""Predict labels with the fitted pipeline, preserving the caller's estimator.""" +function predict_pipeline(pipeline::SampleWeightedPipeline, x::AbstractMatrix) + data = _pipeline_transform(pipeline, x) + name, fitted = pipeline.steps[end] + return pipeline.callbacks.predict_step(name, fitted, data) +end + +"""Predict class probabilities in the order returned by `pipeline_classes`.""" +function predict_pipeline_proba(pipeline::SampleWeightedPipeline, x::AbstractMatrix) + pipeline.callbacks.predict_proba_step !== nothing || throw(ArgumentError("pipeline has no probability callback")) + data = _pipeline_transform(pipeline, x) + name, fitted = pipeline.steps[end] + return pipeline.callbacks.predict_proba_step(name, fitted, data) +end + +"""Return the fitted final estimator's ordered class labels.""" +function pipeline_classes(pipeline::SampleWeightedPipeline) + pipeline.fitted || throw(ArgumentError("pipeline must be fitted before reading classes")) + return collect(pipeline.callbacks.classes_step(pipeline.steps[end]...)) +end diff --git a/src/Validation/Validation.jl b/src/Validation/Validation.jl index 0c13f45..34219bc 100644 --- a/src/Validation/Validation.jl +++ b/src/Validation/Validation.jl @@ -5,9 +5,9 @@ Cross-validation submodule, mirroring the Python `RiskLabAI.backtest.validation` sub-package (López de Prado, AFML Ch. 7): standard K-Fold, Purged K-Fold with embargo, Combinatorial Purged Cross-Validation (CPCV), and Walk-Forward. -This slice wires the **index-generating** logic (`cv_split`, `backtest_paths`, -`get_n_splits`). The estimator-driven `backtest_predictions` lands with the -cross-validation-scoring slice once the ML backend is wired. +The index-generating interfaces (`cv_split`, `backtest_paths`, `get_n_splits`) +include supplied-feature adaptive boundaries. `backtest_predictions` accepts +explicit estimator callbacks and preserves aligned observation weights. Deliberate divergence: Python nests this under `backtest.validation`; the Julia package exposes it as the top-level `RiskLabAI.Validation` submodule. The @@ -17,12 +17,18 @@ by plain structs + multiple dispatch. module Validation include("CrossValidators.jl") +include("AdaptiveCombinatorialCV.jl") +include("BacktestPredictions.jl") +include("BaggedCombinatorialCV.jl") # Estimator-driven scoring over the cross-validators (DecisionTree.jl backend). include("CrossValScore.jl") # Grid / randomised hyper-parameter search over the cross-validators. include("HyperParameterTuning.jl") +include("LeakageAwareSearch.jl") +include("SampleWeightedPipeline.jl") +include("PipelineHyperparameterSearch.jl") # Path-level Bagged / Adaptive CPCV PBO (Arian–Norouzi–Seco 2024). Loaded after the # Backtest submodule (these call its CSCV PBO primitives via `..Backtest`). @@ -33,13 +39,26 @@ export KFoldCV, PurgedKFoldCV, CombinatorialPurgedCV, + AdaptiveCombinatorialPurgedCV, + BaggedCombinatorialPurgedCV, WalkForwardCV, cv_split, get_n_splits, backtest_paths, + backtest_predictions, + filtered_training_indices_with_embargo, cross_val_score, grid_search_cv, random_search_cv, + SampleWeightedPipeline, + clone_pipeline, + configure_pipeline!, + fit_pipeline!, + predict_pipeline, + predict_pipeline_proba, + pipeline_classes, + pipeline_parameter_grid, + clf_hyper_fit, leakage_aware_hpo, deflated_sharpe_gate, # path-level Bagged / Adaptive CPCV diff --git a/test/adaptation_regressions.jl b/test/adaptation_regressions.jl new file mode 100644 index 0000000..d3251cb --- /dev/null +++ b/test/adaptation_regressions.jl @@ -0,0 +1,165 @@ +using Test +using TOML +using LinearAlgebra +using Statistics +using RiskLabAI +using RiskLabAI.Backtest: prequential_adaptation + +mutable struct AdaptationFixtureEstimator + family::String + penalty::Float64 + trained::Bool + center::Float64 + feature_mean::Vector{Float64} + weights::Vector{Float64} +end + +AdaptationFixtureEstimator(family, penalty) = AdaptationFixtureEstimator(family, penalty, false, NaN, Float64[], Float64[]) +adaptation_fixture_clone(model) = AdaptationFixtureEstimator(model.family, model.penalty) + +function adaptation_fixture_fit!(model, features, targets) + model.trained && error("Fixture estimator must be a fresh clone") + model.center = mean(targets) + if model.family == "ridge" + model.feature_mean = vec(mean(features; dims = 1)) + centered = features .- model.feature_mean' + model.weights = (centered' * centered + model.penalty * I) \ (centered' * (targets .- model.center)) + end + model.trained = true + return model +end + +function adaptation_fixture_predict(model, features) + model.trained || error("Prediction needs a fitted model") + return model.family == "mean" ? fill(model.center, size(features, 1)) : model.center .+ (features .- model.feature_mean') * model.weights +end + +adaptation_fixture_loss(target, prediction) = min(1.0, abs(target - prediction)) +adaptation_fixture_vector(values) = [value == "nan" ? NaN : Float64(value) for value in values] + +@testset "ADWIN2 fixed-default streaming traces against River 0.26.1" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "adaptation_python_320.toml")) + for case in fixture["detectors"] + window = RiskLabAI.Backtest._ADWINWindow(case["delta"]) + widths, totals, squares, alarms = Int[], Float64[], Float64[], Int[] + summary_valid = true + maximum_centered_error = 0.0 + for (index, value) in enumerate(case["values"]) + alarm = RiskLabAI.Backtest._adwin_update!(window, value) + alarm && push!(alarms, index) + push!(widths, window.width) + push!(totals, window.total) + push!(squares, window.centered_squares) + counts = getfield.(window.summaries, :count) + summary_valid &= sum(counts) == window.width && issorted(counts; rev = true) + summary_valid &= all(ispow2, counts) && all(count(==(size), counts) <= 5 for size in unique(counts)) + summary_valid &= isapprox(sum(item.total for item in window.summaries), window.total; rtol = 1e-10, atol = 1e-11) + reconstructed = sum(item.centered_squares + item.count * (item.total / item.count - window.total / window.width)^2 for item in window.summaries) + maximum_centered_error = max(maximum_centered_error, abs(reconstructed - window.centered_squares)) + end + @test widths == case["widths"] + @test alarms == case["alarm_indices"] .+ 1 + @test isapprox(totals, case["totals"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(squares, case["centered_squares"]; rtol = 1e-10, atol = 1e-12) + @test summary_valid + @test maximum_centered_error < 1e-9 + end +end + +@testset "Prequential forecasts, alarm rows and observed rolling refits" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "adaptation_python_320.toml")) + for case in fixture["adaptations"] + features = permutedims(hcat(case["features"]...)) + targets = Float64.(case["targets"]) + original_x, original_y = copy(features), copy(targets) + template = AdaptationFixtureEstimator(case["family"], case["penalty"]) + history = Tuple{Matrix{Float64}, Vector{Float64}}[] + function fit_observed!(model, x, y) + push!(history, (copy(x), copy(y))) + return adaptation_fixture_fit!(model, x, y) + end + settings = (fit_estimator = fit_observed!, predict_estimator = adaptation_fixture_predict, + clone_estimator = adaptation_fixture_clone, loss_function = adaptation_fixture_loss, + window_size = case["window_size"], min_samples = case["min_samples"], delta = case["delta"]) + output = prequential_adaptation(features, targets, template; settings...) + warmup = case["min_samples"] + @test all(isnan, output.predictions[1:warmup]) + @test all(isnan, output.losses[1:warmup]) + @test isapprox(output.predictions[warmup+1:end], adaptation_fixture_vector(case["predictions"])[warmup+1:end]; rtol = 1e-10, atol = 1e-12) + @test isapprox(output.losses[warmup+1:end], adaptation_fixture_vector(case["losses"])[warmup+1:end]; rtol = 1e-10, atol = 1e-12) + @test output.alarm_indices == case["alarm_indices"] .+ 1 + @test output.refit_indices == case["refit_indices"] .+ 1 + @test length(history) == length(case["fit_intervals"]) + for (observed, interval) in zip(history, case["fit_intervals"]) + indices = (interval[1]+1):interval[2] + @test observed[1] == features[indices, :] + @test observed[2] == targets[indices] + end + @test !template.trained + @test features == original_x && targets == original_y + for stop in (warmup + 1, 257, 513, length(targets) - 1) + prefix = prequential_adaptation(features[1:stop, :], targets[1:stop], template; settings...) + @test isequal(prefix.predictions, output.predictions[1:stop]) + @test isequal(prefix.losses, output.losses[1:stop]) + @test prefix.alarm_indices == filter(<=(stop), output.alarm_indices) + @test prefix.refit_indices == filter(<=(stop), output.refit_indices) + end + end +end + +@testset "Adaptation domains, independent input slices and alarm reset timing" begin + x, y = reshape(collect(1.0:12.0), :, 1), zeros(12) + model = AdaptationFixtureEstimator("mean", 0.1) + settings = (fit_estimator = adaptation_fixture_fit!, predict_estimator = adaptation_fixture_predict, + clone_estimator = adaptation_fixture_clone, loss_function = adaptation_fixture_loss, + window_size = 5, min_samples = 2, delta = 0.01) + for bad_delta in (0, 1, -0.1, NaN, Inf, true, "0.1") + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., delta = bad_delta) + end + for count in (0, -1, true, 2.5) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., window_size = count) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., min_samples = count) + end + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., min_samples = 6) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., min_samples = 12, window_size = 12) + @test_throws ArgumentError prequential_adaptation(x, y[1:end-1], model; settings...) + @test_throws ArgumentError prequential_adaptation(vec(x), y, model; settings...) + @test_throws ArgumentError prequential_adaptation(x, reshape(y, :, 1), model; settings...) + @test_throws ArgumentError prequential_adaptation(zeros(12, 0), y, model; settings...) + @test_throws ArgumentError prequential_adaptation(fill(NaN, size(x)), y, model; settings...) + @test_throws ArgumentError prequential_adaptation(x, fill(Inf, length(y)), model; settings...) + @test_throws ArgumentError prequential_adaptation(trues(size(x)), y, model; settings...) + for bad_loss in (-0.1, 1.1, NaN, Inf, true, [0.1]) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., loss_function = (a, b) -> bad_loss) + end + for bad_prediction in (1.0, [1.0, 2.0], reshape([1.0], 1, 1), [NaN], [Inf], [true]) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., predict_estimator = (m, a) -> bad_prediction) + end + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., loss_function = nothing) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., clone_estimator = nothing) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., fit_estimator = nothing) + @test_throws ArgumentError prequential_adaptation(x, y, model; settings..., predict_estimator = nothing) + function destructive_fit!(fresh, training, targets) + fitted = adaptation_fixture_fit!(fresh, training, targets) + training .= -100 + targets .= -100 + return fitted + end + function destructive_predict(fitted, row) + prediction = adaptation_fixture_predict(fitted, row) + row .= -100 + return prediction + end + output = prequential_adaptation(x, y, model; settings..., fit_estimator = destructive_fit!, predict_estimator = destructive_predict) + @test x == reshape(collect(1.0:12.0), :, 1) + @test y == zeros(12) + @test output.predictions[3:end] == zeros(10) + window = RiskLabAI.Backtest._ADWINWindow(0.01) + for value in [zeros(400); ones(400)] + RiskLabAI.Backtest._adwin_update!(window, value) && break + end + @test window.drift_detected + @test !RiskLabAI.Backtest._adwin_update!(window, 0.25) + @test window.width == 1 && window.clock == 1 + @test window.total == 0.25 && window.centered_squares == 0.0 +end diff --git a/test/allocation_regressions.jl b/test/allocation_regressions.jl new file mode 100644 index 0000000..e0ae919 --- /dev/null +++ b/test/allocation_regressions.jl @@ -0,0 +1,91 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +const AllocationContracts = RiskLabAI.CausalFactorAnalysis +const allocation_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "allocation_python_320.toml")) +allocation_matrix(rows) = reduce(vcat, permutedims.(rows)) + +@testset "Factor equality allocations against Python 3.2.0" begin + for row in allocation_fixtures["weights"] + covariance, exposures, targets = allocation_matrix(row["covariance"]), allocation_matrix(row["exposures"]), copy(row["targets"]) + result = AllocationContracts.minimum_variance_factor_weights(covariance, exposures, targets) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + scales = vec(maximum(abs.(exposures); dims = 1)) + @test all(abs.((exposures ./ scales')' * result - targets ./ scales) .<= [value == 0 ? 1e-12 : 1e-10abs(value) for value in targets ./ scales]) + @test covariance == allocation_matrix(row["covariance"]) && exposures == allocation_matrix(row["exposures"]) && targets == row["targets"] + @test all(isfinite, result) + end + covariance = Matrix{Float64}(I, 3, 3) + exposures = [1.0 0.0; 0.0 1.0; 1.0 1.0] + @test AllocationContracts.minimum_variance_factor_weights(covariance, exposures, zeros(2)) == zeros(3) + @test AllocationContracts.minimum_variance_factor_weights([1.0;;], [1e-300;;], [1e-200]) ≈ [1e100] + @test AllocationContracts.minimum_variance_factor_weights([1.0;;], [1e300;;], [1e200]) ≈ [1e-100] rtol = 1e-10 atol = 0.0 + for invalid in (zeros(0, 0), ones(2, 3), zeros(3, 3), [1.0 1.0; 1.0 1.0], [-1.0 0.0; 0.0 1.0], [1.0 0.1; 0.0 1.0], [1.0 NaN; NaN 1.0], [1im;;]) + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights(invalid, exposures, [0.0, 1.0]) + end + for invalid in (zeros(3, 0), zeros(3, 1), ones(3, 4), ones(3, 2), ones(2, 2), [1.0 NaN; 0.0 1.0; 1.0 1.0], complex.(exposures)) + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights(covariance, invalid, [0.0, 1.0]) + end + for invalid in (Float64[], [1.0], [1.0, NaN], ones(2, 1), [1im, 1im]) + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights(covariance, exposures, invalid) + end + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights([1.0;;], [1e300;;], [1e-300]) + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights([1.0;;], [1e-300;;], [1e300]) +end + +@testset "Nearly dependent equality constraints" begin + covariance = Matrix(Diagonal([1.0, 1.3, 1.8, 2.4])) + base_one = [1.0, -1.0, 0.5, -0.5] + base_two = [0.5, 0.2, -0.8, 0.1] + separation = 1e-6 + exposures = hcat(ones(4), base_one, base_one + separation * base_two) + targets = [1.0, 0.0, separation] + weights = AllocationContracts.minimum_variance_factor_weights(covariance, exposures, targets) + expected = [0.871437914635384, 0.500046715886808, -0.5571335140096719, 0.18564888348747993] + @test weights ≈ expected rtol = 1e-10 atol = 1e-12 + scales = vec(maximum(abs.(exposures); dims = 1)) + scaled_exposures = exposures ./ scales' + scaled_targets = targets ./ scales + tolerances = [value == 0 ? 1e-12 : 1e-10 * abs(value) for value in scaled_targets] + precise_feasibility = setprecision(BigFloat, 256) do + residuals = abs.(BigFloat.(scaled_exposures)' * BigFloat.(weights) - BigFloat.(scaled_targets)) + all(residuals .<= BigFloat.(tolerances)) + end + @test precise_feasibility + unresolved_separation = 1e-8 + unresolved_exposures = hcat(ones(4), base_one, base_one + unresolved_separation * base_two) + @test_throws ArgumentError AllocationContracts.minimum_variance_factor_weights(covariance, unresolved_exposures, [1.0, 0.0, unresolved_separation]) +end + +@testset "Allocation comparison evidence and copied inputs" begin + for row in allocation_fixtures["diagnostics"] + covariance, reference, misspecified = allocation_matrix(row["covariance"]), allocation_matrix(row["reference"]), allocation_matrix(row["misspecified"]) + reference_targets, misspecified_targets = copy(row["reference_targets"]), copy(row["misspecified_targets"]) + result = AllocationContracts.allocation_misspecification_diagnostics(covariance, reference, reference_targets, misspecified, misspecified_targets) + @test result isa AllocationContracts.AllocationMisspecificationDiagnostics + for name in propertynames(result) + actual, expected = getproperty(result, name), row["expected"][string(name)] + if name == :weight_sign_reversals + @test collect(actual) == expected + else + @test actual ≈ expected rtol = 1e-10 atol = 1e-12 + end + end + @test all(isapprox.(reference' * result.reference_weights, reference_targets; rtol = 1e-10, atol = 1e-12)) + @test all(isapprox.(misspecified' * result.misspecified_weights, misspecified_targets; rtol = 1e-10, atol = 1e-12)) + saved = collect(result.reference_target_exposures) + reference_targets .= 99 + @test collect(result.reference_target_exposures) == saved + @test result.reference_weights isa Vector{Float64} + end + tiny = AllocationContracts.allocation_misspecification_diagnostics([1.0;;], [1.0;;], [1e-200], [1.0;;], [-1e-200]) + @test tiny.weight_sign_reversals == [true] + @test tiny.reference_weights[1] * tiny.misspecified_weights[1] == -0.0 + zero = AllocationContracts.allocation_misspecification_diagnostics([1.0;;], [1.0;;], [0.0], [1.0;;], [-1.0]) + @test zero.weight_sign_reversals == [false] + @test_throws ArgumentError AllocationContracts.allocation_misspecification_diagnostics([true;;], [1.0;;], [1.0], [1.0;;], [1.0]) + @test_throws ArgumentError AllocationContracts.allocation_misspecification_diagnostics([1.0;;], [true;;], [1.0], [1.0;;], [1.0]) + @test_throws ArgumentError AllocationContracts.allocation_misspecification_diagnostics([1.0;;], [1.0;;], [true], [1.0;;], [1.0]) +end diff --git a/test/backtest_statistics_regressions.jl b/test/backtest_statistics_regressions.jl new file mode 100644 index 0000000..518d25e --- /dev/null +++ b/test/backtest_statistics_regressions.jl @@ -0,0 +1,116 @@ +using Test +using TOML +using Dates +using RiskLabAI + +backstat_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-11, atol = 1e-13) +backstat_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(backstat_equal(a, b) for (a, b) in zip(left, right)) +backstat_dates(offsets) = DateTime(2020, 1, 1) .+ Millisecond.(Int.(offsets)) + +@testset "Shared backtest moments, positions and holding periods" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "backtest_statistics_python_320.toml")) + for case in fixture["sharpe"] + @test backstat_equal(sharpe_ratio(Float64[case["values"]...]; risk_free_rate = case["risk_free"]), case["output"]) + end + for case in fixture["hhi"] + @test backstat_equal(calculate_hhi(Float64[case["values"]...]), case["output"]) + end + for case in fixture["positions"] + values, dates = Float64[case["values"]...], backstat_dates(case["offsets"]) + original = copy(values) + @test bet_timing(dates, values) == dates[Int.(case["bets"])] + result = calculate_holding_period(dates, values) + expected = case["holding"] + @test result.holding_periods.index == dates[Int[row["position"] for row in expected]] + @test backstat_equal(result.holding_periods.dT, Float64[row["duration"] for row in expected]) + @test backstat_equal(result.holding_periods.w, Float64[row["weight"] for row in expected]) + @test backstat_equal(result.mean_holding_period, case["mean_holding_period"]) + @test values == original + end + dates = Date(2020, 1, 1) .+ Day.(0:3) + dated = calculate_holding_period(dates, [0., 2., 1., 0.]) + timestamped = calculate_holding_period(DateTime.(dates), [0., 2., 1., 0.]) + @test dated.holding_periods.dT == timestamped.holding_periods.dT + @test dated.mean_holding_period == timestamped.mean_holding_period +end + +@testset "Shared calendar concentration and missing-value drawdowns" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "backtest_statistics_python_320.toml")) + for case in fixture["concentration"] + values, dates = Float64[case["values"]...], DateTime.(case["dates"]) + actual = calculate_hhi_concentration(dates, values) + @test backstat_equal(collect(actual), Float64.(case["output"])) + @test backstat_equal(collect(calculate_hhi_concentration(Date.(dates), values)), collect(actual)) + end + for case in fixture["drawdown"] + values, dates = Float64[case["values"]...], backstat_dates(case["offsets"]) + original = copy(values) + result = compute_drawdowns_time_under_water(dates, values; dollars = case["dollars"]) + rows = case["rows"] + @test result.start == dates[Int[row["position"] for row in rows]] + @test backstat_equal(result.drawdown, Float64[row["drawdown"] for row in rows]) + @test backstat_equal(result.time_under_water, Float64[row["time_under_water"] for row in rows]) + @test isequal(values, original) + end + values = [1., .8, 1.1, .9] + dates = Date(2020, 1, 1) .+ Day.(0:3) + @test compute_drawdowns_time_under_water(dates, values).time_under_water == compute_drawdowns_time_under_water(DateTime.(dates), values).time_under_water + @test all(isnan, calculate_hhi_concentration(DateTime[], Float64[])) +end + +@testset "Shared conditional drawdown and studentized Sharpe bootstrap" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "backtest_statistics_python_320.toml")) + for case in fixture["ced"] + actual = conditional_expected_drawdown(Float64[case["values"]...], case["horizon"], case["alpha"]) + @test backstat_equal(actual, case["output"]) + end + for case in fixture["difference"] + a, b = Float64.(case["a"]), Float64.(case["b"]) + original = copy(a), copy(b) + draws = case["method"] == "naive" ? nothing : [Int.(indices) for indices in case["bootstrap_indices"]] + actual = sharpe_difference_test(a, b; method = case["method"], block_length = case["block_length"], n_boot = case["n_boot"], bandwidth = case["bandwidth"], bootstrap_indices = draws) + for (key, value) in pairs(actual) + @test backstat_equal(value, case["output"][string(key)]) + end + @test (a, b) == original + end + a, b = [.01, -.02, .04, -.01], [.02, .01, -.03, .01] + @test sharpe_difference_test(a, b; n_boot = 0).pvalue == 1. + @test sharpe_difference_test(a, b; n_boot = 7, random_state = 11) == sharpe_difference_test(a, b; n_boot = 7, random_state = 11) + @test_throws ArgumentError sharpe_difference_test(a, b; method = "invalid") + @test_throws ArgumentError sharpe_difference_test(a, b; block_length = 0) + @test_throws ArgumentError sharpe_difference_test(a, b; n_boot = -1) + @test_throws DimensionMismatch sharpe_difference_test(a, b; n_boot = 2, bootstrap_indices = [[1, 2, 3, 4]]) + @test_throws ArgumentError sharpe_difference_test(a, b; n_boot = 1, bootstrap_indices = [[1, 2, 3, 5]]) + @test_throws ArgumentError sharpe_difference_test(a, b; block_length = 2, n_boot = 1, bootstrap_indices = [[1, 3, 2, 3]]) + @test_throws ArgumentError conditional_expected_drawdown(a, 3, 1.) +end + +@testset "Shared Sharpe probability, influence and HAC inference" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "backtest_statistics_python_320.toml")) + for case in fixture["psr"] + actual = probabilistic_sharpe_ratio(case["observed"], case["benchmark"], case["count"]; skewness_of_returns = case["skew"], kurtosis_of_returns = case["kurtosis"]) + statistic = probabilistic_sharpe_ratio(case["observed"], case["benchmark"], case["count"]; skewness_of_returns = case["skew"], kurtosis_of_returns = case["kurtosis"], return_test_statistic = true) + @test backstat_equal(actual, case["output"]) + @test backstat_equal(statistic, case["statistic"]) + end + for case in fixture["benchmark"] + @test backstat_equal(benchmark_sharpe_ratio(Float64[case["values"]...]), case["output"]) + end + for case in fixture["influence"] + @test backstat_equal(sharpe_ratio_influence_function(Float64[case["values"]...]), Float64.(case["output"])) + end + for case in fixture["lrv"] + @test backstat_equal(newey_west_long_run_variance(Float64[case["values"]...], case["lag"]), case["output"]) + end + for case in fixture["lag"] + @test newey_west_automatic_lag(case["count"]) == case["output"] + end + for case in fixture["inference"] + actual = lplz_sharpe_inference(Float64[case["values"]...]; confidence_level = case["confidence"], lag = case["supplied_lag"] == -1 ? nothing : case["supplied_lag"], null_sharpe_ratio = case["null"]) + for (key, value) in pairs(actual) + expected = case["output"][string(key)] + @test value isa Tuple ? backstat_equal(collect(value), expected) : backstat_equal(value, expected) + end + end +end diff --git a/test/bagged_validation_regressions.jl b/test/bagged_validation_regressions.jl new file mode 100644 index 0000000..d151ab0 --- /dev/null +++ b/test/bagged_validation_regressions.jl @@ -0,0 +1,119 @@ +using Test +using TOML +using Random +using RiskLabAI + +bagged_matrix(values) = permutedims(reduce(hcat, [Float64.(row) for row in values])) + +function bagged_diagnostic_probability(model, data) + scores = log.(model.prior)' .+ (0.03 .* sum(data; dims = 2) .+ 0.02 * model.feature .+ mod(model.seed, 97) * 1e-5) .* (model.classes .+ 1)' + exponential = exp.(scores .- maximum(scores; dims = 2)) + return exponential ./ sum(exponential; dims = 2) +end + +function bagged_validator(case) + return BaggedCombinatorialPurgedCV(case["groups"], case["test_groups"], case["starts"], case["ends"]; embargo = case["embargo"], classifier = case["classifier"], n_estimators = case["n_estimators"], max_samples = case["max_samples"], max_features = case["max_features"], bootstrap = case["bootstrap"], bootstrap_features = case["bootstrap_features"], random_seed = case["random_seed"]) +end + +function bagged_callbacks(classifier, calls) + fit_estimator = function(model, x, y, sample_weights) + weights = sample_weights === nothing ? ones(length(y)) : sample_weights + feature = sum(weights .* vec(sum(x; dims = 2))) / sum(weights) + classes = classifier ? sort(unique(y)) : Int[] + push!(calls, (x = copy(x), y = copy(y), weights = copy(weights), seed = model.seed, classes = classes, weight_argument_missing = sample_weights === nothing)) + if classifier + prior = [1.0 + sum(weights[y .== code]) for code in classes] + return (feature = feature, prior = prior, classes = classes, seed = model.seed) + end + return (feature = feature, mean = sum(weights .* y) / sum(weights), seed = model.seed) + end + predict = classifier ? (model, data) -> [model.classes[argmax(row)] for row in eachrow(bagged_diagnostic_probability(model, data))] : (model, data) -> model.mean .+ .003 * model.feature .+ .01 .* vec(sum(data; dims = 2)) .+ mod(model.seed, 97)*1e-5 + return fit_estimator, predict +end + +@testset "Shared bagged CPCV draws, fitted inputs and predictions" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "bagged_validation_python_320.toml")) + for case in fixture["cases"] + validator = bagged_validator(case) + x = bagged_matrix(case["x"]) + y = case["classifier"] ? String.(case["y"]) : Float64.(case["y"]) + weights = Float64.(case["weights"]) + originals = deepcopy((x, y, weights, case["sample_indices"], case["feature_indices"], case["estimator_seeds"])) + calls = [] + fit_estimator, predict = bagged_callbacks(case["classifier"], calls) + prototype = (seed = 991,) + arguments = (clone_estimator = (model, seed) -> (seed = seed,), fit_estimator = fit_estimator, predict = predict, predict_proba = case["learner"] == "probability" ? bagged_diagnostic_probability : nothing, estimator_classes = case["classifier"] ? model -> model.classes : nothing, fit_sample_weights = case["consumes_weights"], sample_weights = case["weight_mode"] == "omitted" ? nothing : weights, sample_indices = case["sample_indices"], feature_indices = case["feature_indices"], estimator_seeds = case["estimator_seeds"]) + predictions = backtest_predictions(validator, prototype, x, y; arguments...) + expected_traces = reduce(vcat, case["traces"]) + @test length(calls) == length(expected_traces) == 18 + for (observed, expected) in zip(calls, expected_traces) + @test observed.x == bagged_matrix(expected["x"]) + @test observed.y == expected["y"] + @test observed.weights == expected["weights"] + @test observed.seed == expected["seed"] + @test observed.classes == expected["classes"] + @test observed.weight_argument_missing == !case["consumes_weights"] + end + @test get_n_splits(validator) == 6 + @test cv_split(validator) == cv_split(validator.base) + @test backtest_paths(validator) == backtest_paths(validator.base) + @test Set(keys(predictions)) == Set(1:3) + for (path, actual) in predictions + expected = case["predictions"][string(path)] + @test case["classifier"] ? actual == expected : isapprox(actual, Float64.(expected); rtol = 1e-12, atol = 1e-13) + end + if case["classifier"] + probabilities = backtest_predictions(validator, prototype, x, y; arguments..., predict_probability = true) + @test length(calls) == 36 + for (path, actual) in probabilities + @test isapprox(actual, bagged_matrix(case["probabilities"][string(path)]); rtol = 1e-12, atol = 1e-13) + @test all(isapprox.(vec(sum(actual; dims = 2)), 1.0; atol = 1e-14)) + end + end + @test prototype.seed == 991 + @test (x, y, weights, case["sample_indices"], case["feature_indices"], case["estimator_seeds"]) == originals + end +end + +@testset "Bagged class-column alignment, native draws and failures" begin + V = RiskLabAI.Validation + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "bagged_validation_python_320.toml")) + case = first(filter(item -> item["learner"] == "probability" && !item["consumes_weights"], fixture["cases"])) + validator = bagged_validator(case) + x, y, weights = bagged_matrix(case["x"]), String.(case["y"]), Float64.(case["weights"]) + fit_estimator, predict = bagged_callbacks(true, []) + common = (clone_estimator = (model, seed) -> (seed = seed,), fit_estimator = fit_estimator, predict = predict, sample_weights = weights, fit_sample_weights = false) + replay = (sample_indices = case["sample_indices"], feature_indices = case["feature_indices"], estimator_seeds = case["estimator_seeds"]) + reversed = backtest_predictions(validator, nothing, x, y; common..., replay..., predict_proba = (model, data) -> reverse(bagged_diagnostic_probability(model, data); dims = 2), estimator_classes = model -> reverse(model.classes), predict_probability = true) + @test all(isapprox(actual, bagged_matrix(case["probabilities"][string(path)]); rtol = 1e-12, atol = 1e-13) for (path, actual) in reversed) + native_first = backtest_predictions(validator, nothing, x, y; common..., predict_proba = bagged_diagnostic_probability) + native_second = backtest_predictions(validator, nothing, x, y; common..., predict_proba = bagged_diagnostic_probability) + @test native_first == native_second + @test all(length(prediction) == length(y) for prediction in values(native_first)) + @test V._bagging_sizes(validator, [1.5, .5, 1.], 4) == (1, 4) + @test V._bagging_weighted_draws(MersenneTwister(7), [0., 1., 2., 3.], 3, false) |> sort == [2, 3, 4] + draws = V._bagging_weighted_draws(MersenneTwister(19), [0., 1., 3., 6.], 20000, true) + @test all(index != 1 for index in draws) + @test maximum(abs.([count(==(index), draws)/length(draws) for index in 1:4] .- [0., .1, .3, .6])) < .02 + @test_throws ArgumentError BaggedCombinatorialPurgedCV(4, 2, 1:24, 1:24; n_estimators = 0) + @test_throws ArgumentError BaggedCombinatorialPurgedCV(4, 2, 1:24, 1:24; max_samples = 0.) + @test_throws ArgumentError BaggedCombinatorialPurgedCV(4, 2, 1:24, 1:24; max_features = 1.2) + @test_throws ArgumentError backtest_predictions(BaggedCombinatorialPurgedCV(4, 2, 1:24, 1:24; classifier = false), nothing, x[1:24, :], collect(1.:24.); common..., predict_probability = true) + @test_throws ArgumentError backtest_predictions(validator, nothing, x, y; clone_estimator = common.clone_estimator, fit_estimator = fit_estimator, predict = predict, sample_weights = zeros(length(y))) + @test_throws DimensionMismatch backtest_predictions(validator, nothing, x, y; common..., sample_indices = case["sample_indices"][1:5]) + invalid_features = deepcopy(case["feature_indices"]) + invalid_features[1][1][1] = 10 + @test_throws ArgumentError backtest_predictions(validator, nothing, x, y; common..., sample_indices = case["sample_indices"], feature_indices = invalid_features, estimator_seeds = case["estimator_seeds"]) + invalid_samples = deepcopy(case["sample_indices"]) + invalid_samples[1][1][1] = 1000 + @test_throws ArgumentError backtest_predictions(validator, nothing, x, y; common..., sample_indices = invalid_samples, feature_indices = case["feature_indices"], estimator_seeds = case["estimator_seeds"]) + @test_throws DimensionMismatch backtest_predictions(validator, nothing, x, y; common..., replay..., predict_proba = (model, data) -> zeros(size(data, 1), 9)) + @test_throws ArgumentError backtest_predictions(validator, nothing, x, y; common..., replay..., predict_proba = bagged_diagnostic_probability, estimator_classes = model -> [-1]) + failures = Ref(0) + failing_fit = function(model, x, y, weights) + failures[] += 1 + throw(ArgumentError("unsupported estimator fit")) + end + @test_throws ArgumentError backtest_predictions(validator, nothing, x, y; clone_estimator = common.clone_estimator, fit_estimator = failing_fit, predict = predict, replay...) + @test failures[] == 1 +end diff --git a/test/bar_stream_regressions.jl b/test/bar_stream_regressions.jl new file mode 100644 index 0000000..1a43388 --- /dev/null +++ b/test/bar_stream_regressions.jl @@ -0,0 +1,82 @@ +using Test +using TOML +using Dates +using RiskLabAI + +bar_close(left, right) = isequal(left, right) || isapprox(left, right; atol = 1e-10, rtol = 2e-11, nans = true) +bar_matrix(rows) = isempty(rows) ? zeros(0, 12) : reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) +bar_milliseconds(value::DateTime) = Dates.value(value - DateTime(1970)) +bar_numeric(rows) = isempty(rows) ? zeros(0, 12) : reduce(vcat, [permutedims(Float64[bar_milliseconds(row[1]); row[2:end]]) for row in rows]) + +function bar_fixture_model(kind, metric, config) + D, U = RiskLabAI.Data, RiskLabAI.Utils + if kind == "TimeBars" + return D.TimeBars(; resolution_type = config["resolution"], resolution_units = config["units"]) + end + constructor = Core.apply_type(getfield(D, Symbol(kind)), getfield(D, Symbol(metric))) + if kind == "StandardBars" + key = metric == "Tick" ? U.CUMULATIVE_TICKS : metric == "Volume" ? U.CUMULATIVE_VOLUME : U.CUMULATIVE_DOLLAR + return constructor(; bar_type = key, threshold = Float64(config["threshold"])) + end + window = config["tick_window"] == -1 ? nothing : Int(config["tick_window"]) + arguments = (bar_type = lowercase(metric) * (occursin("Imbalance", kind) ? "_imbalance" : "_run"), window_size_for_expected_n_ticks_estimation = window, expected_imbalance_window = Int(config["imbalance_window"]), initial_estimate_of_expected_n_ticks_in_bar = Float64(config["initial"]), does_analyse_thresholds = true) + if config["expected"] + return constructor(; arguments..., expected_ticks_number_bounds = Tuple(Float64.(config["bounds"]))) + end + return constructor(; arguments...) +end + +@testset "Bar streams: outputs, persistent batch state and threshold traces" begin + D, U = RiskLabAI.Data, RiskLabAI.Utils + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "bars_python_320.toml")) + for group in ("standard", "time", "information"), case in fixtures[group] + model = bar_fixture_model(case["kind"], case["metric"], case["config"]) + data = Matrix{Any}(undef, length(case["times"]), 3) + data[:, 1] = DateTime(1970) .+ Millisecond.(case["times"]) + data[:, 2] = case["prices"] + data[:, 3] = case["volumes"] + combined = zeros(0, 12) + for batch in case["batches"] + rows = D.construct_bars_from_data(model; data = data[(batch["left"]+1):batch["right"], :]) + numeric = bar_numeric(rows) + @test bar_close(numeric, bar_matrix(batch["output"])) + combined = vcat(combined, numeric) + @test all(bar_close(Float64(getfield(model, Symbol(key))), Float64(value)) for (key, value) in batch["state"]) + for (key, value) in batch["histories"] + @test bar_close(Vector{Float64}(getfield(model, Symbol(key))), Vector{Float64}(value)) + end + end + @test bar_close(combined, bar_matrix(case["output"])) + @test model.tick_counter == length(case["times"]) + if group == "information" + @test length(model.analyse_thresholds) == length(case["traces"]) + for (snapshot, trace) in zip(model.analyse_thresholds, case["traces"]) + actual = Float64[bar_milliseconds(snapshot["timestamp"]); [snapshot[key] for key in case["trace_columns"]]] + @test bar_close(actual, Vector{Float64}(trace)) + @test snapshot[U.PREVIOUS_BARS_N_TICKS_LIST] === model.previous_bars_number_of_ticks + end + if startswith(case["kind"], "Fixed") + @test model.window_size_for_expected_n_ticks_estimation === nothing + end + whole = bar_fixture_model(case["kind"], case["metric"], case["config"]) + @test bar_close(bar_numeric(D.construct_bars_from_data(whole; data = data)), combined) + end + end +end + +@testset "Bar streams: expected-count fallback and disabled diagnostics" begin + D = RiskLabAI.Data + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "bars_python_320.toml")) + for case in fixtures["expected_ticks"] + model = bar_fixture_model(case["kind"], "Tick", case["config"]) + append!(model.previous_bars_number_of_ticks, Int.(case["history"])) + @test bar_close(D.expected_number_of_ticks(model), Float64(case["output"])) + end + for constructor in (D.FixedImbalanceBars{D.Tick}, D.FixedRunBars{D.Tick}) + model = constructor(; bar_type = "tick_imbalance", expected_imbalance_window = 8, initial_estimate_of_expected_n_ticks_in_bar = 3.) + @test model.analyse_thresholds === nothing + @test isempty(D.construct_bars_from_data(model; data = Matrix{Any}(undef, 0, 3))) + @test model.tick_counter == 0 + end + @test_throws ArgumentError D.TimeBars(; resolution_type = "year", resolution_units = 1) +end diff --git a/test/bet_sizing_regressions.jl b/test/bet_sizing_regressions.jl new file mode 100644 index 0000000..111be10 --- /dev/null +++ b/test/bet_sizing_regressions.jl @@ -0,0 +1,107 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +const BetSizingContracts = RiskLabAI.Backtest +const bet_sizing_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "bet_sizing_python_320.toml")) + +@testset "Kelly and pessimistic-box sizing against Python 3.2.0" begin + for row in bet_sizing_fixtures["kelly"] + result = BetSizingContracts.kelly_bet_fraction(row["probabilities"]; payout_ratio = row["payout"], max_fraction = row["cap"]) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + @test BetSizingContracts.kelly_bet_fraction(row["probabilities"], row["payout"], row["cap"]) == result + @test size(BetSizingContracts.kelly_bet_fraction(reshape(row["probabilities"], 2, :); payout_ratio = row["payout"], max_fraction = row["cap"])) == (2, 4) + @test all(0 .<= result .<= row["cap"]) + end + for row in bet_sizing_fixtures["robust"] + result = BetSizingContracts.distributionally_robust_kelly_fraction(row["probabilities"], row["count"]; payout_ratio = row["payout"], max_fraction = row["cap"], confidence_z = row["z"]) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + @test BetSizingContracts.distributionally_robust_kelly_fraction(row["probabilities"], row["count"], row["payout"], row["cap"], row["z"]) == result + @test all(0 .<= result .<= row["cap"]) + end + @test BetSizingContracts.kelly_bet_fraction(0.6) ≈ 0.2 + @test BetSizingContracts.distributionally_robust_kelly_fraction(0.6, 100_000) <= 0.2 + @test BetSizingContracts.distributionally_robust_kelly_fraction(0.6, -2) == BetSizingContracts.distributionally_robust_kelly_fraction(0.6, 1) + @test BetSizingContracts.distributionally_robust_kelly_fraction(0.6, big(10)^30) ≈ BetSizingContracts.kelly_bet_fraction(0.6) rtol = 1e-10 atol = 1e-12 + @test BetSizingContracts.distributionally_robust_kelly_fraction([0.0, 1.0], 100; confidence_z = 0) == BetSizingContracts.kelly_bet_fraction([1e-4, 1 - 1e-4]) + for payout in (0, -1, NaN, Inf, true) + @test_throws ArgumentError BetSizingContracts.kelly_bet_fraction(0.5; payout_ratio = payout) + end + for cap in (-1, NaN, Inf, true) + @test_throws ArgumentError BetSizingContracts.kelly_bet_fraction(0.5; max_fraction = cap) + end + for count in (true, 1.5, Inf) + @test_throws ArgumentError BetSizingContracts.distributionally_robust_kelly_fraction(0.5, count) + end + @test_throws ArgumentError BetSizingContracts.distributionally_robust_kelly_fraction(0.5, 10; confidence_z = -1) + @test_throws ArgumentError BetSizingContracts.kelly_bet_fraction([NaN]) +end + +@testset "Public right-closed calibration error bins" begin + for row in bet_sizing_fixtures["ece"] + result = BetSizingContracts.expected_calibration_error(row["probabilities"], row["outcomes"]; n_bins = row["bins"]) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + @test BetSizingContracts.expected_calibration_error(row["probabilities"], row["outcomes"], row["bins"]) == result + end + @test BetSizingContracts.expected_calibration_error([0.0, 1.0], [0, 1]) == 0 + @test BetSizingContracts.expected_calibration_error([0.0, 1.0], [1, 0]) == 1 + @test BetSizingContracts.expected_calibration_error([0.3, nextfloat(0.3)], [0, 1]; n_bins = 10) ≈ 0.2 + for bins in (0, -1, 1.5, true) + @test_throws ArgumentError BetSizingContracts.expected_calibration_error([0.5], [1]; n_bins = bins) + end + for (probabilities, outcomes) in ((Float64[], Int[]), ([0.5], [0, 1]), ([0.5], [2]), ([NaN], [1]), ([1.1], [1]), ([0.5], [NaN]), (ones(1, 1), [1])) + @test_throws ArgumentError BetSizingContracts.expected_calibration_error(probabilities, outcomes) + end +end + +function independent_platt_objective(parameters, probabilities, outcomes) + clipped = clamp.(probabilities, 1e-4, 1 - 1e-4) + logits = log.(clipped ./ (1 .- clipped)) + score = parameters[1] * logits .+ parameters[2] + losses = [outcome == 1 ? log1p(exp(-abs(value))) + max(-value, 0) : log1p(exp(-abs(value))) + max(value, 0) for (value, outcome) in zip(score, outcomes)] + return sum(losses) / length(losses) + 0.5e-6 / length(losses) * parameters[1]^2 +end + +@testset "Fixed-objective Platt calibration and identity behavior" begin + for row in bet_sizing_fixtures["platt"] + calibrator = BetSizingContracts.PlattCalibrator() + @test BetSizingContracts.transform(calibrator, row["query"]) == row["query"] + @test BetSizingContracts.fit!(calibrator, row["probabilities"], row["outcomes"]) === calibrator + result = BetSizingContracts.transform(calibrator, row["query"]) + if row["identity"] + @test isnothing(calibrator.coefficients) + @test result == row["expected"] + else + parameters = collect(calibrator.coefficients) + @test parameters ≈ row["tight_parameters"] rtol = 1e-5 atol = 1e-5 + @test result ≈ row["tight_predictions"] rtol = 1e-5 atol = 1e-5 + @test norm(parameters - row["default_parameters"]) <= row["coefficient_error_bound"] + 1e-5 + @test all(abs.(result - row["expected"]) .<= row["prediction_error_bounds"] .+ 1e-5) + loss = independent_platt_objective(parameters, row["probabilities"], row["outcomes"]) + @test loss ≈ row["tight_loss"] rtol = 1e-9 atol = 1e-11 + @test loss <= row["reference_loss"] + 1e-11 + score = [(independent_platt_objective(parameters + step, row["probabilities"], row["outcomes"]) - independent_platt_objective(parameters - step, row["probabilities"], row["outcomes"])) / 2e-5 for step in ([1e-5, 0.0], [0.0, 1e-5])] + @test norm(score) < 1e-8 + matrix_query = [0.01 0.2; 0.8 0.99] + @test BetSizingContracts.transform(calibrator, matrix_query) == BetSizingContracts.transform(calibrator, [0.01, 0.2, 0.8, 0.99]) + @test BetSizingContracts.transform(calibrator, 0.5) == BetSizingContracts.transform(calibrator, [0.5]) + end + @test all(0 .<= result .<= 1) + @test BetSizingContracts.fit!(calibrator, [0.1, 0.9], [1, 1]) === calibrator + @test isnothing(calibrator.coefficients) + matrix_query = [0.0 0.3; 0.8 1.0] + @test BetSizingContracts.transform(calibrator, matrix_query) == matrix_query + @test size(BetSizingContracts.transform(calibrator, matrix_query)) == size(matrix_query) + end + calibrator = BetSizingContracts.PlattCalibrator() + @test BetSizingContracts.transform(calibrator, 0.5) == 0.5 + for (probabilities, outcomes) in ((Float64[], Int[]), ([0.2], [0, 1]), ([0.2, 0.8], [0, 2]), ([NaN, 0.8], [0, 1]), ([0.2, 0.8], [0, NaN])) + @test_throws ArgumentError BetSizingContracts.fit!(calibrator, probabilities, outcomes) + end + @test_throws ArgumentError BetSizingContracts.transform(calibrator, [Inf]) + probabilities = [0.1 0.2; 0.8 0.9] + first_fit = BetSizingContracts.fit!(BetSizingContracts.PlattCalibrator(), probabilities, [0, 1, 0, 1]) + second_fit = BetSizingContracts.fit!(BetSizingContracts.PlattCalibrator(), [0.1, 0.2, 0.8, 0.9], [0, 1, 0, 1]) + @test first_fit.coefficients == second_fit.coefficients +end diff --git a/test/causal_estimator_regressions.jl b/test/causal_estimator_regressions.jl new file mode 100644 index 0000000..7b45371 --- /dev/null +++ b/test/causal_estimator_regressions.jl @@ -0,0 +1,93 @@ +using Test +using TOML +using RiskLabAI + +function causal_fixture_value(value) + if value isa AbstractVector && !isempty(value) && all(item -> item isa AbstractVector, value) + if all(item -> all(element -> element isa Number, item), value) + return reduce(vcat, permutedims.(value)) + end + matrices = causal_fixture_value.(value) + return permutedims(cat(matrices...; dims = 3), (3, 1, 2)) + end + return value +end + +function assert_causal_fixture(actual, expected, rtol, atol) + if expected isa AbstractDict + @test Set(String.(propertynames(actual))) == Set(keys(expected)) + for (key, value) in expected + assert_causal_fixture(getproperty(actual, Symbol(key)), value, rtol, atol) + end + elseif expected isa Integer + @test actual == expected + else + @test isapprox(actual, causal_fixture_value(expected); rtol, atol) + end +end + +@testset "Causal estimators against Python 3.2.0" begin + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "causal_estimators_python_320.toml")) + for fixture in fixtures["case"] + method = getproperty(RiskLabAI.CausalFactorAnalysis, Symbol(fixture["method"])) + arguments = causal_fixture_value.(fixture["arguments"]) + keywords = (; (Symbol(key) => causal_fixture_value(value) for (key, value) in fixture["keywords"])...) + result = method(arguments...; keywords...) + assert_causal_fixture(result, fixture["expected"], fixtures["rtol"], fixtures["atol"]) + end +end + +@testset "Causal domains and identification boundaries" begin + C = RiskLabAI.CausalFactorAnalysis + @test C.manski_ate_bounds([1.0, 2.0], [1, 1]; lower = 0, upper = 3).lower == -1.5 + @test C.manski_ate_bounds([1.0, 2.0], [0, 0]; lower = 0, upper = 3).upper == 1.5 + @test C.manski_ate_bounds([2.0, 2.0], [true, false]; lower = 2, upper = 2).lower == 0 + @test C.transported_ate([2.0, 4.0], [1.0, 1.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]) == 1.0 + @test_throws ArgumentError C.transported_ate([2.0, 4.0], [1.0, 1.0], [0.5, 0.5], [1.0, 0.0], [1.0, 0.0]) + @test_throws ArgumentError C.transported_ate([2.0], [1.0], [1.0], [-1.0], [1.0]) + @test_throws ArgumentError C.manski_ate_bounds([4.0], [1]; lower = 0, upper = 3) + @test_throws ArgumentError C.manski_ate_bounds([1.0], [2]; lower = 0, upper = 3) + @test_throws ArgumentError C.manski_ate_bounds([1.0], [1]; lower = 3, upper = 0) + @test_throws ArgumentError C.manski_ate_bounds(Float64[], Int[]; lower = 0, upper = 3) + @test_throws ArgumentError C.average_treatment_effect(true, 1.0) + @test_throws ArgumentError C.average_treatment_effect(Inf, 1.0) + @test_throws ArgumentError C.average_treatment_effect(floatmax(Float64), -floatmax(Float64)) + @test_throws ArgumentError C.randomized_mean_difference([true], [false]) + @test_throws ArgumentError C.backdoor_adjusted_expectation([1.0], [0.5]) + @test_throws ArgumentError C.backdoor_adjusted_expectation([1.0, 2.0], [1.0]) + @test_throws ArgumentError C.backdoor_adjusted_average_treatment_effect([1.0], [2.0], [-1.0]) + @test_throws ArgumentError C.frontdoor_adjusted_probability([1.0], [1.1;;], [1.0]) + @test_throws ArgumentError C.frontdoor_adjusted_probability([1.0], [0.1 0.2], [1.0]) + @test_throws ArgumentError C.linear_instrumental_variable_effect(1.0, 0.0) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [1], [0.0 1.0]; first = 1, second = 2) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [1], [0.7 0.7]; first = 1, second = 2) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [true], [0.5 0.5]; first = 1, second = 2) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [1], [0.5 0.5]; first = 1, second = 1) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [1], [0.5 0.5]; first = true, second = 2) + @test_throws ArgumentError C.exposure_mean_contrast([1.0], [3], [0.4 0.4 0.0]; first = 1, second = 2) + @test_throws ArgumentError C.exposure_mean_contrast([floatmax(Float64)], [1], [1e-100 0.5]; first = 1, second = 2) + omitted = C.exposure_mean_contrast([1.0, 2.0], [1, 2], [0.2 0.3; 0.2 0.3]; first = 1, second = 2) + @test omitted.contrast ≈ (5 - 2 / 0.3) / 2 + behavior = fill(0.5, 4, 2) + deterministic = [1.0 0.0; 1.0 0.0; 1.0 0.0; 1.0 0.0] + zero_weights = C.evaluate_policy([2, 2, 2, 2], ones(4), behavior, deterministic, zeros(4, 2)) + @test zero_weights.effective_sample_size == 0 + @test zero_weights.max_weight == 0 + @test zero_weights.ipw_value == 0 + @test_throws ArgumentError C.evaluate_policy([1], [1.0], [1.0 0.0], [0.0 1.0], zeros(1, 2)) + @test_throws ArgumentError C.evaluate_policy([2], [1.0], [1.0 0.0], [1.0 0.0], zeros(1, 2)) + @test_throws ArgumentError C.evaluate_policy([1.0], [1.0], [0.5 0.5], [0.5 0.5], zeros(1, 2)) + @test_throws ArgumentError C.evaluate_policy([1], [1.0], [0.5 0.5], [0.6 0.6], zeros(1, 2)) + @test_throws ArgumentError C.evaluate_policy([1], [floatmax(Float64)], [1e-100 1.0], [1.0 0.0], zeros(1, 2)) + candidates = permutedims(cat(behavior, deterministic; dims = 3), (3, 1, 2)) + actions = [1, 2, 1, 2] + first_result = C.select_policy(actions, [1.0, 0.0, 1.0, 0.0], behavior, candidates, zeros(4, 2); training_indices = [1, 2], evaluation_indices = [3, 4]) + perturbed = C.select_policy(actions, [1.0, 0.0, -1e8, 1e8], behavior, candidates, zeros(4, 2); training_indices = [1, 2], evaluation_indices = [3, 4]) + @test first_result.selected_policy == perturbed.selected_policy + @test first_result.training_values == perturbed.training_values + tied = C.select_policy(actions, zeros(4), behavior, candidates, zeros(4, 2); training_indices = [1, 2], evaluation_indices = [3, 4]) + @test tied.selected_policy == 1 + for (train, evaluation) in [([1, 1], [3, 4]), ([1, 3], [2, 4]), (Int[], [3, 4]), ([1, 2], [4, 5]), ([true], [3, 4])] + @test_throws ArgumentError C.select_policy(actions, ones(4), behavior, candidates, zeros(4, 2); training_indices = train, evaluation_indices = evaluation) + end +end diff --git a/test/clustering_regressions.jl b/test/clustering_regressions.jl new file mode 100644 index 0000000..9b2ea32 --- /dev/null +++ b/test/clustering_regressions.jl @@ -0,0 +1,76 @@ +using Test +using TOML +using Random +using LinearAlgebra +using RiskLabAI + +clustering_matrix(rows) = isempty(rows) ? zeros(0, 0) : reduce(vcat, [permutedims(Float64.(row)) for row in rows]) + +@testset "Clustering: supplied candidate selection and recursive refinement" begin + C = RiskLabAI.Cluster + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "clustering_python_320.toml")) + for mode in ("base", "top"), case in fixtures[mode] + correlation = clustering_matrix(case["correlation"]) + preserved = copy(correlation) + position = Ref(0) + records = case["records"] + provider = function (features, count, rng) + position[] += 1 + row = records[position[]] + @test count == row["k"] + @test features ≈ clustering_matrix(row["features"]) atol = 2e-14 rtol = 2e-13 + return Int.(row["labels"]) + end + method = mode == "base" ? C.cluster_k_means_base : C.cluster_k_means_top + sorted_correlation, clusters, scores = method(correlation; max_clusters = case["maximum"], iterations = case["iterations"], label_provider = provider, random_state = 7) + expected_clusters = Dict(row["key"] => Int.(row["items"]) for row in case["clusters"]) + @test clusters == expected_clusters + @test scores ≈ Vector{Float64}(case["silhouette"]) atol = 2e-10 rtol = 2e-9 + order = reduce(vcat, (clusters[key] for key in sort(collect(keys(clusters)))); init = Int[]) + @test isequal(sorted_correlation, correlation[order, order]) + @test sort(order) == collect(1:size(correlation, 1)) + @test position[] == length(records) + @test isequal(correlation, preserved) + end +end + +@testset "Clustering: recorded block and market primitives" begin + C = RiskLabAI.Cluster + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "clustering_python_320.toml")) + for case in fixtures["random"] + blocks = [(common = Vector{Float64}(row["common"]), noise = clustering_matrix(row["noise"])) for row in case["draws"]] + market = (common = Vector{Float64}(case["market"]["common"]), noise = clustering_matrix(case["market"]["noise"])) + plan = (cuts = Vector{Int}(case["cuts"]), blocks = blocks, market = market) + actual = C.random_block_correlation(case["columns"], case["blocks"]; block_size_min = case["minimum"], draws = plan) + @test actual ≈ clustering_matrix(case["output"]) atol = 2e-12 rtol = 2e-11 + @test actual ≈ transpose(actual) atol = 1e-14 + @test diag(actual) == ones(case["columns"]) + @test minimum(eigvals(Symmetric(actual))) >= -1e-12 + end +end + +@testset "Clustering: native seed isolation and failure boundaries" begin + C = RiskLabAI.Cluster + correlation = [1.0 0.8 0.1 0.1; 0.8 1.0 0.1 0.1; 0.1 0.1 1.0 0.6; 0.1 0.1 0.6 1.0] + for method in (C.cluster_k_means_base, C.cluster_k_means_top) + Random.seed!(193) + expected = rand(5) + Random.seed!(193) + first = method(correlation; max_clusters = 3, iterations = 2, random_state = 15) + @test rand(5) == expected + second = method(correlation; max_clusters = 3, iterations = 2, random_state = 15) + @test first == second + @test sort(reduce(vcat, collect(values(first[2])))) == collect(1:4) + @test all(isfinite, first[3]) + end + @test_throws ArgumentError C.cluster_k_means_base(correlation; iterations = 0) + @test_throws ArgumentError C.cluster_k_means_base(correlation; max_clusters = 1) + @test_throws ArgumentError C.cluster_k_means_base(correlation; max_clusters = 4) + @test_throws ArgumentError C.cluster_k_means_base(correlation; max_clusters = 3, label_provider = (x, k, rng) -> ones(Int, 4)) + @test_throws DimensionMismatch C.cluster_k_means_base(correlation; max_clusters = 3, label_provider = (x, k, rng) -> [0, 1]) + @test_throws ArgumentError C.cluster_k_means_base(ones(4, 4); max_clusters = 3, label_provider = (x, k, rng) -> [0, 0, 1, 1]) + @test_throws ArgumentError C.random_block_covariance(5, 3; block_size_min = 2) + @test_throws ArgumentError C.random_block_covariance(5, 3; cuts = [1, 1]) + @test_throws DimensionMismatch C.random_covariance_sub(5, 2, .5; common_draws = zeros(4), idiosyncratic_draws = zeros(5, 2)) + @test_throws DimensionMismatch C.random_covariance_sub(5, 2, .5; common_draws = zeros(5), idiosyncratic_draws = zeros(2, 5)) +end diff --git a/test/constants_regressions.jl b/test/constants_regressions.jl new file mode 100644 index 0000000..b3e6a8e --- /dev/null +++ b/test/constants_regressions.jl @@ -0,0 +1,13 @@ +using Test +using TOML +using RiskLabAI + +@testset "Exact public bar state constant names and values" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "constants_python_320.toml")) + @test length(fixture) == 31 + for (name, value) in fixture + @test isascii(name) + @test isdefined(RiskLabAI.Utils, Symbol(name)) + @test getproperty(RiskLabAI.Utils, Symbol(name)) == value + end +end diff --git a/test/convex_regressions.jl b/test/convex_regressions.jl new file mode 100644 index 0000000..42196fa --- /dev/null +++ b/test/convex_regressions.jl @@ -0,0 +1,154 @@ +using Test +using TOML +using LinearAlgebra +using Zygote +using RiskLabAI + +const ConvexUnderTest = RiskLabAI.Optimization +convex_fixture_matrix(rows) = reduce(vcat, permutedims.(rows)) +const convex_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "convex_python_320.toml")) + +@testset "Greatest clearing payments against Python 3.2.0" begin + for row in convex_fixtures["clearing"] + assets, liabilities = row["external_assets"], row["nominal_liabilities"] + relative = convex_fixture_matrix(row["relative_liabilities"]) + original = (copy(assets), copy(liabilities), copy(relative)) + result = ConvexUnderTest.eisenberg_noe_clearing(assets, liabilities, relative) + scale = max(maximum(liabilities), floatmin(Float64)) + @test maximum(abs.(result - row["expected"])) / scale <= 1e-8 + @test maximum(abs.(result - row["independent"])) / scale <= 1e-8 + @test maximum(abs.(result - min.(liabilities, assets + relative' * result))) / scale <= 1e-8 + @test all(0 .<= result .<= liabilities) + @test original == (assets, liabilities, relative) + end + @test ConvexUnderTest.eisenberg_noe_clearing([0.0, 0.0], [1.0, 1.0], [0.0 1.0; 1.0 0.0]) ≈ [1, 1] atol = 1e-8 + for (assets, liabilities, relative) in (([], [], zeros(0, 0)), ([0.0], [1.0, 2.0], zeros(1, 1)), ([-1.0], [1.0], zeros(1, 1)), ([0.0], [NaN], zeros(1, 1)), ([0.0], [1.0], fill(1.01, 1, 1)), ([true], [1.0], zeros(1, 1)), ([0.0], [1.0], fill(-0.1, 1, 1))) + @test_throws ArgumentError ConvexUnderTest.eisenberg_noe_clearing(assets, liabilities, relative) + end + for tolerance in (0, 1, true, NaN) + @test_throws ArgumentError ConvexUnderTest.eisenberg_noe_clearing([0.0], [1.0], zeros(1, 1); atol = tolerance) + end +end + +@testset "Public-book clearing payment and unpaid-balance accuracy" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "clearing_accuracy_book_cases.toml")) + for row in fixture["cases"] + assets, liabilities = row["external_assets"], row["nominal_liabilities"] + relative = convex_fixture_matrix(row["relative_liabilities"]) + payments = ConvexUnderTest.eisenberg_noe_clearing(assets, liabilities, relative) + unpaid = max.(liabilities - payments, 0) + @test all(isapprox.(payments, row["expected_payments"]; rtol = 1e-9, atol = 1e-11)) + @test all(isapprox.(unpaid, row["expected_unpaid"]; rtol = 1e-9, atol = 1e-11)) + @test (unpaid .> 1e-8) == row["expected_defaults"] + @test all(0 .<= payments .<= liabilities) + @test maximum(abs.(payments - min.(liabilities, assets + relative' * payments))) / maximum(liabilities) <= 1e-12 + @test sum(unpaid) ≈ sum(row["expected_unpaid"]) rtol = 1e-9 atol = 1e-11 + end +end + +@testset "Discrete execution against Python 3.2.0 and first-order equations" begin + for row in convex_fixtures["execution"] + kwargs = (; (Symbol(name) => row[name] for name in ("volatility", "temporary_impact", "permanent_impact", "risk_aversion"))...) + result = ConvexUnderTest.almgren_chriss_execution(row["inventory"], row["horizon"], row["intervals"]; kwargs...) + for name in (:times, :inventory, :trades, :expected_cost, :cost_variance) + @test getproperty(result, name) ≈ row["expected_" * string(name)] rtol = 1e-10 atol = 1e-12 + end + @test result.inventory ≈ row["independent_inventory"] rtol = 1e-10 atol = 1e-12 + @test all(result.trades .>= 0) + @test result.inventory[1] == row["inventory"] && result.inventory[end] == 0 + @test sum(result.trades) ≈ row["inventory"] rtol = 1e-12 + tau = row["horizon"] / row["intervals"] + eta = row["temporary_impact"] - row["permanent_impact"] * tau / 2 + @test result.expected_cost ≈ row["permanent_impact"] * row["inventory"]^2 / 2 + eta / tau * sum(abs2, result.trades) rtol = 1e-12 + @test result.cost_variance ≈ row["volatility"]^2 * tau * sum(abs2, result.inventory[2:end]) rtol = 1e-12 + end + @test ConvexUnderTest.almgren_chriss_execution(1.0, 1.0, 1; volatility = 2.0, temporary_impact = 1.0).cost_variance == 0 + @test ConvexUnderTest.almgren_chriss_execution(0.0, 1.0, 3; volatility = 2.0, temporary_impact = 1.0).trades == zeros(3) + @test ConvexUnderTest.almgren_chriss_execution(3.0, 1.0, 3; volatility = 0.0, temporary_impact = 1.0, risk_aversion = 1.0).trades ≈ ones(3) + for count in (0, -1, true, 2.0) + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(1.0, 1.0, count; volatility = 0.2, temporary_impact = 0.1) + end + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(1.0, 0.0, 2; volatility = 0.2, temporary_impact = 0.1) + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(1.0, 1.0, 2; volatility = 0.2, temporary_impact = 0.1, permanent_impact = 0.4) + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(1e200, 1.0, 2; volatility = 0.2, temporary_impact = 0.1) + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(true, 1.0, 2; volatility = 0.2, temporary_impact = 0.1) + @test_throws ArgumentError ConvexUnderTest.almgren_chriss_execution(-1, 1.0, 2; volatility = 0.2, temporary_impact = 0.1) +end + +@testset "Finite-hull SPO+ values, ties and native gradients" begin + for row in convex_fixtures["spo"] + predicted, truth = row["predicted_cost"], row["true_cost"] + vertices = convex_fixture_matrix(row["vertices"]) + result = ConvexUnderTest.spo_plus_loss(predicted, truth, vertices) + @test result.loss == row["expected_loss"] + @test result.subgradient == row["expected_subgradient"] + @test result.true_vertex == row["expected_true_vertex"] + 1 + @test result.transformed_vertex == row["expected_transformed_vertex"] + 1 + @test ConvexUnderTest.spo_plus(predicted, truth, vertices) == result.loss + gradients = Zygote.gradient((p, t, v) -> ConvexUnderTest.spo_plus(p, t, v), predicted, truth, vertices) + @test gradients[1] == result.subgradient + @test isnothing(gradients[2]) && isnothing(gradients[3]) + batch_prediction = permutedims(hcat(predicted, -predicted)) + batch_truth = permutedims(hcat(truth, truth)) + batch = ConvexUnderTest.spo_plus(batch_prediction, batch_truth, vertices) + expected_batch = [ConvexUnderTest.spo_plus_loss(vec(p), vec(t), vertices) for (p, t) in zip(eachrow(batch_prediction), eachrow(batch_truth))] + @test batch == [result.loss for result in expected_batch] + batch_gradient = only(Zygote.gradient(p -> sum(ConvexUnderTest.spo_plus(p, batch_truth, vertices)), batch_prediction)) + @test batch_gradient == reduce(vcat, permutedims.([result.subgradient for result in expected_batch])) + end + @test_throws ArgumentError ConvexUnderTest.spo_plus_loss([1.0], [1.0, 2.0], ones(2, 1)) + @test_throws ArgumentError ConvexUnderTest.spo_plus_loss([NaN], [1.0], ones(1, 1)) + @test_throws ArgumentError ConvexUnderTest.spo_plus_loss([1e308], [-1e308], fill(2.0, 1, 1)) + @test_throws ArgumentError ConvexUnderTest.spo_plus([1], [1], ones(1, 1)) + @test_throws ArgumentError ConvexUnderTest.spo_plus(Float32[1], [1.0], ones(1, 1)) + @test_throws ArgumentError ConvexUnderTest.spo_plus([1.0], [1.0], zeros(0, 1)) +end + +function check_birkhoff(matrix) + original = copy(matrix) + weights, permutations = ConvexUnderTest.birkhoff_von_neumann_decomposition(matrix) + n = size(matrix, 1) + reconstruction = zeros(n, n) + @test all(weights .> 0) + @test abs(sum(weights) - 1) <= 1e-10 + for (weight, permutation) in zip(weights, eachrow(permutations)) + @test sort(permutation) == collect(1:n) + for i in 1:n + reconstruction[i, permutation[i]] += weight + end + end + @test maximum(abs.(reconstruction - matrix)) <= 1e-10 + @test matrix == original +end + +@testset "Majorization witnesses and Birkhoff reconstructions" begin + for row in convex_fixtures["majorization"] + target, source = row["target"], row["source"] + @test ConvexUnderTest.is_majorized(target, source) == row["expected"] + if row["expected"] + witness = ConvexUnderTest.majorization_matrix(target, source) + @test ConvexUnderTest.is_doubly_stochastic(witness) + @test maximum(abs.(witness * source - target)) <= 1e-10 + check_birkhoff(witness) + else + @test_throws ArgumentError ConvexUnderTest.majorization_matrix(target, source) + end + end + for row in convex_fixtures["doubly_stochastic"] + matrix = convex_fixture_matrix(row["matrix"]) + @test ConvexUnderTest.is_doubly_stochastic(matrix) == row["expected"] + check_birkhoff(matrix) + end + @test ConvexUnderTest.is_doubly_stochastic([true false; false true]) + for matrix in (zeros(0, 0), ones(2, 3), ones(2, 2), [1.0 0.0; NaN 1.0], [1.1 -0.1; -0.1 1.1], ones(ComplexF64, 2, 2)) + @test !ConvexUnderTest.is_doubly_stochastic(matrix) + @test_throws ArgumentError ConvexUnderTest.birkhoff_von_neumann_decomposition(matrix) + end + for tolerance in (0, 1, true, NaN) + @test_throws ArgumentError ConvexUnderTest.is_doubly_stochastic(ones(1, 1); atol = tolerance) + @test_throws ArgumentError ConvexUnderTest.is_majorized([1.0], [1.0]; atol = tolerance) + end + for (target, source) in (([], []), ([1.0], [1.0, 2.0]), ([NaN], [1.0]), ([1.0im], [1.0])) + @test_throws ArgumentError ConvexUnderTest.is_majorized(target, source) + end +end diff --git a/test/covariance_algebra_regressions.jl b/test/covariance_algebra_regressions.jl new file mode 100644 index 0000000..7cc53a4 --- /dev/null +++ b/test/covariance_algebra_regressions.jl @@ -0,0 +1,107 @@ +using Test +using TOML +using Random +using Statistics +using LinearAlgebra +using RiskLabAI + +algebra_equal(left::Number, right::Number; rtol = 2e-9, atol = 2e-11) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol, atol) +algebra_equal(left::AbstractArray, right::AbstractArray; kwargs...) = size(left) == size(right) && all(algebra_equal(a, b; kwargs...) for (a, b) in zip(left, right)) +algebra_matrix(rows, columns) = isempty(rows) ? zeros(0, columns) : reduce(vcat, permutedims.([Float64[row...] for row in rows])) + +@testset "Shared Marcenko Pastur grid and covariance conversions" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "covariance_algebra_python_320.toml")) + for case in fixture["mp"] + grid, density = D.marcenko_pastur_pdf(case["variance"], case["q"]; num_points = case["count"]) + @test algebra_equal(grid, Float64[case["grid"]...]) + @test algebra_equal(density, Float64[case["pdf"]...]; atol = 1e-7) + @test all(>=(0), density) + end + for case in fixture["correlation"] + covariance = algebra_matrix(case["covariance"], length(case["standard"])) + original = copy(covariance) + expected = algebra_matrix(case["correlation"], size(covariance, 2)) + @test algebra_equal(D.cov_to_corr(covariance), expected) + @test algebra_equal(RiskLabAI.Cluster.covariance_to_correlation(covariance), expected) + @test algebra_equal(D.corr_to_cov(expected, case["standard"]), algebra_matrix(case["recovered"], size(covariance, 2))) + @test covariance == original + end +end + +@testset "Shared spectral reconstruction and noise eigenvalues" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "covariance_algebra_python_320.toml")) + for case in fixture["pca"] + matrix = algebra_matrix(case["matrix"], length(case["eigenvalues"])) + original = copy(matrix) + values, vectors = D.pca(matrix) + @test algebra_equal(values, case["eigenvalues"]) + @test isapprox(vectors * Diagonal(values) * vectors', matrix; atol = 1e-10) + @test isapprox(vectors' * vectors, I; atol = 1e-12) + @test matrix == original + @test issorted(values; rev = true) + end + for case in fixture["spectral"] + values, vectors = case["eigenvalues"], algebra_matrix(case["eigenvectors"], length(case["eigenvalues"])) + original = deepcopy((values, vectors)) + actual = D.denoised_corr(values, vectors, case["factors"]) + @test algebra_equal(actual, algebra_matrix(case["output"], length(values))) + @test diag(actual) == ones(length(values)) + @test isapprox(actual, actual'; atol = 1e-12) + @test minimum(eigvals(Symmetric(actual))) >= -1e-10 + @test isequal((values, vectors), original) + end +end + +@testset "Shared normalized covariance weights and PCA risk allocation" begin + D, O = RiskLabAI.Data, RiskLabAI.Optimization + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "covariance_algebra_python_320.toml")) + for case in fixture["weights"] + covariance = algebra_matrix(case["covariance"], length(case["mu"])) + target = case["with_mean"] ? case["mu"] : nothing + weight_data = D.optimal_portfolio(covariance; mu = target) + weight_nco = O.get_optimal_portfolio_weights(covariance; mu = target) + @test algebra_equal(weight_data, case["output"]) + @test algebra_equal(weight_nco, case["output"]) + @test isapprox(sum(weight_data), 1; atol = 1e-11) + @test isapprox(sum(weight_nco), 1; atol = 1e-11) + @test algebra_equal(covariance * weight_data, case["mu"] .* (covariance * weight_data)[1] ./ case["mu"][1]) + end + for case in fixture["hedging"] + count = length(case["eigenvalues"]) + covariance = algebra_matrix(case["covariance"], count) + expected_vectors = algebra_matrix(case["eigenvectors"], count) + distribution = case["with_distribution"] ? case["distribution"] : nothing + original = deepcopy((covariance, distribution)) + weights = O.pca_weights(covariance; risk_distribution = distribution, risk_target = case["target"]) + actual_risks = (expected_vectors' * weights) .^ 2 .* case["eigenvalues"] + remaining = collect(1:count) + while !isempty(remaining) + reference = case["eigenvalues"][first(remaining)] + group = [index for index in remaining if isapprox(case["eigenvalues"][index], reference; rtol = 1e-10)] + @test isapprox(sum(actual_risks[group]), case["target"]^2 * sum(case["distribution"][group]); atol = 2e-10, rtol = 2e-9) + remaining = setdiff(remaining, group) + end + @test isapprox(weights' * covariance * weights, case["target"]^2 * sum(case["distribution"]); atol = 2e-10) + @test isequal((covariance, distribution), original) + end +end + +@testset "Shared NERCOME permutations and held out covariance" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "covariance_algebra_python_320.toml")) + for case in fixture["nercome"] + values = algebra_matrix(case["values"], length(first(case["values"]))) + permutations = isempty(case["permutations"]) ? zeros(Int, 0, size(values, 1)) : Int.(algebra_matrix(case["permutations"], size(values, 1))) + original = deepcopy((values, permutations)) + actual = D.nercome_denoised_covariance(values; n_splits = case["splits"], split_fraction = case["fraction"], permutations = permutations) + @test algebra_equal(actual, algebra_matrix(case["output"], size(values, 2)); rtol = 2e-8, atol = 2e-9) + @test isapprox(actual, actual'; atol = 1e-10) + @test isposdef(actual) + @test isequal((values, permutations), original) + end + @test_throws ArgumentError D.nercome_denoised_covariance(ones(3, 2)) + @test_throws ArgumentError D.nercome_denoised_covariance(ones(6, 2); n_splits = 1, permutations = ones(Int, 1, 6)) + @test_throws ArgumentError D.nercome_denoised_covariance(ones(6, 2); n_splits = 2, permutations = permutedims(1:6)) +end diff --git a/test/covariance_regressions.jl b/test/covariance_regressions.jl new file mode 100644 index 0000000..7990a02 --- /dev/null +++ b/test/covariance_regressions.jl @@ -0,0 +1,48 @@ +using Test +using TOML +using LinearAlgebra +using Statistics +using Random +using Distributions +using RiskLabAI + +struct CovarianceFixtureRNG <: AbstractRNG + samples::Matrix{Float64} +end + +function Random.rand(rng::CovarianceFixtureRNG, distribution::MvNormal, count::Int) + size(rng.samples) == (count, length(distribution)) || error("Unexpected fixture sample request") + return permutedims(rng.samples) +end + +covariance_fixture_matrix(rows) = reduce(vcat, permutedims.(rows)) + +@testset "Ledoit-Wolf simulation adapter against supplied Python 3.2.0 draws" begin + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "covariance_python_320.toml")) + for row in fixtures["cases"] + samples = covariance_fixture_matrix(row["data"]) + n, p = size(samples) + expected = covariance_fixture_matrix(row["covariance"]) + mean_result, covariance = RiskLabAI.Data.simulates_cov_mu(zeros(p), Matrix{Float64}(I, p, p), n; shrink = true, rng = CovarianceFixtureRNG(samples)) + @test mean_result ≈ row["mean"] rtol = 1e-10 atol = 1e-12 + @test covariance ≈ expected rtol = 1e-10 atol = 1e-12 + @test covariance ≈ covariance' rtol = 1e-12 atol = 1e-14 + @test eigmin(Symmetric(covariance)) >= -1e-12 * max(1, opnorm(covariance, 2)) + _, unshrunk = RiskLabAI.Data.simulates_cov_mu(zeros(p), Matrix{Float64}(I, p, p), n; rng = CovarianceFixtureRNG(samples)) + @test unshrunk ≈ covariance_fixture_matrix(row["unshrunk"]) rtol = 1e-10 atol = 1e-12 + for scale in (1e-100, 1e100) + scaled = RiskLabAI.Data._ledoit_wolf_covariance(samples * scale) + @test scaled / scale^2 ≈ expected rtol = 1e-10 atol = 1e-12 + end + end + @test RiskLabAI.Data._ledoit_wolf_covariance([1.0 2.0 3.0]) == zeros(3, 3) + @test RiskLabAI.Data._ledoit_wolf_covariance([1.0 0.0; -1.0 0.0; 0.0 1.0; 0.0 -1.0]) == 0.5 * Matrix{Float64}(I, 2, 2) + for samples in (zeros(0, 3), zeros(3, 0), [NaN 1.0; 2.0 3.0], [Inf 1.0; 2.0 3.0]) + @test_throws ArgumentError RiskLabAI.Data._ledoit_wolf_covariance(samples) + end + rng = MersenneTwister(419) + samples = permutedims(rand(copy(rng), MvNormal(zeros(2), Matrix{Float64}(I, 2, 2)), 30)) + mean_result, covariance = RiskLabAI.Data.simulates_cov_mu(zeros(2), Matrix{Float64}(I, 2, 2), 30; shrink = true, rng) + @test mean_result ≈ vec(mean(samples; dims = 1)) + @test covariance ≈ RiskLabAI.Data._ledoit_wolf_covariance(samples) +end diff --git a/test/debiased_importance_regressions.jl b/test/debiased_importance_regressions.jl new file mode 100644 index 0000000..32d014d --- /dev/null +++ b/test/debiased_importance_regressions.jl @@ -0,0 +1,99 @@ +using Test +using TOML +using Random +using RiskLabAI + +debiased_matrix(rows) = reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) + +@testset "Debiased importance: supplied tree bases and unique in-bag rows" begin + F = RiskLabAI.Features + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "debiased_importance_python_320.toml")) + for case in fixtures["mdi"] + x, y = debiased_matrix(case["x"]), Vector{Int}(case["y"]) + snapshot = copy(x) + visits = Ref(0) + fitting = function (model, values, labels, weights) + visits[] += 1 + @test values == x && values !== x + @test labels == y && labels !== y + @test weights === nothing + return model + end + records = [(features = Vector{Int}(row["features"]), thresholds = Vector{Float64}(row["thresholds"]), bootstrap_indices = Vector{Int}(row["bootstrap_indices"])) for row in case["records"]] + actual = F.mdi_plus_importance(:supplied_forest, x, y; fit_estimator = fitting, tree_records = model -> records, n_trees = case["requested"]) + @test actual ≈ Vector{Float64}(case["output"]) atol = 2e-12 rtol = 2e-10 + @test visits[] == 1 + @test x == snapshot + @test all(actual .>= -1e-14) + end +end + +@testset "Debiased importance: independent clones and conditional Gaussian losses" begin + F = RiskLabAI.Features + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "debiased_importance_python_320.toml")) + for case in fixtures["cpi"] + x, y = debiased_matrix(case["x"]), Vector{Int}(case["y"]) + snapshot = copy(x) + template = Dict{Symbol,Any}(:unfitted => true) + clone_count, fit_count = Ref(0), Ref(0) + clone_model = function (model) + @test model === template + clone_count[] += 1 + return Dict{Symbol,Any}(:unfitted => true, :index => clone_count[], :predictions => 0) + end + fit_model = function (model, values, labels, weights) + fit_count[] += 1 + expected = case["folds"][fit_count[]]["trace"] + @test model !== template && model[:unfitted] + @test values == debiased_matrix(expected["x"]) + @test labels == Vector{Int}(expected["y"]) + @test weights === nothing + model[:unfitted] = false + model[:classes] = sort(unique(labels)) + model[:prior] = Float64[1 + count(==(label), labels) for label in model[:classes]] + model[:feature] = sum(values * collect(1:size(values, 2))) / size(values, 1) + return model + end + predict_model = function (model, values) + model[:predictions] += 1 + expected = case["folds"][model[:index]]["trace"]["predictions"][model[:predictions]] + @test values ≈ debiased_matrix(expected["x"]) atol = 3e-12 rtol = 3e-10 + scaled = .07 .* (values * collect(1:size(values, 2))) .+ .01 * model[:feature] + scores = transpose(log.(model[:prior])) .+ scaled * transpose(collect(1:length(model[:classes]))) + exponentials = exp.(scores .- maximum(scores; dims = 2)) + output = exponentials ./ sum(exponentials; dims = 2) + @test output ≈ debiased_matrix(expected["output"]) atol = 2e-13 rtol = 2e-12 + return output + end + folds = [(Vector{Int}(row["train"]), Vector{Int}(row["test"])) for row in case["folds"]] + draws = [debiased_matrix(row["gaussian"]) for row in case["folds"]] + actual = F.conditional_predictive_impact(template, x, y; clone_estimator = clone_model, fit_estimator = fit_model, predict_proba = predict_model, estimator_classes = model -> model[:classes], random_state = case["seed"], splits = folds, gaussian_draws = draws) + @test actual.importance ≈ Vector{Float64}(case["importance"]) atol = 2e-12 rtol = 2e-8 + @test actual.p_value ≈ Vector{Float64}(case["p_value"]) atol = 2e-6 rtol = 1e-5 + @test clone_count[] == fit_count[] == length(folds) + @test template == Dict{Symbol,Any}(:unfitted => true) + @test x == snapshot + end +end + +@testset "Debiased importance: population-deviation gate and domains" begin + F = RiskLabAI.Features + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "debiased_importance_python_320.toml")) + for case in fixtures["gate"] + actual = F._cpi_summary([Vector{Float64}(row) for row in case["differences"]]) + @test actual.importance ≈ Vector{Float64}(case["importance"]) atol = 1e-15 + @test actual.p_value ≈ Vector{Float64}(case["p_value"]) atol = 1e-13 + end + x, y = ones(6, 2), [0, 1, 0, 1, 0, 1] + @test_throws ArgumentError F.mdi_plus_importance(nothing, x, y; fit_estimator = (m, x, y, w) -> m, tree_records = m -> [], n_trees = -1) + @test_throws ArgumentError F._cpi_splits(6, 4, 1, [([1, 2, 3], [3, 4, 5])]) + @test_throws ArgumentError F._cpi_splits(6, 4, 1, [([1, 2, 3], [4, 5, 6])]) + @test_throws ArgumentError F._cpi_splits(6, 1, 1, nothing) + @test_throws ArgumentError F._cpi_summary([[1.]]) + Random.seed!(491) + expected = rand(5) + Random.seed!(491) + folds = F._cpi_splits(9, 4, 19, nothing) + @test rand(5) == expected + @test sort(reduce(vcat, (test for (_, test) in folds))) == collect(1:9) +end diff --git a/test/density_tail_regressions.jl b/test/density_tail_regressions.jl new file mode 100644 index 0000000..36ca433 --- /dev/null +++ b/test/density_tail_regressions.jl @@ -0,0 +1,170 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +const DensityContracts = RiskLabAI.Data +const TailContracts = RiskLabAI.Backtest +const density_tail_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "density_tail_python_320.toml")) +density_tail_matrix(rows) = reduce(vcat, permutedims.(rows)) + +@testset "Fitted univariate density against Python 3.2.0" begin + for row in density_tail_fixtures["density"] + observations = copy(row["observations"]) + estimate = DensityContracts.fit_kde(observations; bandwidth = row["bandwidth"], kernel = row["kernel"]) + result = DensityContracts.score_samples(estimate, row["query"]) + @test all(isapprox.(result, row["expected"]; rtol = 1e-10, atol = 1e-12)) + @test DensityContracts.score_samples(estimate, reshape(row["query"], :, 1)) == result + @test DensityContracts.score_samples(DensityContracts.fit_kde(observations, row["bandwidth"], row["kernel"]), row["query"]) == result + observations .= 999.0 + @test DensityContracts.score_samples(estimate, row["query"]) == result + @test estimate.observations isa Tuple + end + @test DensityContracts.score_samples(DensityContracts.fit_kde([0.0]), [0.0]) ≈ [-log(0.01 * sqrt(2pi))] + @test DensityContracts.score_samples(DensityContracts.fit_kde([-1e308]; bandwidth = 1e308), [1e308]) ≈ [-2 - log(1e308) - log(2pi) / 2] + for kernel in ("tophat", "epanechnikov", "linear", "cosine") + @test DensityContracts.score_samples(DensityContracts.fit_kde([0.0]; bandwidth = 1, kernel), [-1.0, 1.0]) == [-Inf, -Inf] + end + @test DensityContracts.score_samples(DensityContracts.fit_kde([1.0 2.0; 3.0 4.0]; bandwidth = 1), [0.0, 1.0]) ≈ DensityContracts.score_samples(DensityContracts.fit_kde([1.0, 2.0, 3.0, 4.0]; bandwidth = 1), [0.0, 1.0]) + for observations in (Float64[], [NaN], [Inf], [1im], ["1"], 1.0) + @test_throws ArgumentError DensityContracts.fit_kde(observations) + end + for bandwidth in (0, -1, Inf, NaN, "scott") + @test_throws ArgumentError DensityContracts.fit_kde([0.0]; bandwidth) + end + @test_throws ArgumentError DensityContracts.fit_kde([0.0]; kernel = "quartic") + estimate = DensityContracts.fit_kde([0.0]) + for query in (Float64[], zeros(1, 2), [NaN], [Inf], [1im], 1.0) + @test_throws ArgumentError DensityContracts.score_samples(estimate, query) + end +end + +@testset "Targeted eigenvector shrinkage against Python 3.2.0" begin + for row in density_tail_fixtures["shrinkage"] + values, vectors = density_tail_matrix(row["values"]), density_tail_matrix(row["vectors"]) + result = DensityContracts.denoised_corr2(values, vectors, row["count"]; alpha = row["alpha"]) + @test result ≈ density_tail_matrix(row["expected"]) rtol = 1e-10 atol = 1e-12 + @test DensityContracts.denoised_corr2(values, vectors, row["count"], row["alpha"]) == result + signal_vectors, noise_vectors = vectors[:, 1:row["count"]], vectors[:, row["count"]+1:end] + signal = signal_vectors * values[1:row["count"], 1:row["count"]] * signal_vectors' + noise = noise_vectors * values[row["count"]+1:end, row["count"]+1:end] * noise_vectors' + @test diag(result) ≈ diag(signal + noise) rtol = 1e-10 atol = 1e-12 + @test values == density_tail_matrix(row["values"]) && vectors == density_tail_matrix(row["vectors"]) + end + values, vectors = [2.0 0.0; 0.0 0.5], [0.6 -0.8; 0.8 0.6] + @test DensityContracts.denoised_corr2(values, vectors, 0) ≈ Diagonal(diag(vectors * values * vectors')) + @test DensityContracts.denoised_corr2(values, vectors, 2) ≈ vectors * values * vectors' + for invalid in (zeros(0, 0), ones(2, 3), [1.0, 2.0], [1.0 2e-12; 0.0 2.0], [1.0 0.0; 0.0 NaN], [true false; false true], complex.(values)) + @test_throws ArgumentError DensityContracts.denoised_corr2(invalid, vectors, 1) + end + for invalid in (ones(3, 3), [1.0 0.0; 0.0 Inf], [true false; false true], complex.(vectors)) + @test_throws ArgumentError DensityContracts.denoised_corr2(values, invalid, 1) + end + for count in (-1, 3, true, 1.0) + @test_throws ArgumentError DensityContracts.denoised_corr2(values, vectors, count) + end + for alpha in (-0.1, 1.1, NaN, Inf, true, 1im) + @test_throws ArgumentError DensityContracts.denoised_corr2(values, vectors, 1; alpha) + end +end + +@testset "Empirical and Pareto tail risk against Python 3.2.0" begin + for row in density_tail_fixtures["empirical"] + result = TailContracts.expected_shortfall(row["returns"], 0.0123; confidence_level = row["level"]) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + @test TailContracts.expected_shortfall(row["returns"], 100.0, row["level"]) == result + end + @test TailContracts.expected_shortfall([-0.3, 0.2], 0.0) == -0.3 + for row in density_tail_fixtures["pareto"] + result = TailContracts.generalized_pareto_tail_risk(row["threshold"], row["rate"], row["shape"], row["scale"], row["probability"]) + for key in (:value_at_risk, :expected_shortfall) + @test getproperty(result, key) ≈ row["expected"][string(key)] rtol = 1e-10 atol = 1e-12 + end + @test result.expected_shortfall >= result.value_at_risk >= row["threshold"] + if row["shape"] < 0 + @test result.expected_shortfall <= row["threshold"] - row["scale"] / row["shape"] + 1e-12 + end + end + exponential = TailContracts.generalized_pareto_tail_risk(2, 0.1, 0, 3, 0.99) + @test exponential.value_at_risk ≈ 2 - 3log(0.1) + @test exponential.expected_shortfall ≈ exponential.value_at_risk + 3 + for values in (Float64[], [NaN], [Inf], [true], [1im], ones(2, 1)) + @test_throws ArgumentError TailContracts.expected_shortfall(values, 0.0) + end + for level in (-0.1, 1.1, true, NaN) + @test_throws ArgumentError TailContracts.expected_shortfall([0.0], 0; confidence_level = level) + end + for parameters in ((0, 0, 0, 1, 0.99), (0, 1.1, 0, 1, 0.99), (0, 0.1, 0, 1, 0.9), (0, 0.1, 0, 1, 1), (0, 0.1, 0, 0, 0.99), (0, 0.1, NaN, 1, 0.99), (Inf, 0.1, 0, 1, 0.99), (true, 0.1, 0, 1, 0.99), (0, 0.1, 1000, 1, 0.999)) + @test_throws ArgumentError TailContracts.generalized_pareto_tail_risk(parameters...) + end +end + +@testset "Quantile exceptions against exhaustive Python 3.2.0 fixtures" begin + for row in density_tail_fixtures["exceptions"] + result = TailContracts.quantile_exception_tests(row["hits"]; exception_probability = row["probability"]) + for name in propertynames(result) + expected = row["expected"][string(name)] + actual = getproperty(result, name) + if expected == "undefined" + @test isnothing(actual) + elseif name == :transitions + @test actual == density_tail_matrix(expected) + elseif name in (:exception_count, :sample_count) + @test actual == expected + else + @test actual ≈ expected rtol = 1e-10 atol = 1e-12 + end + end + @test sum(result.transitions) == length(row["hits"]) - 1 + end + @test TailContracts.quantile_exception_tests([true, false, true]; exception_probability = 0.1) == TailContracts.quantile_exception_tests([1.0, 0.0, 1.0]; exception_probability = 0.1) + for hits in (Int[], [0, 2], [-1, 1], [0.0, NaN], [0.0, Inf], [0.5], ["0"], ones(2, 1), [0im]) + @test_throws ArgumentError TailContracts.quantile_exception_tests(hits; exception_probability = 0.1) + end + for probability in (0, 1, -0.1, 1.1, true, NaN, Inf) + @test_throws ArgumentError TailContracts.quantile_exception_tests([0, 1]; exception_probability = probability) + end +end + +function independent_pareto_loss(shape, scale, excesses) + return length(excesses) * log(scale) + sum(shape == 0 ? value / scale : (1 + inv(shape)) * log1p(shape * value / scale) for value in excesses) +end + +@testset "Supplied-threshold Pareto likelihood fitting" begin + for row in density_tail_fixtures["fit"] + result = TailContracts.fit_pot_tail_risk(row["losses"]; threshold = row["threshold"], probability = row["probability"]) + expected = row["expected"] + @test result.shape ≈ expected["shape"] rtol = 5e-4 atol = 5e-4 + @test result.scale ≈ expected["scale"] rtol = 5e-4 atol = 5e-4 + @test result.shape ≈ row["tight_shape"] rtol = 1e-5 atol = 1e-5 + @test result.scale ≈ row["tight_scale"] rtol = 1e-5 atol = 1e-5 + for name in (:threshold, :exceedance_count, :sample_count, :exceedance_probability) + @test getproperty(result, name) == expected[string(name)] + end + for name in (:value_at_risk, :expected_shortfall) + @test getproperty(result, name) ≈ expected[string(name)] rtol = 5e-4 atol = 5e-4 + end + excesses = (row["losses"][row["losses"] .> row["threshold"]] .- row["threshold"]) / row["unit"] + objective = independent_pareto_loss(result.shape, result.scale / row["unit"], excesses) + @test abs(objective - row["normalized_objective"]) / length(excesses) < 1e-7 + @test all(1 .+ result.shape * excesses / (result.scale / row["unit"]) .> 0) + @test isfinite(objective) + if result.shape >= 1 + @test result.expected_shortfall == Inf + end + end + row = first(density_tail_fixtures["fit"]) + base = TailContracts.fit_pot_tail_risk(row["losses"]; threshold = row["threshold"], probability = row["probability"]) + for factor in (1e-50, 1e50) + scaled = TailContracts.fit_pot_tail_risk(factor * row["losses"]; threshold = factor * row["threshold"], probability = row["probability"]) + @test scaled.shape ≈ base.shape rtol = 1e-5 atol = 1e-5 + @test scaled.scale / factor ≈ base.scale rtol = 1e-5 atol = 1e-5 + @test scaled.value_at_risk / factor ≈ base.value_at_risk rtol = 1e-5 atol = 1e-5 + @test scaled.expected_shortfall / factor ≈ base.expected_shortfall rtol = 1e-5 atol = 1e-5 + end + for losses in (Float64[], [0.0, 0.5], [0.0, 2.0], [2.0, 2.0], [NaN, 2.0, 3.0], [1.0, Inf], [true, false]) + @test_throws ArgumentError TailContracts.fit_pot_tail_risk(losses; threshold = 1.0, probability = 0.99) + end + @test_throws ArgumentError TailContracts.fit_pot_tail_risk([0.0, 2.0, 3.0]; threshold = NaN, probability = 0.99) + @test_throws ArgumentError TailContracts.fit_pot_tail_risk(row["losses"]; threshold = row["threshold"], probability = 1.0) +end diff --git a/test/differencing_regressions.jl b/test/differencing_regressions.jl new file mode 100644 index 0000000..b812723 --- /dev/null +++ b/test/differencing_regressions.jl @@ -0,0 +1,88 @@ +using Test +using TOML +using RiskLabAI + +differencing_equal(actual::Number, expected::Number; rtol = 1e-10, atol = 1e-12) = isnan(expected) ? isnan(actual) : isapprox(actual, expected; rtol, atol) +differencing_equal(actual::AbstractArray, expected::AbstractArray; rtol = 1e-10, atol = 1e-12) = size(actual) == size(expected) && all(differencing_equal(left, right; rtol, atol) for (left, right) in zip(actual, expected)) + +@testset "Shared generalized-binomial differencing weights" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "differencing_python_320.toml")) + for case in fixture["weights"] + actual = case["kind"] == "standard" ? RiskLabAI.Data.calculate_weights_std(case["degree"], case["count"]) : RiskLabAI.Data.calculate_weights_ffd(case["degree"], case["threshold"]) + @test differencing_equal(actual, Float64.(case["output"])) + end +end + +@testset "Shared expanding and fixed filters preserve missing-value positions" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "differencing_python_320.toml")) + for case in fixture["filters"] + values = Float64[case["series"]...] + original = copy(values) + fixed = RiskLabAI.Data.fractional_difference_fixed(values, case["degree"]; threshold = case["threshold"]) + standard = RiskLabAI.Data.fractional_difference_std(values, case["degree"]; threshold = case["threshold"]) + @test differencing_equal(fixed, Float64.(case["fixed"])) + @test differencing_equal(standard, Float64.(case["standard"])) + @test isequal(values, original) + end +end + +@testset "Shared db2 and Haar reflection, octave powers and Hurst estimates" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "differencing_python_320.toml")) + for case in fixture["hurst"] + values = Float64[case["series"]...] + original = copy(values) + details = RiskLabAI.Data._differencing_wavelet_details(values, case["wavelet"]) + @test length(details) == length(case["details"]) + for (actual, expected) in zip(details, case["details"]) + @test differencing_equal(actual, Float64.(expected)) + end + actual = RiskLabAI.Data.wavelet_variance_hurst(values; wavelet = case["wavelet"], skip_fine = case["skip_fine"]) + @test differencing_equal(actual, case["wavelet_hurst"]) + @test differencing_equal(RiskLabAI.Data.rescaled_range_hurst(values), case["rescaled_range"]) + if case["wavelet"] == "db2" && case["skip_fine"] == 1 + estimates = filter(isfinite, [case["wavelet_hurst"], case["rescaled_range"]]) + expected = isempty(estimates) ? NaN : clamp(sum(estimates) / length(estimates) - 0.5, 0.0, 0.99) + @test differencing_equal(RiskLabAI.Data.adaptive_differencing_order(values), expected) + end + @test values == original + end +end + +@testset "Shared ADF probabilities and complete adaptive selection" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "differencing_python_320.toml")) + for case in fixture["adf"] + actual = RiskLabAI.Data._adf_pvalue(Float64[case["series"]...], case["lag"]) + @test differencing_equal(actual, case["pvalue"]; rtol = 1e-9, atol = 1e-11) + end + for case in fixture["adaptive"] + values = Float64[case["series"]...] + original = copy(values) + actual = RiskLabAI.Data.adaptive_fractional_difference(values) + for name in (:order, :d_hat, :memory_retained, :threshold) + @test differencing_equal(getproperty(actual, name), case[string(name)]; rtol = 1e-9, atol = 1e-11) + end + @test differencing_equal(actual.adf_pvalue, case["adf_pvalue"]; rtol = 1e-9, atol = 1e-11) + @test differencing_equal(actual.series, Float64.(case["differenced"]); rtol = 1e-9, atol = 1e-11) + @test findall(!isnan, actual.series) == Int.(case["retained_indices"]) + @test isequal(values, original) + end +end + +@testset "Native wavelet domains and finite filter termination" begin + D = RiskLabAI.Data + @test_throws ArgumentError D.wavelet_variance_hurst(Float64[]) + @test_throws ArgumentError D.wavelet_variance_hurst(ones(32); wavelet = "unsupported") + @test isnan(D.wavelet_variance_hurst(ones(32); skip_fine = 20)) + @test D.wavelet_variance_hurst(collect(1.:64.); wavelet = "db1") == D.wavelet_variance_hurst(collect(1.:64.); wavelet = "haar") + for wavelet in ("db2", "haar") + low, high = D._differencing_wavelet_filters(wavelet) + @test sum(low) ≈ sqrt(2.) + @test abs(sum(high)) < 1e-15 + @test sum(abs2, low) ≈ 1. + @test sum(abs2, high) ≈ 1. + end + @test_throws ArgumentError D.calculate_weights_ffd(0.5, 0.) + @test_throws ArgumentError D.calculate_weights_ffd(NaN, 0.001) + @test_throws ArgumentError D.calculate_weights_ffd(-1., 0.001) + @test D.calculate_weights_ffd(-1., 2.) == [1.] +end diff --git a/test/distance_regressions.jl b/test/distance_regressions.jl new file mode 100644 index 0000000..d82f249 --- /dev/null +++ b/test/distance_regressions.jl @@ -0,0 +1,79 @@ +using Test +using TOML +using Statistics +using RiskLabAI + +distance_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-10, atol = 1e-12) +distance_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(distance_equal(a, b) for (a, b) in zip(left, right)) +distance_matrix(rows) = permutedims(reduce(hcat, Float64.(row) for row in rows)) + +@testset "Shared floating histogram edges and automatic information bins" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "distance_python_320.toml")) + for case in fixture["bins"] + correlation = case["automatic"] ? nothing : case["correlation"] + @test D.calculate_number_of_bins(case["observations"]; correlation = correlation) == case["output"] + end + for case in fixture["histograms"] + x, y = Float64.(case["x"]), Float64.(case["y"]) + @test D._histogram2d(x, y, case["bins"]) == distance_matrix(case["histogram"]) + @test distance_equal(D.calculate_variation_of_information(x, y, case["bins"]), case["variation"]) + @test distance_equal(D.calculate_variation_of_information(x, y, case["bins"]; norm = true), case["normalized"]) + @test distance_equal(D.calculate_variation_of_information(y, x, case["bins"]), case["variation"]) + end + for case in fixture["automatic"] + x, y = Float64.(case["x"]), Float64.(case["y"]) + @test distance_equal(D.calculate_mutual_information(x, y), case["mi"]) + @test distance_equal(D.calculate_mutual_information(x, y; norm = true), case["normalized_mi"]) + @test distance_equal(D.calculate_variation_of_information_extended(x, y), case["vi"]) + @test distance_equal(D.calculate_variation_of_information_extended(x, y; norm = true), case["normalized_vi"]) + end + @test_throws ArgumentError D.calculate_variation_of_information(Float64[], Float64[], 2) + @test_throws ArgumentError D.calculate_variation_of_information([1., NaN], [1., 2.], 2) + @test_throws ArgumentError D.calculate_variation_of_information([1., 2.], [1., 2.], 0) + @test_throws DimensionMismatch D.calculate_variation_of_information([1.], [1., 2.], 2) +end + +@testset "Shared angular distances and centered distance correlation" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "distance_python_320.toml")) + for case in fixture["distances"] + @test distance_equal(D.calculate_distance(distance_matrix(case["dependence"]); metric = case["metric"]), distance_matrix(case["output"])) + end + for case in fixture["dcor"] + x, y = Float64[case["x"]...], Float64[case["y"]...] + original = deepcopy((x, y)) + @test distance_equal(D.distance_correlation(x, y), case["output"]) + @test distance_equal(D.distance_correlation(y, x), case["output"]) + @test isequal((x, y), original) + @test distance_equal(D.distance_correlation(-3 .* x, 2 .* y), case["output"]) + end + @test_throws ArgumentError D.calculate_distance(ones(2, 2); metric = "unknown") +end + +@testset "Shared KSG jitter and integer neighbor counts" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "distance_python_320.toml")) + for case in fixture["ksg"] + x, y = Float64[case["x"]...], Float64[case["y"]...] + draws = (Float64[case["x_noise"]...], Float64[case["y_noise"]...]) + original = deepcopy((x, y, draws)) + actual = D.ksg_mutual_information(x, y; k = case["k"], jitter_draws = draws) + @test distance_equal(actual, case["output"]) + @test isequal((x, y, draws), original) + if length(x) >= 2 + jitter(values, noise) = values .+ noise .* (std(values; corrected = false) == 0 ? 1. : std(values; corrected = false)) .* 1e-10 + xj, yj = jitter(x, draws[1]), jitter(y, draws[2]) + k = min(case["k"], length(x)-1) + for index in eachindex(x) + radius = sort(max.(abs.(xj .- xj[index]), abs.(yj .- yj[index])))[k+1]*(1-1e-10) + @test count(value -> abs(value-xj[index]) <= radius, xj)-1 == case["nx"][index] + @test count(value -> abs(value-yj[index]) <= radius, yj)-1 == case["ny"][index] + end + end + end + @test_throws DimensionMismatch D.ksg_mutual_information([1.], [1., 2.]) + @test_throws DimensionMismatch D.ksg_mutual_information([1., 2.], [1., 2.]; jitter_draws = ([1.], [1., 2.])) + @test_throws ArgumentError D.ksg_mutual_information([1., 2.], [1., 2.]; jitter_draws = ([1., NaN], [1., 2.])) + @test_throws ArgumentError D.ksg_mutual_information([1., 2.], [1., 2.]; k = 0) +end diff --git a/test/ensemble_contract_regressions.jl b/test/ensemble_contract_regressions.jl new file mode 100644 index 0000000..0c65754 --- /dev/null +++ b/test/ensemble_contract_regressions.jl @@ -0,0 +1,93 @@ +using Test +using TOML +using Random +using RiskLabAI + +ensemble_matrix(rows) = reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) + +@testset "Ensembles: theoretical tails and fitted caller-member weighting" begin + E = RiskLabAI.Ensemble + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ensemble_python_320.toml")) + for case in fixtures["theory"] + actual = E.bagging_classifier_accuracy(case["count"], case["probability"]) + @test actual ≈ case["output"] atol = 3e-16 rtol = 2e-12 + @test actual ≈ case["mathematical_tail"] atol = 3e-16 rtol = 2e-12 + end + for case in fixtures["fitted"] + x, y = ensemble_matrix(case["x"]), Vector{Int}(case["y"]) + x_test, y_test = ensemble_matrix(case["x_test"]), Vector{Int}(case["y_test"]) + fitting, predicting = Ref(0), Ref(0) + fit_model = function (model, values, labels, weights) + fitting[] += 1 + @test values == x && values !== x + @test labels == y && labels !== y + @test weights === nothing + return model + end + members = function (model, values) + predicting[] += 1 + if predicting[] == 1 + @test values == x + return ensemble_matrix(case["train_predictions"]) + end + @test values == x_test + return ensemble_matrix(case["test_predictions"]) + end + actual = E.bagging_evaluate_schemes(:fitted_members, x, y, x_test, y_test; fit_estimator = fit_model, tree_predictions = members, estimator_classes = model -> case["classes"]) + @test fitting[] == 1 && predicting[] == 4 + weights = E._bagging_member_weights(ensemble_matrix(case["train_predictions"]), y) + for name in ("uniform", "c_i", "variance") + @test actual[name] ≈ case["accuracies"][name] atol = 1e-14 + @test weights[name] ≈ Vector{Float64}(case["weights"][name]) atol = 1e-14 + @test E._bagging_member_prediction(ensemble_matrix(case["test_predictions"]), weights[name], case["classes"]) == case["predictions"][name] + end + end + for case in fixtures["votes"] + weights = E._bagging_member_weights(ensemble_matrix(case["train_predictions"]), Vector{Int}(case["y"])) + for name in ("uniform", "c_i", "variance") + @test weights[name] ≈ Vector{Float64}(case["weights"][name]) atol = 1e-14 + @test E._bagging_member_prediction(ensemble_matrix(case["test_predictions"]), weights[name], case["classes"]) == case["predictions"][name] + end + end +end + +@testset "Ensembles: caller-predictor bootstrap and duplicate-index expansion" begin + E = RiskLabAI.Ensemble + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ensemble_python_320.toml")) + for case in fixtures["bootstrap"] + x, y = ensemble_matrix(case["x"]), Vector{Int}(case["y"]) + position = Ref(0) + plans = [Vector{Int}(rows) for rows in case["row_draws"]] + prediction = function (model, values) + position[] += 1 + @test values == x[plans[position[]], :] + return case["predicted"][Int.(values[:, 1]) .+ 1] + end + values, average, deviation = E.calculate_bootstrap_accuracy(:fitted_predictor, x, y; predict = prediction, n_bootstraps = length(plans), bootstrap_rows = plans) + @test values ≈ Vector{Float64}(case["values"]) atol = 1e-14 + @test isapprox(average, case["mean"]; atol = 1e-14, nans = true) + @test isapprox(deviation, case["std"]; atol = 1e-14, nans = true) + @test position[] == length(plans) + end +end + +@testset "Ensembles: native sampling isolation and invalid dimensions" begin + E = RiskLabAI.Ensemble + x = reshape(collect(1.:12.), 6, 2) + y = [0, 1, 0, 1, 0, 1] + predictor = (model, values) -> iseven.(Int.(values[:, 1])) + Random.seed!(702) + expected = rand(4) + Random.seed!(702) + first = E.calculate_bootstrap_accuracy(nothing, x, y; predict = predictor, n_bootstraps = 8, random_state = 7) + @test rand(4) == expected + @test first == E.calculate_bootstrap_accuracy(nothing, x, y; predict = predictor, n_bootstraps = 8, random_state = 7) + @test_throws ArgumentError E.bagging_classifier_accuracy(2, .5) + @test_throws ArgumentError E._bagging_member_weights(zeros(6, 0), y) + @test_throws DimensionMismatch E._bagging_member_weights(zeros(5, 2), y) + @test_throws ArgumentError E._bagging_member_prediction(ones(6, 2), [.5, .5], [0]) + @test_throws DimensionMismatch E._bagging_member_prediction(ones(6, 2), [1.], [0, 1]) + @test_throws ArgumentError E.calculate_bootstrap_accuracy(nothing, x, y; predict = predictor, n_bootstraps = -1) + @test_throws DimensionMismatch E.calculate_bootstrap_accuracy(nothing, x, y; predict = predictor, n_bootstraps = 2, bootstrap_rows = [[1, 2]]) + @test_throws ArgumentError E.calculate_bootstrap_accuracy(nothing, x, y; predict = predictor, n_bootstraps = 1, bootstrap_rows = [[0]]) +end diff --git a/test/entropy_regressions.jl b/test/entropy_regressions.jl new file mode 100644 index 0000000..7d873f6 --- /dev/null +++ b/test/entropy_regressions.jl @@ -0,0 +1,47 @@ +using Test +using TOML +using RiskLabAI + +@testset "Shared string entropy and overlapping word probabilities" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "entropy_python_320.toml")) + for case in fixture["messages"] + @test isapprox(F.shannon_entropy(case["message"]), case["shannon"]; rtol = 1e-12, atol = 1e-13) + @test F.lempel_ziv_entropy(case["message"]) == case["lempel"] + end + for case in fixture["words"] + message, width = case["message"], case["length"] + actual = F.probability_mass_function(message, width) + @test Set(keys(actual)) == Set(keys(case["pmf"])) + @test all(isapprox(actual[key], value; rtol = 1e-12, atol = 1e-13) for (key, value) in case["pmf"]) + @test isapprox(F.plug_in_entropy_estimator(message, width), case["plugin"]; rtol = 1e-12, atol = 1e-13) + @test isapprox(F.miller_madow_entropy(message, width), case["miller"]; rtol = 1e-12, atol = 1e-13) + @test isapprox(F.grassberger_entropy(message, width), case["grassberger"]; rtol = 1e-11, atol = 1e-12) + end +end + +@testset "Shared Unicode and overlapping Kontoyiannis match conventions" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "entropy_python_320.toml")) + for case in fixture["matches"] + @test F.longest_match_length(case["message"], case["position"], case["window"]) == (case["length"], case["word"]) + end + for case in fixture["kontoyiannis"] + window = case["window"] == -1 ? nothing : case["window"] + @test isapprox(F.kontoyiannis_entropy(case["message"]; window = window), case["output"]; rtol = 1e-12, atol = 1e-13) + end + @test_throws DomainError F.kontoyiannis_entropy(""; window = 3) +end + +@testset "Shared finite-grid NSB posterior entropy" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "entropy_python_320.toml")) + for case in fixture["nsb"] + actual = F.nsb_entropy(case["message"], case["length"], case["alphabet"]; n_points = case["points"]) + @test isapprox(actual, case["output"]; rtol = 1e-9, atol = 2e-10) + effective = max(case["alphabet"]^case["length"], length(F.probability_mass_function(case["message"], case["length"])), 1) + @test 0 <= actual <= log2(effective)/case["length"]+1e-12 + end + @test F.nsb_entropy("aaaa") == 0. + @test isnan(F.nsb_entropy("aabb", 1, 2; n_points = 1)) +end diff --git a/test/factor_diagnostic_regressions.jl b/test/factor_diagnostic_regressions.jl new file mode 100644 index 0000000..89fd648 --- /dev/null +++ b/test/factor_diagnostic_regressions.jl @@ -0,0 +1,143 @@ +using Test +using TOML +using Random +using RiskLabAI + +const FactorDiagnostics = RiskLabAI.CausalFactorAnalysis +const factor_diagnostic_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "factor_diagnostics_python_320.toml")) + +function check_factor_fields(actual, expected; atol = 1e-12) + for name in propertynames(actual) + result, reference = getproperty(actual, name), expected[string(name)] + if result isa Bool || result isa String || result isa Integer + @test result == reference + elseif reference isa AbstractDict + check_factor_fields(result, reference; atol) + elseif result isa Tuple + if all(value -> value isa String, result) + @test collect(result) == reference + else + @test collect(result) ≈ reference rtol = 1e-10 atol = atol + end + else + @test result ≈ reference rtol = 1e-10 atol = atol + end + end +end + +mutable struct SpecificationFixtureRNG <: AbstractRNG + draws::Vector{Vector{Float64}} + position::Int +end + +function Random.randn(rng::SpecificationFixtureRNG, count::Int) + result = copy(rng.draws[rng.position]) + rng.position += 1 + length(result) == count || error("incorrect fixture draw count") + return result +end + +@testset "Closed-form public factor diagnostics against Python 3.2.0" begin + for row in factor_diagnostic_fixtures["factor"] + beta, gamma, delta = row["beta"], row["gamma"], row["delta"] + @test FactorDiagnostics.confounder_undercontrolled_coefficient(beta, gamma, delta) ≈ row["confounder_coefficient"] rtol = 1e-10 atol = 1e-12 + coefficients = FactorDiagnostics.collider_overcontrolled_coefficients(beta, gamma, delta) + @test coefficients isa FactorDiagnostics.ColliderCoefficients + check_factor_fields(coefficients, row["collider_coefficients"]) + @test FactorDiagnostics.generalized_confounder_undercontrolled_coefficient(beta, gamma, delta; confounder_variance = row["confounder_variance"], exposure_noise_variance = row["exposure_variance"]) ≈ row["generalized_confounder"] rtol = 1e-10 atol = 1e-12 + check_factor_fields(FactorDiagnostics.generalized_collider_overcontrolled_coefficients(beta, gamma, delta; outcome_noise_variance = row["outcome_variance"], collider_noise_variance = row["collider_variance"]), row["generalized_collider"]) + factor_return = FactorDiagnostics.confounder_factor_return(row["x"], row["z"], beta, gamma, delta; delta_realized = row["realized"]) + @test factor_return isa FactorDiagnostics.StrategyPerformance + check_factor_fields(factor_return, row["confounder_factor"]) + check_factor_fields(FactorDiagnostics.confounder_forecast_return(beta, gamma, delta; delta_realized = row["realized"]), row["confounder_forecast"]) + check_factor_fields(FactorDiagnostics.collider_factor_return(row["x"], row["z"], beta, gamma, delta), row["collider_factor"]) + check_factor_fields(FactorDiagnostics.collider_forecast_return(beta, gamma, delta), row["collider_forecast"]) + check_factor_fields(FactorDiagnostics.confounder_factor_return(row["x"], row["z"], beta, gamma, delta), row["confounder_factor_default"]) + check_factor_fields(FactorDiagnostics.confounder_forecast_return(beta, gamma, delta), row["confounder_forecast_default"]) + @test FactorDiagnostics.generalized_confounder_undercontrolled_coefficient(beta, gamma, delta; confounder_variance = 1, exposure_noise_variance = 1) == FactorDiagnostics.confounder_undercontrolled_coefficient(beta, gamma, delta) + standardized = FactorDiagnostics.generalized_collider_overcontrolled_coefficients(beta, gamma, delta; outcome_noise_variance = 1, collider_noise_variance = 1) + @test standardized.beta_hat == coefficients.beta_hat && standardized.theta_hat == coefficients.theta_hat + end + for row in factor_diagnostic_fixtures["diagnostics"] + result = FactorDiagnostics.collider_model_diagnostics(row["beta"], row["gamma"], row["delta"], row["n"]) + @test result isa FactorDiagnostics.ColliderDiagnostics + check_factor_fields(result, row["expected"]) + end + @test !FactorDiagnostics.collider_model_diagnostics(0, 1, 0, 4).adjusted_r_squared_prefers_overcontrolled + @test FactorDiagnostics.collider_model_diagnostics(0, nextfloat(1.0), 0, 4).adjusted_r_squared_prefers_overcontrolled + @test !FactorDiagnostics.collider_model_diagnostics(0, prevfloat(1.0), 0, 4).adjusted_r_squared_prefers_overcontrolled + @test !FactorDiagnostics.collider_model_diagnostics(0, 0, 0, 10).absolute_beta_t_prefers_overcontrolled +end + +@testset "Factor diagnostic numerical and domain boundaries" begin + for row in factor_diagnostic_fixtures["extreme"] + parameters = (row["beta"], row["gamma"], row["delta"]) + check_factor_fields(FactorDiagnostics.collider_overcontrolled_coefficients(parameters...), row["collider"]; atol = 0.0) + @test FactorDiagnostics.confounder_undercontrolled_coefficient(parameters...) ≈ row["confounder"] rtol = 1e-10 atol = 0.0 + check_factor_fields(FactorDiagnostics.collider_model_diagnostics(parameters..., 8), row["diagnostics"]; atol = 0.0) + end + zero_result = FactorDiagnostics.collider_model_diagnostics(0, 0, 0, big(10)^600) + @test zero_result.correct_beta_variance == zero_result.correct_beta_t_statistic == zero_result.collider_t_statistic == 0 + @test FactorDiagnostics.collider_overcontrolled_coefficients(1e308, 1e308, 1e308).beta_hat == -1.0 + @test FactorDiagnostics.confounder_factor_return(1e200, -5e199, 1e200, 2e200, -1).correct == 0.0 + for function_name in (:confounder_undercontrolled_coefficient, :collider_overcontrolled_coefficients, :collider_forecast_return) + method = getproperty(FactorDiagnostics, function_name) + for invalid in (true, NaN, Inf, -Inf, "0", 1im) + @test_throws ArgumentError method(invalid, 1, 1) + @test_throws ArgumentError method(1, invalid, 1) + @test_throws ArgumentError method(1, 1, invalid) + end + end + for count in (3, 0, -1, true, 4.0, Inf) + @test_throws ArgumentError FactorDiagnostics.collider_model_diagnostics(1, 1, 1, count) + end + for invalid in (0, -1, true, NaN, Inf) + @test_throws ArgumentError FactorDiagnostics.generalized_confounder_undercontrolled_coefficient(1, 1, 1; confounder_variance = invalid, exposure_noise_variance = 1) + @test_throws ArgumentError FactorDiagnostics.generalized_confounder_undercontrolled_coefficient(1, 1, 1; confounder_variance = 1, exposure_noise_variance = invalid) + @test_throws ArgumentError FactorDiagnostics.generalized_collider_overcontrolled_coefficients(1, 1, 1; outcome_noise_variance = invalid, collider_noise_variance = 1) + @test_throws ArgumentError FactorDiagnostics.generalized_collider_overcontrolled_coefficients(1, 1, 1; outcome_noise_variance = 1, collider_noise_variance = invalid) + end + @test_throws ArgumentError FactorDiagnostics.confounder_factor_return(1, 1, 1, 1, 1; delta_realized = NaN) + @test_throws ArgumentError FactorDiagnostics.confounder_forecast_return(1, 1, 1; delta_realized = true) + @test_throws ArgumentError FactorDiagnostics.collider_factor_return(1, NaN, 1, 1, 1) + @test_throws ArgumentError FactorDiagnostics.collider_forecast_return(1e308, 0, 0) + @test_throws ArgumentError FactorDiagnostics.collider_model_diagnostics(1e308, 0, 0, 5) +end + +@testset "Public structural specification experiments with shared draws" begin + for row in factor_diagnostic_fixtures["population"] + result = getproperty(FactorDiagnostics, Symbol(row["structure"] * "_population_diagnostics"))() + @test result isa FactorDiagnostics.SpecificationPopulationResult + @test result.unconditioned isa FactorDiagnostics.PopulationRegressionDiagnostics + check_factor_fields(result, row["expected"]) + end + for row in factor_diagnostic_fixtures["experiment"] + rng = SpecificationFixtureRNG([Vector{Float64}(values) for values in row["draws"]], 1) + result = getproperty(FactorDiagnostics, Symbol(row["structure"] * "_specification_experiment"))(row["count"], 17; rng) + @test result isa FactorDiagnostics.SpecificationExperimentResult + @test result.unconditioned isa FactorDiagnostics.RegressionDiagnostics + @test rng.position == length(row["draws"]) + 1 + check_factor_fields(result, row["expected"]) + @test result.unconditioned.coefficients isa Tuple && result.conditioned.coefficients isa Tuple + end + for method in (FactorDiagnostics.fork_specification_experiment, FactorDiagnostics.collider_specification_experiment, FactorDiagnostics.confounded_mediator_specification_experiment) + first_result = method(100, 71) + second_result = method(100, 71) + @test first_result.unconditioned == second_result.unconditioned + @test first_result.conditioned == second_result.conditioned + @test first_result.conditioned.coefficients == method(100, 0; rng = MersenneTwister(71)).conditioned.coefficients + @test first_result.conditioned.n_observations == 100 + @test all(isfinite, first_result.conditioned.standard_errors) + for count in (3, 0, -1, true, 4.0, 1_000_001) + @test_throws ArgumentError method(count) + end + for seed in (-1, 2^32, true, 0.0) + @test_throws ArgumentError method(10, seed) + end + @test_throws ArgumentError method(10, 0; rng = 5) + end + constant_rng = SpecificationFixtureRNG([ones(5), ones(5), ones(5)], 1) + @test_throws ArgumentError FactorDiagnostics.fork_specification_experiment(5; rng = constant_rng) + @test_throws ArgumentError FactorDiagnostics._specification_ols(ones(5), (("X", collect(1.0:5.0)),)) + @test_throws ArgumentError FactorDiagnostics._specification_ols([1.0, NaN, 2.0, 3.0], (("X", collect(1.0:4.0)),)) +end diff --git a/test/feature_contract_regressions.jl b/test/feature_contract_regressions.jl new file mode 100644 index 0000000..e9e5ba8 --- /dev/null +++ b/test/feature_contract_regressions.jl @@ -0,0 +1,131 @@ +using Test +using TOML +using Random +using RiskLabAI + +const FeatureContracts = RiskLabAI.Features +feature_fixture_matrix(rows) = reduce(vcat, permutedims.(rows)) +const feature_contract_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "features_python_320.toml")) + +mutable struct HawkesFixtureRNG <: AbstractRNG + exponential::Vector{Float64} + uniform::Vector{Float64} + exponential_index::Int + uniform_index::Int +end + +function Random.randexp(rng::HawkesFixtureRNG) + result = rng.exponential[rng.exponential_index] + rng.exponential_index += 1 + return result +end + +function Random.rand(rng::HawkesFixtureRNG) + result = rng.uniform[rng.uniform_index] + rng.uniform_index += 1 + return result +end + +@testset "Supplied-prior probability ambiguity against Python 3.2.0" begin + for row in feature_contract_fixtures["ambiguity"] + probabilities = feature_fixture_matrix(row["probabilities"]) + result = FeatureContracts.probability_ambiguity(probabilities; prior_weights = row["prior_weights"], bin_width = row["bin_width"]) + for name in (:unscaled, :scaled, :mean_probabilities, :probability_variances, :prior_weights, :bin_width) + @test getproperty(result, name) ≈ row["expected_" * string(name)] rtol = 1e-10 atol = 1e-12 + end + uniform = FeatureContracts.probability_ambiguity(probabilities) + explicit = FeatureContracts.probability_ambiguity(probabilities; prior_weights = fill(1 / size(probabilities, 1), size(probabilities, 1))) + @test uniform == explicit + @test isnothing(uniform.scaled) && isnothing(uniform.bin_width) + end + for row in feature_contract_fixtures["normal"] + options = haskey(row, "edges") ? (; edges = row["edges"]) : (;) + result = FeatureContracts.normal_prior_bin_probabilities(row["means"], row["standard_deviations"]; options...) + @test result ≈ feature_fixture_matrix(row["expected"]) rtol = 1e-10 atol = 1e-12 + @test all(result .>= 0) + @test all(abs.(sum(result; dims = 2) .- 1) .<= 1e-12) + end + @test FeatureContracts.probability_ambiguity([0.2 0.8; 0.2 0.8]).unscaled == 0 + @test FeatureContracts.probability_ambiguity([0.0 1.0; 1.0 0.0]; prior_weights = [1.0, 0.0]).unscaled == 0 + for probabilities in (zeros(0, 2), ones(2, 2), [1.1 -0.1], [NaN 1.0], [true false]) + @test_throws ArgumentError FeatureContracts.probability_ambiguity(probabilities) + end + for weights in ([0.2, 0.2], [1.0], [-0.1, 1.1], [NaN, 0.0]) + @test_throws ArgumentError FeatureContracts.probability_ambiguity([0.2 0.8; 0.4 0.6]; prior_weights = weights) + end + for width in (0, 1, -0.1, true, NaN) + @test_throws ArgumentError FeatureContracts.probability_ambiguity([0.2 0.8]; bin_width = width) + end + @test_throws ArgumentError FeatureContracts.normal_prior_bin_probabilities([0.0], [0.0]) + @test_throws ArgumentError FeatureContracts.normal_prior_bin_probabilities([0.0], [1.0, 2.0]) + @test_throws ArgumentError FeatureContracts.normal_prior_bin_probabilities([0.0], [1.0]; edges = [0.0, 0.0]) +end + +@testset "Hawkes intensity, likelihood, score and supplied thinning draws" begin + for row in feature_contract_fixtures["hawkes"] + events = Vector{Float64}(row["events"]) + mu, alpha, beta = row["mu"], row["alpha"], row["beta"] + @test FeatureContracts.hawkes_intensity(row["times"], events, mu, alpha, beta) ≈ row["expected_intensity"] rtol = 1e-10 atol = 1e-12 + @test FeatureContracts.hawkes_integrated_intensity(events, row["horizon"], mu, alpha, beta) ≈ row["expected_integrated"] rtol = 1e-10 atol = 1e-12 + @test FeatureContracts.hawkes_log_likelihood(events, row["horizon"], mu, alpha, beta) ≈ row["expected_likelihood"] rtol = 1e-10 atol = 1e-12 + gradient = zeros(3) + objective = FeatureContracts._hawkes_scaled_objective_gradient!(gradient, [mu, alpha / beta, beta], events) + @test objective ≈ -row["expected_likelihood"] rtol = 1e-10 atol = 1e-12 + @test gradient ≈ row["scaled_objective_gradient"] rtol = 1e-5 atol = 1e-7 + @test FeatureContracts.hawkes_intensity(row["times"], [events; 2.0], mu, alpha, beta) == FeatureContracts.hawkes_intensity(row["times"], events, mu, alpha, beta) + end + for row in feature_contract_fixtures["simulation"] + rng = HawkesFixtureRNG(row["exponential"], row["uniform"], 1, 1) + result = FeatureContracts.simulate_hawkes(row["horizon"], row["mu"], row["alpha"], row["beta"]; random_state = rng) + @test result ≈ Vector{Float64}(row["expected_events"]) rtol = 1e-10 atol = 1e-12 + @test rng.exponential_index == length(row["exponential"]) + 1 + @test rng.uniform_index == length(row["uniform"]) + 1 + @test all(diff(result) .> 0) && all(0 .< result .<= row["horizon"]) + end + @test FeatureContracts.hawkes_intensity([0.0], [0.0], 1.0, 0.5, 1.0) == [1.0] + @test FeatureContracts.hawkes_intensity(Float64[], Float64[], 1.0, 0.5, 1.0) == Float64[] + @test FeatureContracts.simulate_hawkes(10, 1.0, 0.5, 1.0; random_state = 112) == FeatureContracts.simulate_hawkes(10, 1.0, 0.5, 1.0; random_state = 112) + @test_throws ErrorException FeatureContracts.simulate_hawkes(1, 1.0, 0.0, 1.0; random_state = HawkesFixtureRNG([0.01, 0.01], [0.1, 0.1], 1, 1), max_events = 1) + @test_throws ErrorException FeatureContracts.simulate_hawkes(1, 1.0, 0.0, 1.0; random_state = HawkesFixtureRNG([0.0], [0.1], 1, 1)) + for events in ([0.1, 0.1], [0.2, 0.1], [-0.1], [Inf], [NaN], [1.1]) + @test_throws ArgumentError FeatureContracts.hawkes_log_likelihood(events, 1.0, 1.0, 0.5, 1.0) + end + for parameters in ((0.0, 0.2, 1.0), (1.0, 1.0, 1.0), (1.0, -0.1, 1.0), (1.0, 0.2, 0.0)) + @test_throws ArgumentError FeatureContracts.hawkes_intensity([0.1], Float64[], parameters...) + end + @test_throws ArgumentError FeatureContracts.simulate_hawkes(1.0, 1.0, 0.5, 1.0; max_events = true) + @test_throws ArgumentError FeatureContracts.hawkes_intensity([-0.1], Float64[], 1.0, 0.5, 1.0) +end + +@testset "Bounded Hawkes likelihood fitting and completed-bucket VPIN" begin + for row in feature_contract_fixtures["fit"] + events, horizon = row["events"], row["horizon"] + result = FeatureContracts.fit_hawkes(events, horizon) + @test result.mu > 0 && result.beta > 0 && 0 <= result.alpha < result.beta + @test 1 <= result.converged_starts <= 3 + @test abs(result.log_likelihood - row["expected_log_likelihood"]) <= 1e-4 + @test result.log_likelihood ≈ FeatureContracts.hawkes_log_likelihood(events, horizon, result.mu, result.alpha, result.beta) rtol = 1e-12 atol = 1e-12 + @test result.branching_ratio ≈ result.alpha / result.beta rtol = 1e-12 + @test result.log_likelihood >= FeatureContracts.hawkes_log_likelihood(events, horizon, length(events) / horizon, 0.0, 1.0) - 1e-4 + if row["case"] == 1 + scaled = FeatureContracts.fit_hawkes(events * 10, horizon * 10; initial = [result.mu, result.alpha, result.beta] / 10) + @test scaled.converged_starts == 1 + @test abs(scaled.log_likelihood + length(events) * log(10) - result.log_likelihood) <= 1e-4 + end + end + for row in feature_contract_fixtures["vpin"] + result = FeatureContracts.volume_synchronized_pin(row["buy_volume"], row["sell_volume"]; bucket_volume = row["bucket_volume"], window = row["window"]) + @test result ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + @test all(0 .<= result .<= 1) + end + @test FeatureContracts.volume_synchronized_pin([100.0, 0.0], [0.0, 100.0]; bucket_volume = 100, window = 2) == [1.0] + @test FeatureContracts.volume_synchronized_pin([50.0, 50.0], [50.0, 50.0]; bucket_volume = 100, window = 2) == [0.0] + @test_throws ArgumentError FeatureContracts.fit_hawkes(Float64[], 1) + @test_throws ArgumentError FeatureContracts.fit_hawkes([0.1], 1) + @test_throws ArgumentError FeatureContracts.fit_hawkes([0.1, 0.2], 1; initial = [1, 1, 1]) + @test_throws ArgumentError FeatureContracts.fit_hawkes([0.1, 0.2], 1; initial = [1, 0.5]) + @test_throws ArgumentError FeatureContracts.volume_synchronized_pin([50.0], [49.0]; bucket_volume = 100, window = 1) + @test_throws ArgumentError FeatureContracts.volume_synchronized_pin([-1.0], [101.0]; bucket_volume = 100, window = 1) + @test_throws ArgumentError FeatureContracts.volume_synchronized_pin([50.0], [50.0]; bucket_volume = 100, window = 2) + @test_throws ArgumentError FeatureContracts.volume_synchronized_pin([50.0], [50.0]; bucket_volume = 100, window = true) +end diff --git a/test/fixtures/adaptation_python_320.toml b/test/fixtures/adaptation_python_320.toml new file mode 100644 index 0000000..f4fae33 --- /dev/null +++ b/test/fixtures/adaptation_python_320.toml @@ -0,0 +1,481 @@ +reference = "RiskLabAI 3.2.0 with installed River 0.26.1" + +[[detectors]] +name = "zeros_0.002" +delta = 0.002 +values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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-0.2946482494944718, "pvalue" = 0.7682625997603432, "reject" = false} + +[[difference]] +a = [-0.005098073538285683, 0.012457975881241538, -0.021820617541168923, 0.018782592373179356, 0.0016913853327435856, -0.001793555766943449, 0.01753713737528664, -0.014545156383598984, -0.02391124065956586, 0.008697916827771731, -0.010820765873630688, -0.0012127373896068435, 0.03092271066764156] +b = [0.009867270405484272, 0.010477801759830842, 0.0008117561292071626, 0.018807494468852638, 0.003442437002383336, -0.007579306644007059, -0.020139948372585377, 0.0028069200299377017, 0.0015869573511971009, 0.011326072651086698, -0.010195548462534043, -0.010526202946247466, 0.011343533681027586] +method = "ledoit_wolf" +block_length = 45 +bandwidth = 41 +n_boot = 19 +bootstrap_indices = [[9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8], [7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6], [11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1, 2, 3], [8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7], [10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9], [6, 7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5], [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1], [7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], [9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8], [8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7], [9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8], [7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6], [8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7], [7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6], [11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7], [10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9]] +output = {"delta" = -0.10576997663728396, "se" = 0.12049180561525581, "stat" = 0.8778188367018057, "pvalue" = 0.05, "reject" = false} + +[[difference]] +a = [0.007032183274132643, -0.013441900602960185, -0.004178385972140518, 0.0030461080485575743, -0.013678397871412866, -0.000712473235983324, 0.009410311807640863, 0.001956945642186191, 0.0007670383445497163, 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+method = "ledoit_wolf" +block_length = 1 +bandwidth = 2 +n_boot = 19 +bootstrap_indices = [[24, 19, 31, 10, 22, 27, 15, 5, 18, 3, 25, 21, 25, 18, 23, 18, 31, 22, 28, 29, 34, 33, 13, 28, 24, 6, 19, 22, 23, 15, 27, 21, 24, 34, 2, 1, 15], [28, 16, 8, 19, 20, 4, 22, 15, 8, 10, 15, 29, 11, 22, 15, 34, 23, 19, 13, 6, 25, 34, 36, 27, 7, 28, 33, 4, 20, 35, 30, 29, 31, 13, 23, 2, 9], [25, 18, 6, 23, 20, 35, 9, 35, 20, 2, 5, 15, 30, 27, 29, 17, 22, 13, 9, 20, 35, 32, 33, 23, 1, 20, 5, 36, 30, 9, 24, 25, 35, 8, 27, 33, 3], [25, 30, 17, 27, 33, 36, 33, 16, 15, 27, 33, 23, 20, 27, 34, 11, 12, 26, 14, 24, 31, 25, 17, 16, 1, 23, 5, 4, 3, 31, 2, 17, 3, 5, 22, 16, 7], [15, 29, 25, 27, 30, 27, 13, 23, 29, 11, 24, 22, 37, 16, 35, 4, 34, 34, 8, 37, 5, 32, 11, 35, 22, 20, 32, 18, 22, 37, 36, 13, 37, 16, 15, 32, 34], [17, 14, 19, 25, 6, 4, 6, 3, 24, 4, 22, 7, 12, 3, 2, 25, 26, 1, 15, 35, 2, 36, 34, 25, 12, 24, 34, 14, 37, 25, 31, 6, 31, 1, 1, 22, 8], [34, 31, 18, 3, 37, 10, 18, 13, 27, 2, 8, 18, 11, 34, 32, 8, 21, 5, 13, 37, 30, 29, 16, 13, 34, 6, 26, 2, 20, 10, 10, 18, 14, 25, 31, 36, 27], [5, 10, 27, 36, 35, 24, 9, 11, 27, 35, 30, 18, 17, 3, 35, 32, 23, 17, 13, 36, 37, 9, 9, 13, 15, 2, 4, 27, 19, 25, 30, 5, 21, 19, 26, 2, 37], [9, 33, 20, 5, 18, 31, 24, 35, 9, 2, 11, 26, 19, 3, 15, 1, 27, 28, 28, 3, 5, 29, 16, 24, 13, 24, 35, 33, 30, 6, 17, 14, 1, 5, 25, 14, 23], [12, 8, 16, 27, 21, 24, 5, 7, 18, 1, 20, 6, 11, 16, 23, 6, 26, 33, 28, 4, 2, 13, 18, 28, 24, 25, 37, 8, 19, 26, 17, 15, 35, 19, 22, 33, 26], [35, 7, 12, 2, 21, 37, 21, 25, 14, 32, 4, 16, 13, 22, 7, 15, 12, 32, 19, 29, 12, 1, 27, 33, 19, 32, 37, 21, 29, 25, 7, 12, 1, 11, 30, 35, 22], [8, 33, 4, 6, 1, 33, 18, 27, 32, 23, 6, 18, 12, 27, 37, 24, 5, 13, 21, 2, 6, 16, 8, 11, 5, 6, 20, 2, 22, 26, 6, 4, 17, 5, 24, 17, 26], [20, 23, 11, 3, 1, 22, 10, 9, 1, 11, 24, 8, 5, 25, 15, 27, 34, 20, 32, 9, 22, 15, 23, 35, 21, 8, 23, 3, 26, 27, 23, 19, 30, 14, 11, 6, 17], [10, 21, 30, 26, 32, 13, 29, 32, 18, 28, 20, 24, 22, 15, 34, 8, 30, 5, 24, 20, 12, 32, 13, 10, 28, 29, 27, 28, 15, 13, 29, 16, 21, 10, 23, 23, 29], [32, 10, 7, 13, 22, 29, 14, 14, 15, 31, 7, 37, 37, 28, 4, 23, 13, 19, 34, 22, 26, 29, 30, 32, 8, 18, 15, 8, 31, 36, 10, 34, 5, 30, 28, 30, 1], [5, 21, 13, 6, 11, 23, 33, 24, 6, 3, 24, 19, 9, 25, 3, 1, 28, 22, 37, 34, 32, 20, 37, 30, 27, 35, 7, 3, 32, 31, 33, 26, 32, 24, 31, 27, 28], [5, 7, 31, 16, 27, 17, 9, 2, 13, 11, 18, 2, 33, 4, 13, 16, 12, 8, 21, 35, 17, 13, 13, 29, 4, 9, 11, 32, 26, 30, 35, 3, 27, 2, 28, 30, 11], [28, 23, 32, 12, 5, 16, 16, 9, 25, 37, 12, 35, 7, 15, 21, 7, 1, 18, 12, 23, 10, 37, 29, 23, 2, 19, 7, 10, 15, 1, 29, 27, 12, 6, 9, 17, 36], [15, 19, 7, 6, 25, 10, 12, 5, 9, 35, 14, 31, 8, 4, 8, 13, 29, 3, 17, 3, 18, 32, 29, 30, 28, 3, 9, 26, 5, 19, 5, 22, 2, 8, 9, 15, 32]] +output = {"delta" = -0.0285532245257554, "se" = 0.1631593591498985, "stat" = 0.1750020634704924, "pvalue" = 0.9, "reject" = false} + +[[difference]] +a = [0.0031843962301035186, -0.0017040966770402398, 0.008093778331237176, -0.005132555413752912, 0.0044048054167667924, 0.001798877491485421, -0.003099034503597874, 0.006003768518296897, 0.01812663881680568, 0.029341106180352662, -0.019504627980421706, 0.006426110250399967, 0.0003960502897397567, 0.02619502974848558, 0.001805319929252999, 0.006305400125119207, -0.01445965547987168, -0.0014239422547423462, 0.021016022589101353, 0.007382596614331484, 0.0004911448902144542, -0.011351051856762242, 0.009765516214732015, 0.003655659709441511, -0.013903204149814117, -0.022667589539862726, -0.01574034471470634, -0.023826345282911644, -0.012534440557255647, 0.02470436890429123, -0.003850596593908128, 0.001366066870999126, 0.010428176071338084, -0.020747162656004965, 0.00828152170029161, -0.002431452452619426, -0.01421005763852779] +b = [-0.0037892374603047136, 0.01582741478054421, -0.008420528693645417, -0.00748388340555832, -0.022490263769591765, 0.012285377380352496, 0.0008635819529962521, 0.022783487573081736, -0.008785726455806813, -0.001171386253190779, -0.004970724318035837, 0.025893235154575225, -0.00673918974432194, 0.002276122338101108, 0.008575368768803819, 0.008033728381820213, 0.002513052477873057, 0.010466063153603055, 0.004624772258855209, -0.01103470071541551, -0.012232423310218333, -0.013699548495860792, 0.018005641596179952, 0.015659763653751872, -0.007715009947407334, 0.012453858641488683, -0.003454132084494152, -0.007375253745247646, 0.008044349657480902, 0.011139882837432459, -0.0017394542015146581, -0.005759329619744892, 0.01079969602004749, -0.007884785434629133, 0.005292180053693257, 0.00029157164813910393, -0.004516183084405594] +method = "naive" +block_length = 1 +bandwidth = 41 +n_boot = 19 +bootstrap_indices = [] +output = {"delta" = -0.11559542588332929, "se" = 0.2055592228333407, "stat" = -0.56234609320862, "pvalue" = 0.5738802395015861, "reject" = false} + +[[difference]] +a = [0.0031843962301035186, -0.0017040966770402398, 0.008093778331237176, -0.005132555413752912, 0.0044048054167667924, 0.001798877491485421, -0.003099034503597874, 0.006003768518296897, 0.01812663881680568, 0.029341106180352662, -0.019504627980421706, 0.006426110250399967, 0.0003960502897397567, 0.02619502974848558, 0.001805319929252999, 0.006305400125119207, -0.01445965547987168, -0.0014239422547423462, 0.021016022589101353, 0.007382596614331484, 0.0004911448902144542, -0.011351051856762242, 0.009765516214732015, 0.003655659709441511, -0.013903204149814117, -0.022667589539862726, -0.01574034471470634, -0.023826345282911644, -0.012534440557255647, 0.02470436890429123, -0.003850596593908128, 0.001366066870999126, 0.010428176071338084, -0.020747162656004965, 0.00828152170029161, -0.002431452452619426, -0.01421005763852779] +b = [-0.0037892374603047136, 0.01582741478054421, -0.008420528693645417, -0.00748388340555832, -0.022490263769591765, 0.012285377380352496, 0.0008635819529962521, 0.022783487573081736, -0.008785726455806813, -0.001171386253190779, -0.004970724318035837, 0.025893235154575225, -0.00673918974432194, 0.002276122338101108, 0.008575368768803819, 0.008033728381820213, 0.002513052477873057, 0.010466063153603055, 0.004624772258855209, -0.01103470071541551, -0.012232423310218333, -0.013699548495860792, 0.018005641596179952, 0.015659763653751872, -0.007715009947407334, 0.012453858641488683, -0.003454132084494152, -0.007375253745247646, 0.008044349657480902, 0.011139882837432459, -0.0017394542015146581, -0.005759329619744892, 0.01079969602004749, -0.007884785434629133, 0.005292180053693257, 0.00029157164813910393, -0.004516183084405594] +method = "ledoit_wolf" +block_length = 1 +bandwidth = 41 +n_boot = 19 +bootstrap_indices = [[24, 19, 31, 10, 22, 27, 15, 5, 18, 3, 25, 21, 25, 18, 23, 18, 31, 22, 28, 29, 34, 33, 13, 28, 24, 6, 19, 22, 23, 15, 27, 21, 24, 34, 2, 1, 15], [28, 16, 8, 19, 20, 4, 22, 15, 8, 10, 15, 29, 11, 22, 15, 34, 23, 19, 13, 6, 25, 34, 36, 27, 7, 28, 33, 4, 20, 35, 30, 29, 31, 13, 23, 2, 9], [25, 18, 6, 23, 20, 35, 9, 35, 20, 2, 5, 15, 30, 27, 29, 17, 22, 13, 9, 20, 35, 32, 33, 23, 1, 20, 5, 36, 30, 9, 24, 25, 35, 8, 27, 33, 3], [25, 30, 17, 27, 33, 36, 33, 16, 15, 27, 33, 23, 20, 27, 34, 11, 12, 26, 14, 24, 31, 25, 17, 16, 1, 23, 5, 4, 3, 31, 2, 17, 3, 5, 22, 16, 7], [15, 29, 25, 27, 30, 27, 13, 23, 29, 11, 24, 22, 37, 16, 35, 4, 34, 34, 8, 37, 5, 32, 11, 35, 22, 20, 32, 18, 22, 37, 36, 13, 37, 16, 15, 32, 34], [17, 14, 19, 25, 6, 4, 6, 3, 24, 4, 22, 7, 12, 3, 2, 25, 26, 1, 15, 35, 2, 36, 34, 25, 12, 24, 34, 14, 37, 25, 31, 6, 31, 1, 1, 22, 8], [34, 31, 18, 3, 37, 10, 18, 13, 27, 2, 8, 18, 11, 34, 32, 8, 21, 5, 13, 37, 30, 29, 16, 13, 34, 6, 26, 2, 20, 10, 10, 18, 14, 25, 31, 36, 27], [5, 10, 27, 36, 35, 24, 9, 11, 27, 35, 30, 18, 17, 3, 35, 32, 23, 17, 13, 36, 37, 9, 9, 13, 15, 2, 4, 27, 19, 25, 30, 5, 21, 19, 26, 2, 37], [9, 33, 20, 5, 18, 31, 24, 35, 9, 2, 11, 26, 19, 3, 15, 1, 27, 28, 28, 3, 5, 29, 16, 24, 13, 24, 35, 33, 30, 6, 17, 14, 1, 5, 25, 14, 23], [12, 8, 16, 27, 21, 24, 5, 7, 18, 1, 20, 6, 11, 16, 23, 6, 26, 33, 28, 4, 2, 13, 18, 28, 24, 25, 37, 8, 19, 26, 17, 15, 35, 19, 22, 33, 26], [35, 7, 12, 2, 21, 37, 21, 25, 14, 32, 4, 16, 13, 22, 7, 15, 12, 32, 19, 29, 12, 1, 27, 33, 19, 32, 37, 21, 29, 25, 7, 12, 1, 11, 30, 35, 22], [8, 33, 4, 6, 1, 33, 18, 27, 32, 23, 6, 18, 12, 27, 37, 24, 5, 13, 21, 2, 6, 16, 8, 11, 5, 6, 20, 2, 22, 26, 6, 4, 17, 5, 24, 17, 26], [20, 23, 11, 3, 1, 22, 10, 9, 1, 11, 24, 8, 5, 25, 15, 27, 34, 20, 32, 9, 22, 15, 23, 35, 21, 8, 23, 3, 26, 27, 23, 19, 30, 14, 11, 6, 17], [10, 21, 30, 26, 32, 13, 29, 32, 18, 28, 20, 24, 22, 15, 34, 8, 30, 5, 24, 20, 12, 32, 13, 10, 28, 29, 27, 28, 15, 13, 29, 16, 21, 10, 23, 23, 29], [32, 10, 7, 13, 22, 29, 14, 14, 15, 31, 7, 37, 37, 28, 4, 23, 13, 19, 34, 22, 26, 29, 30, 32, 8, 18, 15, 8, 31, 36, 10, 34, 5, 30, 28, 30, 1], [5, 21, 13, 6, 11, 23, 33, 24, 6, 3, 24, 19, 9, 25, 3, 1, 28, 22, 37, 34, 32, 20, 37, 30, 27, 35, 7, 3, 32, 31, 33, 26, 32, 24, 31, 27, 28], [5, 7, 31, 16, 27, 17, 9, 2, 13, 11, 18, 2, 33, 4, 13, 16, 12, 8, 21, 35, 17, 13, 13, 29, 4, 9, 11, 32, 26, 30, 35, 3, 27, 2, 28, 30, 11], [28, 23, 32, 12, 5, 16, 16, 9, 25, 37, 12, 35, 7, 15, 21, 7, 1, 18, 12, 23, 10, 37, 29, 23, 2, 19, 7, 10, 15, 1, 29, 27, 12, 6, 9, 17, 36], [15, 19, 7, 6, 25, 10, 12, 5, 9, 35, 14, 31, 8, 4, 8, 13, 29, 3, 17, 3, 18, 32, 29, 30, 28, 3, 9, 26, 5, 19, 5, 22, 2, 8, 9, 15, 32]] +output = {"delta" = -0.11559542588332929, "se" = 0.14513775899878592, "stat" = 0.7964531537537123, "pvalue" = 0.8, "reject" = false} + +[[difference]] +a = [0.011891931003159826, 0.0017222954380453825, 0.01833470727755343, 0.02428603742954226, 0.033021211649074275, -0.005524415682007924, -0.0004792468452544537, 0.0013884617997557574, 0.008739604838942507, -0.014347336277792102, -0.017497755897903984, -0.011444146640483776, -0.005313092328832851, 0.015127363475698824, -0.007465106849488822, 0.02496271870339503, -0.00017181746230544642, 0.008722324346253604, -0.004147690771659504, 0.006882833776043396, 0.03039710547923248, 0.0038624356294013, -0.0011934549041005631, -0.006339116170645007, 0.00384635218273175, 0.010183569305008128, 0.0105945050346294, 0.011064323331488676, 0.012559046906346242, -0.020582312207892133, -0.0009607522079794896, -0.015136058388128258, -0.0023300448992267034, 0.01224257753519896, 0.017534036969564893, -0.014807221091140228, -0.007569810742202964] +b = [-0.0049333385992605715, -0.020804076492428013, 0.013297551641115363, 0.011065270648730132, 0.015630259945706035, 0.01669639101081276, 0.01686330227715985, -0.021440896463580408, 0.002142275042144471, 0.0010577481873987405, -0.006636912739584418, 0.0131792979032136, -0.026369532389182968, -0.013382925018631978, 0.004147847675637719, 0.0040803172172026924, -0.008242895116179117, 0.019751734688234254, 0.008839732132286598, -0.0014903389458614654, 0.006333206674901257, 0.007770985217256908, -0.0007302233733108867, 0.01451067185402133, 0.0030834516494753026, 0.008858045377798822, -0.010210554487818976, 0.02053998209610653, 0.0047715730160368995, -0.014068906788330369, -0.00029465133392662506, -0.005226974530538511, 0.00027251871768482023, 0.0019455952278187227, 0.004429800994664836, -0.009137191727483219, -0.003985940673678793] +method = "naive" +block_length = 4 +bandwidth = 0 +n_boot = 19 +bootstrap_indices = [] +output = {"delta" = 0.148664843952505, "se" = 0.19467192521657845, "stat" = 0.7636686378228953, "pvalue" = 0.4450647348166855, "reject" = false} + +[[difference]] +a = [0.011891931003159826, 0.0017222954380453825, 0.01833470727755343, 0.02428603742954226, 0.033021211649074275, -0.005524415682007924, -0.0004792468452544537, 0.0013884617997557574, 0.008739604838942507, -0.014347336277792102, -0.017497755897903984, -0.011444146640483776, -0.005313092328832851, 0.015127363475698824, -0.007465106849488822, 0.02496271870339503, -0.00017181746230544642, 0.008722324346253604, -0.004147690771659504, 0.006882833776043396, 0.03039710547923248, 0.0038624356294013, -0.0011934549041005631, -0.006339116170645007, 0.00384635218273175, 0.010183569305008128, 0.0105945050346294, 0.011064323331488676, 0.012559046906346242, -0.020582312207892133, -0.0009607522079794896, -0.015136058388128258, -0.0023300448992267034, 0.01224257753519896, 0.017534036969564893, -0.014807221091140228, -0.007569810742202964] +b = [-0.0049333385992605715, -0.020804076492428013, 0.013297551641115363, 0.011065270648730132, 0.015630259945706035, 0.01669639101081276, 0.01686330227715985, -0.021440896463580408, 0.002142275042144471, 0.0010577481873987405, -0.006636912739584418, 0.0131792979032136, -0.026369532389182968, -0.013382925018631978, 0.004147847675637719, 0.0040803172172026924, -0.008242895116179117, 0.019751734688234254, 0.008839732132286598, -0.0014903389458614654, 0.006333206674901257, 0.007770985217256908, -0.0007302233733108867, 0.01451067185402133, 0.0030834516494753026, 0.008858045377798822, -0.010210554487818976, 0.02053998209610653, 0.0047715730160368995, -0.014068906788330369, -0.00029465133392662506, -0.005226974530538511, 0.00027251871768482023, 0.0019455952278187227, 0.004429800994664836, -0.009137191727483219, -0.003985940673678793] +method = "ledoit_wolf" +block_length = 4 +bandwidth = 0 +n_boot = 19 +bootstrap_indices = [[24, 25, 26, 27, 19, 20, 21, 22, 31, 32, 33, 34, 10, 11, 12, 13, 22, 23, 24, 25, 27, 28, 29, 30, 15, 16, 17, 18, 5, 6, 7, 8, 18, 19, 20, 21, 3], [25, 26, 27, 28, 21, 22, 23, 24, 25, 26, 27, 28, 18, 19, 20, 21, 23, 24, 25, 26, 18, 19, 20, 21, 31, 32, 33, 34, 22, 23, 24, 25, 28, 29, 30, 31, 29], [34, 35, 36, 37, 33, 34, 35, 36, 13, 14, 15, 16, 28, 29, 30, 31, 24, 25, 26, 27, 6, 7, 8, 9, 19, 20, 21, 22, 22, 23, 24, 25, 23, 24, 25, 26, 15], [27, 28, 29, 30, 21, 22, 23, 24, 24, 25, 26, 27, 34, 35, 36, 37, 2, 3, 4, 5, 1, 2, 3, 4, 15, 16, 17, 18, 28, 29, 30, 31, 16, 17, 18, 19, 8], [19, 20, 21, 22, 20, 21, 22, 23, 4, 5, 6, 7, 22, 23, 24, 25, 15, 16, 17, 18, 8, 9, 10, 11, 10, 11, 12, 13, 15, 16, 17, 18, 29, 30, 31, 32, 11], [22, 23, 24, 25, 15, 16, 17, 18, 34, 35, 36, 37, 23, 24, 25, 26, 19, 20, 21, 22, 13, 14, 15, 16, 6, 7, 8, 9, 25, 26, 27, 28, 34, 35, 36, 37, 36], [27, 28, 29, 30, 7, 8, 9, 10, 28, 29, 30, 31, 33, 34, 35, 36, 4, 5, 6, 7, 20, 21, 22, 23, 35, 36, 37, 1, 30, 31, 32, 33, 29, 30, 31, 32, 31], [13, 14, 15, 16, 23, 24, 25, 26, 2, 3, 4, 5, 9, 10, 11, 12, 25, 26, 27, 28, 18, 19, 20, 21, 6, 7, 8, 9, 23, 24, 25, 26, 20, 21, 22, 23, 35], [9, 10, 11, 12, 35, 36, 37, 1, 20, 21, 22, 23, 2, 3, 4, 5, 5, 6, 7, 8, 15, 16, 17, 18, 30, 31, 32, 33, 27, 28, 29, 30, 29, 30, 31, 32, 17], [22, 23, 24, 25, 13, 14, 15, 16, 9, 10, 11, 12, 20, 21, 22, 23, 35, 36, 37, 1, 32, 33, 34, 35, 33, 34, 35, 36, 23, 24, 25, 26, 1, 2, 3, 4, 20], [5, 6, 7, 8, 36, 37, 1, 2, 30, 31, 32, 33, 9, 10, 11, 12, 24, 25, 26, 27, 25, 26, 27, 28, 35, 36, 37, 1, 8, 9, 10, 11, 27, 28, 29, 30, 33], [3, 4, 5, 6, 25, 26, 27, 28, 30, 31, 32, 33, 17, 18, 19, 20, 27, 28, 29, 30, 33, 34, 35, 36, 36, 37, 1, 2, 33, 34, 35, 36, 16, 17, 18, 19, 15], [27, 28, 29, 30, 33, 34, 35, 36, 23, 24, 25, 26, 20, 21, 22, 23, 27, 28, 29, 30, 34, 35, 36, 37, 11, 12, 13, 14, 12, 13, 14, 15, 26, 27, 28, 29, 14], [24, 25, 26, 27, 31, 32, 33, 34, 25, 26, 27, 28, 17, 18, 19, 20, 16, 17, 18, 19, 1, 2, 3, 4, 23, 24, 25, 26, 5, 6, 7, 8, 4, 5, 6, 7, 3], [31, 32, 33, 34, 2, 3, 4, 5, 17, 18, 19, 20, 3, 4, 5, 6, 5, 6, 7, 8, 22, 23, 24, 25, 16, 17, 18, 19, 7, 8, 9, 10, 15, 16, 17, 18, 29], [25, 26, 27, 28, 27, 28, 29, 30, 30, 31, 32, 33, 27, 28, 29, 30, 13, 14, 15, 16, 23, 24, 25, 26, 29, 30, 31, 32, 11, 12, 13, 14, 24, 25, 26, 27, 22], [37, 1, 2, 3, 16, 17, 18, 19, 35, 36, 37, 1, 4, 5, 6, 7, 34, 35, 36, 37, 34, 35, 36, 37, 8, 9, 10, 11, 37, 1, 2, 3, 5, 6, 7, 8, 32], [11, 12, 13, 14, 35, 36, 37, 1, 22, 23, 24, 25, 20, 21, 22, 23, 32, 33, 34, 35, 18, 19, 20, 21, 22, 23, 24, 25, 37, 1, 2, 3, 36, 37, 1, 2, 13], [37, 1, 2, 3, 16, 17, 18, 19, 15, 16, 17, 18, 32, 33, 34, 35, 34, 35, 36, 37, 17, 18, 19, 20, 14, 15, 16, 17, 19, 20, 21, 22, 25, 26, 27, 28, 6]] +output = {"delta" = 0.148664843952505, "se" = 0.19467192521657845, "stat" = 0.7636686378228953, "pvalue" = 0.35, "reject" = false} + +[[difference]] +a = [0.008694548189093137, -0.007954035844916925, -0.0002478595817807889, 0.009907623054064134, 0.008053839474821647, 0.0016748624109103945, -0.0037351263051922587, 0.018019610570840575, -0.0070164948272976296, 0.0017843634184885832, 0.022128983769295812, 0.005869755531686967, -0.016775930849470086, 0.001847150709025303, 0.005352293988326797, -0.013503431187287272, 0.021097802108157544, -0.020890761658167084, -0.013652511583347767, 0.005160749385284034, 0.003010225587860016, 0.005660038022590181, 0.002360130612094484, 0.02079869795004234, -0.011351414502480717, 0.02466749474794091, 0.04064310817224997, 0.005151393354995121, -0.018324400927753608, 0.02038331782587348, -0.022239468512881888, 0.011093719645053717, 0.01831008011625588, -0.0005125453921867129, -0.015573159428829234, 0.015037708349922515, -0.014024132475169268] +b = [-0.005662924626123231, -0.0036859886979817625, -0.01791001369483622, -0.009894805735283885, 0.012776515393048747, -0.007177773792842326, 0.012618181975237485, 0.006720072748403255, -0.016133833847371697, 0.0009414077454979889, -0.0017167792646186767, -0.011862950678461713, 0.021360281977022395, 0.005538034482369695, -0.0007527552841273919, -0.005918251494667971, 0.007144384897418322, -0.0005393958515376735, -0.00010147873458555851, 0.006421549325029195, -0.011472976150107738, 0.009226095656023763, 0.005979733534135019, 0.006822605068735032, -0.015209709721756552, 0.016601436482793677, 0.003066278253048881, -0.012001620604863037, 0.00032235681686238957, 0.015255509594017478, -0.021845555972281373, 0.003646683644957409, 0.010550397831319096, -0.006749838627289116, -0.006793134811824153, 0.0033600371975336177, -0.0022214633873688092] +method = "naive" +block_length = 4 +bandwidth = 2 +n_boot = 19 +bootstrap_indices = [] +output = {"delta" = 0.23200012395838443, "se" = 0.18415770402586154, "stat" = 1.2597904887313556, "pvalue" = 0.20774495180130256, "reject" = false} + +[[difference]] +a = [0.008694548189093137, -0.007954035844916925, -0.0002478595817807889, 0.009907623054064134, 0.008053839474821647, 0.0016748624109103945, -0.0037351263051922587, 0.018019610570840575, -0.0070164948272976296, 0.0017843634184885832, 0.022128983769295812, 0.005869755531686967, -0.016775930849470086, 0.001847150709025303, 0.005352293988326797, -0.013503431187287272, 0.021097802108157544, -0.020890761658167084, -0.013652511583347767, 0.005160749385284034, 0.003010225587860016, 0.005660038022590181, 0.002360130612094484, 0.02079869795004234, -0.011351414502480717, 0.02466749474794091, 0.04064310817224997, 0.005151393354995121, -0.018324400927753608, 0.02038331782587348, -0.022239468512881888, 0.011093719645053717, 0.01831008011625588, -0.0005125453921867129, -0.015573159428829234, 0.015037708349922515, -0.014024132475169268] +b = [-0.005662924626123231, -0.0036859886979817625, -0.01791001369483622, -0.009894805735283885, 0.012776515393048747, -0.007177773792842326, 0.012618181975237485, 0.006720072748403255, -0.016133833847371697, 0.0009414077454979889, -0.0017167792646186767, -0.011862950678461713, 0.021360281977022395, 0.005538034482369695, -0.0007527552841273919, -0.005918251494667971, 0.007144384897418322, -0.0005393958515376735, -0.00010147873458555851, 0.006421549325029195, -0.011472976150107738, 0.009226095656023763, 0.005979733534135019, 0.006822605068735032, -0.015209709721756552, 0.016601436482793677, 0.003066278253048881, -0.012001620604863037, 0.00032235681686238957, 0.015255509594017478, -0.021845555972281373, 0.003646683644957409, 0.010550397831319096, -0.006749838627289116, -0.006793134811824153, 0.0033600371975336177, -0.0022214633873688092] +method = "ledoit_wolf" +block_length = 4 +bandwidth = 2 +n_boot = 19 +bootstrap_indices = [[24, 25, 26, 27, 19, 20, 21, 22, 31, 32, 33, 34, 10, 11, 12, 13, 22, 23, 24, 25, 27, 28, 29, 30, 15, 16, 17, 18, 5, 6, 7, 8, 18, 19, 20, 21, 3], [25, 26, 27, 28, 21, 22, 23, 24, 25, 26, 27, 28, 18, 19, 20, 21, 23, 24, 25, 26, 18, 19, 20, 21, 31, 32, 33, 34, 22, 23, 24, 25, 28, 29, 30, 31, 29], [34, 35, 36, 37, 33, 34, 35, 36, 13, 14, 15, 16, 28, 29, 30, 31, 24, 25, 26, 27, 6, 7, 8, 9, 19, 20, 21, 22, 22, 23, 24, 25, 23, 24, 25, 26, 15], [27, 28, 29, 30, 21, 22, 23, 24, 24, 25, 26, 27, 34, 35, 36, 37, 2, 3, 4, 5, 1, 2, 3, 4, 15, 16, 17, 18, 28, 29, 30, 31, 16, 17, 18, 19, 8], [19, 20, 21, 22, 20, 21, 22, 23, 4, 5, 6, 7, 22, 23, 24, 25, 15, 16, 17, 18, 8, 9, 10, 11, 10, 11, 12, 13, 15, 16, 17, 18, 29, 30, 31, 32, 11], [22, 23, 24, 25, 15, 16, 17, 18, 34, 35, 36, 37, 23, 24, 25, 26, 19, 20, 21, 22, 13, 14, 15, 16, 6, 7, 8, 9, 25, 26, 27, 28, 34, 35, 36, 37, 36], [27, 28, 29, 30, 7, 8, 9, 10, 28, 29, 30, 31, 33, 34, 35, 36, 4, 5, 6, 7, 20, 21, 22, 23, 35, 36, 37, 1, 30, 31, 32, 33, 29, 30, 31, 32, 31], [13, 14, 15, 16, 23, 24, 25, 26, 2, 3, 4, 5, 9, 10, 11, 12, 25, 26, 27, 28, 18, 19, 20, 21, 6, 7, 8, 9, 23, 24, 25, 26, 20, 21, 22, 23, 35], [9, 10, 11, 12, 35, 36, 37, 1, 20, 21, 22, 23, 2, 3, 4, 5, 5, 6, 7, 8, 15, 16, 17, 18, 30, 31, 32, 33, 27, 28, 29, 30, 29, 30, 31, 32, 17], [22, 23, 24, 25, 13, 14, 15, 16, 9, 10, 11, 12, 20, 21, 22, 23, 35, 36, 37, 1, 32, 33, 34, 35, 33, 34, 35, 36, 23, 24, 25, 26, 1, 2, 3, 4, 20], [5, 6, 7, 8, 36, 37, 1, 2, 30, 31, 32, 33, 9, 10, 11, 12, 24, 25, 26, 27, 25, 26, 27, 28, 35, 36, 37, 1, 8, 9, 10, 11, 27, 28, 29, 30, 33], [3, 4, 5, 6, 25, 26, 27, 28, 30, 31, 32, 33, 17, 18, 19, 20, 27, 28, 29, 30, 33, 34, 35, 36, 36, 37, 1, 2, 33, 34, 35, 36, 16, 17, 18, 19, 15], [27, 28, 29, 30, 33, 34, 35, 36, 23, 24, 25, 26, 20, 21, 22, 23, 27, 28, 29, 30, 34, 35, 36, 37, 11, 12, 13, 14, 12, 13, 14, 15, 26, 27, 28, 29, 14], [24, 25, 26, 27, 31, 32, 33, 34, 25, 26, 27, 28, 17, 18, 19, 20, 16, 17, 18, 19, 1, 2, 3, 4, 23, 24, 25, 26, 5, 6, 7, 8, 4, 5, 6, 7, 3], [31, 32, 33, 34, 2, 3, 4, 5, 17, 18, 19, 20, 3, 4, 5, 6, 5, 6, 7, 8, 22, 23, 24, 25, 16, 17, 18, 19, 7, 8, 9, 10, 15, 16, 17, 18, 29], [25, 26, 27, 28, 27, 28, 29, 30, 30, 31, 32, 33, 27, 28, 29, 30, 13, 14, 15, 16, 23, 24, 25, 26, 29, 30, 31, 32, 11, 12, 13, 14, 24, 25, 26, 27, 22], [37, 1, 2, 3, 16, 17, 18, 19, 35, 36, 37, 1, 4, 5, 6, 7, 34, 35, 36, 37, 34, 35, 36, 37, 8, 9, 10, 11, 37, 1, 2, 3, 5, 6, 7, 8, 32], [11, 12, 13, 14, 35, 36, 37, 1, 22, 23, 24, 25, 20, 21, 22, 23, 32, 33, 34, 35, 18, 19, 20, 21, 22, 23, 24, 25, 37, 1, 2, 3, 36, 37, 1, 2, 13], [37, 1, 2, 3, 16, 17, 18, 19, 15, 16, 17, 18, 32, 33, 34, 35, 34, 35, 36, 37, 17, 18, 19, 20, 14, 15, 16, 17, 19, 20, 21, 22, 25, 26, 27, 28, 6]] +output = {"delta" = 0.23200012395838443, "se" = 0.16039915594045343, "stat" = 1.4463924239383912, "pvalue" = 0.1, "reject" = false} + +[[difference]] +a = [0.015393602013538673, 0.008001670397721906, -0.0012071080768650579, -0.021489722399969817, 0.018915940921459408, -0.03014106534682269, 0.007881159586557192, -0.006091037590012031, -0.00634271808674622, -0.0048723637801244835, -0.024963819853795317, -0.005153836477364091, 0.003158378336479152, 0.003875292435098187, 0.008373335053799466, 0.0008073374348759931, 0.013973515727253956, 0.0071977273085344195, 0.00559123463904632, 0.0006006732440152428, 0.008355938626543559, 0.015432235573159134, 0.03465505621061654, 0.008809238946882328, 0.015085740390128907, 0.011096179737038584, 0.036822428391157014, 0.01634945870903278, 0.02285721260646978, 0.010901023669595134, -0.017003338066046182, -0.01770192497855156, -0.011441237786112939, 0.013724320638536092, -0.002133208930976474, 0.009091691920163306, 0.0210689289489418] +b = [0.024983255108844044, 0.010859089807561511, 0.008555005582976524, -0.01339865682668825, 0.004062068819257357, -0.028709232969167664, -0.010488752570556583, -0.011371867705879994, 0.0021226009153633473, -0.013446814290565507, -0.01818251772697099, -0.007825274351532893, -0.008555887678044466, 0.01562672803156193, 0.0046059640677745504, -0.0018537811996570573, 0.00611429223369459, 0.014452813399698387, -0.008737587050923685, 0.00032610298837566266, -0.005042094835763622, -0.01377945659698641, 0.0021708466378311704, -0.010310183309316604, 0.0069399631385160376, 0.0015061141144800541, 0.0009023239971306888, -0.010541970230687578, 0.012606731998262514, 0.01205538249068132, 0.00917504515793597, -0.009172926862132313, -0.002708559229029752, 0.002537708404208549, 0.0009314424379525837, 0.013330841868758507, 0.006487626675968698] +method = "naive" +block_length = 4 +bandwidth = 41 +n_boot = 19 +bootstrap_indices = [] +output = {"delta" = 0.3433776347286244, "se" = 0.16470176093695013, "stat" = 2.0848449511118075, "pvalue" = 0.03708337691402852, "reject" = true} + +[[difference]] +a = [0.015393602013538673, 0.008001670397721906, -0.0012071080768650579, -0.021489722399969817, 0.018915940921459408, -0.03014106534682269, 0.007881159586557192, -0.006091037590012031, -0.00634271808674622, -0.0048723637801244835, -0.024963819853795317, -0.005153836477364091, 0.003158378336479152, 0.003875292435098187, 0.008373335053799466, 0.0008073374348759931, 0.013973515727253956, 0.0071977273085344195, 0.00559123463904632, 0.0006006732440152428, 0.008355938626543559, 0.015432235573159134, 0.03465505621061654, 0.008809238946882328, 0.015085740390128907, 0.011096179737038584, 0.036822428391157014, 0.01634945870903278, 0.02285721260646978, 0.010901023669595134, -0.017003338066046182, -0.01770192497855156, -0.011441237786112939, 0.013724320638536092, -0.002133208930976474, 0.009091691920163306, 0.0210689289489418] +b = [0.024983255108844044, 0.010859089807561511, 0.008555005582976524, -0.01339865682668825, 0.004062068819257357, -0.028709232969167664, -0.010488752570556583, -0.011371867705879994, 0.0021226009153633473, -0.013446814290565507, -0.01818251772697099, -0.007825274351532893, -0.008555887678044466, 0.01562672803156193, 0.0046059640677745504, -0.0018537811996570573, 0.00611429223369459, 0.014452813399698387, -0.008737587050923685, 0.00032610298837566266, -0.005042094835763622, -0.01377945659698641, 0.0021708466378311704, -0.010310183309316604, 0.0069399631385160376, 0.0015061141144800541, 0.0009023239971306888, -0.010541970230687578, 0.012606731998262514, 0.01205538249068132, 0.00917504515793597, -0.009172926862132313, -0.002708559229029752, 0.002537708404208549, 0.0009314424379525837, 0.013330841868758507, 0.006487626675968698] +method = "ledoit_wolf" +block_length = 4 +bandwidth = 41 +n_boot = 19 +bootstrap_indices = [[24, 25, 26, 27, 19, 20, 21, 22, 31, 32, 33, 34, 10, 11, 12, 13, 22, 23, 24, 25, 27, 28, 29, 30, 15, 16, 17, 18, 5, 6, 7, 8, 18, 19, 20, 21, 3], [25, 26, 27, 28, 21, 22, 23, 24, 25, 26, 27, 28, 18, 19, 20, 21, 23, 24, 25, 26, 18, 19, 20, 21, 31, 32, 33, 34, 22, 23, 24, 25, 28, 29, 30, 31, 29], [34, 35, 36, 37, 33, 34, 35, 36, 13, 14, 15, 16, 28, 29, 30, 31, 24, 25, 26, 27, 6, 7, 8, 9, 19, 20, 21, 22, 22, 23, 24, 25, 23, 24, 25, 26, 15], [27, 28, 29, 30, 21, 22, 23, 24, 24, 25, 26, 27, 34, 35, 36, 37, 2, 3, 4, 5, 1, 2, 3, 4, 15, 16, 17, 18, 28, 29, 30, 31, 16, 17, 18, 19, 8], [19, 20, 21, 22, 20, 21, 22, 23, 4, 5, 6, 7, 22, 23, 24, 25, 15, 16, 17, 18, 8, 9, 10, 11, 10, 11, 12, 13, 15, 16, 17, 18, 29, 30, 31, 32, 11], [22, 23, 24, 25, 15, 16, 17, 18, 34, 35, 36, 37, 23, 24, 25, 26, 19, 20, 21, 22, 13, 14, 15, 16, 6, 7, 8, 9, 25, 26, 27, 28, 34, 35, 36, 37, 36], [27, 28, 29, 30, 7, 8, 9, 10, 28, 29, 30, 31, 33, 34, 35, 36, 4, 5, 6, 7, 20, 21, 22, 23, 35, 36, 37, 1, 30, 31, 32, 33, 29, 30, 31, 32, 31], [13, 14, 15, 16, 23, 24, 25, 26, 2, 3, 4, 5, 9, 10, 11, 12, 25, 26, 27, 28, 18, 19, 20, 21, 6, 7, 8, 9, 23, 24, 25, 26, 20, 21, 22, 23, 35], [9, 10, 11, 12, 35, 36, 37, 1, 20, 21, 22, 23, 2, 3, 4, 5, 5, 6, 7, 8, 15, 16, 17, 18, 30, 31, 32, 33, 27, 28, 29, 30, 29, 30, 31, 32, 17], [22, 23, 24, 25, 13, 14, 15, 16, 9, 10, 11, 12, 20, 21, 22, 23, 35, 36, 37, 1, 32, 33, 34, 35, 33, 34, 35, 36, 23, 24, 25, 26, 1, 2, 3, 4, 20], [5, 6, 7, 8, 36, 37, 1, 2, 30, 31, 32, 33, 9, 10, 11, 12, 24, 25, 26, 27, 25, 26, 27, 28, 35, 36, 37, 1, 8, 9, 10, 11, 27, 28, 29, 30, 33], [3, 4, 5, 6, 25, 26, 27, 28, 30, 31, 32, 33, 17, 18, 19, 20, 27, 28, 29, 30, 33, 34, 35, 36, 36, 37, 1, 2, 33, 34, 35, 36, 16, 17, 18, 19, 15], [27, 28, 29, 30, 33, 34, 35, 36, 23, 24, 25, 26, 20, 21, 22, 23, 27, 28, 29, 30, 34, 35, 36, 37, 11, 12, 13, 14, 12, 13, 14, 15, 26, 27, 28, 29, 14], [24, 25, 26, 27, 31, 32, 33, 34, 25, 26, 27, 28, 17, 18, 19, 20, 16, 17, 18, 19, 1, 2, 3, 4, 23, 24, 25, 26, 5, 6, 7, 8, 4, 5, 6, 7, 3], [31, 32, 33, 34, 2, 3, 4, 5, 17, 18, 19, 20, 3, 4, 5, 6, 5, 6, 7, 8, 22, 23, 24, 25, 16, 17, 18, 19, 7, 8, 9, 10, 15, 16, 17, 18, 29], [25, 26, 27, 28, 27, 28, 29, 30, 30, 31, 32, 33, 27, 28, 29, 30, 13, 14, 15, 16, 23, 24, 25, 26, 29, 30, 31, 32, 11, 12, 13, 14, 24, 25, 26, 27, 22], [37, 1, 2, 3, 16, 17, 18, 19, 35, 36, 37, 1, 4, 5, 6, 7, 34, 35, 36, 37, 34, 35, 36, 37, 8, 9, 10, 11, 37, 1, 2, 3, 5, 6, 7, 8, 32], [11, 12, 13, 14, 35, 36, 37, 1, 22, 23, 24, 25, 20, 21, 22, 23, 32, 33, 34, 35, 18, 19, 20, 21, 22, 23, 24, 25, 37, 1, 2, 3, 36, 37, 1, 2, 13], [37, 1, 2, 3, 16, 17, 18, 19, 15, 16, 17, 18, 32, 33, 34, 35, 34, 35, 36, 37, 17, 18, 19, 20, 14, 15, 16, 17, 19, 20, 21, 22, 25, 26, 27, 28, 6]] +output = {"delta" = 0.3433776347286244, "se" = 0.11343436231792227, "stat" = 3.027104201161202, "pvalue" = 0.25, "reject" = false} + +[[difference]] +a = [0.030459904022725928, 0.0016621633951632497, -0.008564212235214443, 0.007301641516565389, 0.016628328205457066, 0.01103223436975263, -0.002682836617903547, 0.019470404374955716, 0.0011593401683467185, -0.018717639682830238, 0.015658531274987647, 0.02954403707481469, -0.0024278493441821456, -0.0036122281079068877, -0.010323783355430596, -0.006564356093099683, 0.0041220741641158545, 0.020470889402561823, 0.013753559203910113, -0.013002871431241913, -0.011420536269590538, 0.01154978789970011, 0.004445403398284556, -0.006412544878966017, 0.0016288424638116034, -0.016897397794974317, 0.006517963151282513, 0.009073923877545846, 0.01776709822013657, 0.014589303298397588, -0.01559263099134787, -0.0029138102868636806, 0.023036310804556044, 0.010615685204949566, 0.024953379831959867, 0.005501722885549056, -0.009057935614254258] +b = [-0.004508939075661257, 0.0011275384476511193, 0.00926749952801674, -0.005274200355287865, -0.013542307332091257, 0.017839192976074267, -0.005166093623858262, -0.013575408842960133, 0.0005645346044413995, -0.004843252320943398, 0.01792113552665251, 0.022849743666813783, -0.0036661326940831728, -0.017086844705247607, 0.009647387633354548, 0.004001915876417412, -0.007185992077607948, -0.004927667258279623, -0.008856906350712113, -0.016645554515525653, -0.0033473821878160503, -0.008298806378611244, 0.016355538544817808, 0.009620149639348192, 0.0004746982461350227, -0.01598952372426688, 0.009267229742874833, 0.02749654388344592, -0.009521482153308244, 0.015330389536649022, -0.007046072550439477, -0.006550889759583094, 0.00647652821096525, -0.013502212971535974, 0.0009300029885774223, -0.010472562460517516, -0.027028984212423227] +method = "naive" +block_length = 45 +bandwidth = 0 +n_boot = 19 +bootstrap_indices = [] +output = {"delta" = 0.43961683532961515, "se" = 0.20046157717904664, "stat" = 2.193022929960098, "pvalue" = 0.028305727063858557, "reject" = true} + +[[difference]] +a = [0.030459904022725928, 0.0016621633951632497, -0.008564212235214443, 0.007301641516565389, 0.016628328205457066, 0.01103223436975263, -0.002682836617903547, 0.019470404374955716, 0.0011593401683467185, -0.018717639682830238, 0.015658531274987647, 0.02954403707481469, -0.0024278493441821456, -0.0036122281079068877, -0.010323783355430596, -0.006564356093099683, 0.0041220741641158545, 0.020470889402561823, 0.013753559203910113, -0.013002871431241913, -0.011420536269590538, 0.01154978789970011, 0.004445403398284556, -0.006412544878966017, 0.0016288424638116034, -0.016897397794974317, 0.006517963151282513, 0.009073923877545846, 0.01776709822013657, 0.014589303298397588, -0.01559263099134787, -0.0029138102868636806, 0.023036310804556044, 0.010615685204949566, 0.024953379831959867, 0.005501722885549056, -0.009057935614254258] +b = [-0.004508939075661257, 0.0011275384476511193, 0.00926749952801674, -0.005274200355287865, -0.013542307332091257, 0.017839192976074267, -0.005166093623858262, -0.013575408842960133, 0.0005645346044413995, -0.004843252320943398, 0.01792113552665251, 0.022849743666813783, -0.0036661326940831728, -0.017086844705247607, 0.009647387633354548, 0.004001915876417412, -0.007185992077607948, -0.004927667258279623, -0.008856906350712113, -0.016645554515525653, -0.0033473821878160503, -0.008298806378611244, 0.016355538544817808, 0.009620149639348192, 0.0004746982461350227, -0.01598952372426688, 0.009267229742874833, 0.02749654388344592, -0.009521482153308244, 0.015330389536649022, -0.007046072550439477, -0.006550889759583094, 0.00647652821096525, -0.013502212971535974, 0.0009300029885774223, -0.010472562460517516, -0.027028984212423227] +method = "ledoit_wolf" +block_length = 45 +bandwidth = 0 +n_boot = 19 +bootstrap_indices = [[24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9], [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2], [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]] +output = {"delta" = 0.43961683532961515, "se" = 0.20046157717904664, "stat" = 2.193022929960098, "pvalue" = 0.05, "reject" = false} + +[[difference]] +a = [0.010090960548327692, -0.01752045537100739, -0.0024786327264632183, 0.015117872807785475, -0.017413518925339717, 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-0.014536269653622468, 0.019453233903360012, 0.004838440650999303, -0.007790430233082901, -0.016549726253573482, 0.016477010765881504, -0.02510356790117174, -0.0039178501000948045, 0.005172902405894722, 0.020600339006019028, -0.0112132114997136, -0.002671584480744613, 0.03147841660632557, -0.00796578082047721, 0.0075053249067885235, 0.021238370241577632, -0.012883047865370116, 0.0032688620340901786, -0.004331896704743545, 0.00762155905255674, -0.0016701424540916852, -0.012802348672489363, 0.02428812347951901, -0.018601580396790003, -0.03132054092038737, 0.009011198649667235, -0.0160257944229062, 0.019462507815880985, -0.010295457696322851, -0.00019999039289148252] +b = [-0.0009601210493082241, -0.0036959926363470314, -0.002894211574202657, 0.011970956999305341, 0.010829970522324612, -0.00218913722016266, 0.02271390833637866, -0.021946263241173755, 0.017944560799496714, -0.013066852883728027, -0.0142716565520147, -0.001895465981391088, 0.007140390324548857, -0.014810560644048697, -0.0005759287749882987, 0.001635290620379035, 0.0017263818763870549, -0.005572600035606725, -0.008706087289417094, 0.021050177224272007, 0.0019187379378333039, -0.0048247423871787595, 0.022511438145486506, -0.01485837551326513, 0.0075320460463213735, -0.02878647270939081, -0.006802587726850103, -0.00015998484127999115, -0.0048218055274559255, 0.009613509845991396, -0.004328461298537645, -0.0044016969415347825, -0.011648468286719806, -0.007193126838387578, 0.013068680582641757, 0.01663728381683913, -0.010907942226155029] +method = "ledoit_wolf" +block_length = 45 +bandwidth = 2 +n_boot = 19 +bootstrap_indices = [[24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9], [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2], [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [31, 32, 33, 34, 35, 36, 37, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 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0.0, 5.0, 8.0, 2.0, 1.0], [1704067242000, 1.0, 3.0, 994.75, 9.0, 9.0, 0.0, 5.0, 8.0, 3.0, 1.0], [1704067243000, 1.0, 4.0, 1660.75, 15.0, 15.0, 0.0, 5.0, 8.0, 4.0, 1.0], [1704067244000, 1.0, 5.0, 1772.0, 16.0, 16.0, 0.0, 5.0, 8.0, 5.0, 1.0], [1704067245000, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 5.0, 8.0, 1.0, 1.0], [1704067246000, 1.0, 2.0, 335.25, 3.0, 3.0, 0.0, 5.0, 8.0, 2.0, 1.0], [1704067247000, 1.0, 3.0, 1007.25, 9.0, 9.0, 0.0, 5.0, 8.0, 3.0, 1.0], [1704067248000, 1.0, 4.0, 1344.0, 12.0, 12.0, 0.0, 5.0, 8.0, 4.0, 1.0], [1704067249000, 1.0, 5.0, 1344.0, 12.0, 12.0, 0.0, 5.0, 8.0, 5.0, 1.0], [1704067250000, 1.0, 1.0, 563.75, 5.0, 5.0, 0.0, 5.0, 8.0, 1.0, 1.0], [1704067251000, 1.0, 2.0, 563.75, 5.0, 5.0, 0.0, 5.0, 8.0, 2.0, 1.0], [1704067252000, 1.0, 3.0, 790.25, 7.0, 7.0, 0.0, 5.0, 8.0, 3.0, 1.0], [1704067253000, 1.0, 4.0, 903.75, 8.0, 8.0, 0.0, 5.0, 8.0, 4.0, 1.0], [1704067254000, 1.0, 5.0, 1017.5, 9.0, 9.0, 0.0, 5.0, 8.0, 5.0, 1.0], [1704067255000, 1.0, 1.0, 456.0, 4.0, 4.0, 0.0, 5.0, 8.0, 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1.0], [1704067270000, 1.0, 1.0, 471.0, 4.0, 4.0, 0.0, 5.0, 8.0, 1.0, 1.0], [1704067271000, 1.0, 2.0, 825.0, 7.0, 7.0, 0.0, 5.0, 8.0, 2.0, 1.0]] + +[[information]] +kind = "ExpectedImbalanceBars" +metric = "Tick" +config = {"initial" = 3.0, "imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0]}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 1.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 1.0]}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 6.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 35.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 71.0, 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1368.0, 13.0, 13.0, 0.0, 4.75, 15.0, 13.0, 2.1592566069973858], [1704067222000, 1.0, 1.0, 211.5, 2.0, 2.0, 0.0, 4.4, 15.0, 2.0, 2.9607714546507657], [1704067223000, 1.0, 2.0, 635.5, 6.0, 6.0, 0.0, 4.4, 15.0, 6.0, 2.9607714546507657], [1704067224000, 1.0, 3.0, 1166.75, 11.0, 11.0, 0.0, 4.4, 15.0, 11.0, 2.9607714546507657], [1704067225000, 1.0, 4.0, 1699.25, 16.0, 16.0, 0.0, 4.4, 15.0, 16.0, 2.9607714546507657], [1704067226000, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 4.333333333333333, 15.0, 0.0, 3.5991136214463575], [1704067227000, 1.0, 2.0, 214.0, 2.0, 2.0, 0.0, 4.333333333333333, 15.0, 2.0, 3.5991136214463575], [1704067228000, 1.0, 3.0, 321.25, 3.0, 3.0, 0.0, 4.333333333333333, 15.0, 3.0, 3.5991136214463575], [1704067229000, 1.0, 4.0, 428.75, 4.0, 4.0, 0.0, 4.333333333333333, 15.0, 4.0, 3.5991136214463575], [1704067230000, 1.0, 5.0, 752.0, 7.0, 7.0, 0.0, 4.333333333333333, 15.0, 7.0, 3.5991136214463575], [1704067231000, 1.0, 6.0, 1400.0, 13.0, 13.0, 0.0, 4.333333333333333, 15.0, 13.0, 3.5991136214463575], [1704067232000, 1.0, 7.0, 1508.25, 14.0, 14.0, 0.0, 4.333333333333333, 15.0, 14.0, 3.5991136214463575], [1704067233000, 1.0, 8.0, 2050.75, 19.0, 19.0, 0.0, 4.333333333333333, 15.0, 19.0, 3.5991136214463575], [1704067234000, 1.0, 1.0, 108.75, 1.0, 1.0, 0.0, 4.857142857142857, 15.0, 1.0, 3.1058377558165486], [1704067235000, 1.0, 2.0, 544.75, 5.0, 5.0, 0.0, 4.857142857142857, 15.0, 5.0, 3.1058377558165486], [1704067236000, 1.0, 3.0, 1200.25, 11.0, 11.0, 0.0, 4.857142857142857, 15.0, 11.0, 3.1058377558165486], [1704067237000, 1.0, 4.0, 1200.25, 11.0, 11.0, 0.0, 4.857142857142857, 15.0, 11.0, 3.1058377558165486], [1704067238000, 1.0, 5.0, 1200.25, 11.0, 11.0, 0.0, 4.857142857142857, 15.0, 11.0, 3.1058377558165486], [1704067239000, 1.0, 6.0, 1310.25, 12.0, 12.0, 0.0, 4.857142857142857, 15.0, 12.0, 3.1058377558165486], [1704067240000, 1.0, 7.0, 1530.75, 14.0, 14.0, 0.0, 4.857142857142857, 15.0, 14.0, 3.1058377558165486], [1704067241000, 1.0, 8.0, 1972.75, 18.0, 18.0, 0.0, 4.857142857142857, 15.0, 18.0, 3.1058377558165486], [1704067242000, 1.0, 1.0, 332.25, 3.0, 3.0, 0.0, 5.25, 15.0, 3.0, 2.4210232045356364], [1704067243000, 1.0, 2.0, 998.25, 9.0, 9.0, 0.0, 5.25, 15.0, 9.0, 2.4210232045356364], [1704067244000, 1.0, 3.0, 1109.5, 10.0, 10.0, 0.0, 5.25, 15.0, 10.0, 2.4210232045356364], [1704067245000, 1.0, 4.0, 1109.5, 10.0, 10.0, 0.0, 5.25, 15.0, 10.0, 2.4210232045356364], [1704067246000, 1.0, 5.0, 1444.75, 13.0, 13.0, 0.0, 5.25, 15.0, 13.0, 2.4210232045356364], [1704067247000, 1.0, 1.0, 672.0, 6.0, 6.0, 0.0, 5.222222222222222, 15.0, 6.0, 2.401791536254368], [1704067248000, 1.0, 2.0, 1008.75, 9.0, 9.0, 0.0, 5.222222222222222, 15.0, 9.0, 2.401791536254368], [1704067249000, 1.0, 3.0, 1008.75, 9.0, 9.0, 0.0, 5.222222222222222, 15.0, 9.0, 2.401791536254368], [1704067250000, 1.0, 4.0, 1572.5, 14.0, 14.0, 0.0, 5.222222222222222, 15.0, 14.0, 2.401791536254368], [1704067251000, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 5.1, 15.0, 0.0, 2.8603277556669613], [1704067252000, 1.0, 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1.0, 4.0, 1155.25, 10.0, 10.0, 0.0, 5.454545454545454, 15.0, 10.0, 2.3892142168766757], [1704067264000, 1.0, 5.0, 1387.75, 12.0, 12.0, 0.0, 5.454545454545454, 15.0, 12.0, 2.3892142168766757], [1704067265000, 1.0, 6.0, 1737.25, 15.0, 15.0, 0.0, 5.454545454545454, 15.0, 15.0, 2.3892142168766757], [1704067266000, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 5.5, 15.0, 0.0, 2.365845285282071], [1704067267000, 1.0, 2.0, 351.0, 3.0, 3.0, 0.0, 5.5, 15.0, 3.0, 2.365845285282071], [1704067268000, 1.0, 3.0, 1054.5, 9.0, 9.0, 0.0, 5.5, 15.0, 9.0, 2.365845285282071], [1704067269000, 1.0, 4.0, 1289.5, 11.0, 11.0, 0.0, 5.5, 15.0, 11.0, 2.365845285282071], [1704067270000, 1.0, 5.0, 1760.5, 15.0, 15.0, 0.0, 5.5, 15.0, 15.0, 2.365845285282071], [1704067271000, 1.0, 1.0, 354.0, 3.0, 3.0, 0.0, 5.461538461538462, 15.0, 3.0, 2.8624142348672508]] + +[[information]] +kind = "ExpectedImbalanceBars" +metric = "Volume" +config = {"initial" = 4.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 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{"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0]}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, 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"cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0]}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 0.0]}}, {"left" = 2, "right" = 7, "output" = [], "state" = 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100.5, 100.5, 100.75, 101.25, 101.0, 101.5, 101.75, 101.5, 101.25, 101.0, 101.25, 101.0, 101.0, 101.25, 101.75, 101.5, 101.5, 102.0, 102.25, 102.5, 102.25, 102.5, 102.5, 102.75, 103.0, 103.25, 103.25, 103.75, 104.0, 104.5, 104.75, 105.0, 104.75, 105.0] +volumes = [2.0, 4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 0.0, 0.0, 1.0, 6.0, 4.0, 4.0, 5.0, 5.0, 0.0, 2.0, 6.0, 2.0, 6.0, 3.0, 2.0, 4.0, 0.0, 4.0, 6.0, 2.0, 1.0, 2.0, 6.0, 1.0, 2.0, 0.0, 6.0, 5.0, 1.0, 3.0, 0.0, 5.0, 6.0, 4.0, 5.0, 2.0, 6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0]}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = -4.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, -4.0]}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = -18.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, -4.0, -4.0, -3.0, -1.0, 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"cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 0.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0]}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 6.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 6.0]}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 13.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0]}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 92.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0]}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, 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100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_theta" = 183.0, "expected_imbalance" = nan}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances" = [0.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0]}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", 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"cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 6.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = 1.0000000000000002, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [[1704067213000, 14.0, 100.0, 100.0, 98.5, 98.75, 40.0, 10.0, 30.0, 14.0, 3962.25, 2.357142857142857], [1704067231000, 32.0, 98.75, 100.0, 98.5, 100.0, 54.0, 32.0, 22.0, 18.0, 5359.25, 9.428571428571429]], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 100.0, "previous_tick_rule" = 1.0, "cumulative_ticks" = 4.0, "cumulative_dollar" = 1204.75, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 12.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 12.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 4.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = 1.0, "expected_buy_ticks_proportion" = 0.4417989417989418, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 18], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.5555555555555556]}}, {"left" = 36, "right" = 72, "output" = [[1704067240000, 41.0, 100.0, 100.75, 100.0, 100.75, 30.0, 19.0, 11.0, 9.0, 3015.5, 6.698412698412698], [1704067254000, 55.0, 101.25, 101.75, 101.0, 101.5, 53.0, 27.0, 26.0, 14.0, 5370.5, 7.605442176870748], [1704067262000, 63.0, 102.0, 103.0, 102.0, 103.0, 28.0, 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"previous_tick_price" = 100.75, "open_price" = 98.5, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 18.0, "cumulative_dollar" = 5280.75, "cumulative_volume" = 53.0, "cumulative_buy_volume" = 33.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 18.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 12.0, "cumulative_sell_theta" = 6.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = 1.0000000000000002, "expected_buy_ticks_proportion" = 0.2777777777777778, "buy_ticks_number" = 12.0}, "histories" = {"previous_bars_number_of_ticks" = [18], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = [0.2777777777777778]}}, {"left" = 36, "right" = 72, 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= 5.0, "cumulative_sell_theta" = 1.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = 1.0, "expected_buy_ticks_proportion" = 0.601535640983845, "buy_ticks_number" = 5.0}, "histories" = {"previous_bars_number_of_ticks" = [18, 20, 17, 11], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = [0.2777777777777778, 0.7, 0.4117647058823529, 0.9090909090909091]}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance 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1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 99.75, 99.5, 99.25, 99.25, 99.0, 99.0, 99.0, 98.75, 99.0, 98.75, 98.5, 98.75, 98.75, 98.75, 98.5, 98.5, 98.75, 98.5, 99.0, 99.0, 99.5, 99.75, 99.5, 99.5, 99.5, 99.75, 99.75, 99.5, 99.5, 99.75, 100.0, 100.0, 100.25, 100.5, 100.75, 100.75, 100.75, 100.5, 100.5, 100.75, 101.25, 101.0, 101.5, 101.75, 101.5, 101.25, 101.0, 101.25, 101.0, 101.0, 101.25, 101.75, 101.5, 101.5, 102.0, 102.25, 102.5, 102.25, 102.5, 102.5, 102.75, 103.0, 103.25, 103.25, 103.75, 104.0, 104.5, 104.75, 105.0, 104.75, 105.0] +volumes = [2.0, 4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 0.0, 0.0, 1.0, 6.0, 4.0, 4.0, 5.0, 5.0, 0.0, 2.0, 6.0, 2.0, 6.0, 3.0, 2.0, 4.0, 0.0, 4.0, 6.0, 2.0, 1.0, 2.0, 6.0, 1.0, 2.0, 0.0, 6.0, 5.0, 1.0, 3.0, 0.0, 5.0, 6.0, 4.0, 5.0, 2.0, 6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 6.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 17.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4722222222222222, "buy_ticks_number" = 17.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 41.0, "cumulative_sell_theta" = 30.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5694444444444444, "buy_ticks_number" = 41.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 41.0, "cumulative_sell_theta" = 30.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5694444444444444, "buy_ticks_number" = 41.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 200.0, 2.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, -1.0, 2.0, 599.0, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 1.0, nan, nan, 0.0, 0.0], [1704067202000, -1.0, 3.0, 997.0, 10.0, 0.0, 0.0, 3.0, 0.0, 0.0, 2.0, nan, nan, 0.0, 0.0], [1704067203000, -1.0, 4.0, 1294.75, 13.0, 0.0, 0.0, 3.0, 0.0, 0.0, 3.0, nan, nan, 0.0, 0.0], [1704067204000, -1.0, 5.0, 1394.0, 14.0, 0.0, 0.0, 3.0, 0.0, 0.0, 4.0, nan, nan, 0.0, 0.0], [1704067205000, -1.0, 6.0, 1790.0, 18.0, 0.0, 0.0, 3.0, 0.0, 0.0, 5.0, nan, nan, 0.0, 0.0], [1704067206000, -1.0, 7.0, 1988.0, 20.0, 0.0, 0.0, 3.0, 0.0, 0.0, 6.0, nan, nan, 0.0, 0.0], [1704067207000, -1.0, 8.0, 1988.0, 20.0, 0.0, 0.0, 3.0, 0.0, 0.0, 7.0, nan, nan, 0.0, 0.0], [1704067208000, -1.0, 9.0, 1988.0, 20.0, 0.0, 0.0, 3.0, 0.0, 0.0, 8.0, nan, nan, 0.0, 0.0], [1704067209000, 1.0, 10.0, 2087.0, 21.0, 1.0, 0.0, 3.0, 0.0, 1.0, 8.0, nan, nan, 0.1, 1.0], [1704067210000, -1.0, 11.0, 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= true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 1.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 2.0, 0.0, nan, nan, 0.6666666666666666, 2.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 3.0, 0.0, 1.0, nan, 0.75, 3.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 3.0, 8.0, 4.0, 0.0, 1.0, nan, 0.8, 4.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 3.0, 8.0, 5.0, 0.0, 1.0000000000000002, nan, 0.8333333333333334, 5.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 3.0, 8.0, 6.0, 0.0, 1.0000000000000002, nan, 0.8571428571428571, 6.0], [1704067207000, 1.0, 8.0, 2221.5, 22.0, 16.0, 0.0, 3.0, 8.0, 7.0, 0.0, 1.0, nan, 0.875, 7.0], [1704067208000, 1.0, 9.0, 2323.75, 23.0, 17.0, 0.0, 3.0, 8.0, 8.0, 0.0, 1.0, nan, 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71.0]] + +[[information]] +kind = "ExpectedRunBars" +metric = "Tick" +config = {"initial" = 5.0, "imbalance_window" = 15, "tick_window" = 0, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 1.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 2.0, 0.0, nan, nan, 0.6666666666666666, 2.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 3.0, 0.0, nan, nan, 0.75, 3.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 5.0, 15.0, 4.0, 0.0, nan, nan, 0.8, 4.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 5.0, 15.0, 5.0, 0.0, 1.0000000000000002, nan, 0.8333333333333334, 5.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 5.0, 15.0, 6.0, 0.0, 1.0000000000000002, nan, 0.8571428571428571, 6.0], 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1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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"cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 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1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 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1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 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1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 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= 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 4.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = 2.716694111189524, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [[1704067213000, 14.0, 100.0, 100.0, 98.5, 98.75, 40.0, 10.0, 30.0, 14.0, 3962.25, 8.801509893114027], [1704067233000, 34.0, 98.75, 100.25, 98.5, 100.25, 60.0, 38.0, 22.0, 20.0, 5960.75, 35.206039572456106]], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.5, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 100.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 603.25, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 12.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 2.940634441573825, "expected_sell_imbalance" = 4.178974687998902, "expected_buy_ticks_proportion" = 0.4380952380952381, "buy_ticks_number" = 2.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 20], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.55]}}, {"left" = 36, "right" = 72, "output" = [[1704067248000, 49.0, 100.5, 101.75, 100.5, 101.25, 51.0, 33.0, 18.0, 15.0, 5150.75, 28.178229324792596], [1704067261000, 62.0, 101.0, 102.75, 101.0, 102.75, 49.0, 29.0, 20.0, 13.0, 4989.75, 26.527867964757537], [1704067271000, 72.0, 103.0, 105.0, 103.0, 105.0, 35.0, 29.0, 6.0, 10.0, 3639.75, 28.321641235333253]], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = nan, "close_price" = 105.0, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 1.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 11.571428571428571, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3277234112406235, "expected_sell_imbalance" = 4.1727786551765575, "expected_buy_ticks_proportion" = 0.6871794871794873, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 20, 15, 13, 10], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.55, 0.5333333333333333, 0.5384615384615384, 0.8]}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = nan, "close_price" = 105.0, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 1.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 11.571428571428571, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3277234112406235, "expected_sell_imbalance" = 4.1727786551765575, "expected_buy_ticks_proportion" = 0.6871794871794873, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 20, 15, 13, 10], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 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"expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = 2.716694111189524, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], 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5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0], "previous_bars_buy_ticks_proportions" = [0.2777777777777778]}}, {"left" = 36, "right" = 72, "output" = [[1704067241000, 42.0, 98.5, 101.25, 98.5, 101.25, 76.0, 45.0, 31.0, 24.0, 7597.75, 43.02328766556218], [1704067263000, 64.0, 101.0, 103.25, 101.0, 103.25, 82.0, 55.0, 27.0, 22.0, 8354.75, 50.11808974944838]], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 103.25, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 103.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 8.0, "cumulative_dollar" = 2505.25, "cumulative_volume" = 24.0, "cumulative_buy_volume" = 18.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 21.333333333333332, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 18.0, "cumulative_sell_theta" = 6.0, "expected_buy_imbalance" = 4.6719111314095345, "expected_sell_imbalance" = 3.5816356138681598, "expected_buy_ticks_proportion" = 0.480395672703365, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [18, 24, 22], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = [0.2777777777777778, 0.5833333333333334, 0.5454545454545454]}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 103.25, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 103.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 8.0, "cumulative_dollar" = 2505.25, 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"bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 99.75, 99.5, 99.25, 99.25, 99.0, 99.0, 99.0, 98.75, 99.0, 98.75, 98.5, 98.75, 98.75, 98.75, 98.5, 98.5, 98.75, 98.5, 99.0, 99.0, 99.5, 99.75, 99.5, 99.5, 99.5, 99.75, 99.75, 99.5, 99.5, 99.75, 100.0, 100.0, 100.25, 100.5, 100.75, 100.75, 100.75, 100.5, 100.5, 100.75, 101.25, 101.0, 101.5, 101.75, 101.5, 101.25, 101.0, 101.25, 101.0, 101.0, 101.25, 101.75, 101.5, 101.5, 102.0, 102.25, 102.5, 102.25, 102.5, 102.5, 102.75, 103.0, 103.25, 103.25, 103.75, 104.0, 104.5, 104.75, 105.0, 104.75, 105.0] +volumes = [2.0, 4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 0.0, 0.0, 1.0, 6.0, 4.0, 4.0, 5.0, 5.0, 0.0, 2.0, 6.0, 2.0, 6.0, 3.0, 2.0, 4.0, 0.0, 4.0, 6.0, 2.0, 1.0, 2.0, 6.0, 1.0, 2.0, 0.0, 6.0, 5.0, 1.0, 3.0, 0.0, 5.0, 6.0, 4.0, 5.0, 2.0, 6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 4.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 54.0, "cumulative_sell_theta" = 50.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4444444444444444, "buy_ticks_number" = 16.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 139.0, "cumulative_sell_theta" = 94.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 139.0, "cumulative_sell_theta" = 94.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = 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"expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.040441176470588, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.006167600810259, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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"tick_window" = 0, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.076276035215111, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.2655242849235484, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.2655242849235484, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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[], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.040441176470588, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.006167600810259, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 6.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 6.0, 0.0, nan, nan, 0.3333333333333333, 1.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 6.0, 0.0, nan, nan, 0.25, 1.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 4.0, 8.0, 8.0, 0.0, nan, nan, 0.4, 2.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 4.0, 8.0, 9.0, 0.0, nan, nan, 0.5, 3.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 4.0, 8.0, 13.0, 0.0, 3.040441176470588, nan, 0.5714285714285714, 4.0], [1704067207000, 1.0, 8.0, 2221.5, 22.0, 16.0, 0.0, 4.0, 8.0, 16.0, 0.0, 3.0284360189573465, nan, 0.625, 5.0], [1704067208000, 1.0, 9.0, 2323.75, 23.0, 17.0, 0.0, 4.0, 8.0, 17.0, 0.0, 2.3670312867560575, nan, 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"imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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"imbalance_window" = 8, "tick_window" = 3, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 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2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell 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1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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+times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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"previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, 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"high_price" = 100.75, "low_price" = 100.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 603.25, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 12.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 603.25, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 294.0700154927643, "expected_sell_imbalance" = 415.0481118193484, "expected_buy_ticks_proportion" = 0.4380952380952381, "buy_ticks_number" = 2.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 20], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.55]}}, {"left" = 36, "right" = 72, "output" = [[1704067244000, 45.0, 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20, 11, 16, 9], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.55, 0.6363636363636364, 0.4375, 0.8888888888888888]}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 104.75, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 104.75, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1153.5, "cumulative_volume" = 11.0, "cumulative_buy_volume" = 5.0, 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"expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1788.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = 269.4401023288638, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], 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0.0, "cumulative_sell_theta" = 1788.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = 269.4401023288638, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [[1704067214000, 15.0, 100.0, 100.0, 98.5, 98.75, 45.0, 15.0, 30.0, 15.0, 4456.0, 1082.9141805572249]], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 98.5, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 21.0, "cumulative_dollar" = 6070.25, "cumulative_volume" = 61.0, "cumulative_buy_volume" = 39.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 15.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 3885.25, "cumulative_sell_theta" = 2185.0, "expected_buy_imbalance" = 438.2279411764706, "expected_sell_imbalance" = 369.1752888263266, "expected_buy_ticks_proportion" = 0.26666666666666666, "buy_ticks_number" = 12.0}, "histories" = {"previous_bars_number_of_ticks" = [15], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0], "previous_bars_buy_ticks_proportions" = [0.26666666666666666]}}, {"left" = 36, "right" = 72, "output" = [[1704067236000, 37.0, 98.5, 100.75, 98.5, 100.75, 64.0, 42.0, 22.0, 22.0, 6372.5, 4060.928177089593], [1704067257000, 58.0, 100.75, 102.5, 100.5, 102.5, 81.0, 44.0, 37.0, 21.0, 8209.25, 4230.385740478401]], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 102.25, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 102.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 14.0, "cumulative_dollar" = 4665.5, "cumulative_volume" = 45.0, "cumulative_buy_volume" = 38.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 19.333333333333332, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 3934.75, "cumulative_sell_theta" = 730.75, "expected_buy_imbalance" = 447.8126471624853, "expected_sell_imbalance" = 422.3433357643598, "expected_buy_ticks_proportion" = 0.4604423210640827, "buy_ticks_number" = 10.0}, "histories" = {"previous_bars_number_of_ticks" = [15, 22, 21], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = [0.26666666666666666, 0.5909090909090909, 0.47619047619047616]}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 102.25, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 102.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 14.0, "cumulative_dollar" = 4665.5, "cumulative_volume" = 45.0, "cumulative_buy_volume" = 38.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 19.333333333333332, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 3934.75, "cumulative_sell_theta" = 730.75, "expected_buy_imbalance" = 447.8126471624853, "expected_sell_imbalance" = 422.3433357643598, "expected_buy_ticks_proportion" = 0.4604423210640827, "buy_ticks_number" = 10.0}, "histories" = {"previous_bars_number_of_ticks" = [15, 22, 21], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = [0.26666666666666666, 0.5909090909090909, 0.47619047619047616]}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 200.0, 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447.8126471624853, 422.3433357643598, 0.4604423210640827, 10.0]] + +[[information]] +kind = "ExpectedRunBars" +metric = "Dollar" +config = {"initial" = 3.0, "imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = true} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 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6.0, 2.0, 1.0, 2.0, 6.0, 1.0, 2.0, 0.0, 6.0, 5.0, 1.0, 3.0, 0.0, 5.0, 6.0, 4.0, 5.0, 2.0, 6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 399.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1788.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 5366.75, "cumulative_sell_theta" = 4959.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4444444444444444, "buy_ticks_number" = 16.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 14074.75, "cumulative_sell_theta" = 9428.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 14074.75, "cumulative_sell_theta" = 9428.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 200.0, 2.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, -1.0, 2.0, 599.0, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 399.0, nan, nan, 0.0, 0.0], [1704067202000, -1.0, 3.0, 997.0, 10.0, 0.0, 0.0, 3.0, 0.0, 0.0, 797.0, nan, nan, 0.0, 0.0], [1704067203000, -1.0, 4.0, 1294.75, 13.0, 0.0, 0.0, 3.0, 0.0, 0.0, 1094.75, nan, nan, 0.0, 0.0], [1704067204000, -1.0, 5.0, 1394.0, 14.0, 0.0, 0.0, 3.0, 0.0, 0.0, 1194.0, nan, nan, 0.0, 0.0], [1704067205000, -1.0, 6.0, 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1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 308.3860294117647, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 326.14402619285704, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 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"cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected 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0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 331.4851332681654, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 381.1846121769321, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 381.1846121769321, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 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1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 308.3860294117647, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 326.14402619285704, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 603.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 603.0, 0.0, nan, nan, 0.3333333333333333, 1.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 4.0, 8.0, 603.0, 0.0, nan, nan, 0.25, 1.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 4.0, 8.0, 805.5, 0.0, nan, nan, 0.4, 2.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 4.0, 8.0, 907.0, 0.0, nan, nan, 0.5, 3.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 4.0, 8.0, 1314.0, 0.0, 308.3860294117647, nan, 0.5714285714285714, 4.0], [1704067207000, 1.0, 8.0, 2221.5, 22.0, 16.0, 0.0, 4.0, 8.0, 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1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 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"open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, 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1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 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= 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 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"cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 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0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 4.0, 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0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], 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"previous_tick_price" = 105.0, "open_price" = nan, "close_price" = 105.0, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 1.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = 1.0, "expected_buy_ticks_proportion" = 0.8330268085263135, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [14, 5, 3, 3, 3, 2, 2, 2, 3, 5, 5, 3, 3, 2, 2, 3, 2, 3, 3, 4], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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{"previous_bars_number_of_ticks" = [14, 5, 3, 3, 3, 2, 2, 2, 3, 5, 5, 3, 3, 2, 2, 3, 2, 3, 3, 4], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = [0.21428571428571427, 0.4, 1.0, 0.3333333333333333, 0.6666666666666666, 0.0, 1.0, 1.0, 1.0, 0.6, 0.4, 0.3333333333333333, 0.6666666666666666, 0.0, 1.0, 0.6666666666666666, 1.0, 1.0, 1.0, 0.75]}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy 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{"initial" = 3.0, "imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 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2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 6.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 17.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4722222222222222, "buy_ticks_number" = 17.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 41.0, "cumulative_sell_theta" = 30.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5694444444444444, "buy_ticks_number" = 41.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 41.0, "cumulative_sell_theta" = 30.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5694444444444444, "buy_ticks_number" = 41.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", 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false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", 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71.0]] + +[[information]] +kind = "FixedRunBars" +metric = "Tick" +config = {"initial" = 5.0, "imbalance_window" = 15, "tick_window" = 0, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 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3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 1.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 2.0, 0.0, nan, nan, 0.6666666666666666, 2.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 3.0, 0.0, nan, nan, 0.75, 3.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 5.0, 15.0, 4.0, 0.0, nan, nan, 0.8, 4.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 5.0, 15.0, 5.0, 0.0, 1.0000000000000002, nan, 0.8333333333333334, 5.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 5.0, 15.0, 6.0, 0.0, 1.0000000000000002, nan, 0.8571428571428571, 6.0], 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114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0000000000000002, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 35.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9722222222222222, "buy_ticks_number" = 35.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 1.0, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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"previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8571428571428571, "buy_ticks_number" = 6.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = 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{"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 71.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.9861111111111112, "buy_ticks_number" = 71.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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"cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 3.0, 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"expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 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"expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 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"expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], 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+kind = "FixedRunBars" +metric = "Volume" +config = {"initial" = 3.0, "imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 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6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 4.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 18.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 54.0, "cumulative_sell_theta" = 50.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4444444444444444, "buy_ticks_number" = 16.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 139.0, "cumulative_sell_theta" = 94.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 139.0, "cumulative_sell_theta" = 94.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [1.0, 4.0, 5.0, 5.0, 6.0, 6.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 2.0, 6.0, 5.0, 1.0, 3.0, 4.0, 5.0, 6.0, 4.0, 5.0, 6.0, 1.0, 2.0, 5.0, 6.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0], "previous_tick_imbalances_sell" = [4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 6.0, 4.0, 2.0, 2.0, 4.0, 6.0, 2.0, 6.0, 5.0, 6.0, 2.0, 1.0, 3.0, 1.0, 2.0, 6.0, 5.0, 6.0, 1.0, 6.0], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.040441176470588, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.006167600810259, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", 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"tick_window" = 0, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.076276035215111, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.2655242849235484, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.2655242849235484, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 6.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 6.0, 0.0, nan, nan, 0.3333333333333333, 1.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 5.0, 15.0, 6.0, 0.0, nan, nan, 0.25, 1.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 5.0, 15.0, 8.0, 0.0, nan, nan, 0.4, 2.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 5.0, 15.0, 9.0, 0.0, nan, nan, 0.5, 3.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 5.0, 15.0, 13.0, 0.0, nan, nan, 0.5714285714285714, 4.0], [1704067207000, 1.0, 8.0, 2221.5, 22.0, 16.0, 0.0, 5.0, 15.0, 16.0, 0.0, 3.0284360189573465, nan, 0.625, 5.0], [1704067208000, 1.0, 9.0, 2323.75, 23.0, 17.0, 0.0, 5.0, 15.0, 17.0, 0.0, 2.3670312867560575, nan, 0.6666666666666666, 6.0], [1704067209000, 1.0, 10.0, 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"Volume" +config = {"initial" = 4.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.040441176470588, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.006167600810259, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 3.3497198642130024, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell 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"imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 6.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 13.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 92.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 183.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [6.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 3.0, 6.0, 3.0, 5.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 2.0, 3.0, 3.0, 6.0, 2.0, 4.0, 3.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 0.0, 6.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 0.0, 6.0, 0.0, nan, nan, 0.3333333333333333, 1.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 0.0, 6.0, 0.0, nan, nan, 0.25, 1.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 3.0, 0.0, 8.0, 0.0, nan, nan, 0.4, 2.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 3.0, 0.0, 9.0, 0.0, nan, nan, 0.5, 3.0], [1704067206000, 1.0, 7.0, 1915.5, 19.0, 13.0, 0.0, 3.0, 0.0, 13.0, 0.0, nan, nan, 0.5714285714285714, 4.0], [1704067207000, 1.0, 8.0, 2221.5, 22.0, 16.0, 0.0, 3.0, 0.0, 16.0, 0.0, nan, nan, 0.625, 5.0], [1704067208000, 1.0, 9.0, 2323.75, 23.0, 17.0, 0.0, 3.0, 0.0, 17.0, 0.0, nan, nan, 0.6666666666666666, 6.0], [1704067209000, 1.0, 10.0, 2528.75, 25.0, 19.0, 0.0, 3.0, 0.0, 19.0, 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2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell 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"right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 10.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067205000, 0.0, 6.0, 1400.0, 14.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067206000, 0.0, 7.0, 1700.0, 17.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067207000, 0.0, 8.0, 2200.0, 22.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067208000, 0.0, 9.0, 2800.0, 28.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067209000, 0.0, 10.0, 3100.0, 31.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067210000, 0.0, 11.0, 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+times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 10.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067205000, 0.0, 6.0, 1400.0, 14.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067206000, 0.0, 7.0, 1700.0, 17.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067207000, 0.0, 8.0, 2200.0, 22.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067208000, 0.0, 9.0, 2800.0, 28.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067209000, 0.0, 10.0, 3100.0, 31.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067210000, 0.0, 11.0, 3700.0, 37.0, 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1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, 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"previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [[1704067217000, 18.0, 100.0, 100.0, 98.5, 98.75, 53.0, 21.0, 32.0, 18.0, 5245.5, 1181.1727986759033], [1704067222000, 23.0, 98.5, 99.75, 98.5, 99.75, 17.0, 15.0, 2.0, 5.0, 1686.0, 1181.1727986759033], [1704067228000, 29.0, 99.5, 99.75, 99.5, 99.5, 15.0, 3.0, 12.0, 6.0, 1493.25, 1083.167015432002], [1704067233000, 34.0, 99.5, 100.25, 99.5, 100.25, 15.0, 9.0, 6.0, 5.0, 1498.25, 897.2160923157519]], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.5, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 100.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 603.25, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 603.25, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 319.78539152303154, "expected_sell_imbalance" = 379.1693785691483, "expected_buy_ticks_proportion" = 0.5105670272041953, "buy_ticks_number" = 2.0}, "histories" = {"previous_bars_number_of_ticks" = [18, 5, 6, 5], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0], "previous_bars_buy_ticks_proportions" = [0.2777777777777778, 0.8, 0.3333333333333333, 0.6]}}, {"left" = 36, "right" = 72, "output" = [[1704067239000, 40.0, 100.5, 100.75, 100.5, 100.5, 20.0, 9.0, 11.0, 6.0, 2011.0, 927.8899807311807], [1704067243000, 44.0, 100.75, 101.5, 100.75, 101.5, 17.0, 15.0, 2.0, 4.0, 1720.25, 1046.6343008778588], [1704067250000, 51.0, 101.75, 101.75, 101.0, 101.0, 22.0, 9.0, 13.0, 7.0, 2227.5, 1065.6419037076166], [1704067254000, 55.0, 101.25, 101.75, 101.25, 101.5, 18.0, 7.0, 11.0, 4.0, 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"expected_sell_imbalance" = 347.78391252381857, "expected_buy_ticks_proportion" = 0.7176827776037287, "buy_ticks_number" = 5.0}, "histories" = {"previous_bars_number_of_ticks" = [15, 5, 3, 3, 8, 3, 3, 4, 7, 4, 3, 5, 2], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = [0.26666666666666666, 0.4, 1.0, 0.0, 0.625, 1.0, 0.0, 0.75, 0.2857142857142857, 0.5, 1.0, 0.6, 1.0]}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, 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102.25, 102.5, 102.25, 102.5, 102.5, 102.75, 103.0, 103.25, 103.25, 103.75, 104.0, 104.5, 104.75, 105.0, 104.75, 105.0] +volumes = [2.0, 4.0, 4.0, 3.0, 1.0, 4.0, 2.0, 0.0, 0.0, 1.0, 6.0, 4.0, 4.0, 5.0, 5.0, 0.0, 2.0, 6.0, 2.0, 6.0, 3.0, 2.0, 4.0, 0.0, 4.0, 6.0, 2.0, 1.0, 2.0, 6.0, 1.0, 2.0, 0.0, 6.0, 5.0, 1.0, 3.0, 0.0, 5.0, 6.0, 4.0, 5.0, 2.0, 6.0, 4.0, 1.0, 3.0, 1.0, 5.0, 2.0, 6.0, 6.0, 1.0, 5.0, 6.0, 2.0, 5.0, 6.0, 1.0, 0.0, 5.0, 4.0, 5.0, 6.0, 6.0, 1.0, 0.0, 2.0, 2.0, 2.0, 6.0, 5.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 200.0, "cumulative_volume" = 2.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 99.75, "open_price" = 100.0, "close_price" = 99.75, "high_price" = 100.0, "low_price" = 99.75, "previous_tick_rule" = -1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 599.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 399.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 99.0, "open_price" = 100.0, "close_price" = 99.0, "high_price" = 100.0, "low_price" = 99.0, "previous_tick_rule" = -1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1988.0, "cumulative_volume" = 20.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 1788.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.75, "open_price" = 100.0, "close_price" = 100.75, "high_price" = 100.75, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10526.25, "cumulative_volume" = 106.0, "cumulative_buy_volume" = 54.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 5366.75, "cumulative_sell_theta" = 4959.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.4444444444444444, "buy_ticks_number" = 16.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 14074.75, "cumulative_sell_theta" = 9428.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 609.0, 407.0, 506.25, 607.5, 101.75, 204.0, 511.25, 615.0, 512.5, 411.0, 515.0, 619.5, 619.5, 103.75, 209.0, 209.5, 210.0, 525.0], "previous_tick_imbalances_sell" = [399.0, 398.0, 297.75, 99.25, 396.0, 198.0, 592.5, 394.0, 197.0, 197.0, 398.0, 597.0, 199.0, 597.0, 502.5, 603.0, 202.0, 101.5, 303.75, 101.0, 202.0, 606.0, 507.5, 609.0, 102.25, 628.5], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 105.0, "open_price" = 100.0, "close_price" = 105.0, "high_price" = 105.0, "low_price" = 98.5, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 23703.25, "cumulative_volume" = 235.0, "cumulative_buy_volume" = 139.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 14074.75, "cumulative_sell_theta" = 9428.5, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5138888888888888, "buy_ticks_number" = 37.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [99.0, 395.0, 493.75, 493.75, 592.5, 594.0, 297.0, 199.0, 399.0, 199.5, 99.75, 99.75, 200.0, 601.5, 502.5, 100.75, 302.25, 403.0, 506.25, 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"tick_window" = 3, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.25, 100.5, 100.75, 101.0, 101.25, 101.5, 101.75, 102.0, 102.25, 102.5, 102.75, 103.0, 103.25, 103.5, 103.75, 104.0, 104.25, 104.5, 104.75, 105.0, 105.25, 105.5, 105.75, 106.0, 106.25, 106.5, 106.75, 107.0, 107.25, 107.5, 107.75, 108.0, 108.25, 108.5, 108.75, 109.0, 109.25, 109.5, 109.75, 110.0, 110.25, 110.5, 110.75, 111.0, 111.25, 111.5, 111.75, 112.0, 112.25, 112.5, 112.75, 113.0, 113.25, 113.5, 113.75, 114.0, 114.25, 114.5, 114.75, 115.0, 115.25, 115.5, 115.75, 116.0, 116.25, 116.5, 116.75, 117.0, 117.25, 117.5, 117.75, 118.0] +volumes = [6.0, 6.0, 0.0, 0.0, 2.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 6.0, 2.0, 2.0, 1.0, 1.0, 0.0, 1.0, 5.0, 6.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 0.0, 2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 308.3860294117647, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 326.14402619285704, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 601.5, 6.0, 0.0, 0.0, 3.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 1.0, 2.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 603.0, 0.0, nan, nan, 0.5, 1.0], [1704067202000, 1.0, 3.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 603.0, 0.0, nan, nan, 0.3333333333333333, 1.0], [1704067203000, 1.0, 4.0, 1204.5, 12.0, 6.0, 0.0, 3.0, 8.0, 603.0, 0.0, nan, nan, 0.25, 1.0], [1704067204000, 1.0, 5.0, 1407.0, 14.0, 8.0, 0.0, 3.0, 8.0, 805.5, 0.0, nan, nan, 0.4, 2.0], [1704067205000, 1.0, 6.0, 1508.5, 15.0, 9.0, 0.0, 3.0, 8.0, 907.0, 0.0, 202.0, nan, 0.5, 3.0], 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"n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 331.4851332681654, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 381.1846121769321, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 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2.0, 1.0, 1.0, 3.0, 6.0, 1.0, 5.0, 1.0, 4.0, 6.0, 0.0, 0.0, 1.0, 2.0, 4.0, 3.0, 6.0, 1.0, 0.0, 3.0, 6.0, 3.0, 0.0, 5.0, 0.0, 2.0, 1.0, 1.0, 4.0, 2.0, 3.0, 1.0, 4.0, 2.0, 5.0, 3.0, 0.0, 2.0, 3.0, 0.0, 3.0, 6.0, 2.0, 4.0, 3.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 308.3860294117647, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 326.14402619285704, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 343.5, 114.75, 460.0, 230.5, 577.5, 347.25, 232.5, 349.5, 351.0, 703.5, 235.0, 471.0, 354.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = 393.261554300315, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0, 655.5, 110.0, 220.5, 442.0, 332.25, 666.0, 111.25, 335.25, 672.0, 336.75, 563.75, 226.5, 113.5, 113.75, 456.0, 228.5, 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"cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.25, "open_price" = 100.25, "close_price" = 100.25, "high_price" = 100.25, "low_price" = 100.25, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 601.5, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.5, "open_price" = 100.25, "close_price" = 100.5, "high_price" = 100.5, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 1204.5, "cumulative_volume" = 12.0, "cumulative_buy_volume" = 6.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 603.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5, "buy_ticks_number" = 1.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 101.75, "open_price" = 100.25, "close_price" = 101.75, "high_price" = 101.75, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1915.5, "cumulative_volume" = 19.0, "cumulative_buy_volume" = 13.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 1314.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.5714285714285714, "buy_ticks_number" = 4.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 109.0, "open_price" = 100.25, "close_price" = 109.0, "high_price" = 109.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 10264.5, "cumulative_volume" = 98.0, "cumulative_buy_volume" = 92.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 9663.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8611111111111112, "buy_ticks_number" = 31.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 210.5, 527.5, 211.5, 424.0, 531.25, 532.5, 214.0, 107.25, 107.5, 323.25, 648.0, 108.25, 542.5, 108.75, 436.0], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 118.0, "open_price" = 100.25, "close_price" = 118.0, "high_price" = 118.0, "low_price" = 100.25, "previous_tick_rule" = 1.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20618.0, "cumulative_volume" = 189.0, "cumulative_buy_volume" = 183.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 20016.5, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.8333333333333334, "buy_ticks_number" = 60.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [603.0, 202.5, 101.5, 407.0, 306.0, 102.25, 205.0, 205.5, 618.0, 206.5, 207.0, 103.75, 104.0, 104.5, 523.75, 630.0, 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"previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 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"high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 5.0, "expected_imbalance_window" = 15.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 10.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067205000, 0.0, 6.0, 1400.0, 14.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067206000, 0.0, 7.0, 1700.0, 17.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067207000, 0.0, 8.0, 2200.0, 22.0, 0.0, 0.0, 5.0, 15.0, 0.0, 0.0, nan, nan, 0.0, 0.0], 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0.0, nan, nan, 0.0, 0.0]] + +[[information]] +kind = "FixedRunBars" +metric = "Dollar" +config = {"initial" = 4.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 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[]}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 4.0, "expected_imbalance_window" = 8.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 10.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067205000, 0.0, 6.0, 1400.0, 14.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067206000, 0.0, 7.0, 1700.0, 17.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067207000, 0.0, 8.0, 2200.0, 22.0, 0.0, 0.0, 4.0, 8.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067208000, 0.0, 9.0, 2800.0, 28.0, 0.0, 0.0, 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= 3.0, "imbalance_window" = 0, "tick_window" = 2, "bounds" = [4.0, 12.0], "expected" = false} +times = [1704067200000, 1704067201000, 1704067202000, 1704067203000, 1704067204000, 1704067205000, 1704067206000, 1704067207000, 1704067208000, 1704067209000, 1704067210000, 1704067211000, 1704067212000, 1704067213000, 1704067214000, 1704067215000, 1704067216000, 1704067217000, 1704067218000, 1704067219000, 1704067220000, 1704067221000, 1704067222000, 1704067223000, 1704067224000, 1704067225000, 1704067226000, 1704067227000, 1704067228000, 1704067229000, 1704067230000, 1704067231000, 1704067232000, 1704067233000, 1704067234000, 1704067235000, 1704067236000, 1704067237000, 1704067238000, 1704067239000, 1704067240000, 1704067241000, 1704067242000, 1704067243000, 1704067244000, 1704067245000, 1704067246000, 1704067247000, 1704067248000, 1704067249000, 1704067250000, 1704067251000, 1704067252000, 1704067253000, 1704067254000, 1704067255000, 1704067256000, 1704067257000, 1704067258000, 1704067259000, 1704067260000, 1704067261000, 1704067262000, 1704067263000, 1704067264000, 1704067265000, 1704067266000, 1704067267000, 1704067268000, 1704067269000, 1704067270000, 1704067271000] +prices = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +volumes = [6.0, 1.0, 1.0, 0.0, 2.0, 4.0, 3.0, 5.0, 6.0, 3.0, 6.0, 0.0, 6.0, 0.0, 0.0, 0.0, 0.0, 4.0, 1.0, 0.0, 3.0, 1.0, 0.0, 3.0, 4.0, 2.0, 1.0, 5.0, 2.0, 3.0, 6.0, 0.0, 3.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 0.0, 4.0, 3.0, 5.0, 2.0, 0.0, 0.0, 2.0, 2.0, 2.0, 6.0, 0.0, 4.0, 0.0, 5.0, 3.0, 5.0, 5.0, 2.0, 3.0, 5.0, 0.0, 2.0, 5.0, 6.0, 6.0, 5.0, 2.0, 6.0, 3.0, 5.0, 4.0] +output = [] +batches = [{"left" = 0, "right" = 0, "output" = [], "state" = {"tick_counter" = 0.0, "previous_tick_price" = nan, "open_price" = nan, "close_price" = nan, "high_price" = -inf, "low_price" = inf, "previous_tick_rule" = 0.0, "cumulative_ticks" = 0.0, "cumulative_dollar" = 0.0, "cumulative_volume" = 0.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = nan, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 0, "right" = 1, "output" = [], "state" = {"tick_counter" = 1.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 1.0, "cumulative_dollar" = 600.0, "cumulative_volume" = 6.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 1, "right" = 2, "output" = [], "state" = {"tick_counter" = 2.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 2.0, "cumulative_dollar" = 700.0, "cumulative_volume" = 7.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 2, "right" = 7, "output" = [], "state" = {"tick_counter" = 7.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 7.0, "cumulative_dollar" = 1700.0, "cumulative_volume" = 17.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 7, "right" = 36, "output" = [], "state" = {"tick_counter" = 36.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 36.0, "cumulative_dollar" = 8700.0, "cumulative_volume" = 87.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 36, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}, {"left" = 72, "right" = 72, "output" = [], "state" = {"tick_counter" = 72.0, "previous_tick_price" = 100.0, "open_price" = 100.0, "close_price" = 100.0, "high_price" = 100.0, "low_price" = 100.0, "previous_tick_rule" = 0.0, "cumulative_ticks" = 72.0, "cumulative_dollar" = 20200.0, "cumulative_volume" = 202.0, "cumulative_buy_volume" = 0.0, "n_ticks_on_bar_formation" = 0.0, "expected_ticks_number" = 3.0, "expected_imbalance_window" = 0.0, "cumulative_buy_theta" = 0.0, "cumulative_sell_theta" = 0.0, "expected_buy_imbalance" = nan, "expected_sell_imbalance" = nan, "expected_buy_ticks_proportion" = 0.0, "buy_ticks_number" = 0.0}, "histories" = {"previous_bars_number_of_ticks" = [], "previous_tick_imbalances_buy" = [], "previous_tick_imbalances_sell" = [], "previous_bars_buy_ticks_proportions" = []}}] +trace_columns = ["Previous tick rule", "Cumulative Ticks", "Cumulative Dollar Value", "Cumulative Volume", "Cumulative Buy Volume", "Number of ticks while bar is formed.", "exp_num_ticks", "Expected Imbalance Window", "Cumulative Buy \u03b8", "Cumulative Sell \u03b8", "exp_imbalance_buy", "exp_imbalance_sell", "exp_buy_ticks_proportion", "buy_ticks_num"] +traces = [[1704067200000, 0.0, 1.0, 600.0, 6.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067201000, 0.0, 2.0, 700.0, 7.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067202000, 0.0, 3.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067203000, 0.0, 4.0, 800.0, 8.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067204000, 0.0, 5.0, 1000.0, 10.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067205000, 0.0, 6.0, 1400.0, 14.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067206000, 0.0, 7.0, 1700.0, 17.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067207000, 0.0, 8.0, 2200.0, 22.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067208000, 0.0, 9.0, 2800.0, 28.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0], [1704067209000, 0.0, 10.0, 3100.0, 31.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, nan, nan, 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+[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 2.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 5.666666666666667 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = nan + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 2.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 5.666666666666667 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = 5.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 4.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 7.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = 5.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 4.0 + +[[expected_ticks]] +kind = "ExpectedImbalanceBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 6.795918367346939 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = nan + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 2.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = -1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 5.666666666666667 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = nan + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 2.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 0, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 5.666666666666667 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = 5.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 4.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 1, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 7.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [] +output = 5.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [2] +output = 4.0 + +[[expected_ticks]] +kind = "ExpectedRunBars" +config = {"initial" = 5.0, "imbalance_window" = 8, "tick_window" = 4, "bounds" = [4.0, 7.0], "expected" = true} +history = [2, 6, 9] +output = 6.795918367346939 diff --git a/test/fixtures/bet_sizing_python_320.toml b/test/fixtures/bet_sizing_python_320.toml new file mode 100644 index 0000000..e99d836 --- /dev/null +++ b/test/fixtures/bet_sizing_python_320.toml @@ -0,0 +1,1068 @@ +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 0.5 +cap = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 0.5 +cap = 0.2 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.2, 0.2] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 0.5 +cap = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.8499999999999996, 1.0] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 1.0 +cap = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 1.0 +cap = 0.2 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.2, 0.2, 0.2] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 1.0 +cap = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.3999999999999999, 0.8999999999999999, 1.0] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 3.0 +cap = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 3.0 +cap = 0.2 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[kelly]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +payout = 3.0 +cap = 1.0 +expected = [0.0, 0.0, 0.0, 0.3333333333333333, 0.4000000000000001, 0.6, 0.9333333333333332, 1.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19616515846889815, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19616515846889815, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0956525869761431, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09147907047324022, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.8499999999999996, 0.9996999999999998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19616515846889815, 0.9697015000375035] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 0.5 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9409029400735069] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.8499999999999996, 0.9996999999999998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19616515846889815, 0.9697015000375035] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 0.5 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9409029400735069] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.8499999999999996, 0.9996999999999998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7037980848278655, 0.9929921314860835] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 0.5 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5634442462626161, 0.9865525777127235] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09999999999999964, 0.8499999999999996, 0.9996999999999998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0956525869761431, 0.8479323926871865, 0.9996051364137299] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 0.5 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.09147907047324022, 0.8459474896668855, 0.9995140673709111] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.045655807066027565, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.045655807066027565, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.19506098468080801, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.09685357345548962, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.09383300397275951, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.3999999999999999, 0.8999999999999999, 0.9998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.46411010564593225, 0.9798010000250024] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 1.0 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.045655807066027565, 0.9606019600490046] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.3999999999999999, 0.8999999999999999, 0.9998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.46411010564593225, 0.9798010000250024] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 1.0 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.045655807066027565, 0.9606019600490046] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.3999999999999999, 0.8999999999999999, 0.9998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.19506098468080801, 0.8025320565519103, 0.9953280876573889] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 1.0 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7089628308417442, 0.9910350518084823] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.10000000000000009, 0.3999999999999999, 0.8999999999999999, 0.9998] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.09685357345548962, 0.397101724650762, 0.8986215951247909, 0.9997367576091534] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 1.0 +cap = 1.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.09383300397275951, 0.39431938031549363, 0.8972983264445902, 0.9996760449139406] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.0 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 1 +payout = 3.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.18426213483334739, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 20 +payout = 3.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.04115378427336084, 0.1092850193058501, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.2 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.2 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 100000 +payout = 3.0 +cap = 0.2 +z = 1.96 +expected = [0.0, 0.0, 0.0, 0.2, 0.2, 0.2, 0.2, 0.2] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 1.0 +z = 0.0 +expected = [0.0, 0.0, 0.0, 0.3333333333333333, 0.4000000000000001, 0.6, 0.9333333333333332, 0.9998666666666667] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 0.7, 0.95, 1.0] +count = 0 +payout = 3.0 +cap = 1.0 +z = 1.0 +expected = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6427400704306215, 0.9865340000166682] + +[[robust]] +probabilities = [0.0, 0.05, 0.25, 0.5, 0.55, 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[-0.7935389963759513, 0.10440538432057772] +tight_predictions = [0.9993970603221843, 0.9993970603221843, 0.9770408559545062, 0.8638907885982611, 0.6849834020588982, 0.5339853113475068, 0.5260776621204303, 0.5181569103559545, 0.36170802146187425, 0.16257694307592602, 0.028140500466982764, 0.00074284538214796, 0.00074284538214796] +tight_loss = 0.5324193885116776 +reference_loss = 0.5324193895188644 +reference_score_norm = 2.2798762981275623e-05 +minimum_segment_curvature = 0.17866116094499285 +coefficient_error_bound = 0.00012760894903342254 +prediction_error_bounds = [0.0002955540886742946, 0.0002955540886742946, 0.0001500257663636199, 7.701464276226621e-05, 4.1814008925571053e-05, 3.192775565006311e-05, 3.1902237258355636e-05, 3.192775565006311e-05, 4.1814008925571053e-05, 7.701464276226621e-05, 0.00015002576636361986, 0.000295554088674298, 0.000295554088674298] + +[[platt]] +name = "constant_predictor" +probabilities = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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0.23000388150148074, 0.23000388150148074, 0.23000388150148074, 0.23000388150148074, 0.23000388150148074, 0.23000388150148074] +identity = false +default_parameters = [0.0, -1.20828928904982] +tight_parameters = [0.0, -1.2083112059245344] +tight_predictions = [0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993, 0.22999999999999993] +tight_loss = 0.5392763414970504 +reference_loss = 0.5392763415395855 +reference_score_norm = 3.881501480754724e-06 +minimum_segment_curvature = 1e-08 +coefficient_error_bound = 388.15014808646356 +prediction_error_bounds = [898.9915217971165, 898.9915217971165, 456.3357340680891, 234.25665064579655, 127.18632885455567, 97.11515671518134, 97.03753702161589, 97.11515671518134, 127.18632885455567, 234.25665064579655, 456.335734068089, 898.9915217971271, 898.9915217971271] + +[[platt]] +name = "separable" +probabilities = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9] +outcomes = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +query = [0.0, 0.0001, 0.01, 0.1, 0.3, 0.49, 0.5, 0.51, 0.7, 0.9, 0.99, 0.9999, 1.0] +expected = [6.75235379939351e-20, 6.75235379939351e-20, 2.7261055638941796e-10, 2.6705551286339766e-05, 0.01694263095570433, 0.45221302989963513, 0.4999999999999998, 0.5477869701003644, 0.9830573690442956, 0.9999732944487136, 0.9999999997273894, 1.0, 1.0] +identity = false +default_parameters = [4.7926882411272596, -8.587049774571028e-16] +tight_parameters = [7.710692966952584, 8.515859695268779e-10] +tight_predictions = [1.4373516122258953e-31, 1.4373516122258953e-31, 4.0951493093469814e-16, 4.3866095018724455e-08, 0.0014521546467928666, 0.4234885188034525, 0.5000000002128965, 0.57651148161237, 0.9985478453556769, 0.9999999561339049, 0.9999999999999996, 1.0, 1.0] +tight_loss = 4.15458508634811e-07 +reference_loss = 2.6849469514527273e-05 +reference_score_norm = 5.861818503467296e-05 +minimum_segment_curvature = 4.386609305713433e-08 +coefficient_error_bound = 1336.2982875728203 +prediction_error_bounds = [3094.9900110623344, 3094.9900110623344, 1571.0432238650992, 806.4836833214113, 437.8688873078998, 334.34179596642724, 334.0745718932051, 334.34179596642724, 437.8688873078998, 806.4836833214113, 1571.043223865099, 3094.9900110623707, 3094.9900110623707] + +[[platt]] +name = "clipped_endpoints" +probabilities = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] +outcomes = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] +query = [0.0, 0.0001, 0.01, 0.1, 0.3, 0.49, 0.5, 0.51, 0.7, 0.9, 0.99, 0.9999, 1.0] +expected = [0.1666669017113125, 0.1666669017113125, 0.3093904355318872, 0.4051740091697987, 0.4630523723385297, 0.4982523319221498, 0.4999999999999999, 0.5017476680778499, 0.53694762766147, 0.5948259908302012, 0.6906095644681125, 0.8333330982886901, 0.8333330982886901] +identity = false +default_parameters = [0.17474421469871557, -5.493005064053357e-16] +tight_parameters = [0.17474439819487617, -5.1631507625283956e-15] +tight_predictions = [0.16666666698288124, 0.16666666698288124, 0.3093902553698724, 0.4051739119996351, 0.4630523336817971, 0.4982523300869648, 0.49999999999999867, 0.5017476699130327, 0.5369476663182002, 0.5948260880003624, 0.6906097446301254, 0.83333333301712, 0.83333333301712] +tight_loss = 0.5314231996004938 +reference_loss = 0.531423199600626 +reference_score_norm = 1.441270179755403e-06 +minimum_segment_curvature = 0.1759259260664654 +coefficient_error_bound = 8.192483120781433e-06 +prediction_error_bounds = [1.897454607284572e-05, 1.897454607284572e-05, 9.631640789505609e-06, 4.944333180877666e-06, 2.6844556352017577e-06, 2.0497590586618493e-06, 2.0481207801953583e-06, 2.0497590586618493e-06, 2.6844556352017577e-06, 4.944333180877666e-06, 9.631640789505607e-06, 1.897454607284594e-05, 1.897454607284594e-05] + +[[platt]] +name = "one_class" +probabilities = [0.0, 0.2, 0.5, 1.0] +outcomes = [1, 1, 1, 1] +query = [0.0, 0.0001, 0.01, 0.1, 0.3, 0.49, 0.5, 0.51, 0.7, 0.9, 0.99, 0.9999, 1.0] +expected = [0.0, 0.0001, 0.01, 0.1, 0.3, 0.49, 0.5, 0.51, 0.7, 0.9, 0.99, 0.9999, 1.0] +identity = true diff --git a/test/fixtures/causal_estimators_python_320.toml b/test/fixtures/causal_estimators_python_320.toml new file mode 100644 index 0000000..2149226 --- /dev/null +++ b/test/fixtures/causal_estimators_python_320.toml @@ -0,0 +1,1012 @@ +reference = "RiskLabAI.py 3.2.0" +index_convention = "Actions, exposures, split rows and selected policies are converted to one-based Julia indices." +rtol = 1e-10 +atol = 1e-12 + +[[case]] +method = "average_treatment_effect" +arguments = [1.328350430127939, -0.28623132046114125] +keywords = { } +expected = 1.6145817505890803 + +[[case]] +method = "treatment_effect_decomposition" +arguments = [1.328350430127939, -0.28623132046114125, 0.15233134865876535] +keywords = { } +expected = { "observed_difference" = 1.6145817505890803, "average_treatment_effect_on_treated" = 1.1760190814691736, "sample_selection_bias" = 0.4385626691199066 } + +[[case]] +method = "difference_in_differences" +arguments = [1.328350430127939, -0.28623132046114125, 0.15233134865876535, 0.11903147376885438] +keywords = { } +expected = { "treated_change" = -1.6145817505890803, "control_change" = -0.03329987488991097, "estimate" = -1.5812818756991693 } + +[[case]] +method = "linear_instrumental_variable_effect" +arguments = [1.328350430127939, 0.48623132046114126] +keywords = { } +expected = 2.7319310258914067 + +[[case]] +method = "randomized_mean_difference" +arguments = [[-0.8027901699561943, 0.1690242071562458, 0.3478431437056975, 2.2023489189248684], [-0.06414255145542662, 1.1678969846125458, 0.6461865977203617, 0.5177896806558041]] +keywords = { } +expected = -0.08782615292566692 + +[[case]] +method = "backdoor_adjusted_expectation" +arguments = [[-0.8027901699561943, 0.1690242071562458, 0.3478431437056975, 2.2023489189248684], [0.1440191118393694, 0.6636699899370365, 0.14445057007555134, 0.047860328148042815]] +keywords = { } +expected = 0.15221044895342517 + +[[case]] +method = "backdoor_adjusted_average_treatment_effect" +arguments = [[-0.8027901699561943, 0.1690242071562458, 0.3478431437056975, 2.2023489189248684], [-0.06414255145542662, 1.1678969846125458, 0.6461865977203617, 0.5177896806558041], [0.1440191118393694, 0.6636699899370365, 0.14445057007555134, 0.047860328148042815]] +keywords = { } +expected = -0.731773584223901 + +[[case]] +method = "frontdoor_adjusted_probability" +arguments = [[0.7029481020733489, 0.0582395571832451, 0.23881234074340593], [[0.7149111887648321, 0.24778295736745448], [0.6036062233555495, 0.8025958110498065], [0.28102102095812154, 0.748577613738636]], [0.36526794774382704, 0.634732052256173]] +keywords = { } +expected = 0.4746145548867089 + +[[case]] +method = "transported_ate" +arguments = [[-0.8027901699561943, 0.1690242071562458, 0.3478431437056975, 2.2023489189248684], [-0.06414255145542662, 1.1678969846125458, 0.6461865977203617, 0.5177896806558041], [0.1440191118393694, 0.6636699899370365, 0.14445057007555134, 0.047860328148042815], [1.0, 1.0, 1.0, 1.0], [1, 2, 3, 4]] +keywords = { } +expected = -0.731773584223901 + +[[case]] +method = "manski_ate_bounds" +arguments = [[0.2773869418779191, 2.9396460032003597, 2.204962406847666, -1.513297880601446], [1, 1, 0, 0]] +keywords = { "lower" = -2.0, "upper" = 3.0 } +expected = { "lower" = -1.8686578952919852, "upper" = 3.1313421047080148, "treated_fraction" = 0.5, "sample_count" = 4 } + +[[case]] +method = "average_treatment_effect" +arguments = [0.5714866927072363, 0.5056257383185577] +keywords = { } +expected = 0.06586095438867856 + +[[case]] +method = "treatment_effect_decomposition" +arguments = [0.5714866927072363, 0.5056257383185577, 0.7275192670545999] +keywords = { } +expected = { "observed_difference" = 0.06586095438867856, "average_treatment_effect_on_treated" = -0.15603257434736362, "sample_selection_bias" = 0.22189352873604218 } + +[[case]] +method = "difference_in_differences" +arguments = [0.5714866927072363, 0.5056257383185577, 0.7275192670545999, -0.2666192538350946] +keywords = { } +expected = { "treated_change" = -0.06586095438867856, "control_change" = -0.9941385208896945, "estimate" = 0.9282775665010159 } + +[[case]] +method = "linear_instrumental_variable_effect" +arguments = [0.5714866927072363, 0.7056257383185578] +keywords = { } +expected = 0.8099005771374458 + +[[case]] +method = "randomized_mean_difference" +arguments = [[-1.6070489421561953, 1.3125323242947, -0.7329256211690662, -1.1878725584353242], [-0.44267836534910376, -0.5443486546981012, 0.34013168478322997, -0.3492217282363501]] +keywords = { } +expected = -0.3047994334913901 + +[[case]] +method = "backdoor_adjusted_expectation" +arguments = [[-1.6070489421561953, 1.3125323242947, -0.7329256211690662, -1.1878725584353242], [0.09317772199812951, 0.40380457218301724, 0.32121094389279753, 0.1818067619260557]] +keywords = { } +expected = -0.07112159989029188 + +[[case]] +method = "backdoor_adjusted_average_treatment_effect" +arguments = [[-1.6070489421561953, 1.3125323242947, -0.7329256211690662, -1.1878725584353242], [-0.44267836534910376, -0.5443486546981012, 0.34013168478322997, -0.3492217282363501], [0.09317772199812951, 0.40380457218301724, 0.32121094389279753, 0.1818067619260557]] +keywords = { } +expected = 0.1441734894873641 + +[[case]] +method = "frontdoor_adjusted_probability" +arguments = [[0.389604407176731, 0.18681588647681327, 0.4235797063464557], [[0.4912357448561776, 0.7986094597204766], [0.06743836927020475, 0.18323683294554494], [0.2544391263607485, 0.49621009474212796]], [0.4707187462383964, 0.5292812537616035]] +keywords = { } +expected = 0.44079829643157764 + 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"average_treatment_effect_on_treated" = 1.4152603435480537, "sample_selection_bias" = -0.9607075873661353 } + +[[case]] +method = "difference_in_differences" +arguments = [0.5075861182724786, 0.05303336209056004, -0.9076742252755752, -0.6886338556309085] +keywords = { } +expected = { "treated_change" = -0.45455275618191854, "control_change" = 0.21904036964466667, "estimate" = -0.6735931258265853 } + +[[case]] +method = "linear_instrumental_variable_effect" +arguments = [0.5075861182724786, 0.25303336209056004] +keywords = { } +expected = 2.0060047184245007 + +[[case]] +method = "randomized_mean_difference" +arguments = [[1.4199426528378398, 0.2948998291871173, 0.38754286015178324, -0.10211865370435637], [-0.9027897380198868, 0.7803433091893326, 0.5030645385639404, -0.9156651668745835]] +keywords = { } +expected = 0.6338284364033955 + +[[case]] +method = "backdoor_adjusted_expectation" +arguments = [[1.4199426528378398, 0.2948998291871173, 0.38754286015178324, -0.10211865370435637], [0.10982247643959363, 0.1631227456129967, 0.17506793516792846, 0.5519868427794812]] +keywords = { } +expected = 0.2155246634233512 + +[[case]] +method = "backdoor_adjusted_average_treatment_effect" +arguments = [[1.4199426528378398, 0.2948998291871173, 0.38754286015178324, -0.10211865370435637], [-0.9027897380198868, 0.7803433091893326, 0.5030645385639404, -0.9156651668745835], [0.10982247643959363, 0.1631227456129967, 0.17506793516792846, 0.5519868427794812]] +keywords = { } +expected = 0.604744179524904 + +[[case]] +method = "frontdoor_adjusted_probability" +arguments = [[0.5496741295729128, 0.15881128419017368, 0.2915145862369135], [[0.41991194229053075, 0.42697561063114253], [0.10570279723605003, 0.5886666808652062], [0.350239468833095, 0.4893556442055712]], [0.2732104752782527, 0.7267895247217472]] +keywords = { } +expected = 0.43774270810005966 + +[[case]] +method = "transported_ate" +arguments = [[1.4199426528378398, 0.2948998291871173, 0.38754286015178324, 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[0.3838862598263405, -1.2200979134264334, -0.7651062508403759, 0.7956028407119079], [0.17419797081204466, 0.68279945159476, 0.024453627022091536, 0.11854895057110372], [1.0, 1.0, 1.0, 1.0], [1, 2, 3, 4]] +keywords = { } +expected = 0.2668874885095404 + +[[case]] +method = "manski_ate_bounds" +arguments = [[1.3316083920998523, 0.5752033975784894, 0.8812163030730753, -1.9909178661823677], [0, 1, 1, 0]] +keywords = { "lower" = -2.0, "upper" = 3.0 } +expected = { "lower" = -1.97106770631648, "upper" = 3.0289322936835203, "treated_fraction" = 0.5, "sample_count" = 4 } + +[[case]] +method = "average_treatment_effect" +arguments = [0.21843306506647753, 2.1055437965469133] +keywords = { } +expected = -1.8871107314804356 + +[[case]] +method = "treatment_effect_decomposition" +arguments = [0.21843306506647753, 2.1055437965469133, -1.6493433880813633] +keywords = { } +expected = { "observed_difference" = -1.8871107314804356, "average_treatment_effect_on_treated" = 1.8677764531478407, "sample_selection_bias" = -3.7548871846282763 } + +[[case]] +method = "difference_in_differences" +arguments = [0.21843306506647753, 2.1055437965469133, -1.6493433880813633, 1.4918448093523815] +keywords = { } +expected = { "treated_change" = 1.8871107314804356, "control_change" = 3.1411881974337446, "estimate" = -1.254077465953309 } + +[[case]] +method = "linear_instrumental_variable_effect" +arguments = [0.21843306506647753, 2.3055437965469134] +keywords = { } +expected = 0.09474253553267203 + +[[case]] +method = "randomized_mean_difference" +arguments = [[0.5343169692718697, 0.13181454030646803, 1.0900350177481928, -0.1850121698873727], [-0.6047995001463162, 0.7117256982248671, -0.9829948335081216, 0.08228206119501473]] +keywords = { } +expected = 0.5912352329184284 + +[[case]] +method = "backdoor_adjusted_expectation" +arguments = [[0.5343169692718697, 0.13181454030646803, 1.0900350177481928, -0.1850121698873727], [0.08887439008025058, 0.46427565018671174, 0.11907901001635252, 0.32777094971668513]] +keywords = { } +expected = 0.17784405232189657 + +[[case]] +method = "backdoor_adjusted_average_treatment_effect" +arguments = [[0.5343169692718697, 0.13181454030646803, 1.0900350177481928, -0.1850121698873727], [-0.6047995001463162, 0.7117256982248671, -0.9829948335081216, 0.08228206119501473], [0.08887439008025058, 0.46427565018671174, 0.11907901001635252, 0.32777094971668513]] +keywords = { } +expected = -0.008757289996900702 + +[[case]] +method = "frontdoor_adjusted_probability" +arguments = [[0.519889744910029, 0.09820889142650471, 0.3819013636634662], [[0.7313453687022375, 0.025358799414276212], [0.32423200507639904, 0.5813314871504681], [0.49199152533567314, 0.8661941935190881]], [0.877036955055803, 0.12296304494419698]] +keywords = { } +expected = 0.5754991097116061 + +[[case]] +method = "transported_ate" +arguments = [[0.5343169692718697, 0.13181454030646803, 1.0900350177481928, -0.1850121698873727], [-0.6047995001463162, 0.7117256982248671, 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0.0843667724966145, 0.3438227728207817], [1.0, 1.0, 1.0, 1.0], [1, 2, 3, 4]] +keywords = { } +expected = -1.5798591236951633 + +[[case]] +method = "manski_ate_bounds" +arguments = [[-0.5697588481504272, -1.2698952259529048, 2.7838954915053318, -0.38791772133131985], [0, 0, 0, 1]] +keywords = { "lower" = -2.0, "upper" = 3.0 } +expected = { "lower" = -2.58303978468333, "upper" = 2.41696021531667, "treated_fraction" = 0.25, "sample_count" = 4 } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.0, -1.0, 3.0], [1, 1, 1, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 0.0, "second_mean" = 2.5, "contrast" = -2.5, "unit_contributions" = [-2.0, -4.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.0, -1.0, 1.0], [1, 1, 1, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 0.5, "second_mean" = 1.0, "contrast" = -0.5, "unit_contributions" = [-2.0, -4.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.0, 1.0, 3.0], [1, 1, 2, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 0.5, "second_mean" = 3.0, "contrast" = -2.5, "unit_contributions" = [-2.0, -4.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.0, 1.0, 1.0], [1, 1, 2, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.0, "second_mean" = 1.5, "contrast" = -0.5, "unit_contributions" = [-2.0, -4.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.5, -1.0, 3.0], [1, 2, 1, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.25, "second_mean" = 1.5, "contrast" = -0.25, "unit_contributions" = [-2.0, 5.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.5, -1.0, 1.0], [1, 2, 1, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.75, "second_mean" = 0.0, "contrast" = 1.75, "unit_contributions" = [-2.0, 5.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.5, 1.0, 3.0], [1, 2, 2, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.75, "second_mean" = 2.0, "contrast" = -0.25, "unit_contributions" = [-2.0, 5.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[1.0, 2.5, 1.0, 1.0], [1, 2, 2, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 2.25, "second_mean" = 0.5, "contrast" = 1.75, "unit_contributions" = [-2.0, 5.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.0, -1.0, 3.0], [2, 1, 1, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.0, "second_mean" = 2.0, "contrast" = -1.0, "unit_contributions" = [4.0, -4.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.0, -1.0, 1.0], [2, 1, 1, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.5, "second_mean" = 0.5, "contrast" = 1.0, "unit_contributions" = [4.0, -4.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.0, 1.0, 3.0], [2, 1, 2, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 1.5, "second_mean" = 2.5, "contrast" = -1.0, "unit_contributions" = [4.0, -4.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.0, 1.0, 1.0], [2, 1, 2, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 2.0, "second_mean" = 1.0, "contrast" = 1.0, "unit_contributions" = [4.0, -4.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.5, -1.0, 3.0], [2, 2, 1, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 2.25, "second_mean" = 1.0, "contrast" = 1.25, "unit_contributions" = [4.0, 5.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.5, -1.0, 1.0], [2, 2, 1, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 2.75, "second_mean" = -0.5, "contrast" = 3.25, "unit_contributions" = [4.0, 5.0, 2.0, 2.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.5, 1.0, 3.0], [2, 2, 2, 1], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 2.75, "second_mean" = 1.5, "contrast" = 1.25, "unit_contributions" = [4.0, 5.0, 2.0, -6.0] } + +[[case]] +method = "exposure_mean_contrast" +arguments = [[2.0, 2.5, 1.0, 1.0], [2, 2, 2, 2], [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]] +keywords = { "first" = 2, "second" = 1 } +expected = { "first_mean" = 3.25, "second_mean" = 0.0, "contrast" = 3.25, "unit_contributions" = [4.0, 5.0, 2.0, 2.0] } + +[[case]] +method = "evaluate_policy" +arguments = [[3, 3, 1, 1, 1, 3, 1, 2], [0.6907094296022304, 0.45362893755745937, -1.456650208258506, -0.5400936165999215, -0.2743005199729853, -0.03147956807586248, 0.24929157013493927, -1.8014573925944868], [[0.273031533119959, 0.04847019074693973, 0.6784982761331014], [0.13246708586270364, 0.4118052431061402, 0.45572767103115613], [0.37848985639322313, 0.03916008369246686, 0.5823500599143102], [0.23749464660421463, 0.27571866497394076, 0.4867866884218447], [0.11836936752325436, 0.6290046046303643, 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0.7943132135993376 } + +[[case]] +method = "select_policy" +arguments = [[3, 3, 1, 1, 1, 3, 1, 2], [0.6907094296022304, 0.45362893755745937, -1.456650208258506, -0.5400936165999215, -0.2743005199729853, -0.03147956807586248, 0.24929157013493927, -1.8014573925944868], [[0.273031533119959, 0.04847019074693973, 0.6784982761331014], [0.13246708586270364, 0.4118052431061402, 0.45572767103115613], [0.37848985639322313, 0.03916008369246686, 0.5823500599143102], [0.23749464660421463, 0.27571866497394076, 0.4867866884218447], [0.11836936752325436, 0.6290046046303643, 0.2526260278463814], [0.026372573615772707, 0.5231420856381623, 0.450485340746065], [0.5497031182850837, 0.10756488903386253, 0.3427319926810538], [0.14018006140874564, 0.6782903495499024, 0.18152958904135186]], [[[0.06553009172314653, 0.28631933935935416, 0.6481505689174993], [0.49579162005801425, 0.355604292261105, 0.14860408768088076], [0.01442900188297944, 0.823032099770884, 0.16253889834613663], [0.2603132424198449, 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[-1.444775935408129, -0.5105212373008834, 1.6067322424107864], [0.24298835976752864, -0.7055525518352304, -0.25080093072530674], [1.1003036720405588, 2.520839531766327, -1.1417254774217866], [1.3242828228655306, -1.8096874195956036, -0.3713903156395131], [-0.6342909891854156, 0.5537291040481538, 0.6171960169494328]]] +keywords = { "training_indices" = [1, 2, 3, 4], "evaluation_indices" = [5, 6, 7, 8] } +expected = { "selected_policy" = 1, "training_values" = [0.49913555667313575, -0.07797355936372796], "evaluation" = { "ipw_value" = -0.267663091294456, "dr_value" = -0.4761557559864671, "ipw_scores" = [-0.9189936372938592, -0.010296030831751343, 0.04009466047734059, -0.18145735752955386], "dr_scores" = [-1.8626103745793763, 1.1919175475541812, -0.5624062859356109, -0.6715239109850624], "importance_weights" = [3.350316788988832, 0.32707027005386413, 0.16083440148272046, 0.10072808731169387], "effective_sample_size" = 1.3648710352511029, "max_weight" = 3.350316788988832, "mean_weight" = 0.9847373869592776 } } + +[[case]] +method = "evaluate_policy" +arguments = [[3, 2, 2, 2, 2, 1, 2, 3], [-0.5379940529548681, 1.3495552386185716, 0.19904680721794105, -0.4860809005255703, -0.3392754617768362, 1.6354560384936623, -0.5534940408641104, -1.8158048946237904], [[0.028787457376430386, 0.3192120123936752, 0.6520005302298945], [0.17764089667877794, 0.0730234852647839, 0.7493356180564382], [0.3795149428056165, 0.4266305482877961, 0.19385450890658731], [0.26546685411312804, 0.6646590258589575, 0.06987412002791438], [0.3502903971302198, 0.6145225810764414, 0.0351870217933388], [0.18670495216379876, 0.22413384711681866, 0.5891612007193826], [0.8082254139431188, 0.1829574459189504, 0.008817140137931001], [0.13195689220632897, 0.7127646216953621, 0.1552784860983089]], [[0.5907782579398765, 0.20395754248063172, 0.20526419957949185], [0.11286352235119776, 0.0880634289173791, 0.799073048731423], [0.4766973423147769, 0.08936884126843554, 0.4339338164167875], [0.9282033659155551, 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4.631842933202768, "max_weight" = 8.903487701601955, "mean_weight" = 3.0966871633434336 } + +[[case]] +method = "select_policy" +arguments = [[1, 1, 2, 1, 3, 3, 2, 2], [1.5230972037567114, -2.2129436531019686, 1.244205628860733, -0.08396437242237147, 0.5154800194173893, -0.17210313002183575, -0.5602519075735927, -0.06198310505316284], [[0.6895475284995493, 0.1364685827121334, 0.1739838887883174], [0.24500181325233347, 0.3424929294705061, 0.41250525727716053], [0.13180313375620512, 0.3048629503488919, 0.563333915894903], [0.14352910962082446, 0.5521286017416943, 0.3043422886374812], [0.6460641481858886, 0.04525191874759669, 0.3086839330665144], [0.24064067719497056, 0.7329719826013027, 0.026387340203726635], [0.21291542311008313, 0.4315331367329558, 0.35555144015696116], [0.5052261562061756, 0.2704307060446861, 0.22434313774913822]], [[[0.16829575558275273, 0.4511296586698851, 0.3805745857473623], [0.5165195128999439, 0.2664451803031936, 0.21703530679686253], [0.1078776604033029, 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7, 8] } +expected = { "selected_policy" = 2, "training_values" = [-1.452321526789072, -1.3476285680687872], "evaluation" = { "ipw_value" = 0.11599506434016285, "dr_value" = 0.2395985923448924, "ipw_scores" = [-0.29982815472429397, -0.7571965233779195, 0.5825886387938991, 0.9384162966689658], "dr_scores" = [0.02739241334700726, -2.1003315828077502, 1.2110581643012703, 1.8202753745390423], "importance_weights" = [0.7257693368034714, 1.006982521307782, 0.729253492046919, 1.1625625663613457], "effective_sample_size" = 3.836753070032948, "max_weight" = 1.1625625663613457, "mean_weight" = 0.9061419791298796 } } + +[[case]] +method = "evaluate_policy" +arguments = [[2, 2, 1, 1, 1, 1, 3, 2], [-1.0546899725164476, -0.18505634775571125, 0.8968898135646696, -1.4483082313676625, 0.18723812467764264, -0.5901463394033213, -0.19361133685192156, 0.9101071859485872], [[0.879551107426778, 0.06897293896464314, 0.051475953608578706], [0.43144516116449144, 0.20646474272689516, 0.3620900961086136], 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0.3333333333333333], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]]], [[1.3139282349720427, 0.7123440161317002, -2.1174416882368203], [0.6862610830427601, -1.4618419414007888, -1.048043227640724], [-0.9826914618262647, -1.3531783035240934, 1.3323188938046937], [-0.9428569987601987, -0.8102451068625386, -0.6679466432664559], [0.298446573648046, -0.5194277295881851, -0.9307720566075156], [-1.2400931350926039, 0.4690549132222658, 1.4372000231302033], [-0.2274022964760141, -0.1837198124750133, -0.8210122402499738], [0.11769636066286128, 0.5253088472706865, -0.012948094901322257]]] +keywords = { "training_indices" = [1, 2, 3, 4], "evaluation_indices" = [5, 6, 7, 8] } +expected = { "selected_policy" = 2, "training_values" = [-2.5239579427083623, -1.4455693247627535], "evaluation" = { "ipw_value" = -0.03644355209258221, "dr_value" = -0.5579929837783812, "ipw_scores" = [0.4633417859730411, -1.0456712583471357, -1.3090657879610885, 1.7456210519648543], "dr_scores" = [0.3615115211988317, -3.044602998138757, -1.4313228355138967, 1.8824423773402972], "importance_weights" = [0.5430735724589081, 1.5453559962430499, 1.2684762056098429, 0.6219199292970389], "effective_sample_size" = 3.383520555080041, "max_weight" = 1.5453559962430499, "mean_weight" = 0.9947064259022099 } } diff --git a/test/fixtures/clearing_accuracy_book_cases.toml b/test/fixtures/clearing_accuracy_book_cases.toml new file mode 100644 index 0000000..92e79e4 --- /dev/null +++ b/test/fixtures/clearing_accuracy_book_cases.toml @@ -0,0 +1,170 @@ +[[cases]] +name = "two" +external_assets = [1.0, 0.0] +nominal_liabilities = [10.0, 10.0] +relative_liabilities = [[0.0, 0.5], [0.5, 0.0]] +expected_payments = [1.3333333333333333, 0.6666666666666665] +expected_unpaid = [8.666666666666666, 9.333333333333334] +expected_defaults = [true, true] + +[[cases]] +name = "chain" +external_assets = [3.0, 0.0, 0.0] +nominal_liabilities = [5.0, 4.0, 2.0] +relative_liabilities = [[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]] +expected_payments = [3.0, 3.0, 2.0] +expected_unpaid = [2.0, 1.0, 0.0] +expected_defaults = [true, true, false] + +[[cases]] +name = "scale_1e-12" +external_assets = [1e-12, 0.0] +nominal_liabilities = [1e-11, 1e-11] +relative_liabilities = [[0.0, 0.5], [0.5, 0.0]] +expected_payments = [1.3333333333333332e-12, 6.666666666666665e-13] +expected_unpaid = [8.666666666666666e-12, 9.333333333333333e-12] +expected_defaults = [false, false] + +[[cases]] +name = "scale_1" +external_assets = [1.0, 0.0] +nominal_liabilities = [10.0, 10.0] +relative_liabilities = [[0.0, 0.5], [0.5, 0.0]] +expected_payments = [1.3333333333333333, 0.6666666666666665] +expected_unpaid = [8.666666666666666, 9.333333333333334] +expected_defaults = [true, true] + +[[cases]] +name = "scale_1e+12" +external_assets = [1000000000000.0, 0.0] +nominal_liabilities = [10000000000000.0, 10000000000000.0] +relative_liabilities = [[0.0, 0.5], [0.5, 0.0]] +expected_payments = [1333333333333.3333, 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0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8], [0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] +expected_payments = [1.0120545435552188, 1.185145807720178, 0.9787817054694735, 0.6265703226894404, 0.46756763563363324, 1.373165842271652, 0.7278697257279149, 1.211863550874819, 0.9035797982556963, 2.221889735596981, 0.5211678499318995, 0.7703434822730957] +expected_unpaid = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +expected_defaults = [false, false, false, false, false, false, false, false, false, false, false, false] + +[[cases]] +name = "grid_Complete_0" +external_assets = [0.5172263029633686, 0.5939219750451503, 0.3347286626182655, 0.07518843872273284, 0.15686516185379237, 1.191834540868671, 0.45435326481868565, 1.0074890649865762, 0.6551647762790074, 2.161804704669047, 0.21812254962323946, 0.5028947008703207] +nominal_liabilities = [1.0120545435552188, 1.185145807720178, 0.9787817054694735, 0.6265703226894404, 0.46756763563363324, 1.373165842271652, 0.7278697257279149, 1.211863550874819, 0.9035797982556963, 2.221889735596981, 0.5211678499318995, 0.7703434822730956] +relative_liabilities = [[0.0, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274], [0.07272727272727274, 0.0, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274, 0.07272727272727274], [0.07272727272727274, 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0.45435326481868565, 1.0074890649865762, 0.6551647762790074, 2.161804704669047, 0.21812254962323946, 0.5028947008703207] +nominal_liabilities = [1.0120545435552188, 1.185145807720178, 0.9787817054694735, 0.6265703226894404, 0.46756763563363324, 1.373165842271652, 0.7278697257279149, 1.211863550874819, 0.9035797982556963, 2.221889735596981, 0.5211678499318995, 0.7703434822730956] +relative_liabilities = [[0.0, 0.13333333333333333, 0.13333333333333333, 0.13333333333333333, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.13333333333333333, 0.0, 0.13333333333333333, 0.13333333333333333, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.13333333333333333, 0.13333333333333333, 0.0, 0.13333333333333333, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.13333333333333333, 0.13333333333333333, 0.13333333333333333, 0.0, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.16, 0.16, 0.16, 0.16, 0.0, 0.022857142857142857, 0.022857142857142857, 0.022857142857142857, 0.022857142857142857, 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51.31132454451208] +expected_unpaid = [0.0, 0.0, 0.0, 0.0, 0.0, 1.4210854715202004e-14, 0.0, 0.0, 0.0, 0.0, 18.464474913053756, 0.0] +expected_defaults = [false, false, false, false, false, false, false, false, false, false, true, false] + +[[cases]] +name = "repeated_32_0.25_Core-periphery" +external_assets = [2.322709444836779, 3.2741140116370433, 112.12962549033931, 8.233021243020023, 229.6924959022931, 17.601917175795837, 39.519475100351855, 21.082538261084494, 274.1728953743532, 117.88782744355169, 77.1946849933126, 72.35172807745336] +nominal_liabilities = [21.581409442141336, 18.72429747900071, 119.74351772068015, 52.45438179886684, 227.2933621540892, 44.30770763781893, 78.79349646053838, 46.71726586537197, 280.63087392565063, 124.68269506642962, 91.95629333975073, 93.11469910966143] +relative_liabilities = [[0.0, 0.13333333333333333, 0.13333333333333333, 0.13333333333333333, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.13333333333333333, 0.0, 0.13333333333333333, 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91.95629333975073, 93.11469910966143] +expected_unpaid = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.878489104465842, 0.0, 0.0, 0.0, 0.0, 0.0] +expected_defaults = [false, false, false, false, false, false, true, false, false, false, false, false] + +[[cases]] +name = "repeated_59_0.85_Two-community" +external_assets = [54.418254971789736, 8.344702879249574, 8.252336036721438, 80.72313105658723, 54.789182541668005, 13.557428656259559, 37.07523441307423, 14.310328501356627, 112.61519137344919, 132.53283620441303, 9.478652271445698, 46.589769289336246] +nominal_liabilities = [102.90730475661456, 72.38794948638554, 60.24740499108421, 117.53045930919653, 78.89460616908462, 42.72837615724824, 61.709405389152856, 46.1313981832447, 122.86724962018195, 148.3714552093844, 43.203985215869835, 303.02040551255254] +relative_liabilities = [[0.0, 0.136, 0.136, 0.136, 0.136, 0.136, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], [0.136, 0.0, 0.136, 0.136, 0.136, 0.136, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], [0.136, 0.136, 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113.51424643875391] +expected_unpaid = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 189.50615907379864] +expected_defaults = [false, false, false, false, false, false, false, false, false, false, false, true] + +[[cases]] +name = "repeated_52_0.85_Two-community" +external_assets = [22.466165824868234, 16.113078361223497, 27.262266991054553, 71.83907487869054, 48.45113859116935, 55.692813529324766, 74.42447316799131, 233.56537332561115, 102.01872436837876, 45.35403751109541, 88.84409235505058, 1.194880742110767] +nominal_liabilities = [33.122064228765765, 119.0414167680881, 92.54942766156054, 126.81033967042912, 73.43190342385194, 77.6391831922383, 97.79414493373666, 252.2525569095772, 118.77294263868089, 75.80790104118827, 112.29263914176742, 20.485480390115896] +relative_liabilities = [[0.0, 0.136, 0.136, 0.136, 0.136, 0.136, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], [0.136, 0.0, 0.136, 0.136, 0.136, 0.136, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02], [0.136, 0.136, 0.0, 0.136, 0.136, 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1.3037600621934924, -0.8920711221040412, 0.6281473506064575], [-2.7823750542368626, -0.9299096959747637, -0.014787741903240401, -0.8020114347238579, 0.6412014825102231, 0.0005240335454889029, -0.7532482312912829, -2.3050053018747607, -1.1960995901602407, -0.43290195755752786, 1.1276909166287645, 0.17568560773080083], [1.1162054178140293, -0.8297004826333318, -0.25955820478423247, -0.8511739458318733, -0.07634142406024888, 1.8918553938040976, 1.2437937414518268, -0.6015560019348611, 1.0216821830800402, -0.02391133823605972, 0.955010778119892, -0.09814480802890961], [1.0020591908652488, 0.17966819644004708, 2.2890913969322466, -0.5342377748178745, -0.3316344391330058, -0.2434207592132264, 0.7323314855499332, -0.06553106654648781, -0.01331930518704134, -1.214235053493836, -0.4431235035143657, 0.5636702030209957], [0.5933191290580827, -1.0030610471024932, -0.5684381717759858, 1.7124290399276418, -0.21573985576847146, 0.2846157174696945, -0.56051283420228, 1.0179711306131536, 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1.3713335816559258, 0.9343935900658147, 1.7375749594375531, -0.3616327952348603, 0.4679315397766971], [1.1117105501903544, -0.1339581313044233, 0.3298327873738296, 0.25664983058345747, 1.6052278309143095, -0.5316228954449176, -0.7537035186137179, -2.439727818537395, -0.6657141875571474, -0.003336778343369604, 0.6982723104949955, 0.468487797056408]]} +output = [[1.0, 0.6889430449067863, 0.6654469881425249, 0.6566311119985623, 0.6589339960982128, 0.6010825948074412, 0.36905326906871155, 0.33518200249536245, 0.31462969829303084, 0.3134459938072252, 0.30674421650751715, 0.3099334938038326], [0.6889430449067863, 1.0, 0.6816491124463472, 0.6570793309110973, 0.7283321811153979, 0.6532283650171156, 0.3744408221191457, 0.35689329144884036, 0.3889335355734729, 0.3422068825434753, 0.3348280981976698, 0.4216036253133412], [0.6654469881425249, 0.6816491124463472, 1.0, 0.6928010466723429, 0.6374471440050181, 0.6677056039333098, 0.38551993991408745, 0.36946384802332477, 0.35878384699972793, 0.39427032060655903, 0.3410208084970693, 0.3962648011234295], [0.6566311119985623, 0.6570793309110973, 0.6928010466723429, 1.0, 0.6690335707632622, 0.6657823004481126, 0.3078691277678899, 0.3687411800995861, 0.3435841091256543, 0.37680983353432573, 0.3323665654304721, 0.3539577552509031], [0.6589339960982128, 0.7283321811153979, 0.6374471440050181, 0.6690335707632622, 1.0, 0.6662298002666063, 0.3643462983920934, 0.3250254891173933, 0.3347844093346288, 0.35006399431307356, 0.31963544938411576, 0.36695864815644585], [0.6010825948074412, 0.6532283650171156, 0.6677056039333098, 0.6657823004481126, 0.6662298002666063, 1.0, 0.3568462358862965, 0.33506981044481154, 0.31014079542308937, 0.32243491964840243, 0.29520592093324777, 0.34412984760472054], [0.36905326906871155, 0.3744408221191457, 0.38551993991408745, 0.3078691277678899, 0.3643462983920934, 0.3568462358862965, 1.0, 0.6433221988971645, 0.6275166806835788, 0.6703796158963512, 0.30202671470954023, 0.37593668555682214], [0.33518200249536245, 0.35689329144884036, 0.36946384802332477, 0.3687411800995861, 0.3250254891173933, 0.33506981044481154, 0.6433221988971645, 1.0, 0.6535063593326079, 0.6326243967547963, 0.30493083951548455, 0.2987640906291211], [0.31462969829303084, 0.3889335355734729, 0.35878384699972793, 0.3435841091256543, 0.3347844093346288, 0.31014079542308937, 0.6275166806835788, 0.6535063593326079, 1.0, 0.5774690213152247, 0.3218558196593757, 0.3340470824039213], [0.3134459938072252, 0.3422068825434753, 0.39427032060655903, 0.37680983353432573, 0.35006399431307356, 0.32243491964840243, 0.6703796158963512, 0.6326243967547963, 0.5774690213152247, 1.0, 0.3206759323956983, 0.39550876468265894], [0.30674421650751715, 0.3348280981976698, 0.3410208084970693, 0.3323665654304721, 0.31963544938411576, 0.29520592093324777, 0.30202671470954023, 0.30493083951548455, 0.3218558196593757, 0.3206759323956983, 1.0, 0.6348775780005986], [0.3099334938038326, 0.4216036253133412, 0.3962648011234295, 0.3539577552509031, 0.36695864815644585, 0.34412984760472054, 0.37593668555682214, 0.2987640906291211, 0.3340470824039213, 0.39550876468265894, 0.6348775780005986, 1.0]] + +[[errors]] +name = "no_iterations" +correlation = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] +maximum = 3 +iterations = 0 +error = "Clustering failed to find a valid solution." + +[[errors]] +name = "no_candidates" +correlation = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] +maximum = 1 +iterations = 3 +error = "Clustering failed to find a valid solution." + +[[errors]] +name = "one_label" +correlation = [[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]] +maximum = 3 +iterations = 2 +error = "Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)" + +[[errors]] +name = "singleton_labels" +correlation = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] +maximum = 4 +iterations = 1 +error = "Number of labels is 4. Valid values are 2 to n_samples - 1 (inclusive)" diff --git a/test/fixtures/constants_python_320.toml b/test/fixtures/constants_python_320.toml new file mode 100644 index 0000000..a8d7c0b --- /dev/null +++ b/test/fixtures/constants_python_320.toml @@ -0,0 +1,31 @@ +BUY_TICKS_NUMBER = "buy_ticks_num" +CLOSE_PRICE = "Close" +CUMULATIVE_BUY_THETA = "Cumulative Buy θ" +CUMULATIVE_BUY_VOLUME = "Cumulative Buy Volume" +CUMULATIVE_DOLLAR = "Cumulative Dollar Value" +CUMULATIVE_SELL_THETA = "Cumulative Sell θ" +CUMULATIVE_SELL_VOLUME = "Cumulative Sell Volume" +CUMULATIVE_THETA = "Cumulative θ" +CUMULATIVE_TICKS = "Cumulative Ticks" +CUMULATIVE_VOLUME = "Cumulative Volume" +DATE_TIME = "Date Time" +EXPECTED_BUY_IMBALANCE = "exp_imbalance_buy" +EXPECTED_BUY_TICKS_PROPORTION = "exp_buy_ticks_proportion" +EXPECTED_IMBALANCE = "expected_imbalance" +EXPECTED_IMBALANCE_WINDOW = "Expected Imbalance Window" +EXPECTED_SELL_IMBALANCE = "exp_imbalance_sell" +EXPECTED_TICKS_NUMBER = "exp_num_ticks" +HIGH_PRICE = "High" +LOW_PRICE = "Low" +N_PREVIOUS_BARS_FOR_EXPECTED_N_TICKS_ESTIMATION = "Window size for E[T]" +N_TICKS_ON_BAR_FORMATION = "Number of ticks while bar is formed." +OPEN_PRICE = "Open" +PREVIOUS_BARS_BUY_TICKS_PROPORTIONS_LIST = "List of previous bars buy ticks proportion" +PREVIOUS_BARS_N_TICKS_LIST = "List of previous bars number of ticks" +PREVIOUS_TICK_IMBALANCES_BUY_LIST = "List of previous (buy) tick imbalances" +PREVIOUS_TICK_IMBALANCES_LIST = "List of previous tick imbalances" +PREVIOUS_TICK_IMBALANCES_SELL_LIST = "List of previous (sell) tick imbalances" +PREVIOUS_TICK_RULE = "Previous tick rule" +THRESHOLD = "Threshold" +TICK_NUMBER = "Tick Number" +TIMESTAMP = "Timestamp" diff --git a/test/fixtures/convex_python_320.toml b/test/fixtures/convex_python_320.toml new file mode 100644 index 0000000..92a5696 --- /dev/null +++ b/test/fixtures/convex_python_320.toml @@ -0,0 +1,1166 @@ +[[clearing]] +case = 1 +external_assets = [2e-13, 0.0] +nominal_liabilities = [1e-12, 1e-12] +relative_liabilities = [[0.0, 0.5], [0.3, 0.0]] +expected = [2.3529411764705886e-13, 1.1764705882352946e-13] +independent = [2.3529411764706144e-13, 1.176470588235332e-13] + +[[clearing]] +case = 2 +external_assets = [0.2, 0.0] +nominal_liabilities = [1.0, 1.0] +relative_liabilities = [[0.0, 0.5], [0.3, 0.0]] +expected = [0.23529411764705885, 0.11764705882352947] +independent = [0.23529411764706143, 0.11764705882353318] + +[[clearing]] +case = 3 +external_assets = [200000000000.0, 0.0] +nominal_liabilities = [1000000000000.0, 1000000000000.0] +relative_liabilities = [[0.0, 0.5], [0.3, 0.0]] +expected = [235294117647.05884, 117647058823.52946] +independent = [235294117647.06143, 117647058823.53319] + +[[clearing]] +case = 4 +external_assets = [0.0, 0.0] +nominal_liabilities = [1e-12, 2e-12] +relative_liabilities = [[0.0, 1.0], [1.0, 0.0]] +expected = [1e-12, 1e-12] +independent = [1e-12, 1e-12] + +[[clearing]] +case = 5 +external_assets = [0.0, 0.0] +nominal_liabilities = [1.0, 2.0] +relative_liabilities = [[0.0, 1.0], [1.0, 0.0]] +expected = [1.0, 1.0] +independent = [1.0, 1.0] + +[[clearing]] +case = 6 +external_assets = [0.0, 0.0] +nominal_liabilities = [1000000000000.0, 2000000000000.0] +relative_liabilities = [[0.0, 1.0], [1.0, 0.0]] +expected = [1000000000000.0, 1000000000000.0] +independent = [1000000000000.0, 1000000000000.0] + +[[clearing]] +case = 7 +external_assets = [2e-12] +nominal_liabilities = [1e-12] +relative_liabilities = [[0.0]] +expected = [1e-12] +independent = [1e-12] + +[[clearing]] +case = 8 +external_assets = [2.0] +nominal_liabilities = [1.0] +relative_liabilities = [[0.0]] +expected = [1.0] +independent = [1.0] + +[[clearing]] +case = 9 +external_assets = [2000000000000.0] +nominal_liabilities = [1000000000000.0] +relative_liabilities = [[0.0]] +expected = [1000000000000.0] +independent = [1000000000000.0] + +[[clearing]] +case = 10 +external_assets = [0.0] +nominal_liabilities = [0.0] +relative_liabilities = [[1.0]] +expected = [0.0] +independent = [0.0] + +[[clearing]] +case = 11 +external_assets = [0.0] +nominal_liabilities = [0.0] +relative_liabilities = [[1.0]] +expected = [0.0] +independent = [0.0] + +[[clearing]] +case = 12 +external_assets = [0.0] +nominal_liabilities = [0.0] +relative_liabilities = [[1.0]] +expected = [0.0] +independent = [0.0] + +[[clearing]] +case = 13 +external_assets = [1.0569939786649216e-13, 5.825894708646947e-14] +nominal_liabilities = [1.4050760126833504e-12, 1.7166175267408266e-12] +relative_liabilities = [[0.3585495823618605, 0.3081170843048062], [0.15971576028645, 0.5069509063802167]] +expected = [2.2998923082627514e-13, 2.618858040326096e-13] +independent = [2.299892308262825e-13, 2.618858040326239e-13] + +[[clearing]] +case = 14 +external_assets = [0.10569939786649216, 0.058258947086469476] +nominal_liabilities = [1.4050760126833504, 1.7166175267408266] +relative_liabilities = [[0.3585495823618605, 0.3081170843048062], [0.15971576028645, 0.5069509063802167]] +expected = [0.22998923082627512, 0.2618858040326096] +independent = [0.2299892308262825, 0.2618858040326239] + +[[clearing]] +case = 15 +external_assets = [105699397866.49216, 58258947086.469475] +nominal_liabilities = [1405076012683.3503, 1716617526740.8267] +relative_liabilities = [[0.3585495823618605, 0.3081170843048062], [0.15971576028645, 0.5069509063802167]] +expected = [229989230826.27515, 261885804032.60962] +independent = [229989230826.2825, 261885804032.6239] + +[[clearing]] +case = 16 +external_assets = [2.5186035522104654e-13, 5.612320745486998e-14] +nominal_liabilities = [1.9395235461764237e-12, 1.6542999497849396e-12] +relative_liabilities = [[0.2512655373333209, 0.4154011293333458], [0.1941344444648326, 0.4725322222018341]] +expected = [4.573605147996097e-13, 4.6659017322814e-13] +independent = [4.573605147996167e-13, 4.665901732281548e-13] + +[[clearing]] +case = 17 +external_assets = [0.25186035522104655, 0.05612320745486998] +nominal_liabilities = [1.9395235461764235, 1.6542999497849395] +relative_liabilities = [[0.2512655373333209, 0.4154011293333458], [0.1941344444648326, 0.4725322222018341]] +expected = [0.4573605147996097, 0.46659017322813995] +independent = [0.4573605147996167, 0.4665901732281549] + +[[clearing]] +case = 18 +external_assets = [251860355221.04654, 56123207454.86998] +nominal_liabilities = [1939523546176.4236, 1654299949784.9395] +relative_liabilities = [[0.2512655373333209, 0.4154011293333458], [0.1941344444648326, 0.4725322222018341]] +expected = [457360514799.60974, 466590173228.13995] +independent = [457360514799.6167, 466590173228.1549] + +[[clearing]] +case = 19 +external_assets = [9.18117108875633e-14, 8.991693471022472e-14] +nominal_liabilities = [1.9044601649964774e-12, 1.5968958784841353e-12] +relative_liabilities = [[0.4941112676547628, 0.17255539901190384], [0.3111811506136888, 0.35548551605297785]] +expected = [3.2000114488428384e-13, 2.2518479190908018e-13] +independent = [3.2000114488429944e-13, 2.251847919090888e-13] + +[[clearing]] +case = 20 +external_assets = [0.0918117108875633, 0.08991693471022472] +nominal_liabilities = [1.9044601649964774, 1.5968958784841354] +relative_liabilities = [[0.4941112676547628, 0.17255539901190384], [0.3111811506136888, 0.35548551605297785]] +expected = [0.3200011448842839, 0.2251847919090802] +independent = [0.3200011448842994, 0.2251847919090888] + +[[clearing]] +case = 21 +external_assets = [91811710887.56331, 89916934710.22472] +nominal_liabilities = [1904460164996.4773, 1596895878484.1355] +relative_liabilities = [[0.4941112676547628, 0.17255539901190384], [0.3111811506136888, 0.35548551605297785]] +expected = [320001144884.2839, 225184791909.08017] +independent = [320001144884.29944, 225184791909.0888] + +[[clearing]] +case = 22 +external_assets = [8.757332338613003e-14, 1.6248858595658507e-13, 2.882373275648816e-13] +nominal_liabilities = [7.805100012735597e-13, 1.8183762603763721e-12, 7.180689537660796e-13] +relative_liabilities = [[0.0049894157271977435, 0.09084295671000951, 0.5708342942294594], [0.2992116366557325, 0.1676698944527125, 0.1997851355582217], [0.20642431633228936, 0.14868228134467773, 0.3115600689896996]] +expected = [3.456036815746775e-13, 3.612129063284725e-13, 7.180689537660796e-13] +independent = [3.456036815746797e-13, 3.6121290632847446e-13, 7.180689537660796e-13] + +[[clearing]] +case = 23 +external_assets = [0.08757332338613004, 0.16248858595658508, 0.28823732756488163] +nominal_liabilities = [0.7805100012735597, 1.8183762603763722, 0.7180689537660796] +relative_liabilities = [[0.0049894157271977435, 0.09084295671000951, 0.5708342942294594], [0.2992116366557325, 0.1676698944527125, 0.1997851355582217], [0.20642431633228936, 0.14868228134467773, 0.3115600689896996]] +expected = [0.3456036815746775, 0.3612129063284725, 0.7180689537660796] +independent = [0.34560368157467974, 0.36121290632847447, 0.7180689537660796] + +[[clearing]] +case = 24 +external_assets = [87573323386.13004, 162488585956.58508, 288237327564.88165] +nominal_liabilities = [780510001273.5597, 1818376260376.3723, 718068953766.0796] +relative_liabilities = [[0.0049894157271977435, 0.09084295671000951, 0.5708342942294594], [0.2992116366557325, 0.1676698944527125, 0.1997851355582217], [0.20642431633228936, 0.14868228134467773, 0.3115600689896996]] +expected = [345603681574.67755, 361212906328.47253, 718068953766.0796] +independent = [345603681574.67975, 361212906328.4745, 718068953766.0796] + +[[clearing]] +case = 25 +external_assets = [1.280769590911108e-13, 1.6829134079513799e-13, 5.965824156987231e-15] +nominal_liabilities = [1.0364163066775898e-12, 6.9400291623126e-13, 1.8767181106965756e-12] +relative_liabilities = [[0.0670359739743398, 0.06914749886836444, 0.5304831938239624], [0.2399074227101092, 0.2858477797843124, 0.14091146417224512], [0.08753295091177331, 0.16639016963366351, 0.41274354612122993]] +expected = [2.53254614848934e-13, 3.345443304220558e-13, 3.192034268587186e-13] +independent = [2.5325461484893994e-13, 3.3454433042206434e-13, 3.19203426858736e-13] + +[[clearing]] +case = 26 +external_assets = [0.1280769590911108, 0.168291340795138, 0.005965824156987231] +nominal_liabilities = [1.0364163066775898, 0.69400291623126, 1.8767181106965758] +relative_liabilities = [[0.0670359739743398, 0.06914749886836444, 0.5304831938239624], [0.2399074227101092, 0.2858477797843124, 0.14091146417224512], [0.08753295091177331, 0.16639016963366351, 0.41274354612122993]] +expected = [0.253254614848934, 0.33454433042205584, 0.3192034268587186] +independent = [0.25325461484893996, 0.33454433042206433, 0.319203426858736] + +[[clearing]] +case = 27 +external_assets = [128076959091.11081, 168291340795.138, 5965824156.987231] +nominal_liabilities = [1036416306677.5897, 694002916231.26, 1876718110696.5757] +relative_liabilities = [[0.0670359739743398, 0.06914749886836444, 0.5304831938239624], [0.2399074227101092, 0.2858477797843124, 0.14091146417224512], [0.08753295091177331, 0.16639016963366351, 0.41274354612122993]] +expected = [253254614848.93405, 334544330422.0558, 319203426858.71893] +independent = [253254614848.93997, 334544330422.06433, 319203426858.73596] + +[[clearing]] +case = 28 +external_assets = [9.652080221391885e-14, 2.5650063214157693e-13, 1.3692468798575597e-13] +nominal_liabilities = [7.201962200923417e-13, 1.4833667371679095e-12, 1.4399662102526296e-12] +relative_liabilities = [[0.2611835186547719, 0.1453709571520462, 0.26011219085984855], [0.3063283261655537, 0.35605482921460946, 0.004283511286503534], [0.13993365056479756, 0.29160454161331684, 0.23512847448855229]] +expected = [4.686918212916515e-13, 6.590496284662185e-13, 3.4209691726588507e-13] +independent = [4.686918212916693e-13, 6.590496284662371e-13, 3.4209691726589603e-13] + +[[clearing]] +case = 29 +external_assets = [0.09652080221391886, 0.2565006321415769, 0.13692468798575597] +nominal_liabilities = [0.7201962200923417, 1.4833667371679096, 1.4399662102526296] +relative_liabilities = [[0.2611835186547719, 0.1453709571520462, 0.26011219085984855], [0.3063283261655537, 0.35605482921460946, 0.004283511286503534], [0.13993365056479756, 0.29160454161331684, 0.23512847448855229]] +expected = [0.46869182129165143, 0.6590496284662182, 0.34209691726588504] +independent = [0.4686918212916693, 0.6590496284662372, 0.34209691726589603] + +[[clearing]] +case = 30 +external_assets = [96520802213.91885, 256500632141.57693, 136924687985.75597] +nominal_liabilities = [720196220092.3418, 1483366737167.9097, 1439966210252.6296] +relative_liabilities = [[0.2611835186547719, 0.1453709571520462, 0.26011219085984855], [0.3063283261655537, 0.35605482921460946, 0.004283511286503534], [0.13993365056479756, 0.29160454161331684, 0.23512847448855229]] +expected = [468691821291.6515, 659049628466.2183, 342096917265.8851] +independent = [468691821291.6693, 659049628466.2372, 342096917265.89606] + +[[clearing]] +case = 31 +external_assets = [1.6319191283653826e-13, 1.8235044327580047e-13, 1.4740171928818892e-13, 2.9723950895541367e-13] +nominal_liabilities = [1.9640121753659748e-12, 5.132559279925561e-13, 1.0348806832778378e-12, 1.0242952059448078e-12] +relative_liabilities = [[0.2589968610248899, 0.21038501141913168, 0.03185446368936585, 0.16543033053327924], [0.06165111475691512, 0.24183057403773794, 0.10652704173764335, 0.2566579361343702], [0.2304635806173113, 0.10528067793758675, 0.14248144985847835, 0.1884409582532903], [0.34197254460741044, 0.10821451080584303, 0.21622180413668615, 0.00025780711672705845]] +expected = [6.788488655682763e-13, 5.132559279925561e-13, 4.172163857534604e-13, 6.200534180701017e-13] +independent = [6.788488655682822e-13, 5.132559279925561e-13, 4.172163857534624e-13, 6.200534180701044e-13] + +[[clearing]] +case = 32 +external_assets = [0.16319191283653828, 0.18235044327580047, 0.14740171928818893, 0.2972395089554137] +nominal_liabilities = [1.964012175365975, 0.5132559279925561, 1.0348806832778379, 1.0242952059448078] +relative_liabilities = [[0.2589968610248899, 0.21038501141913168, 0.03185446368936585, 0.16543033053327924], [0.06165111475691512, 0.24183057403773794, 0.10652704173764335, 0.2566579361343702], [0.2304635806173113, 0.10528067793758675, 0.14248144985847835, 0.1884409582532903], [0.34197254460741044, 0.10821451080584303, 0.21622180413668615, 0.00025780711672705845]] +expected = [0.6788488655682763, 0.5132559279925561, 0.41721638575346043, 0.6200534180701017] +independent = [0.6788488655682823, 0.5132559279925561, 0.4172163857534624, 0.6200534180701044] + +[[clearing]] +case = 33 +external_assets = [163191912836.53827, 182350443275.80048, 147401719288.18893, 297239508955.4137] +nominal_liabilities = [1964012175365.9749, 513255927992.55615, 1034880683277.8379, 1024295205944.8079] +relative_liabilities = [[0.2589968610248899, 0.21038501141913168, 0.03185446368936585, 0.16543033053327924], [0.06165111475691512, 0.24183057403773794, 0.10652704173764335, 0.2566579361343702], [0.2304635806173113, 0.10528067793758675, 0.14248144985847835, 0.1884409582532903], [0.34197254460741044, 0.10821451080584303, 0.21622180413668615, 0.00025780711672705845]] +expected = [678848865568.2762, 513255927992.55615, 417216385753.4604, 620053418070.1017] +independent = [678848865568.2822, 513255927992.55615, 417216385753.4624, 620053418070.1044] + +[[clearing]] +case = 34 +external_assets = [2.525359797074681e-13, 1.4018110147688078e-14, 1.1314528193378084e-13, 2.1436943592323167e-13] +nominal_liabilities = [1.425717705917918e-12, 1.8224673455958276e-12, 1.8578561029955563e-12, 1.6122844300903901e-12] +relative_liabilities = [[0.11475075275019617, 0.21096332431765263, 0.14762155338140415, 0.19333103621741363], [0.07068108856392617, 0.3392256997795557, 0.03637301553970332, 0.2203868627834815], [0.2291824424676213, 0.014085610515635921, 0.2696245004174637, 0.15377411326594576], [0.14527009317940398, 0.15858726044526278, 0.13969123871237607, 0.22311807432962394]] +expected = [5.037316266667077e-13, 3.268935216783901e-13, 3.8195299587693204e-13, 5.696282789144762e-13] +independent = [5.037316266667199e-13, 3.268935216784077e-13, 3.819529958769441e-13, 5.696282789144948e-13] + +[[clearing]] +case = 35 +external_assets = [0.2525359797074681, 0.014018110147688079, 0.11314528193378084, 0.21436943592323168] +nominal_liabilities = [1.425717705917918, 1.8224673455958276, 1.8578561029955563, 1.6122844300903902] +relative_liabilities = [[0.11475075275019617, 0.21096332431765263, 0.14762155338140415, 0.19333103621741363], [0.07068108856392617, 0.3392256997795557, 0.03637301553970332, 0.2203868627834815], [0.2291824424676213, 0.014085610515635921, 0.2696245004174637, 0.15377411326594576], [0.14527009317940398, 0.15858726044526278, 0.13969123871237607, 0.22311807432962394]] +expected = [0.5037316266667077, 0.3268935216783901, 0.38195299587693204, 0.5696282789144762] +independent = [0.5037316266667199, 0.3268935216784077, 0.3819529958769441, 0.5696282789144949] + +[[clearing]] +case = 36 +external_assets = [252535979707.4681, 14018110147.68808, 113145281933.78084, 214369435923.2317] +nominal_liabilities = [1425717705917.918, 1822467345595.8276, 1857856102995.5562, 1612284430090.3901] +relative_liabilities = [[0.11475075275019617, 0.21096332431765263, 0.14762155338140415, 0.19333103621741363], [0.07068108856392617, 0.3392256997795557, 0.03637301553970332, 0.2203868627834815], [0.2291824424676213, 0.014085610515635921, 0.2696245004174637, 0.15377411326594576], [0.14527009317940398, 0.15858726044526278, 0.13969123871237607, 0.22311807432962394]] +expected = [503731626666.70776, 326893521678.3907, 381952995876.9318, 569628278914.4766] +independent = [503731626666.7199, 326893521678.40765, 381952995876.9441, 569628278914.4949] + +[[clearing]] +case = 37 +external_assets = [1.8594436780798844e-13, 1.0553828538704025e-13, 4.143222298299931e-15, 1.5366828651532644e-13] +nominal_liabilities = [1.2272902768765962e-12, 5.706053377502077e-13, 1.2672856191732556e-12, 9.999463966652671e-13] +relative_liabilities = [[0.13553976205698834, 0.07084606655351508, 0.14522088735454403, 0.3150599507016193], [0.20399163782775326, 0.2939838240428043, 0.06648466319212777, 0.10220654160398127], [0.17002032970056355, 0.19839424119038027, 0.2664562047933037, 0.0317958909824192], [0.1789557078257162, 0.12446361297095401, 0.08847048470739052, 0.2747768611626059]] +expected = [4.155397442364234e-13, 3.174204293274756e-13, 1.7030645511057477e-13, 4.4461585735149146e-13] +independent = [4.155397442364409e-13, 3.174204293274926e-13, 1.7030645511058832e-13, 4.4461585735151125e-13] + +[[clearing]] +case = 38 +external_assets = [0.18594436780798845, 0.10553828538704026, 0.004143222298299931, 0.15366828651532644] +nominal_liabilities = [1.2272902768765963, 0.5706053377502077, 1.2672856191732556, 0.9999463966652671] +relative_liabilities = [[0.13553976205698834, 0.07084606655351508, 0.14522088735454403, 0.3150599507016193], [0.20399163782775326, 0.2939838240428043, 0.06648466319212777, 0.10220654160398127], [0.17002032970056355, 0.19839424119038027, 0.2664562047933037, 0.0317958909824192], [0.1789557078257162, 0.12446361297095401, 0.08847048470739052, 0.2747768611626059]] +expected = [0.4155397442364234, 0.3174204293274755, 0.17030645511057493, 0.4446158573514915] +independent = [0.4155397442364409, 0.3174204293274926, 0.17030645511058834, 0.44461585735151127] + +[[clearing]] +case = 39 +external_assets = [185944367807.98846, 105538285387.04025, 4143222298.299931, 153668286515.32645] +nominal_liabilities = [1227290276876.5962, 570605337750.2078, 1267285619173.2556, 999946396665.2671] +relative_liabilities = [[0.13553976205698834, 0.07084606655351508, 0.14522088735454403, 0.3150599507016193], [0.20399163782775326, 0.2939838240428043, 0.06648466319212777, 0.10220654160398127], [0.17002032970056355, 0.19839424119038027, 0.2664562047933037, 0.0317958909824192], [0.1789557078257162, 0.12446361297095401, 0.08847048470739052, 0.2747768611626059]] +expected = [415539744236.42346, 317420429327.4755, 170306455110.57495, 444615857351.4915] +independent = [415539744236.4409, 317420429327.4926, 170306455110.58835, 444615857351.5113] + +[[execution]] +case = 1 +inventory = 120.0 +horizon = 2.0 +intervals = 1 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.0 +expected_times = [0.0, 2.0] +expected_inventory = [120.0, 0.0] +expected_trades = [120.0] +expected_expected_cost = 288.0 +expected_cost_variance = 0.0 +independent_inventory = [120.0, 0.0] + +[[execution]] +case = 2 +inventory = 120.0 +horizon = 2.0 +intervals = 1 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1e-12 +expected_times = [0.0, 2.0] +expected_inventory = [120.0, 0.0] +expected_trades = [120.0] +expected_expected_cost = 288.0 +expected_cost_variance = 0.0 +independent_inventory = [120.0, 0.0] + +[[execution]] +case = 3 +inventory = 120.0 +horizon = 2.0 +intervals = 1 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.1 +expected_times = [0.0, 2.0] +expected_inventory = [120.0, 0.0] +expected_trades = [120.0] +expected_expected_cost = 288.0 +expected_cost_variance = 0.0 +independent_inventory = [120.0, 0.0] + +[[execution]] +case = 4 +inventory = 120.0 +horizon = 2.0 +intervals = 1 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 100.0 +expected_times = [0.0, 2.0] +expected_inventory = [120.0, 0.0] +expected_trades = [120.0] +expected_expected_cost = 288.0 +expected_cost_variance = 0.0 +independent_inventory = [120.0, 0.0] + +[[execution]] +case = 5 +inventory = 120.0 +horizon = 2.0 +intervals = 1 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1000000000000.0 +expected_times = [0.0, 2.0] +expected_inventory = [120.0, 0.0] +expected_trades = [120.0] +expected_expected_cost = 288.0 +expected_cost_variance = 0.0 +independent_inventory = [120.0, 0.0] + +[[execution]] +case = 6 +inventory = 120.0 +horizon = 2.0 +intervals = 2 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.0 +expected_times = [0.0, 1.0, 2.0] +expected_inventory = [120.0, 60.0, 0.0] +expected_trades = [60.0, 60.0] +expected_expected_cost = 324.0 +expected_cost_variance = 324.0 +independent_inventory = [120.0, 60.0, 0.0] + +[[execution]] +case = 7 +inventory = 120.0 +horizon = 2.0 +intervals = 2 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1e-12 +expected_times = [0.0, 1.0, 2.0] +expected_inventory = [120.0, 59.99999999992286, 0.0] +expected_trades = [60.00000000007714, 59.99999999992286] +expected_expected_cost = 324.0 +expected_cost_variance = 323.9999999991669 +independent_inventory = [120.0, 59.99999999992286, 0.0] + +[[execution]] +case = 8 +inventory = 120.0 +horizon = 2.0 +intervals = 2 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.1 +expected_times = [0.0, 1.0, 2.0] +expected_inventory = [120.0, 53.16455696202533, 0.0] +expected_trades = [66.83544303797467, 53.16455696202533] +expected_expected_cost = 327.27062970677775 +expected_cost_variance = 254.3823105271592 +independent_inventory = [120.0, 53.164556962025316, 0.0] + +[[execution]] +case = 9 +inventory = 120.0 +horizon = 2.0 +intervals = 2 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 100.0 +expected_times = [0.0, 1.0, 2.0] +expected_inventory = [120.0, 0.4630650496141119, 0.0] +expected_trades = [119.53693495038588, 0.4630650496141119] +expected_expected_cost = 572.1252636300536 +expected_cost_variance = 0.019298631615670792 +independent_inventory = [120.0, 0.4630650496141125, 0.0] + +[[execution]] +case = 10 +inventory = 120.0 +horizon = 2.0 +intervals = 2 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1000000000000.0 +expected_times = [0.0, 1.0, 2.0] +expected_inventory = [120.0, 4.666666666663046e-11, 0.0] +expected_trades = [119.99999999995333, 4.666666666663046e-11] +expected_expected_cost = 575.999999999608 +expected_cost_variance = 1.9599999999969584e-22 +independent_inventory = [120.0, 4.6666666666630375e-11, 0.0] + +[[execution]] +case = 11 +inventory = 120.0 +horizon = 2.0 +intervals = 5 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.0 +expected_times = [0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0] +expected_inventory = [120.0, 96.0, 72.0, 48.0, 23.999999999999993, 0.0] +expected_trades = [24.0, 24.0, 24.0, 24.000000000000007, 23.999999999999993] +expected_expected_cost = 345.59999999999997 +expected_cost_variance = 622.0799999999999 +independent_inventory = [120.0, 96.0, 72.0, 47.99999999999999, 23.999999999999996, 0.0] + +[[execution]] +case = 12 +inventory = 120.0 +horizon = 2.0 +intervals = 5 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1e-12 +expected_times = [0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0] +expected_inventory = [120.0, 95.99999999994546, 71.99999999992727, 47.99999999993634, 23.999999999963627, 0.0] +expected_trades = [24.00000000005454, 24.00000000001819, 23.999999999990926, 23.999999999972715, 23.999999999963627] +expected_expected_cost = 345.59999999999997 +expected_cost_variance = 622.0799999989631 +independent_inventory = [120.0, 95.99999999994547, 71.99999999992728, 47.99999999993637, 23.99999999996364, 0.0] + +[[execution]] +case = 13 +inventory = 120.0 +horizon = 2.0 +intervals = 5 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.1 +expected_times = [0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0] +expected_inventory = [120.0, 90.95883219029184, 65.36452538989998, 42.24719007796751, 20.730800916358028, 0.0] +expected_trades = [29.041167809708156, 25.594306800391863, 23.11733531193247, 21.51638916160948, 20.730800916358028] +expected_expected_cost = 349.93107559832293 +expected_cost_variance = 531.382374323501 +independent_inventory = [120.0, 90.95883219029184, 65.36452538990001, 42.24719007796753, 20.730800916358035, 0.0] + +[[execution]] +case = 14 +inventory = 120.0 +horizon = 2.0 +intervals = 5 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 100.0 +expected_times = [0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0] +expected_inventory = [120.0, 3.009807826735718, 0.07549119293022521, 0.0018934491122148483, 4.746112550406616e-05, 0.0] +expected_trades = [116.99019217326428, 2.934316633805493, 0.07359774381801036, 0.0018459879867107822, 4.746112550406616e-05] +expected_expected_cost = 1373.0554663938422 +expected_cost_variance = 0.32632724381370165 +independent_inventory = [120.0, 3.009807826735717, 0.07549119293022519, 0.001893449112214849, 4.746112550406612e-05, 0.0] + +[[execution]] +case = 15 +inventory = 120.0 +horizon = 2.0 +intervals = 5 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1000000000000.0 +expected_times = [0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0] +expected_inventory = [120.0, 3.166666666649946e-10, 8.3564814813932335e-22, 2.2051826131338087e-33, 5.819231895739007e-45, 0.0] +expected_trades = [119.99999999968334, 3.1666666666415896e-10, 8.356481481371181e-22, 2.2051826131279894e-33, 5.819231895739007e-45] +expected_expected_cost = 1439.99999999278 +expected_cost_variance = 3.609999999961877e-21 +independent_inventory = [120.0, 3.1666666666499534e-10, 8.356481481393272e-22, 2.2051826131338084e-33, 5.819231895739059e-45, 0.0] + +[[execution]] +case = 16 +inventory = 120.0 +horizon = 2.0 +intervals = 20 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.0 +expected_times = [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0] +expected_inventory = [120.0, 114.0, 108.0, 102.0, 96.0, 90.0, 84.0, 78.0, 72.0, 66.0, 60.0, 53.99999999999999, 48.0, 42.0, 36.00000000000001, 30.0, 23.999999999999993, 18.000000000000004, 11.999999999999996, 6.000000000000005, 0.0] +expected_trades = [6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.000000000000007, 5.999999999999993, 6.0, 5.999999999999993, 6.000000000000007, 6.000000000000007, 5.999999999999989, 6.000000000000007, 5.999999999999991, 6.000000000000005] +expected_expected_cost = 356.4 +expected_cost_variance = 800.28 +independent_inventory = [120.0, 114.0, 108.0, 102.00000000000001, 96.00000000000003, 90.00000000000003, 84.00000000000003, 78.00000000000003, 72.00000000000001, 66.00000000000001, 60.00000000000002, 54.00000000000003, 48.00000000000002, 42.00000000000002, 36.00000000000002, 30.000000000000014, 24.00000000000001, 18.000000000000007, 12.000000000000004, 6.000000000000002, 0.0] + +[[execution]] +case = 17 +inventory = 120.0 +horizon = 2.0 +intervals = 20 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1e-12 +expected_times = [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0] +expected_inventory = [120.0, 113.99999999998312, 107.99999999996882, 101.99999999995701, 95.99999999994749, 89.99999999994019, 83.99999999993491, 77.99999999993156, 71.99999999993, 65.99999999993007, 59.99999999993164, 53.99999999993458, 47.99999999993875, 41.99999999994401, 35.999999999950234, 29.99999999995728, 23.999999999965, 17.999999999973273, 11.999999999981954, 5.999999999990909, 0.0] +expected_trades = [6.0000000000168825, 6.000000000014296, 6.000000000011809, 6.000000000009521, 6.000000000007304, 6.000000000005272, 6.000000000003354, 6.000000000001563, 5.999999999999929, 5.99999999999843, 5.999999999997058, 5.999999999995829, 5.999999999994742, 5.999999999993776, 5.999999999992955, 5.99999999999228, 5.999999999991726, 5.999999999991319, 5.999999999991045, 5.999999999990909] +expected_expected_cost = 356.4 +expected_cost_variance = 800.2799999989533 +independent_inventory = [120.0, 113.99999999998319, 107.99999999996896, 101.99999999995721, 95.99999999994775, 89.99999999994044, 83.9999999999352, 77.99999999993186, 71.99999999993028, 65.99999999993035, 59.99999999993192, 53.99999999993486, 47.99999999993902, 41.99999999994426, 35.99999999995045, 29.999999999957456, 23.999999999965148, 17.999999999973383, 11.999999999982029, 5.999999999990947, 0.0] + +[[execution]] +case = 18 +inventory = 120.0 +horizon = 2.0 +intervals = 20 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 0.1 +expected_times = [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0] +expected_inventory = [120.0, 112.41324187917515, 105.08261519554337, 97.9914172553951, 91.12349089886672, 84.46318768615853, 77.99533224286186, 71.70518768315651, 65.57842203209633, 59.60107557047638, 53.75952902787775, 48.04047255141859, 42.43087537950697, 36.917956151498025, 31.489153785606415, 26.132098858719985, 20.834585422904055, 15.584543194380823, 10.370010051616937, 5.179104779856731, 0.0] +expected_trades = [7.586758120824854, 7.3306266836317775, 7.0911979401482625, 6.867926356528386, 6.660303212708186, 6.467855443296671, 6.290144559705354, 6.1267656510601824, 5.977346461619945, 5.8415465425986355, 5.7190564764591585, 5.609597171911616, 5.512919228008947, 5.42880236589161, 5.3570549268864305, 5.2975134358159295, 5.250042228523233, 5.2145331427638855, 5.190905271760206, 5.179104779856731] +expected_expected_cost = 360.83299130881977 +expected_cost_variance = 707.793436182778 +independent_inventory = [120.0, 112.41324187917519, 105.08261519554344, 97.99141725539519, 91.12349089886682, 84.46318768615866, 77.99533224286199, 71.70518768315664, 65.57842203209646, 59.60107557047649, 53.75952902787785, 48.040472551418695, 42.430875379507064, 36.9179561514981, 31.489153785606483, 26.132098858720045, 20.834585422904105, 15.584543194380856, 10.370010051616958, 5.179104779856743, 0.0] + +[[execution]] +case = 19 +inventory = 120.0 +horizon = 2.0 +intervals = 20 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 100.0 +expected_times = [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0] +expected_inventory = [120.0, 29.773982011344625, 7.387416706765611, 1.8329401011462179, 0.45478271332833053, 0.11283910271423506, 0.027997245119915897, 0.006946578938063178, 0.0017235609695189819, 0.0004276439441825961, 0.00010610552584453111, 2.632653348134737e-05, 6.5320477845245844e-06, 1.620708938517274e-06, 4.021246359670458e-07, 9.977368118983402e-08, 2.4755164566674615e-08, 6.140820373913132e-09, 1.5182188052575242e-09, 3.5484995744184717e-10, 0.0] +expected_trades = [90.22601798865537, 22.386565304579015, 5.554476605619394, 1.3781573878178874, 0.3419436106140955, 0.08484185759431916, 0.02105066618185272, 0.005223017968544196, 0.0012959170253363857, 0.000321538418338065, 7.977899236318374e-05, 1.9794485696822785e-05, 4.91133884600731e-06, 1.218584302550228e-06, 3.023509547772118e-07, 7.50185166231594e-08, 1.8614344192761482e-08, 4.6226015686556085e-09, 1.163368847815677e-09, 3.5484995744184717e-10] +expected_expected_cost = 3498.5336570980594 +expected_cost_variance = 8.501795955642047 +independent_inventory = [120.0, 29.77398201134462, 7.387416706765609, 1.8329401011462163, 0.4547827133283303, 0.11283910271423504, 0.02799724511991585, 0.00694657893806317, 0.0017235609695189806, 0.0004276439441825958, 0.00010610552584453107, 2.6326533481347312e-05, 6.532047784524562e-06, 1.620708938517269e-06, 4.0212463596704474e-07, 9.977368118983378e-08, 2.4755164566674566e-08, 6.140820373913121e-09, 1.518218805257522e-09, 3.5484995744184676e-10, 0.0] + +[[execution]] +case = 20 +inventory = 120.0 +horizon = 2.0 +intervals = 20 +volatility = 0.3 +temporary_impact = 0.04 +permanent_impact = 0.01 +risk_aversion = 1000000000000.0 +expected_times = [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0, 1.1, 1.2000000000000002, 1.3, 1.4000000000000001, 1.5, 1.6, 1.7000000000000002, 1.8, 1.9000000000000001, 2.0] +expected_inventory = [120.0, 5.266666666204368e-09, 2.311481481075686e-19, 1.0144835388274979e-29, 4.4524555311298726e-40, 1.954133260713254e-50, 8.576473754599696e-61, 3.764119036410636e-71, 1.6520300213907496e-81, 7.250576204356215e-92, 3.1821973338548454e-102, 1.3966310519581688e-112, 6.129658505278117e-123, 2.6902390104137434e-133, 1.1807160100224117e-143, 5.182031376865513e-154, 2.2743359929802673e-164, 9.981807968315165e-175, 4.380904607931381e-185, 1.9227303555344776e-195, 0.0] +expected_trades = [119.99999999473333, 5.26666666597322e-09, 2.3114814809742376e-19, 1.0144835387829733e-29, 4.452455530934459e-40, 1.9541332606274892e-50, 8.576473754223284e-61, 3.7641190362454335e-71, 1.6520300213182438e-81, 7.250576204037995e-92, 3.1821973337151825e-102, 1.3966310518968722e-112, 6.129658505009093e-123, 2.6902390102956717e-133, 1.1807160099705914e-143, 5.18203137663808e-154, 2.2743359928804492e-164, 9.981807967877074e-175, 4.380904607739108e-185, 1.9227303555344776e-195] +expected_expected_cost = 5759.999999500719 +expected_cost_variance = 2.4963999995617404e-19 +independent_inventory = [120.0, 5.2666666662043695e-09, 2.3114814810756874e-19, 1.0144835388275024e-29, 4.4524555311298775e-40, 1.9541332607132497e-50, 8.57647375459977e-61, 3.764119036410603e-71, 1.652030021390753e-81, 7.250576204356308e-92, 3.1821973338548303e-102, 1.3966310519581376e-112, 6.129658505278221e-123, 2.6902390104137422e-133, 1.1807160100223906e-143, 5.182031376865624e-154, 2.2743359929802762e-164, 9.981807968315029e-175, 4.380904607931493e-185, 1.922730355534493e-195, 0.0] + +[[spo]] +case = 1 +predicted_cost = [0.0, 0.0, 0.0] +true_cost = [0.0, 0.0, 0.0] +vertices = [[-2.0, 2.0, 0.0], [-2.0, 1.0, -3.0], [1.0, 2.0, -2.0], [3.0, 3.0, 0.0], [-1.0, 3.0, 2.0]] +expected_loss = 0.0 +expected_subgradient = [0.0, 0.0, 0.0] +expected_true_vertex = 0 +expected_transformed_vertex = 0 + +[[spo]] +case = 2 +predicted_cost = [-2.0, 2.0, 0.0] +true_cost = [-2.0, -2.0, -1.0] +vertices = [[-3.0, 1.0, -2.0], [-2.0, 2.0, 0.0], [3.0, 1.0, 0.0], [2.0, 3.0, 3.0], [1.0, -3.0, -2.0]] +expected_loss = 39.0 +expected_subgradient = [2.0, 12.0, 10.0] +expected_true_vertex = 3 +expected_transformed_vertex = 4 + +[[spo]] +case = 3 +predicted_cost = [-2.0, 0.0, 1.0] +true_cost = [-1.0, -1.0, 2.0] +vertices = [[-2.0, 0.0, -1.0], [3.0, -1.0, -1.0], [-1.0, -2.0, 3.0], [-3.0, 1.0, 0.0], [0.0, -1.0, 2.0]] +expected_loss = 0.0 +expected_subgradient = [0.0, 0.0, 0.0] +expected_true_vertex = 1 +expected_transformed_vertex = 1 + +[[spo]] +case = 4 +predicted_cost = [1.0, 1.0, -1.0] +true_cost = [2.0, -2.0, -1.0] +vertices = [[-2.0, 3.0, -3.0], [-3.0, 0.0, 3.0], [-1.0, -1.0, 2.0], [-3.0, -2.0, 1.0], [-3.0, -3.0, -1.0]] +expected_loss = 8.0 +expected_subgradient = [0.0, 6.0, 8.0] +expected_true_vertex = 1 +expected_transformed_vertex = 4 + +[[spo]] +case = 5 +predicted_cost = [-2.0, -1.0, 0.0] +true_cost = [2.0, 1.0, 2.0] +vertices = [[-3.0, -1.0, -2.0], [3.0, -3.0, 2.0], [-2.0, -1.0, -1.0], [-1.0, -1.0, 1.0], [-3.0, -3.0, -2.0]] +expected_loss = 44.0 +expected_subgradient = [-12.0, 0.0, -8.0] +expected_true_vertex = 4 +expected_transformed_vertex = 1 + +[[spo]] +case = 6 +predicted_cost = [-1.0, -2.0, -1.0] +true_cost = [1.0, 2.0, -2.0] +vertices = [[2.0, -3.0, -1.0], [-3.0, 0.0, -1.0], [-3.0, -3.0, -2.0], [0.0, 3.0, -1.0], [-3.0, -3.0, -1.0]] +expected_loss = 45.0 +expected_subgradient = [-6.0, -12.0, 0.0] +expected_true_vertex = 4 +expected_transformed_vertex = 3 + +[[spo]] +case = 7 +predicted_cost = [0.0, -2.0, -2.0] +true_cost = [0.0, -2.0, -2.0] +vertices = [[-3.0, 3.0, 2.0], [0.0, -1.0, 1.0], [0.0, 1.0, -3.0], [-2.0, 0.0, -2.0], [3.0, 1.0, 3.0]] +expected_loss = 0.0 +expected_subgradient = [0.0, 0.0, 0.0] +expected_true_vertex = 0 +expected_transformed_vertex = 0 + +[[spo]] +case = 8 +predicted_cost = [-1.0, -2.0, 2.0] +true_cost = [-1.0, 1.0, -2.0] +vertices = [[0.0, 1.0, -2.0], [-1.0, -1.0, -3.0], [-3.0, -1.0, 0.0], [2.0, 0.0, -2.0], [3.0, 0.0, 1.0]] +expected_loss = 20.0 +expected_subgradient = [6.0, -2.0, 6.0] +expected_true_vertex = 4 +expected_transformed_vertex = 0 + +[[spo]] +case = 9 +predicted_cost = [2.0, -1.0, -2.0] +true_cost = [2.0, -1.0, 0.0] +vertices = [[1.0, 3.0, -3.0], [-2.0, 1.0, -1.0], [3.0, -2.0, 0.0], [-3.0, -3.0, 2.0], [0.0, -2.0, 1.0]] +expected_loss = 10.0 +expected_subgradient = [2.0, 8.0, -6.0] +expected_true_vertex = 1 +expected_transformed_vertex = 3 + +[[spo]] +case = 10 +predicted_cost = [-2.0, -2.0, 2.0] +true_cost = [-1.0, 1.0, 0.0] +vertices = [[0.0, 3.0, -3.0], [0.0, 2.0, 3.0], [3.0, 0.0, -2.0], [2.0, 0.0, 1.0], [0.0, -3.0, 1.0]] +expected_loss = 10.0 +expected_subgradient = [6.0, -6.0, 2.0] +expected_true_vertex = 2 +expected_transformed_vertex = 0 + +[[spo]] +case = 11 +predicted_cost = [2.0, -2.0, -2.0] +true_cost = [-2.0, 1.0, -2.0] +vertices = [[-1.0, -1.0, 2.0], [0.0, 1.0, 3.0], [1.0, -2.0, 0.0], [-3.0, 3.0, 1.0], [1.0, 0.0, 2.0]] +expected_loss = 37.0 +expected_subgradient = [8.0, -6.0, 2.0] +expected_true_vertex = 4 +expected_transformed_vertex = 3 + +[[spo]] +case = 12 +predicted_cost = [-2.0, 2.0, 0.0] +true_cost = [-1.0, 2.0, 1.0] +vertices = [[2.0, -3.0, 2.0], [-1.0, 2.0, 0.0], [-1.0, -1.0, 1.0], [2.0, -2.0, -3.0], [-2.0, -3.0, -1.0]] +expected_loss = 7.0 +expected_subgradient = [0.0, 2.0, -10.0] +expected_true_vertex = 3 +expected_transformed_vertex = 0 + +[[spo]] +case = 13 +predicted_cost = [-2.0, -2.0, 1.0] +true_cost = [2.0, 1.0, 1.0] +vertices = [[-3.0, -1.0, 3.0], [0.0, -1.0, 0.0], [1.0, 3.0, -2.0], [3.0, -3.0, 3.0], [0.0, -2.0, 0.0]] +expected_loss = 49.0 +expected_subgradient = [-8.0, -8.0, 10.0] +expected_true_vertex = 0 +expected_transformed_vertex = 2 + +[[spo]] +case = 14 +predicted_cost = [-2.0, 1.0, -1.0] +true_cost = [-2.0, 0.0, 1.0] +vertices = [[-2.0, -1.0, -2.0], [-2.0, -1.0, 3.0], [-1.0, 3.0, 3.0], [-2.0, -1.0, -3.0], [-1.0, -3.0, 0.0]] +expected_loss = 18.0 +expected_subgradient = [0.0, 0.0, -12.0] +expected_true_vertex = 3 +expected_transformed_vertex = 1 + +[[spo]] +case = 15 +predicted_cost = [0.0, -2.0, -2.0] +true_cost = [-1.0, -1.0, 1.0] +vertices = [[-1.0, 3.0, -1.0], [-3.0, -2.0, -3.0], [-1.0, 0.0, -1.0], [-3.0, 2.0, 3.0], [-1.0, 3.0, -1.0]] +expected_loss = 19.0 +expected_subgradient = [4.0, 2.0, -8.0] +expected_true_vertex = 0 +expected_transformed_vertex = 3 + +[[spo]] +case = 16 +predicted_cost = [-2.0, -2.0, 2.0] +true_cost = [2.0, 1.0, 1.0] +vertices = [[0.0, 2.0, 1.0], [-2.0, 3.0, -3.0], [1.0, -1.0, 3.0], [3.0, 3.0, -3.0], [2.0, -2.0, 0.0]] +expected_loss = 30.0 +expected_subgradient = [-10.0, 0.0, 0.0] +expected_true_vertex = 1 +expected_transformed_vertex = 3 + +[[spo]] +case = 17 +predicted_cost = [-2.0, 1.0, 2.0] +true_cost = [-1.0, 0.0, -2.0] +vertices = [[-3.0, -1.0, 3.0], [-2.0, -3.0, -1.0], [-1.0, -2.0, -3.0], [0.0, 3.0, 2.0], [-3.0, 1.0, -2.0]] +expected_loss = 37.0 +expected_subgradient = [2.0, 10.0, 10.0] +expected_true_vertex = 3 +expected_transformed_vertex = 2 + +[[spo]] +case = 18 +predicted_cost = [-2.0, -1.0, 1.0] +true_cost = [0.0, -2.0, -2.0] +vertices = [[0.0, 1.0, 0.0], [1.0, -2.0, 2.0], [-3.0, -3.0, 1.0], [-2.0, -1.0, -2.0], [-2.0, 0.0, 0.0]] +expected_loss = 0.0 +expected_subgradient = [0.0, 0.0, 0.0] +expected_true_vertex = 0 +expected_transformed_vertex = 0 + +[[spo]] +case = 19 +predicted_cost = [0.0, -1.0, -1.0] +true_cost = [2.0, 1.0, 2.0] +vertices = [[3.0, 2.0, -2.0], [-3.0, -1.0, 1.0], [3.0, -3.0, 0.0], [-3.0, 3.0, -1.0], [1.0, 0.0, 3.0]] +expected_loss = 19.0 +expected_subgradient = [-8.0, -2.0, -4.0] +expected_true_vertex = 1 +expected_transformed_vertex = 4 + +[[spo]] +case = 20 +predicted_cost = [0.0, 2.0, 1.0] +true_cost = [2.0, 1.0, 1.0] +vertices = [[0.0, -2.0, 1.0], [3.0, -1.0, -2.0], [-1.0, 0.0, -1.0], [0.0, 0.0, 3.0], [-1.0, -1.0, -2.0]] +expected_loss = 8.0 +expected_subgradient = [-8.0, 0.0, 0.0] +expected_true_vertex = 4 +expected_transformed_vertex = 1 + +[[spo]] +case = 21 +predicted_cost = [0.0, 1.0, 2.0] +true_cost = [0.0, -2.0, 1.0] +vertices = [[-3.0, 2.0, 2.0], [2.0, 1.0, 3.0], [-1.0, 3.0, 1.0], [-1.0, -2.0, -2.0], [3.0, 3.0, 1.0]] +expected_loss = 29.0 +expected_subgradient = [0.0, 10.0, 6.0] +expected_true_vertex = 2 +expected_transformed_vertex = 3 + +[[spo]] +case = 22 +predicted_cost = [-2.0, 1.0, 0.0] +true_cost = [-2.0, 0.0, -2.0] +vertices = [[-3.0, -3.0, -1.0], [-3.0, 2.0, 0.0], [-2.0, 2.0, -1.0], [-2.0, -2.0, 0.0], [-1.0, 3.0, 2.0]] +expected_loss = 14.0 +expected_subgradient = [4.0, 12.0, 6.0] +expected_true_vertex = 4 +expected_transformed_vertex = 0 + +[[spo]] +case = 23 +predicted_cost = [2.0, -1.0, -1.0] +true_cost = [2.0, 1.0, 2.0] +vertices = [[2.0, 0.0, 1.0], [2.0, -3.0, 1.0], [-2.0, -2.0, 2.0], [-1.0, -1.0, -3.0], [1.0, 2.0, 0.0]] +expected_loss = 19.0 +expected_subgradient = [2.0, 2.0, -10.0] +expected_true_vertex = 3 +expected_transformed_vertex = 2 + +[[spo]] +case = 24 +predicted_cost = [1.0, 2.0, 1.0] +true_cost = [0.0, 0.0, -2.0] +vertices = [[0.0, 1.0, 1.0], [0.0, 2.0, 1.0], [-1.0, 0.0, 2.0], [2.0, 1.0, 1.0], [3.0, -1.0, 0.0]] +expected_loss = 4.0 +expected_subgradient = [-8.0, 2.0, 4.0] +expected_true_vertex = 2 +expected_transformed_vertex = 4 + +[[spo]] +case = 25 +predicted_cost = [-2.0, -1.0, 0.0] +true_cost = [2.0, -2.0, -1.0] +vertices = [[-2.0, 1.0, -3.0], [-2.0, 1.0, 3.0], [-2.0, -1.0, 0.0], [0.0, -3.0, 1.0], [-3.0, 3.0, -1.0]] +expected_loss = 16.0 +expected_subgradient = [-6.0, 12.0, -4.0] +expected_true_vertex = 4 +expected_transformed_vertex = 3 + +[[spo]] +case = 26 +predicted_cost = [1.0, -2.0, 0.0] +true_cost = [0.0, -2.0, 0.0] +vertices = [[2.0, 0.0, -2.0], [-1.0, -3.0, 0.0], [-3.0, 3.0, 2.0], [2.0, -2.0, -2.0], [0.0, -1.0, 1.0]] +expected_loss = 0.0 +expected_subgradient = [0.0, 0.0, 0.0] +expected_true_vertex = 2 +expected_transformed_vertex = 2 + +[[spo]] +case = 27 +predicted_cost = [0.0, 2.0, 0.0] +true_cost = [0.0, 1.0, 2.0] +vertices = [[2.0, 2.0, 1.0], [3.0, 2.0, 3.0], [-3.0, 2.0, -2.0], [1.0, 0.0, 3.0], [-1.0, 1.0, 1.0]] +expected_loss = 16.0 +expected_subgradient = [-8.0, 4.0, -10.0] +expected_true_vertex = 2 +expected_transformed_vertex = 3 + +[[spo]] +case = 28 +predicted_cost = [0.0, 1.0, -2.0] +true_cost = [-1.0, -1.0, 0.0] +vertices = [[-1.0, -3.0, -1.0], [-1.0, 2.0, -3.0], [3.0, -3.0, 2.0], [-3.0, 1.0, 1.0], [2.0, 0.0, 3.0]] +expected_loss = 4.0 +expected_subgradient = [-2.0, 6.0, 2.0] +expected_true_vertex = 4 +expected_transformed_vertex = 2 + +[[spo]] +case = 29 +predicted_cost = [-1.0, 0.0, -2.0] +true_cost = [-2.0, -2.0, -1.0] +vertices = [[-3.0, 3.0, -3.0], [-2.0, -2.0, 1.0], [-1.0, 2.0, -3.0], [-2.0, 3.0, -2.0], [2.0, 3.0, 2.0]] +expected_loss = 7.0 +expected_subgradient = [8.0, 10.0, 2.0] +expected_true_vertex = 4 +expected_transformed_vertex = 1 + +[[spo]] +case = 30 +predicted_cost = [-1.0, 0.0, 1.0] +true_cost = [-1.0, -2.0, -1.0] +vertices = [[2.0, -1.0, 1.0], [-1.0, -3.0, 1.0], [2.0, 0.0, 1.0], [3.0, -3.0, -1.0], [2.0, 0.0, 3.0]] +expected_loss = 19.0 +expected_subgradient = [-2.0, 6.0, 8.0] +expected_true_vertex = 4 +expected_transformed_vertex = 3 + +[[spo]] +case = 31 +predicted_cost = [0.0, 2.0, 2.0] +true_cost = [0.0, -2.0, 0.0] +vertices = [[-1.0, 1.0, 0.0], [2.0, -1.0, -1.0], [-1.0, -3.0, -1.0], [-2.0, 1.0, 3.0], [3.0, 0.0, 3.0]] +expected_loss = 28.0 +expected_subgradient = [0.0, 8.0, 2.0] +expected_true_vertex = 0 +expected_transformed_vertex = 2 + +[[spo]] +case = 32 +predicted_cost = [-2.0, 2.0, -2.0] +true_cost = [2.0, -2.0, 1.0] +vertices = [[0.0, 2.0, -1.0], [0.0, 2.0, 0.0], [3.0, 1.0, 3.0], [3.0, 0.0, 1.0], [-2.0, 3.0, -2.0]] +expected_loss = 67.0 +expected_subgradient = [-10.0, 4.0, -10.0] +expected_true_vertex = 4 +expected_transformed_vertex = 2 + +[[spo]] +case = 33 +predicted_cost = [-2.0, 2.0, 1.0] +true_cost = [-2.0, -2.0, 1.0] +vertices = [[-3.0, -2.0, 1.0], [1.0, -2.0, -2.0], [-1.0, 1.0, 1.0], [2.0, 3.0, 1.0], [-3.0, 3.0, -1.0]] +expected_loss = 31.0 +expected_subgradient = [2.0, 10.0, 6.0] +expected_true_vertex = 3 +expected_transformed_vertex = 1 + +[[spo]] +case = 34 +predicted_cost = [1.0, 0.0, 0.0] +true_cost = [-2.0, -1.0, 0.0] +vertices = [[-1.0, 3.0, 2.0], [-3.0, -3.0, 1.0], [2.0, 3.0, -3.0], [2.0, 0.0, 1.0], [0.0, 0.0, -2.0]] +expected_loss = 26.0 +expected_subgradient = [10.0, 12.0, -8.0] +expected_true_vertex = 2 +expected_transformed_vertex = 1 + +[[spo]] +case = 35 +predicted_cost = [-1.0, -2.0, 1.0] +true_cost = [2.0, 0.0, -1.0] +vertices = [[0.0, 0.0, -1.0], [0.0, 0.0, -3.0], [3.0, -1.0, 2.0], [1.0, 1.0, 3.0], [-2.0, 2.0, 2.0]] +expected_loss = 15.0 +expected_subgradient = [-4.0, 4.0, 10.0] +expected_true_vertex = 4 +expected_transformed_vertex = 1 + +[[spo]] +case = 36 +predicted_cost = [1.0, 1.0, -1.0] +true_cost = [-1.0, 1.0, 0.0] +vertices = [[0.0, 0.0, 3.0], [-2.0, 0.0, 3.0], [1.0, -3.0, 1.0], [1.0, 1.0, 0.0], [2.0, 2.0, -1.0]] +expected_loss = 10.0 +expected_subgradient = [6.0, -6.0, -4.0] +expected_true_vertex = 2 +expected_transformed_vertex = 1 + +[[spo]] +case = 37 +predicted_cost = [-2.0, 0.0, 0.0] +true_cost = [1.0, -1.0, 1.0] +vertices = [[3.0, 0.0, 2.0], [-2.0, 3.0, 2.0], [1.0, 3.0, 1.0], [-1.0, 0.0, 2.0], [-3.0, 1.0, 3.0]] +expected_loss = 28.0 +expected_subgradient = [-10.0, 6.0, 0.0] +expected_true_vertex = 1 +expected_transformed_vertex = 0 + +[[spo]] +case = 38 +predicted_cost = [1.0, -1.0, -1.0] +true_cost = [0.0, 2.0, 0.0] +vertices = [[-2.0, -1.0, 2.0], [2.0, 2.0, 2.0], [-3.0, -2.0, 0.0], [1.0, -2.0, 2.0], [-3.0, 2.0, 3.0]] +expected_loss = 22.0 +expected_subgradient = [0.0, -8.0, -6.0] +expected_true_vertex = 2 +expected_transformed_vertex = 4 + +[[spo]] +case = 39 +predicted_cost = [-2.0, 0.0, -2.0] +true_cost = [0.0, 1.0, 0.0] +vertices = [[-2.0, 0.0, 1.0], [-1.0, 0.0, 2.0], [-2.0, 3.0, -3.0], [1.0, -2.0, -2.0], [1.0, 3.0, 3.0]] +expected_loss = 25.0 +expected_subgradient = [0.0, -10.0, -10.0] +expected_true_vertex = 3 +expected_transformed_vertex = 4 + +[[spo]] +case = 40 +predicted_cost = [1.0, -1.0, -1.0] +true_cost = [1.0, -2.0, 1.0] +vertices = [[-3.0, -2.0, 1.0], [0.0, 3.0, -1.0], [-3.0, -1.0, -2.0], [0.0, -3.0, -2.0], [-3.0, -3.0, -2.0]] +expected_loss = 9.0 +expected_subgradient = [6.0, 10.0, -4.0] +expected_true_vertex = 1 +expected_transformed_vertex = 0 + +[[majorization]] +case = 1 +target = [0.5, 0.5] +source = [1.0, 0.0] +expected = true + +[[majorization]] +case = 2 +target = [1.0, 0.0] +source = [0.5, 0.5] +expected = false + +[[majorization]] +case = 3 +target = [-1.0, -1.0] +source = [-2.0, 0.0] +expected = true + +[[majorization]] +case = 4 +target = [0.0] +source = [0.0] +expected = true + +[[majorization]] +case = 5 +target = [1.0] +source = [2.0] +expected = false + +[[majorization]] +case = 6 +target = [2.0, 1.0, 0.0] +source = [0.0, 1.0, 2.0] +expected = true + +[[majorization]] +case = 7 +target = [-0.02591059991942605, -0.03408641032915294] +source = [0.02603285152215437, -0.08602986177073335] +expected = true + +[[majorization]] +case = 8 +target = [-0.6302075716698684, 0.6383727235129312] +source = [-0.6926984471974397, 0.7008635990405024] +expected = true + +[[majorization]] +case = 9 +target = [-0.3040142831686919, 1.2278787776968516] +source = [-0.9833773685829941, 1.907241863111154] +expected = true + +[[majorization]] +case = 10 +target = [0.9072197220143444, 0.2760012634560256, 0.7715005724418413] +source = [-0.7537544651439352, 1.5111362269265949, 1.1973397961295515] +expected = true + +[[majorization]] +case = 11 +target = [-0.5161169431030499, -0.05203023454398321, -0.686392858831489] +source = [-0.24858301859690649, -1.953134769985848, 0.947177752104232] +expected = true + +[[majorization]] +case = 12 +target = [-0.19935985361960185, 0.6383407402858449, 0.49765266700259697] +source = [0.44776055506202983, 1.8184858923301048, -1.3296128937232945] +expected = true + +[[majorization]] +case = 13 +target = [-0.3958859566516536, 1.1292789494142452, 0.9013251588924266, 1.1214246501164202, 1.127208851705798] +source = [0.8659263547434167, 0.8743577189840287, 1.4849857157673734, -0.6811846807843337, 1.3392665447667509] +expected = true + +[[majorization]] +case = 14 +target = [-0.6756196536094606, 0.5140285294056244, 0.735179345024651, -0.11359540276092317, -0.43573713342296133] +source = [-0.13343709467219322, 1.0338800539530315, -1.6256287783235166, -0.7315720867663875, 1.481013590445996] +expected = true + +[[majorization]] +case = 15 +target = [-0.8716227781370148, -0.5421262267195331, 0.052155253375050205, 1.1032563562680513, -0.06378912742052215] +source = [1.2977464335594924, -0.5750154263237758, -1.1646357513667132, 0.021202034162741218, 0.09857618733428677] +expected = true + +[[doubly_stochastic]] +case = 1 +matrix = [[0.5364787277118597, 0.4635212722881404], [0.4635212722881404, 0.5364787277118597]] +expected = true + +[[doubly_stochastic]] +case = 2 +matrix = [[0.9551574501499438, 0.04484254985005626], [0.04484254985005626, 0.9551574501499438]] +expected = true + +[[doubly_stochastic]] +case = 3 +matrix = [[0.7649766257812731, 0.23502337421872668], [0.23502337421872668, 0.7649766257812731]] +expected = true + +[[doubly_stochastic]] +case = 4 +matrix = [[0.14869608294877804, 0.0, 0.851303917051222], [0.5247381992997002, 0.3265657177515218, 0.14869608294877804], [0.3265657177515218, 0.6734342822484782, 0.0]] +expected = true + +[[doubly_stochastic]] +case = 5 +matrix = [[0.843047344096097, 0.15695265590390284, 0.0], [0.006957339957330309, 0.3416489309037409, 0.6513937291389288], [0.14999531594657253, 0.5013984131923562, 0.3486062708610712]] +expected = true + +[[doubly_stochastic]] +case = 6 +matrix = [[0.1099480667171101, 0.2969520110731261, 0.5930999222097638], [0.6084499600375426, 0.28160197324534725, 0.1099480667171101], [0.28160197324534725, 0.4214460156815267, 0.2969520110731261]] +expected = true + +[[doubly_stochastic]] +case = 7 +matrix = [[0.1844074003617711, 0.0, 0.0, 0.8155925996382289, 0.0], [0.27172008378295737, 0.3070542226965722, 0.42122569352047057, 0.0, 0.0], [0.0, 0.23681829315869943, 0.3070542226965722, 0.1844074003617711, 0.27172008378295737], [0.5438725158552716, 0.0, 0.27172008378295737, 0.0, 0.1844074003617711], [0.0, 0.4561274841447285, 0.0, 0.0, 0.5438725158552716]] +expected = true + +[[doubly_stochastic]] +case = 8 +matrix = [[0.4881660469159051, 0.0, 0.26400648156290846, 0.24782747152118642, 0.0], [0.24782747152118642, 0.0, 0.0, 0.2562069508495103, 0.49596557762930327], [0.0, 0.5202134324124188, 0.0, 0.2319590960663948, 0.24782747152118642], [0.0, 0.4797865675875812, 0.2562069508495103, 0.26400648156290846, 0.0], [0.26400648156290846, 0.0, 0.4797865675875812, 0.0, 0.2562069508495103]] +expected = true + +[[doubly_stochastic]] +case = 9 +matrix = [[0.0, 0.07898452095780971, 0.7259238346720318, 0.0, 0.1950916443701585], [0.07898452095780971, 0.7259238346720318, 0.1950916443701585, 0.0, 0.0], [0.0, 0.0, 0.0, 0.599954016379567, 0.400045983620433], [0.9210154790421903, 0.0, 0.07898452095780971, 0.0, 0.0], [0.0, 0.1950916443701585, 0.0, 0.400045983620433, 0.40486237200940856]] +expected = true diff --git a/test/fixtures/covariance_algebra_python_320.toml b/test/fixtures/covariance_algebra_python_320.toml new file mode 100644 index 0000000..fb2071a --- /dev/null +++ b/test/fixtures/covariance_algebra_python_320.toml @@ -0,0 +1,2156 @@ +reference = "RiskLabAI Python 3.2.0" +contract = "Spectral algebra and supplied NERCOME splits; 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0.1] +level = 0.0 +expected = 0.02 + +[[empirical]] +returns = [0.02, 0.03, 0.04, 0.1] +level = 0.05 +expected = 0.02 + +[[empirical]] +returns = [0.02, 0.03, 0.04, 0.1] +level = 0.25 +expected = 0.02 + +[[empirical]] +returns = [0.02, 0.03, 0.04, 0.1] +level = 0.5 +expected = 0.025 + +[[empirical]] +returns = [0.02, 0.03, 0.04, 0.1] +level = 1.0 +expected = 0.0475 + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -2.0 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = -0.01750000000000007, "expected_shortfall" = 0.027500000000000004} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -2.0 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 0.04999925000000005, "expected_shortfall" = 0.049999750000000044} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -2.0 +scale = 1.5 +probability = 0.7 +expected = {"value_at_risk" = -0.01749999999999996, "expected_shortfall" = 0.027500000000000045} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -2.0 +scale = 1.5 +probability = 0.999 +expected = {"value_at_risk" = 0.04999925000000005, "expected_shortfall" = 0.049999750000000044} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -1.0 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 0.34999999999999964, "expected_shortfall" = 0.5749999999999998} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -1.0 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 0.7985000000000002, "expected_shortfall" = 0.7992500000000001} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -1.0 +scale = 1.5 +probability = 0.7 +expected = {"value_at_risk" = 0.34999999999999987, "expected_shortfall" = 0.575} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -1.0 +scale = 1.5 +probability = 0.999 +expected = {"value_at_risk" = 0.7985, "expected_shortfall" = 0.79925} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -0.5 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 0.6568323274845009, "expected_shortfall" = 1.2045548849896672} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -0.5 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 2.2051316701949535, "expected_shortfall" = 2.2367544467966356} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -0.5 +scale = 1.5 +probability = 0.7 +expected = {"value_at_risk" = 0.6568323274845016, "expected_shortfall" = 1.2045548849896677} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = -0.5 +scale = 1.5 +probability = 0.999 +expected = {"value_at_risk" = 2.205131670194949, "expected_shortfall" = 2.236754446796633} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -1e-09 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 1.1059592054017398, "expected_shortfall" = 2.6059592020957805} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = -1e-09 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 9.66163288268556, "expected_shortfall" = 11.161632870823926} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape 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9.661632918473204, "expected_shortfall" = 11.161632918473204} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 1e-09 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 1.1059592075760656, "expected_shortfall" = 2.6059592108820246} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 1e-09 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 9.661632954261183, "expected_shortfall" = 11.161632966122816} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = 1e-09 +scale = 1.5 +probability = 0.7 +expected = {"value_at_risk" = 1.105959207576067, "expected_shortfall" = 2.605959210882026} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = 1e-09 +scale = 1.5 +probability = 0.999 +expected = {"value_at_risk" = 9.661632954261018, "expected_shortfall" = 11.161632966122651} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 0.25 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 1.4072009288422043, "expected_shortfall" = 4.109601238456272} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 0.25 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 27.040479511421875, "expected_shortfall" = 38.28730601522916} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = 0.25 +scale = 1.5 +probability = 0.7 +expected = {"value_at_risk" = 1.4072009288422065, "expected_shortfall" = 4.109601238456276} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = 0.25 +scale = 1.5 +probability = 0.999 +expected = {"value_at_risk" = 27.040479511420937, "expected_shortfall" = 38.28730601522791} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 0.9 +scale = 1.5 +probability = 0.97 +expected = {"value_at_risk" = 2.5587119475845146, "expected_shortfall" = 46.887119475845154} + +[[pareto]] +threshold = -0.7 +rate = 0.1 +shape = 0.9 +scale = 1.5 +probability = 0.9999 +expected = {"value_at_risk" = 832.9453893788701, "expected_shortfall" = 8350.753893788704} + +[[pareto]] +threshold = -0.7 +rate = 1.0 +shape = 0.9 +scale = 1.5 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= 1} + +[[exceptions]] +hits = [0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 0.2051731775502023, "coverage_pvalue" = 0.650577214270368, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 0], [0, 0]], "exception_count" = 0, "sample_count" = 2} + +[[exceptions]] +hits = [0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 2.043302495063963, "coverage_pvalue" = 0.15287742860036369, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 0], [0, 0]], "exception_count" = 0, "sample_count" = 2} + +[[exceptions]] +hits = [0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.98292909421596, "coverage_pvalue" = 0.0005369012484783263, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 0], [0, 0]], "exception_count" = 0, "sample_count" = 2} + +[[exceptions]] +hits = [0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 3.3214624136433017, "coverage_pvalue" = 0.06838097690650383, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 1], [0, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.08164398904051007, "coverage_pvalue" = 0.7750817674116173, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 1], [0, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 3.3214624136433, "coverage_pvalue" = 0.06838097690650355, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 1], [0, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 3.3214624136433017, "coverage_pvalue" = 0.06838097690650383, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.08164398904051007, "coverage_pvalue" = 0.7750817674116173, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 3.3214624136433, "coverage_pvalue" = 0.06838097690650355, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 0]], "exception_count" = 1, "sample_count" = 2} + +[[exceptions]] +hits = [1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.982929094215963, "coverage_pvalue" = 0.0005369012484783257, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 1]], "exception_count" = 2, "sample_count" = 2} + +[[exceptions]] +hits = [1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 3.66516292749662, "coverage_pvalue" = 0.055561210835573825, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 1]], "exception_count" = 2, "sample_count" = 2} + +[[exceptions]] +hits = [1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 0.2051731775502023, "coverage_pvalue" = 0.650577214270368, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 1]], "exception_count" = 2, "sample_count" = 2} + +[[exceptions]] +hits = [0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 0.30775976632530344, "coverage_pvalue" = 0.5790581467152065, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 0], [0, 0]], "exception_count" = 0, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 3.0649537425959443, "coverage_pvalue" = 0.07999743934701063, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 0], [0, 0]], "exception_count" = 0, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 17.97439364132394, "coverage_pvalue" = 2.238966098276823e-05, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 0], [0, 0]], "exception_count" = 0, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 2.3775527148893074, "coverage_pvalue" = 0.12309024313681263, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 1], [0, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.05679894904339644, "coverage_pvalue" = 0.8116287993424329, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 1], [0, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 8.266430673222184, "coverage_pvalue" = 0.004038482412760525, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[1, 1], [0, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 2.3775527148893074, "coverage_pvalue" = 0.12309024313681263, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 5.150141437129088, "conditional_coverage_pvalue" = 0.07614843750000004, "transitions" = [[0, 1], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.05679894904339644, "coverage_pvalue" = 0.8116287993424329, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 2.8293876712831776, "conditional_coverage_pvalue" = 0.24299999999999997, "transitions" = [[0, 1], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 8.266430673222184, "coverage_pvalue" = 0.004038482412760525, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 11.039019395461965, "conditional_coverage_pvalue" = 0.00400781250000001, "transitions" = [[0, 1], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 8.266430673222187, "coverage_pvalue" = 0.0040384824127605206, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 8.266430673222187, "conditional_coverage_pvalue" = 0.016031250000000007, "transitions" = [[0, 1], [0, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8677291652597248, "coverage_pvalue" = 0.3515847397015699, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.8677291652597248, "conditional_coverage_pvalue" = 0.6480000000000001, "transitions" = [[0, 1], [0, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 2.3775527148893056, "coverage_pvalue" = 0.1230902431368129, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 2.3775527148893056, "conditional_coverage_pvalue" = 0.30459375000000016, "transitions" = [[0, 1], [0, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 2.3775527148893074, "coverage_pvalue" = 0.12309024313681263, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 2.3775527148893074, "conditional_coverage_pvalue" = 0.30459374999999994, "transitions" = [[1, 0], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.05679894904339644, "coverage_pvalue" = 0.8116287993424329, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.05679894904339644, "conditional_coverage_pvalue" = 0.9719999999999998, "transitions" = [[1, 0], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 8.266430673222184, "coverage_pvalue" = 0.004038482412760525, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 8.266430673222184, "conditional_coverage_pvalue" = 0.016031250000000042, "transitions" = [[1, 0], [1, 0]], "exception_count" = 1, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 8.266430673222187, "coverage_pvalue" = 0.0040384824127605206, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 11.039019395461969, "conditional_coverage_pvalue" = 0.004007812500000001, "transitions" = [[0, 1], [1, 0]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8677291652597248, "coverage_pvalue" = 0.3515847397015699, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 3.640317887499506, "conditional_coverage_pvalue" = 0.16200000000000003, "transitions" = [[0, 1], [1, 0]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 2.3775527148893056, "coverage_pvalue" = 0.1230902431368129, "independence_statistic" = 2.772588722239781, "independence_pvalue" = 0.09589096714246556, "conditional_coverage_statistic" = 5.150141437129086, "conditional_coverage_pvalue" = 0.07614843750000005, "transitions" = [[0, 1], [1, 0]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 8.266430673222187, "coverage_pvalue" = 0.0040384824127605206, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.8677291652597248, "coverage_pvalue" = 0.3515847397015699, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 2.3775527148893056, "coverage_pvalue" = 0.1230902431368129, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 1]], "exception_count" = 2, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 17.974393641323946, "coverage_pvalue" = 2.238966098276813e-05, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 2]], "exception_count" = 3, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 5.49774439124493, "coverage_pvalue" = 0.019041019186388137, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 2]], "exception_count" = 3, "sample_count" = 3} + +[[exceptions]] +hits = [1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 0.30775976632530344, "coverage_pvalue" = 0.5790581467152065, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 2]], "exception_count" = 3, "sample_count" = 3} + +[[exceptions]] +hits = [0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 0.4103463551004046, "coverage_pvalue" = 0.5217937615347963, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 0], [0, 0]], "exception_count" = 0, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 4.086604990127926, "coverage_pvalue" = 0.04322438145248494, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 0], [0, 0]], "exception_count" = 0, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 23.96585818843192, "coverage_pvalue" = 9.805925279415386e-07, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 0], [0, 0]], "exception_count" = 0, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 1.800543156482819, "coverage_pvalue" = 0.17964684377728118, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 1], [0, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.39885404939378777, "coverage_pvalue" = 0.5276816675912053, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 1], [0, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 13.578299073148573, "coverage_pvalue" = 0.00022881548112780808, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[2, 1], [0, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.800543156482819, "coverage_pvalue" = 0.17964684377728118, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 2.8470394440119144, "conditional_coverage_pvalue" = 0.24086474622770918, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.39885404939378777, "coverage_pvalue" = 0.5276816675912053, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.4453503369228833, "conditional_coverage_pvalue" = 0.4854518518518518, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 13.578299073148573, "coverage_pvalue" = 0.00022881548112780808, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 14.624795360677668, "conditional_coverage_pvalue" = 0.0006672153635116622, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.6894211148156995, "conditional_coverage_pvalue" = 0.02139259259259259, "transitions" = [[1, 1], [0, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.2097842656101156, "conditional_coverage_pvalue" = 0.5461333333333335, "transitions" = [[1, 1], [0, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.689421114815696, "conditional_coverage_pvalue" = 0.021392592592592633, "transitions" = [[1, 1], [0, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.800543156482819, "coverage_pvalue" = 0.17964684377728118, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 2.8470394440119144, "conditional_coverage_pvalue" = 0.24086474622770918, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.39885404939378777, "coverage_pvalue" = 0.5276816675912053, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.4453503369228833, "conditional_coverage_pvalue" = 0.4854518518518518, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 13.578299073148573, "coverage_pvalue" = 0.00022881548112780808, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 14.624795360677668, "conditional_coverage_pvalue" = 0.0006672153635116622, "transitions" = [[1, 1], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 10.46200983705548, "conditional_coverage_pvalue" = 0.0053481481481481505, "transitions" = [[0, 2], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 3.982372987849897, "conditional_coverage_pvalue" = 0.1365333333333334, "transitions" = [[0, 2], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 10.462009837055476, "conditional_coverage_pvalue" = 0.00534814814814816, "transitions" = [[0, 2], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.6894211148156995, "conditional_coverage_pvalue" = 0.02139259259259259, "transitions" = [[0, 1], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.2097842656101156, "conditional_coverage_pvalue" = 0.5461333333333335, "transitions" = [[0, 1], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.689421114815696, "conditional_coverage_pvalue" = 0.021392592592592633, "transitions" = [[0, 1], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 13.57829907314858, "coverage_pvalue" = 0.00022881548112780743, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 13.57829907314858, "conditional_coverage_pvalue" = 0.0011259259259259256, "transitions" = [[0, 1], [0, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 2.0207144818264453, "coverage_pvalue" = 0.15516611643249756, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 2.0207144818264453, "conditional_coverage_pvalue" = 0.36408888888888874, "transitions" = [[0, 1], [0, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.8005431564828172, "coverage_pvalue" = 0.1796468437772814, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.8005431564828172, "conditional_coverage_pvalue" = 0.4064592592592595, "transitions" = [[0, 1], [0, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.800543156482819, "coverage_pvalue" = 0.17964684377728118, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.800543156482819, "conditional_coverage_pvalue" = 0.4064592592592592, "transitions" = [[2, 0], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.39885404939378777, "coverage_pvalue" = 0.5276816675912053, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.39885404939378777, "conditional_coverage_pvalue" = 0.8191999999999999, "transitions" = [[2, 0], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 13.578299073148573, "coverage_pvalue" = 0.00022881548112780808, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 13.578299073148573, "conditional_coverage_pvalue" = 0.0011259259259259297, "transitions" = [[2, 0], [1, 0]], "exception_count" = 1, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.6894211148156995, "conditional_coverage_pvalue" = 0.02139259259259259, "transitions" = [[1, 1], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.2097842656101156, "conditional_coverage_pvalue" = 0.5461333333333335, "transitions" = [[1, 1], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.689421114815696, "conditional_coverage_pvalue" = 0.021392592592592633, "transitions" = [[1, 1], [1, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 10.46200983705548, "conditional_coverage_pvalue" = 0.0053481481481481505, "transitions" = [[0, 1], [2, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 3.982372987849897, "conditional_coverage_pvalue" = 0.1365333333333334, "transitions" = [[0, 1], [2, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 3.8190850097688767, "independence_pvalue" = 0.050671902346990164, "conditional_coverage_statistic" = 10.462009837055476, "conditional_coverage_pvalue" = 0.00534814814814816, "transitions" = [[0, 1], [2, 0]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 13.57829907314858, "coverage_pvalue" = 0.00022881548112780743, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 14.624795360677675, "conditional_coverage_pvalue" = 0.0006672153635116603, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 2.0207144818264453, "coverage_pvalue" = 0.15516611643249756, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 3.067210769355541, "conditional_coverage_pvalue" = 0.21575637860082306, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.8005431564828172, "coverage_pvalue" = 0.1796468437772814, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 2.8470394440119127, "conditional_coverage_pvalue" = 0.24086474622770934, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 6.6429248272866035, "coverage_pvalue" = 0.009955036458494698, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.6894211148156995, "conditional_coverage_pvalue" = 0.02139259259259259, "transitions" = [[1, 0], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.16328797808102014, "coverage_pvalue" = 0.686147220673451, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 1.2097842656101156, "conditional_coverage_pvalue" = 0.5461333333333335, "transitions" = [[1, 0], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 6.6429248272866, "coverage_pvalue" = 0.009955036458494706, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 7.689421114815696, "conditional_coverage_pvalue" = 0.021392592592592633, "transitions" = [[1, 0], [1, 1]], "exception_count" = 2, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 13.57829907314858, "coverage_pvalue" = 0.00022881548112780743, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 14.624795360677675, "conditional_coverage_pvalue" = 0.0006672153635116603, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 2.0207144818264453, "coverage_pvalue" = 0.15516611643249756, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 3.067210769355541, "conditional_coverage_pvalue" = 0.21575637860082306, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.8005431564828172, "coverage_pvalue" = 0.1796468437772814, "independence_statistic" = 1.0464962875290955, "independence_pvalue" = 0.30631540550273384, "conditional_coverage_statistic" = 2.8470394440119127, "conditional_coverage_pvalue" = 0.24086474622770934, "transitions" = [[0, 1], [1, 1]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 13.57829907314858, "coverage_pvalue" = 0.00022881548112780743, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 2.0207144818264453, "coverage_pvalue" = 0.15516611643249756, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 1.8005431564828172, "coverage_pvalue" = 0.1796468437772814, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 2]], "exception_count" = 3, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 23.965858188431927, "coverage_pvalue" = 9.805925279415346e-07, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 3]], "exception_count" = 4, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 7.33032585499324, "coverage_pvalue" = 0.00678007610398573, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 3]], "exception_count" = 4, "sample_count" = 4} + +[[exceptions]] +hits = [1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 0.4103463551004046, "coverage_pvalue" = 0.5217937615347963, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 3]], "exception_count" = 4, "sample_count" = 4} + +[[exceptions]] +hits = [0, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 0.5129329438755058, "coverage_pvalue" = 0.47387195607914057, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 0], [0, 0]], "exception_count" = 0, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 5.108256237659907, "coverage_pvalue" = 0.023812241842379248, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 0], [0, 0]], "exception_count" = 0, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 29.9573227355399, "coverage_pvalue" = 4.4166078433662186e-08, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 0], [0, 0]], "exception_count" = 0, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 1.3977866668265078, "coverage_pvalue" = 0.2370945064965105, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 1], [0, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.9151622184943573, "coverage_pvalue" = 0.3387484301648956, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 1], [0, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 19.064420541825143, "coverage_pvalue" = 1.2637916264054544e-05, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[3, 1], [0, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.3977866668265078, "coverage_pvalue" = 0.2370945064965105, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080974, "conditional_coverage_pvalue" = 0.3539175130426884, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.9151622184943573, "coverage_pvalue" = 0.3387484301648956, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 1.594758365675947, "conditional_coverage_pvalue" = 0.45050811767578103, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 19.064420541825143, "coverage_pvalue" = 1.2637916264054544e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.744016689006735, "conditional_coverage_pvalue" = 5.1598995923996134e-05, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[2, 1], [0, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 1.7260924347106856, "conditional_coverage_pvalue" = 0.4218749999999999, "transitions" = [[2, 1], [0, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.175542583492263, "conditional_coverage_pvalue" = 0.00137710571289063, "transitions" = [[2, 1], [0, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.3977866668265078, "coverage_pvalue" = 0.2370945064965105, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080974, "conditional_coverage_pvalue" = 0.3539175130426884, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.9151622184943573, "coverage_pvalue" = 0.3387484301648956, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 1.594758365675947, "conditional_coverage_pvalue" = 0.45050811767578103, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 19.064420541825143, "coverage_pvalue" = 1.2637916264054544e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.744016689006735, "conditional_coverage_pvalue" = 5.1598995923996134e-05, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 1.7260924347106856, "conditional_coverage_pvalue" = 0.4218749999999999, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.175542583492263, "conditional_coverage_pvalue" = 0.00137710571289063, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 5.560572190448702, "conditional_coverage_pvalue" = 0.06202076099537042, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.0, "conditional_coverage_pvalue" = 1.0, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 11.449450148781576, "conditional_coverage_pvalue" = 0.0032642505787037167, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.175542583492268, "conditional_coverage_pvalue" = 0.0013771057128906257, "transitions" = [[1, 1], [0, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 2.5370226509270135, "conditional_coverage_pvalue" = 0.28125000000000017, "transitions" = [[1, 1], [0, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[1, 1], [0, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.3977866668265078, "coverage_pvalue" = 0.2370945064965105, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080974, "conditional_coverage_pvalue" = 0.3539175130426884, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.9151622184943573, "coverage_pvalue" = 0.3387484301648956, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 1.594758365675947, "conditional_coverage_pvalue" = 0.45050811767578103, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 19.064420541825143, "coverage_pvalue" = 1.2637916264054544e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.744016689006735, "conditional_coverage_pvalue" = 5.1598995923996134e-05, "transitions" = [[2, 1], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 1.7260924347106856, "conditional_coverage_pvalue" = 0.4218749999999999, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.175542583492263, "conditional_coverage_pvalue" = 0.00137710571289063, "transitions" = [[1, 2], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 11.105749634928264, "conditional_coverage_pvalue" = 0.003876297562210651, "transitions" = [[0, 2], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 5.545177444479562, "conditional_coverage_pvalue" = 0.0625, "transitions" = [[0, 2], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 16.99462759326114, "conditional_coverage_pvalue" = 0.00020401566116898213, "transitions" = [[0, 2], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.175542583492268, "conditional_coverage_pvalue" = 0.0013771057128906257, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 2.5370226509270135, "conditional_coverage_pvalue" = 0.28125000000000017, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 5.560572190448702, "conditional_coverage_pvalue" = 0.06202076099537042, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.0, "conditional_coverage_pvalue" = 1.0, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 11.449450148781576, "conditional_coverage_pvalue" = 0.0032642505787037167, "transitions" = [[1, 1], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.175542583492268, "conditional_coverage_pvalue" = 0.0013771057128906257, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 2.5370226509270135, "conditional_coverage_pvalue" = 0.28125000000000017, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[0, 2], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 12.129046295963173, "conditional_coverage_pvalue" = 0.0023238658905029297, "transitions" = [[0, 1], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 1.490526363397918, "conditional_coverage_pvalue" = 0.47460937500000033, "transitions" = [[0, 1], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 6.240168337630288, "conditional_coverage_pvalue" = 0.044153451919555775, "transitions" = [[0, 1], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 19.06442054182515, "coverage_pvalue" = 1.2637916264054485e-05, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 19.06442054182515, "conditional_coverage_pvalue" = 7.247924804687503e-05, "transitions" = [[0, 1], [0, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 3.347952867143343, "coverage_pvalue" = 0.06728864668786298, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 3.347952867143343, "conditional_coverage_pvalue" = 0.18749999999999997, "transitions" = [[0, 1], [0, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [0, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.397786666826506, "coverage_pvalue" = 0.2370945064965108, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.397786666826506, "conditional_coverage_pvalue" = 0.49713516235351607, "transitions" = [[0, 1], [0, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.3977866668265078, "coverage_pvalue" = 0.2370945064965105, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.3977866668265078, "conditional_coverage_pvalue" = 0.49713516235351574, "transitions" = [[3, 0], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.9151622184943573, "coverage_pvalue" = 0.3387484301648956, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.9151622184943573, "conditional_coverage_pvalue" = 0.6328124999999998, "transitions" = [[3, 0], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 19.064420541825143, "coverage_pvalue" = 1.2637916264054544e-05, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 19.064420541825143, "conditional_coverage_pvalue" = 7.247924804687526e-05, "transitions" = [[3, 0], [1, 0]], "exception_count" = 1, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 6.240168337630291, "conditional_coverage_pvalue" = 0.04415345191955569, "transitions" = [[2, 1], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 0.6795961471815897, "conditional_coverage_pvalue" = 0.7119140625000001, "transitions" = [[2, 1], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 12.129046295963166, "conditional_coverage_pvalue" = 0.0023238658905029397, "transitions" = [[2, 1], [1, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 1.7260924347106852, "conditional_coverage_pvalue" = 0.4218750000000001, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.17554258349226, "conditional_coverage_pvalue" = 0.0013771057128906313, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 11.449450148781583, "conditional_coverage_pvalue" = 0.003264250578703705, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.8109302162163283, "conditional_coverage_pvalue" = 0.666666666666667, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 5.560572190448698, "conditional_coverage_pvalue" = 0.0620207609953705, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 1.7260924347106852, "conditional_coverage_pvalue" = 0.4218750000000001, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.17554258349226, "conditional_coverage_pvalue" = 0.0013771057128906313, "transitions" = [[1, 1], [2, 0]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 16.994627593261146, "conditional_coverage_pvalue" = 0.00020401566116898134, "transitions" = [[0, 2], [2, 0]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 6.356107660695891, "conditional_coverage_pvalue" = 0.04166666666666668, "transitions" = [[0, 2], [2, 0]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 5.545177444479562, "independence_pvalue" = 0.018531677751199058, "conditional_coverage_statistic" = 11.10574963492826, "conditional_coverage_pvalue" = 0.003876297562210658, "transitions" = [[0, 2], [2, 0]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.17554258349227, "conditional_coverage_pvalue" = 0.0013771057128906241, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 2.537022650927014, "conditional_coverage_pvalue" = 0.28125000000000006, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 19.06442054182515, "coverage_pvalue" = 1.2637916264054485e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.74401668900674, "conditional_coverage_pvalue" = 5.1598995923995924e-05, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 3.347952867143343, "coverage_pvalue" = 0.06728864668786298, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 4.027549014324933, "conditional_coverage_pvalue" = 0.13348388671875, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.397786666826506, "coverage_pvalue" = 0.2370945064965108, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080957, "conditional_coverage_pvalue" = 0.3539175130426888, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 5.560572190448702, "coverage_pvalue" = 0.01836940942069374, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 7.286664625159387, "conditional_coverage_pvalue" = 0.02616500854492189, "transitions" = [[2, 0], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.0, "coverage_pvalue" = 1.0, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 1.7260924347106852, "conditional_coverage_pvalue" = 0.4218750000000001, "transitions" = [[2, 0], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 11.449450148781576, "coverage_pvalue" = 0.0007151515800603236, "independence_statistic" = 1.7260924347106852, "independence_pvalue" = 0.18891070000568827, "conditional_coverage_statistic" = 13.17554258349226, "conditional_coverage_pvalue" = 0.0013771057128906313, "transitions" = [[2, 0], [1, 1]], "exception_count" = 2, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 11.449450148781583, "conditional_coverage_pvalue" = 0.003264250578703705, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 0.8109302162163283, "conditional_coverage_pvalue" = 0.666666666666667, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 5.560572190448698, "conditional_coverage_pvalue" = 0.0620207609953705, "transitions" = [[1, 1], [1, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.17554258349227, "conditional_coverage_pvalue" = 0.0013771057128906241, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 2.537022650927014, "conditional_coverage_pvalue" = 0.28125000000000006, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[0, 1], [2, 1]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 19.06442054182515, "coverage_pvalue" = 1.2637916264054485e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.74401668900674, "conditional_coverage_pvalue" = 5.1598995923995924e-05, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 3.347952867143343, "coverage_pvalue" = 0.06728864668786298, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 4.027549014324933, "conditional_coverage_pvalue" = 0.13348388671875, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.397786666826506, "coverage_pvalue" = 0.2370945064965108, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080957, "conditional_coverage_pvalue" = 0.3539175130426888, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 11.449450148781583, "coverage_pvalue" = 0.0007151515800603213, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 13.17554258349227, "conditional_coverage_pvalue" = 0.0013771057128906241, "transitions" = [[1, 0], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.8109302162163283, "coverage_pvalue" = 0.3678453748467996, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 2.537022650927014, "conditional_coverage_pvalue" = 0.28125000000000006, "transitions" = [[1, 0], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 5.560572190448698, "coverage_pvalue" = 0.018369409420693806, "independence_statistic" = 1.7260924347106856, "independence_pvalue" = 0.18891070000568821, "conditional_coverage_statistic" = 7.286664625159384, "conditional_coverage_pvalue" = 0.026165008544921927, "transitions" = [[1, 0], [1, 2]], "exception_count" = 3, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 19.06442054182515, "coverage_pvalue" = 1.2637916264054485e-05, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 19.74401668900674, "conditional_coverage_pvalue" = 5.1598995923995924e-05, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 3.347952867143343, "coverage_pvalue" = 0.06728864668786298, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 4.027549014324933, "conditional_coverage_pvalue" = 0.13348388671875, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.397786666826506, "coverage_pvalue" = 0.2370945064965108, "independence_statistic" = 0.6795961471815897, "independence_pvalue" = 0.40972582406331504, "conditional_coverage_statistic" = 2.0773828140080957, "conditional_coverage_pvalue" = 0.3539175130426888, "transitions" = [[0, 1], [1, 2]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 19.06442054182515, "coverage_pvalue" = 1.2637916264054485e-05, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 3.347952867143343, "coverage_pvalue" = 0.06728864668786298, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 1.397786666826506, "coverage_pvalue" = 0.2370945064965108, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 3]], "exception_count" = 4, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 29.957322735539908, "coverage_pvalue" = 4.4166078433662014e-08, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 4]], "exception_count" = 5, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 9.16290731874155, "coverage_pvalue" = 0.0024696989194303944, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 4]], "exception_count" = 5, "sample_count" = 5} + +[[exceptions]] +hits = [1, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 0.5129329438755058, "coverage_pvalue" = 0.47387195607914057, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 4]], "exception_count" = 5, "sample_count" = 5} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 0.6155195326506069, "coverage_pvalue" = 0.4327171451180195, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[5, 0], [0, 0]], "exception_count" = 0, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 6.129907485191889, "coverage_pvalue" = 0.013291382602506965, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[5, 0], [0, 0]], "exception_count" = 0, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 35.94878728264788, "coverage_pvalue" = 2.0257241590118135e-09, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[5, 0], [0, 0]], "exception_count" = 0, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 1], [0, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 1], [0, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[4, 1], [0, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.6030060630192438, "conditional_coverage_pvalue" = 0.4486541158121471, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 2.039446273443973, "conditional_coverage_pvalue" = 0.36069478949191663, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350755, "conditional_coverage_pvalue" = 3.4426847232000216e-06, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 6.986540942920713, "conditional_coverage_pvalue" = 0.030401282879999988, "transitions" = [[3, 1], [0, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 2.345033411228891, "conditional_coverage_pvalue" = 0.3095868211199998, "transitions" = [[3, 1], [0, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 18.764296859586466, "conditional_coverage_pvalue" = 8.421408000000032e-05, "transitions" = [[3, 1], [0, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.6030060630192438, "conditional_coverage_pvalue" = 0.4486541158121471, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 2.039446273443973, "conditional_coverage_pvalue" = 0.36069478949191663, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350755, "conditional_coverage_pvalue" = 3.4426847232000216e-06, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862521, "conditional_coverage_pvalue" = 0.08657240320124998, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.25204083617069895, "conditional_coverage_pvalue" = 0.8815968460799997, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.671304284528272, "conditional_coverage_pvalue" = 0.0002398127512500014, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[2, 1], [0, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[2, 1], [0, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[2, 1], [0, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.6030060630192438, "conditional_coverage_pvalue" = 0.4486541158121471, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 2.039446273443973, "conditional_coverage_pvalue" = 0.36069478949191663, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350755, "conditional_coverage_pvalue" = 3.4426847232000216e-06, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.6661370901023025, "conditional_coverage_pvalue" = 0.021643100800312495, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.0246295584104805, "conditional_coverage_pvalue" = 0.22039921151999986, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.443893006768054, "conditional_coverage_pvalue" = 5.995318781250034e-05, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862521, "conditional_coverage_pvalue" = 0.08657240320124998, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.25204083617069895, "conditional_coverage_pvalue" = 0.8815968460799997, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.671304284528272, "conditional_coverage_pvalue" = 0.0002398127512500014, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 18.764296859586473, "conditional_coverage_pvalue" = 8.421408e-05, "transitions" = [[1, 1], [0, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 3.966893843661547, "conditional_coverage_pvalue" = 0.13759414271999998, "transitions" = [[1, 1], [0, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 6.986540942920708, "conditional_coverage_pvalue" = 0.03040128288000006, "transitions" = [[1, 1], [0, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.6030060630192438, "conditional_coverage_pvalue" = 0.4486541158121471, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 2.039446273443973, "conditional_coverage_pvalue" = 0.36069478949191663, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350755, "conditional_coverage_pvalue" = 3.4426847232000216e-06, "transitions" = [[3, 1], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 2], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.6661370901023025, "conditional_coverage_pvalue" = 0.021643100800312495, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.0246295584104805, "conditional_coverage_pvalue" = 0.22039921151999986, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.443893006768054, "conditional_coverage_pvalue" = 5.995318781250034e-05, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.6661370901023025, "conditional_coverage_pvalue" = 0.021643100800312495, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.0246295584104805, "conditional_coverage_pvalue" = 0.22039921151999986, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.443893006768054, "conditional_coverage_pvalue" = 5.995318781250034e-05, "transitions" = [[1, 2], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 16.694503911022473, "conditional_coverage_pvalue" = 0.0002370470399999997, "transitions" = [[0, 3], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 6.975048637214095, "conditional_coverage_pvalue" = 0.030576476160000005, "transitions" = [[0, 3], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 16.694503911022466, "conditional_coverage_pvalue" = 0.00023704704000000043, "transitions" = [[0, 3], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862521, "conditional_coverage_pvalue" = 0.08657240320124998, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.25204083617069895, "conditional_coverage_pvalue" = 0.8815968460799997, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.671304284528272, "conditional_coverage_pvalue" = 0.0002398127512500014, "transitions" = [[2, 1], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 2], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[0, 2], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 2], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 17.038204424875786, "conditional_coverage_pvalue" = 0.00019961856000000024, "transitions" = [[0, 1], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 2.240801408950862, "conditional_coverage_pvalue" = 0.32614907903999985, "transitions" = [[0, 1], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.260448508210024, "conditional_coverage_pvalue" = 0.07206230016000009, "transitions" = [[0, 1], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 24.65317481791935, "conditional_coverage_pvalue" = 4.432320000000008e-06, "transitions" = [[0, 1], [0, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 4.777824059877874, "conditional_coverage_pvalue" = 0.09172942848000004, "transitions" = [[0, 1], [0, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [0, 1, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.0976629845878296, "conditional_coverage_pvalue" = 0.5776243747200003, "transitions" = [[0, 1], [0, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 1.0976629845878314, "coverage_pvalue" = 0.2947795541152114, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.0976629845878314, "conditional_coverage_pvalue" = 0.5776243747199998, "transitions" = [[4, 0], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.5341031950125608, "coverage_pvalue" = 0.2154976714038855, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 1.5341031950125608, "conditional_coverage_pvalue" = 0.46438023168, "transitions" = [[4, 0], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 24.653174817919343, "coverage_pvalue" = 6.86315964802285e-07, "independence_statistic" = 0.0, "independence_pvalue" = 1.0, "conditional_coverage_statistic" = 24.653174817919343, "conditional_coverage_pvalue" = 4.432320000000024e-06, "transitions" = [[4, 0], [1, 0]], "exception_count" = 1, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.260448508210027, "conditional_coverage_pvalue" = 0.07206230015999995, "transitions" = [[3, 1], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 0.6189409765182052, "conditional_coverage_pvalue" = 0.7338354278399997, "transitions" = [[3, 1], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 17.03820442487578, "conditional_coverage_pvalue" = 0.000199618560000001, "transitions" = [[3, 1], [1, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.67130428452828, "conditional_coverage_pvalue" = 0.00023981275125000041, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 1.8739012686033556, "conditional_coverage_pvalue" = 0.39182082048, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862517, "conditional_coverage_pvalue" = 0.08657240320125015, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391617, "conditional_coverage_pvalue" = 0.05130216485999999, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 1.298537123699795, "conditional_coverage_pvalue" = 0.5224277606399998, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.71780057205737, "conditional_coverage_pvalue" = 0.00014211126000000056, "transitions" = [[2, 1], [2, 0]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[1, 2], [2, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 16.694503911022473, "conditional_coverage_pvalue" = 0.0002370470399999997, "transitions" = [[0, 2], [3, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 6.975048637214095, "conditional_coverage_pvalue" = 0.030576476160000005, "transitions" = [[0, 2], [3, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 6.730116670092564, "independence_pvalue" = 0.00947983990870854, "conditional_coverage_statistic" = 16.694503911022466, "conditional_coverage_pvalue" = 0.00023704704000000043, "transitions" = [[0, 2], [3, 0]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.44389300676806, "conditional_coverage_pvalue" = 5.99531878125001e-05, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 4.646489990843137, "conditional_coverage_pvalue" = 0.09795520511999999, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.666137090102299, "conditional_coverage_pvalue" = 0.02164310080031253, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.44389300676806, "conditional_coverage_pvalue" = 5.99531878125001e-05, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 4.646489990843137, "conditional_coverage_pvalue" = 0.09795520511999999, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.666137090102299, "conditional_coverage_pvalue" = 0.02164310080031253, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350762, "conditional_coverage_pvalue" = 3.4426847232000085e-06, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.283167138309286, "conditional_coverage_pvalue" = 0.07124835347988485, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 0, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.603006063019242, "conditional_coverage_pvalue" = 0.4486541158121474, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 4.755105429778615, "coverage_pvalue" = 0.029211503268054877, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 6.986540942920712, "conditional_coverage_pvalue" = 0.03040128288, "transitions" = [[3, 0], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.11359789808679288, "coverage_pvalue" = 0.7360845811488791, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 2.3450334112288904, "conditional_coverage_pvalue" = 0.30958682111999986, "transitions" = [[3, 0], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 16.532861346444367, "coverage_pvalue" = 4.781419117133618e-05, "independence_statistic" = 2.2314355131420975, "independence_pvalue" = 0.13522815768706553, "conditional_coverage_statistic" = 18.764296859586466, "conditional_coverage_pvalue" = 8.421408000000032e-05, "transitions" = [[3, 0], [1, 1]], "exception_count" = 2, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[2, 1], [1, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.67130428452828, "conditional_coverage_pvalue" = 0.00023981275125000041, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 1.8739012686033556, "conditional_coverage_pvalue" = 0.39182082048, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862517, "conditional_coverage_pvalue" = 0.08657240320125015, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013814, "conditional_coverage_pvalue" = 0.006400270079999993, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 0.3833749052054367, "conditional_coverage_pvalue" = 0.82556485632, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 10.102830179013807, "conditional_coverage_pvalue" = 0.006400270080000013, "transitions" = [[1, 1], [2, 1]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 19.44389300676806, "conditional_coverage_pvalue" = 5.99531878125001e-05, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 4.646489990843137, "conditional_coverage_pvalue" = 0.09795520511999999, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 7.666137090102299, "conditional_coverage_pvalue" = 0.02164310080031253, "transitions" = [[0, 2], [2, 1]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350762, "conditional_coverage_pvalue" = 3.4426847232000085e-06, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.283167138309286, "conditional_coverage_pvalue" = 0.07124835347988485, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 0, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.603006063019242, "conditional_coverage_pvalue" = 0.4486541158121474, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 9.964387240929907, "coverage_pvalue" = 0.0015959728570581263, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253596, "conditional_coverage_pvalue" = 0.001600067519999997, "transitions" = [[2, 0], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 0.24493196712153065, "coverage_pvalue" = 0.6206664621414028, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 3.1559636274452183, "conditional_coverage_pvalue" = 0.20639121408, "transitions" = [[2, 0], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 9.9643872409299, "coverage_pvalue" = 0.001595972857058133, "independence_statistic" = 2.9110316603236877, "independence_pvalue" = 0.08797558211282346, "conditional_coverage_statistic" = 12.875418901253589, "conditional_coverage_pvalue" = 0.0016000675200000035, "transitions" = [[2, 0], [1, 2]], "exception_count" = 3, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 16.67130428452828, "conditional_coverage_pvalue" = 0.00023981275125000041, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 1.8739012686033556, "conditional_coverage_pvalue" = 0.39182082048, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 0.13844293808390606, "independence_pvalue" = 0.7098338753206082, "conditional_coverage_statistic" = 4.893548367862517, "conditional_coverage_pvalue" = 0.08657240320125015, "transitions" = [[1, 1], [1, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 17.717800572057378, "conditional_coverage_pvalue" = 0.00014211125999999999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 2.9203975561324516, "conditional_coverage_pvalue" = 0.2321901158399999, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 1.184939225613002, "independence_pvalue" = 0.27635275644239965, "conditional_coverage_statistic" = 5.940044655391613, "conditional_coverage_pvalue" = 0.05130216486000007, "transitions" = [[0, 1], [2, 2]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350762, "conditional_coverage_pvalue" = 3.4426847232000085e-06, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.283167138309286, "conditional_coverage_pvalue" = 0.07124835347988485, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 0, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.603006063019242, "conditional_coverage_pvalue" = 0.4486541158121474, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 0] +probability = 0.05 +expected = {"coverage_statistic" = 16.532861346444374, "coverage_pvalue" = 4.781419117133599e-05, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 18.764296859586473, "conditional_coverage_pvalue" = 8.421408e-05, "transitions" = [[1, 0], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 0] +probability = 0.4 +expected = {"coverage_statistic" = 1.7354583305194495, "coverage_pvalue" = 0.1877153166980975, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 3.9668938436615475, "conditional_coverage_pvalue" = 0.13759414271999992, "transitions" = [[1, 0], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 0] +probability = 0.95 +expected = {"coverage_statistic" = 4.755105429778611, "coverage_pvalue" = 0.029211503268054977, "independence_statistic" = 2.231435513142098, "independence_pvalue" = 0.13522815768706709, "conditional_coverage_statistic" = 6.986540942920709, "conditional_coverage_pvalue" = 0.030401282880000043, "transitions" = [[1, 0], [1, 3]], "exception_count" = 4, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 25.158517896350762, "conditional_coverage_pvalue" = 3.4426847232000085e-06, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 5.283167138309286, "conditional_coverage_pvalue" = 0.07124835347988485, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = 0.5053430784314124, "independence_pvalue" = 0.47716178085961247, "conditional_coverage_statistic" = 1.603006063019242, "conditional_coverage_pvalue" = 0.4486541158121474, "transitions" = [[0, 1], [1, 3]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 0] +probability = 0.05 +expected = {"coverage_statistic" = 24.65317481791935, "coverage_pvalue" = 6.863159648022814e-07, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 0] +probability = 0.4 +expected = {"coverage_statistic" = 4.777824059877874, "coverage_pvalue" = 0.028828526025108103, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 0] +probability = 0.95 +expected = {"coverage_statistic" = 1.0976629845878296, "coverage_pvalue" = 0.2947795541152118, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [1, 4]], "exception_count" = 5, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 35.94878728264789, "coverage_pvalue" = 2.0257241590117965e-09, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 5]], "exception_count" = 6, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 10.99548878248986, "coverage_pvalue" = 0.0009133392314647706, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 5]], "exception_count" = 6, "sample_count" = 6} + +[[exceptions]] +hits = [1, 1, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 0.6155195326506069, "coverage_pvalue" = 0.4327171451180195, "independence_statistic" = "undefined", "independence_pvalue" = "undefined", "conditional_coverage_statistic" = "undefined", "conditional_coverage_pvalue" = "undefined", "transitions" = [[0, 0], [0, 5]], "exception_count" = 6, "sample_count" = 6} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +probability = 0.05 +expected = {"coverage_statistic" = 64.75213721651782, "coverage_pvalue" = 8.493744079604845e-16, "independence_statistic" = 110.97255354000923, "independence_pvalue" = 5.999559947398882e-26, "conditional_coverage_statistic" = 175.72469075652705, "conditional_coverage_pvalue" = 6.948134793378789e-39, "transitions" = [[69, 1], [0, 29]], "exception_count" = 30, "sample_count" = 100} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.320170828709294, "coverage_pvalue" = 0.03766314102443897, "independence_statistic" = 110.97255354000923, "independence_pvalue" = 5.999559947398882e-26, "conditional_coverage_statistic" = 115.29272436871852, "conditional_coverage_pvalue" = 9.215162575819668e-26, "transitions" = [[69, 1], [0, 29]], "exception_count" = 30, "sample_count" = 100} + +[[exceptions]] +hits = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +probability = 0.95 +expected = {"coverage_statistic" = 300.30725554983286, "coverage_pvalue" = 2.8237799308289845e-67, "independence_statistic" = 110.97255354000923, "independence_pvalue" = 5.999559947398882e-26, "conditional_coverage_statistic" = 411.2798090898421, "conditional_coverage_pvalue" = 4.917271603469958e-90, "transitions" = [[69, 1], [0, 29]], "exception_count" = 30, "sample_count" = 100} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] +probability = 0.05 +expected = {"coverage_statistic" = 166.07312068216507, "coverage_pvalue" = 5.332042478978189e-38, "independence_statistic" = 137.2330405689928, "independence_pvalue" = 1.0723230018984475e-31, "conditional_coverage_statistic" = 303.30616125115785, "conditional_coverage_pvalue" = 1.3737380282225823e-66, "transitions" = [[0, 50], [49, 0]], "exception_count" = 50, "sample_count" = 100} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] +probability = 0.4 +expected = {"coverage_statistic" = 4.082199452025492, "coverage_pvalue" = 0.04333721240358853, "independence_statistic" = 137.2330405689928, "independence_pvalue" = 1.0723230018984475e-31, "conditional_coverage_statistic" = 141.3152400210183, "conditional_coverage_pvalue" = 2.0596125288246743e-31, "transitions" = [[0, 50], [49, 0]], "exception_count" = 50, "sample_count" = 100} + +[[exceptions]] +hits = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] +probability = 0.95 +expected = {"coverage_statistic" = 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0.1476086752194659 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [] +y = [] +x_noise = [] +y_noise = [] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.5761449599992041] +y = [0.8290350459463168] +x_noise = [1.777227321327075] +y_noise = [0.8433672884092134] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.5761449599992041] +y = [0.8290350459463168] +x_noise = [0.033527407347317066] +y_noise = [0.7198904663676241] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.5761449599992041] +y = [0.8290350459463168] +x_noise = [0.7660631324367637] +y_noise = [-0.0168651702698701] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.5761449599992041] +y = [0.8290350459463168] +x_noise = [-0.41751344633597764] +y_noise = [0.5259026078723864] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [0.7726600012259313] +y = [0.5884750046022599] +x_noise = [0.05168826890596037] +y_noise = [1.0536380632558868] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [0.7726600012259313] +y = [0.5884750046022599] +x_noise = [-0.48474281501416894] +y_noise = [1.3122015213818734] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [0.7726600012259313] +y = [0.5884750046022599] +x_noise = [-0.007932267206797454] +y_noise = [1.183671894249895] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [0.7726600012259313] +y = [0.5884750046022599] +x_noise = [-0.33991632009509315] +y_noise = [0.30821716541795935] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [1.0] +y = [0.0] +x_noise = [-0.6701679221922467] +y_noise = [-0.6929520624359633] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [1.0] +y = [0.0] +x_noise = [-0.26456163860261706] +y_noise = [0.15786415132685966] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [1.0] +y = [0.0] +x_noise = [-0.26452534781861575] +y_noise = [0.8932296842155699] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [1.0] +y = [0.0] +x_noise = [0.3997868070595132] +y_noise = [0.5789129690330113] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.0] +y = [-1.0] +x_noise = [-0.620126775709413] +y_noise = [2.1161384302562665] +k = 1 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.0] +y = [-1.0] +x_noise = [0.5626905747261748] +y_noise = [0.3009562255334394] +k = 2 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.0] +y = [-1.0] +x_noise = [-0.01930679419854481] +y_noise = [1.2345942751692651] +k = 4 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.0] +y = [-1.0] +x_noise = [1.0734861432629272] +y_noise = [-2.655987234463515] +k = 10 +nx = [] +ny = [] +output = 0.0 + +[[ksg]] +x = [-1.2606149191120708, -0.1874061020756856] +y = [0.5253988738421749, -1.2901248928370377] +x_noise = [-1.293893682446736, -0.5055976339659952] +y_noise = [-0.5776752841951329, 1.18141491770124] +k = 1 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-1.2606149191120708, -0.1874061020756856] +y = [0.5253988738421749, -1.2901248928370377] +x_noise = [-0.39200396757195677, -1.1996725315709689] +y_noise = [-1.89726348110454, 0.8601725673092254] +k = 2 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-1.2606149191120708, -0.1874061020756856] +y = [0.5253988738421749, -1.2901248928370377] +x_noise = [0.5715817582815961, -0.7058403129850842] +y_noise = [-0.11242571316538229, -1.2632620941078434] +k = 4 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-1.2606149191120708, -0.1874061020756856] +y = [0.5253988738421749, -1.2901248928370377] +x_noise = [0.3135861844897114, -0.07782836008224983] +y_noise = [0.5456583958824729, 0.3405511584949959] +k = 10 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-0.5352845176352281, -1.0238078078403345] +y = [0.27242106867486643, 1.040459498670975] +x_noise = [-0.6544773552400867, 0.9884000447534346] +y_noise = [0.20799624979432824, -0.5207965032830836] +k = 1 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-0.5352845176352281, -1.0238078078403345] +y = [0.27242106867486643, 1.040459498670975] +x_noise = [-1.1939862298111903, 1.2497085059860151] +y_noise = [0.2685903134369455, 0.5582099823120564] +k = 2 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-0.5352845176352281, -1.0238078078403345] +y = [0.27242106867486643, 1.040459498670975] +x_noise = [-1.1036092995527012, -1.7378677005399201] +y_noise = [-0.5935034990605127, 0.4304898894467286] +k = 4 +nx = [1, 1] +ny = [0, 0] +output = 0.0 + +[[ksg]] +x = [-0.5352845176352281, -1.0238078078403345] +y = [0.27242106867486643, 1.040459498670975] +x_noise = [0.7862466250983008, 1.2177434540192988] +y_noise = [-2.0211523999494974, 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-1.0] +y = [-0.0, -1.0, 2.0, 2.0, -0.0, -0.0, -1.0, -0.0, 1.0, 1.0, 0.0, -1.0, -0.0, -0.0, -0.0, -0.0, -1.0, 1.0, -1.0, 1.0, -0.0, -0.0, 0.0, 1.0, -1.0] +x_noise = [-0.4157614364671829, -0.7350506961618527, 0.18292987390961707, -0.7614703776520593, -0.4097467519397327, 1.1990927681285901, -1.1486401688631087, -0.1938343888363115, -1.4907777569951284, 1.8876954607200866, 1.8029862974706847, 0.3727356596279831, 0.9386635762722221, 1.8013052416221425, 1.7401064327228435, 1.2056386344978691, 1.1999274682277907, 1.2666380206784358, -0.03009458415313136, -0.9199569246809365, -0.0506133016035667, -1.089503164651187, -2.0120845246602914, -1.3047456175180323, -0.6667791103329023] +y_noise = [1.8393283217668057, -0.43357590226511034, 0.8087698633316577, 0.3572840799658396, 1.0253115104092945, 0.8495653124367445, -1.2302986626789825, -1.1774191000393996, -1.185700575350865, 1.077728029728675, 1.007324717828604, 2.425479780821695, -0.43373781234743286, -0.2062687255636347, 2.0155955909020835, 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11, 12, 11] +output = 0.3911811101358813 diff --git a/test/fixtures/ensemble_python_320.toml b/test/fixtures/ensemble_python_320.toml new file mode 100644 index 0000000..7ae60ff --- /dev/null +++ b/test/fixtures/ensemble_python_320.toml @@ -0,0 +1,680 @@ +reference = "RiskLabAI Python 3.2.0" +contract = "Fitted member predictions and supplied bootstrap row expansion" + +[[theory]] +count = 1 +probability = 0.0 +output = 0.0 +mathematical_tail = 0.0 + +[[theory]] +count = 1 +probability = 1e-06 +output = 1.0000000000287557e-06 +mathematical_tail = 1e-06 + +[[theory]] +count = 1 +probability = 0.01 +output = 0.010000000000000009 +mathematical_tail = 0.01 + +[[theory]] +count = 1 +probability = 0.2 +output = 0.19999999999999996 +mathematical_tail = 0.2 + +[[theory]] +count = 1 +probability = 0.5 +output = 0.5 +mathematical_tail = 0.5 + +[[theory]] +count = 1 +probability = 0.8 +output = 0.8 +mathematical_tail = 0.8 + +[[theory]] +count = 1 +probability = 0.99 +output = 0.99 +mathematical_tail = 0.99 + +[[theory]] +count = 1 +probability = 0.999999 +output = 0.999999 +mathematical_tail = 0.999999 + +[[theory]] +count = 1 +probability = 1.0 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 3 +probability = 0.0 +output = 0.0 +mathematical_tail = 0.0 + +[[theory]] +count = 3 +probability = 1e-06 +output = 3.000044657142098e-12 +mathematical_tail = 2.9999979999999998e-12 + +[[theory]] +count = 3 +probability = 0.01 +output = 0.00029800000000002047 +mathematical_tail = 0.00029800000000000003 + +[[theory]] +count = 3 +probability = 0.2 +output = 0.10400000000000009 +mathematical_tail = 0.10400000000000001 + +[[theory]] +count = 3 +probability = 0.5 +output = 0.5 +mathematical_tail = 0.5 + +[[theory]] +count = 3 +probability = 0.8 +output = 0.896 +mathematical_tail = 0.896 + +[[theory]] +count = 3 +probability = 0.99 +output = 0.999702 +mathematical_tail = 0.999702 + +[[theory]] +count = 3 +probability = 0.999999 +output = 0.999999999997 +mathematical_tail = 0.999999999997 + +[[theory]] +count = 3 +probability = 1.0 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 11 +probability = 0.0 +output = 0.0 +mathematical_tail = 0.0 + +[[theory]] +count = 11 +probability = 1e-06 +output = 0.0 +mathematical_tail = 4.6199802000346486e-34 + +[[theory]] +count = 11 +probability = 0.01 +output = 4.42543446332877e-10 +mathematical_tail = 4.4254343383480005e-10 + +[[theory]] +count = 11 +probability = 0.2 +output = 0.011654205439999954 +mathematical_tail = 0.011654205440000003 + +[[theory]] +count = 11 +probability = 0.5 +output = 0.5 +mathematical_tail = 0.5 + +[[theory]] +count = 11 +probability = 0.8 +output = 0.98834579456 +mathematical_tail = 0.98834579456 + +[[theory]] +count = 11 +probability = 0.99 +output = 0.9999999995574566 +mathematical_tail = 0.9999999995574566 + +[[theory]] +count = 11 +probability = 0.999999 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 11 +probability = 1.0 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 51 +probability = 0.0 +output = 0.0 +mathematical_tail = 0.0 + +[[theory]] +count = 51 +probability = 1e-06 +output = 0.0 +mathematical_tail = 2.479532971533771e-142 + +[[theory]] +count = 51 +probability = 0.01 +output = 0.0 +mathematical_tail = 1.9468761102961738e-38 + +[[theory]] +count = 51 +probability = 0.2 +output = 8.128990065525699e-07 +mathematical_tail = 8.128990065206694e-07 + +[[theory]] +count = 51 +probability = 0.5 +output = 0.5 +mathematical_tail = 0.5 + +[[theory]] +count = 51 +probability = 0.8 +output = 0.9999991871009934 +mathematical_tail = 0.9999991871009934 + +[[theory]] +count = 51 +probability = 0.99 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 51 +probability = 0.999999 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 51 +probability = 1.0 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 101 +probability = 0.0 +output = 0.0 +mathematical_tail = 0.0 + +[[theory]] +count = 101 +probability = 1e-06 +output = 0.0 +mathematical_tail = 1.9979462956716233e-277 + +[[theory]] +count = 101 +probability = 0.01 +output = 0.0 +mathematical_tail = 1.2206803070165445e-73 + +[[theory]] +count = 101 +probability = 0.2 +output = 8.422373909411363e-12 +mathematical_tail = 8.422415435259088e-12 + +[[theory]] +count = 101 +probability = 0.5 +output = 0.4999999999999998 +mathematical_tail = 0.5 + +[[theory]] +count = 101 +probability = 0.8 +output = 0.9999999999915776 +mathematical_tail = 0.9999999999915776 + +[[theory]] +count = 101 +probability = 0.99 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 101 +probability = 0.999999 +output = 1.0 +mathematical_tail = 1.0 + +[[theory]] +count = 101 +probability = 1.0 +output = 1.0 +mathematical_tail = 1.0 + +[[fitted]] +x = [[1.1097286489720684, -0.38697025151688486, -0.4796393003157726, 0.5038333671685818], [0.31873281298522255, -0.16069827087639202, 0.7545370589006447, -0.7036301640793745], [2.4748603869583214, 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= [0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1], "c_i" = [1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1], "variance" = [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1]} +accuracies = {"uniform" = 0.38461538461538464, "c_i" = 0.38461538461538464, "variance" = 0.3076923076923077} + +[[fitted]] +x = [[-0.6455307394717998, 0.2670432912936477, 0.30558878440748255, 0.6817935906973404], [0.26074269154591406, 0.3751761034144892, 0.22556439365299358, 1.6218499793604617], [0.07149181822129137, 1.4269917491417192, -1.4219040627407515, 0.8987533191292185], [-0.9642693146795656, -0.6312797244542242, -1.5071083385371788, 0.3340160659766807], [1.34765247955303, 1.0050699973502668, -1.3548405413319369, -0.30034591863415855], [0.5323310458522588, -0.03589911067269107, -0.7743973721246871, 0.9072607919170507], [-0.21147403619472266, -0.49249050124544513, 0.2075133537325555, 0.19289724092554303], [-0.555794282281137, 0.5826564883700218, -0.20013422107312007, -1.4209594246879305], [0.9177317957514004, 0.2547681565906008, 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1, 1, 1], [0, 1, 0, 0, 0, 0, 0, 1], [0, 1, 0, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1], [0, 1, 1, 1, 1, 0, 0, 1], [0, 1, 1, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 0, 0]] +test_predictions = [[0, 1, 1, 1, 0, 0, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 0, 0, 1, 0], [0, 1, 1, 1, 0, 0, 1, 0], [0, 1, 1, 1, 0, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 0], [1, 1, 0, 0, 0, 1, 0, 0], [0, 1, 1, 1, 0, 0, 0, 1], [1, 1, 1, 1, 1, 0, 1, 1], [0, 1, 1, 1, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 1, 1]] +weights = {"uniform" = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], "c_i" = [0.11458333333333333, 0.13541666666666666, 0.15625, 0.14583333333333334, 0.13541666666666666, 0.125, 0.08333333333333333, 0.10416666666666667], "variance" = [0.12892632781267846, 0.12207310108509423, 0.11407766990291263, 0.1182181610508281, 0.12207310108509423, 0.125642490005711, 0.13706453455168474, 0.13192461450599657]} +predictions = {"uniform" = [-1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1], "c_i" = [1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1], "variance" = [-1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1]} +accuracies = {"uniform" = 0.5384615384615384, "c_i" = 0.6153846153846154, "variance" = 0.5384615384615384} + +[[fitted]] +x = [[0.32053391316799296, 0.8498522026762443, 0.27801515416139366, 0.362602127155858], [0.10375310791105782, -0.5906896340062966, 0.383180412844235, -0.4931665507050555], [0.7565968723840392, -2.6694180475489007, -1.582152130749241, -0.5588400065237008], [-0.5020952551441339, -1.2264210088025784, -1.3181974402240149, -0.11657990688479951], [1.5582724072618745, -1.138407641388002, -0.9308185046551221, 0.8695970129750664], [-0.8290814690735654, 0.5287053627349583, -0.5934236567874734, -0.9949927359423693], [1.157120086818499, 1.1938283587268599, 1.4806136783618147, -0.8808308351048286], [1.2707787908340504, -0.5762386176825847, 1.712992416847575, 0.8837799800483014], [-1.0625054234078977, 0.7270363342232495, 1.433924657016713, 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11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11, 7, 11] +x_test = [[1.2698260659793588, 1.1156495388934256, -1.235250491315714, 0.1700475864290776], [0.13650684074054506, 0.5065525452530988, 0.3684416262918051, 1.8329655505218163], [-0.6566777476965542, 0.5180042547163368, -0.046617942670130016, 1.5277468607622842], [-0.09191150135749192, -0.2781912767418271, -0.14268225371531137, 0.8446000389544446], [0.4353213894257005, -0.38033418111332795, 1.091251120968184, 0.9988067446314267], [-1.3612117744230476, 0.24275897000176608, 0.5673752310792536, -1.4473141095995237], [0.08711752141645873, 0.41980688814186917, -1.244047548761209, 2.1361216131251073], [2.1016998328371588, 2.2961332212834766, -0.3535656690214264, -1.2462746003740903], [-1.9530367133366424, -1.7131268056561106, 0.43773026998191394, -2.4206238259376534], [0.066538696650672, -1.3612397727402414, 0.42870957630415657, -1.3304249379812447], [1.7887533016319561, -1.575529605439473, 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0, 1], [0, 1, 1, 0, 1, 1, 1, 1], [0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 1, 0, 1, 0], [1, 1, 1, 1, 0, 1, 0, 0]] +test_predictions = [[0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0], [1, 0, 1, 1, 0, 1, 1, 0], [0, 0, 0, 1, 0, 1, 1, 0], [0, 1, 0, 0, 1, 0, 1, 0], [0, 0, 1, 1, 0, 0, 0, 0], [1, 0, 0, 0, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [0, 1, 1, 1, 0, 0, 1, 1], [0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 1, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 1, 0]] +weights = {"uniform" = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], "c_i" = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], "variance" = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125]} +predictions = {"uniform" = [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], "c_i" = [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], "variance" = [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]} +accuracies = {"uniform" = 0.5384615384615384, "c_i" = 0.5384615384615384, "variance" = 0.5384615384615384} + +[[votes]] +train_predictions = [[0, 0], [1, 1]] +y = [0, 1] +test_predictions = [[0, 1], [1, 0], [1, 1], [0, 0]] +classes = [0, 1] +weights = {"uniform" = [0.5, 0.5], "c_i" = [0.5, 0.5], "variance" = [0.5, 0.5]} +predictions = {"uniform" = [0, 0, 1, 0], "c_i" = [0, 0, 1, 0], "variance" = [0, 0, 1, 0]} + +[[votes]] +train_predictions = [[1, 1], [0, 0]] +y = [0, 1] +test_predictions = [[0, 1], [1, 0], [1, 1], [0, 0]] +classes = [0, 1] +weights = {"uniform" = [0.5, 0.5], "c_i" = [0.5, 0.5], "variance" = [0.5, 0.5]} +predictions = {"uniform" = [0, 0, 1, 0], "c_i" = [0, 0, 1, 0], "variance" = [0, 0, 1, 0]} + +[[votes]] +train_predictions = [[0, 1], [1, 0]] +y = [0, 1] +test_predictions = [[0, 1], [1, 0], [1, 1], [0, 0]] +classes = [0, 1] +weights = {"uniform" = [0.5, 0.5], "c_i" = [1.0, 0.0], "variance" = [0.0, 1.0]} +predictions = {"uniform" = [0, 0, 1, 0], "c_i" = [0, 1, 1, 0], "variance" = [1, 0, 1, 0]} + +[[bootstrap]] +x = [[0.0, -0.3028839247028385], [1.0, -0.043417668798498704], 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12, 9, 10, 9, 10, 1, 2, 11, 12, 11, 12], [3, 4, 7, 8, 11, 12, 7, 8, 13, 5, 6, 7, 8, 7, 8, 5, 6, 5, 6, 7, 8, 7, 8, 3, 4], [9, 10, 7, 8, 3, 4, 11, 12, 11, 12, 11, 12, 5, 6, 7, 8, 9, 10, 9, 10, 13, 13, 11, 12], [7, 8, 7, 8, 13, 9, 10, 11, 12, 5, 6, 11, 12, 3, 4, 11, 12, 7, 8, 9, 10, 3, 4, 9, 10]] +values = [0.5769230769230769, 0.6, 0.44, 0.5769230769230769, 0.32, 0.2692307692307692, 0.6, 0.4166666666666667, 0.48] +mean = 0.4755270655270654 +std = 0.12401757997631083 +duplicate_index = true + +[[bootstrap]] +x = [[0.0, -0.6874267067222517], [1.0, -0.1827458788346187], [2.0, -1.3169792527496806], [3.0, 0.19230517307285136], [4.0, 0.3610666256806479], [5.0, 0.01115152677551667], [6.0, 0.32504165865364903], [7.0, -0.010428010600555952], [8.0, 1.2966687262176286], [9.0, 0.1019039362130353], [10.0, -1.6128800802104248], [11.0, 0.6168337201693126], [12.0, 0.2149817169060441]] +y = [7, 8, 7, 8, 7, 8, 7, 8, 7, 8, 7, 8, 7] +predicted = [7, 8, 7, 7, 8, 7, 7, 8, 7, 7, 8, 7, 7] +row_draws = [[13, 3, 4, 7, 8, 1, 2, 3, 4, 1, 2, 11, 12, 1, 2, 13, 7, 8, 11, 12, 1, 2, 13], [11, 12, 11, 12, 9, 10, 1, 2, 1, 2, 1, 2, 7, 8, 7, 8, 5, 6, 7, 8, 7, 8, 3, 4, 9, 10], [3, 4, 3, 4, 11, 12, 5, 6, 3, 4, 9, 10, 5, 6, 7, 8, 3, 4, 11, 12, 9, 10, 3, 4, 9, 10], [3, 4, 5, 6, 9, 10, 5, 6, 11, 12, 5, 6, 3, 4, 11, 12, 5, 6, 11, 12, 11, 12, 11, 12, 5, 6], [3, 4, 5, 6, 7, 8, 1, 2, 9, 10, 9, 10, 11, 12, 9, 10, 3, 4, 3, 4, 3, 4, 13, 1, 2], [3, 4, 1, 2, 7, 8, 11, 12, 1, 2, 1, 2, 1, 2, 5, 6, 1, 2, 5, 6, 3, 4, 11, 12, 5, 6], [5, 6, 3, 4, 9, 10, 11, 12, 3, 4, 7, 8, 5, 6, 9, 10, 1, 2, 3, 4, 1, 2, 5, 6, 1, 2], [7, 8, 1, 2, 11, 12, 13, 5, 6, 13, 7, 8, 3, 4, 3, 4, 1, 2, 3, 4, 11, 12, 13], [13, 3, 4, 7, 8, 13, 9, 10, 11, 12, 5, 6, 7, 8, 9, 10, 3, 4, 3, 4, 3, 4, 9, 10]] +values = [0.7391304347826086, 0.6538461538461539, 0.38461538461538464, 0.11538461538461539, 0.56, 0.5384615384615384, 0.5, 0.6086956521739131, 0.5416666666666666] +mean = 0.5157556051034312 +std = 0.17975351229765446 +duplicate_index = true diff --git a/test/fixtures/entropy_python_320.toml b/test/fixtures/entropy_python_320.toml new file mode 100644 index 0000000..d1d3ef3 --- /dev/null +++ b/test/fixtures/entropy_python_320.toml @@ -0,0 +1,24389 @@ +reference = "RiskLabAI 3.2.0 discrete-message entropy" + +[[messages]] +message = "" +shannon = 0.0 +lempel = 0.0 + +[[messages]] +message = "0" +shannon = -0.0 +lempel = 1.0 + +[[messages]] +message = "1" +shannon = -0.0 +lempel = 1.0 + +[[messages]] +message = "00" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "01" +shannon = 1.0 +lempel = 1.0 + +[[messages]] +message = "10" +shannon = 1.0 +lempel = 1.0 + +[[messages]] +message = "11" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "000" +shannon = -0.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "001" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "010" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "100" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "110" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "111" +shannon = -0.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "0000" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "0001" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "0010" +shannon = 0.8112781244591328 +lempel = 0.5 + +[[messages]] +message = "0011" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "0100" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "0101" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "0110" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "0111" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "1000" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "1001" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "1010" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "1011" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "1100" +shannon = 1.0 +lempel = 0.75 + +[[messages]] +message = "1101" +shannon = 0.8112781244591328 +lempel = 0.5 + +[[messages]] +message = "1110" +shannon = 0.8112781244591328 +lempel = 0.75 + +[[messages]] +message = "1111" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "00000" +shannon = -0.0 +lempel = 0.4 + +[[messages]] +message = "00001" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "00010" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "00011" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "00100" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "00101" +shannon = 0.9709505944546686 +lempel = 0.4 + +[[messages]] +message = "00110" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "00111" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01000" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "01001" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01010" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01011" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01100" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01101" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01110" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "01111" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "10000" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "10001" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10010" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10011" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10100" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10101" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10110" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "10111" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "11000" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "11001" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "11010" +shannon = 0.9709505944546686 +lempel = 0.4 + +[[messages]] +message = "11011" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "11100" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] +message = "11101" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "11110" +shannon = 0.7219280948873623 +lempel = 0.6 + +[[messages]] +message = "11111" +shannon = -0.0 +lempel = 0.4 + +[[messages]] +message = "000000" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "000001" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "000010" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "000011" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "000100" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "000101" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "000110" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "000111" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "001000" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "001001" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "001010" +shannon = 0.9182958340544896 +lempel = 0.5 + +[[messages]] +message = "001011" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "001100" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "001101" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "001110" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "001111" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "010000" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "010001" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "010010" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "010011" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "010100" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "010101" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "010110" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "010111" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011000" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011001" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "011010" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "011011" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011100" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "011101" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011110" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "011111" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "100000" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "100001" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "100010" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "100011" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "100100" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "100101" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "100110" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "100111" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101000" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101001" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "101010" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "101011" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101100" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "101101" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101110" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "101111" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "110000" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "110001" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "110010" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "110011" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "110100" +shannon = 1.0 +lempel = 0.5 + +[[messages]] +message = "110101" +shannon = 0.9182958340544896 +lempel = 0.5 + +[[messages]] +message = "110110" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "110111" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "111000" +shannon = 1.0 +lempel = 0.6666666666666666 + +[[messages]] +message = "111001" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "111010" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "111011" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "111100" +shannon = 0.9182958340544896 +lempel = 0.6666666666666666 + +[[messages]] +message = "111101" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "111110" +shannon = 0.6500224216483541 +lempel = 0.5 + +[[messages]] +message = "111111" +shannon = -0.0 +lempel = 0.5 + +[[messages]] +message = "0000000" +shannon = -0.0 +lempel = 0.42857142857142855 + +[[messages]] +message = "0000001" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "0000010" +shannon = 0.5916727785823275 +lempel = 0.42857142857142855 + +[[messages]] +message = "0000011" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0000100" +shannon = 0.5916727785823275 +lempel = 0.42857142857142855 + +[[messages]] +message = "0000101" +shannon = 0.863120568566631 +lempel = 0.42857142857142855 + +[[messages]] +message = "0000110" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0000111" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001000" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001001" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001010" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001100" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0001111" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0010000" +shannon = 0.5916727785823275 +lempel = 0.42857142857142855 + +[[messages]] +message = "0010001" +shannon = 0.863120568566631 +lempel = 0.42857142857142855 + +[[messages]] +message = "0010010" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0010011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0010100" +shannon = 0.863120568566631 +lempel = 0.42857142857142855 + +[[messages]] +message = "0010101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0010110" +shannon = 0.9852281360342515 +lempel = 0.42857142857142855 + +[[messages]] +message = "0010111" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011000" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011001" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0011111" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100000" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100001" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100010" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100100" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0100111" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101000" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101001" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0101110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] 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0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111011" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111101" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111110" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "0111111" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000000" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000001" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000010" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000100" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1000111" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001000" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001001" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1001101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] 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0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011001" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011011" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011101" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011110" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1011111" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100000" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100001" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100011" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100101" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100110" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1100111" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1101000" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1101001" +shannon = 0.9852281360342515 +lempel = 0.42857142857142855 + +[[messages]] +message = "1101010" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1101011" +shannon = 0.863120568566631 +lempel = 0.42857142857142855 + +[[messages]] +message = "1101100" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] 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0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "1111000" +shannon = 0.9852281360342515 +lempel = 0.5714285714285714 + +[[messages]] +message = "1111001" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1111010" +shannon = 0.863120568566631 +lempel = 0.42857142857142855 + +[[messages]] +message = "1111011" +shannon = 0.5916727785823275 +lempel = 0.42857142857142855 + +[[messages]] +message = "1111100" +shannon = 0.863120568566631 +lempel = 0.5714285714285714 + +[[messages]] +message = "1111101" +shannon = 0.5916727785823275 +lempel = 0.42857142857142855 + +[[messages]] +message = "1111110" +shannon = 0.5916727785823275 +lempel = 0.5714285714285714 + +[[messages]] +message = "1111111" +shannon = -0.0 +lempel = 0.42857142857142855 + +[[messages]] +message = "αβαγβαα" +shannon = 1.3787834934861753 +lempel = 0.5714285714285714 + +[[messages]] +message = "🙂🙃🙂🙂🙃" +shannon = 0.9709505944546686 +lempel = 0.6 + +[[messages]] 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2 +word = "0" + +[[matches]] +message = "00000" +position = 4 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "00000" +position = 4 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "00001" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00001" +position = 0 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "00001" +position = 0 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "00001" +position = 1 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "00001" +position = 1 +window = 3 +length = 4 +word = "000" + +[[matches]] +message = "00001" +position = 1 +window = 5 +length = 4 +word = "000" + +[[matches]] +message = "00001" +position = 2 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "00001" +position = 2 +window = 3 +length = 3 +word = "00" + +[[matches]] +message = "00001" +position = 2 +window = 5 +length = 3 +word = "00" + +[[matches]] +message = "00001" +position = 3 +window = 1 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= 1 +length = 2 +word = "0" + +[[matches]] +message = "00010" +position = 2 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "00010" +position = 2 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "00010" +position = 3 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00010" +position = 3 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "00010" +position = 3 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "00010" +position = 4 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00010" +position = 4 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "00010" +position = 4 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "00011" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 0 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 0 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 1 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "00011" +position = 1 +window = 3 +length = 3 +word = "00" + +[[matches]] +message = "00011" +position = 1 +window = 5 +length = 3 +word = "00" + +[[matches]] +message = "00011" +position = 2 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "00011" +position = 2 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "00011" +position = 2 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "00011" +position = 3 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 3 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 3 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "00011" +position = 4 +window = 1 +length = 2 +word = "1" + +[[matches]] +message = "00011" +position = 4 +window = 3 +length = 2 +word = "1" + +[[matches]] +message = "00011" +position = 4 +window = 5 +length = 2 +word = "1" + +[[matches]] +message = "00100" +position = 0 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= 3 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00101" +position = 3 +window = 3 +length = 3 +word = "01" + +[[matches]] +message = "00101" +position = 3 +window = 5 +length = 3 +word = "01" + +[[matches]] +message = "00101" +position = 4 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00101" +position = 4 +window = 3 +length = 2 +word = "1" + +[[matches]] +message = "00101" +position = 4 +window = 5 +length = 2 +word = "1" + +[[matches]] +message = "00110" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "00110" +position = 0 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "00110" +position = 0 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "00110" +position = 1 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "00110" +position = 1 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "00110" +position = 1 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "00110" 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= "1" + +[[matches]] +message = "01101" +position = 3 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "01101" +position = 3 +window = 3 +length = 3 +word = "01" + +[[matches]] +message = "01101" +position = 3 +window = 5 +length = 3 +word = "01" + +[[matches]] +message = "01101" +position = 4 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "01101" +position = 4 +window = 3 +length = 2 +word = "1" + +[[matches]] +message = "01101" +position = 4 +window = 5 +length = 2 +word = "1" + +[[matches]] +message = "01110" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "01110" +position = 0 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "01110" +position = 0 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "01110" +position = 1 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "01110" +position = 1 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "01110" +position = 1 +window = 5 +length = 1 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= 1 +word = "" + +[[matches]] +message = "01111" +position = 1 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "01111" +position = 1 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "01111" +position = 1 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "01111" +position = 2 +window = 1 +length = 2 +word = "1" + +[[matches]] +message = "01111" +position = 2 +window = 3 +length = 4 +word = "111" + +[[matches]] +message = "01111" +position = 2 +window = 5 +length = 4 +word = "111" + +[[matches]] +message = "01111" +position = 3 +window = 1 +length = 2 +word = "1" + +[[matches]] +message = "01111" +position = 3 +window = 3 +length = 3 +word = "11" + +[[matches]] +message = "01111" +position = 3 +window = 5 +length = 3 +word = "11" + +[[matches]] +message = "01111" +position = 4 +window = 1 +length = 2 +word = "1" + +[[matches]] +message = "01111" +position = 4 +window = 3 +length = 2 +word = "1" + +[[matches]] +message = "01111" +position = 4 +window = 5 +length = 2 +word = "1" + +[[matches]] +message = "10000" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 0 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 0 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 1 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 1 +window = 3 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 1 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "10000" +position = 2 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "10000" +position = 2 +window = 3 +length = 4 +word = "000" + +[[matches]] +message = "10000" +position = 2 +window = 5 +length = 4 +word = "000" + +[[matches]] +message = "10000" +position = 3 +window = 1 +length = 2 +word = "0" + +[[matches]] +message = "10000" +position = 3 +window = 3 +length = 3 +word = "00" + +[[matches]] +message = "10000" +position = 3 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"" + +[[matches]] +message = "10110" +position = 1 +window = 5 +length = 1 +word = "" + +[[matches]] +message = "10110" +position = 2 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "10110" +position = 2 +window = 3 +length = 2 +word = "1" + +[[matches]] +message = "10110" +position = 2 +window = 5 +length = 2 +word = "1" + +[[matches]] +message = "10110" +position = 3 +window = 1 +length = 2 +word = "1" + +[[matches]] +message = "10110" +position = 3 +window = 3 +length = 3 +word = "10" + +[[matches]] +message = "10110" +position = 3 +window = 5 +length = 3 +word = "10" + +[[matches]] +message = "10110" +position = 4 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "10110" +position = 4 +window = 3 +length = 2 +word = "0" + +[[matches]] +message = "10110" +position = 4 +window = 5 +length = 2 +word = "0" + +[[matches]] +message = "10111" +position = 0 +window = 1 +length = 1 +word = "" + +[[matches]] +message = "10111" +position = 0 +window = 3 +length = 1 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2.8465285635230813 +collider_variance = 1.2888736016393312 +confounder_variance = 2.6383659178937005 +exposure_variance = 2.7255046795479356 +confounder_coefficient = -1.0 +collider_coefficients = {"beta_hat" = -1.0, "theta_hat" = 0.0} +generalized_confounder = -1.0 +generalized_collider = {"beta_hat" = -1.0, "theta_hat" = 0.0} +confounder_factor = {"correct" = 5.196307034889735, "misspecified" = 5.196307034889735} +confounder_forecast = {"correct" = 4.44317285003174, "misspecified" = 4.44317285003174} +collider_factor = {"correct" = 5.196307034889735, "misspecified" = 5.196307034889735} +collider_forecast = {"correct" = 1.0, "misspecified" = 1.0} +confounder_factor_default = {"correct" = 5.196307034889735, "misspecified" = 5.196307034889735} +confounder_forecast_default = {"correct" = 2.0, "misspecified" = 2.0} + +[[factor]] +beta = -1.0 +gamma = 0.0 +delta = 0.0 +x = -0.505334684838431 +z = 0.12346306623836345 +realized = 0.284109324027794 +outcome_variance = 0.2954879275707957 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0.7374980945080843 +realized = 0.1981791214699154 +outcome_variance = 2.047237541921416 +collider_variance = 1.8141341041547783 +confounder_variance = 0.48807191134219485 +exposure_variance = 2.496395760925397 +confounder_coefficient = -1.0 +collider_coefficients = {"beta_hat" = -0.5, "theta_hat" = 0.5} +generalized_confounder = -1.0 +generalized_collider = {"beta_hat" = -0.4698159800283003, "theta_hat" = 0.5301840199716997} +confounder_factor = {"correct" = 0.0002174437987440262, "misspecified" = 0.4579652938069733} +confounder_forecast = {"correct" = 1.6429167212467566, "misspecified" = 0.841095842716672} +collider_factor = {"correct" = 0.565871141132568, "misspecified" = 0.005546286382064186} +collider_forecast = {"correct" = 1.0, "misspecified" = 0.5} +confounder_factor_default = {"correct" = 0.0002174437987440262, "misspecified" = 0.565871141132568} +confounder_forecast_default = {"correct" = 2.0, "misspecified" = 1.0} + +[[factor]] +beta = -1.0 +gamma = 1.0 +delta = 0.8 +x = 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0.4302439024390244} + +[[factor]] +beta = 0.0 +gamma = -0.7 +delta = -1.0 +x = -0.32576934457850315 +z = -0.23775903769210835 +realized = -1.0141810746359659 +outcome_variance = 0.7074212080312401 +collider_variance = 2.3334249216122225 +confounder_variance = 2.108269967841709 +exposure_variance = 1.3080724412784492 +confounder_coefficient = 0.35 +collider_coefficients = {"beta_hat" = -0.4697986577181208, "theta_hat" = -0.4697986577181208} +generalized_confounder = 0.4319792341509672 +generalized_collider = {"beta_hat" = -0.18476996892522246, "theta_hat" = -0.18476996892522246} +confounder_factor = {"correct" = 0.027699386402095922, "misspecified" = 0.012999105266580838} +confounder_forecast = {"correct" = 0.48999999999999994, "misspecified" = 0.2484743632858116} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.027699386402095922, "misspecified" = 0.01300039406872067} 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{"correct" = 0.48999999999999994, "misspecified" = 0.0} + +[[factor]] +beta = 0.0 +gamma = -0.7 +delta = 0.8 +x = 0.9467803215387025 +z = 1.5866092820260365 +realized = -0.5174050143661578 +outcome_variance = 1.1123183652779982 +collider_variance = 1.1469249150300227 +confounder_variance = 2.7624610997317247 +exposure_variance = 1.1717308522343257 +confounder_coefficient = -0.3414634146341463 +collider_coefficients = {"beta_hat" = 0.37583892617449666, "theta_hat" = -0.4697986577181208} +generalized_confounder = -0.5262356980497976 +generalized_collider = {"beta_hat" = 0.3681516986566154, "theta_hat" = -0.46018962332076924} +confounder_factor = {"correct" = 1.2334912167674756, "misspecified" = -0.08744844357330882} +confounder_forecast = {"correct" = 0.48999999999999994, "misspecified" = -0.12367241806800844} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 1.2334912167674756, "misspecified" = 0.10451696819843787} +confounder_forecast_default = {"correct" = 0.48999999999999994, "misspecified" = 0.19121951219512193} + +[[factor]] +beta = 0.0 +gamma = 0.0 +delta = -1.0 +x = 1.237393516965442 +z = -0.6503905179386973 +realized = 0.5207216031867412 +outcome_variance = 2.3811409990544234 +collider_variance = 0.9219439339832396 +confounder_variance = 2.7200146626537554 +exposure_variance = 0.9636693111037452 +confounder_coefficient = 0.0 +collider_coefficients = {"beta_hat" = 0.0, "theta_hat" = 0.0} +generalized_confounder = 0.0 +generalized_collider = {"beta_hat" = 0.0, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast = {"correct" = 0.0, "misspecified" = 0.0} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast_default = {"correct" = 0.0, "misspecified" = 0.0} + +[[factor]] +beta = 0.0 +gamma = 0.0 +delta = 0.0 +x = 0.6208663009239946 +z = -1.4063455537277214 +realized = 0.21592093372642845 +outcome_variance = 1.5217636556262248 +collider_variance = 1.036400974274169 +confounder_variance = 2.7334837033189445 +exposure_variance = 2.233771854387299 +confounder_coefficient = 0.0 +collider_coefficients = {"beta_hat" = 0.0, "theta_hat" = 0.0} +generalized_confounder = 0.0 +generalized_collider = {"beta_hat" = 0.0, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast = {"correct" = 0.0, "misspecified" = 0.0} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast_default = {"correct" = 0.0, "misspecified" = 0.0} + +[[factor]] +beta = 0.0 +gamma = 0.0 +delta = 0.8 +x = -0.5404167825525331 +z = -0.6175989733371436 +realized = -1.0476548202481228 +outcome_variance = 1.7167713564233127 +collider_variance = 2.5140914434594723 +confounder_variance = 2.0162208186645647 +exposure_variance = 1.0798341205845032 +confounder_coefficient = 0.0 +collider_coefficients = {"beta_hat" = 0.0, "theta_hat" = 0.0} +generalized_confounder = 0.0 +generalized_collider = {"beta_hat" = 0.0, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast = {"correct" = 0.0, "misspecified" = 0.0} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.0, "misspecified" = 0.0} +confounder_forecast_default = {"correct" = 0.0, "misspecified" = 0.0} + +[[factor]] +beta = 0.0 +gamma = 1.0 +delta = -1.0 +x = 1.0252571146435805 +z = -0.36738655071687937 +realized = -0.12331392769966755 +outcome_variance = 0.9320293888992683 +collider_variance = 0.9536195502309859 +confounder_variance = 0.8453976889901675 +exposure_variance = 2.593654017547221 +confounder_coefficient = -0.5 +collider_coefficients = {"beta_hat" = 0.5, "theta_hat" = 0.5} +generalized_confounder = -0.2458229073389992 +generalized_collider = {"beta_hat" = 0.49427513762405956, "theta_hat" = 0.49427513762405956} +confounder_factor = {"correct" = 0.13497287764764618, "misspecified" = 0.06384007723528523} +confounder_forecast = {"correct" = 1.0, "misspecified" = 0.061656963849833775} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.13497287764764618, "misspecified" = 0.26278803778181997} +confounder_forecast_default = {"correct" = 1.0, "misspecified" = 0.5} + +[[factor]] +beta = 0.0 +gamma = 1.0 +delta = 0.0 +x = 0.35027284073123743 +z = -0.8666796312949308 +realized = 1.3413773931562405 +outcome_variance = 0.5845540010846646 +collider_variance = 2.136741176633426 +confounder_variance = 2.461111133308666 +exposure_variance = 1.0793492246062475 +confounder_coefficient = 0.0 +collider_coefficients = {"beta_hat" = 0.0, "theta_hat" = 0.5} +generalized_confounder = 0.0 +generalized_collider = {"beta_hat" = 0.0, "theta_hat" = 0.2148072748120016} +confounder_factor = {"correct" = 0.7511335833015171, "misspecified" = 0.0} +confounder_forecast = {"correct" = 1.0, "misspecified" = 0.0} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.7511335833015171, "misspecified" = 0.0} +confounder_forecast_default = {"correct" = 1.0, "misspecified" = 0.0} + +[[factor]] +beta = 0.0 +gamma = 1.0 +delta = 0.8 +x = 1.153385844960876 +z = -0.31807155357882627 +realized = 0.6871806712070428 +outcome_variance = 0.8944371401417344 +collider_variance = 1.5215323687764295 +confounder_variance = 0.6670013463979605 +exposure_variance = 2.4019065209956385 +confounder_coefficient = 0.4878048780487805 +collider_coefficients = {"beta_hat" = -0.4, "theta_hat" = 0.5} +generalized_confounder = 0.18863244384626895 +generalized_collider = {"beta_hat" = -0.29617497632815754, "theta_hat" = 0.3702187204101969} +confounder_factor = {"correct" = 0.10116951319604815, "misspecified" = 0.3028966005522334} +confounder_forecast = {"correct" = 1.0, "misspecified" = 0.33521008351563064} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.10116951319604815, "misspecified" = 0.31654941281525617} +confounder_forecast_default = {"correct" = 1.0, "misspecified" = 0.3902439024390244} + +[[factor]] +beta = 0.4 +gamma = -0.7 +delta = -1.0 +x = -0.4926590308901675 +z = 1.2450839490107666 +realized = -1.246942954833217 +outcome_variance = 2.281234511532814 +collider_variance = 1.2361954448940962 +confounder_variance = 0.5684670848077751 +exposure_variance = 0.8859669646109665 +confounder_coefficient = 0.75 +collider_coefficients = {"beta_hat" = -0.2013422818791946, "theta_hat" = -0.4697986577181208} +generalized_confounder = 0.6735957395417637 +generalized_collider = {"beta_hat" = -0.4683032343298113, "theta_hat" = -0.6783619018201651} +confounder_factor = {"correct" = 1.1419537839061686, "misspecified" = 0.135005304990567} +confounder_forecast = {"correct" = 1.5970667319239125, "misspecified" = 1.4211050710698971} +collider_factor = {"correct" = 0.038834067314822245, "misspecified" = 0.09572279730799764} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = -0.08053691275167785} +confounder_factor_default = {"correct" = 1.1419537839061686, "misspecified" = 0.13652601790367194} +confounder_forecast_default = {"correct" = 1.3699999999999999, "misspecified" = 1.125} + +[[factor]] +beta = 0.4 +gamma = -0.7 +delta = 0.0 +x = 0.43064208460339043 +z = 0.6617933220221799 +realized = 0.3358869099185431 +outcome_variance = 0.17065608512208946 +collider_variance = 1.2311556418399985 +confounder_variance = 0.3791621906460052 +exposure_variance = 1.7738320314867722 +confounder_coefficient = 0.4 +collider_coefficients = {"beta_hat" = 0.2684563758389262, "theta_hat" = -0.4697986577181208} +generalized_confounder = 0.4 +generalized_collider = {"beta_hat" = 0.374559495989997, "theta_hat" = -0.09085894289286804} +confounder_factor = {"correct" = 0.08468012209844213, "misspecified" = 0.013999164681122837} +confounder_forecast = {"correct" = 0.4799545330463562, "misspecified" = 0.08400286782354835} +collider_factor = {"correct" = 0.029672416805048594, "misspecified" = -0.033641932088038284} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.10738255033557048} +confounder_factor_default = {"correct" = 0.08468012209844213, "misspecified" = 0.029672416805048594} +confounder_forecast_default = {"correct" = 0.6499999999999999, "misspecified" = 0.16000000000000003} + +[[factor]] +beta = 0.4 +gamma = -0.7 +delta = 0.8 +x = -0.25740645613531515 +z = 0.05485701792346716 +realized = 0.3381222222677833 +outcome_variance = 1.8276754563154904 +collider_variance = 0.7769384594887075 +confounder_variance = 1.8364644431844364 +exposure_variance = 2.26654471274157 +confounder_coefficient = 0.0585365853658537 +collider_coefficients = {"beta_hat" = 0.6442953020134229, "theta_hat" = -0.4697986577181208} +generalized_confounder = 0.10120413738272359 +generalized_collider = {"beta_hat" = 0.7977722520792657, "theta_hat" = -0.7649466386139725} +confounder_factor = {"correct" = 0.019983354992781394, "misspecified" = 0.0007276017491875031} +confounder_forecast = {"correct" = 0.47894381748064996, "misspecified" = 0.012236786299848208} +collider_factor = {"correct" = 0.010601293385622709, "misspecified" = 0.019729435092139074} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.25771812080536916} +confounder_factor_default = {"correct" = 0.019983354992781394, "misspecified" = 0.0002270354324107186} +confounder_forecast_default = {"correct" = 0.30439999999999995, "misspecified" = 0.0056195121951219595} + +[[factor]] +beta = 0.4 +gamma = 0.0 +delta = -1.0 +x = -1.5269938324982162 +z = -0.2977106751547432 +realized = -0.7825308172344965 +outcome_variance = 0.6534519737138211 +collider_variance = 1.5683112574157043 +confounder_variance = 2.0715794751027845 +exposure_variance = 2.336845159455497 +confounder_coefficient = 0.4 +collider_coefficients = {"beta_hat" = 0.4, "theta_hat" = 0.0} +generalized_confounder = 0.4 +generalized_collider = {"beta_hat" = 0.4, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.3730736263180145, "misspecified" = 0.3730736263180145} +confounder_forecast = {"correct" = 0.25797671678747025, "misspecified" = 0.25797671678747025} +collider_factor = {"correct" = 0.3730736263180145, "misspecified" = 0.3730736263180145} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.16000000000000003} +confounder_factor_default = {"correct" = 0.3730736263180145, "misspecified" = 0.3730736263180145} +confounder_forecast_default = {"correct" = 0.32000000000000006, "misspecified" = 0.32000000000000006} + +[[factor]] +beta = 0.4 +gamma = 0.0 +delta = 0.0 +x = -0.6205257172830102 +z = 0.486586177265474 +realized = -1.0740346270135244 +outcome_variance = 1.5905315823635042 +collider_variance = 0.484625585742234 +confounder_variance = 1.5113423676525164 +exposure_variance = 1.7929518863863678 +confounder_coefficient = 0.4 +collider_coefficients = {"beta_hat" = 0.4, "theta_hat" = 0.0} +generalized_confounder = 0.4 +generalized_collider = {"beta_hat" = 0.4, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.0616083465295351, "misspecified" = 0.0616083465295351} +confounder_forecast = {"correct" = 0.3445680608038529, "misspecified" = 0.3445680608038529} +collider_factor = {"correct" = 0.0616083465295351, "misspecified" = 0.0616083465295351} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.16000000000000003} +confounder_factor_default = {"correct" = 0.0616083465295351, "misspecified" = 0.0616083465295351} +confounder_forecast_default = {"correct" = 0.16000000000000003, "misspecified" = 0.16000000000000003} + +[[factor]] +beta = 0.4 +gamma = 0.0 +delta = 0.8 +x = -0.04466236451375794 +z = -1.4617482409533362 +realized = -0.3092630432391984 +outcome_variance = 0.7368186559685659 +collider_variance = 1.9122912457860164 +confounder_variance = 1.5261326779411677 +exposure_variance = 0.9330403017120235 +confounder_coefficient = 0.4 +collider_coefficients = {"beta_hat" = 0.4, "theta_hat" = 0.0} +generalized_confounder = 0.4 +generalized_collider = {"beta_hat" = 0.4, "theta_hat" = 0.0} +confounder_factor = {"correct" = 0.0003191562886335656, "misspecified" = 0.0003191562886335656} +confounder_forecast = {"correct" = 0.17530298078617126, "misspecified" = 0.17530298078617126} +collider_factor = {"correct" = 0.0003191562886335656, "misspecified" = 0.0003191562886335656} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.16000000000000003} +confounder_factor_default = {"correct" = 0.0003191562886335656, "misspecified" = 0.0003191562886335656} +confounder_forecast_default = {"correct" = 0.2624, "misspecified" = 0.2624} + +[[factor]] +beta = 0.4 +gamma = 1.0 +delta = -1.0 +x = -0.027888349947725242 +z = 0.5929283942403945 +realized = 2.9091306644821135 +outcome_variance = 2.26243076021023 +collider_variance = 0.17194555016644458 +confounder_variance = 1.17876669006799 +exposure_variance = 2.8533972941771553 +confounder_coefficient = -0.09999999999999998 +collider_coefficients = {"beta_hat" = 0.7, "theta_hat" = 0.5} +generalized_confounder = 0.10765903999098764 +generalized_collider = {"beta_hat" = 0.9576206317568448, "theta_hat" = 0.9293677195947412} +confounder_factor = {"correct" = 0.3384598866645266, "misspecified" = -5.50203231709099e-05} +confounder_forecast = {"correct" = 4.841391127270514, "misspecified" = -0.669434715369417} +collider_factor = {"correct" = 0.00012444161004908586, "misspecified" = -0.003089386092917883} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.28} +confounder_factor_default = {"correct" = 0.3384598866645266, "misspecified" = 7.777600628067861e-06} +confounder_forecast_default = {"correct" = 0.52, "misspecified" = 0.01999999999999999} + +[[factor]] +beta = 0.4 +gamma = 1.0 +delta = 0.0 +x = -0.04636089751745937 +z = -0.7612484937537777 +realized = 1.2130577102735487 +outcome_variance = 2.831041192266038 +collider_variance = 1.5515869337546182 +confounder_variance = 1.5089139224745582 +exposure_variance = 2.3970065835408247 +confounder_coefficient = 0.4 +collider_coefficients = {"beta_hat" = 0.2, "theta_hat" = 0.5} +generalized_confounder = 0.4 +generalized_collider = {"beta_hat" = 0.14161246531892543, "theta_hat" = 0.6459688367026865} +confounder_factor = {"correct" = 0.6080768932167665, "misspecified" = 0.0007658645630257462} +confounder_forecast = {"correct" = 2.365887609571496, "misspecified" = 0.8806645254620763} +collider_factor = {"correct" = 0.0003438932509798993, "misspecified" = 0.00723037930633779} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = 0.08000000000000002} +confounder_factor_default = {"correct" = 0.6080768932167665, "misspecified" = 0.0003438932509798993} +confounder_forecast_default = {"correct" = 1.16, "misspecified" = 0.16000000000000003} + +[[factor]] +beta = 0.4 +gamma = 1.0 +delta = 0.8 +x = -0.6612769240367877 +z = -0.2037851709312176 +realized = -0.5919694479842023 +outcome_variance = 2.1457704104558877 +collider_variance = 2.2643953585658227 +confounder_variance = 0.5125302421118452 +exposure_variance = 2.893021406128325 +confounder_coefficient = 0.8878048780487805 +collider_coefficients = {"beta_hat" = -0.2, "theta_hat" = 0.5} +generalized_confounder = 0.5272955619326 +generalized_collider = {"beta_hat" = -0.18386115792610003, "theta_hat" = 0.4865509649384167} +confounder_factor = {"correct" = 0.21930108793179973, "misspecified" = -0.014891160119178674} +confounder_forecast = {"correct" = 0.7424928939881136, "misspecified" = -0.045986798547724424} +collider_factor = {"correct" = 0.0699659472421689, "misspecified" = -0.008031287421543128} +collider_forecast = {"correct" = 0.16000000000000003, "misspecified" = -0.08000000000000002} +confounder_factor_default = {"correct" = 0.21930108793179973, "misspecified" = 0.3446686550341467} +confounder_forecast_default = {"correct" = 1.9024, "misspecified" = 1.2926439024390246} + +[[factor]] +beta = -0.8653954825018496 +gamma = -1.412232900777426 +delta = 0.15014288305709964 +x = -0.8114115525014082 +z = 0.9218303993202377 +realized = -1.3857198950916054 +outcome_variance = 2.960654530467036 +collider_variance = 1.4344987857571119 +confounder_variance = 2.0866339315304123 +exposure_variance = 1.35980245691807 +confounder_coefficient = -1.072757659983359 +collider_coefficients = {"beta_hat" = -0.2181934203490234, "theta_hat" = -0.4716243881481197} +generalized_confounder = -1.1798894077282431 +generalized_collider = {"beta_hat" = -0.08361108865705129, "theta_hat" = -0.5696962478524011} +confounder_factor = {"correct" = 0.3595769166250964, "misspecified" = 0.13790650828545079} +confounder_forecast = {"correct" = 0.7942941741889817, "misspecified" = 0.6116710199744613} +collider_factor = {"correct" = 0.49307345316881523, "misspecified" = -0.18096400441147253} +collider_forecast = {"correct" = 0.748909341134609, "misspecified" = 0.188823600281672} +confounder_factor_default = {"correct" = 0.3595769166250964, "misspecified" = 0.7576796481867615} +confounder_forecast_default = {"correct" = 3.1271849225375656, "misspecified" = 1.1767515523133745} + +[[factor]] +beta = -0.7276755945534111 +gamma = -0.8728895619002268 +delta = -1.2790674889082458 +x = -0.7186830920185775 +z = 0.6396207530059645 +realized = 0.09329263272381495 +outcome_variance = 2.21357483538635 +collider_variance = 2.840145674354006 +confounder_variance = 1.6206736353661555 +exposure_variance = 2.045837814627763 +confounder_coefficient = -0.3041251839670357 +collider_coefficients = {"beta_hat" = -1.046666881528882, "theta_hat" = -0.49541496917124156} +generalized_confounder = -0.3424619206323221 +generalized_collider = {"beta_hat" = -1.0025136353862298, "theta_hat" = -0.4268420019156992} +confounder_factor = {"correct" = 0.0012496318743827905, "misspecified" = 0.12698644504519138} +confounder_forecast = {"correct" = 1.4145718813067274, "misspecified" = 0.24799678144751544} +collider_factor = {"correct" = 0.27349568202364644, "misspecified" = 0.22767109440785133} +collider_forecast = {"correct" = 0.5295117709086604, "misspecified" = 0.761633945315894} +confounder_factor_default = {"correct" = 0.0012496318743827905, "misspecified" = 0.047772682097901725} +confounder_forecast_default = {"correct" = 0.5328591609561373, "misspecified" = 0.24381050985253333} + +[[factor]] +beta = 1.0471233533088071 +gamma = 0.8098627841596172 +delta = 0.00870487723880685 +x = -0.9497363390751328 +z = -0.05656718023188829 +realized = -0.2507130636062984 +outcome_variance = 2.220574151188338 +collider_variance = 1.1996604676699762 +confounder_variance = 0.949790044955503 +exposure_variance = 2.7321356349917063 +confounder_coefficient = 1.0541725752711921 +collider_coefficients = {"beta_hat" = 0.6281101429607255, "theta_hat" = 0.48908368649123507} +generalized_confounder = 1.0495740412308752 +generalized_collider = {"beta_hat" = 0.4670549721046729, "theta_hat" = 0.677071689602924} +confounder_factor = {"correct" = 1.082229820409087, "misspecified" = 0.8140222525912308} +confounder_forecast = {"correct" = 1.3960432260815308, "misspecified" = 0.959190833892085} +collider_factor = {"correct" = 0.9890125482409269, "misspecified" = 0.6207664292901899} +collider_forecast = {"correct" = 1.0964673170446808, "misspecified" = 0.6577087991443091} +confounder_factor_default = {"correct" = 1.082229820409087, "misspecified" = 1.0023734113846061} +confounder_forecast_default = {"correct" = 1.7671920594285129, "misspecified" = 1.111364025557391} + +[[factor]] +beta = 0.9492622026200077 +gamma = 0.29965100702971786 +delta = -0.6186355958139929 +x = 1.7429918823178983 +z = -0.4538567885372455 +realized = 0.7755618177726734 +outcome_variance = 0.4108186681618089 +collider_variance = 1.561840583790205 +confounder_variance = 1.5877228508626617 +exposure_variance = 0.38325332716603333 +confounder_coefficient = 0.8151959275403631 +collider_coefficients = {"beta_hat" = 1.0411512548287212, "theta_hat" = 0.27496197194275745} +generalized_confounder = 0.6522327107502551 +generalized_collider = {"beta_hat" = 0.9749947268291754, "theta_hat" = 0.07700009336853914} +confounder_factor = {"correct" = 2.3060173959781927, "misspecified" = 2.710309997444472} +confounder_forecast = {"correct" = 1.974109902998569, "misspecified" = 1.4287430556467173} +collider_factor = {"correct" = 2.737556594072424, "misspecified" = 2.7960757930620477} +collider_forecast = {"correct" = 0.9010987293229886, "misspecified" = 0.9883255334192969} +confounder_factor_default = {"correct" = 2.3060173959781927, "misspecified" = 2.018899645328361} +confounder_forecast_default = {"correct" = 0.983810407843631, "misspecified" = 0.9188721879801597} + +[[factor]] +beta = -1.008594675705287 +gamma = 1.3862541132718083 +delta = 0.03127947620302537 +x = -0.9086237784977299 +z = -0.4440215508854386 +realized = -1.0483357854113755 +outcome_variance = 1.8547233210185627 +collider_variance = 2.712815349106316 +confounder_variance = 0.5547360366003935 +exposure_variance = 1.6774687528530359 +confounder_coefficient = -0.9652757566322286 +collider_coefficients = {"beta_hat" = -0.3600492214351477, "theta_hat" = 0.47446825201166226} +generalized_confounder = -0.9942598055463365 +generalized_collider = {"beta_hat" = -0.4487075475992445, "theta_hat" = 0.4096068598541941} +confounder_factor = {"correct" = 0.09054466391751673, "misspecified" = 1.3555364544780562} +confounder_forecast = {"correct" = 6.9884442032660585, "misspecified" = 3.4463317618602933} +collider_factor = {"correct" = 0.8398496363285645, "misspecified" = 0.10674168790471522} +collider_forecast = {"correct" = 1.0172632198610532, "misspecified" = 0.36314372773132386} +confounder_factor_default = {"correct" = 0.09054466391751673, "misspecified" = 0.7692561795241536} +confounder_forecast_default = {"correct" = 2.8524910247251554, "misspecified" = 0.9326689229181028} + +[[factor]] +beta = -0.8099692228530592 +gamma = -1.0989659071665383 +delta = 2.311407160329961 +x = 0.31349324918171934 +z = -2.6798964586993157 +realized = -1.0048455360903326 +outcome_variance = 2.403900701512665 +collider_variance = 0.4136132853222313 +confounder_variance = 1.3335034147234912 +exposure_variance = 1.267099917344922 +confounder_coefficient = -1.210460573553003 +collider_coefficients = {"beta_hat" = 0.7836970678880648, "theta_hat" = -0.49778182381046704} +generalized_confounder = -1.2136297437637618 +generalized_collider = {"beta_hat" = 1.7399772370159834, "theta_hat" = -0.796476029384024} +confounder_factor = {"correct" = 7.24253030970175, "misspecified" = 0.030988644024058352} +confounder_forecast = {"correct" = 0.7373161581967668, "misspecified" = 0.6336954137393628} +collider_factor = {"correct" = 0.0644753071906421, "misspecified" = -0.40111406252764986} +collider_forecast = {"correct" = 0.6560501419691886, "misspecified" = -0.6347705050295169} +confounder_factor_default = {"correct" = 7.24253030970175, "misspecified" = 0.14399840544940082} +confounder_forecast_default = {"correct" = 9.483690766199905, "misspecified" = 9.293275876046373} + +[[factor]] +beta = -0.09129052483690066 +gamma = 0.6911101587122943 +delta = 0.20657847204841562 +x = -1.086540252238297 +z = -0.5759788544574094 +realized = 0.14870375974452157 +outcome_variance = 1.1053053262156864 +collider_variance = 0.6247709896207736 +confounder_variance = 1.1116564891119285 +exposure_variance = 2.019848563578305 +confounder_coefficient = 0.04563471692902806 +collider_coefficients = {"beta_hat" = -0.15840128476205434, "theta_hat" = 0.46771427079233646} +generalized_confounder = -0.014518687641402132 +generalized_collider = {"beta_hat" = -0.1863781917408619, "theta_hat" = 0.6626934166284358} +confounder_factor = {"correct" = 0.08932567244024425, "misspecified" = 0.0004987082185296144} +confounder_forecast = {"correct" = 0.46738752021021024, "misspecified" = 0.00043177124499658927} +collider_factor = {"correct" = 0.009838820732928348, "misspecified" = -0.009649696366922096} +collider_forecast = {"correct" = 0.008333959924996776, "misspecified" = 0.014460536420767296} +confounder_factor_default = {"correct" = 0.08932567244024425, "misspecified" = 0.002458568776197521} +confounder_forecast_default = {"correct" = 0.4602560413002028, "misspecified" = 0.002171398548116748} + +[[factor]] +beta = -1.403525342095066 +gamma = -0.8425034617378222 +delta = -0.7345091104069601 +x = -0.6264967786944249 +z = 0.43863802082997727 +realized = 0.36116301795926814 +outcome_variance = 0.4179580650179937 +collider_variance = 1.9108119879682042 +confounder_variance = 1.5560994775685706 +exposure_variance = 2.5515136522234547 +confounder_coefficient = -1.001560413385495 +collider_coefficients = {"beta_hat" = -1.1827918578601677, "theta_hat" = -0.4927462322290811} +generalized_confounder = -1.119554356475847 +generalized_collider = {"beta_hat" = -1.3320671852106964, "theta_hat" = -0.1595169744583809} +confounder_factor = {"correct" = 0.2598451182049393, "misspecified" = 0.6575547053255124} +confounder_forecast = {"correct" = 3.7907769972273098, "misspecified" = 1.8938310470953101} +collider_factor = {"correct" = 0.7731757101928094, "misspecified" = 0.4615274302501833} +collider_forecast = {"correct" = 1.969883385903072, "misspecified" = 1.6600783469304505} +confounder_factor_default = {"correct" = 0.2598451182049393, "misspecified" = 0.3937240883373667} +confounder_forecast_default = {"correct" = 2.0053774518137524, "misspecified" = 1.5443119059454715} + +[[factor]] +beta = -1.272775386252895 +gamma = -0.5907661499290734 +delta = 0.02472402568181034 +x = -1.0213265543764467 +z = 0.9897113105161922 +realized = 1.0857732952475239 +outcome_variance = 1.6385933179093983 +collider_variance = 2.606793859569309 +confounder_variance = 0.7937656142935178 +exposure_variance = 2.4510780608788822 +confounder_coefficient = -1.2873725807798977 +collider_coefficients = {"beta_hat" = -0.9326648907233017, "theta_hat" = -0.4379274397605271} +generalized_confounder = -1.2775045457786565 +generalized_collider = {"beta_hat" = -1.0362601397949958, "theta_hat" = -0.3045378419867834} +confounder_factor = {"correct" = 0.5115558973002512, "misspecified" = 2.1044913541802583} +confounder_forecast = {"correct" = 5.511548537206465, "misspecified" = 4.395982194535239} +collider_factor = {"correct" = 1.6897901858282653, "misspecified" = 0.6748318883635858} +collider_forecast = {"correct" = 1.6199571838512061, "misspecified" = 1.1870729165348644} +confounder_factor_default = {"correct" = 0.5115558973002512, "misspecified" = 1.7287721492451882} +confounder_forecast_default = {"correct" = 2.0071326846337794, "misspecified" = 1.6583412490696476} + +[[factor]] +beta = -0.7371946848133772 +gamma = -0.5855937051960315 +delta = 1.3343028988246413 +x = 1.832566395038623 +z = -1.87843983990729 +realized = -0.06966967373432274 +outcome_variance = 2.6519509859283543 +collider_variance = 0.15333073374491157 +confounder_variance = 2.7494161775851684 +exposure_variance = 1.9513173472971734 +confounder_coefficient = -1.0182223901487735 +collider_coefficients = {"beta_hat" = 0.03288706249969727, "theta_hat" = -0.43606001148120754} +generalized_confounder = -1.0509829400520794 +generalized_collider = {"beta_hat" = 1.8434389890692016, "theta_hat" = -1.4612879131188343} +confounder_factor = {"correct" = 0.06297874336179138, "misspecified" = 2.381999033302117} +confounder_forecast = {"correct" = 0.8288615341665316, "misspecified" = 0.7127300187456119} +collider_factor = {"correct" = 1.8250880743319482, "misspecified" = -1.1880059294269698} +collider_forecast = {"correct" = 0.5434560033170946, "misspecified" = -0.024244167673902167} +confounder_factor_default = {"correct" = 0.06297874336179138, "misspecified" = 3.4818072248962593} +confounder_forecast_default = {"correct" = 3.005953578827268, "misspecified" = 2.8826172244094987} + +[[factor]] +beta = -1.7401111329459416 +gamma = -0.332720202989102 +delta = -0.15529274409203062 +x = -1.0041065703070635 +z = -0.6225544484961357 +realized = 1.2502086516539366 +outcome_variance = 2.01135694703571 +collider_variance = 2.632896623458399 +confounder_variance = 1.8325567235836262 +exposure_variance = 0.36087215219850355 +confounder_coefficient = -1.689658799818962 +collider_coefficients = {"beta_hat" = -1.6131950631594478, "theta_hat" = -0.2995582823025069} +generalized_confounder = -1.506355440129555 +generalized_collider = {"beta_hat" = -1.6408195540840276, "theta_hat" = -0.23435657014131855} +confounder_factor = {"correct" = 3.8196538126375432, "misspecified" = 3.240874639874026} +confounder_forecast = {"correct" = 9.319165340700634, "misspecified" = 8.23862799465643} +collider_factor = {"correct" = 3.0529070997244063, "misspecified" = 3.1560897585991583} +collider_forecast = {"correct" = 3.0279867550024084, "misspecified" = 2.8071386890171866} +confounder_factor_default = {"correct" = 3.8196538126375432, "misspecified" = 2.8784430854056535} +confounder_forecast_default = {"correct" = 3.031892201309856, "misspecified" = 2.9237962911150155} + +[[factor]] +beta = 0.6875313903168299 +gamma = -1.131398378883464 +delta = -1.2207282067573215 +x = -0.8252507798883695 +z = -1.1617788475644155 +realized = -0.19756301623860367 +outcome_variance = 2.807493465050413 +collider_variance = 1.3648720850342149 +confounder_variance = 2.2514821307297166 +exposure_variance = 0.19512433307863555 +confounder_coefficient = 1.2421625339722515 +collider_coefficients = {"beta_hat" = -0.3042015678161018, "theta_hat" = -0.4962138021325669} +generalized_confounder = 1.5634147988897134 +generalized_collider = {"beta_hat" = -0.5927273675243115, "theta_hat" = -0.6405777490120651} +confounder_factor = {"correct" = 0.5580820421060823, "misspecified" = 0.7636127851139306} +confounder_forecast = {"correct" = 2.078569141705393, "misspecified" = 1.165010582127398} +collider_factor = {"correct" = 0.321926664262326, "misspecified" = -0.46953064554043006} +collider_forecast = {"correct" = 0.472699412670993, "misspecified" = -0.20914812685716389} +confounder_factor_default = {"correct" = 0.5580820421060823, "misspecified" = 1.050820988952206} +confounder_forecast_default = {"correct" = 4.356308005000097, "misspecified" = 3.842263377099749} + +[[factor]] +beta = -0.6554342688098205 +gamma = 0.6817085787435635 +delta = 2.5989353606378356 +x = 0.20969664333245852 +z = 1.106734567648782 +realized = 1.0293251642769945 +outcome_variance = 2.2205998731223895 +collider_variance = 0.2979497053965746 +confounder_variance = 2.9429704786295514 +exposure_variance = 0.12784407425588948 +confounder_coefficient = -0.42695731663552894 +collider_coefficients = {"beta_hat" = -1.6570674844059532, "theta_hat" = 0.4654169488726547} +generalized_confounder = -0.3948074431289013 +generalized_collider = {"beta_hat" = -3.105115556499524, "theta_hat" = 1.1382641598485939} +confounder_factor = {"correct" = 0.3807236552893134, "misspecified" = 0.005908740842503004} +confounder_forecast = {"correct" = 0.4296438338949954, "misspecified" = 0.27674255862705477} +collider_factor = {"correct" = 0.018890403997650074, "misspecified" = -0.023036926151905064} +collider_forecast = {"correct" = 0.429594080730264, "misspecified" = 1.0860988150101445} +confounder_factor_default = {"correct" = 0.3807236552893134, "misspecified" = 0.008015892383169406} +confounder_forecast_default = {"correct" = 1.473511395332925, "misspecified" = 1.4135812021078917} + +[[factor]] +beta = -0.40757059559002246 +gamma = 0.05930998657023708 +delta = -0.0839085282554313 +x = -0.784762631860068 +z = 1.1568409740240826 +realized = -0.8218743866041791 +outcome_variance = 1.7090902907254233 +collider_variance = 1.2738054368833067 +confounder_variance = 1.4764133077185635 +exposure_variance = 0.5658873605538758 +confounder_coefficient = -0.41251241569219116 +collider_coefficients = {"beta_hat" = -0.4011827515682401, "theta_hat" = 0.059102084673672214} +generalized_confounder = -0.42032049129443666 +generalized_collider = {"beta_hat" = -0.3990101513046916, "theta_hat" = 0.07920357811972882} +confounder_factor = {"correct" = 0.15089992534307997, "misspecified" = 0.11093307499398633} +confounder_forecast = {"correct" = 0.321571943829141, "misspecified" = 0.30180263239382243} +collider_factor = {"correct" = 0.10230157455162564, "misspecified" = 0.12256663529112094} +collider_forecast = {"correct" = 0.16611379038960564, "misspecified" = 0.16351029299711164} +confounder_factor_default = {"correct" = 0.15089992534307997, "misspecified" = 0.10479744119527858} +confounder_forecast_default = {"correct" = 0.17485765528516994, "misspecified" = 0.17136457430775237} + +[[factor]] +beta = 0.32509180040772895 +gamma = -0.8127661428850748 +delta = 1.6614818265983582 +x = -0.23055918722094398 +z = -0.4998475517952904 +realized = -0.101568095429486 +outcome_variance = 2.4036007543397853 +collider_variance = 2.1712673720316475 +confounder_variance = 0.9044917753755527 +exposure_variance = 2.974847083257687 +confounder_coefficient = -0.03400627850521196 +collider_coefficients = {"beta_hat" = 1.0089722230080949, "theta_hat" = -0.4894445520810536} +generalized_confounder = 0.1018671036618389 +generalized_collider = {"beta_hat" = 1.05124031369778, "theta_hat" = -0.5196952889515012} +confounder_factor = {"correct" = 0.10976384151251528, "misspecified" = -0.0007353683227905996} +confounder_forecast = {"correct" = 0.8210371105089084, "misspecified" = -0.01397646423025349} +collider_factor = {"correct" = 0.005617937409419872, "misspecified" = -0.0009009222879959624} +collider_forecast = {"correct" = 0.10568467869233868, "misspecified" = 0.3280085965390902} +confounder_factor_default = {"correct" = 0.10976384151251528, "misspecified" = 6.147281195424698e-05} +confounder_forecast_default = {"correct" = 0.18001289949247895, "misspecified" = 0.004348768929547875} + +[[factor]] +beta = 0.26240158502891675 +gamma = 0.055498542223747116 +delta = 0.7906073859060696 +x = 2.6408120129365242 +z = -0.5413171300657122 +realized = 0.7810823933713242 +outcome_variance = 0.9946852083933164 +collider_variance = 2.1366362090313604 +confounder_variance = 1.7393141660614084 +exposure_variance = 1.965718226480764 +confounder_coefficient = 0.28940216122029605 +collider_coefficients = {"beta_hat" = 0.21785302112251348, "theta_hat" = 0.05532812671413756} +generalized_confounder = 0.28739978574617514 +generalized_collider = {"beta_hat" = 0.24162844705180797, "theta_hat" = 0.025799682626414272} +confounder_factor = {"correct" = 0.43945092280199977, "misspecified" = 0.5839323537296254} +confounder_forecast = {"correct" = 0.1366918157265179, "misspecified" = 0.1348148205813353} +collider_factor = {"correct" = 0.4801842177145347, "misspecified" = 0.37790816585719117} +collider_forecast = {"correct" = 0.06885459182568783, "misspecified" = 0.05716497804588562} +confounder_factor_default = {"correct" = 0.43945092280199977, "misspecified" = 0.5840883094871985} +confounder_forecast_default = {"correct" = 0.1380000150545919, "misspecified" = 0.13610464619700727} + +[[factor]] +beta = -2.2895539097396664 +gamma = -0.8256186344016126 +delta = 0.9112667097324645 +x = -0.24397500230233227 +z = -0.7387109214753453 +realized = 0.8351240919218336 +outcome_variance = 1.1145529019444622 +collider_variance = 0.9436418786852243 +confounder_variance = 2.7608632685031194 +exposure_variance = 1.8370979531192255 +confounder_coefficient = -2.7005875048793464 +collider_coefficients = {"beta_hat" = -0.9141014309342775, "theta_hat" = -0.4909586029619915} +generalized_confounder = -2.792530035347925 +generalized_collider = {"beta_hat" = -0.7760928722406799, "theta_hat" = -0.5402198389676547} +confounder_factor = {"correct" = 1.365362856788601, "misspecified" = 0.43334009840261123} +confounder_forecast = {"correct" = 12.736950334479586, "misspecified" = 12.357501304814784} +collider_factor = {"correct" = 0.31202716790789625, "misspecified" = 0.32716532028101947} +collider_forecast = {"correct" = 5.242057105604193, "misspecified" = 2.0928845050941987} +confounder_factor_default = {"correct" = 1.365362856788601, "misspecified" = 0.43411737612076534} +confounder_forecast_default = {"correct" = 13.721876191604089, "misspecified" = 13.34947479485819} + +[[factor]] +beta = -0.05948383325374731 +gamma = 0.6998965824144787 +delta = -0.15521879433361277 +x = 0.5565527473452334 +z = 0.1292550005340139 +realized = -0.5367605415110749 +outcome_variance = 1.0345058485576448 +collider_variance = 2.7205616002830064 +confounder_variance = 1.43173036020108 +exposure_variance = 2.5170966210922576 +confounder_coefficient = -0.16556513352110744 +collider_coefficients = {"beta_hat" = 0.032991977721434196, "theta_hat" = 0.4697748950132059} +generalized_confounder = -0.12044149936987168 +generalized_collider = {"beta_hat" = -0.015320364298543434, "theta_hat" = 0.2243493598279218} +confounder_factor = {"correct" = 0.0032900826788062414, "misspecified" = 0.018007494902516158} +confounder_forecast = {"correct" = 0.5391063867823542, "misspecified" = 0.07488489478881064} +collider_factor = {"correct" = 0.001096000006985682, "misspecified" = -0.00261809760881635} +collider_forecast = {"correct" = 0.003538326418559614, "misspecified" = -0.0019624893014931377} +confounder_factor_default = {"correct" = 0.0032900826788062414, "misspecified" = 0.008490835543549765} +confounder_forecast_default = {"correct" = 0.5064031036680174, "misspecified" = 0.02807224280826743} + +[[factor]] +beta = 1.4453211799919978 +gamma = -1.7554676337446538 +delta = 0.27981980963164965 +x = -0.542397681395061 +z = 0.055027503358555746 +realized = -0.7498244196619394 +outcome_variance = 2.5327643606328416 +collider_variance = 1.9204710472217679 +confounder_variance = 0.9819445280688157 +exposure_variance = 0.5001812978550917 +confounder_coefficient = 0.9897753974651524 +collider_coefficients = {"beta_hat" = 0.47444732327079403, "theta_hat" = -0.43008599185924495} +generalized_confounder = 0.6094637121724303 +generalized_collider = {"beta_hat" = 0.4133241865541067, "theta_hat" = -0.4571628409250177} +confounder_factor = {"correct" = 0.7753469193898128, "misspecified" = 0.6662044299787151} +confounder_forecast = {"correct" = 10.150036918699254, "misspecified" = 3.537681190427974} +collider_factor = {"correct" = 0.6145601313558492, "misspecified" = 0.22029127661586148} +collider_forecast = {"correct" = 2.088953313333461, "misspecified" = 0.6857287651137889} +confounder_factor_default = {"correct" = 0.7753469193898128, "misspecified" = 0.2882099417971405} +confounder_forecast_default = {"correct" = 3.9142573590777383, "misspecified" = 1.0563614939941874} + +[[factor]] +beta = 1.2664098099997454 +gamma = 0.2282517924023317 +delta = 0.6986699064866807 +x = 0.1228917173283526 +z = -0.18532914186387253 +realized = -1.0078931902379715 +outcome_variance = 0.5579140886738531 +collider_variance = 1.2489086612804827 +confounder_variance = 0.9530309048699079 +exposure_variance = 1.2334336484980248 +confounder_coefficient = 1.3735722390865885 +collider_coefficients = {"beta_hat" = 1.0521227346743398, "theta_hat" = 0.21694899270580048} +generalized_confounder = 1.355882477654529 +generalized_collider = {"beta_hat" = 1.1679866320279726, "theta_hat" = 0.09964581059055733} +confounder_factor = {"correct" = 0.012843590887309152, "misspecified" = 0.02390328335633361} +confounder_forecast = {"correct" = 2.7024206798732453, "misspecified" = 3.1905845843821283} +collider_factor = {"correct" = 0.024221094191509376, "misspecified" = 0.01386522139343416} +collider_forecast = {"correct" = 1.6037938068635913, "misspecified" = 1.3324185525153434} +confounder_factor_default = {"correct" = 0.012843590887309152, "misspecified" = 0.028493659891424553} +confounder_forecast_default = {"correct" = 2.842683494460175, "misspecified" = 2.8076740911780744} + +[[factor]] +beta = 0.0 +gamma = 0.9999999999999999 +delta = 0.5 +x = -1.7456072334117525 +z = -0.6863487017921613 +realized = -0.8729780143565936 +outcome_variance = 2.793799720812601 +collider_variance = 0.9606572127823892 +confounder_variance = 0.45336012529368697 +exposure_variance = 0.2705156331908687 +confounder_coefficient = 0.39999999999999997 +collider_coefficients = {"beta_hat" = -0.25, "theta_hat" = 0.5} +generalized_confounder = 0.5905346295558039 +generalized_collider = {"beta_hat" = -0.372064425058868, "theta_hat" = 0.744128850117736} +confounder_factor = {"correct" = 0.4710745404517851, "misspecified" = -0.6038486859994832} +confounder_forecast = {"correct" = 0.9999999999999998, "misspecified" = -0.34919120574263735} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 0.4710745404517851, "misspecified" = 0.4875431381343091} +confounder_forecast_default = {"correct" = 0.9999999999999998, "misspecified" = 0.19999999999999996} + +[[factor]] +beta = 0.0 +gamma = 1.0000000000000002 +delta = 0.5 +x = 0.5805578031798989 +z = 2.1583247279716224 +realized = -0.801959165304815 +outcome_variance = 0.7540185521419513 +collider_variance = 1.9782235377296151 +confounder_variance = 0.22294007887881845 +exposure_variance = 1.3337808330630618 +confounder_coefficient = 0.4000000000000001 +collider_coefficients = {"beta_hat" = -0.25, "theta_hat" = 0.5} +generalized_confounder = 0.08022221496514025 +generalized_collider = {"beta_hat" = -0.13798531157562896, "theta_hat" = 0.2759706231512579} +confounder_factor = {"correct" = 4.65836563137378, "misspecified" = -0.065800471792852} +confounder_forecast = {"correct" = 1.0000000000000004, "misspecified" = -0.32078366612192616} +collider_factor = {"correct" = 0.0, "misspecified" = 0.0} +collider_forecast = {"correct" = 0.0, "misspecified" = 0.0} +confounder_factor_default = {"correct" = 4.65836563137378, "misspecified" = 0.053927578053291264} +confounder_forecast_default = {"correct" = 1.0000000000000004, "misspecified" = 0.2000000000000001} + +[[factor]] +beta = 0.5 +gamma = 1.0 +delta = -0.5 +x = -0.39841698013171506 +z = -0.5056334310820477 +realized = -1.2247964699549798 +outcome_variance = 1.0581564598411035 +collider_variance = 0.4944008253331016 +confounder_variance = 1.8630352376679085 +exposure_variance = 0.4934745218357035 +confounder_coefficient = 0.1 +collider_coefficients = {"beta_hat" = 0.5, "theta_hat" = 0.5} +generalized_confounder = -0.4711063914110116 +generalized_collider = {"beta_hat" = 0.5, "theta_hat" = 0.6815571122210623} +confounder_factor = {"correct" = 0.4968021338074699, "misspecified" = 0.00016042154417001843} +confounder_forecast = {"correct" = 0.4002351282485651, "misspecified" = 0.0025266726452110072} +collider_factor = {"correct" = 0.03968402251431886, "misspecified" = 0.09004725868065563} +collider_forecast = {"correct" = 0.25, "misspecified" = 0.25} +confounder_factor_default = {"correct" = 0.4968021338074699, "misspecified" = 0.0015873609005727543} +confounder_forecast_default = {"correct" = 0.8125, "misspecified" = 0.0125} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.006711409395973197, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.17791991351740913, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -2.7048947661974823, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.5685522531160115, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.07116796540696366, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -4.276814146126335, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.6642952214281589, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00014233593081392732, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -95.63247157871203, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.006711409395973197, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.22296292959776587, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -1.4213381090374029, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.5685522531160115, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.08918517183910635, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -2.2473328748774737, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.6642952214281589, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0001783703436782127, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -50.251890762960606, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.006711409395973197, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.42115220035133555, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -0.4550371671956169, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.5685522531160115, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.16846088014053423, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -0.7194769341845014, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = -0.7 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.6644295302013423, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.6642952214281589, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0003369217602810684, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -16.087993330796873, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.5, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -1.4142135623730951, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.2, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -2.23606797749979, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0004, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -50.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.25, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -2.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.1, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -3.1622776601683795, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0002, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -70.71067811865476, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.41000000000000003, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -1.5617376188860606, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.164, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -2.4693239916239738, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 0.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000328, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -55.21576303742327, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = 0.25, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.375, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.6785714285714286, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.15, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.7498999399639784, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0003, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = 0.25, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.1875, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -1.1547005383792515, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.6785714285714286, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.075, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -1.8257418583505538, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.7498999399639784, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00015, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -40.8248290463863, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.25, "overcontrolled_adjusted_r_squared" = 0.25, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.1275, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = -2.0, "overcontrolled_beta_t_statistic" = -2.520504151250418, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4375, "overcontrolled_adjusted_r_squared" = 0.6785714285714286, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.051, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = -3.1622776601683795, "overcontrolled_beta_t_statistic" = -3.985266984930429, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.0 +gamma = 1.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 2.0, "correct_r_squared" = 0.5, "overcontrolled_r_squared" = 0.75, "correct_adjusted_r_squared" = 0.4998999599839936, "overcontrolled_adjusted_r_squared" = 0.7498999399639784, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000102, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = -70.71067811865476, "overcontrolled_beta_t_statistic" = -89.11327886790069, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -1.0134228187919463, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.2803927751002207, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -0.887213950734269, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.13710450623202297, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.11215711004008828, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -1.402808428098354, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.3285904428563178, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00022431422008017658, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -31.367750046375455, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -1.0134228187919463, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.16778523489932887, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.13710450623202297, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.06711409395973154, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.3285904428563178, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0001342281879194631, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -1.0134228187919463, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.23985406062789966, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.7674113550068354, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.13710450623202297, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.09594162425115986, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 1.2133838920488305, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = -0.7 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.32885906040268453, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.3285904428563178, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00019188324850231972, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 27.132088654244516, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -2.0, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.5, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = -0.2857142857142857, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.2, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = -0.00040024014408645187, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0004, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -2.0, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.25, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = -0.2857142857142857, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.1, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = -0.00040024014408645187, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0002, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -2.0, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.41000000000000003, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = -0.2857142857142857, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.164, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.0, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = -0.00040024014408645187, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000328, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.1875, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 1.1547005383792515, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.075, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 1.8257418583505538, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00015, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 40.8248290463863, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.125, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0001, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = 0.0, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -0.5, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.165, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -0.9847319278346619, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.35714285714285715, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.066, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -1.556997888323046, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.49979987992795677, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000132, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -34.81553119113957, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.7357093265447814, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.3522814287644701, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = -0.3392267657763791, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.2561245743379508, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.14091257150578804, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = -0.5363646115729075, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.4211986576347567, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0002818251430115761, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = -11.993477322022915, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.7357093265447814, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.17661366605107878, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 0.6387950912299217, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.2561245743379508, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.07064546642043151, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 1.0100237232108018, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.4211986576347567, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00014129093284086304, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 22.584817039867847, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.7357093265447814, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.19823431376965003, "collider_coefficient_variance" = 0.11260754020089186, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 1.4470900212676308, "collider_t_statistic" = -1.4, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.2561245743379508, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.07929372550786001, "collider_coefficient_variance" = 0.04504301608035675, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 2.288050223253607, "collider_t_statistic" = -2.2135943621178655, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = -0.7 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.6711409395973155, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.4214302244850729, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.4211986576347567, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00015858745101572002, "collider_coefficient_variance" = 9.008603216071348e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 51.16235835128634, "collider_t_statistic" = -49.49747468305832, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -1.5862068965517242, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.5, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 0.565685424949238, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = -0.10837438423645318, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.2, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 0.894427190999916, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.13758599987578757, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0004, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 20.0, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -1.5862068965517242, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.25, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 0.8, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = -0.10837438423645318, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.1, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 1.2649110640673518, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.13758599987578757, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0002, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 28.284271247461902, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -1.5862068965517242, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.41000000000000003, "collider_coefficient_variance" = 0.25, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 0.6246950475544243, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = -0.10837438423645318, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.164, "collider_coefficient_variance" = 0.1, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 0.9877295966495896, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 0.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 1.0, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.13793103448275865, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.13758599987578757, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000328, "collider_coefficient_variance" = 0.0002, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 22.08630521496931, "collider_t_statistic" = 0.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = -1.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.29310344827586204, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.1475, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 1.8226447537315342, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = -1.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.4458128078817734, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.059, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 2.881854393574164, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = -1.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.5687929999378938, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000118, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 64.44022325288263, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.0 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.29310344827586204, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.135, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = 0.5443310539518174, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.0 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.4458128078817734, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.054, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = 0.8606629658238705, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.0 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.5687929999378938, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000108, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = 19.245008972987527, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.8 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = -0.29310344827586204, "overcontrolled_adjusted_r_squared" = -0.29310344827586204, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.215, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 0.8, "overcontrolled_beta_t_statistic" = -0.4313310928137537, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.8 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.030172413793103463, "overcontrolled_adjusted_r_squared" = 0.4458128078817734, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.08600000000000001, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 1.2649110640673518, "overcontrolled_beta_t_statistic" = -0.6819943394704735, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.4 +gamma = 1.0 +delta = 0.8 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.16, "correct_r_squared" = 0.13793103448275865, "overcontrolled_r_squared" = 0.5689655172413793, "correct_adjusted_r_squared" = 0.1377585516965407, "overcontrolled_adjusted_r_squared" = 0.5687929999378938, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.000172, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 28.284271247461902, "overcontrolled_beta_t_statistic" = -15.249857033260467, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8653954825018496 +gamma = -1.412232900777426 +delta = 0.15014288305709964 +n = 4 +expected = {"residual_variance" = 0.33395652224820227, "outcome_variance" = 1.748909341134609, "correct_r_squared" = 0.42821507297156547, "overcontrolled_r_squared" = 0.8090486942956419, "correct_adjusted_r_squared" = 0.14232260945734818, "overcontrolled_adjusted_r_squared" = 0.4271460828869256, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.13599491404906278, "collider_coefficient_variance" = 0.02788173968802851, "correct_beta_t_statistic" = -1.7307909650036992, "overcontrolled_beta_t_statistic" = -0.5916710454918079, "collider_t_statistic" = -2.824465801554852, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8653954825018496 +gamma = -1.412232900777426 +delta = 0.15014288305709964 +n = 10 +expected = {"residual_variance" = 0.33395652224820227, "outcome_variance" = 1.748909341134609, "correct_r_squared" = 0.42821507297156547, "overcontrolled_r_squared" = 0.8090486942956419, "correct_adjusted_r_squared" = 0.35674195709301115, "overcontrolled_adjusted_r_squared" = 0.754491178380111, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05439796561962511, "collider_coefficient_variance" = 0.011152695875211402, "correct_beta_t_statistic" = -2.7366208015262345, "overcontrolled_beta_t_statistic" = -0.9355140646636065, "collider_t_statistic" = -4.4658725530832415, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8653954825018496 +gamma = -1.412232900777426 +delta = 0.15014288305709964 +n = 5000 +expected = {"residual_variance" = 0.33395652224820227, "outcome_variance" = 1.748909341134609, "correct_r_squared" = 0.42821507297156547, "overcontrolled_r_squared" = 0.8090486942956419, "correct_adjusted_r_squared" = 0.42810067022506115, "overcontrolled_adjusted_r_squared" = 0.8089722679175333, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00010879593123925021, "collider_coefficient_variance" = 2.2305391750422806e-05, "correct_beta_t_statistic" = -61.19270140852621, "overcontrolled_beta_t_statistic" = -20.918730424949583, "collider_t_statistic" = -99.85994607544666, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.7276755945534111 +gamma = -0.8728895619002268 +delta = -1.2790674889082458 +n = 4 +expected = {"residual_variance" = 0.5675574446013006, "outcome_variance" = 1.5295117709086603, "correct_r_squared" = 0.34619659748946247, "overcontrolled_r_squared" = 0.6289290115994838, "correct_adjusted_r_squared" = 0.019294896234193656, "overcontrolled_adjusted_r_squared" = -0.11321296520154878, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.17527648820432132, "collider_coefficient_variance" = 0.0805303632305896, "correct_beta_t_statistic" = -1.4553511891068223, "overcontrolled_beta_t_statistic" = -2.5000382359967124, "collider_t_statistic" = -1.7457791238004536, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.7276755945534111 +gamma = -0.8728895619002268 +delta = -1.2790674889082458 +n = 10 +expected = {"residual_variance" = 0.5675574446013006, "outcome_variance" = 1.5295117709086603, "correct_r_squared" = 0.34619659748946247, "overcontrolled_r_squared" = 0.6289290115994838, "correct_adjusted_r_squared" = 0.26447117217564525, "overcontrolled_adjusted_r_squared" = 0.5229087291993363, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.07011059528172853, "collider_coefficient_variance" = 0.03221214529223584, "correct_beta_t_statistic" = -2.3011122765059953, "overcontrolled_beta_t_statistic" = -3.952907531629583, "collider_t_statistic" = -2.760319161391251, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.7276755945534111 +gamma = -0.8728895619002268 +delta = -1.2790674889082458 +n = 5000 +expected = {"residual_variance" = 0.5675574446013006, "outcome_variance" = 1.5295117709086603, "correct_r_squared" = 0.34619659748946247, "overcontrolled_r_squared" = 0.6289290115994838, "correct_adjusted_r_squared" = 0.3460657844837581, "overcontrolled_adjusted_r_squared" = 0.62878049409362, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00014022119056345705, "collider_coefficient_variance" = 6.442429058447168e-05, "correct_beta_t_statistic" = -51.45443474126698, "overcontrolled_beta_t_statistic" = -88.38969949494648, "collider_t_statistic" = -61.7226128446605, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 1.0471233533088071 +gamma = 0.8098627841596172 +delta = 0.00870487723880685 +n = 4 +expected = {"residual_variance" = 0.603909323971159, "outcome_variance" = 2.096467317044681, "correct_r_squared" = 0.5230071120738166, "overcontrolled_r_squared" = 0.7119395475134478, "correct_adjusted_r_squared" = 0.28451066811072484, "overcontrolled_adjusted_r_squared" = 0.13581864254034323, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.2178998924087926, "collider_coefficient_variance" = 0.09117661789482556, "correct_beta_t_statistic" = 2.0942467066176143, "overcontrolled_beta_t_statistic" = 1.3455731073077475, "collider_t_statistic" = 1.6197255683192344, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.0471233533088071 +gamma = 0.8098627841596172 +delta = 0.00870487723880685 +n = 10 +expected = {"residual_variance" = 0.603909323971159, "outcome_variance" = 2.096467317044681, "correct_r_squared" = 0.5230071120738166, "overcontrolled_r_squared" = 0.7119395475134478, "correct_adjusted_r_squared" = 0.4633830010830436, "overcontrolled_adjusted_r_squared" = 0.6296365610887186, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.08715995696351704, "collider_coefficient_variance" = 0.036470647157930226, "correct_beta_t_statistic" = 3.3112947876090417, "overcontrolled_beta_t_statistic" = 2.1275378886813194, "collider_t_statistic" = 2.5610109901497236, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.0471233533088071 +gamma = 0.8098627841596172 +delta = 0.00870487723880685 +n = 5000 +expected = {"residual_variance" = 0.603909323971159, "outcome_variance" = 2.096467317044681, "correct_r_squared" = 0.5230071120738166, "overcontrolled_r_squared" = 0.7119395475134478, "correct_adjusted_r_squared" = 0.5229116753215304, "overcontrolled_adjusted_r_squared" = 0.7118242541564389, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00017431991392703408, "collider_coefficient_variance" = 7.294129431586045e-05, "correct_beta_t_statistic" = 74.04280238634546, "overcontrolled_beta_t_statistic" = 47.573193437978105, "collider_t_statistic" = 57.26594665098826, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.9492622026200077 +gamma = 0.29965100702971786 +delta = -0.6186355958139929 +n = 4 +expected = {"residual_variance" = 0.9176073682124757, "outcome_variance" = 1.9010987293229886, "correct_r_squared" = 0.47398839177799257, "overcontrolled_r_squared" = 0.517327872530192, "correct_adjusted_r_squared" = 0.21098258766698888, "overcontrolled_adjusted_r_squared" = -0.4480163824094242, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.25291094563945793, "collider_coefficient_variance" = 0.21050082054945649, "correct_beta_t_statistic" = 1.8985244052400154, "overcontrolled_beta_t_statistic" = 2.0702844215916287, "collider_t_statistic" = 0.5993020140594357, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.9492622026200077 +gamma = 0.29965100702971786 +delta = -0.6186355958139929 +n = 10 +expected = {"residual_variance" = 0.9176073682124757, "outcome_variance" = 1.9010987293229886, "correct_r_squared" = 0.47398839177799257, "overcontrolled_r_squared" = 0.517327872530192, "correct_adjusted_r_squared" = 0.40823694075024164, "overcontrolled_adjusted_r_squared" = 0.37942155039596104, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.10116437825578316, "collider_coefficient_variance" = 0.0842003282197826, "correct_beta_t_statistic" = 3.00183065698748, "overcontrolled_beta_t_statistic" = 3.2734070882969113, "collider_t_statistic" = 0.9475796853770347, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.9492622026200077 +gamma = 0.29965100702971786 +delta = -0.6186355958139929 +n = 5000 +expected = {"residual_variance" = 0.9176073682124757, "outcome_variance" = 1.9010987293229886, "correct_r_squared" = 0.47398839177799257, "overcontrolled_r_squared" = 0.517327872530192, "correct_adjusted_r_squared" = 0.4738831473585804, "overcontrolled_adjusted_r_squared" = 0.5171346877683469, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00020232875651156633, "collider_coefficient_variance" = 0.0001684006564395652, "correct_beta_t_statistic" = 67.12297405966859, "overcontrolled_beta_t_statistic" = 73.1956076746155, "collider_t_statistic" = 21.188525906009133, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.008594675705287 +gamma = 1.3862541132718083 +delta = 0.03127947620302537 +n = 4 +expected = {"residual_variance" = 0.34226643403194823, "outcome_variance" = 2.017263219861053, "correct_r_squared" = 0.5042788714162553, "overcontrolled_r_squared" = 0.8303312970453488, "correct_adjusted_r_squared" = 0.25641830712438296, "overcontrolled_adjusted_r_squared" = 0.4909938911360464, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.14028523057903378, "collider_coefficient_variance" = 0.029286577966236495, "correct_beta_t_statistic" = -2.017189351410574, "overcontrolled_beta_t_statistic" = -0.9612932681270512, "collider_t_statistic" = 2.7725082265436165, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.008594675705287 +gamma = 1.3862541132718083 +delta = 0.03127947620302537 +n = 10 +expected = {"residual_variance" = 0.34226643403194823, "outcome_variance" = 2.017263219861053, "correct_r_squared" = 0.5042788714162553, "overcontrolled_r_squared" = 0.8303312970453488, "correct_adjusted_r_squared" = 0.4423137303432872, "overcontrolled_adjusted_r_squared" = 0.7818545247725913, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05611409223161351, "collider_coefficient_variance" = 0.011714631186494599, "correct_beta_t_statistic" = -3.1894564111476003, "overcontrolled_beta_t_statistic" = -1.519938113334213, "collider_t_statistic" = 4.383720413715965, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.008594675705287 +gamma = 1.3862541132718083 +delta = 0.03127947620302537 +n = 5000 +expected = {"residual_variance" = 0.34226643403194823, "outcome_variance" = 2.017263219861053, "correct_r_squared" = 0.5042788714162553, "overcontrolled_r_squared" = 0.8303312970453488, "correct_adjusted_r_squared" = 0.5041796875169788, "overcontrolled_adjusted_r_squared" = 0.8302633888192312, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00011222818446322703, "collider_coefficient_variance" = 2.34292623729892e-05, "correct_beta_t_statistic" = -71.31841346598553, "overcontrolled_beta_t_statistic" = -33.986849430080795, "collider_t_statistic" = 98.022968394224, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8099692228530592 +gamma = -1.0989659071665383 +delta = 2.311407160329961 +n = 4 +expected = {"residual_variance" = 0.45295474642511613, "outcome_variance" = 1.6560501419691886, "correct_r_squared" = 0.39615354954716986, "overcontrolled_r_squared" = 0.7264848841554319, "correct_adjusted_r_squared" = 0.09423032432075477, "overcontrolled_adjusted_r_squared" = 0.1794546524662956, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.6389730231431109, "collider_coefficient_variance" = 0.05129200057726031, "correct_beta_t_statistic" = -1.6199384457061183, "overcontrolled_beta_t_statistic" = 0.9804082573823265, "collider_t_statistic" = -2.1979318143330766, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8099692228530592 +gamma = -1.0989659071665383 +delta = 2.311407160329961 +n = 10 +expected = {"residual_variance" = 0.45295474642511613, "outcome_variance" = 1.6560501419691886, "correct_r_squared" = 0.39615354954716986, "overcontrolled_r_squared" = 0.7264848841554319, "correct_adjusted_r_squared" = 0.3206727432405661, "overcontrolled_adjusted_r_squared" = 0.6483377081998409, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.2555892092572443, "collider_coefficient_variance" = 0.020516800230904124, "correct_beta_t_statistic" = -2.5613475788521725, "overcontrolled_beta_t_statistic" = 1.5501615650823708, "collider_t_statistic" = -3.475235337519421, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.8099692228530592 +gamma = -1.0989659071665383 +delta = 2.311407160329961 +n = 5000 +expected = {"residual_variance" = 0.45295474642511613, "outcome_variance" = 1.6560501419691886, "correct_r_squared" = 0.39615354954716986, "overcontrolled_r_squared" = 0.7264848841554319, "correct_adjusted_r_squared" = 0.3960327319300324, "overcontrolled_adjusted_r_squared" = 0.7263754124260564, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0005111784185144886, "collider_coefficient_variance" = 4.103360046180825e-05, "correct_beta_t_statistic" = -57.2734730031796, "overcontrolled_beta_t_statistic" = 34.66266635631646, "collider_t_statistic" = -77.70862452502851, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.09129052483690066 +gamma = 0.6911101587122943 +delta = 0.20657847204841562 +n = 4 +expected = {"residual_variance" = 0.6767579160807033, "outcome_variance" = 1.0083339599249967, "correct_r_squared" = 0.00826507908710789, "overcontrolled_r_squared" = 0.32883554161852996, "correct_adjusted_r_squared" = -0.48760238136933814, "overcontrolled_adjusted_r_squared" = -1.01349337514441, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.17154685996246782, "collider_coefficient_variance" = 0.11450031924447408, "correct_beta_t_statistic" = -0.18258104967380132, "overcontrolled_beta_t_statistic" = -0.3824435256864414, "collider_t_statistic" = 1.3822203174245886, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.09129052483690066 +gamma = 0.6911101587122943 +delta = 0.20657847204841562 +n = 10 +expected = {"residual_variance" = 0.6767579160807033, "outcome_variance" = 1.0083339599249967, "correct_r_squared" = 0.00826507908710789, "overcontrolled_r_squared" = 0.32883554161852996, "correct_adjusted_r_squared" = -0.11570178602700362, "overcontrolled_adjusted_r_squared" = 0.13707426779525284, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.06861874398498713, "collider_coefficient_variance" = 0.045800127697789635, "correct_beta_t_statistic" = -0.2886859872767775, "overcontrolled_beta_t_statistic" = -0.6046963087771327, "collider_t_statistic" = 2.1854822156113114, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.09129052483690066 +gamma = 0.6911101587122943 +delta = 0.20657847204841562 +n = 5000 +expected = {"residual_variance" = 0.6767579160807033, "outcome_variance" = 1.0083339599249967, "correct_r_squared" = 0.00826507908710789, "overcontrolled_r_squared" = 0.32883554161852996, "correct_adjusted_r_squared" = 0.008066652732383422, "overcontrolled_adjusted_r_squared" = 0.3285669146590017, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00013723748796997426, "collider_coefficient_variance" = 9.160025539557927e-05, "correct_beta_t_statistic" = -6.455214917025139, "overcontrolled_beta_t_statistic" = -13.521420521688714, "collider_t_statistic" = 48.86886797723744, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.403525342095066 +gamma = -0.8425034617378222 +delta = -0.7345091104069601 +n = 4 +expected = {"residual_variance" = 0.5848595935887303, "outcome_variance" = 2.969883385903072, "correct_r_squared" = 0.6632864425766255, "overcontrolled_r_squared" = 0.8030698456495495, "correct_adjusted_r_squared" = 0.4949296638649382, "overcontrolled_adjusted_r_squared" = 0.4092095369486487, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.16337552168982583, "collider_coefficient_variance" = 0.0855151860531937, "correct_beta_t_statistic" = -2.807050684190132, "overcontrolled_beta_t_statistic" = -2.9262729485748844, "collider_t_statistic" = -1.6850069234756444, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.403525342095066 +gamma = -0.8425034617378222 +delta = -0.7345091104069601 +n = 10 +expected = {"residual_variance" = 0.5848595935887303, "outcome_variance" = 2.969883385903072, "correct_r_squared" = 0.6632864425766255, "overcontrolled_r_squared" = 0.8030698456495495, "correct_adjusted_r_squared" = 0.6211972478987037, "overcontrolled_adjusted_r_squared" = 0.7468040872637066, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.06535020867593033, "collider_coefficient_variance" = 0.03420607442127748, "correct_beta_t_statistic" = -4.4383368347874095, "overcontrolled_beta_t_statistic" = -4.626843786416704, "collider_t_statistic" = -2.66422987566804, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.403525342095066 +gamma = -0.8425034617378222 +delta = -0.7345091104069601 +n = 5000 +expected = {"residual_variance" = 0.5848595935887303, "outcome_variance" = 2.969883385903072, "correct_r_squared" = 0.6632864425766255, "overcontrolled_r_squared" = 0.8030698456495495, "correct_adjusted_r_squared" = 0.6632190729172771, "overcontrolled_adjusted_r_squared" = 0.8029910262961973, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00013070041735186065, "collider_coefficient_variance" = 6.841214884255496e-05, "correct_beta_t_statistic" = -99.244228696259, "overcontrolled_beta_t_statistic" = -103.4593722770027, "collider_t_statistic" = -59.5739910967955, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -1.272775386252895 +gamma = -0.5907661499290734 +delta = 0.02472402568181034 +n = 4 +expected = {"residual_variance" = 0.7412872924643772, "outcome_variance" = 2.619957183851206, "correct_r_squared" = 0.6183143731646599, "overcontrolled_r_squared" = 0.7170612951106621, "correct_adjusted_r_squared" = 0.4274715597469898, "overcontrolled_adjusted_r_squared" = 0.15118388533198632, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.26818257525114336, "collider_coefficient_variance" = 0.13737671249229177, "correct_beta_t_statistic" = -2.54555077250579, "overcontrolled_beta_t_statistic" = -1.8009860548445398, "collider_t_statistic" = -1.1815322998581468, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.272775386252895 +gamma = -0.5907661499290734 +delta = 0.02472402568181034 +n = 10 +expected = {"residual_variance" = 0.7412872924643772, "outcome_variance" = 2.619957183851206, "correct_r_squared" = 0.6183143731646599, "overcontrolled_r_squared" = 0.7170612951106621, "correct_adjusted_r_squared" = 0.5706036698102424, "overcontrolled_adjusted_r_squared" = 0.6362216651422798, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.10727303010045734, "collider_coefficient_variance" = 0.05495068499691671, "correct_beta_t_statistic" = -4.02486917035971, "overcontrolled_beta_t_statistic" = -2.847608983754836, "collider_t_statistic" = -1.8681665983043922, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.272775386252895 +gamma = -0.5907661499290734 +delta = 0.02472402568181034 +n = 5000 +expected = {"residual_variance" = 0.7412872924643772, "outcome_variance" = 2.619957183851206, "correct_r_squared" = 0.6183143731646599, "overcontrolled_r_squared" = 0.7170612951106621, "correct_adjusted_r_squared" = 0.6182380054922239, "overcontrolled_adjusted_r_squared" = 0.7169480516826495, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0002145460602009147, "collider_coefficient_variance" = 0.00010990136999383341, "correct_beta_t_statistic" = -89.99881065467493, "overcontrolled_beta_t_statistic" = -63.674472610149074, "collider_t_statistic" = -41.77347507103164, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.7371946848133772 +gamma = -0.5855937051960315 +delta = 1.3343028988246413 +n = 4 +expected = {"residual_variance" = 0.7446460021888957, "outcome_variance" = 1.5434560033170945, "correct_r_squared" = 0.352103333136244, "overcontrolled_r_squared" = 0.5175463371883934, "correct_adjusted_r_squared" = 0.02815499970436593, "overcontrolled_adjusted_r_squared" = -0.44736098843482003, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.6184969716838955, "collider_coefficient_variance" = 0.1386244171439762, "correct_beta_t_statistic" = -1.4743893696267545, "overcontrolled_beta_t_statistic" = 0.041817329503379026, "collider_t_statistic" = -1.171187410392063, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.7371946848133772 +gamma = -0.5855937051960315 +delta = 1.3343028988246413 +n = 10 +expected = {"residual_variance" = 0.7446460021888957, "outcome_variance" = 1.5434560033170945, "correct_r_squared" = 0.352103333136244, "overcontrolled_r_squared" = 0.5175463371883934, "correct_adjusted_r_squared" = 0.2711162497782744, "overcontrolled_adjusted_r_squared" = 0.37970243352793426, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.2473987886735582, "collider_coefficient_variance" = 0.055449766857590475, "correct_beta_t_statistic" = -2.3312142829802123, "overcontrolled_beta_t_statistic" = 0.06611900344821778, "collider_t_statistic" = -1.8518098918766381, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.7371946848133772 +gamma = -0.5855937051960315 +delta = 1.3343028988246413 +n = 5000 +expected = {"residual_variance" = 0.7446460021888957, "outcome_variance" = 1.5434560033170945, "correct_r_squared" = 0.352103333136244, "overcontrolled_r_squared" = 0.5175463371883934, "correct_adjusted_r_squared" = 0.35197370195039684, "overcontrolled_adjusted_r_squared" = 0.5173532398648746, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0004947975773471163, "collider_coefficient_variance" = 0.00011089953371518095, "correct_beta_t_statistic" = -52.127536068621865, "overcontrolled_beta_t_statistic" = 1.4784658631475796, "collider_t_statistic" = -41.407727996426985, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.7401111329459416 +gamma = -0.332720202989102 +delta = -0.15529274409203062 +n = 4 +expected = {"residual_variance" = 0.9003309075052431, "outcome_variance" = 4.027986755002408, "correct_r_squared" = 0.751737018807699, "overcontrolled_r_squared" = 0.7764811648431786, "correct_adjusted_r_squared" = 0.6276055282115487, "overcontrolled_adjusted_r_squared" = 0.32944349452953586, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.2614587228976304, "collider_coefficient_variance" = 0.2026489357523037, "correct_beta_t_statistic" = -3.480222265891883, "overcontrolled_beta_t_statistic" = -3.1548979545106826, "collider_t_statistic" = -0.665440405978204, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.7401111329459416 +gamma = -0.332720202989102 +delta = -0.15529274409203062 +n = 10 +expected = {"residual_variance" = 0.9003309075052431, "outcome_variance" = 4.027986755002408, "correct_r_squared" = 0.751737018807699, "overcontrolled_r_squared" = 0.7764811648431786, "correct_adjusted_r_squared" = 0.7207041461586615, "overcontrolled_adjusted_r_squared" = 0.7126186405126582, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.10458348915905216, "collider_coefficient_variance" = 0.08105957430092148, "correct_beta_t_statistic" = -5.50271456192524, "overcontrolled_beta_t_statistic" = -4.988331660830023, "collider_t_statistic" = -1.0521536649991257, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -1.7401111329459416 +gamma = -0.332720202989102 +delta = -0.15529274409203062 +n = 5000 +expected = {"residual_variance" = 0.9003309075052431, "outcome_variance" = 4.027986755002408, "correct_r_squared" = 0.751737018807699, "overcontrolled_r_squared" = 0.7764811648431786, "correct_adjusted_r_squared" = 0.7516873463424746, "overcontrolled_adjusted_r_squared" = 0.7763917036323894, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00020916697831810434, "collider_coefficient_variance" = 0.00016211914860184295, "correct_beta_t_statistic" = -123.04443821242812, "overcontrolled_beta_t_statistic" = -111.54248687930358, "collider_t_statistic" = -23.526871177135863, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.6875313903168299 +gamma = -1.131398378883464 +delta = -1.2207282067573215 +n = 4 +expected = {"residual_variance" = 0.43858450868761384, "outcome_variance" = 1.4726994126709931, "correct_r_squared" = 0.3209748089826909, "overcontrolled_r_squared" = 0.7021900702111603, "correct_adjusted_r_squared" = -0.018537786525963672, "overcontrolled_adjusted_r_squared" = 0.10657021063348102, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.30173331437239276, "collider_coefficient_variance" = 0.04808909281518891, "correct_beta_t_statistic" = 1.3750627806336597, "overcontrolled_beta_t_statistic" = -0.5537960023202984, "collider_t_statistic" = -2.262796757766928, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.6875313903168299 +gamma = -1.131398378883464 +delta = -1.2207282067573215 +n = 10 +expected = {"residual_variance" = 0.43858450868761384, "outcome_variance" = 1.4726994126709931, "correct_r_squared" = 0.3209748089826909, "overcontrolled_r_squared" = 0.7021900702111603, "correct_adjusted_r_squared" = 0.23609666010552724, "overcontrolled_adjusted_r_squared" = 0.6171015188429204, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.12069332574895711, "collider_coefficient_variance" = 0.019235637126075563, "correct_beta_t_statistic" = 2.1741651562634177, "overcontrolled_beta_t_statistic" = -0.8756283632140178, "collider_t_statistic" = -3.577795818293898, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.6875313903168299 +gamma = -1.131398378883464 +delta = -1.2207282067573215 +n = 5000 +expected = {"residual_variance" = 0.43858450868761384, "outcome_variance" = 1.4726994126709931, "correct_r_squared" = 0.3209748089826909, "overcontrolled_r_squared" = 0.7021900702111603, "correct_adjusted_r_squared" = 0.3208389496007346, "overcontrolled_adjusted_r_squared" = 0.7020708747219513, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00024138665149791422, "collider_coefficient_variance" = 3.8471274252151125e-05, "correct_beta_t_statistic" = 48.61581083716454, "overcontrolled_beta_t_statistic" = -19.5796454317342, "collider_t_statistic" = -80.00194659319642, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.6554342688098205 +gamma = 0.6817085787435635 +delta = 2.5989353606378356 +n = 4 +expected = {"residual_variance" = 0.6827212732608567, "outcome_variance" = 1.429594080730264, "correct_r_squared" = 0.30050074109905317, "overcontrolled_r_squared" = 0.5224369753181198, "correct_adjusted_r_squared" = -0.04924888835142022, "overcontrolled_adjusted_r_squared" = -0.4326890740456404, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.7103896485381929, "collider_coefficient_variance" = 0.11652708424073137, "correct_beta_t_statistic" = -1.310868537619641, "overcontrolled_beta_t_statistic" = -1.9660378893371007, "collider_t_statistic" = 1.363417157487127, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.6554342688098205 +gamma = 0.6817085787435635 +delta = 2.5989353606378356 +n = 10 +expected = {"residual_variance" = 0.6827212732608567, "outcome_variance" = 1.429594080730264, "correct_r_squared" = 0.30050074109905317, "overcontrolled_r_squared" = 0.5224369753181198, "correct_adjusted_r_squared" = 0.21306333373643482, "overcontrolled_adjusted_r_squared" = 0.3859903968375827, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.28415585941527716, "collider_coefficient_variance" = 0.04661083369629255, "correct_beta_t_statistic" = -2.072665145966092, "overcontrolled_beta_t_statistic" = -3.1085788482476526, "collider_t_statistic" = 2.155751809305907, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.6554342688098205 +gamma = 0.6817085787435635 +delta = 2.5989353606378356 +n = 5000 +expected = {"residual_variance" = 0.6827212732608567, "outcome_variance" = 1.429594080730264, "correct_r_squared" = 0.30050074109905317, "overcontrolled_r_squared" = 0.5224369753181198, "correct_adjusted_r_squared" = 0.30036078526493937, "overcontrolled_adjusted_r_squared" = 0.5222458354243108, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0005683117188305544, "collider_coefficient_variance" = 9.322166739258509e-05, "correct_beta_t_statistic" = -46.34620160974705, "overcontrolled_beta_t_statistic" = -69.50993618099754, "collider_t_statistic" = 48.204075882261726, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = -0.40757059559002246 +gamma = 0.05930998657023708 +delta = -0.0839085282554313 +n = 4 +expected = {"residual_variance" = 0.9964946561517315, "outcome_variance" = 1.1661137903896057, "correct_r_squared" = 0.14245075545681182, "overcontrolled_r_squared" = 0.14545676042575859, "correct_adjusted_r_squared" = -0.2863238668147823, "overcontrolled_adjusted_r_squared" = -1.5636297187227242, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.25202363041954845, "collider_coefficient_variance" = 0.24825039993473938, "correct_beta_t_statistic" = -0.8151411911800449, "overcontrolled_beta_t_statistic" = -0.7991377031150568, "collider_t_statistic" = 0.11861997314047416, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.40757059559002246 +gamma = 0.05930998657023708 +delta = -0.0839085282554313 +n = 10 +expected = {"residual_variance" = 0.9964946561517315, "outcome_variance" = 1.1661137903896057, "correct_r_squared" = 0.14245075545681182, "overcontrolled_r_squared" = 0.14545676042575859, "correct_adjusted_r_squared" = 0.0352570998889133, "overcontrolled_adjusted_r_squared" = -0.09869845088116752, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.10080945216781938, "collider_coefficient_variance" = 0.09930015997389575, "correct_beta_t_statistic" = -1.288851389375849, "overcontrolled_beta_t_statistic" = -1.2635476529795073, "collider_t_statistic" = 0.1875546455559473, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.40757059559002246 +gamma = 0.05930998657023708 +delta = -0.0839085282554313 +n = 5000 +expected = {"residual_variance" = 0.9964946561517315, "outcome_variance" = 1.1661137903896057, "correct_r_squared" = 0.14245075545681182, "overcontrolled_r_squared" = 0.14545676042575859, "correct_adjusted_r_squared" = 0.14227917697651107, "overcontrolled_adjusted_r_squared" = 0.1451147379164233, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00020161890433563877, "collider_coefficient_variance" = 0.00019860031994779152, "correct_beta_t_statistic" = -28.819593195394486, "overcontrolled_beta_t_statistic" = -28.253784448724932, "collider_t_statistic" = 4.1938493695897705, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.32509180040772895 +gamma = -0.8127661428850748 +delta = 1.6614818265983582 +n = 4 +expected = {"residual_variance" = 0.6021960392489689, "outcome_variance" = 1.1056846786923387, "correct_r_squared" = 0.09558301813255556, "overcontrolled_r_squared" = 0.45536367568991837, "correct_adjusted_r_squared" = -0.3566254728011667, "overcontrolled_adjusted_r_squared" = -0.6339089729302448, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.32754732944683035, "collider_coefficient_variance" = 0.09066001742178645, "correct_beta_t_statistic" = 0.6501836008154579, "overcontrolled_beta_t_statistic" = 1.7629588640919303, "collider_t_statistic" = -1.6255322857701495, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.32509180040772895 +gamma = -0.8127661428850748 +delta = 1.6614818265983582 +n = 10 +expected = {"residual_variance" = 0.6021960392489689, "outcome_variance" = 1.1056846786923387, "correct_r_squared" = 0.09558301813255556, "overcontrolled_r_squared" = 0.45536367568991837, "correct_adjusted_r_squared" = -0.017469104600874995, "overcontrolled_adjusted_r_squared" = 0.29975329731560935, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.13101893177873214, "collider_coefficient_variance" = 0.036264006968714575, "correct_beta_t_statistic" = 1.0280305379332788, "overcontrolled_beta_t_statistic" = 2.7874827158568665, "collider_t_statistic" = -2.570192216586693, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.32509180040772895 +gamma = -0.8127661428850748 +delta = 1.6614818265983582 +n = 5000 +expected = {"residual_variance" = 0.6021960392489689, "outcome_variance" = 1.1056846786923387, "correct_r_squared" = 0.09558301813255556, "overcontrolled_r_squared" = 0.45536367568991837, "correct_adjusted_r_squared" = 0.0954020623538706, "overcontrolled_adjusted_r_squared" = 0.45514569036900177, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00026203786355746426, "collider_coefficient_variance" = 7.252801393742915e-05, "correct_beta_t_statistic" = 22.98746165764488, "overcontrolled_beta_t_statistic" = 62.33000838761684, "collider_t_statistic" = -57.471245115287076, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.26240158502891675 +gamma = 0.055498542223747116 +delta = 0.7906073859060696 +n = 4 +expected = {"residual_variance" = 0.9969293696233946, "outcome_variance" = 1.0688545918256878, "correct_r_squared" = 0.06441904479081552, "overcontrolled_r_squared" = 0.06729186809165434, "correct_adjusted_r_squared" = -0.40337143281377674, "overcontrolled_adjusted_r_squared" = -1.798124395725037, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.4103133264857495, "collider_coefficient_variance" = 0.24846704200442474, "correct_beta_t_statistic" = 0.5248031700578335, "overcontrolled_beta_t_statistic" = 0.34009932947927585, "collider_t_statistic" = 0.11099708444749423, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.26240158502891675 +gamma = 0.055498542223747116 +delta = 0.7906073859060696 +n = 10 +expected = {"residual_variance" = 0.9969293696233946, "outcome_variance" = 1.0688545918256878, "correct_r_squared" = 0.06441904479081552, "overcontrolled_r_squared" = 0.06729186809165434, "correct_adjusted_r_squared" = -0.052528574610332536, "overcontrolled_adjusted_r_squared" = -0.19919616959644443, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.1641253305942998, "collider_coefficient_variance" = 0.09938681680176989, "correct_beta_t_statistic" = 0.8297866703297169, "overcontrolled_beta_t_statistic" = 0.5377442559252796, "collider_t_statistic" = 0.17550180024606704, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.26240158502891675 +gamma = 0.055498542223747116 +delta = 0.7906073859060696 +n = 5000 +expected = {"residual_variance" = 0.9969293696233946, "outcome_variance" = 1.0688545918256878, "correct_r_squared" = 0.06441904479081552, "overcontrolled_r_squared" = 0.06729186809165434, "correct_adjusted_r_squared" = 0.0642318537233467, "overcontrolled_adjusted_r_squared" = 0.06691856085454874, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00032825066118859957, "collider_coefficient_variance" = 0.0001987736336035398, "correct_beta_t_statistic" = 18.554594016804547, "overcontrolled_beta_t_statistic" = 12.024327107589691, "collider_t_statistic" = 3.924339555237952, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -2.2895539097396664 +gamma = -0.8256186344016126 +delta = 0.9112667097324645 +n = 4 +expected = {"residual_variance" = 0.594655428674797, "outcome_variance" = 6.242057105604193, "correct_r_squared" = 0.8397964031597551, "overcontrolled_r_squared" = 0.9047340614457198, "correct_adjusted_r_squared" = 0.7596946047396327, "overcontrolled_adjusted_r_squared" = 0.7142021843371594, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.8425244395355308, "collider_coefficient_variance" = 0.08840376971310165, "correct_beta_t_statistic" = -4.579107819479333, "overcontrolled_beta_t_statistic" = -0.995871112881541, "collider_t_statistic" = -1.6512372688032253, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -2.2895539097396664 +gamma = -0.8256186344016126 +delta = 0.9112667097324645 +n = 10 +expected = {"residual_variance" = 0.594655428674797, "outcome_variance" = 6.242057105604193, "correct_r_squared" = 0.8397964031597551, "overcontrolled_r_squared" = 0.9047340614457198, "correct_adjusted_r_squared" = 0.8197709535547245, "overcontrolled_adjusted_r_squared" = 0.8775152218587826, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.3370097758142123, "collider_coefficient_variance" = 0.035361507885240666, "correct_beta_t_statistic" = -7.240205180520917, "overcontrolled_beta_t_statistic" = -1.5746104863361596, "collider_t_statistic" = -2.6108353633869443, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -2.2895539097396664 +gamma = -0.8256186344016126 +delta = 0.9112667097324645 +n = 5000 +expected = {"residual_variance" = 0.594655428674797, "outcome_variance" = 6.242057105604193, "correct_r_squared" = 0.8397964031597551, "overcontrolled_r_squared" = 0.9047340614457198, "correct_adjusted_r_squared" = 0.8397643496189707, "overcontrolled_adjusted_r_squared" = 0.9046959321927464, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0006740195516284247, "collider_coefficient_variance" = 7.072301577048132e-05, "correct_beta_t_statistic" = -161.89590954690908, "overcontrolled_beta_t_statistic" = -35.20936085531657, "collider_t_statistic" = -58.38005350593573, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.05948383325374731 +gamma = 0.6998965824144787 +delta = -0.15521879433361277 +n = 4 +expected = {"residual_variance" = 0.6712061564761367, "outcome_variance" = 1.0035383264185596, "correct_r_squared" = 0.0035258508074995387, "overcontrolled_r_squared" = 0.3311604162926734, "correct_adjusted_r_squared" = -0.4947112237887507, "overcontrolled_adjusted_r_squared" = -1.0065187511219797, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.17216597944767528, "collider_coefficient_variance" = 0.11262942612286701, "correct_beta_t_statistic" = -0.11896766650749462, "overcontrolled_beta_t_statistic" = 0.07951236688844329, "collider_t_statistic" = 1.3997931648289574, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.05948383325374731 +gamma = 0.6998965824144787 +delta = -0.15521879433361277 +n = 10 +expected = {"residual_variance" = 0.6712061564761367, "outcome_variance" = 1.0035383264185596, "correct_r_squared" = 0.0035258508074995387, "overcontrolled_r_squared" = 0.3311604162926734, "correct_adjusted_r_squared" = -0.12103341784156302, "overcontrolled_adjusted_r_squared" = 0.14006339237629437, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.06886639177907011, "collider_coefficient_variance" = 0.04505177044914681, "correct_beta_t_statistic" = -0.18810439703950607, "overcontrolled_beta_t_statistic" = 0.12572009075921808, "collider_t_statistic" = 2.213267326997503, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = -0.05948383325374731 +gamma = 0.6998965824144787 +delta = -0.15521879433361277 +n = 5000 +expected = {"residual_variance" = 0.6712061564761367, "outcome_variance" = 1.0035383264185596, "correct_r_squared" = 0.0035258508074995387, "overcontrolled_r_squared" = 0.3311604162926734, "correct_adjusted_r_squared" = 0.0033264762278291705, "overcontrolled_adjusted_r_squared" = 0.3308927198413196, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00013773278355814022, "collider_coefficient_variance" = 9.01035408982936e-05, "correct_beta_t_statistic" = -4.2061421864694575, "overcontrolled_beta_t_statistic" = 2.811186690750548, "collider_t_statistic" = 49.49016195545672, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.4453211799919978 +gamma = -1.7554676337446538 +delta = 0.27981980963164965 +n = 4 +expected = {"residual_variance" = 0.24499796156412887, "outcome_variance" = 3.088953313333461, "correct_r_squared" = 0.6762657448775603, "overcontrolled_r_squared" = 0.9206857674065206, "correct_adjusted_r_squared" = 0.5143986173163405, "overcontrolled_adjusted_r_squared" = 0.7620573022195619, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.13771753220078833, "collider_coefficient_variance" = 0.015006000292644591, "correct_beta_t_statistic" = 2.8906423599839957, "overcontrolled_beta_t_statistic" = 1.278478370183728, "collider_t_statistic" = -3.5109352674893075, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.4453211799919978 +gamma = -1.7554676337446538 +delta = 0.27981980963164965 +n = 10 +expected = {"residual_variance" = 0.24499796156412887, "outcome_variance" = 3.088953313333461, "correct_r_squared" = 0.6762657448775603, "overcontrolled_r_squared" = 0.9206857674065206, "correct_adjusted_r_squared" = 0.6357989629872554, "overcontrolled_adjusted_r_squared" = 0.898024558094098, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05508701288031533, "collider_coefficient_variance" = 0.006002400117057837, "correct_beta_t_statistic" = 4.570506879256896, "overcontrolled_beta_t_statistic" = 2.021451794520241, "collider_t_statistic" = -5.551276081339365, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.4453211799919978 +gamma = -1.7554676337446538 +delta = 0.27981980963164965 +n = 5000 +expected = {"residual_variance" = 0.24499796156412887, "outcome_variance" = 3.088953313333461, "correct_r_squared" = 0.6762657448775603, "overcontrolled_r_squared" = 0.9206857674065206, "correct_adjusted_r_squared" = 0.6762009721174318, "overcontrolled_adjusted_r_squared" = 0.9206540226666393, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00011017402576063067, "collider_coefficient_variance" = 1.2004800234115674e-05, "correct_beta_t_statistic" = 102.19964073648843, "overcontrolled_beta_t_statistic" = 45.201036257861965, "collider_t_statistic" = -124.13030679743473, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.2664098099997454 +gamma = 0.2282517924023317 +delta = 0.6986699064866807 +n = 4 +expected = {"residual_variance" = 0.9504810035550206, "outcome_variance" = 2.603793806863591, "correct_r_squared" = 0.6159450117117555, "overcontrolled_r_squared" = 0.6349630293114777, "correct_adjusted_r_squared" = 0.4239175175676333, "overcontrolled_adjusted_r_squared" = -0.0951109120655669, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.45796543874984486, "collider_coefficient_variance" = 0.2258535345297398, "correct_beta_t_statistic" = 2.532819619999491, "overcontrolled_beta_t_statistic" = 1.554712366068087, "collider_t_statistic" = 0.4565035848046634, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.2664098099997454 +gamma = 0.2282517924023317 +delta = 0.6986699064866807 +n = 10 +expected = {"residual_variance" = 0.9504810035550206, "outcome_variance" = 2.603793806863591, "correct_r_squared" = 0.6159450117117555, "overcontrolled_r_squared" = 0.6349630293114777, "correct_adjusted_r_squared" = 0.567938138175725, "overcontrolled_adjusted_r_squared" = 0.5306667519718999, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.18318617549993796, "collider_coefficient_variance" = 0.09034141381189592, "correct_beta_t_statistic" = 4.004739450780277, "overcontrolled_beta_t_statistic" = 2.4582160916023175, "collider_t_statistic" = 0.7217955440072842, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 1.2664098099997454 +gamma = 0.2282517924023317 +delta = 0.6986699064866807 +n = 5000 +expected = {"residual_variance" = 0.9504810035550206, "outcome_variance" = 2.603793806863591, "correct_r_squared" = 0.6159450117117555, "overcontrolled_r_squared" = 0.6349630293114777, "correct_adjusted_r_squared" = 0.6158681699774041, "overcontrolled_adjusted_r_squared" = 0.6348169268617324, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0003663723509998759, "collider_coefficient_variance" = 0.00018068282762379184, "correct_beta_t_statistic" = 89.54869644119871, "overcontrolled_beta_t_statistic" = 54.96738284206632, "collider_t_statistic" = 16.139839022567283, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = false} + +[[diagnostics]] +beta = 0.0 +gamma = 0.9999999999999999 +delta = 0.5 +n = 4 +expected = {"residual_variance" = 0.5000000000000001, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.49999999999999994, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -0.5000000000000002, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.14062500000000003, "collider_coefficient_variance" = 0.06250000000000001, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -0.6666666666666666, "collider_t_statistic" = 1.9999999999999998, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 0.9999999999999999 +delta = 0.5 +n = 10 +expected = {"residual_variance" = 0.5000000000000001, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.49999999999999994, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.3571428571428571, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05625000000000001, "collider_coefficient_variance" = 0.025000000000000005, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -1.0540925533894596, "collider_t_statistic" = 3.162277660168379, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 0.9999999999999999 +delta = 0.5 +n = 5000 +expected = {"residual_variance" = 0.5000000000000001, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.49999999999999994, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.4997998799279567, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00011250000000000001, "collider_coefficient_variance" = 5.000000000000001e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -23.57022603955158, "collider_t_statistic" = 70.71067811865474, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0000000000000002 +delta = 0.5 +n = 4 +expected = {"residual_variance" = 0.4999999999999999, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5000000000000001, "correct_adjusted_r_squared" = -0.5, "overcontrolled_adjusted_r_squared" = -0.49999999999999967, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.14062499999999997, "collider_coefficient_variance" = 0.06249999999999997, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -0.6666666666666667, "collider_t_statistic" = 2.0000000000000004, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0000000000000002 +delta = 0.5 +n = 10 +expected = {"residual_variance" = 0.4999999999999999, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5000000000000001, "correct_adjusted_r_squared" = -0.125, "overcontrolled_adjusted_r_squared" = 0.35714285714285726, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05624999999999999, "collider_coefficient_variance" = 0.024999999999999988, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -1.0540925533894598, "collider_t_statistic" = 3.16227766016838, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.0 +gamma = 1.0000000000000002 +delta = 0.5 +n = 5000 +expected = {"residual_variance" = 0.4999999999999999, "outcome_variance" = 1.0, "correct_r_squared" = 0.0, "overcontrolled_r_squared" = 0.5000000000000001, "correct_adjusted_r_squared" = -0.00020008003201280514, "overcontrolled_adjusted_r_squared" = 0.4997998799279569, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.00011249999999999997, "collider_coefficient_variance" = 4.9999999999999975e-05, "correct_beta_t_statistic" = 0.0, "overcontrolled_beta_t_statistic" = -23.57022603955159, "collider_t_statistic" = 70.71067811865477, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.5 +gamma = 1.0 +delta = -0.5 +n = 4 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.25, "correct_r_squared" = 0.2, "overcontrolled_r_squared" = 0.6, "correct_adjusted_r_squared" = -0.2, "overcontrolled_adjusted_r_squared" = -0.2, "correct_beta_variance" = 0.25, "overcontrolled_beta_variance" = 0.125, "collider_coefficient_variance" = 0.0625, "correct_beta_t_statistic" = 1.0, "overcontrolled_beta_t_statistic" = 1.4142135623730951, "collider_t_statistic" = 2.0, "adjusted_r_squared_prefers_overcontrolled" = false, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.5 +gamma = 1.0 +delta = -0.5 +n = 10 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.25, "correct_r_squared" = 0.2, "overcontrolled_r_squared" = 0.6, "correct_adjusted_r_squared" = 0.1, "overcontrolled_adjusted_r_squared" = 0.4857142857142857, "correct_beta_variance" = 0.1, "overcontrolled_beta_variance" = 0.05, "collider_coefficient_variance" = 0.025, "correct_beta_t_statistic" = 1.5811388300841898, "overcontrolled_beta_t_statistic" = 2.23606797749979, "collider_t_statistic" = 3.1622776601683795, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[diagnostics]] +beta = 0.5 +gamma = 1.0 +delta = -0.5 +n = 5000 +expected = {"residual_variance" = 0.5, "outcome_variance" = 1.25, "correct_r_squared" = 0.2, "overcontrolled_r_squared" = 0.6, "correct_adjusted_r_squared" = 0.19983993597438976, "overcontrolled_adjusted_r_squared" = 0.5998399039423654, "correct_beta_variance" = 0.0002, "overcontrolled_beta_variance" = 0.0001, "collider_coefficient_variance" = 5e-05, "correct_beta_t_statistic" = 35.35533905932738, "overcontrolled_beta_t_statistic" = 50.0, "collider_t_statistic" = 70.71067811865476, "adjusted_r_squared_prefers_overcontrolled" = true, "absolute_beta_t_prefers_overcontrolled" = true} + +[[extreme]] +beta = 1.0 +gamma = 1e+300 +delta = 1e+300 +collider = {"beta_hat" = -1.0, "theta_hat" = 1e-300} +confounder = 2.0 +diagnostics = {"residual_variance" = 0.0, 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"conditioning_variable_role" = "confounded_mediator", "unconditioned" = {"coefficient_names" = ["intercept", "X"], "coefficients" = [-0.030351697317427876, 1.1618489079741188], "standard_errors" = [0.17280477064994532, 0.17164606270271793], "t_statistics" = [-0.1756415474137113, 6.768864311128306], "r_squared" = 0.18791727230700594, "adjusted_r_squared" = 0.1838158443893646, "residual_variance" = 5.877913419389918, "n_observations" = 200}, "conditioned" = {"coefficient_names" = ["intercept", "X", "Z"], "coefficients" = [0.0013852591803322472, -0.5490335003273477, 1.4875692682998225], "standard_errors" = [0.0840796375601277, 0.10747180008759065, 0.058822035446786304], "t_statistics" = [0.016475560796056114, -5.108628494915686, 25.289319844185954], "r_squared" = 0.8087617535416255, "adjusted_r_squared" = 0.806820248501439, "residual_variance" = 1.3912226130367256, "n_observations" = 200}} diff --git a/test/fixtures/features_python_320.toml b/test/fixtures/features_python_320.toml new file mode 100644 index 0000000..0749680 --- /dev/null +++ b/test/fixtures/features_python_320.toml @@ -0,0 +1,611 @@ +[[ambiguity]] +case = 1 +probabilities = [[0.321114448759955, 0.678885551240045]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.321114448759955, 0.678885551240045] +expected_probability_variances = [0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 2 +probabilities = [[0.321114448759955, 0.678885551240045]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.321114448759955, 0.678885551240045] +expected_probability_variances = [0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 3 +probabilities = [[0.25557451141976095, 0.015774253330096365, 0.7000459875885074, 0.010355937398322463, 0.018249310263312904]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.25557451141976095, 0.015774253330096365, 0.7000459875885074, 0.010355937398322463, 0.018249310263312904] +expected_probability_variances = [0.0, 0.0, 0.0, 0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 4 +probabilities = [[0.25557451141976095, 0.015774253330096365, 0.7000459875885074, 0.010355937398322463, 0.018249310263312904]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.25557451141976095, 0.015774253330096365, 0.7000459875885074, 0.010355937398322463, 0.018249310263312904] +expected_probability_variances = [0.0, 0.0, 0.0, 0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 5 +probabilities = [[0.13487426894607898, 0.04560314369350535, 0.13775992708512927, 0.05388360184504834, 0.003494906606399321, 0.014364984349608152, 0.039213719445321975, 0.15349510347497924, 0.045403030687731355, 0.007279093638156099, 0.01138733838377611, 0.3532408818442658]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.13487426894607898, 0.04560314369350535, 0.13775992708512927, 0.05388360184504834, 0.003494906606399321, 0.014364984349608152, 0.039213719445321975, 0.15349510347497924, 0.045403030687731355, 0.007279093638156099, 0.01138733838377611, 0.3532408818442658] +expected_probability_variances = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 6 +probabilities = [[0.13487426894607898, 0.04560314369350535, 0.13775992708512927, 0.05388360184504834, 0.003494906606399321, 0.014364984349608152, 0.039213719445321975, 0.15349510347497924, 0.045403030687731355, 0.007279093638156099, 0.01138733838377611, 0.3532408818442658]] +prior_weights = [1.0] +bin_width = 0.2 +expected_unscaled = 0.0 +expected_scaled = 0.0 +expected_mean_probabilities = [0.13487426894607898, 0.04560314369350535, 0.13775992708512927, 0.05388360184504834, 0.003494906606399321, 0.014364984349608152, 0.039213719445321975, 0.15349510347497924, 0.045403030687731355, 0.007279093638156099, 0.01138733838377611, 0.3532408818442658] +expected_probability_variances = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] +expected_prior_weights = [1.0] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 7 +probabilities = [[0.988232814035703, 0.011767185964297064], [0.4887041434121974, 0.5112958565878027]] +prior_weights = [0.5, 0.5] +bin_width = 0.2 +expected_unscaled = 0.06238222319372168 +expected_scaled = 0.3898888949607604 +expected_mean_probabilities = [0.7384684787239502, 0.2615315212760499] +expected_probability_variances = [0.06238222319372167, 0.062382223193721706] +expected_prior_weights = [0.5, 0.5] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 8 +probabilities = [[0.988232814035703, 0.011767185964297064], [0.4887041434121974, 0.5112958565878027]] +prior_weights = [0.18577630235962828, 0.8142236976403717] +bin_width = 0.2 +expected_unscaled = 0.03774460564770739 +expected_scaled = 0.23590378529817113 +expected_mean_probabilities = [0.581504732763253, 0.41849526723674724] +expected_probability_variances = [0.03774460564770737, 0.037744605647707395] +expected_prior_weights = [0.18577630235962828, 0.8142236976403717] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 9 +probabilities = [[0.07537285199423818, 0.24163254412482682, 0.34964706077549185, 0.129661866820406, 0.20368567628503703], [0.08858897695045395, 0.13732822183612348, 0.4034003696534669, 0.3156076831224877, 0.05507474843746796]] +prior_weights = [0.5, 0.5] +bin_width = 0.2 +expected_unscaled = 0.003429714941163173 +expected_scaled = 0.021435718382269826 +expected_mean_probabilities = [0.08198091447234607, 0.18948038298047515, 0.37652371521447936, 0.22263477497144685, 0.1293802123612525] +expected_probability_variances = [4.366648971457731e-05, 0.0027198479120264245, 0.0007223545538327472, 0.008643961650061878, 0.005521301968928845] +expected_prior_weights = [0.5, 0.5] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 10 +probabilities = [[0.07537285199423818, 0.24163254412482682, 0.34964706077549185, 0.129661866820406, 0.20368567628503703], [0.08858897695045395, 0.13732822183612348, 0.4034003696534669, 0.3156076831224877, 0.05507474843746796]] +prior_weights = [0.9156054037719624, 0.0843945962280376] +bin_width = 0.2 +expected_unscaled = 0.0009903972236659131 +expected_scaled = 0.006189982647911956 +expected_mean_probabilities = [0.07648822152361731, 0.23282982296043261, 0.3541835495741696, 0.14535468890751305, 0.1911437170342674] +expected_probability_variances = [1.3496813885562638e-05, 0.0008406739654504909, 0.00022327155299626763, 0.002671749948012861, 0.0017065714594353102] +expected_prior_weights = [0.9156054037719624, 0.0843945962280376] +expected_bin_width = 0.2 + +[[ambiguity]] +case = 11 +probabilities = [[0.11778757568924456, 0.06414880398656546, 0.019561254323767006, 0.4201743363572697, 0.09282052745532442, 0.03148776945536989, 0.0783904689738976, 0.05712508926584205, 0.022776658358905112, 0.020902063458405458, 0.05100547848674651, 0.023819974188662312], [0.013115074875366846, 0.026598611587215756, 0.10279497917448452, 0.22958122225021485, 0.044438678329792446, 0.03954722304550827, 0.06307122300341056, 0.09234462550133876, 0.0006343761516709094, 0.25053326422475897, 0.0882667903680095, 0.049073931488228785]] +prior_weights = [0.5, 0.5] +bin_width = 0.2 +expected_unscaled = 0.005140180872299163 +expected_scaled = 0.03212613045186976 +expected_mean_probabilities = [0.0654513252823057, 0.04537370778689061, 0.061178116749125766, 0.3248777793037423, 0.06862960289255843, 0.03551749625043908, 0.07073084598865409, 0.0747348573835904, 0.011705517255288012, 0.1357176638415822, 0.06963613442737801, 0.03644695283844555] 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4.366731040837309e-40, 1.9943736868622054e-42, 7.7935368191928e-45], [0.022750131948179195, 0.002837927573459416, 0.003128500294363193, 0.0034402149796119094, 0.003773544317312097, 0.004128837750891276, 0.0045063058947259205, 0.004906005275105069, 0.005327823665909899, 0.005771466302501034, 0.006236443266799063, 0.006722058340790288, 0.007227399624122707, 0.007751332203631001, 0.00829249314820829, 0.008849289081244993, 0.009419896554852869, 0.010002265415442135, 0.010594125309232316, 0.011192995429513297, 0.011796197555561105, 0.012400872377024752, 0.013003999038277647, 0.013602417775932907, 0.014192855460704312, 0.01477195379347157, 0.01533629984620477, 0.015882458582791692, 0.016407006944208824, 0.016906569038272867, 0.017377851937640465, 0.01781768156193314, 0.01822303810175585, 0.018591090434705138, 0.018919228986666425, 0.019205096506028918, 0.019446616243820714, 0.019642017068860373, 0.01978985509321357, 0.019889031438656557, 0.01993880583837243, 0.01993880583837243, 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0.0007978521141679407, 0.0007978361572905657, 0.0007978170094588566, 0.0007977946709028516, 0.0007977691418903365, 0.0007977404227277884, 0.000797708513759654, 0.0007976734153689047, 0.0007976351279767036, 0.0007975936520421834, 0.0007975489880632236, 0.0007975011365753959, 0.0007974500981529631, 0.0007973958734079911, 0.0007973384629910152, 0.0007972778675907621, 0.0007972140879337619, 0.0007971471247852358, 0.0007970769789480414, 0.0007970036512636169, 0.0007969271426110369, 0.0007968474539078452, 0.0007967645861094441, 0.0007966785402093723, 0.0007965893172392491, 0.0007964969182684412, 0.47607781734589316], [3.814722443652784e-16, 6.291850346301272e-18, 6.394071256063331e-18, 6.497926911978679e-18, 6.603443028820274e-18, 6.710645718870254e-18, 6.819561498007673e-18, 6.9302172917621746e-18, 7.042640441599138e-18, 7.156858711191027e-18, 7.272900292818451e-18, 7.390793813866833e-18, 7.51056834346378e-18, 7.632253399065145e-18, 7.755878953386687e-18, 7.881475441158897e-18, 8.00907376621018e-18, 8.138705308575571e-18, 8.27040193157815e-18, 8.404195989276375e-18, 8.54012033384526e-18, 8.67820832302312e-18, 8.818493827890734e-18, 8.96101124048384e-18, 9.10579548175216e-18, 9.252882009537542e-18, 9.4023068266159e-18, 9.554106488978487e-18, 9.708318114193021e-18, 9.8649793897529e-18, 1.0024128581828817e-17, 1.0185804543870902e-17, 1.0350046725512783e-17, 1.0516895181528814e-17, 1.0686390580947287e-17, 1.0858574216318463e-17, 1.1033488013082397e-17, 1.1211174539044438e-17, 1.1391677014154834e-17, 1.1575039320159069e-17, 1.1761306010691037e-17, 1.1950522321280142e-17, 1.2142734179583709e-17, 1.2337988215890371e-17, 1.2536331773511834e-17, 1.2737812919640954e-17, 1.2942480456084189e-17, 1.3150383930368467e-17, 1.3361573646895384e-17, 1.357610067825514e-17, 1.3794016876781879e-17, 1.4015374886251112e-17, 1.424022815367891e-17, 1.4468630941394524e-17, 1.470063833920276e-17, 1.4936306276817008e-17, 1.517569153640736e-17, 1.541885176535156e-17, 1.566584548911257e-17, 1.5916732124501092e-17, 1.6171571992832388e-17, 0.999999999999999]] + +[[normal]] +case = 10 +means = [0.0, 8.0] +standard_deviations = [1.0, 1.0] +edges = [-0.01, 0.0, 0.02] +expected = [[0.4960106436853684, 0.003989356314631598, 0.007978313716901997, 0.492021686283098], [5.735422180258049e-16, 4.8553839401369124e-17, 1.0957039614531002e-16, 0.9999999999999992]] + +[[normal]] +case = 11 +means = [0.0, 8.0] +standard_deviations = [1.0, 1.0] +edges = [0.0] +expected = [[0.5, 0.5], [6.22096057427174e-16, 0.9999999999999993]] + +[[normal]] +case = 12 +means = [0.0, 8.0] +standard_deviations = [1.0, 1.0] +edges = [-10.0, -8.0, 8.0, 8.5, 10.0] +expected = [[7.61985302416047e-24, 6.22096049807321e-16, 0.9999999999999987, 6.126165226049708e-16, 9.479527202350226e-18, 7.61985302416047e-24], [9.740948918936876e-73, 6.388754400537896e-58, 0.5, 0.19146246127401312, 0.2857874067778077, 0.022750131948179195]] + +[[hawkes]] +case = 1 +events = [] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.2 +alpha = 0.0 +beta = 1.0 +expected_intensity = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2] +expected_integrated = 0.2 +expected_likelihood = -0.2 +scaled_objective_gradient = [1.0, -4.583566078126286e-12, 0.0] + +[[hawkes]] +case = 2 +events = [] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 1.2 +alpha = 0.4 +beta = 0.8 +expected_intensity = [1.2, 1.2, 1.2, 1.2, 1.2, 1.2] +expected_integrated = 1.2 +expected_likelihood = -1.2 +scaled_objective_gradient = [1.0, 0.0, 0.0] + +[[hawkes]] +case = 3 +events = [] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.1 +alpha = 3.0 +beta = 4.0 +expected_intensity = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1] +expected_integrated = 0.1 +expected_likelihood = -0.1 +scaled_objective_gradient = [1.0, 0.0, 0.0] + +[[hawkes]] +case = 4 +events = [0.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.2 +alpha = 0.0 +beta = 1.0 +expected_intensity = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2] +expected_integrated = 0.2 +expected_likelihood = -1.8094379124341002 +scaled_objective_gradient = [-4.000000001521744, 0.6321205588798628, 0.0] + +[[hawkes]] +case = 5 +events = [0.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 1.2 +alpha = 0.4 +beta = 0.8 +expected_intensity = [1.2, 1.5692465385546543, 1.5408575155864845, 1.5408302480759526, 1.4109169696172195, 1.3797315856468886] +expected_integrated = 1.4753355179413892 +expected_likelihood = -1.2930139611474345 +scaled_objective_gradient = [0.1666666666513881, 0.5506710358905337, 0.22466448206516232] + +[[hawkes]] +case = 6 +events = [0.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.1 +alpha = 3.0 +beta = 4.0 +expected_intensity = [0.1, 2.110960138106918, 1.4479868923516648, 1.4474478054192985, 0.22228661193509863, 0.15494691666620253] +expected_integrated = 0.8362632708334493 +expected_likelihood = -3.1388483638274947 +scaled_objective_gradient = [-9.000000012233539, 0.9816843611183544, 0.01373672916594362] + +[[hawkes]] +case = 7 +events = [0.0, 0.2, 0.2001, 1.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.2 +alpha = 0.0 +beta = 1.0 +expected_intensity = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2] +expected_integrated = 0.2 +expected_likelihood = -6.637751649736401 +scaled_objective_gradient = [-19.00000000608239, -17.785892012006354, 0.0] + +[[hawkes]] +case = 8 +events = [0.0, 0.2, 0.2001, 1.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 1.2 +alpha = 0.4 +beta = 0.8 +expected_intensity = [1.2, 1.5692465385546543, 1.5408575155864845, 1.9407982493559186, 1.9059634849227336, 1.801582398913849] +expected_integrated = 1.9480220013576885 +expected_likelihood = -0.08159660840279415 +scaled_objective_gradient = [-1.5526421043863625, -0.377614946823966, -0.15806844255491945] + +[[hawkes]] +case = 9 +events = [0.0, 0.2, 0.2001, 1.0] +times = [0.0, 0.1, 0.2, 0.2001, 0.8, 1.0] +horizon = 1.0 +mu = 0.1 +alpha = 3.0 +beta = 4.0 +expected_intensity = [0.1, 2.110960138106918, 1.4479868923516648, 4.446248045387302, 0.7667032149907328, 0.39956906496540723] +expected_integrated = 2.2751077337586483 +expected_likelihood = -3.6328266306082186 +scaled_objective_gradient = [-12.418218977297938, -0.6440938264929909, 0.2722663722852872] + +[[hawkes]] +case = 10 +events = [0.3, 0.5, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.3, 0.5, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 0.2 +alpha = 0.0 +beta = 1.0 +expected_intensity = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] +expected_integrated = 0.2 +expected_likelihood = -5.028313737302301 +scaled_objective_gradient = [-14.0000000045744, -9.19726555025584, 0.0] + +[[hawkes]] +case = 11 +events = [0.3, 0.5, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.3, 0.5, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 1.2 +alpha = 0.4 +beta = 0.8 +expected_intensity = [1.2, 1.2, 1.2, 1.2, 1.2, 1.5408575155864845, 1.782779162840877, 1.7379729715519328, 2.065858182508436] +expected_integrated = 1.617677271864455 +expected_likelihood = -0.45029715005722104 +scaled_objective_gradient = [-1.0577055482596174, -0.22615214053500024, -0.05324092127106899] + +[[hawkes]] +case = 12 +events = [0.3, 0.5, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.3, 0.5, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 0.1 +alpha = 3.0 +beta = 4.0 +expected_intensity = [0.1, 0.1, 0.1, 0.1, 0.1, 1.4479868923516648, 1.4095884854464442, 0.9778434138522035, 2.6993961756924105] +expected_integrated = 1.7001509560768977 +expected_likelihood = -3.654967537693355 +scaled_objective_gradient = [-10.713272622519801, -0.304695710588754, 0.2767273065014177] + +[[hawkes]] +case = 13 +events = [0.1, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 0.2 +alpha = 0.0 +beta = 1.0 +expected_intensity = [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] +expected_integrated = 0.2 +expected_likelihood = -13.0755032994728 +scaled_objective_gradient = [-39.000000012334375, -98.27048833792627, 0.0] + +[[hawkes]] +case = 14 +events = [0.1, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 1.2 +alpha = 0.4 +beta = 0.8 +expected_intensity = [1.2, 1.2, 1.5692465385546543, 1.5692170000131274, 1.5650506005724731, 1.8982054530182815, 2.202251400440722, 2.4797317897111926, 2.7329677465868647, 2.964077591946888, 3.339486067270772, 3.1749945615641075, 3.392396302359189] +expected_integrated = 2.459504622051014 +expected_likelihood = 3.756534548296134 +scaled_objective_gradient = [-2.8746907107469575, -4.181733050284531, -1.977176600421172] + +[[hawkes]] +case = 15 +events = [0.1, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.9] +times = [0.0, 0.1, 0.2, 0.2001, 0.2142857142857143, 0.3285714285714286, 0.44285714285714284, 0.5571428571428572, 0.6714285714285715, 0.7857142857142857, 0.8, 0.9, 1.0] +horizon = 1.0 +mu = 0.1 +alpha = 3.0 +beta = 4.0 +expected_intensity = [0.1, 0.1, 2.110960138106918, 2.1101559149070384, 1.9992696765675442, 3.2016781113438713, 3.9629107376837798, 4.444839419024198, 4.7499435959368785, 4.943101966404998, 7.507489314553628, 5.065388578340095, 5.439359638524689] +expected_integrated = 4.765160090368827 +expected_likelihood = 2.4359910421591895 +scaled_objective_gradient = [-10.900087802733582, -2.859774840994754, 0.1055682179359856] + +[[simulation]] +case = 1 +mu = 1.0 +alpha = 0.0 +beta = 1.0 +horizon = 2.0 +exponential = [0.21534519416996648, 0.7612268578910141, 0.8190132279110384, 2.0189751563766043] +uniform = [0.011609310673685869, 0.25429455577753934, 0.306539049266201] +expected_events = [0.21534519416996648, 0.9765720520609805, 1.7955852799720189] + +[[simulation]] +case = 2 +mu = 1.0 +alpha = 0.0 +beta = 1.0 +horizon = 10.0 +exponential = [1.1012925695010871, 0.2721317818441246, 0.765314694764488, 0.8063130184318529, 0.17035139181133402, 1.78502233585847, 0.739760385687763, 1.987761585879321, 0.25465700349378195, 0.2391201189871668, 2.245375349062709] +uniform = [0.041622158475958626, 0.32476033194146203, 0.48752510686287254, 0.16879363184314966, 0.8247568987334111, 0.9126237619021201, 0.5817979348893184, 0.7197410944758074, 0.584208887114349, 0.9379965614298019] +expected_events = [1.1012925695010871, 1.3734243513452118, 2.1387390461097, 2.9450520645415526, 3.1154034563528867, 4.900425792211356, 5.640186177899119, 7.62794776377844, 7.882604767272222, 8.12172488625939] + +[[simulation]] +case = 3 +mu = 1.0 +alpha = 0.5 +beta = 1.5 +horizon = 2.0 +exponential = [2.440280020865283] +uniform = [] +expected_events = [] + +[[simulation]] +case = 4 +mu = 1.0 +alpha = 0.5 +beta = 1.5 +horizon = 10.0 +exponential = [0.3789965977640432, 0.12723968842272101, 3.1041269995127085, 2.0757843089570414, 1.0209726959492973, 3.222808626096414, 0.16982424323197198, 0.06929548125914624, 1.7972569478507283, 0.9654846930787172, 0.6102796714805363, 0.4445645487235599, 0.6775787232563022, 0.04304863355393492, 0.3064308758020365, 2.4472485049395205] +uniform = [0.6374190117332038, 0.1984908890493865, 0.16801334423907155, 0.7939761710857797, 0.399004387953222, 0.42738641080914885, 0.580112791442339, 0.30343339133796876, 0.6718599510107238, 0.4432696317355377, 0.5676802639491135, 0.8643019139235029, 0.02906214780929328, 0.482868920526486, 0.2615834963057815] +expected_events = [0.3789965977640432, 0.4638230567125239, 2.0636730369851426, 4.316553647112463, 6.436903223089379, 6.548511423671841, 6.58420857287889, 8.000891005093749, 8.36714759523363, 9.021034729070365, 9.04453624925786, 9.177597650215494] + +[[simulation]] +case = 5 +mu = 0.4 +alpha = 1.0 +beta = 1.3 +horizon = 2.0 +exponential = [0.9886669998811901] +uniform = [] +expected_events = [] + +[[simulation]] +case = 6 +mu = 0.4 +alpha = 1.0 +beta = 1.3 +horizon = 10.0 +exponential = [2.3208203761962807, 1.5774334236350969, 2.773263196690429, 3.480093952910348] +uniform = [0.4126776497342811, 0.147246359395086, 0.4697186023060147] +expected_events = [5.802050940490702, 6.928789100230056] + +[[fit]] +case = 1 +events = [2.3661094652988344, 3.854341210191036, 6.064915981207806, 7.487785894907469, 7.895377944340146, 8.514641974437787, 8.638988070898062, 9.497537549349877, 9.895448081577788, 10.343037339809465, 10.373829304172213, 10.528540306005594, 10.64274374129751, 10.807094053246844, 11.149165946391527, 11.85671802556751, 13.337546126065007, 13.61652287034379, 13.671743340736205, 14.058539236448258, 15.745706284578924, 15.76884332349651, 15.786856814235847, 16.021801993214943, 16.650937266174846, 16.77381574283805, 16.83433821655969, 16.940381067699295, 17.27902907238665, 17.2867296422171, 17.28826278461862, 17.423035323087152, 17.464328943944036, 17.56529337518524, 17.586052273022702, 17.752453149365582, 17.904953037303606, 18.11143035992962, 18.26420772964016, 18.280122599184658, 18.58253047977073, 18.746690809711215, 18.89881703351016, 18.92949992189486, 18.950961057880928, 19.08397579589013, 19.13854290788034, 19.21894386098631, 19.29137559884981, 19.300930054892678, 19.379836436162734, 19.44695536738439, 19.806912260166563, 19.829511348691298, 19.994217948178186, 20.077745048336233, 20.140011869534998, 20.172417302783487, 20.195121656659694, 20.37870413367352, 20.77118880179387, 20.7930740581572, 21.09418484372678, 21.110619335177194, 21.136025556798078, 21.219425950174376, 21.539764586412748, 21.646605430783122, 21.679972910369578, 21.702247721716866, 21.72514802134552, 21.729035018597227, 21.77972071278933, 21.8600257802809, 22.0849497783435, 22.117039446421018, 22.49234606498268, 22.797535981948638, 22.907785099734646, 23.719516918135778, 25.607346398716064, 25.654426378848797, 26.184098972693665, 26.39772618017799, 26.72234551656247, 26.87044392049563, 26.98547261099539, 27.667121665159886, 27.9518033795434, 28.04692752817702, 28.199481281441344, 28.201977237757, 28.316090194108995, 28.40430579972678, 28.488300615584492, 28.71043919714885, 29.00003822331588, 29.01082516112622, 29.103906190863547, 29.203965314753003, 29.279594102766076, 29.497969653088255, 29.534622636863126, 29.573736883347156, 29.652084018126597, 29.673764593592075, 29.690676948070884, 30.074327038753708, 30.152448618866565, 30.28905225827055, 30.366842603706512, 31.92027287001068, 31.9250243310013, 31.959818103071797, 33.5222656824434, 34.11905325606619, 34.48035269110748, 34.85755874254667, 34.917040752178536, 35.045999241222546, 37.09462407385003, 37.118784729172816, 37.26134967134324, 37.323630017082586, 37.7137141013195, 37.737891619306716, 37.755198935981326, 37.94391004662689, 38.32534585684174, 38.42836459609931, 38.75731444206113, 38.98772750917451, 39.1732072654086] +horizon = 40.0 +expected_mu = 0.9249836568002175 +expected_alpha = 1.3392237623909504 +expected_beta = 1.8460416700617315 +expected_log_likelihood = 55.383218243151276 +expected_branching_ratio = 0.7254569515465851 +expected_converged_starts = 3 +expected_message = "CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH" + +[[fit]] +case = 2 +events = [0.7616656437756952, 2.4886302441312873, 2.639173541099517, 2.7910029622697246, 2.9253376519113337, 3.3720778169323853, 3.5953698742338585, 4.586565900475822, 4.615522320560968, 5.08257761329757, 6.589185075997363, 6.655075555391127, 6.863765000218037, 7.235078887700903, 8.273247808559095, 8.479865981503814, 8.982171193159225, 9.009376100000654, 9.153184134767864, 9.317525612915151, 9.45205822345689, 9.513104114982541, 9.671552007089717, 9.911604905254633, 10.265755164540286, 10.549830665332944, 10.710894545653641, 10.758957384135572, 10.882316183751225, 10.990290011297173, 11.056967774582882, 11.066024801437004, 11.297044347429786, 11.464338295500788, 11.541694973461624, 11.772203087108423, 11.852509408386389, 11.885894272606928, 12.07280561152519, 12.090160771553744, 12.09016493064865, 12.147668222619753, 12.501241504513118, 12.833105347791877, 12.878014965301974, 13.3234086824869, 13.95573858589882, 14.093688048439216, 14.395947830664724, 14.50884745598088, 14.581232048106665, 14.801675783461084, 14.842221691432702, 15.388281235059951, 15.662949280634, 15.911600202459653, 16.39090082823972, 17.95349624318851, 18.062554696950418, 18.500320622458357, 18.800694168120625, 19.16056073903265, 19.46942531812413, 19.924828225538974, 23.700977193469043, 26.047431912777466, 27.858048419175585, 28.58559498251111, 29.75686311476296, 30.227745603974594, 30.436163876270687, 30.909220200242533, 31.64147820516629, 31.817814556932777, 32.17887493953593, 32.22243864136013, 32.24416090430605, 32.28327364641832, 32.307294303698804, 32.477603701110624, 32.59430767151271, 33.071137400353464, 33.44559734823055, 33.97496729309458, 33.99692619300904, 34.10422582151478, 34.33026937045085, 35.084972641563766, 35.3131304196332, 35.336887815386824, 37.16236942114864, 37.29738696953683, 37.306189707536966, 38.27374558791522, 38.38513671470265, 38.49263149195001, 38.578787779605285, 38.62850995233302, 38.685780722370275, 38.71325474147768, 38.76143595240515, 38.98104926625192, 39.31206227027881, 39.42952040505867, 39.60056520262405, 39.75494700508053, 39.799197467961925, 39.94502847635465] +horizon = 40.0 +expected_mu = 0.8130254582334147 +expected_alpha = 0.7395569796285095 +expected_beta = 0.9909984566884921 +expected_log_likelihood = 14.722927608749998 +expected_branching_ratio = 0.7462746027877821 +expected_converged_starts = 3 +expected_message = "CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH" + +[[fit]] +case = 3 +events = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5] +horizon = 40.0 +expected_mu = 1.0000005636547553 +expected_alpha = 0.0 +expected_beta = 0.9857789666721846 +expected_log_likelihood = -40.00000000000635 +expected_branching_ratio = 0.0 +expected_converged_starts = 3 +expected_message = "CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH" + +[[vpin]] +case = 1 +buy_volume = [0.0, 49.0, 49.0, 57.0, 59.0, 43.0, 46.0, 8.0] +sell_volume = [100.0, 51.0, 51.0, 43.0, 41.0, 57.0, 54.0, 92.0] +bucket_volume = 100.0 +window = 1 +expected = [1.0, 0.02, 0.02, 0.14, 0.18, 0.14, 0.08, 0.84] + +[[vpin]] +case = 2 +buy_volume = [59.0, 7.0, 80.0, 10.0, 99.0, 68.0, 97.0, 59.0] +sell_volume = [41.0, 93.0, 20.0, 90.0, 1.0, 32.0, 3.0, 41.0] +bucket_volume = 100.0 +window = 3 +expected = [0.5466666666666666, 0.7533333333333332, 0.7933333333333333, 0.7133333333333333, 0.76, 0.4933333333333333] + +[[vpin]] +case = 3 +buy_volume = [87.0, 100.0, 11.0, 15.0, 87.0, 54.0, 61.0, 93.0] +sell_volume = [13.0, 0.0, 89.0, 85.0, 13.0, 46.0, 39.0, 7.0] +bucket_volume = 100.0 +window = 8 +expected = [0.64] diff --git a/test/fixtures/graphs_python_320.toml b/test/fixtures/graphs_python_320.toml new file mode 100644 index 0000000..06cdc1c --- /dev/null +++ b/test/fixtures/graphs_python_320.toml @@ -0,0 +1,17353 @@ +[[graphs]] +id = 1 +nodes = ["A", "B", "C", "D"] +directed_edges = [] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 2 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 3 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 4 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 5 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 6 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 7 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["A", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 8 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["A", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 9 +nodes = ["A", "B", "C", "D"] +directed_edges = [["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 10 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 11 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 12 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 13 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 14 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 15 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["A", "D"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 16 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["A", "D"], ["B", "C"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 17 +nodes = ["A", "B", "C", "D"] +directed_edges = [["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 18 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 19 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 20 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 21 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 22 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 23 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["A", "D"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 24 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["A", "D"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 25 +nodes = ["A", "B", "C", "D"] +directed_edges = [["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 26 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 27 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 28 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 29 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 30 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 31 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["A", "D"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 32 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["A", "D"], ["B", "C"], ["B", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 33 +nodes = ["A", "B", "C", "D"] +directed_edges = [["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 34 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 35 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 36 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 37 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 38 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 39 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["A", "D"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 40 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["A", "D"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 41 +nodes = ["A", "B", "C", "D"] +directed_edges = [["B", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 42 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["B", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 43 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "C"], ["B", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 44 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "C"], ["B", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 45 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "D"], ["B", "C"], ["C", "D"]] +observed_nodes = ["A", "B", "C", "D"] + +[[graphs]] +id = 46 +nodes = ["A", "B", "C", "D"] +directed_edges = [["A", "B"], ["A", "D"], ["B", "C"], ["C", "D"]] +observed_nodes = 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"right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + 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true, "paths" = []} + +[[separation]] +graph = 1 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 1 +left = ["C"] +right = ["D"] 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"activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 2 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 2 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] 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"right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 2 +left = ["C"] +right = ["D"] +conditioned = [] 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"paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 3 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 3 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 3 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 3 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 3 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 4 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 4 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 4 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 4 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 4 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 4 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 5 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 5 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 5 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 5 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 5 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 6 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 6 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 6 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 6 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 6 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 6 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 7 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 7 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 7 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 7 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 7 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 8 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 9 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 9 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 9 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 9 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 9 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 9 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 10 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 10 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 11 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 11 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 12 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 12 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 12 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 12 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 12 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 12 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 13 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 13 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 14 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 14 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 15 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 15 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 15 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 15 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 16 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 16 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 17 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 17 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 17 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 17 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 17 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 17 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 18 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 18 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 19 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 19 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 20 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 20 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 20 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 21 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 21 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 22 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 22 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 22 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 22 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 22 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 22 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 23 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 23 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 23 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 23 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 23 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 24 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 25 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 25 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 25 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 25 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 25 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 25 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 26 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 26 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 26 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 26 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 27 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 27 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 27 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 27 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 28 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 28 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 29 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 29 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 29 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 29 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 30 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 31 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 31 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 31 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 31 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 32 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 32 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 32 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 32 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = true, "paths" = [{"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 33 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 33 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 33 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 33 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 33 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 34 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 34 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 35 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 35 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 36 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 36 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 37 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 37 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 38 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 38 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 39 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 39 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 39 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 39 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 39 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 40 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 41 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 41 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 41 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 42 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 43 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 44 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 44 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 45 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 46 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 46 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 47 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 47 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 47 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 47 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 48 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 48 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 49 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 49 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 49 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 50 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 51 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 52 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 53 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 54 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 54 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 55 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 55 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 55 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 55 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = true, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 56 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 57 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = []} + +[[separation]] +graph = 57 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 57 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 57 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 57 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 57 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 58 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 58 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 58 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 58 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 59 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 59 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 59 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 59 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 60 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 61 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 61 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 61 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 61 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = true, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 62 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 62 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = true, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 63 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 63 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 63 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 63 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["B"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["B"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["B"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["B"] +conditioned = ["C", "D"] +expected = {"left" = ["A"], "right" = ["B"], "conditioned" = ["C", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "B"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B"], "colliders" = ["C"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C", "B"], "colliders" = ["D"], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["C"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["C"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["C"] +conditioned = ["B", "D"] +expected = {"left" = ["A"], "right" = ["C"], "conditioned" = ["B", "D"], "separated" = false, "paths" = [{"nodes" = ["A", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["A", "D", "B", "C"], "colliders" = ["D"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["D"] +conditioned = [] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["A"] +right = ["D"] +conditioned = ["B", "C"] +expected = {"left" = ["A"], "right" = ["D"], "conditioned" = ["B", "C"], "separated" = false, "paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = ["B", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "B", "D"], "colliders" = ["C"], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["C"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["C"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["D"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["C"] +conditioned = ["D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["C"] +conditioned = ["A", "D"] +expected = {"left" = ["B"], "right" = ["C"], "conditioned" = ["A", "D"], "separated" = false, "paths" = [{"nodes" = ["B", "C"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "C"], "colliders" = ["D"], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "D", "A", "C"], "colliders" = ["D"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["D"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["D"] +conditioned = [] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = ["C"], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["D"] +conditioned = ["C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["B"] +right = ["D"] +conditioned = ["A", "C"] +expected = {"left" = ["B"], "right" = ["D"], "conditioned" = ["A", "C"], "separated" = false, "paths" = [{"nodes" = ["B", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = ["C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["B", "A", "C", "D"], "colliders" = [], "noncolliders" = ["A", "C"], "conditioned_noncolliders" = ["A", "C"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["B", "C", "A", "D"], "colliders" = ["C"], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = ["C"], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = false}]} + +[[separation]] +graph = 64 +left = ["C"] +right = ["D"] +conditioned = [] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = [], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 64 +left = ["C"] +right = ["D"] +conditioned = ["A"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 64 +left = ["C"] +right = ["D"] +conditioned = ["B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[separation]] +graph = 64 +left = ["C"] +right = ["D"] +conditioned = ["A", "B"] +expected = {"left" = ["C"], "right" = ["D"], "conditioned" = ["A", "B"], "separated" = false, "paths" = [{"nodes" = ["C", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["C", "A", "D"], "colliders" = [], "noncolliders" = ["A"], "conditioned_noncolliders" = ["A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = ["B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "A", "B", "D"], "colliders" = [], "noncolliders" = ["A", "B"], "conditioned_noncolliders" = ["A", "B"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}, {"nodes" = ["C", "B", "A", "D"], "colliders" = [], "noncolliders" = ["B", "A"], "conditioned_noncolliders" = ["B", "A"], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = false, "is_directed_from_left" = false, "is_backdoor_from_left" = true}]} + +[[backdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = true} + +[[backdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "adjustment_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["B", "C"], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = [] +expected = {"treatment" = "X", "outcome" = "Y", "adjustment_set" = [], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [{"nodes" = ["X", "U", "Y"], "colliders" = [], "noncolliders" = ["U"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "admissible" = false} + +[[backdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["M"] +expected = {"treatment" = "X", "outcome" = "Y", "adjustment_set" = ["M"], "all_observed" = true, "endpoints_in_set" = [], "descendants_in_set" = ["M"], "open_backdoor_paths" = [{"nodes" = ["X", "U", "Y"], "colliders" = [], "noncolliders" = ["U"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "admissible" = false} + +[[backdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["U"] +expected = {"treatment" = "X", "outcome" = "Y", "adjustment_set" = ["U"], "all_observed" = false, "endpoints_in_set" = [], "descendants_in_set" = [], "open_backdoor_paths" = [], "admissible" = false} + +[[backdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["X"] +expected = {"treatment" = "X", "outcome" = "Y", "adjustment_set" = ["X"], "all_observed" = true, "endpoints_in_set" = ["X"], "descendants_in_set" = [], "open_backdoor_paths" = [{"nodes" = ["X", "U", "Y"], "colliders" = [], "noncolliders" = ["U"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "admissible" = false} + +[[backdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["M", "U"] +expected = {"treatment" = "X", "outcome" = "Y", "adjustment_set" = ["M", "U"], "all_observed" = false, "endpoints_in_set" = [], "descendants_in_set" = ["M"], "open_backdoor_paths" = [], "admissible" = false} + +[[frontdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 1 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 2 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 3 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 4 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 5 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 6 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 7 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 8 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 9 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 10 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 11 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 12 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 13 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 14 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 15 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 16 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 17 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 18 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 19 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 20 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 21 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 22 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 23 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 24 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 25 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 26 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 27 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 28 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 29 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 30 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 31 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 32 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 33 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 34 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 35 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 36 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 37 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 38 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 39 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 40 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 41 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 42 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 43 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 44 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 45 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 46 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 47 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 48 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 49 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 50 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 51 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 52 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 53 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 54 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 55 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 56 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 57 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = false, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 58 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 59 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 60 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 61 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 62 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 63 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = [] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "C", "D"], "colliders" = [], "noncolliders" = ["B", "C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["B"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "C", "D"], "colliders" = [], "noncolliders" = ["C"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}, {"nodes" = ["A", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [{"nodes" = ["C", "B", "D"], "colliders" = [], "noncolliders" = ["B"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 64 +treatment = "A" +outcome = "D" +candidate = ["B", "C"] +expected = {"treatment" = "A", "outcome" = "D", "mediator_set" = ["B", "C"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["A", "D"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = [] +expected = {"treatment" = "X", "outcome" = "Y", "mediator_set" = [], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["X", "M", "Y"], "colliders" = [], "noncolliders" = ["M"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["M"] +expected = {"treatment" = "X", "outcome" = "Y", "mediator_set" = ["M"], "all_observed" = true, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = true, "positivity_required" = true} + +[[frontdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["U"] +expected = {"treatment" = "X", "outcome" = "Y", "mediator_set" = ["U"], "all_observed" = false, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["X", "M", "Y"], "colliders" = [], "noncolliders" = ["M"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [{"nodes" = ["X", "U"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["X"] +expected = {"treatment" = "X", "outcome" = "Y", "mediator_set" = ["X"], "all_observed" = true, "endpoints_in_set" = ["X"], "directed_path_exists" = true, "unintercepted_directed_paths" = [{"nodes" = ["X", "M", "Y"], "colliders" = [], "noncolliders" = ["M"], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = true, "is_backdoor_from_left" = false}], "open_treatment_mediator_backdoor_paths" = [], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[frontdoor]] +graph = 65 +treatment = "X" +outcome = "Y" +candidate = ["M", "U"] +expected = {"treatment" = "X", "outcome" = "Y", "mediator_set" = ["M", "U"], "all_observed" = false, "endpoints_in_set" = [], "directed_path_exists" = true, "unintercepted_directed_paths" = [], "open_treatment_mediator_backdoor_paths" = [{"nodes" = ["X", "U"], "colliders" = [], "noncolliders" = [], "conditioned_noncolliders" = [], "activated_colliders" = [], "blocking_colliders" = [], "is_open" = true, "is_directed_from_left" = false, "is_backdoor_from_left" = true}], "open_mediator_outcome_backdoor_paths_given_treatment" = [], "graphically_admissible" = false, "positivity_required" = true} + +[[minimal]] +graph = 1 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 2 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 3 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 4 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 5 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 6 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 7 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 8 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 9 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 10 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 11 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 12 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 13 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 14 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 15 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 16 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 17 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 18 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 19 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 20 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 21 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 22 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 23 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 24 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 25 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 26 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 27 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 28 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 29 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 30 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 31 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 32 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 33 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 34 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 35 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 36 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 37 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 38 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 39 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 40 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 41 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 42 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 43 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 44 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 45 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 46 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 47 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 48 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 49 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 50 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 51 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 52 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 53 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 54 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 55 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 56 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 57 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 58 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 59 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 60 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 61 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 62 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 63 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 64 +treatment = "A" +outcome = "D" +expected = [[]] + +[[minimal]] +graph = 65 +treatment = "X" +outcome = "Y" +expected = [] + +[[instrument]] +graph = 1 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 1 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 2 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 2 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 3 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 3 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 4 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 4 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 5 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 5 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 6 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 6 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 7 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 7 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 8 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 8 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 9 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 9 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 10 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 10 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 11 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 11 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 12 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 12 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 13 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 13 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 14 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 14 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 15 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 15 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 16 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 16 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 17 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 17 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 18 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 18 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 19 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 19 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 20 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 20 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 21 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 21 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 22 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 22 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 23 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 23 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 24 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 24 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 25 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 25 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 26 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 26 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 27 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 27 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 28 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 28 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 29 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 29 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 30 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 30 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 31 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 31 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 32 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 32 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 33 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 33 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 34 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 34 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 35 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 35 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 36 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 36 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 37 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 37 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 38 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 38 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 39 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 39 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 40 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 40 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 41 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 41 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 42 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 42 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 43 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 43 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 44 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 44 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 45 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 45 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 46 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 46 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 47 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 47 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = true, "cfi_simple" = true} + +[[instrument]] +graph = 48 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 48 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = true, "pearl_graphical" = true, "cfi_direct_relevance" = true, "cfi_full_mediation" = true, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 49 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 49 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 50 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 50 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 51 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 51 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 52 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 52 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 53 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 53 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 54 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 54 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 55 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 55 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 56 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 56 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = false, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = false, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 57 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 57 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 58 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 58 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 59 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 59 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 60 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 60 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 61 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 61 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 62 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 62 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 63 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 63 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = true, "cfi_simple" = false} + +[[instrument]] +graph = 64 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = [] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = [], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[instrument]] +graph = 64 +instrument = "B" +treatment = "C" +outcome = "D" +conditioned = ["A"] +expected = {"instrument" = "B", "treatment" = "C", "outcome" = "D", "conditioned" = ["A"], "all_observed" = true, "conditioning_disjoint" = true, "conditioning_unaffected_by_treatment" = true, "pearl_relevance" = true, "pearl_exclusion_exogeneity" = false, "pearl_graphical" = false, "cfi_direct_relevance" = true, "cfi_full_mediation" = false, "cfi_exogeneity" = false, "cfi_simple" = false} + +[[path_roles]] +graph = 1 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 1 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 2 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 2 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 3 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 3 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 4 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 4 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 5 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 5 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 6 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 6 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 7 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 7 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 8 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 8 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 9 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 9 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 10 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 10 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 11 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 11 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 12 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 12 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 13 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 13 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 14 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 14 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 15 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 15 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 16 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 16 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 17 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 17 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 18 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 18 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 19 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 19 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 20 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 20 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 21 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 21 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 22 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 22 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 23 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 23 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 24 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 24 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 25 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 25 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 26 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 26 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 27 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "C", "B", "D"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 27 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "C", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "C", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 28 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"], ["A", "C", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 28 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"], ["D", "B", "C", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"], ["D", "B", "C", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 29 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 29 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 30 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 30 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 31 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "C", "B", "D"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 31 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "C", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "C", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 32 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"], ["A", "C", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 32 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"], ["D", "B", "C", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"], ["D", "B", "C", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 33 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 33 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 34 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 34 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 35 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 35 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 36 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 36 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 37 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 37 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 38 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 38 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 39 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 39 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 40 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 40 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 41 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 41 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 42 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "C", "D"]], "mediator_on_directed_paths" = [["A", "B", "C", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 42 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "C", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "C", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 43 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 43 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 44 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "C", "D"]], "mediator_on_directed_paths" = [["A", "B", "C", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 44 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "C", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "C", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 45 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 45 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + 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"mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 47 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 48 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "C", "D"]], "mediator_on_directed_paths" = [["A", "B", "C", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 48 +treatment = "D" +outcome = "A" +node = "B" 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false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 50 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 50 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 51 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 51 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 52 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 52 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 53 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 53 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 54 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 54 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 55 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 55 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 56 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 56 +treatment = "D" +outcome = "A" +node = "B" +expected = 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"ancestor_of_outcome" = false} + +[[path_roles]] +graph = 58 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", "treatment" = "A", "outcome" = "D", "collider_on_paths" = [], "noncollider_on_paths" = [["A", "B", "D"], ["A", "B", "C", "D"]], "mediator_on_directed_paths" = [["A", "B", "D"], ["A", "B", "C", "D"]], "backdoor_noncollider_on_paths" = [], "common_cause_paths" = [], "descendant_of_treatment" = true, "ancestor_of_outcome" = true} + +[[path_roles]] +graph = 58 +treatment = "D" +outcome = "A" +node = "B" +expected = {"node" = "B", "treatment" = "D", "outcome" = "A", "collider_on_paths" = [], "noncollider_on_paths" = [["D", "B", "A"], ["D", "C", "B", "A"]], "mediator_on_directed_paths" = [], "backdoor_noncollider_on_paths" = [["D", "B", "A"], ["D", "C", "B", "A"]], "common_cause_paths" = [], "descendant_of_treatment" = false, "ancestor_of_outcome" = false} + +[[path_roles]] +graph = 59 +treatment = "A" +outcome = "D" +node = "B" +expected = {"node" = "B", 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a/test/fixtures/hedging_python_320.toml b/test/fixtures/hedging_python_320.toml new file mode 100644 index 0000000..87c59aa --- /dev/null +++ b/test/fixtures/hedging_python_320.toml @@ -0,0 +1,797 @@ +reference = "RiskLabAI 3.2.0 public single-asset hedging benchmark" + +[[gbm]] +spot = 1.0 +maturity = 0.25 +volatility = 0.0 +drift = -0.05 +steps = 5 +paths = 4 +seed = 17 +draws = [[0.6490505844611323, 1.6152885311722915, 1.4531509541109588, -1.3604007599624022, 1.2208651823356647], [-1.1187653751107869, 1.3044177344597176, -1.468203672076882, 0.09750080636857598, 0.8032710779634884], [0.722749919780817, -0.3922633828085346, 1.266794981412932, -0.43373097208061295, -1.1243945975705405], [-1.2331067528522945, -2.3152501577333466, 0.9260108271994983, 0.4019488602204705, 0.5410461082176676]] +expected = [[1.0, 0.9975031223974601, 0.9950124791926823, 0.9925280548191384, 0.9900498337491681, 0.9875778004938814], [1.0, 0.9975031223974601, 0.9950124791926823, 0.9925280548191384, 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nan] +direction = [0, 0, 0, 0, 0, 0] diff --git a/test/fixtures/labeling_python_320.toml b/test/fixtures/labeling_python_320.toml new file mode 100644 index 0000000..f21bfa5 --- /dev/null +++ b/test/fixtures/labeling_python_320.toml @@ -0,0 +1,3705 @@ +reference = "RiskLabAI 3.2.0 public labeling contracts" + +[[cusum]] +values = [100.0, 99.0, 98.0, 97.0, 96.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 98.0, 97.0, 96.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 99.0, 98.0, 97.0, 96.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2, 4] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 98.0, 97.0, 96.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 98.0, 97.0, 97.0] +threshold = 0.0 +dynamic 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0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 98.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 98.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 99.0, 98.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 4] +dynamic_events = [1, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1] +dynamic_events = [1] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 4] +dynamic_events = [1, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 99.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 99.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 99.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 99.0, 100.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 97.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 97.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 97.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 97.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 98.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 99.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 99.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 99.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 100.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 100.0, 101.0, 102.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 98.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 98.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 98.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 98.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 99.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 100.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 99.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 99.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 99.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 99.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 100.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 4] +dynamic_events = [1, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1] +dynamic_events = [1] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 4] +dynamic_events = [1, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 101.0, 102.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 100.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 100.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 100.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 100.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 101.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 101.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 101.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 101.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 101.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 102.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 102.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 102.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 102.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 102.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3] +dynamic_events = [1, 3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 104.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [1, 2, 3, 4] +dynamic_events = [1, 3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 104.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [1, 2, 3, 4] +dynamic_events = [3, 4] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 104.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [2, 4] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, 102.0, 103.0, 104.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [100.0, 101.0, nan, 104.0, 101.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0, 0.5] +events = [1, 4, 5] +dynamic_events = [1, 4, 5] + +[[cusum]] +values = [100.0, 101.0, nan, 104.0, 101.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5, 1.0] +events = [1, 4, 5] +dynamic_events = [4, 5] + +[[cusum]] +values = [100.0, 101.0, nan, 104.0, 101.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0, 1.5] +events = [4, 5] +dynamic_events = [4, 5] + +[[cusum]] +values = [100.0, 101.0, nan, 104.0, 101.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0, 2.5] +events = [4] +dynamic_events = [4] + +[[cusum]] +values = [nan, 100.0, 101.0, 103.0, 103.0] +threshold = 0.0 +dynamic = [0.0, 0.5, nan, 0.5, 0.0] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [nan, 100.0, 101.0, 103.0, 103.0] +threshold = 0.5 +dynamic = [0.5, 1.0, nan, 1.0, 0.5] +events = [2, 3] +dynamic_events = [3] + +[[cusum]] +values = [nan, 100.0, 101.0, 103.0, 103.0] +threshold = 1.0 +dynamic = [1.0, 1.5, nan, 1.5, 1.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [nan, 100.0, 101.0, 103.0, 103.0] +threshold = 2.0 +dynamic = [2.0, 2.5, nan, 2.5, 2.0] +events = [3] +dynamic_events = [3] + +[[cusum]] +values = [] +threshold = 0.0 +dynamic = [] +events = [] +dynamic_events = [] + +[[cusum]] +values = [] +threshold = 0.5 +dynamic = [] +events = [] +dynamic_events = [] + +[[cusum]] +values = [] +threshold = 1.0 +dynamic = [] +events = [] +dynamic_events = [] + +[[cusum]] +values = [] +threshold = 2.0 +dynamic = [] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0] +threshold = 0.0 +dynamic = [0.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0] +threshold = 0.5 +dynamic = [0.5] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0] +threshold = 1.0 +dynamic = [1.0] +events = [] +dynamic_events = [] + +[[cusum]] +values = [100.0] +threshold = 2.0 +dynamic = [2.0] +events = [] +dynamic_events = [] + +[[volatility]] +minutes = [] +prices = [] +span = 1 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 1 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 2 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 2 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 5 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 5 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 100 +output_index = [] +output = [] + +[[volatility]] +minutes = [] +prices = [] +span = 100 +output_index = [] +output = [] + +[[volatility]] +minutes = [0, 500] +prices = [100.0, 101.30004283349628] +span = 1 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101.30004283349628, 100.3450528394007, 99.9741886493747, 101.7085448314009, 102.3251058794016, 101.10619894300355, 101.49289691151652, 103.31285421323837, 103.14518839187674, 102.0595239354503, 103.20117057147135, 104.70722424695713] +span = 2 +output_index = [2160, 2880, 3600, 4320, 5040, 5760, 6480, 7200, 7920, 8640] +output = [nan, 0.0030282782912589607, 0.011369550840235046, 0.006657130443618468, 0.010350411926537384, 0.007549826129836729, 0.009506637170961015, 0.008726051684188515, 0.008377453703144393, 0.009788759383308188] + +[[volatility]] +minutes = [0, 720, 1440, 2160, 2880, 3600, 4320, 5040, 5760, 6480, 7200, 7920, 8640] +prices = [100.0, 101.30004283349628, 100.3450528394007, nan, 101.7085448314009, 102.3251058794016, 101.10619894300355, 101.49289691151652, 103.31285421323837, 103.14518839187674, 102.0595239354503, 103.20117057147135, 104.70722424695713] +span = 2 +output_index = [2160, 2880, 3600, 4320, 5040, 5760, 6480, 7200, 7920, 8640] +output = [nan, nan, 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0.02] +sides = [1.0, nan, -1.0] +with_side = true +multipliers = [2.0, 0.5] +output = [40, -99999, 70] + +[[triple]] +minutes = [0, 10, 20, 40, 70, 100] +prices = [100.0, nan, 102.0, 99.0, 103.0, 101.0] +starts = [0, 10, 40] +ends = [40, -99999, 200] +widths = [0.01, 0.6931471805599453, 0.02] +sides = [1.0, 1.0, 1.0] +with_side = false +multipliers = [-1.0, 2.0] +output = [40, -99999, 200] + +[[triple]] +minutes = [0, 10, 20, 40, 70, 100] +prices = [100.0, nan, 102.0, 99.0, 103.0, 101.0] +starts = [0, 10, 40] +ends = [40, -99999, 200] +widths = [0.01, 0.6931471805599453, 0.02] +sides = [1.0, 1.0, 1.0] +with_side = true +multipliers = [-1.0, 2.0] +output = [40, -99999, 200] + +[[triple]] +minutes = [0, 10, 20, 40, 70, 100] +prices = [100.0, nan, 102.0, 99.0, 103.0, 101.0] +starts = [0, 10, 40] +ends = [40, -99999, 200] +widths = [0.01, 0.6931471805599453, 0.02] +sides = [-1.0, -1.0, -1.0] +with_side = true +multipliers = [-1.0, 2.0] +output = [40, -99999, 200] + +[[triple]] +minutes = [0, 10, 20, 40, 70, 100] +prices = [100.0, nan, 102.0, 99.0, 103.0, 101.0] +starts = [0, 10, 40] +ends = [40, -99999, 200] +widths = [0.01, 0.6931471805599453, 0.02] +sides = [0.0, 0.0, 0.0] +with_side = true +multipliers = [-1.0, 2.0] +output = [40, -99999, 200] + +[[triple]] +minutes = [0, 10, 20, 40, 70, 100] +prices = [100.0, nan, 102.0, 99.0, 103.0, 101.0] +starts = [0, 10, 40] +ends = [40, -99999, 200] +widths = [0.01, 0.6931471805599453, 0.02] +sides = [1.0, nan, -1.0] +with_side = true +multipliers = [-1.0, 2.0] +output = [40, -99999, 200] + +[[meta]] +minutes = [0, 10, 30, 60, 100] +prices = [100.0, 102.0, 99.0, 103.0, 101.0] +starts = [-5, 0, 5, 30, 60, 95] +ends = [10, 25, -99999, 65, 105, 100] +sides = [1.0, -1.0, 1.0, 0.0, 1.0, nan] +with_side = false +output_starts = [-5, 0, 30, 60, 95] +output_ends = [10, 25, 65, 105, 100] +output_sides = [1.0, -1.0, 0.0, 1.0, nan] +returns = [0.019802627296178876, -0.010050335853502013, 0.02000066670666989, nan, 0.0] +labels = [1.0, -1.0, 1.0, nan, 0.0] + +[[meta]] +minutes = [0, 10, 30, 60, 100] +prices = [100.0, 102.0, nan, 103.0, 101.0] +starts = [-5, 0, 5, 30, 60, 95] +ends = [10, 25, -99999, 65, 105, 100] +sides = [1.0, -1.0, 1.0, 0.0, 1.0, nan] +with_side = false +output_starts = [-5, 0, 30, 60, 95] +output_ends = [10, 25, 65, 105, 100] +output_sides = [1.0, -1.0, 0.0, 1.0, nan] +returns = [0.019802627296178876, nan, nan, nan, 0.0] +labels = [1.0, nan, nan, nan, 0.0] + +[[meta]] +minutes = [0, 10, 30, 60, 100] +prices = [100.0, 102.0, 99.0, 103.0, 101.0] +starts = [-5, 0, 5, 30, 60, 95] +ends = [10, 25, -99999, 65, 105, 100] +sides = [1.0, -1.0, 1.0, 0.0, 1.0, nan] +with_side = true +output_starts = [-5, 0, 30, 60, 95] +output_ends = [10, 25, 65, 105, 100] +output_sides = [1.0, -1.0, 0.0, 1.0, nan] +returns = [0.019802627296178876, 0.010050335853502013, 0.0, nan, nan] +labels = [1.0, 1.0, 0.0, nan, nan] + +[[meta]] +minutes = [0, 10, 30, 60, 100] +prices = [100.0, 102.0, nan, 103.0, 101.0] +starts = [-5, 0, 5, 30, 60, 95] +ends = [10, 25, -99999, 65, 105, 100] +sides = [1.0, -1.0, 1.0, 0.0, 1.0, nan] +with_side = true +output_starts = [-5, 0, 30, 60, 95] +output_ends = [10, 25, 65, 105, 100] +output_sides = [1.0, -1.0, 0.0, 1.0, nan] +returns = [0.019802627296178876, nan, nan, nan, nan] +labels = [1.0, nan, nan, nan, nan] diff --git a/test/fixtures/microstructure_python_320.toml b/test/fixtures/microstructure_python_320.toml new file mode 100644 index 0000000..ec262e8 --- /dev/null +++ b/test/fixtures/microstructure_python_320.toml @@ -0,0 +1,1865 @@ +reference = "RiskLabAI 3.2.0 OHLC spread and volatility estimators" + +[[rolling]] +high = [] +low = [] +width = 1 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 1 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 1 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 1 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 1 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 2 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 2 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 2 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 2 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 2 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 5 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 5 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 5 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 5 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 5 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 20 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 20 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 20 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 20 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [] +low = [] +width = 20 +beta = [] +gamma = [] +alpha = [] +sigma = [] +spread = [] +volatility = [] + +[[rolling]] +high = [100.0] +low = [98.0] +width = 1 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [nan] +low = [98.0] +width = 1 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [nan] +width = 1 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [inf] +low = [98.0] +width = 1 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [inf] +width = 1 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [98.0] +width = 2 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [nan] +low = [98.0] +width = 2 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [nan] +width = 2 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [inf] +low = [98.0] +width = 2 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [inf] +width = 2 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [98.0] +width = 5 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [nan] +low = [98.0] +width = 5 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [nan] +width = 5 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [inf] +low = [98.0] +width = 5 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [inf] +width = 5 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [98.0] +width = 20 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [nan] +low = [98.0] +width = 20 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [nan] +width = 20 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [inf] +low = [98.0] +width = 20 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0] +low = [inf] +width = 20 +beta = [nan] +gamma = [nan] +alpha = [nan] +sigma = [nan] +spread = [nan] +volatility = [nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, 98.55969769413186] +width = 1 +beta = [nan, 0.0009783283779188322] +gamma = [nan, 0.00087458450880347] +alpha = [nan, 0.004115911312232379] +sigma = [nan, 0.120253440005919] +spread = [nan, 0.004115905501698347] +volatility = [nan, 0.120253440005919] + +[[rolling]] +high = [100.0, nan] +low = [98.0, 98.55969769413186] +width = 1 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, nan] +width = 1 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, inf] +low = [98.0, 98.55969769413186] +width = 1 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, inf] +width = 1 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, 98.55969769413186] +width = 2 +beta = [nan, nan] +gamma = [nan, 0.00087458450880347] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, nan] +low = [98.0, 98.55969769413186] +width = 2 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, nan] +width = 2 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, inf] +low = [98.0, 98.55969769413186] +width = 2 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, inf] +width = 2 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, 98.55969769413186] +width = 5 +beta = [nan, nan] +gamma = [nan, 0.00087458450880347] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, nan] +low = [98.0, 98.55969769413186] +width = 5 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, nan] +width = 5 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, inf] +low = [98.0, 98.55969769413186] +width = 5 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, inf] +width = 5 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, 98.55969769413186] +width = 20 +beta = [nan, nan] +gamma = [nan, 0.00087458450880347] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, nan] +low = [98.0, 98.55969769413186] +width = 20 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, nan] +width = 20 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, inf] +low = [98.0, 98.55969769413186] +width = 20 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789] +low = [98.0, inf] +width = 20 +beta = [nan, nan] +gamma = [nan, nan] +alpha = [nan, nan] +sigma = [nan, nan] +spread = [nan, nan] +volatility = [nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 1 +beta = [nan, 0.0009783283779188322, 0.0011115752170276893] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049] +alpha = [nan, 0.004115911312232379, 0.018832416368595006] +sigma = [nan, 0.120253440005919, 0.11912912102943499] +spread = [nan, 0.004115905501698347, 0.018831859796415693] +volatility = [nan, 0.120253440005919, 0.11912912102943499] + +[[rolling]] +high = [100.0, nan, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 1 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, nan, 98.78385316345286] +width = 1 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, inf, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 1 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, inf, 98.78385316345286] +width = 1 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 2 +beta = [nan, nan, 0.0010449517974732607] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049] +alpha = [nan, nan, 0.016383003544045135] +sigma = [nan, nan, 0.11667970820488512] +spread = [nan, nan, 0.016382637116871157] +volatility = [nan, nan, 0.11667970820488512] + +[[rolling]] +high = [100.0, nan, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 2 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, nan, 98.78385316345286] +width = 2 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, inf, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 2 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, inf, 98.78385316345286] +width = 2 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 5 +beta = [nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, nan, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 5 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, nan, 98.78385316345286] +width = 5 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, inf, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 5 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, inf, 98.78385316345286] +width = 5 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 20 +beta = [nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, nan, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 20 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, nan, 98.78385316345286] +width = 20 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, inf, 101.10929742682569] +low = [98.0, 98.55969769413186, 98.78385316345286] +width = 20 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569] +low = [98.0, inf, 98.78385316345286] +width = 20 +beta = [nan, nan, nan] +gamma = [nan, nan, nan] +alpha = [nan, nan, nan] +sigma = [nan, nan, nan] +spread = [nan, nan, nan] +volatility = [nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 1 +beta = [nan, 0.0009783283779188322, 0.0011115752170276893, 0.0010013210277196802, 0.00104156931409304, 0.0010812266035334665, 0.001003618948648209, 0.0010835736857536858, 0.0010288224844899252, 0.0009874614755722582, 0.001102159452296705, 0.0009570997707823193] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, 0.0007940502426201395, 0.0007384085245336162, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, 0.004115911312232379, 0.018832416368595006, 0.008612465984096249, 0.008998661879410105, 0.013960157047897132, 0.008452278560750578, 0.013867259773160182, 0.009041072121232466, 0.008511922915056813, 0.018622745734167674, 0.004172896571634166] +sigma = [nan, 0.120253440005919, 0.11912912102943499, 0.11887066535729915, 0.12110153064465926, 0.12038263133699459, 0.11911355656563613, 0.12058092607342055, 0.12029703446146098, 0.1180706632654576, 0.11870479970041498, 0.11887775632929423] +spread = [nan, 0.004115905501698347, 0.018831859796415693, 0.008612412748994163, 0.008998601156994683, 0.013959930332402741, 0.008452228241165025, 0.01386703755356302, 0.009041010536223972, 0.00851187152268598, 0.01862220754514723, 0.004172890516401202] +volatility = [nan, 0.120253440005919, 0.11912912102943499, 0.11887066535729915, 0.12110153064465926, 0.12038263133699459, 0.11911355656563613, 0.12058092607342055, 0.12029703446146098, 0.1180706632654576, 0.11870479970041498, 0.11887775632929423] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, nan, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 1 +beta = [nan, 0.0009783283779188322, 0.0011115752170276893, 0.0010013210277196802, 0.00104156931409304, 0.0010812266035334665, nan, nan, 0.0010288224844899252, 0.0009874614755722582, 0.001102159452296705, 0.0009570997707823193] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, nan, nan, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, 0.004115911312232379, 0.018832416368595006, 0.008612465984096249, 0.008998661879410105, 0.013960157047897132, nan, nan, 0.009041072121232466, 0.008511922915056813, 0.018622745734167674, 0.004172896571634166] +sigma = [nan, 0.120253440005919, 0.11912912102943499, 0.11887066535729915, 0.12110153064465926, 0.12038263133699459, nan, nan, 0.12029703446146098, 0.1180706632654576, 0.11870479970041498, 0.11887775632929423] +spread = [nan, 0.004115905501698347, 0.018831859796415693, 0.008612412748994163, 0.008998601156994683, 0.013959930332402741, nan, nan, 0.009041010536223972, 0.00851187152268598, 0.01862220754514723, 0.004172890516401202] +volatility = [nan, 0.120253440005919, 0.11912912102943499, 0.11887066535729915, 0.12110153064465926, 0.12038263133699459, nan, nan, 0.12029703446146098, 0.1180706632654576, 0.11870479970041498, 0.11887775632929423] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, nan, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 1 +beta = [nan, 0.0009783283779188322, 0.0011115752170276893, 0.0010013210277196802, 0.00104156931409304, 0.0010812266035334665, nan, nan, 0.0010288224844899252, 0.0009874614755722582, 0.001102159452296705, 0.0009570997707823193] +gamma = [nan, 0.00087458450880347, 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0.012536734869562412, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, 0.1189593733621247, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, nan, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 5 +beta = [nan, nan, nan, nan, nan, 0.0010428041080585415, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, nan, nan, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, nan, nan, nan, nan, 0.012536899073027241, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, 0.1189593733621247, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, 0.012536734869562412, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, 0.1189593733621247, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, inf, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 5 +beta = [nan, nan, nan, nan, nan, 0.0010428041080585415, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, nan, nan, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, nan, nan, nan, nan, 0.012536899073027241, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, 0.1189593733621247, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, 0.012536734869562412, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, 0.1189593733621247, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, inf, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 5 +beta = [nan, nan, nan, nan, nan, 0.0010428041080585415, nan, nan, nan, nan, nan, nan] +gamma = [nan, 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99.16092847092355, 100.09557430201194] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, 0.0007940502426201395, 0.0007384085245336162, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, nan, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, nan, nan, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, nan, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, nan, nan, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, inf, 101.35698659871879, 101.78935824662338, 101.31211848524177, 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100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507, 101.73657291800043, 101.72016703682664, 102.39060735569488, 102.15028784015712, 101.88790331666506, 102.66139749187955, 102.55098724677167, 102.04987720966295, 102.91294525072763, 102.93665563853605, 102.20885130929041, 103.14622040417517, 103.30557836200663, 102.63235175009777, 103.3625584504796, 103.6563759284045, 103.07090578830787, 103.56363388421298, 103.98803162409286] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194, 99.35614604126751, 99.3925532185498, 100.26326278179218, 99.74031208714118, 99.64234051967661, 100.4248366619484, 100.13968329175592, 99.91129538181333, 100.59191793818661, 100.55227073977572, 100.20003917360536, 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0.0005492510686093486, 0.0007260451846786666, 0.000839783455136006, 0.000618954593228269, 0.0007532894128842382, 0.0007838619470247386, 0.0006983065139400291, 0.0007602201811597563, 0.0007116614500575701, 0.0007646355757276501] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005826034345220263, 0.020880576783564475, 0.012091548508602307, 0.007062854464463836, 0.01700565470722873, 0.010573220754818174, 0.009222764153888702, 0.012930189865119832, 0.010032976855843187, 0.012284612698771402, 0.009714370708616415] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12207084335931001, 0.11291645173870492, 0.11790807827019521, 0.12086628326924839, 0.11470712180200254, 0.11835680509082513, 0.11917181410381661, 0.11670566649341671, 0.11831126655695097, 0.11704772951889603, 0.118306647855975] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 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102.04987720966295, 102.91294525072763, 102.93665563853605, 102.20885130929041, 103.14622040417517, 103.30557836200663, 102.63235175009777, 103.3625584504796, 103.6563759284045, 103.07090578830787, 103.56363388421298, 103.98803162409286] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194, 99.35614604126751, 99.3925532185498, 100.26326278179218, 99.74031208714118, 99.64234051967661, 100.4248366619484, 100.13968329175592, 99.91129538181333, 100.59191793818661, 100.55227073977572, 100.20003917360536, 100.7671669796666, 100.975820992663, 100.50879718813653, 100.95308067767135, 101.40786119126616, 100.83739413368643, 101.151942470311, 101.84574855011242] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 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98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194, 99.35614604126751, 99.3925532185498, 100.26326278179218, nan, 99.64234051967661, 100.4248366619484, 100.13968329175592, 99.91129538181333, 100.59191793818661, 100.55227073977572, 100.20003917360536, 100.7671669796666, 100.975820992663, 100.50879718813653, 100.95308067767135, 101.40786119126616, 100.83739413368643, 101.151942470311, 101.84574855011242] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, 0.0007940502426201395, 0.0007384085245336162, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685, 0.0007421156480060829, 0.0005605541369626931, 0.0008831470317080073, nan, nan, 0.0008909622027869334, 0.000618523148119376, 0.0006800289811897225, 0.0008762010240235289, 0.0005492510686093486, 0.0007260451846786666, 0.000839783455136006, 0.000618954593228269, 0.0007532894128842382, 0.0007838619470247386, 0.0006983065139400291, 0.0007602201811597563, 0.0007116614500575701, 0.0007646355757276501] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507, 101.73657291800043, 101.72016703682664, 102.39060735569488, inf, 101.88790331666506, 102.66139749187955, 102.55098724677167, 102.04987720966295, 102.91294525072763, 102.93665563853605, 102.20885130929041, 103.14622040417517, 103.30557836200663, 102.63235175009777, 103.3625584504796, 103.6563759284045, 103.07090578830787, 103.56363388421298, 103.98803162409286] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194, 99.35614604126751, 99.3925532185498, 100.26326278179218, 99.74031208714118, 99.64234051967661, 100.4248366619484, 100.13968329175592, 99.91129538181333, 100.59191793818661, 100.55227073977572, 100.20003917360536, 100.7671669796666, 100.975820992663, 100.50879718813653, 100.95308067767135, 101.40786119126616, 100.83739413368643, 101.151942470311, 101.84574855011242] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, 0.0007940502426201395, 0.0007384085245336162, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685, 0.0007421156480060829, 0.0005605541369626931, 0.0008831470317080073, nan, nan, 0.0008909622027869334, 0.000618523148119376, 0.0006800289811897225, 0.0008762010240235289, 0.0005492510686093486, 0.0007260451846786666, 0.000839783455136006, 0.000618954593228269, 0.0007532894128842382, 0.0007838619470247386, 0.0006983065139400291, 0.0007602201811597563, 0.0007116614500575701, 0.0007646355757276501] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[rolling]] +high = [100.0, 100.94147098480789, 101.10929742682569, 100.44112000805987, 101.15680249530793, 101.45892427466313, 100.87941549819892, 101.35698659871879, 101.78935824662338, 101.31211848524177, 101.54402111088937, 102.0999902065507, 101.73657291800043, 101.72016703682664, 102.39060735569488, 102.15028784015712, 101.88790331666506, 102.66139749187955, 102.55098724677167, 102.04987720966295, 102.91294525072763, 102.93665563853605, 102.20885130929041, 103.14622040417517, 103.30557836200663, 102.63235175009777, 103.3625584504796, 103.6563759284045, 103.07090578830787, 103.56363388421298, 103.98803162409286] +low = [98.0, 98.55969769413186, 98.78385316345286, 98.31000750339955, 98.74635637913639, 99.21633781453677, 98.63982971334963, 98.9460977456567, 99.65449996619138, 98.98886973811533, 99.16092847092355, 100.09557430201194, 99.35614604126751, 99.3925532185498, 100.26326278179218, inf, 99.64234051967661, 100.4248366619484, 100.13968329175592, 99.91129538181333, 100.59191793818661, 100.55227073977572, 100.20003917360536, 100.7671669796666, 100.975820992663, 100.50879718813653, 100.95308067767135, 101.40786119126616, 100.83739413368643, 101.151942470311, 101.84574855011242] +width = 20 +beta = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +gamma = [nan, 0.00087458450880347, 0.0006522738203556049, 0.0007882761944513234, 0.0008148732716043198, 0.0007343841983605604, 0.0007940502426201395, 0.0007384085245336162, 0.0008026074914705132, 0.0007783065411921748, 0.0006494848711815439, 0.0008531399051763685, 0.0007421156480060829, 0.0005605541369626931, 0.0008831470317080073, nan, nan, 0.0008909622027869334, 0.000618523148119376, 0.0006800289811897225, 0.0008762010240235289, 0.0005492510686093486, 0.0007260451846786666, 0.000839783455136006, 0.000618954593228269, 0.0007532894128842382, 0.0007838619470247386, 0.0006983065139400291, 0.0007602201811597563, 0.0007116614500575701, 0.0007646355757276501] +alpha = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +sigma = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +spread = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] +volatility = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] + +[[moments]] +beta = 0.0 +gamma = 0.0 +alpha = 0.0 +sigma = 0.0 + +[[moments]] +beta = 0.0 +gamma = 0.001 +alpha = 0.0 +sigma = 0.04784159257001972 + +[[moments]] +beta = 0.0 +gamma = 0.1 +alpha = 0.0 +sigma = 0.47841592570019725 + +[[moments]] +beta = 0.0 +gamma = 4.0 +alpha = 0.0 +sigma = 3.025767988221018 + +[[moments]] +beta = 0.0 +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = 0.0 +gamma = inf +alpha = 0.0 +sigma = inf + +[[moments]] +beta = 0.001 +gamma = 0.0 +alpha = 0.07634413615167969 +sigma = 0.07634413615167969 + +[[moments]] +beta = 0.001 +gamma = 0.001 +alpha = 5.551115123125783e-17 +sigma = 0.12418572872169942 + +[[moments]] +beta = 0.001 +gamma = 0.1 +alpha = 0.0 +sigma = 0.554760061851877 + +[[moments]] +beta = 0.001 +gamma = 4.0 +alpha = 0.0 +sigma = 3.102112124372698 + +[[moments]] +beta = 0.001 +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = 0.001 +gamma = inf +alpha = 0.0 +sigma = inf + +[[moments]] +beta = 0.1 +gamma = 0.0 +alpha = 0.763441361516797 +sigma = 0.763441361516797 + +[[moments]] +beta = 0.1 +gamma = 0.001 +alpha = 0.6870972253651173 +sigma = 0.8112829540868167 + +[[moments]] +beta = 0.1 +gamma = 0.1 +alpha = 6.661338147750939e-16 +sigma = 1.2418572872169942 + +[[moments]] +beta = 0.1 +gamma = 4.0 +alpha = 0.0 +sigma = 3.789209349737815 + +[[moments]] +beta = 0.1 +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = 0.1 +gamma = inf +alpha = 0.0 +sigma = inf + +[[moments]] +beta = 4.0 +gamma = 0.0 +alpha = 4.828427124746197 +sigma = 4.828427124746197 + +[[moments]] +beta = 4.0 +gamma = 0.001 +alpha = 4.752082988594517 +sigma = 4.876268717316217 + +[[moments]] +beta = 4.0 +gamma = 0.1 +alpha = 4.064985763229401 +sigma = 5.306843050446394 + +[[moments]] +beta = 4.0 +gamma = 4.0 +alpha = 4.440892098500626e-15 +sigma = 7.854195112967215 + +[[moments]] +beta = 4.0 +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = 4.0 +gamma = inf +alpha = 0.0 +sigma = inf + +[[moments]] +beta = nan +gamma = 0.0 +alpha = nan +sigma = nan + +[[moments]] +beta = nan +gamma = 0.001 +alpha = nan +sigma = nan + +[[moments]] +beta = nan +gamma = 0.1 +alpha = nan +sigma = nan + +[[moments]] +beta = nan +gamma = 4.0 +alpha = nan +sigma = nan + +[[moments]] +beta = nan +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = nan +gamma = inf +alpha = nan +sigma = nan + +[[moments]] +beta = inf +gamma = 0.0 +alpha = inf +sigma = inf + +[[moments]] +beta = inf +gamma = 0.001 +alpha = inf +sigma = inf + +[[moments]] +beta = inf +gamma = 0.1 +alpha = inf +sigma = inf + +[[moments]] +beta = inf +gamma = 4.0 +alpha = inf +sigma = inf + +[[moments]] +beta = inf +gamma = nan +alpha = nan +sigma = nan + +[[moments]] +beta = inf +gamma = inf +alpha = nan +sigma = inf + +[[edge]] +open = [] +high = [] +low = [] +close = [] +signed = nan +unsigned = nan + +[[edge]] +open = [100.0] +high = [100.0] +low = [100.0] +close = [100.0] +signed = nan +unsigned = nan + +[[edge]] +open = [100.0, 100.0] +high = [100.0, 100.0] +low = [100.0, 100.0] +close = [100.0, 100.0] +signed = nan +unsigned = nan + +[[edge]] +open = [100.0, 100.0, 100.0] +high = [100.0, 100.0, 100.0] +low = [100.0, 100.0, 100.0] +close = [100.0, 100.0, 100.0] +signed = nan +unsigned = nan + +[[edge]] +open = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +high = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +low = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0] +close = [100.0, 100.0, 100.0, 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99.29592689197064, 98.07136272771521] +signed = -0.000156925625128234 +unsigned = 0.000156925625128234 + +[[edge]] +open = [100.22176589294925, 99.86914466765913, 99.61329182912179] +high = [100.22560155105027, 99.88400828498156, 99.6219897333702] +low = [100.20973278733344, nan, 99.60804567124877] +close = [100.21854886458247, 99.87367944732482, 99.62093021549035] +signed = nan +unsigned = nan + +[[edge]] +open = [99.78134607516989, 99.39351594081928, 96.51156572899957] +high = [100.99564578869752, 101.2509942986267, 97.3796673773857] +low = [99.49544762335677, 98.45331220150085, 95.93293688242954] +close = [100.97340209880761, 100.14706645219385, 97.00336479326343] +signed = -0.008905036388369036 +unsigned = 0.008905036388369036 + +[[edge]] +open = [98.35583244514267, 98.43153819368746, 97.94511992525683] +high = [98.35583244514267, 98.79228296883821, 98.40984123843683] +low = [98.35583244514267, 98.01797768000225, 97.7141975705529] +close = [98.35583244514267, 98.28204547488025, 97.89631875175589] +signed = 0.0007067111816471006 +unsigned = 0.0007067111816471006 + +[[edge]] +open = [100.52192972100549, 99.43057318713845, nan] +high = [101.041407583934, 99.60310176056079, 100.45837806442754] +low = [100.48519133275353, 99.39842658253552, 98.86355091213157] +close = [100.49535923603761, 99.50392157778957, 99.52656955249218] +signed = -0.003908626566573059 +unsigned = 0.003908626566573059 + +[[edge]] +open = [101.56339326181896, 101.90870504203903, 101.20978544602659] +high = [101.65409459153108, 102.48962224451512, 101.29965813335056] +low = [100.04263382844245, nan, 99.79029260168883] +close = [101.08544114959243, 102.1103996282744, 100.23429962740309] +signed = nan +unsigned = nan + +[[edge]] +open = [105.18829742713658, 118.79373765921738, 98.84261054017907] +high = [113.68866003991931, 135.610297430578, 109.02918279483453] +low = [87.45905758256472, 99.53968646649993, 93.56050824238449] +close = [89.30383670412812, 106.47289242467562, 108.42091904915222] 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2.5] +probabilities = [[0.050000000000000044, 0.44999999999999996, 0.475, 0.025]] +maximum_mean_error = 1.1102230246251565e-16 +maximum_price_error = 0.0 + +[[grids]] +strikes = [0.5, 1.5] +maturities = [0.1, 0.5, 1.0] +prices = [[0.525, 0.025], [0.5874999999999999, 0.0875], [0.6499999999999999, 0.15]] +matrix = [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1.0, -0.0, -0.0, -0.0, -0.0, -0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [-0.0, -1.0, -0.0, -0.0, -0.0, -0.0], [2.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-2.0, -0.0, -0.0, -0.0, -0.0, -0.0], [-1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [1.0, -1.0, -0.0, -0.0, -0.0, -0.0], [-3.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [-0.0, -0.0, -1.0, -0.0, -0.0, -0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [-0.0, -0.0, -0.0, -1.0, -0.0, -0.0], [0.0, 0.0, 2.0, 0.0, 0.0, 0.0], [-0.0, -0.0, -2.0, -0.0, -0.0, -0.0], [0.0, 0.0, -1.0, 1.0, 0.0, 0.0], [-0.0, -0.0, 1.0, -1.0, -0.0, -0.0], [0.0, 0.0, -3.0, 1.0, 0.0, 0.0], [-1.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, -1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [-0.0, -0.0, -0.0, -0.0, -1.0, -0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [-0.0, -0.0, -0.0, -0.0, -0.0, -1.0], [0.0, 0.0, 0.0, 0.0, 2.0, 0.0], [-0.0, -0.0, -0.0, -0.0, -2.0, -0.0], [0.0, 0.0, 0.0, 0.0, -1.0, 1.0], [-0.0, -0.0, -0.0, -0.0, 1.0, -1.0], [0.0, 0.0, 0.0, 0.0, -3.0, 1.0], [0.0, 0.0, -1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, -1.0, 0.0, 1.0]] +bounds = [0.5, -1.0, 0.0, -1.0, 1.0, -2.0, -1.0, 0.0, -2.0, 0.5, -1.0, 0.0, -1.0, 1.0, -2.0, -1.0, 0.0, -2.0, 0.0, 0.0, 0.5, -1.0, 0.0, -1.0, 1.0, -2.0, -1.0, 0.0, -2.0, 0.0, 0.0] +support = [0.0, 0.5, 1.5, 2.5] +probabilities = [[0.050000000000000044, 0.44999999999999996, 0.475, 0.025], [0.17499999999999982, 0.3250000000000003, 0.41249999999999987, 0.0875], [0.2999999999999998, 0.2000000000000003, 0.34999999999999987, 0.15]] +maximum_mean_error = 1.1102230246251565e-16 +maximum_price_error = 0.0 + +[[grids]] +strikes = [0.4, 0.8, 1.2, 1.8] +maturities = [0.25] +prices = [[0.6200000000000001, 0.23999999999999996, 0.04000000000000001, 0.009999999999999998]] +matrix = [[1.0, 0.0, 0.0, 0.0], [-1.0, -0.0, -0.0, -0.0], [0.0, 1.0, 0.0, 0.0], [-0.0, -1.0, -0.0, -0.0], [0.0, 0.0, 1.0, 0.0], [-0.0, -0.0, -1.0, -0.0], [0.0, 0.0, 0.0, 1.0], [-0.0, -0.0, -0.0, -1.0], [2.5, 0.0, 0.0, 0.0], [-2.5, -0.0, -0.0, -0.0], [-2.5, 2.5, 0.0, 0.0], [2.5, -2.5, -0.0, -0.0], [0.0, -2.5000000000000004, 2.5000000000000004, 0.0], [-0.0, 2.5000000000000004, -2.5000000000000004, -0.0], [0.0, 0.0, -1.6666666666666665, 1.6666666666666665], [-0.0, -0.0, 1.6666666666666665, -1.6666666666666665], [-5.0, 2.5, 0.0, 0.0], [2.5, -5.0, 2.5000000000000004, 0.0], [0.0, 2.5000000000000004, -4.166666666666667, 1.6666666666666665]] +bounds = [0.6, -1.0, 0.19999999999999996, -1.0, 0.0, -1.0, 0.0, -1.0, 1.5, -2.5, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -2.5, 0.0, 0.0] +support = [0.0, 0.4, 0.8, 1.2, 1.8, 2.8] +probabilities = [[0.05000000000000023, 0.0, 0.45, 0.44999999999999973, 0.03999999999999998, 0.009999999999999993]] +maximum_mean_error = 3.3306690738754696e-16 +maximum_price_error = 3.3306690738754696e-16 + +[[grids]] +strikes = [0.4, 0.8, 1.2, 1.8] +maturities = [0.1, 0.5, 1.0] +prices = [[0.6200000000000001, 0.23999999999999996, 0.04000000000000001, 0.009999999999999998], [0.6699999999999999, 0.33999999999999997, 0.13999999999999999, 0.03499999999999999], [0.72, 0.43999999999999995, 0.24, 0.059999999999999984]] +matrix = [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.0, -1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.0, -0.0, -1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.0, -0.0, -0.0, -1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0], [2.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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+normalized_bounds = [0.0, 0.0, -0.7071067811865475] +rho = 0.11785113019775792 diff --git a/test/fixtures/ou_boundary_reference.toml b/test/fixtures/ou_boundary_reference.toml new file mode 100644 index 0000000..f15db43 --- /dev/null +++ b/test/fixtures/ou_boundary_reference.toml @@ -0,0 +1,263 @@ +[[cases]] +variant = "python_fit" +case = "public" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.27312671498613755, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246867, "std_gain" = 0.011308156469046567, "expected_holding_time" = 4.983531328665575, "return_rate" = 0.050786635586424544, "time_scaled_sharpe" = 10.025966075168057} + +[[cases]] +variant = "python_fit" +case = "cost_0.0" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.0 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.1, "stop_loss" = 4.0, "hit_probability" = 0.9999972391477709, "expected_gain" = 0.09998868050586109, "std_gain" = 0.006812473694528697, "expected_holding_time" = 1.7381432472382545, "return_rate" = 0.057526145019827134, "time_scaled_sharpe" = 11.132769473288915} + +[[cases]] +variant = "python_fit" +case = "cost_0.01" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.01 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.19369633060399827, "stop_loss" = 4.0, "hit_probability" = 0.9999948692724169, "expected_gain" = 0.18367481389055973, "std_gain" = 0.009499163080237037, "expected_holding_time" = 3.455068160535042, "return_rate" = 0.053160981305247636, "time_scaled_sharpe" = 10.402457619396303} + +[[cases]] +variant = "python_fit" +case = "cost_0.02" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.27312671498613755, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246867, "std_gain" = 0.011308156469046567, "expected_holding_time" = 4.983531328665575, "return_rate" = 0.050786635586424544, "time_scaled_sharpe" = 10.025966075168057} + +[[cases]] +variant = "python_fit" +case = "cost_0.05" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.05 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.42945788222675485, "stop_loss" = 4.0, "hit_probability" = 0.99998960337072, "expected_gain" = 0.379411830795242, "std_gain" = 0.014282183151331606, "expected_holding_time" = 8.209614868814175, "return_rate" = 0.046215545656899434, "time_scaled_sharpe" = 9.271604100878715} + +[[cases]] +variant = "python_fit" +case = "cost_0.1" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.1 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.6038255890564114, "stop_loss" = 4.0, "hit_probability" = 0.9999861566028458, "expected_gain" = 0.5037618564703534, "std_gain" = 0.017129204555603992, "expected_holding_time" = 12.20012340821004, "return_rate" = 0.04129153776685146, "time_scaled_sharpe" = 8.41988138056398} + +[[cases]] +variant = "python_fit" +case = "cost_0.2" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.2 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.8477502252205524, "stop_loss" = 4.0, "hit_probability" = 0.9999816976775661, "expected_gain" = 0.6476615001328512, "std_gain" = 0.02073907403297452, "expected_holding_time" = 18.636878509537773, "return_rate" = 0.03475160820527956, "time_scaled_sharpe" = 7.233893863363783} + +[[cases]] +variant = "python_fit" +case = "bound_2.0" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 2.0 +expected = {"profit_take" = 0.28625072009459973, "stop_loss" = 2.0, "hit_probability" = 0.9880354236782118, "expected_gain" = 0.23889669886328477, "std_gain" = 0.2485757595986656, "expected_holding_time" = 4.908604497563999, "return_rate" = 0.048668964668439355, "time_scaled_sharpe" = 0.43378282514328065} + +[[cases]] +variant = "python_fit" +case = "bound_3.0" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 3.0 +expected = {"profit_take" = 0.2739857654387698, "stop_loss" = 3.0, "hit_probability" = 0.9995075377527677, "expected_gain" = 0.25237345105131503, "std_gain" = 0.0726367317288919, "expected_holding_time" = 4.983672949995734, "return_rate" = 0.05064005073838785, "time_scaled_sharpe" = 1.556369116290546} + +[[cases]] +variant = "python_fit" +case = "bound_4.0" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.27312671498613755, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246867, "std_gain" = 0.011308156469046567, "expected_holding_time" = 4.983531328665575, "return_rate" = 0.050786635586424544, "time_scaled_sharpe" = 10.025966075168057} + +[[cases]] +variant = "python_fit" +case = "bound_5.0" +theta = 0.03875384332527206 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 5.0 +expected = {"profit_take" = 0.2731085046091358, "stop_loss" = 5.0, "hit_probability" = 0.9999999653727247, "expected_gain" = 0.2531083220157559, "std_gain" = 0.000981241393003243, "expected_holding_time" = 4.983452234257733, "return_rate" = 0.05078975579936615, "time_scaled_sharpe" = 115.54879328551758} + +[[cases]] +variant = "python_fit" +case = "preliminary" +theta = 0.06931471805599453 +sigma = 0.25 +entry_gap = 1.0 +cost = 0.1 +lower = 0.25 +upper = 4.0 +expected = {"profit_take" = 0.5354638197873653, "stop_loss" = 4.0, "hit_probability" = 0.9999999999898131, "expected_gain" = 0.435463819741163, "std_gain" = 1.447580755213817e-05, "expected_holding_time" = 7.285376134589267, "return_rate" = 0.05977231809263523, "time_scaled_sharpe" = 11145.082498910962} + +[[cases]] +variant = "julia_fit" +case = "public" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.2731267149861377, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246883, "std_gain" = 0.011308156469049745, "expected_holding_time" = 4.983531328665764, "return_rate" = 0.05078663558642264, "time_scaled_sharpe" = 10.025966075165053} + +[[cases]] +variant = "julia_fit" +case = "cost_0.0" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.0 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.1, "stop_loss" = 4.0, "hit_probability" = 0.9999972391477709, "expected_gain" = 0.09998868050586107, "std_gain" = 0.006812473694530597, "expected_holding_time" = 1.738143247238321, "return_rate" = 0.057526145019824934, "time_scaled_sharpe" = 11.132769473285597} + +[[cases]] +variant = "julia_fit" +case = "cost_0.01" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.01 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.1936963306039987, "stop_loss" = 4.0, "hit_probability" = 0.9999948692724169, "expected_gain" = 0.18367481389056015, "std_gain" = 0.009499163080239709, "expected_holding_time" = 3.455068160535181, "return_rate" = 0.05316098130524563, "time_scaled_sharpe" = 10.402457619393195} + +[[cases]] +variant = "julia_fit" +case = "cost_0.02" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.2731267149861377, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246883, "std_gain" = 0.011308156469049745, "expected_holding_time" = 4.983531328665764, "return_rate" = 0.05078663558642264, "time_scaled_sharpe" = 10.025966075165053} + +[[cases]] +variant = "julia_fit" +case = "cost_0.05" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.05 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.42945788222675685, "stop_loss" = 4.0, "hit_probability" = 0.99998960337072, "expected_gain" = 0.37941183079524393, "std_gain" = 0.01428218315133567, "expected_holding_time" = 8.209614868814517, "return_rate" = 0.04621554565689775, "time_scaled_sharpe" = 9.271604100875932} + +[[cases]] +variant = "julia_fit" +case = "cost_0.1" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.1 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.603825589056417, "stop_loss" = 4.0, "hit_probability" = 0.9999861566028458, "expected_gain" = 0.5037618564703591, "std_gain" = 0.017129204555608936, "expected_holding_time" = 12.200123408210608, "return_rate" = 0.041291537766849996, "time_scaled_sharpe" = 8.419881380561447} + +[[cases]] +variant = "julia_fit" +case = "cost_0.2" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.2 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.8477502252205595, "stop_loss" = 4.0, "hit_probability" = 0.9999816976775661, "expected_gain" = 0.6476615001328583, "std_gain" = 0.02073907403298053, "expected_holding_time" = 18.636878509538604, "return_rate" = 0.03475160820527839, "time_scaled_sharpe" = 7.233893863361605} + +[[cases]] +variant = "julia_fit" +case = "bound_2.0" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 2.0 +expected = {"profit_take" = 0.28625072009460195, "stop_loss" = 2.0, "hit_probability" = 0.98803542367821, "expected_gain" = 0.23889669886328257, "std_gain" = 0.24857575959868544, "expected_holding_time" = 4.908604497564171, "return_rate" = 0.048668964668437203, "time_scaled_sharpe" = 0.43378282514323446} + +[[cases]] +variant = "julia_fit" +case = "bound_3.0" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 3.0 +expected = {"profit_take" = 0.27398576543876846, "stop_loss" = 3.0, "hit_probability" = 0.9995075377527675, "expected_gain" = 0.25237345105131315, "std_gain" = 0.07263673172890373, "expected_holding_time" = 4.9836729499958885, "return_rate" = 0.05064005073838591, "time_scaled_sharpe" = 1.5563691162902569} + +[[cases]] +variant = "julia_fit" +case = "bound_4.0" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 4.0 +expected = {"profit_take" = 0.2731267149861377, "stop_loss" = 4.0, "hit_probability" = 0.99999299682278, "expected_gain" = 0.25309678952246883, "std_gain" = 0.011308156469049745, "expected_holding_time" = 4.983531328665764, "return_rate" = 0.05078663558642264, "time_scaled_sharpe" = 10.025966075165053} + +[[cases]] +variant = "julia_fit" +case = "bound_5.0" +theta = 0.0387538433252701 +sigma = 0.27822884144301363 +entry_gap = 1.0 +cost = 0.02 +lower = 0.1 +upper = 5.0 +expected = {"profit_take" = 0.27310850460913694, "stop_loss" = 5.0, "hit_probability" = 0.9999999653727247, "expected_gain" = 0.253108322015757, "std_gain" = 0.0009812413930036577, "expected_holding_time" = 4.983452234257941, "return_rate" = 0.05078975579936425, "time_scaled_sharpe" = 115.54879328546681} + +[[cases]] +variant = "julia_fit" +case = "preliminary" +theta = 0.06931471805599453 +sigma = 0.25 +entry_gap = 1.0 +cost = 0.1 +lower = 0.25 +upper = 4.0 +expected = {"profit_take" = 0.5354638197873653, "stop_loss" = 4.0, "hit_probability" = 0.9999999999898131, "expected_gain" = 0.435463819741163, "std_gain" = 1.447580755213817e-05, "expected_holding_time" = 7.285376134589267, "return_rate" = 0.05977231809263523, "time_scaled_sharpe" = 11145.082498910962} diff --git a/test/fixtures/ou_python_320.toml b/test/fixtures/ou_python_320.toml new file mode 100644 index 0000000..bc25a92 --- /dev/null +++ b/test/fixtures/ou_python_320.toml @@ -0,0 +1,1911 @@ +reference = "Isolated OU correction: independent 70-digit time-dependent references; all original inputs and unaffected outputs retained" + +[[scales]] +half_life = 0.1 +sigma = 0.1 +theta = 6.931471805599452 +stationary_std = 0.026857913553447927 + +[[scales]] +half_life = 0.1 +sigma = 1.0 +theta = 6.931471805599452 +stationary_std = 0.26857913553447926 + +[[scales]] +half_life = 0.1 +sigma = 3.0 +theta = 6.931471805599452 +stationary_std = 0.8057374066034377 + +[[scales]] +half_life = 1.0 +sigma = 0.1 +theta = 0.6931471805599453 +stationary_std = 0.08493218002880192 + +[[scales]] +half_life = 1.0 +sigma = 1.0 +theta = 0.6931471805599453 +stationary_std = 0.8493218002880191 + +[[scales]] +half_life = 1.0 +sigma = 3.0 +theta = 0.6931471805599453 +stationary_std = 2.547965400864057 + +[[scales]] +half_life = 5.0 +sigma = 0.1 +theta = 0.13862943611198905 +stationary_std = 0.1899141280216511 + +[[scales]] +half_life = 5.0 +sigma = 1.0 +theta = 0.13862943611198905 +stationary_std = 1.899141280216511 + +[[scales]] +half_life = 5.0 +sigma = 3.0 +theta = 0.13862943611198905 +stationary_std = 5.697423840649533 + +[[scales]] +half_life = 100.0 +sigma = 0.1 +theta = 0.006931471805599453 +stationary_std = 0.8493218002880191 + +[[scales]] +half_life = 100.0 +sigma = 1.0 +theta = 0.006931471805599453 +stationary_std = 8.493218002880191 + +[[scales]] +half_life = 100.0 +sigma = 3.0 +theta = 0.006931471805599453 +stationary_std = 25.47965400864057 + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5387154934399117, "expected_gain" = -0.0037064786080485967, "std_gain" = 0.2741743714257963, "expected_holding_time" = 0.15357377129248334, "return_rate" = -0.02413484136551823, "time_scaled_sharpe" = -0.03449658600973734} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5387154934399117, "expected_gain" = -0.033706478608048596, "std_gain" = 0.2741743714257963, "expected_holding_time" = 0.15357377129248334, "return_rate" = -0.2194806985878729, "time_scaled_sharpe" = -0.3137097394446131} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8553545832317144, "expected_gain" = -0.0031294793444997526, "std_gain" = 0.6155502055394483, "expected_holding_time" = 0.7919322931961423, "return_rate" = -0.00395170063323152, "time_scaled_sharpe" = -0.005713004712911952} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8553545832317144, "expected_gain" = -0.03312947934449975, "std_gain" = 0.6155502055394483, "expected_holding_time" = 0.7919322931961423, "return_rate" = -0.04183372698541329, "time_scaled_sharpe" = -0.060479348414330114} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.21365922496276546, "expected_gain" = -0.022243007548404886, "std_gain" = 0.532855649619405, "expected_holding_time" = 0.6107105101987375, "return_rate" = -0.03642152407229183, "time_scaled_sharpe" = -0.05341536955130873} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.21365922496276546, "expected_gain" = -0.052243007548404885, "std_gain" = 0.532855649619405, "expected_holding_time" = 0.6107105101987375, "return_rate" = -0.08554463477532753, "time_scaled_sharpe" = -0.1254587334287883} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5790927739324524, "expected_gain" = -0.05226806516886906, "std_gain" = 1.2342617558475744, "expected_holding_time" = 3.2501525591993774, "return_rate" = -0.01608172669339082, "time_scaled_sharpe" = -0.023489689610128598} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5790927739324524, "expected_gain" = -0.08226806516886906, "std_gain" = 1.2342617558475744, "expected_holding_time" = 3.2501525591993774, "return_rate" = -0.02531206264026404, "time_scaled_sharpe" = -0.03697193131980589} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.10303929214908171, "expected_gain" = -0.06300962805711205, "std_gain" = 0.6992234400211522, "expected_holding_time" = 1.2072447761135927, "return_rate" = -0.05219291837398131, "time_scaled_sharpe" = -0.08201499634750661} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.10303929214908171, "expected_gain" = -0.09300962805711205, "std_gain" = 0.6992234400211522, "expected_holding_time" = 1.2072447761135927, "return_rate" = -0.07704289129875727, "time_scaled_sharpe" = -0.12106378883037987} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.36775884223405736, "expected_gain" = -0.21284405218079916, "std_gain" = 1.6876834369516274, "expected_holding_time" = 6.704491923023419, "return_rate" = -0.031746484987160144, "time_scaled_sharpe" = -0.04870657532534971} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.36775884223405736, "expected_gain" = -0.24284405218079916, "std_gain" = 1.6876834369516274, "expected_holding_time" = 6.704491923023419, "return_rate" = -0.03622109698527131, "time_scaled_sharpe" = -0.05557168264119466} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.545686928018814, "expected_gain" = 0.00012781041034770624, "std_gain" = 0.27384958123755027, "expected_holding_time" = 0.15345247147761903, "return_rate" = 0.000832899001995403, "time_scaled_sharpe" = 0.0011914263593660933} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.545686928018814, "expected_gain" = -0.029872189589652293, "std_gain" = 0.27384958123755027, "expected_holding_time" = 0.15345247147761903, "return_rate" = -0.1946673735653023, "time_scaled_sharpe" = -0.2784633426365271} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8662890814869543, "expected_gain" = 0.016005892602169974, "std_gain" = 0.5955975534278959, "expected_holding_time" = 0.7747225737578455, "return_rate" = 0.020660160352024167, "time_scaled_sharpe" = 0.030531903995091817} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8662890814869543, "expected_gain" = -0.013994107397830025, "std_gain" = 0.5955975534278959, "expected_holding_time" = 0.7747225737578455, "return_rate" = -0.0180633789073044, "time_scaled_sharpe" = -0.026694340277506636} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.2252399357730971, "expected_gain" = -0.007188083494973768, "std_gain" = 0.5430623104296483, "expected_holding_time" = 0.6184829651246119, "return_rate" = -0.011622120414465838, "time_scaled_sharpe" = -0.016830599240777924} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.2252399357730971, "expected_gain" = -0.03718808349497377, "std_gain" = 0.5430623104296483, "expected_holding_time" = 0.6184829651246119, "return_rate" = -0.06012790261326189, "time_scaled_sharpe" = -0.08707435441924445} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.6106136687037435, "expected_gain" = 0.026534171759358682, "std_gain" = 1.2190278306295412, "expected_holding_time" = 3.223357883121778, "return_rate" = 0.00823184167612827, "time_scaled_sharpe" = 0.012123768967181713} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.6106136687037435, "expected_gain" = -0.0034658282406413166, "std_gain" = 1.2190278306295412, "expected_holding_time" = 3.223357883121778, "return_rate" = -0.0010752229092492035, "time_scaled_sharpe" = -0.0015835768777916434} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.11530144253956776, "expected_gain" = -0.03480668215899413, "std_gain" = 0.7345863701477129, "expected_holding_time" = 1.2475417103593969, "return_rate" = -0.02790021517514411, "time_scaled_sharpe" = -0.042422105931065726} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.11530144253956776, "expected_gain" = -0.06480668215899413, "std_gain" = 0.7345863701477129, "expected_holding_time" = 1.2475417103593969, "return_rate" = -0.0519475073425198, "time_scaled_sharpe" = -0.07898586607684853} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.4127963336621919, "expected_gain" = -0.0552128321823282, "std_gain" = 1.7231787855795004, "expected_holding_time" = 6.798819055631636, "return_rate" = -0.008120944494999307, "time_scaled_sharpe" = -0.012288332225860487} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.4127963336621919, "expected_gain" = -0.0852128321823282, "std_gain" = 1.7231787855795004, "expected_holding_time" = 6.798819055631636, "return_rate" = -0.012533475517596567, "time_scaled_sharpe" = -0.018965221496065464} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5595911921430626, "expected_gain" = 0.007775155678684448, "std_gain" = 0.2730399039888456, "expected_holding_time" = 0.1531498612893349, "return_rate" = 0.050768284170988054, "time_scaled_sharpe" = 0.07276535743810247} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5595911921430626, "expected_gain" = -0.02222484432131555, "std_gain" = 0.2730399039888456, "expected_holding_time" = 0.1531498612893349, "return_rate" = -0.1451182791431189, "time_scaled_sharpe" = -0.20799567338314218} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8866441390939919, "expected_gain" = 0.05162724341448577, "std_gain" = 0.5547977770947845, "expected_holding_time" = 0.7389919065131633, "return_rate" = 0.06986171696802791, "time_scaled_sharpe" = 0.10824912227706848} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8866441390939919, "expected_gain" = 0.02162724341448577, "std_gain" = 0.5547977770947845, "expected_holding_time" = 0.7389919065131633, "return_rate" = 0.029265873176515935, "time_scaled_sharpe" = 0.04534679680832471} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.24926597025291972, "expected_gain" = 0.024045761328795656, "std_gain" = 0.5623645030514244, "expected_holding_time" = 0.6330478090095469, "return_rate" = 0.037984115870201104, "time_scaled_sharpe" = 0.05374058346403667} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.24926597025291972, "expected_gain" = -0.005954238671204343, "std_gain" = 0.5623645030514244, "expected_holding_time" = 0.6330478090095469, "return_rate" = -0.009405669819030424, "time_scaled_sharpe" = -0.013307304181359614} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.671478027500141, "expected_gain" = 0.1786950687503524, "std_gain" = 1.1741893109840214, "expected_holding_time" = 3.1437657574075097, "return_rate" = 0.05684108885316962, "time_scaled_sharpe" = 0.08583202440821852} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.671478027500141, "expected_gain" = 0.1486950687503524, "std_gain" = 1.1741893109840214, "expected_holding_time" = 3.1437657574075097, "return_rate" = 0.0472983931452237, "time_scaled_sharpe" = 0.07142222143909502} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.14237797747549916, "expected_gain" = 0.027469348193648102, "std_gain" = 0.8037059952737803, "expected_holding_time" = 1.3246308017420623, "return_rate" = 0.020737361804906148, "time_scaled_sharpe" = 0.029696394344096703} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.14237797747549916, "expected_gain" = -0.002530651806351897, "std_gain" = 0.8037059952737803, "expected_holding_time" = 1.3246308017420623, "return_rate" = -0.001910458222037273, "time_scaled_sharpe" = -0.002735821522201458} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5054323838066684, "expected_gain" = 0.2690133433233395, "std_gain" = 1.7498967091733353, "expected_holding_time" = 6.867837704034254, "return_rate" = 0.0391700204513158, "time_scaled_sharpe" = 0.058661261167540495} + +[[metrics]] +theta = 0.05 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5054323838066684, "expected_gain" = 0.23901334332333948, "std_gain" = 1.7498967091733353, "expected_holding_time" = 6.867837704034254, "return_rate" = 0.03480183336058452, "time_scaled_sharpe" = 0.05211943757884592} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.543772320696523, "expected_gain" = -0.0009252236169123296, "std_gain" = 0.2739441642782418, "expected_holding_time" = 0.038297719261424584, "return_rate" = -0.024158713227697526, "time_scaled_sharpe" = -0.01725830742891873} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.543772320696523, "expected_gain" = -0.03092522361691233, "std_gain" = 0.2739441642782418, "expected_holding_time" = 0.038297719261424584, "return_rate" = -0.8074951776060744, "time_scaled_sharpe" = -0.5768519163726649} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8566893905920394, "expected_gain" = -0.000793566463931028, "std_gain" = 0.6131813176640178, "expected_holding_time" = 0.1929626439622533, "return_rate" = -0.004112539337332642, "time_scaled_sharpe" = -0.002946169785850453} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8566893905920394, "expected_gain" = -0.030793566463931027, "std_gain" = 0.6131813176640178, "expected_holding_time" = 0.1929626439622533, "return_rate" = -0.15958304587676675, "time_scaled_sharpe" = -0.1143232221095441} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.22646127507201982, "expected_gain" = -0.00560034240637422, "std_gain" = 0.5441032957816644, "expected_holding_time" = 0.1529884996919006, "return_rate" = -0.03660629666708078, "time_scaled_sharpe" = -0.02631502768799617} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.22646127507201982, "expected_gain" = -0.03560034240637422, "std_gain" = 0.5441032957816644, "expected_holding_time" = 0.1529884996919006, "return_rate" = -0.23269946746368436, "time_scaled_sharpe" = -0.1672797711546587} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5948665613910737, "expected_gain" = -0.012833596522315638, "std_gain" = 1.2272946252067838, "expected_holding_time" = 0.7767981309265239, "return_rate" = -0.0165211475303219, "time_scaled_sharpe" = -0.01186439133607788} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5948665613910737, "expected_gain" = -0.04283359652231564, "std_gain" = 1.2272946252067838, "expected_holding_time" = 0.7767981309265239, "return_rate" = -0.05514121985750114, "time_scaled_sharpe" = -0.03959876333876882} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.12338964613363707, "expected_gain" = -0.016203813892634755, "std_gain" = 0.7564330458129481, "expected_holding_time" = 0.30536669816184664, "return_rate" = -0.0530634610459244, "time_scaled_sharpe" = -0.038764654409640525} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.12338964613363707, "expected_gain" = -0.046203813892634754, "std_gain" = 0.7564330458129481, "expected_holding_time" = 0.30536669816184664, "return_rate" = -0.15130600085326049, "time_scaled_sharpe" = -0.11053415509601233} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.41382068251062887, "expected_gain" = -0.05162761121279891, "std_gain" = 1.7238099617283977, "expected_holding_time" = 1.56660760871598, "return_rate" = -0.0329550366828061, "time_scaled_sharpe" = -0.023928337687034982} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.41382068251062887, "expected_gain" = -0.0816276112127989, "std_gain" = 1.7238099617283977, "expected_holding_time" = 1.56660760871598, "return_rate" = -0.052104694729334135, "time_scaled_sharpe" = -0.03783272167358474} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5455125526530525, "expected_gain" = 3.190395917890276e-05, "std_gain" = 0.2738583644590365, "expected_holding_time" = 0.038289722263104314, "return_rate" = 0.0008332251396307092, "time_scaled_sharpe" = 0.0005953569978917509} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5455125526530525, "expected_gain" = -0.029968096040821096, "std_gain" = 0.2738583644590365, "expected_holding_time" = 0.038289722263104314, "return_rate" = -0.7826668429428105, "time_scaled_sharpe" = -0.5592320248166847} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8594231991154758, "expected_gain" = 0.003990598452082689, "std_gain" = 0.6082728228836883, "expected_holding_time" = 0.19193148209880248, "return_rate" = 0.020791786779556683, "time_scaled_sharpe" = 0.014974988870797765} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8594231991154758, "expected_gain" = -0.02600940154791731, "std_gain" = 0.6082728228836883, "expected_holding_time" = 0.19193148209880248, "return_rate" = -0.13551399313703147, "time_scaled_sharpe" = -0.09760202721296277} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.22939338731167447, "expected_gain" = -0.0017885964948231858, "std_gain" = 0.5465755056496792, "expected_holding_time" = 0.15345156067514115, "return_rate" = -0.01165577259008189, "time_scaled_sharpe" = -0.008353655383818515} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.22939338731167447, "expected_gain" = -0.031788596494823185, "std_gain" = 0.5465755056496792, "expected_holding_time" = 0.15345156067514115, "return_rate" = -0.20715720553744937, "time_scaled_sharpe" = -0.1484689146052183} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.6025765427001257, "expected_gain" = 0.00644135675031432, "std_gain" = 1.2234123714218594, "expected_holding_time" = 0.7751591377038438, "return_rate" = 0.008309721755192744, "time_scaled_sharpe" = 0.0059801083062196786} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.6025765427001257, "expected_gain" = -0.02355864324968568, "std_gain" = 1.2234123714218594, "expected_holding_time" = 0.7751591377038438, "return_rate" = -0.03039200869059058, "time_scaled_sharpe" = -0.021871671394981113} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.12665832124729076, "expected_gain" = -0.008685861131231265, "std_gain" = 0.7649565947773219, "expected_holding_time" = 0.30765612386218816, "return_rate" = -0.028232368731010118, "time_scaled_sharpe" = -0.02047120024507471} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.12665832124729076, "expected_gain" = -0.038685861131231264, "std_gain" = 0.7649565947773219, "expected_holding_time" = 0.30765612386218816, "return_rate" = -0.12574383583068088, "time_scaled_sharpe" = -0.09117645307764333} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.42485315287169223, "expected_gain" = -0.01301396494907725, "std_gain" = 1.730122439089721, "expected_holding_time" = 1.5704321102839374, "return_rate" = -0.008286868858485277, "time_scaled_sharpe" = -0.006002376188310128} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.42485315287169223, "expected_gain" = -0.04301396494907725, "std_gain" = 1.730122439089721, "expected_holding_time" = 1.5704321102839374, "return_rate" = -0.027389891398298182, "time_scaled_sharpe" = -0.01983914971228303} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5489907494722234, "expected_gain" = 0.001944912209722871, "std_gain" = 0.27367676500939997, "expected_holding_time" = 0.038272793441057454, "return_rate" = 0.05081709577113294, "time_scaled_sharpe" = 0.03632595112978181} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5489907494722234, "expected_gain" = -0.028055087790277128, "std_gain" = 0.27367676500939997, "expected_holding_time" = 0.038272793441057454, "return_rate" = -0.7330295300624874, "time_scaled_sharpe" = -0.5239967865472369} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8648016949159526, "expected_gain" = 0.013402966102917052, "std_gain" = 0.5983867084678585, "expected_holding_time" = 0.1898464331799128, "return_rate" = 0.07059898823706227, "time_scaled_sharpe" = 0.051406467315513626} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8648016949159526, "expected_gain" = -0.016597033897082947, "std_gain" = 0.5983867084678585, "expected_holding_time" = 0.1898464331799128, "return_rate" = -0.08742346969118196, "time_scaled_sharpe" = -0.06365716916788988} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.23531054904905144, "expected_gain" = 0.005903713763766888, "std_gain" = 0.5514505832787125, "expected_holding_time" = 0.154362645636316, "return_rate" = 0.038245740991487066, "time_scaled_sharpe" = 0.027248812833660953} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.23531054904905144, "expected_gain" = -0.02409628623623311, "std_gain" = 0.5514505832787125, "expected_holding_time" = 0.154362645636316, "return_rate" = -0.156101796110728, "time_scaled_sharpe" = -0.1112173150512711} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.6178895885230677, "expected_gain" = 0.04472397130766936, "std_gain" = 1.214758321945825, "expected_holding_time" = 0.7714989797862335, "return_rate" = 0.057970227413730264, "time_scaled_sharpe" = 0.041916286407987245} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.6178895885230677, "expected_gain" = 0.014723971307669365, "std_gain" = 1.214758321945825, "expected_holding_time" = 0.7714989797862335, "return_rate" = 0.01908488759343344, "time_scaled_sharpe" = 0.013799628708048262} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.13334388604309289, "expected_gain" = 0.006690937899113625, "std_gain" = 0.7818758378275437, "expected_holding_time" = 0.31217608151079973, "return_rate" = 0.02143321764669722, "time_scaled_sharpe" = 0.015316142353086374} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.13334388604309289, "expected_gain" = -0.023309062100886374, "std_gain" = 0.7818758378275437, "expected_holding_time" = 0.31217608151079973, "return_rate" = -0.0746663933638989, "time_scaled_sharpe" = -0.053356482848446414} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.44706239341818343, "expected_gain" = 0.06471837696364202, "std_gain" = 1.7401639923194057, "expected_holding_time" = 1.5764989431530236, "return_rate" = 0.04105196343119877, "time_scaled_sharpe" = 0.029620378063801807} + +[[metrics]] +theta = 0.05 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.44706239341818343, "expected_gain" = 0.03471837696364202, "std_gain" = 1.7401639923194057, "expected_holding_time" = 1.5764989431530236, "return_rate" = 0.02202245495591904, "time_scaled_sharpe" = 0.0158899450151907} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5183537508887617, "expected_gain" = -0.014905437011181077, "std_gain" = 0.27481466445236286, "expected_holding_time" = 0.15501178992211506, "return_rate" = -0.0961567956777367, "time_scaled_sharpe" = -0.1377597812687837} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5183537508887617, "expected_gain" = -0.044905437011181076, "std_gain" = 0.27481466445236286, "expected_holding_time" = 0.15501178992211506, "return_rate" = -0.289690461827088, "time_scaled_sharpe" = -0.41502729344996736} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8504425327039861, "expected_gain" = -0.011725567768024386, "std_gain" = 0.6241149499655877, "expected_holding_time" = 0.8794478886397279, "return_rate" = -0.013332873862669488, "time_scaled_sharpe" = -0.0200338418003867} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8504425327039861, "expected_gain" = -0.041725567768024385, "std_gain" = 0.6241149499655877, "expected_holding_time" = 0.8794478886397279, "return_rate" = -0.047445184992783204, "time_scaled_sharpe" = -0.07129065647255665} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.16498324859776992, "expected_gain" = -0.08552177682289908, "std_gain" = 0.4825150587422375, "expected_holding_time" = 0.6000681270580464, "return_rate" = -0.1425201122448989, "time_scaled_sharpe" = -0.2288050279601352} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.16498324859776992, "expected_gain" = -0.11552177682289907, "std_gain" = 0.4825150587422375, "expected_holding_time" = 0.6000681270580464, "return_rate" = -0.19251443563461298, "time_scaled_sharpe" = -0.3090670511991815} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5107534114777533, "expected_gain" = -0.22311647130561674, "std_gain" = 1.2497108769171126, "expected_holding_time" = 3.9026566891531287, "return_rate" = -0.05717040700139898, "time_scaled_sharpe" = -0.09037366784661935} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5107534114777533, "expected_gain" = -0.25311647130561676, "std_gain" = 1.2497108769171126, "expected_holding_time" = 3.9026566891531287, "return_rate" = -0.06485747824273586, "time_scaled_sharpe" = -0.10252521371651108} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.04036215715976302, "expected_gain" = -0.20716703853254503, "std_gain" = 0.4526564399633881, "expected_holding_time" = 1.0797452048970326, "return_rate" = -0.19186659740924808, "time_scaled_sharpe" = -0.4404446228840009} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.04036215715976302, "expected_gain" = -0.23716703853254503, "std_gain" = 0.4526564399633881, "expected_holding_time" = 1.0797452048970326, "return_rate" = -0.21965093010546122, "time_scaled_sharpe" = -0.5042257088140595} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.18182445859297802, "expected_gain" = -0.8636143949245769, "std_gain" = 1.3499492506819537, "expected_holding_time" = 8.283905503863885, "return_rate" = -0.10425208188598468, "time_scaled_sharpe" = -0.22227205379694556} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.18182445859297802, "expected_gain" = -0.893614394924577, "std_gain" = 1.3499492506819537, "expected_holding_time" = 8.283905503863885, "return_rate" = -0.10787356211484618, "time_scaled_sharpe" = -0.22999327944243833} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5463897495384712, "expected_gain" = 0.0005143622461591668, "std_gain" = 0.27381383697536504, "expected_holding_time" = 0.15463568697693034, "return_rate" = 0.0033262842246484098, "time_scaled_sharpe" = 0.004777039463496344} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5463897495384712, "expected_gain" = -0.029485637753840832, "std_gain" = 0.27381383697536504, "expected_holding_time" = 0.15463568697693034, "return_rate" = -0.19067809203861102, "time_scaled_sharpe" = -0.27384213403739216} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8936686884933597, "expected_gain" = 0.06392020486337943, "std_gain" = 0.5394570894250991, "expected_holding_time" = 0.7978556501486566, "return_rate" = 0.0801149993128073, "time_scaled_sharpe" = 0.13265361538561035} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8936686884933597, "expected_gain" = 0.03392020486337943, "std_gain" = 0.5394570894250991, "expected_holding_time" = 0.7978556501486566, "return_rate" = 0.04251421276149267, "time_scaled_sharpe" = 0.07039460870573197} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.20828241543670786, "expected_gain" = -0.029232859932279753, "std_gain" = 0.527903815053068, "expected_holding_time" = 0.6370017689144813, "return_rate" = -0.045891332424548556, "time_scaled_sharpe" = -0.06938189956182586} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.20828241543670786, "expected_gain" = -0.05923285993227975, "std_gain" = 0.527903815053068, "expected_holding_time" = 0.6370017689144813, "return_rate" = -0.09298696302401013, "time_scaled_sharpe" = -0.14058454588779684} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.6473409263049268, "expected_gain" = 0.11835231576231697, "std_gain" = 1.1944942743573694, "expected_holding_time" = 3.7871348059141736, "return_rate" = 0.031251149438222214, "time_scaled_sharpe" = 0.05091401022836009} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.6473409263049268, "expected_gain" = 0.08835231576231697, "std_gain" = 1.1944942743573694, "expected_holding_time" = 3.7871348059141736, "return_rate" = 0.023329593555619213, "time_scaled_sharpe" = 0.03800830325496824} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.07195485697705627, "expected_gain" = -0.13450382895277055, "std_gain" = 0.594350242514743, "expected_holding_time" = 1.2860363434141728, "return_rate" = -0.10458789103555931, "time_scaled_sharpe" = -0.1995563640695416} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.07195485697705627, "expected_gain" = -0.16450382895277055, "std_gain" = 0.594350242514743, "expected_holding_time" = 1.2860363434141728, "return_rate" = -0.12791538108172387, "time_scaled_sharpe" = -0.2440658101477525} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.351065191163636, "expected_gain" = -0.271271830927274, "std_gain" = 1.6705614859327171, "expected_holding_time" = 9.600239367741146, "return_rate" = -0.02825677782981176, "time_scaled_sharpe" = -0.05240843643862658} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.351065191163636, "expected_gain" = -0.30127183092727405, "std_gain" = 1.6705614859327171, "expected_holding_time" = 9.600239367741146, "return_rate" = -0.03138169991256799, "time_scaled_sharpe" = -0.058204294739816385} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.6016955695073217, "expected_gain" = 0.03093256322902696, "std_gain" = 0.2692518307656067, "expected_holding_time" = 0.15291433267031185, "return_rate" = 0.20228687977678655, "time_scaled_sharpe" = 0.2937873876493135} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.6016955695073217, "expected_gain" = 0.0009325632290269614, "std_gain" = 0.2692518307656067, "expected_holding_time" = 0.15291433267031185, "return_rate" = 0.006098599213963512, "time_scaled_sharpe" = 0.00885718111509525} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.9525715500877646, "expected_gain" = 0.16700021265358808, "std_gain" = 0.37196863195257646, "expected_holding_time" = 0.6277480181053996, "return_rate" = 0.26603064898175177, "time_scaled_sharpe" = 0.566654094984078} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.9525715500877646, "expected_gain" = 0.13700021265358808, "std_gain" = 0.37196863195257646, "expected_holding_time" = 0.6277480181053996, "return_rate" = 0.21824077289334495, "time_scaled_sharpe" = 0.46486007580647964} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.30971689761681576, "expected_gain" = 0.1026319669018605, "std_gain" = 0.6010898902836063, "expected_holding_time" = 0.6942538147422969, "return_rate" = 0.14783061284287916, "time_scaled_sharpe" = 0.2049198791332125} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.30971689761681576, "expected_gain" = 0.0726319669018605, "std_gain" = 0.6010898902836063, "expected_holding_time" = 0.6942538147422969, "return_rate" = 0.10461875089418274, "time_scaled_sharpe" = 0.145020448579817} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8564318362396939, "expected_gain" = 0.6410795905992347, "std_gain" = 0.876628292503442, "expected_holding_time" = 3.08717498749936, "return_rate" = 0.20765897404426523, "time_scaled_sharpe" = 0.4162131950800187} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8564318362396939, "expected_gain" = 0.6110795905992347, "std_gain" = 0.876628292503442, "expected_holding_time" = 3.08717498749936, "return_rate" = 0.19794135190704382, "time_scaled_sharpe" = 0.396736056772232} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.18323374183894758, "expected_gain" = 0.12143760622957944, "std_gain" = 0.8897734758822127, "expected_holding_time" = 1.6832303332912872, "return_rate" = 0.07214556666889882, "time_scaled_sharpe" = 0.10519666410116219} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.18323374183894758, "expected_gain" = 0.09143760622957944, "std_gain" = 0.8897734758822127, "expected_holding_time" = 1.6832303332912872, "return_rate" = 0.054322693942181845, "time_scaled_sharpe" = 0.07920883363397893} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.7489119261798577, "expected_gain" = 1.1211917416295019, "std_gain" = 1.517736785255123, "expected_holding_time" = 8.92140149480453, "return_rate" = 0.1256743956969585, "time_scaled_sharpe" = 0.247324356451041} + +[[metrics]] +theta = 0.2 +sigma = 0.7 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.7489119261798577, "expected_gain" = 1.0911917416295018, "std_gain" = 1.517736785255123, "expected_holding_time" = 8.92140149480453, "return_rate" = 0.12231169533900793, "time_scaled_sharpe" = 0.24070663851927349} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5387154934399117, "expected_gain" = -0.0037064786080485967, "std_gain" = 0.2741743714257963, "expected_holding_time" = 0.038393442823120835, "return_rate" = -0.09653936546207292, "time_scaled_sharpe" = -0.06899317201947468} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5387154934399117, "expected_gain" = -0.033706478608048596, "std_gain" = 0.2741743714257963, "expected_holding_time" = 0.038393442823120835, "return_rate" = -0.8779227943514916, "time_scaled_sharpe" = -0.6274194788892262} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8553545832317144, "expected_gain" = -0.0031294793444997526, "std_gain" = 0.6155502055394483, "expected_holding_time" = 0.19798307329903558, "return_rate" = -0.01580680253292608, "time_scaled_sharpe" = -0.011426009425823905} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8553545832317144, "expected_gain" = -0.03312947934449975, "std_gain" = 0.6155502055394483, "expected_holding_time" = 0.19798307329903558, "return_rate" = -0.16733490794165315, "time_scaled_sharpe" = -0.12095869682866023} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.21365922496276546, "expected_gain" = -0.022243007548404886, "std_gain" = 0.532855649619405, "expected_holding_time" = 0.15267762754968436, "return_rate" = -0.14568609628916732, "time_scaled_sharpe" = -0.10683073910261746} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.21365922496276546, "expected_gain" = -0.052243007548404885, "std_gain" = 0.532855649619405, "expected_holding_time" = 0.15267762754968436, "return_rate" = -0.34217853910131013, "time_scaled_sharpe" = -0.2509174668575766} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5790927739324524, "expected_gain" = -0.05226806516886906, "std_gain" = 1.2342617558475744, "expected_holding_time" = 0.8125381397998444, "return_rate" = -0.06432690677356329, "time_scaled_sharpe" = -0.046979379220257196} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5790927739324524, "expected_gain" = -0.08226806516886906, "std_gain" = 1.2342617558475744, "expected_holding_time" = 0.8125381397998444, "return_rate" = -0.10124825056105616, "time_scaled_sharpe" = -0.07394386263961178} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.10303929214908171, "expected_gain" = -0.06300962805711205, "std_gain" = 0.6992234400211522, "expected_holding_time" = 0.3018111940283982, "return_rate" = -0.20877167349592524, "time_scaled_sharpe" = -0.16402999269501323} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.10303929214908171, "expected_gain" = -0.09300962805711205, "std_gain" = 0.6992234400211522, "expected_holding_time" = 0.3018111940283982, "return_rate" = -0.3081715651950291, "time_scaled_sharpe" = -0.24212757766075974} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.36775884223405736, "expected_gain" = -0.21284405218079916, "std_gain" = 1.6876834369516274, "expected_holding_time" = 1.6761229807558546, "return_rate" = -0.12698593994864057, "time_scaled_sharpe" = -0.09741315065069941} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.36775884223405736, "expected_gain" = -0.24284405218079916, "std_gain" = 1.6876834369516274, "expected_holding_time" = 1.6761229807558546, "return_rate" = -0.14488438794108524, "time_scaled_sharpe" = -0.11114336528238931} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.545686928018814, "expected_gain" = 0.00012781041034770624, "std_gain" = 0.27384958123755027, "expected_holding_time" = 0.03836311786940476, "return_rate" = 0.003331596007981612, "time_scaled_sharpe" = 0.0023828527187321866} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.545686928018814, "expected_gain" = -0.029872189589652293, "std_gain" = 0.27384958123755027, "expected_holding_time" = 0.03836311786940476, "return_rate" = -0.7786694942612092, "time_scaled_sharpe" = -0.5569266852730542} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8662890814869543, "expected_gain" = 0.016005892602169974, "std_gain" = 0.5955975534278959, "expected_holding_time" = 0.19368064343946137, "return_rate" = 0.08264064140809667, "time_scaled_sharpe" = 0.061063807990183634} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8662890814869543, "expected_gain" = -0.013994107397830025, "std_gain" = 0.5955975534278959, "expected_holding_time" = 0.19368064343946137, "return_rate" = -0.0722535156292176, "time_scaled_sharpe" = -0.05338868055501327} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.2252399357730971, "expected_gain" = -0.007188083494973768, "std_gain" = 0.5430623104296483, "expected_holding_time" = 0.15462074128115297, "return_rate" = -0.04648848165786335, "time_scaled_sharpe" = -0.03366119848155585} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.2252399357730971, "expected_gain" = -0.03718808349497377, "std_gain" = 0.5430623104296483, "expected_holding_time" = 0.15462074128115297, "return_rate" = -0.24051161045304756, "time_scaled_sharpe" = -0.1741487088384889} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.6106136687037435, "expected_gain" = 0.026534171759358682, "std_gain" = 1.2190278306295412, "expected_holding_time" = 0.8058394707804445, "return_rate" = 0.03292736670451308, "time_scaled_sharpe" = 0.024247537934363426} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.6106136687037435, "expected_gain" = -0.0034658282406413166, "std_gain" = 1.2190278306295412, "expected_holding_time" = 0.8058394707804445, "return_rate" = -0.004300891636996814, "time_scaled_sharpe" = -0.0031671537555832867} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.11530144253956776, "expected_gain" = -0.03480668215899413, "std_gain" = 0.7345863701477129, "expected_holding_time" = 0.3118854275898492, "return_rate" = -0.11160086070057644, "time_scaled_sharpe" = -0.08484421186213145} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.11530144253956776, "expected_gain" = -0.06480668215899413, "std_gain" = 0.7345863701477129, "expected_holding_time" = 0.3118854275898492, "return_rate" = -0.2077900293700792, "time_scaled_sharpe" = -0.15797173215369706} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.4127963336621919, "expected_gain" = -0.0552128321823282, "std_gain" = 1.7231787855795004, "expected_holding_time" = 1.699704763907909, "return_rate" = -0.03248377797999723, "time_scaled_sharpe" = -0.024576664451720975} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.4127963336621919, "expected_gain" = -0.0852128321823282, "std_gain" = 1.7231787855795004, "expected_holding_time" = 1.699704763907909, "return_rate" = -0.05013390207038627, "time_scaled_sharpe" = -0.03793044299213093} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.5595911921430626, "expected_gain" = 0.007775155678684448, "std_gain" = 0.2730399039888456, "expected_holding_time" = 0.03828746532233372, "return_rate" = 0.20307313668395222, "time_scaled_sharpe" = 0.14553071487620495} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.5595911921430626, "expected_gain" = -0.02222484432131555, "std_gain" = 0.2730399039888456, "expected_holding_time" = 0.03828746532233372, "return_rate" = -0.5804731165724756, "time_scaled_sharpe" = -0.41599134676628435} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.8866441390939919, "expected_gain" = 0.05162724341448577, "std_gain" = 0.5547977770947845, "expected_holding_time" = 0.18474797662829082, "return_rate" = 0.27944686787211165, "time_scaled_sharpe" = 0.21649824455413696} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.8866441390939919, "expected_gain" = 0.02162724341448577, "std_gain" = 0.5547977770947845, "expected_holding_time" = 0.18474797662829082, "return_rate" = 0.11706349270606374, "time_scaled_sharpe" = 0.09069359361664941} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.24926597025291972, "expected_gain" = 0.024045761328795656, "std_gain" = 0.5623645030514244, "expected_holding_time" = 0.15826195225238673, "return_rate" = 0.15193646348080442, "time_scaled_sharpe" = 0.10748116692807334} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.24926597025291972, "expected_gain" = -0.005954238671204343, "std_gain" = 0.5623645030514244, "expected_holding_time" = 0.15826195225238673, "return_rate" = -0.037622679276121694, "time_scaled_sharpe" = -0.026614608362719228} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.671478027500141, "expected_gain" = 0.1786950687503524, "std_gain" = 1.1741893109840214, "expected_holding_time" = 0.7859414393518774, "return_rate" = 0.2273643554126785, "time_scaled_sharpe" = 0.17166404881643704} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.671478027500141, "expected_gain" = 0.1486950687503524, "std_gain" = 1.1741893109840214, "expected_holding_time" = 0.7859414393518774, "return_rate" = 0.1891935725808948, "time_scaled_sharpe" = 0.14284444287819004} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.0 +output = {"hit_probability" = 0.14237797747549916, "expected_gain" = 0.027469348193648102, "std_gain" = 0.8037059952737803, "expected_holding_time" = 0.33115770043551557, "return_rate" = 0.08294944721962459, "time_scaled_sharpe" = 0.05939278868819341} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 0.3 +cost = 0.03 +output = {"hit_probability" = 0.14237797747549916, "expected_gain" = -0.002530651806351897, "std_gain" = 0.8037059952737803, "expected_holding_time" = 0.33115770043551557, "return_rate" = -0.007641832888149092, "time_scaled_sharpe" = -0.005471643044402916} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.0 +output = {"hit_probability" = 0.5054323838066684, "expected_gain" = 0.2690133433233395, "std_gain" = 1.7498967091733353, "expected_holding_time" = 1.7169594260085634, "return_rate" = 0.1566800818052632, "time_scaled_sharpe" = 0.11732252233508099} + +[[metrics]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 2.0 +sl = 1.5 +cost = 0.03 +output = {"hit_probability" = 0.5054323838066684, "expected_gain" = 0.23901334332333948, "std_gain" = 1.7498967091733353, "expected_holding_time" = 1.7169594260085634, "return_rate" = 0.13920733344233807, "time_scaled_sharpe" = 0.10423887515769184} + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 0.3 +time = 0.038393442823120835 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 0.25 +sl = 1.5 +time = 0.19798307329903558 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 0.3 +time = 0.15267762754968436 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 1.0 +sl = 1.5 +time = 0.8125381397998444 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 0.3 +time = 0.3018111940283982 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = -0.5 +pt = 2.0 +sl = 1.5 +time = 1.6761229807558546 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 0.3 +time = 0.03836311786940476 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 0.25 +sl = 1.5 +time = 0.19368064343946137 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 0.3 +time = 0.15462074128115297 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 1.0 +sl = 1.5 +time = 0.8058394707804445 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 0.3 +time = 0.3118854275898492 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 0.0 +pt = 2.0 +sl = 1.5 +time = 1.699704763907909 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 0.3 +time = 0.03828746532233372 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 0.25 +sl = 1.5 +time = 0.18474797662829082 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 +sl = 0.3 +time = 0.15826195225238673 + +[[boundary]] +theta = 0.2 +sigma = 1.4 +gap = 1.0 +pt = 1.0 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+[[cases]] +name = "gamma_2.5_radius_0" +risk_aversion = 2.5 +radius = 0.0 +zero_loading = false +expected_weights = [0.14999999999981856, 6.1562466315191e-13, 0.14999999999713626, 0.14999999999841898, 0.07369641217628697, 1.587965945599587e-13, 1.4595697246965793e-09, 0.1499999999995314, 0.02630358636436325, 3.1610360225310046e-12, 3.2039027844194595e-13, 8.999776391995339e-13, 0.14999999999929103, 5.313317322173035e-13, 0.14999999999977104, 1.2532132716424277e-13] +variance = 0.03413904483493794 +expected_return = 0.14155215877848604 +worst_case_expected_return = 0.14155215877848604 +objective = 0.09887835273481362 +solver_status = "optimal" + +[[cases]] +name = "gamma_2.5_radius_0.5" +risk_aversion = 2.5 +radius = 0.5 +zero_loading = false +expected_weights = [0.14999999999933755, 2.177615515464483e-11, 0.12565058532572365, 0.14999999997155186, 0.07064307220072742, 1.4068130761928826e-13, 0.022187133571711044, 0.0925995805003546, 0.0829540883330354, 0.005965540072256653, 5.412726241237708e-13, 3.956832812583828e-12, 0.14999999999766156, 1.595664145247693e-12, 0.14999999999957642, 5.312784739524001e-14] +variance = 0.03359949369677746 +expected_return = 0.14012985893074628 +worst_case_expected_return = 0.12369999442783804 +objective = 0.08170062730686621 +solver_status = "optimal" + +[[cases]] +name = "gamma_2.5_radius_1" +risk_aversion = 2.5 +radius = 1.0 +zero_loading = false +expected_weights = [0.14999999993693836, 0.0536018080639326, 0.10109118199347769, 0.12620603527754515, 0.06563649502674385, 5.61055749730169e-12, 0.02401162280470746, 0.06690953355764075, 0.08515937092120608, 0.017930640600899727, 1.5363379869030764e-11, 0.009453311622954005, 0.14999999994861665, 2.4056292497345975e-10, 0.14999999997965935, 4.141291732355923e-12] +variance = 0.03313095559524155 +expected_return = 0.13817289809521446 +worst_case_expected_return = 0.1072242133224534 +objective = 0.06581051882840147 +solver_status = "optimal" + +[[cases]] +name = "gamma_2.5_radius_2" +risk_aversion = 2.5 +radius = 2.0 +zero_loading = false +expected_weights = [0.107937296160676, 0.10007098227771385, 0.08955767065764007, 0.11270600100025895, 0.06502767982984438, 9.34778169374766e-12, 0.026727764303880665, 0.053578173210190545, 0.0909872720117509, 0.02647671718667849, 0.005942123841665095, 0.034670598453193806, 0.14856488980224455, 0.025720891739185785, 0.11203193950988764, 5.841505028981158e-12] +variance = 0.03238701272883465 +expected_return = 0.13145622037699145 +worst_case_expected_return = 0.07739668840771162 +objective = 0.036912922496668304 +solver_status = "optimal" + +[[cases]] +name = "gamma_5_radius_0" +risk_aversion = 5.0 +radius = 0.0 +zero_loading = false +expected_weights = [0.14999999994907234, 0.027200646205660053, 0.14999999783744977, 0.14999999555601065, 0.06540136495232673, 2.562506624030869e-12, 1.9124571251170524e-10, 0.08724907676254919, 0.0701489185287333, 1.736847781369023e-11, 5.89611494498756e-12, 2.324976060452245e-11, 0.14999999996662078, 7.0155340838461215e-12, 0.1499999999907133, 3.525471848053112e-12] +variance = 0.032958907804111155 +expected_return = 0.1391150722302535 +worst_case_expected_return = 0.1391150722302535 +objective = 0.056717802719975605 +solver_status = "optimal" + +[[cases]] +name = "gamma_5_radius_0.5" +risk_aversion = 5.0 +radius = 0.5 +zero_loading = false +expected_weights = [0.14623055206986063, 0.09947219895141417, 0.1135623820141239, 0.12002644591096094, 0.0649437541536492, 1.823393822739712e-12, 0.007588848264225053, 0.06053906496949611, 0.08763675365522261, 7.77184344975117e-12, 3.2634474938411908e-12, 9.13369692007577e-12, 0.1499999999911231, 3.930038883192931e-12, 0.14999999999276367, 1.2383180582743798e-12] +variance = 0.031948537852518735 +expected_return = 0.13616210754675545 +worst_case_expected_return = 0.12068312916849219 +objective = 0.04081178453719536 +solver_status = "optimal" + +[[cases]] +name = "gamma_5_radius_1" +risk_aversion = 5.0 +radius = 1.0 +zero_loading = false +expected_weights = [0.12077615911624466, 0.12307706873583918, 0.10324404582741341, 0.11611799131780363, 0.06829327654070544, 1.877210682722844e-12, 0.017624421960317112, 0.055875861591411766, 0.09499117491583099, 3.0104221172886194e-11, 4.867036311393095e-12, 4.941584808161414e-11, 0.14999999998964333, 7.2101093182535936e-12, 0.14999999991029123, 1.0246546888200815e-12] +variance = 0.031310396349492745 +expected_return = 0.1338080904421873 +worst_case_expected_return = 0.10393154329721162 +objective = 0.025655552423479763 +solver_status = "optimal" + +[[cases]] +name = "gamma_5_radius_2" +risk_aversion = 5.0 +radius = 2.0 +zero_loading = false +expected_weights = [0.09529902173838792, 0.140143387310145, 0.09584075305160242, 0.11446212153046811, 0.07016773017485639, 4.71292511884179e-12, 0.024233923364173543, 0.051188856861164624, 0.10079411888037479, 0.014525175364524634, 2.356219602129069e-10, 0.027674204403488268, 0.14999999998626662, 0.009568996546067434, 0.10610171054560208, 2.5431695180770073e-12] +variance = 0.03104505832924831 +expected_return = 0.12905955927136636 +worst_case_expected_return = 0.0749580223180249 +objective = -0.0026546235050958766 +solver_status = "optimal" + +[[cases]] +name = "gamma_10_radius_0" +risk_aversion = 10.0 +radius = 0.0 +zero_loading = false +expected_weights = [0.09903994177033071, 0.14999999998982994, 0.14999999721591792, 0.05616448215376117, 0.07558136748548477, 4.923628574136588e-13, 5.4258261472835935e-11, 0.04290230938401344, 0.12631190195105377, 1.0894597985757478e-12, 9.863784206368975e-13, 9.309718188813503e-13, 0.14999999999743752, 8.869653947593119e-13, 0.14999999999326905, 2.5733738285943073e-13] +variance = 0.030472817892863654 +expected_return = 0.1308978136638212 +worst_case_expected_return = 0.1308978136638212 +objective = -0.021466275800497064 +solver_status = "optimal" + +[[cases]] +name = "gamma_10_radius_0.5" +risk_aversion = 10.0 +radius = 0.5 +zero_loading = false +expected_weights = [0.08956129125807287, 0.14999999996525412, 0.12093554553254988, 0.09605278122505438, 0.07643182724136764, 5.218229579236368e-12, 0.004374842849682028, 0.04651431634422815, 0.11612939574828975, 1.0429454782234623e-11, 9.041368064558946e-12, 1.9752232508051934e-11, 0.14999999998576696, 7.621900303972923e-12, 0.14999999979488815, 2.7830462122962803e-12] +variance = 0.03043745116605416 +expected_return = 0.13054906914455555 +worst_case_expected_return = 0.1157754537351557 +objective = -0.03641180209511512 +solver_status = "optimal" + +[[cases]] +name = "gamma_10_radius_1" +risk_aversion = 10.0 +radius = 1.0 +zero_loading = false +expected_weights = [0.08591700415130554, 0.14999999968359456, 0.1144893885288656, 0.10952025463431007, 0.07864347732088578, 4.1675319774530344e-11, 0.015029259137048036, 0.049672681147408214, 0.11980837984243939, 1.3050667593593834e-10, 9.05531625233005e-11, 8.292836905043758e-10, 0.14999999990082571, 6.887739342879204e-11, 0.12691955447352526, 1.8895448331675848e-11] +variance = 0.030292932844341636 +expected_return = 0.12910007197487564 +worst_case_expected_return = 0.10053315530087875 +objective = -0.05093150892082943 +solver_status = "optimal" + +[[cases]] +name = "gamma_10_radius_2" +risk_aversion = 10.0 +radius = 2.0 +zero_loading = false +expected_weights = [0.08034985100379843, 0.14999999994295385, 0.10831767704125438, 0.118088314199824, 0.08000043722325964, 2.789054748117741e-11, 0.02304119014891966, 0.05075362817429128, 0.12065466118896821, 5.711222960690029e-10, 1.8308283252030406e-10, 0.018492866933625667, 0.14999999996692695, 1.0494696569602695e-10, 0.10030137327802517, 1.1110081714603434e-11] +variance = 0.0302330113546449 +expected_return = 0.12714511663734765 +worst_case_expected_return = 0.07223615492877361 +objective = -0.0789289018444509 +solver_status = "optimal" + +[[cases]] +name = "zero_loading" +risk_aversion = 5.0 +radius = 2.0 +zero_loading = true +expected_weights = [0.1499999999223106, 0.027200648688947136, 0.14999999626693164, 0.14999999217593346, 0.06540136534780423, 1.2176860989985708e-11, 3.929404877889941e-10, 0.08724907663524112, 0.07014892052478404, 3.170162348781445e-11, 1.549239422017223e-11, 3.0402993409897955e-11, 0.14999999995130833, 1.6963598426536967e-11, 0.14999999997938535, 7.675943053916899e-12] +variance = 0.032958907770099403 +expected_return = 0.13911507213366447 +worst_case_expected_return = 0.13911507213366447 +objective = 0.05671780270841596 +solver_status = "optimal" + +[[cases]] +name = "feasible_floor" +risk_aversion = 5.0 +radius = 1.0 +zero_loading = false +expected_weights = [0.1207760748426268, 0.1230767040250899, 0.10324409323342898, 0.11611778577225375, 0.06829344523844899, 1.4516055915618841e-12, 0.017624600471346317, 0.05587591305781733, 0.09499138335831164, 2.0161747053494185e-11, 3.734827516655899e-12, 3.436271294241379e-11, 0.1499999999922402, 5.4798701817491284e-12, 0.14999999994244154, 8.038346575947156e-13] +variance = 0.03131039919258355 +expected_return = 0.13380809521424564 +worst_case_expected_return = 0.10393155040533514 +objective = 0.025655552423876266 +solver_status = "optimal" +return_floor = 0.12137766295707472 diff --git a/test/fixtures/portfolio_python_320.toml b/test/fixtures/portfolio_python_320.toml new file mode 100644 index 0000000..5df2b97 --- /dev/null +++ b/test/fixtures/portfolio_python_320.toml @@ -0,0 +1,327 @@ +[[cases]] +case = 1 +method = "constrained_minimum_variance" +covariance = [[1.0, 0.0], [0.0, 4.0]] +expected_weights = [0.7999999999985745, 0.20000000000142562] +expected_variance = 0.8000000000000003 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 0.8000000000000003 +expected_solver_status = "optimal" +independent_weights = [0.8, 0.19999999999999996] +independent_objective = 0.8 + +[[cases]] +case = 2 +method = "constrained_minimum_variance" +covariance = [[1.0, 0.0], [0.0, 4.0]] +caps = [0.6, 1.0] +expected_weights = [0.5999999999961673, 0.4000000000038328] +expected_variance = 1.0000000000076656 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 1.0000000000076656 +expected_solver_status = "optimal" +independent_weights = [0.6, 0.4] +independent_objective = 1.0 + +[[cases]] +case = 3 +method = "constrained_minimum_variance" +covariance = [[1.0, 0.0], [0.0, 4.0]] +expected_returns = [0.05, 0.1] +return_floor = 0.085 +expected_weights = [0.2999999999992693, 0.7000000000007306] +expected_variance = 2.050000000003653 +expected_expected_return = 0.08500000000003652 +expected_worst_case_expected_return = 0.08500000000003652 +expected_objective = 2.050000000003653 +expected_solver_status = "optimal" +independent_weights = [0.3, 0.7] +independent_objective = 2.05 + +[[cases]] +case = 4 +method = "constrained_minimum_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_weights = [0.7272727272532052, 0.27272727274679476] +expected_variance = 0.031818181818181815 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 0.031818181818181815 +expected_solver_status = "optimal" +independent_weights = [0.7272727272727273, 0.2727272727272727] +independent_objective = 0.031818181818181815 + +[[cases]] +case = 5 +method = "constrained_minimum_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +caps = [0.6, 1.0] +expected_weights = [0.599999999927104, 0.40000000007289616] +expected_variance = 0.033600000002041094 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 0.033600000002041094 +expected_solver_status = "optimal" +independent_weights = [0.6, 0.4] +independent_objective = 0.0336 + +[[cases]] +case = 6 +method = "constrained_minimum_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.05, 0.1] +return_floor = 0.085 +expected_weights = [0.2999999999995121, 0.7000000000004879] +expected_variance = 0.05190000000004586 +expected_expected_return = 0.08500000000002439 +expected_worst_case_expected_return = 0.08500000000002439 +expected_objective = 0.05190000000004586 +expected_solver_status = "optimal" +independent_weights = [0.3, 0.7] +independent_objective = 0.051899999999999995 + +[[cases]] +case = 7 +method = "constrained_minimum_variance" +covariance = [[0.01, -0.02], [-0.02, 0.04]] +expected_weights = [0.6666666666650455, 0.33333333333495435] +expected_variance = 2.365068102725011e-25 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 2.365068102725011e-25 +expected_solver_status = "optimal" +independent_weights = [0.6666666666666667, 0.33333333333333326] +independent_objective = 3.851859888774472e-34 + +[[cases]] +case = 8 +method = "constrained_minimum_variance" +covariance = [[0.01, -0.02], [-0.02, 0.04]] +caps = [0.6, 1.0] +expected_weights = [0.5999999998067368, 0.4000000001932641] +expected_variance = 0.00040000000231916544 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 0.00040000000231916544 +expected_solver_status = "optimal" +independent_weights = [0.6, 0.4] +independent_objective = 0.0004000000000000002 + +[[cases]] +case = 9 +method = "constrained_minimum_variance" +covariance = [[0.01, -0.02], [-0.02, 0.04]] +expected_returns = [0.05, 0.1] +return_floor = 0.085 +expected_weights = [0.2999999999982527, 0.7000000000017472] +expected_variance = 0.012100000000115319 +expected_expected_return = 0.08500000000008737 +expected_worst_case_expected_return = 0.08500000000008737 +expected_objective = 0.012100000000115319 +expected_solver_status = "optimal" +independent_weights = [0.3, 0.7] +independent_objective = 0.0121 + +[[cases]] +case = 10 +method = "constrained_minimum_variance" +covariance = [[1e-12, 0.0], [0.0, 4e-12]] +expected_weights = [0.7999999999985745, 0.20000000000142562] +expected_variance = 8.000000000000002e-13 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 8.000000000000002e-13 +expected_solver_status = "optimal" +independent_weights = [0.8, 0.19999999999999996] +independent_objective = 8e-13 + +[[cases]] +case = 11 +method = "constrained_minimum_variance" +covariance = [[1000000000000.0, 0.0], [0.0, 4000000000000.0]] +expected_weights = [0.7999999999985745, 0.20000000000142562] +expected_variance = 800000000000.0002 +expected_expected_return = 0.0 +expected_worst_case_expected_return = 0.0 +expected_objective = 800000000000.0002 +expected_solver_status = "optimal" +independent_weights = [0.8, 0.19999999999999996] +independent_objective = 800000000000.0 + +[[cases]] +case = 12 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.0 +risk_aversion = 3.0 +expected_weights = [0.5757575757210905, 0.42424242427890957] +expected_variance = 0.03434343434465052 +expected_expected_return = 0.12121212121394549 +expected_worst_case_expected_return = 0.12121212121394549 +expected_objective = 0.0696969696969697 +expected_solver_status = "optimal" +independent_weights = [0.5757575757575757, 0.4242424242424243] +independent_objective = 0.0696969696969697 + +[[cases]] +case = 13 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.0 +risk_aversion = 3.0 +caps = [0.7, 0.7] +expected_weights = [0.5757575757213089, 0.42424242427869147] +expected_variance = 0.03434343434464327 +expected_expected_return = 0.12121212121393463 +expected_worst_case_expected_return = 0.12121212121393463 +expected_objective = 0.06969696969696972 +expected_solver_status = "optimal" +independent_weights = [0.5757575757575757, 0.4242424242424243] +independent_objective = 0.0696969696969697 + +[[cases]] +case = 14 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.0 +risk_aversion = 3.0 +return_floor = 0.13 +expected_weights = [0.399999999936793, 0.6000000000632048] +expected_variance = 0.04360000000455065 +expected_expected_return = 0.13000000000316 +expected_worst_case_expected_return = 0.13000000000316 +expected_objective = 0.06459999999633403 +expected_solver_status = "optimal" +independent_weights = [0.3999999999999999, 0.6000000000000001] +independent_objective = 0.0646 + +[[cases]] +case = 15 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.1 +risk_aversion = 3.0 +expected_weights = [0.5822373484523763, 0.41776265154762315] +expected_variance = 0.03413206054023367 +expected_expected_return = 0.1208881325773811 +expected_worst_case_expected_return = 0.11885136763066265 +expected_objective = 0.06765327682031214 +expected_solver_status = "optimal" +independent_weights = [0.582237330487525, 0.417762669512475] +independent_objective = 0.06765327682031219 + +[[cases]] +case = 16 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.1 +risk_aversion = 3.0 +caps = [0.7, 0.7] +expected_weights = [0.5822373467990752, 0.4177626532009077] +expected_variance = 0.03413206059298541 +expected_expected_return = 0.12088813266004367 +expected_worst_case_expected_return = 0.11885136770978998 +expected_objective = 0.06765327682031186 +expected_solver_status = "optimal" +independent_weights = [0.582237330487525, 0.417762669512475] +independent_objective = 0.06765327682031219 + +[[cases]] +case = 17 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 0.1 +risk_aversion = 3.0 +return_floor = 0.13 +expected_weights = [0.3999999999891162, 0.6000000000108188] +expected_variance = 0.04360000000077609 +expected_expected_return = 0.13000000000053444 +expected_worst_case_expected_return = 0.12747017787236556 +expected_objective = 0.06207017787120142 +expected_solver_status = "optimal" +independent_weights = [0.3999999999999999, 0.6000000000000001] +independent_objective = 0.06207017787186529 + +[[cases]] +case = 18 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 1.0 +risk_aversion = 3.0 +expected_weights = [0.6290230372696315, 0.3709769627303676] +expected_variance = 0.03288001199260923 +expected_expected_return = 0.11854884813651828 +expected_worst_case_expected_return = 0.09909463924566378 +expected_objective = 0.04977462125674993 +expected_solver_status = "optimal" +independent_weights = [0.6290224921450334, 0.37097750785496664] +independent_objective = 0.04977462125681187 + +[[cases]] +case = 19 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 1.0 +risk_aversion = 3.0 +caps = [0.7, 0.7] +expected_weights = [0.6290216295901636, 0.37097837040983755] +expected_variance = 0.03288004241972306 +expected_expected_return = 0.11854891852049199 +expected_worst_case_expected_return = 0.09909468488624137 +expected_objective = 0.049774621256656784 +expected_solver_status = "optimal" +independent_weights = [0.6290224921450334, 0.37097750785496664] +independent_objective = 0.04977462125681187 + +[[cases]] +case = 20 +method = "robust_mean_variance" +covariance = [[0.04, 0.01], [0.01, 0.09]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02, 0.0], [0.0, 0.04]] +radius = 1.0 +risk_aversion = 3.0 +return_floor = 0.13 +expected_weights = [0.3999999999090806, 0.6000000000909236] +expected_variance = 0.04360000000654668 +expected_expected_return = 0.1300000000045466 +expected_worst_case_expected_return = 0.10470177872032427 +expected_objective = 0.03930177871050425 +expected_solver_status = "optimal" +independent_weights = [0.3999999999999999, 0.6000000000000001] +independent_objective = 0.039301778718652966 + +[[cases]] +case = 21 +method = "robust_mean_variance" +covariance = [[1.0, 0.0], [0.0, 4.0]] +expected_returns = [0.1, 0.15] +uncertainty_loading = [[0.02], [0.02]] +radius = 0.0 +risk_aversion = 0.0 +expected_weights = [2.355652336787586e-13, 0.9999999999997643] +expected_variance = 3.9999999999981144 +expected_expected_return = 0.1499999999999882 +expected_worst_case_expected_return = 0.1499999999999882 +expected_objective = 0.1499999999999882 +expected_solver_status = "optimal" +independent_weights = [0.0, 1.0] +independent_objective = 0.15 diff --git a/test/fixtures/protocol_python_320.toml b/test/fixtures/protocol_python_320.toml new file mode 100644 index 0000000..7eb3f00 --- /dev/null +++ b/test/fixtures/protocol_python_320.toml @@ -0,0 +1,5470 @@ +reference = "RiskLabAI 3.2.0 public synthetic protocol records" + +[bases.backdoor] +value = {"kind" = "record", "type" = "CausalFactorProtocolReport", "fields" = {"trial_family_id" = {"kind" = "scalar", "value" = "synthetic-family"}, "declared_trial_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}, "variable_selection" = {"kind" = "record", "type" = "VariableSelectionStage", "fields" = {"purpose" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}, "selected_variables" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "K"}, {"kind" = "scalar", "value" = "Z"}]}, "method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PERMUTATION_FEATURE_IMPORTANCE"}]}, "overlapping_returns" = {"kind" = "scalar", "value" = false}, "strong_time_dependence" = {"kind" = "scalar", "value" = false}, "validation" = {"kind" = "record", "type" = "ValidationEvidence", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PURGED_CROSS_VALIDATION"}]}, "event_horizons" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-0"}, "start" = {"kind" = "scalar", "value" = 0}, "end" = {"kind" = "scalar", "value" = 1}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-1"}, "start" = {"kind" = "scalar", "value" = 3}, "end" = {"kind" = "scalar", "value" = 4}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-2"}, "start" = {"kind" = "scalar", "value" = 6}, "end" = {"kind" = "scalar", "value" = 7}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-3"}, "start" = {"kind" = "scalar", "value" = 9}, "end" = {"kind" = "scalar", "value" = 10}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-4"}, "start" = {"kind" = "scalar", "value" = 12}, "end" = {"kind" = "scalar", "value" = 13}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-5"}, "start" = {"kind" = "scalar", "value" = 15}, "end" = {"kind" = "scalar", "value" = 16}}}]}, "folds" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-0"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-0"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}, {"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-1"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-1"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}, {"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = 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"identified" = {"kind" = "scalar", "value" = true}, "admissible_adjustment_sets" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}]}]}, "selected_adjustment_set" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}]}, "confounders" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}]}, "descendants" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "K"}]}, "mediators" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}]}, "colliders" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "K"}]}, "instruments" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}]}, "control_justifications" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Supplied common cause"}]}]}, "open_backdoor_paths" = {"kind" = "tuple", "items" = []}, "frontdoor_criteria_satisfied" = {"kind" = "none"}, "instrument_relevance_satisfied" = {"kind" = "none"}, "instrument_exclusion_satisfied" = {"kind" = "none"}, "instrument_exogeneity_satisfied" = {"kind" = "none"}}}, "causal_explanatory_and_predictive_power" = {"kind" = "record", "type" = "CausalExplanatoryAndPredictivePowerStage", "fields" = {"task_types" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PROBABILITY"}, {"kind" = "scalar", "value" = "RETURN_SIZE"}]}, "estimator_label" = {"kind" = "scalar", "value" = "synthetic-estimator"}, "explanatory_metric_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "R_SQUARED"}]}, "predictive_metric_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LOG_LOSS"}]}, "explanatory_evidence_id" = {"kind" = "scalar", "value" = "synthetic-explanatory"}, "predictive_evidence_id" = {"kind" = "scalar", "value" = "synthetic-predictive"}, "naive_benchmark_id" = {"kind" = "scalar", "value" = "synthetic-naive"}, "validation" = {"kind" = "record", "type" = "ValidationEvidence", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PURGED_CROSS_VALIDATION"}]}, "event_horizons" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-0"}, "start" = {"kind" = "scalar", "value" = 0}, "end" = {"kind" = "scalar", "value" = 1}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-1"}, "start" = {"kind" = "scalar", "value" = 3}, "end" = {"kind" = "scalar", "value" = 4}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-2"}, "start" = {"kind" = "scalar", "value" = 6}, "end" = {"kind" = "scalar", "value" = 7}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-3"}, "start" = {"kind" = "scalar", "value" = 9}, "end" = {"kind" = "scalar", "value" = 10}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-4"}, "start" = {"kind" = "scalar", "value" = 12}, "end" = {"kind" = "scalar", "value" = 13}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-5"}, "start" = {"kind" = "scalar", "value" = 15}, "end" = {"kind" = "scalar", "value" = 16}}}]}, "folds" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-0"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-0"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}, {"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-1"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-1"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}, {"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-2"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-2"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}]}, "embargo" = {"kind" = "scalar", "value" = 0.5}}}, "multiclass_encoding" = {"kind" = "none"}, "averaging_method" = {"kind" = "none"}}}, "causal_portfolio_construction" = {"kind" = "record", "type" = "CausalPortfolioConstructionStage", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ECONOMIC_RATIONALE"}, {"kind" = "scalar", "value" = "EXPOSURE_CONTROL"}, {"kind" = "scalar", "value" = "FRAGILITY_STRESS_TEST"}, {"kind" = "scalar", "value" = "POSITION_SIZING"}, {"kind" = "scalar", "value" = "TRANSACTION_COST_OPTIMIZATION"}, {"kind" = "scalar", "value" = "TRANSFER_COEFFICIENT"}]}, "causal_exposures" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}]}, "controlled_unintended_exposures" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "K"}]}, "cost_model_id" = {"kind" = "scalar", "value" = "synthetic-cost"}, "constraint_set_id" = {"kind" = "scalar", "value" = "synthetic-constraints"}, "economic_rationale" = {"kind" = "scalar", "value" = "Synthetic evidence contract only"}, "fragility_scenarios" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "synthetic-link-break"}]}, "transfer_coefficient" = {"kind" = "scalar", "value" = 0.7}}}, "backtest" = {"kind" = "record", "type" = "BacktestStage", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "WALK_FORWARD"}]}, "trial_family_id" = {"kind" = "scalar", "value" = "synthetic-family"}, "declared_trial_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}, "validation" = {"kind" = "record", "type" = "ValidationEvidence", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "WALK_FORWARD"}]}, "event_horizons" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-0"}, "start" = {"kind" = "scalar", "value" = 0}, "end" = {"kind" = "scalar", "value" = 1}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-1"}, "start" = {"kind" = "scalar", "value" = 3}, "end" = {"kind" = "scalar", "value" = 4}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-2"}, "start" = {"kind" = "scalar", "value" = 6}, "end" = {"kind" = "scalar", "value" = 7}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-3"}, "start" = {"kind" = "scalar", "value" = 9}, "end" = {"kind" = "scalar", "value" = 10}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-4"}, "start" = {"kind" = "scalar", "value" = 12}, "end" = {"kind" = "scalar", "value" = 13}}}, {"kind" = "record", "type" = "EventHorizon", "fields" = {"observation_id" = {"kind" = "scalar", "value" = "obs-5"}, "start" = {"kind" = "scalar", "value" = 15}, "end" = {"kind" = "scalar", "value" = 16}}}]}, "folds" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "walk-fold"}, "path_id" = {"kind" = "scalar", "value" = "walk-path"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "refit_id" = {"kind" = "scalar", "value" = "walk-refit"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}]}, "embargo" = {"kind" = "scalar", "value" = 0.5}}}, "monte_carlo_dgp" = {"kind" = "none"}}}, "multiple_testing_adjustments" = {"kind" = "record", "type" = "MultipleTestingAdjustmentsStage", "fields" = {"method_labels" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "DEFLATED_SHARPE_RATIO"}]}, "trial_family_id" = {"kind" = "scalar", "value" = "synthetic-family"}, "declared_trial_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}, "family_partitions" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "partition-all"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}]}]}, "alpha" = {"kind" = "scalar", "value" = 0.05}, "backtests_independent" = {"kind" = "scalar", "value" = false}, "p_value_estimator" = {"kind" = "none"}, "time_dependence_model" = {"kind" = "none"}, "p_value_inputs_assume_independence" = {"kind" = "none"}, "sharpe_variance" = {"kind" = "scalar", "value" = 0.1}, "effective_trials" = {"kind" = "scalar", "value" = 2.0}, "sample_length" = {"kind" = "scalar", "value" = 200}, "skewness" = {"kind" = "scalar", "value" = 0.2}, "kurtosis" = {"kind" = "scalar", "value" = 3.0}, "selection_bias_evidence_id" = {"kind" = "scalar", "value" = "synthetic-selection"}}}}} + +[[cases]] +name = "backdoor" +base = "backdoor" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "frontdoor" +base = "frontdoor" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "instrument" +base = "instrument" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "cpcv" +base = "cpcv" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "overlap" +base = "overlap" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "monte_carlo" +base = "monte_carlo" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "mixed_backtest" +base = "mixed_backtest" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "no_selection_validation" +base = "no_selection_validation" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "deflated_sharpe" +base = "deflated_sharpe" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "deflated_sharpe_only" +base = "deflated_sharpe_only" +changes = [] +accepted = true +errors = [] + +[[cases]] +name = "invalid_report" +base = "backdoor" +changes = [{"path" = [], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Protocol: report type is invalid."] + +[[cases]] +name = "field_trial_family_id_0" +base = "backdoor" +changes = [{"path" = ["trial_family_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Protocol: trial-family identifier must be nonblank.", "Stage 6: trial-family identifier must match the report.", "Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_trial_family_id_1" +base = "backdoor" +changes = [{"path" = ["trial_family_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Protocol: trial-family identifier must be nonblank.", "Stage 6: trial-family identifier must match the report.", "Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_trial_family_id_2" +base = "backdoor" +changes = [{"path" = ["trial_family_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Protocol: trial-family identifier must be nonblank.", "Stage 6: trial-family identifier must match the report.", "Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_trial_family_id_3" +base = "backdoor" +changes = [{"path" = ["trial_family_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report.", "Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_trial_family_id_4" +base = "backdoor" +changes = [{"path" = ["trial_family_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Protocol: trial-family identifier must be nonblank.", "Stage 6: trial-family identifier must match the report.", "Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_declared_trial_ids_0" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Protocol: declared trial identifiers must be a tuple.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_declared_trial_ids_1" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Protocol: declared trial identifiers must be a tuple.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_declared_trial_ids_2" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Protocol: declared trial identifiers must be a tuple.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_declared_trial_ids_3" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Protocol: declared trial identifiers must not be empty.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_declared_trial_ids_4" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly.", "Stage 7: family partitions must cover declared trials exactly."] + +[[cases]] +name = "field_declared_trial_ids_5" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Protocol: declared trial identifiers contains duplicate identifiers.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_declared_trial_ids_6" +base = "backdoor" +changes = [{"path" = ["declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Protocol: declared trial identifiers contains an invalid identifier.", "Stage 6: declared trials must match the report exactly.", "Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_variable_selection_0" +base = "backdoor" +changes = [{"path" = ["variable_selection"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: stage record type is invalid."] + +[[cases]] +name = "field_variable_selection_1" +base = "backdoor" +changes = [{"path" = ["variable_selection"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: stage record type is invalid."] + +[[cases]] +name = "field_variable_selection_2" +base = "backdoor" +changes = [{"path" = ["variable_selection"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: stage record type is invalid."] + +[[cases]] +name = "field_causal_discovery_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: stage record type is invalid."] + +[[cases]] +name = "field_causal_discovery_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: stage record type is invalid."] + +[[cases]] +name = "field_causal_discovery_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: stage record type is invalid."] + +[[cases]] +name = "field_causal_adjustment_set_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: stage record type is invalid."] + +[[cases]] +name = "field_causal_adjustment_set_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: stage record type is invalid."] + +[[cases]] +name = "field_causal_adjustment_set_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: stage record type is invalid."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: stage record type is invalid."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: stage record type is invalid."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: stage record type is invalid."] + +[[cases]] +name = "field_causal_portfolio_construction_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: stage record type is invalid."] + +[[cases]] +name = "field_causal_portfolio_construction_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: stage record type is invalid."] + +[[cases]] +name = "field_causal_portfolio_construction_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: stage record type is invalid."] + +[[cases]] +name = "field_backtest_0" +base = "backdoor" +changes = [{"path" = ["backtest"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: stage record type is invalid."] + +[[cases]] +name = "field_backtest_1" +base = "backdoor" +changes = [{"path" = ["backtest"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: stage record type is invalid."] + +[[cases]] +name = "field_backtest_2" +base = "backdoor" +changes = [{"path" = ["backtest"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: stage record type is invalid."] + +[[cases]] +name = "field_multiple_testing_adjustments_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: stage record type is invalid."] + +[[cases]] +name = "field_multiple_testing_adjustments_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: stage record type is invalid."] + +[[cases]] +name = "field_multiple_testing_adjustments_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: stage record type is invalid."] + +[[cases]] +name = "field_variable_selection_purpose_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: research purpose is not source-listed."] + +[[cases]] +name = "field_variable_selection_purpose_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: research purpose is not source-listed."] + +[[cases]] +name = "field_variable_selection_purpose_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: research purpose is not source-listed."] + +[[cases]] +name = "field_variable_selection_purpose_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 1: research purpose is not source-listed."] + +[[cases]] +name = "field_variable_selection_purpose_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 1: research purpose is not source-listed."] + +[[cases]] +name = "field_variable_selection_selected_variables_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: selected variables must be a tuple.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: selected variables must be a tuple.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: selected variables must be a tuple.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 1: selected variables must not be empty.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 1: selected variables contains duplicate identifiers.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_selected_variables_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "selected_variables"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 1: selected variables contains an invalid identifier.", "Stage 2: graph nodes must match the selected variables and outcome."] + +[[cases]] +name = "field_variable_selection_method_labels_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: variable-selection method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_method_labels_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: variable-selection method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_method_labels_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: variable-selection method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_method_labels_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 1: variable-selection method labels must not be empty."] + +[[cases]] +name = "field_variable_selection_method_labels_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 1: variable-selection method labels contains a non-source label."] + +[[cases]] +name = "field_variable_selection_method_labels_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 1: variable-selection method labels contains duplicate identifiers."] + +[[cases]] +name = "field_variable_selection_method_labels_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 1: variable-selection method labels contains an invalid identifier."] + +[[cases]] +name = "field_variable_selection_overlapping_returns_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "overlapping_returns"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: overlapping-returns flag must be boolean."] + +[[cases]] +name = "field_variable_selection_overlapping_returns_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "overlapping_returns"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: overlapping-returns flag must be boolean."] + +[[cases]] +name = "field_variable_selection_overlapping_returns_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "overlapping_returns"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: overlapping-returns flag must be boolean."] + +[[cases]] +name = "field_variable_selection_overlapping_returns_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "overlapping_returns"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 1: overlap declaration must match event horizons."] + +[[cases]] +name = "field_variable_selection_overlapping_returns_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "overlapping_returns"], "value" = {"kind" = "scalar", "value" = "invalid"}}] +accepted = false +errors = ["Stage 1: overlapping-returns flag must be boolean."] + +[[cases]] +name = "field_variable_selection_strong_time_dependence_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "strong_time_dependence"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: time-dependence flag must be boolean."] + +[[cases]] +name = "field_variable_selection_strong_time_dependence_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "strong_time_dependence"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: time-dependence flag must be boolean."] + +[[cases]] +name = "field_variable_selection_strong_time_dependence_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "strong_time_dependence"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: time-dependence flag must be boolean."] + +[[cases]] +name = "field_variable_selection_strong_time_dependence_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "strong_time_dependence"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = true +errors = [] + +[[cases]] +name = "field_variable_selection_strong_time_dependence_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "strong_time_dependence"], "value" = {"kind" = "scalar", "value" = "invalid"}}] +accepted = false +errors = ["Stage 1: time-dependence flag must be boolean."] + +[[cases]] +name = "field_variable_selection_validation_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: the declared selection design requires temporal validation."] + +[[cases]] +name = "field_variable_selection_validation_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: temporal validation evidence has an invalid type."] + +[[cases]] +name = "field_variable_selection_validation_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: temporal validation evidence has an invalid type."] + +[[cases]] +name = "field_causal_discovery_method_labels_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels must be a tuple."] + +[[cases]] +name = "field_causal_discovery_method_labels_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels must be a tuple."] + +[[cases]] +name = "field_causal_discovery_method_labels_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels must be a tuple."] + +[[cases]] +name = "field_causal_discovery_method_labels_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels must not be empty."] + +[[cases]] +name = "field_causal_discovery_method_labels_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels contains a non-source label."] + +[[cases]] +name = "field_causal_discovery_method_labels_5" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels contains duplicate identifiers."] + +[[cases]] +name = "field_causal_discovery_method_labels_6" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 2: causal-discovery method labels contains an invalid identifier."] + +[[cases]] +name = "field_causal_discovery_graph_id_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: graph identifier must be nonblank."] + +[[cases]] +name = "field_causal_discovery_graph_id_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: graph identifier must be nonblank."] + +[[cases]] +name = "field_causal_discovery_graph_id_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: graph identifier must be nonblank."] + +[[cases]] +name = "field_causal_discovery_graph_id_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_discovery_graph_id_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 2: graph identifier must be nonblank."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes must not be empty.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_5" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes contains duplicate identifiers.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_nodes_6" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_nodes"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes.", "Stage 2: graph nodes contains an invalid identifier.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared admissible set must use graph nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every selected control must be a graph node.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: causal exposures must be graph variables.", "Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: directed edges must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: directed edges must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: directed edges must be a tuple.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 2: directed edges contains an invalid edge.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_5" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 2: directed edges contains an invalid edge.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_directed_edges_6" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 2: directed edges contains an invalid edge.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_discovery_graph_kind_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_kind"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: the accepted graph must be a resolved DAG."] + +[[cases]] +name = "field_causal_discovery_graph_kind_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_kind"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: the accepted graph must be a resolved DAG."] + +[[cases]] +name = "field_causal_discovery_graph_kind_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_kind"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: the accepted graph must be a resolved DAG."] + +[[cases]] +name = "field_causal_discovery_graph_kind_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_kind"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 2: the accepted graph must be a resolved DAG."] + +[[cases]] +name = "field_causal_discovery_graph_kind_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "graph_kind"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 2: the accepted graph must be a resolved DAG."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: ambiguous edges must be a tuple."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: ambiguous edges must be a tuple."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: ambiguous edges must be a tuple."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 2: ambiguous edges contains an invalid edge.", "Stage 2: identification-relevant ambiguity must be resolved."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_5" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 2: ambiguous edges contains an invalid edge.", "Stage 2: identification-relevant ambiguity must be resolved."] + +[[cases]] +name = "field_causal_discovery_ambiguous_edges_6" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "ambiguous_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 2: ambiguous edges contains an invalid edge.", "Stage 2: identification-relevant ambiguity must be resolved."] + +[[cases]] +name = "field_causal_discovery_assumptions_0" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: graph assumptions must be a tuple."] + +[[cases]] +name = "field_causal_discovery_assumptions_1" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: graph assumptions must be a tuple."] + +[[cases]] +name = "field_causal_discovery_assumptions_2" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: graph assumptions must be a tuple."] + +[[cases]] +name = "field_causal_discovery_assumptions_3" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 2: graph assumptions must not be empty."] + +[[cases]] +name = "field_causal_discovery_assumptions_4" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_discovery_assumptions_5" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 2: graph assumptions contains duplicate identifiers."] + +[[cases]] +name = "field_causal_discovery_assumptions_6" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "assumptions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 2: graph assumptions contains an invalid identifier."] + +[[cases]] +name = "field_causal_adjustment_set_treatment_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "treatment"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be graph-separated from outcome shocks.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every declared instrument must satisfy graph-level exclusion.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_adjustment_set_treatment_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "treatment"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be graph-separated from outcome shocks.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every declared instrument must satisfy graph-level exclusion.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_adjustment_set_treatment_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "treatment"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be graph-separated from outcome shocks.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every declared instrument must satisfy graph-level exclusion.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_adjustment_set_treatment_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "treatment"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be graph-separated from outcome shocks.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every declared instrument must satisfy graph-level exclusion.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_adjustment_set_treatment_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "treatment"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be graph-separated from outcome shocks.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: every declared instrument must satisfy graph-level exclusion.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_adjustment_set_outcome_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "outcome"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_adjustment_set_outcome_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "outcome"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_adjustment_set_outcome_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "outcome"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_adjustment_set_outcome_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "outcome"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_adjustment_set_outcome_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "outcome"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 2: graph nodes must match the selected variables and outcome.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 3: treatment and outcome must be graph nodes.", "Stage 3: treatment and outcome must be nonblank.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "field_causal_adjustment_set_method_label_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "method_label"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: adjustment method is not source-listed."] + +[[cases]] +name = "field_causal_adjustment_set_method_label_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "method_label"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: adjustment method is not source-listed."] + +[[cases]] +name = "field_causal_adjustment_set_method_label_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "method_label"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: adjustment method is not source-listed."] + +[[cases]] +name = "field_causal_adjustment_set_method_label_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "method_label"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 3: adjustment method is not source-listed."] + +[[cases]] +name = "field_causal_adjustment_set_method_label_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "method_label"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 3: adjustment method is not source-listed."] + +[[cases]] +name = "field_causal_adjustment_set_identified_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "identified"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: the causal effect must be identified."] + +[[cases]] +name = "field_causal_adjustment_set_identified_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "identified"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: the causal effect must be identified."] + +[[cases]] +name = "field_causal_adjustment_set_identified_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "identified"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: the causal effect must be identified."] + +[[cases]] +name = "field_causal_adjustment_set_identified_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "identified"], "value" = {"kind" = "scalar", "value" = false}}] +accepted = false +errors = ["Stage 3: the causal effect must be identified."] + +[[cases]] +name = "field_causal_adjustment_set_identified_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "identified"], "value" = {"kind" = "scalar", "value" = "invalid"}}] +accepted = false +errors = ["Stage 3: the causal effect must be identified."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: admissible adjustment sets must be nonempty.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: admissible adjustment sets must be nonempty.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: admissible adjustment sets must be nonempty.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: admissible adjustment sets must be nonempty.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: an admissible adjustment set must be a tuple.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: an admissible adjustment set must be a tuple.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_admissible_adjustment_sets_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "admissible_adjustment_sets"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: an admissible adjustment set must be a tuple.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected adjustment set must be a tuple.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected adjustment set must be a tuple.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected adjustment set must be a tuple.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected controls must block every graph-implied backdoor path.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: every selected control must be a graph node.", "Stage 3: every selected control needs one justification.", "Stage 3: selected controls must block every graph-implied backdoor path.", "Stage 3: the selected adjustment set must be declared admissible."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected adjustment set contains duplicate identifiers.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "field_causal_adjustment_set_selected_adjustment_set_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "selected_adjustment_set"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification.", "Stage 3: selected adjustment set contains an invalid identifier.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: confounders must be a tuple.", "Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: confounders must be a tuple.", "Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: confounders must be a tuple.", "Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: every declared causal role must be a graph node."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: confounders contains duplicate identifiers.", "Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_confounders_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "confounders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: confounders contains an invalid identifier.", "Stage 3: declared confounders must match graph-implied common causes."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: descendants must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: descendants must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: descendants must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: every declared causal role must be a graph node."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: descendants contains duplicate identifiers."] + +[[cases]] +name = "field_causal_adjustment_set_descendants_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "descendants"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: declared descendants must exactly match the graph.", "Stage 3: descendants contains an invalid identifier."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: mediators must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: mediators must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: mediators must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared causal role must be a graph node."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: mediators contains duplicate identifiers."] + +[[cases]] +name = "field_causal_adjustment_set_mediators_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "mediators"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: mediators contains an invalid identifier."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: colliders must be a tuple.", "Stage 3: declared colliders must match graph-implied converging nodes."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: colliders must be a tuple.", "Stage 3: declared colliders must match graph-implied converging nodes."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: colliders must be a tuple.", "Stage 3: declared colliders must match graph-implied converging nodes."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: declared colliders must match graph-implied converging nodes."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: every declared causal role must be a graph node.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: colliders contains duplicate identifiers.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_adjustment_set_colliders_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "colliders"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: colliders contains an invalid identifier.", "Stage 3: declared colliders must match graph-implied converging nodes."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: instruments must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: instruments must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: instruments must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_adjustment_set_instruments_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: every declared causal role must be a graph node.", "Stage 3: every declared instrument must be relevant in the graph."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: instruments contains duplicate identifiers."] + +[[cases]] +name = "field_causal_adjustment_set_instruments_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instruments"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: instruments contains an invalid identifier."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: control justifications must be a tuple.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: control justifications must be a tuple.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: control justifications must be a tuple.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_3" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: a control justification is invalid.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: a control justification is invalid.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_control_justifications_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "control_justifications"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: a control justification is invalid.", "Stage 3: every selected control needs one justification."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_0" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: open backdoor paths must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: open backdoor paths must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: open backdoor paths must be a tuple."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_4" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 3: no backdoor path may remain open."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_5" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 3: open backdoor paths contains duplicate identifiers."] + +[[cases]] +name = "field_causal_adjustment_set_open_backdoor_paths_6" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "open_backdoor_paths"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 3: open backdoor paths contains an invalid identifier."] + +[[cases]] +name = "field_causal_adjustment_set_frontdoor_criteria_satisfied_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "frontdoor_criteria_satisfied"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: front-door evidence requires the front-door method."] + +[[cases]] +name = "field_causal_adjustment_set_frontdoor_criteria_satisfied_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "frontdoor_criteria_satisfied"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: front-door evidence requires the front-door method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_relevance_satisfied_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_relevance_satisfied"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_relevance_satisfied_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_relevance_satisfied"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_exclusion_satisfied_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_exclusion_satisfied"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_exclusion_satisfied_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_exclusion_satisfied"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_exogeneity_satisfied_1" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_exogeneity_satisfied"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_adjustment_set_instrument_exogeneity_satisfied_2" +base = "backdoor" +changes = [{"path" = ["causal_adjustment_set", "instrument_exogeneity_satisfied"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises require the IV method."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types must not be empty."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types contains a non-source label."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types contains duplicate identifiers."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_task_types_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: at least one performance aspect is required.", "Stage 4: causal attribution requires explanatory evidence.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label.", "Stage 4: task types contains an invalid identifier."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_estimator_label_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "estimator_label"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: estimator label must be nonblank."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_estimator_label_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "estimator_label"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: estimator label must be nonblank."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_estimator_label_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "estimator_label"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: estimator label must be nonblank."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_estimator_label_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "estimator_label"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_estimator_label_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "estimator_label"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 4: estimator label must be nonblank."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains a non-source label."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains duplicate identifiers."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_metric_labels_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: causal attribution requires explanatory evidence.", "Stage 4: every declared task needs a compatible metric.", "Stage 4: explanatory evidence must match reported metrics.", "Stage 4: explanatory metric labels contains an invalid identifier."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains a non-source label."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains duplicate identifiers."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_metric_labels_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: every declared task needs a compatible metric.", "Stage 4: predictive evidence must match reported metrics.", "Stage 4: predictive metric labels contains an invalid identifier."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_evidence_id_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_evidence_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: explanatory evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_evidence_id_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_evidence_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: explanatory evidence identifier is invalid.", "Stage 4: explanatory evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_evidence_id_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_evidence_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: explanatory evidence identifier is invalid.", "Stage 4: explanatory evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_evidence_id_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_evidence_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_explanatory_evidence_id_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "explanatory_evidence_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 4: explanatory evidence identifier is invalid.", "Stage 4: explanatory evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_evidence_id_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_evidence_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: predictive evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_evidence_id_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_evidence_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: predictive evidence identifier is invalid.", "Stage 4: predictive evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_evidence_id_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_evidence_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: predictive evidence identifier is invalid.", "Stage 4: predictive evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_evidence_id_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_evidence_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_predictive_evidence_id_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "predictive_evidence_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 4: predictive evidence identifier is invalid.", "Stage 4: predictive evidence identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_naive_benchmark_id_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "naive_benchmark_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: a naive benchmark identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_naive_benchmark_id_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "naive_benchmark_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: a naive benchmark identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_naive_benchmark_id_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "naive_benchmark_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: a naive benchmark identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_naive_benchmark_id_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "naive_benchmark_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_naive_benchmark_id_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "naive_benchmark_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 4: a naive benchmark identifier is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: purged validation evidence is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: purged validation evidence is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: purged validation evidence is required."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_multiclass_encoding_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: multiclass averaging is not source-listed.", "Stage 4: multiclass encoding is not source-listed."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_multiclass_encoding_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: multiclass averaging is not source-listed.", "Stage 4: multiclass encoding is not source-listed."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_averaging_method_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: multiclass averaging is not source-listed.", "Stage 4: multiclass encoding is not source-listed."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_averaging_method_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: multiclass averaging is not source-listed.", "Stage 4: multiclass encoding is not source-listed."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels must not be empty."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels contains a non-source label."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_5" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels contains duplicate identifiers."] + +[[cases]] +name = "field_causal_portfolio_construction_method_labels_6" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 5: portfolio-construction method labels contains an invalid identifier."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: causal exposures must be a tuple.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: causal exposures must be a tuple.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: causal exposures must be a tuple.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 5: causal exposures must not be empty.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 5: causal exposures must be graph variables.", "Stage 5: every causal exposure must have a directed path to the outcome.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_5" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 5: causal exposures contains duplicate identifiers.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_causal_exposures_6" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "causal_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 5: causal exposures contains an invalid identifier.", "Stage 5: the target causal factor must drive position sizing."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures must be a tuple.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures must be a tuple.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures must be a tuple.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures must be graph variables.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_5" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures contains duplicate identifiers.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_controlled_unintended_exposures_6" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "controlled_unintended_exposures"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 5: controlled unintended exposures contains an invalid identifier.", "Stage 5: declared colliders must be neutralized."] + +[[cases]] +name = "field_causal_portfolio_construction_cost_model_id_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "cost_model_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: cost model identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_cost_model_id_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "cost_model_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: cost model identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_cost_model_id_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "cost_model_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: cost model identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_cost_model_id_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "cost_model_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_cost_model_id_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "cost_model_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 5: cost model identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_constraint_set_id_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "constraint_set_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: constraint-set identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_constraint_set_id_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "constraint_set_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: constraint-set identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_constraint_set_id_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "constraint_set_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: constraint-set identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_constraint_set_id_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "constraint_set_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_constraint_set_id_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "constraint_set_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 5: constraint-set identifier must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_economic_rationale_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "economic_rationale"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: economic rationale must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_economic_rationale_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "economic_rationale"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: economic rationale must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_economic_rationale_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "economic_rationale"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: economic rationale must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_economic_rationale_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "economic_rationale"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_economic_rationale_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "economic_rationale"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 5: economic rationale must be nonblank."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios must be a tuple."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios must not be empty."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_5" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios contains duplicate identifiers."] + +[[cases]] +name = "field_causal_portfolio_construction_fragility_scenarios_6" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "fragility_scenarios"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 5: causal-fragility scenarios contains an invalid identifier."] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_0" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 5: transfer coefficient must be finite and between minus one and one."] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_1" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 5: transfer coefficient must be finite and between minus one and one."] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_2" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_3" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 5: transfer coefficient must be finite and between minus one and one."] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_4" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_5" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 5: transfer coefficient must be finite and between minus one and one."] + +[[cases]] +name = "field_causal_portfolio_construction_transfer_coefficient_6" +base = "backdoor" +changes = [{"path" = ["causal_portfolio_construction", "transfer_coefficient"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 5: transfer coefficient must be finite and between minus one and one."] + +[[cases]] +name = "field_backtest_method_labels_0" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels must be a tuple."] + +[[cases]] +name = "field_backtest_method_labels_1" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels must be a tuple."] + +[[cases]] +name = "field_backtest_method_labels_2" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels must be a tuple."] + +[[cases]] +name = "field_backtest_method_labels_3" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels must not be empty."] + +[[cases]] +name = "field_backtest_method_labels_4" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels contains a non-source label."] + +[[cases]] +name = "field_backtest_method_labels_5" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels contains duplicate identifiers."] + +[[cases]] +name = "field_backtest_method_labels_6" +base = "backdoor" +changes = [{"path" = ["backtest", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 6: Monte Carlo-only evidence cannot carry temporal folds.", "Stage 6: backtest method labels contains an invalid identifier."] + +[[cases]] +name = "field_backtest_trial_family_id_0" +base = "backdoor" +changes = [{"path" = ["backtest", "trial_family_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report."] + +[[cases]] +name = "field_backtest_trial_family_id_1" +base = "backdoor" +changes = [{"path" = ["backtest", "trial_family_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report."] + +[[cases]] +name = "field_backtest_trial_family_id_2" +base = "backdoor" +changes = [{"path" = ["backtest", "trial_family_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report."] + +[[cases]] +name = "field_backtest_trial_family_id_3" +base = "backdoor" +changes = [{"path" = ["backtest", "trial_family_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report."] + +[[cases]] +name = "field_backtest_trial_family_id_4" +base = "backdoor" +changes = [{"path" = ["backtest", "trial_family_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 6: trial-family identifier must match the report."] + +[[cases]] +name = "field_backtest_declared_trial_ids_0" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_1" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_2" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_3" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_4" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_5" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_declared_trial_ids_6" +base = "backdoor" +changes = [{"path" = ["backtest", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 6: declared trials must match the report exactly."] + +[[cases]] +name = "field_backtest_validation_0" +base = "backdoor" +changes = [{"path" = ["backtest", "validation"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: temporal backtests require validation evidence."] + +[[cases]] +name = "field_backtest_validation_1" +base = "backdoor" +changes = [{"path" = ["backtest", "validation"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: temporal backtests require validation evidence."] + +[[cases]] +name = "field_backtest_validation_2" +base = "backdoor" +changes = [{"path" = ["backtest", "validation"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: temporal backtests require validation evidence."] + +[[cases]] +name = "field_backtest_monte_carlo_dgp_1" +base = "backdoor" +changes = [{"path" = ["backtest", "monte_carlo_dgp"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: a Monte Carlo DGP requires the Monte Carlo method."] + +[[cases]] +name = "field_backtest_monte_carlo_dgp_2" +base = "backdoor" +changes = [{"path" = ["backtest", "monte_carlo_dgp"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: a Monte Carlo DGP requires the Monte Carlo method."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels must be a tuple.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels must be a tuple.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels must be a tuple.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels must not be empty.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels contains a non-source label.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_5" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels contains duplicate identifiers.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_method_labels_6" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 7: multiple-testing method labels contains an invalid identifier.", "Stage 7: p-value inputs require a p-value correction."] + +[[cases]] +name = "field_multiple_testing_adjustments_trial_family_id_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "trial_family_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_multiple_testing_adjustments_trial_family_id_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "trial_family_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_multiple_testing_adjustments_trial_family_id_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "trial_family_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_multiple_testing_adjustments_trial_family_id_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "trial_family_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_multiple_testing_adjustments_trial_family_id_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "trial_family_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 7: trial-family identifier must match the report."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_5" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_declared_trial_ids_6" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "declared_trial_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 7: declared trials must match the report exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: family partitions must be a nonempty tuple."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: family partitions must be a nonempty tuple."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: family partitions must be a nonempty tuple."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 7: family partitions must be a nonempty tuple."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 7: a family partition is invalid.", "Stage 7: family partitions must cover declared trials exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_5" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 7: a family partition is invalid.", "Stage 7: family partitions must cover declared trials exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_family_partitions_6" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 7: a family partition is invalid.", "Stage 7: family partitions must cover declared trials exactly."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_5" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_alpha_6" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "alpha"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 7: alpha must be a finite number between zero and one."] + +[[cases]] +name = "field_multiple_testing_adjustments_backtests_independent_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "backtests_independent"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: backtest-dependence flag must be boolean."] + +[[cases]] +name = "field_multiple_testing_adjustments_backtests_independent_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "backtests_independent"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: backtest-dependence flag must be boolean."] + +[[cases]] +name = "field_multiple_testing_adjustments_backtests_independent_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "backtests_independent"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: backtest-dependence flag must be boolean."] + +[[cases]] +name = "field_multiple_testing_adjustments_backtests_independent_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "backtests_independent"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 7: backtest dependence must be represented."] + +[[cases]] +name = "field_multiple_testing_adjustments_backtests_independent_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "backtests_independent"], "value" = {"kind" = "scalar", "value" = "invalid"}}] +accepted = false +errors = ["Stage 7: backtest-dependence flag must be boolean."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_estimator_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_estimator"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: p-value estimator must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_estimator_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_estimator"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: p-value estimator must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_estimator_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_estimator"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: p-value estimator must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_estimator_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_estimator"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_estimator_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_estimator"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 7: p-value estimator must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_time_dependence_model_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "time_dependence_model"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: time dependence treatment must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_time_dependence_model_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "time_dependence_model"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: time dependence treatment must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_time_dependence_model_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "time_dependence_model"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: time dependence treatment must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_time_dependence_model_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "time_dependence_model"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_multiple_testing_adjustments_time_dependence_model_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "time_dependence_model"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 7: time dependence treatment must be declared."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_inputs_assume_independence_0" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_inputs_assume_independence"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: p-value inputs must explicitly reject independence assumptions."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_inputs_assume_independence_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_inputs_assume_independence"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: p-value inputs must explicitly reject independence assumptions."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_inputs_assume_independence_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_inputs_assume_independence"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: p-value inputs must explicitly reject independence assumptions."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_inputs_assume_independence_3" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_inputs_assume_independence"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 7: p-value inputs must explicitly reject independence assumptions."] + +[[cases]] +name = "field_multiple_testing_adjustments_p_value_inputs_assume_independence_4" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "p_value_inputs_assume_independence"], "value" = {"kind" = "scalar", "value" = "invalid"}}] +accepted = false +errors = ["Stage 7: p-value inputs must explicitly reject independence assumptions."] + +[[cases]] +name = "field_multiple_testing_adjustments_sharpe_variance_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_sharpe_variance_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_effective_trials_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_effective_trials_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_sample_length_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_sample_length_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_skewness_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "skewness"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_skewness_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "skewness"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_kurtosis_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "kurtosis"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_kurtosis_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "kurtosis"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_selection_bias_evidence_id_1" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "selection_bias_evidence_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_multiple_testing_adjustments_selection_bias_evidence_id_2" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "selection_bias_evidence_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: Sharpe-correction inputs require the deflated Sharpe ratio."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: validation method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: validation method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: validation method labels must be a tuple."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 1: validation method labels must not be empty."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 1: validation method labels contains a non-source label."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 1: validation method labels contains duplicate identifiers."] + +[[cases]] +name = "field_variable_selection_validation_method_labels_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 1: validation method labels contains an invalid identifier."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: event horizons must be a nonempty tuple.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: event horizons must be a nonempty tuple.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: event horizons must be a nonempty tuple.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 1: event horizons must be a nonempty tuple.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 1: an event-horizon record has an invalid type.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 1: an event-horizon record has an invalid type.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 1: an event-horizon record has an invalid type.", "Stage 1: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_variable_selection_validation_folds_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_variable_selection_validation_folds_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_variable_selection_validation_folds_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_variable_selection_validation_folds_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 1: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_variable_selection_validation_folds_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 1: a fold record has an invalid type.", "Stage 1: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_variable_selection_validation_folds_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 1: a fold record has an invalid type.", "Stage 1: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_variable_selection_validation_folds_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 1: a fold record has an invalid type.", "Stage 1: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_variable_selection_validation_embargo_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: embargo must be a finite nonnegative number."] + +[[cases]] +name = "field_variable_selection_validation_embargo_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: embargo must be a finite nonnegative number."] + +[[cases]] +name = "field_variable_selection_validation_embargo_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = true +errors = [] + +[[cases]] +name = "field_variable_selection_validation_embargo_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 1: embargo must be a finite nonnegative number."] + +[[cases]] +name = "field_variable_selection_validation_embargo_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = false +errors = ["Stage 1: embargo must be a finite nonnegative number."] + +[[cases]] +name = 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"field_variable_selection_validation_event_horizons_0_end_0" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 1: event-horizon bounds must be finite numbers.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_1" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 1: event-horizon bounds must be finite numbers.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_2" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 1: every event horizon must have positive length.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_3" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 1: event-horizon bounds must be finite numbers.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_4" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = false +errors = ["Stage 1: every event horizon must have positive length.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_5" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 1: event-horizon bounds must be finite numbers.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_variable_selection_validation_event_horizons_0_end_6" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 1: event-horizon bounds must be finite numbers.", "Stage 1: every fold identifier must have an event horizon.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels must not be empty."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels contains a non-source label."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels contains duplicate identifiers."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_method_labels_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: purged cross-validation is required.", "Stage 4: validation method labels contains an invalid identifier."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: event horizons must be a nonempty tuple.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: event horizons must be a nonempty tuple.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: event horizons must be a nonempty tuple.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: event horizons must be a nonempty tuple.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: an event-horizon record has an invalid type.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: an event-horizon record has an invalid type.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_event_horizons_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "event_horizons"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: an event-horizon record has an invalid type.", "Stage 4: event horizons must match Stage 1.", "Stage 4: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: validation folds must be a nonempty tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: a fold record has an invalid type.", "Stage 4: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_5" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 4: a fold record has an invalid type.", "Stage 4: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_6" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 4: a fold record has an invalid type.", "Stage 4: purged cross-validation must predict every observation once."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_embargo_0" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "embargo"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 4: embargo must be a finite nonnegative number."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_embargo_1" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 4: embargo must be a finite nonnegative number."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_embargo_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_embargo_3" +base 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"field_causal_explanatory_and_predictive_power_validation_folds_0_fold_id_2" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "fold_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 4: fold and path identifiers must be nonblank."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_0_fold_id_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "fold_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = true +errors = [] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_0_fold_id_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "fold_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 4: fold and path identifiers must be nonblank."] + 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false +errors = ["Stage 4: purged cross-validation must predict every observation once.", "Stage 4: test identifiers must be a tuple."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_0_test_ids_3" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "test_ids"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 4: purged cross-validation must predict every observation once.", "Stage 4: test identifiers must not be empty."] + +[[cases]] +name = "field_causal_explanatory_and_predictive_power_validation_folds_0_test_ids_4" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "test_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 4: every fold identifier must have an event horizon.", "Stage 4: predictions must match the test 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"validation", "folds", "0", "refit_components"], "value" = {"kind" = "tuple", "items" = []}}] +accepted = false +errors = ["Stage 6: refit components must not be empty."] + +[[cases]] +name = "field_backtest_validation_folds_0_refit_components_4" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "folds", "0", "refit_components"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "unknown"}]}}] +accepted = false +errors = ["Stage 6: refit components contains a non-source label."] + +[[cases]] +name = "field_backtest_validation_folds_0_refit_components_5" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "folds", "0", "refit_components"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "duplicate"}, {"kind" = "scalar", "value" = "duplicate"}]}}] +accepted = false +errors = ["Stage 6: refit components contains duplicate identifiers."] + +[[cases]] +name = "field_backtest_validation_folds_0_refit_components_6" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "folds", "0", "refit_components"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = 0}]}}] +accepted = false +errors = ["Stage 6: refit components contains an invalid identifier."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_observation_id_0" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "observation_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: an event horizon has an invalid identifier.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_observation_id_1" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "observation_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: an event horizon has an invalid identifier.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_observation_id_2" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "observation_id"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: an event horizon has an invalid identifier.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_observation_id_3" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "observation_id"], "value" = {"kind" = "scalar", "value" = "unknown"}}] +accepted = false +errors = ["Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_observation_id_4" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "observation_id"], "value" = {"kind" = "scalar", "value" = " "}}] +accepted = false +errors = ["Stage 6: an event horizon has an invalid identifier.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_0" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_1" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_3" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_4" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = true +errors = [] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_5" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_start_6" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "start"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_0" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_1" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_2" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 6: every event horizon must have positive length.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_3" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_4" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = -1.0}}] +accepted = false +errors = ["Stage 6: every event horizon must have positive length.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_5" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "field_backtest_validation_event_horizons_0_end_6" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 6: event-horizon bounds must be finite numbers.", "Stage 6: every fold identifier must have an event horizon."] + +[[cases]] +name = "selection_label_MEAN_DECREASE_IMPURITY" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_DECREASE_IMPURITY"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "selection_label_MUTUAL_INFORMATION" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MUTUAL_INFORMATION"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "selection_label_PERMUTATION_FEATURE_IMPORTANCE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PERMUTATION_FEATURE_IMPORTANCE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "selection_label_SHAPLEY_VALUES" +base = "backdoor" +changes = [{"path" = ["variable_selection", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "SHAPLEY_VALUES"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_DOMAIN_EXPERTISE" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "DOMAIN_EXPERTISE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_ECONOMIC_REASONING" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ECONOMIC_REASONING"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_EX_ANTE_VIEWS" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "EX_ANTE_VIEWS"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_LINGAM" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LINGAM"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_OBSERVED_OUTCOMES" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "OBSERVED_OUTCOMES"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_PC" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PC"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "discovery_label_PEER_REVIEWED_ASSUMPTIONS" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PEER_REVIEWED_ASSUMPTIONS"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "pvalue_label_BENJAMINI_HOCHBERG" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "BENJAMINI_HOCHBERG"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "pvalue_label_HOCHBERG" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "HOCHBERG"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "pvalue_label_HOLM" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "method_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "HOLM"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_PROBABILITY_BRIER_SCORE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PROBABILITY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "BRIER_SCORE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "BRIER_SCORE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_PROBABILITY_LOG_LOSS" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PROBABILITY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LOG_LOSS"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LOG_LOSS"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RANKING_CLASSIFICATION_ACCURACY" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "CLASSIFICATION_ACCURACY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "CLASSIFICATION_ACCURACY"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RANKING_MEAN_RECIPROCAL_RANK" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_RECIPROCAL_RANK"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_RECIPROCAL_RANK"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RANKING_PRECISION_RECALL_CURVE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PRECISION_RECALL_CURVE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PRECISION_RECALL_CURVE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RANKING_ROC_CURVE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ROC_CURVE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ROC_CURVE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RETURN_SIZE_MEAN_SQUARED_ERROR" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_SQUARED_ERROR"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_SQUARED_ERROR"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RETURN_SIZE_R_SQUARED" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "R_SQUARED"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "R_SQUARED"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_CAUSAL_ATTRIBUTION_RETURN_SIZE_SPEARMAN_CORRELATION" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "CAUSAL_ATTRIBUTION"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "SPEARMAN_CORRELATION"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "SPEARMAN_CORRELATION"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_PROBABILITY_BRIER_SCORE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PROBABILITY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "BRIER_SCORE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "BRIER_SCORE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_PROBABILITY_LOG_LOSS" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PROBABILITY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LOG_LOSS"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "LOG_LOSS"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RANKING_CLASSIFICATION_ACCURACY" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "CLASSIFICATION_ACCURACY"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "CLASSIFICATION_ACCURACY"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RANKING_MEAN_RECIPROCAL_RANK" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_RECIPROCAL_RANK"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_RECIPROCAL_RANK"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RANKING_PRECISION_RECALL_CURVE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PRECISION_RECALL_CURVE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "PRECISION_RECALL_CURVE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RANKING_ROC_CURVE" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RANKING"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ROC_CURVE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "ROC_CURVE"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RETURN_SIZE_MEAN_SQUARED_ERROR" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_SQUARED_ERROR"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "MEAN_SQUARED_ERROR"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RETURN_SIZE_R_SQUARED" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "R_SQUARED"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "R_SQUARED"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "metric_RISK_PREMIA_HARVESTING_RETURN_SIZE_SPEARMAN_CORRELATION" +base = "backdoor" +changes = [{"path" = ["variable_selection", "purpose"], "value" = {"kind" = "scalar", "value" = "RISK_PREMIA_HARVESTING"}}, {"path" = ["causal_explanatory_and_predictive_power", "task_types"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "RETURN_SIZE"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "explanatory_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "SPEARMAN_CORRELATION"}]}}, {"path" = ["causal_explanatory_and_predictive_power", "predictive_metric_labels"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "SPEARMAN_CORRELATION"}]}}] +accepted = true +errors = [] + +[[cases]] +name = "multiclass_MICRO" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = "ONE_VS_REST"}}, {"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = "MICRO"}}] +accepted = true +errors = [] + +[[cases]] +name = "multiclass_MACRO" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = "ONE_VS_REST"}}, {"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = "MACRO"}}] +accepted = true +errors = [] + +[[cases]] +name = "multiclass_WEIGHTED" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = "ONE_VS_REST"}}, {"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = "WEIGHTED"}}] +accepted = true +errors = [] + +[[cases]] +name = "multiclass_SAMPLES" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "multiclass_encoding"], "value" = {"kind" = "scalar", "value" = "ONE_VS_REST"}}, {"path" = ["causal_explanatory_and_predictive_power", "averaging_method"], "value" = {"kind" = "scalar", "value" = "SAMPLES"}}] +accepted = true +errors = [] + +[[cases]] +name = "dsr_sharpe_variance_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "scalar", "value" = 0}}] +accepted = false +errors = ["Stage 7: positive Sharpe-ratio variance is required."] + +[[cases]] +name = "dsr_sharpe_variance_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "scalar", "value" = -1}}] +accepted = false +errors = ["Stage 7: positive Sharpe-ratio variance is required."] + +[[cases]] +name = "dsr_sharpe_variance_2" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 7: positive Sharpe-ratio variance is required."] + +[[cases]] +name = "dsr_sharpe_variance_3" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sharpe_variance"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 7: positive Sharpe-ratio variance is required."] + +[[cases]] +name = "dsr_effective_trials_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "scalar", "value" = 0.99}}] +accepted = false +errors = ["Stage 7: effective trials must be below total trials."] + +[[cases]] +name = "dsr_effective_trials_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "scalar", "value" = 3}}] +accepted = false +errors = ["Stage 7: effective trials must be below total trials."] + +[[cases]] +name = "dsr_effective_trials_2" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "scalar", "value" = 3.01}}] +accepted = false +errors = ["Stage 7: effective trials must be below total trials."] + +[[cases]] +name = "dsr_effective_trials_3" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "effective_trials"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 7: effective trials must be below total trials."] + +[[cases]] +name = "dsr_sample_length_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "scalar", "value" = true}}] +accepted = false +errors = ["Stage 7: sample length must be an integer above one."] + +[[cases]] +name = "dsr_sample_length_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "scalar", "value" = 1}}] +accepted = false +errors = ["Stage 7: sample length must be an integer above one."] + +[[cases]] +name = "dsr_sample_length_2" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "scalar", "value" = 1.5}}] +accepted = false +errors = ["Stage 7: sample length must be an integer above one."] + +[[cases]] +name = "dsr_sample_length_3" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "sample_length"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: sample length must be an integer above one."] + +[[cases]] +name = "dsr_skewness_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "skewness"], "value" = {"kind" = "float", "value" = "nan"}}] +accepted = false +errors = ["Stage 7: skewness must be finite."] + +[[cases]] +name = "dsr_skewness_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "skewness"], "value" = {"kind" = "scalar", "value" = 2.0}}] +accepted = false +errors = ["Stage 7: skewness and Pearson kurtosis must satisfy the moment inequality."] + +[[cases]] +name = "dsr_kurtosis_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "kurtosis"], "value" = {"kind" = "scalar", "value" = 0.99}}] +accepted = false +errors = ["Stage 7: Pearson kurtosis must be finite and at least one."] + +[[cases]] +name = "dsr_kurtosis_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "kurtosis"], "value" = {"kind" = "scalar", "value" = 1.0}}] +accepted = false +errors = ["Stage 7: skewness and Pearson kurtosis must satisfy the moment inequality."] + +[[cases]] +name = "dsr_kurtosis_2" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "kurtosis"], "value" = {"kind" = "float", "value" = "inf"}}] +accepted = false +errors = ["Stage 7: Pearson kurtosis must be finite and at least one."] + +[[cases]] +name = "dsr_selection_bias_evidence_id_0" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "selection_bias_evidence_id"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 7: selection-bias evidence is required."] + +[[cases]] +name = "dsr_selection_bias_evidence_id_1" +base = "deflated_sharpe" +changes = [{"path" = ["multiple_testing_adjustments", "selection_bias_evidence_id"], "value" = {"kind" = "scalar", "value" = ""}}] +accepted = false +errors = ["Stage 7: selection-bias evidence is required."] + +[[cases]] +name = "premise_frontdoor_criteria_satisfied_0" +base = "frontdoor" +changes = [{"path" = ["causal_adjustment_set", "frontdoor_criteria_satisfied"], "value" = {"kind" = "scalar", "value" = false}}] +accepted = false +errors = ["Stage 3: front-door criteria must be explicitly satisfied."] + +[[cases]] +name = "premise_frontdoor_criteria_satisfied_1" +base = "frontdoor" +changes = [{"path" = ["causal_adjustment_set", "frontdoor_criteria_satisfied"], "value" = {"kind" = "scalar", "value" = 1}}] +accepted = false +errors = ["Stage 3: front-door criteria must be explicitly satisfied."] + +[[cases]] +name = "premise_frontdoor_criteria_satisfied_2" +base = "frontdoor" +changes = [{"path" = ["causal_adjustment_set", "frontdoor_criteria_satisfied"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: front-door criteria must be explicitly satisfied."] + +[[cases]] +name = "premise_instrument_relevance_satisfied_0" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_relevance_satisfied"], "value" = {"kind" = "scalar", "value" = false}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_relevance_satisfied_1" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_relevance_satisfied"], "value" = {"kind" = "scalar", "value" = 1}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_relevance_satisfied_2" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_relevance_satisfied"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exclusion_satisfied_0" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exclusion_satisfied"], "value" = {"kind" = "scalar", "value" = false}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exclusion_satisfied_1" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exclusion_satisfied"], "value" = {"kind" = "scalar", "value" = 1}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exclusion_satisfied_2" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exclusion_satisfied"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exogeneity_satisfied_0" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exogeneity_satisfied"], "value" = {"kind" = "scalar", "value" = false}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exogeneity_satisfied_1" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exogeneity_satisfied"], "value" = {"kind" = "scalar", "value" = 1}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "premise_instrument_exogeneity_satisfied_2" +base = "instrument" +changes = [{"path" = ["causal_adjustment_set", "instrument_exogeneity_satisfied"], "value" = {"kind" = "none"}}] +accepted = false +errors = ["Stage 3: instrumental-variable premises must all hold."] + +[[cases]] +name = "cycle" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "T"}]}]}}] +accepted = false +errors = ["Stage 2: the resolved graph must be acyclic.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: every declared admissible set must block graph-implied backdoor paths.", "Stage 3: selected controls must block every graph-implied backdoor path."] + +[[cases]] +name = "self_loop" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "C"}]}]}}] +accepted = false +errors = ["Stage 2: directed self-loops are not admissible.", "Stage 2: the resolved graph must be acyclic."] + +[[cases]] +name = "unknown_edge" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "missing"}, {"kind" = "scalar", "value" = "Y"}]}]}}] +accepted = false +errors = ["Stage 2: every directed edge must use graph nodes."] + +[[cases]] +name = "duplicate_edge" +base = "backdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}]}}] +accepted = false +errors = ["Stage 2: directed edges contains duplicate edges.", "Stage 3: declared colliders must have converging arrows.", "Stage 3: declared colliders must match graph-implied converging nodes.", "Stage 3: declared confounders must match graph-implied common causes.", "Stage 3: declared descendants must exactly match the graph.", "Stage 3: declared mediators must lie on a causal path.", "Stage 3: declared mediators must match graph-implied causal-path nodes.", "Stage 3: every declared instrument must be relevant in the graph.", "Stage 3: the graph must retain a treatment-to-outcome path.", "Stage 5: every causal exposure must have a directed path to the outcome."] + +[[cases]] +name = "direct_frontdoor_bypass" +base = "frontdoor" +changes = [{"path" = ["causal_discovery", "directed_edges"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "C"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "M"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "M"}, {"kind" = "scalar", "value" = "Y"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Y"}, {"kind" = "scalar", "value" = "K"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "Z"}, {"kind" = "scalar", "value" = "T"}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "T"}, {"kind" = "scalar", "value" = "Y"}]}]}}] +accepted = false +errors = ["Stage 3: front-door mediators must intercept every directed causal path."] + +[[cases]] +name = "variable_selection_fit_leak" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds", "0", "fit_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}, {"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}}] +accepted = false +errors = ["Stage 1: every fold must refit on its complete training membership only."] + +[[cases]] +name = "variable_selection_duplicate_prediction" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-0"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", 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"train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-1"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}, {"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-2"}, "path_id" = {"kind" = "scalar", "value" = "path-0"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-0-2"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = 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= "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}]}}] +accepted = false +errors = ["Stage 1: a path cannot contain duplicate test contributions.", "Stage 1: ordinary temporal validation cannot duplicate test predictions."] + +[[cases]] +name = "variable_selection_embargo_violation" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = 100.0}}] +accepted = false +errors = ["Stage 1: post-test embargo observations must be excluded."] + +[[cases]] +name = "variable_selection_overlap_violation" +base = "backdoor" +changes = [{"path" = ["variable_selection", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = 100.0}}] +accepted = false +errors = ["Stage 1: overlap declaration must match event horizons.", "Stage 1: train and test event horizons must be purged.", "Stage 4: event horizons must match Stage 1."] + +[[cases]] +name = "causal_explanatory_and_predictive_power_fit_leak" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds", "0", "fit_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}, {"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}}] +accepted = false +errors = ["Stage 4: every fold must refit on its complete training membership only."] + +[[cases]] +name = "causal_explanatory_and_predictive_power_duplicate_prediction" +base = "backdoor" +changes = [{"path" = ["causal_explanatory_and_predictive_power", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "fold-0-0"}, "path_id" = {"kind" = "scalar", "value" 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{"kind" = "scalar", "value" = 100.0}}] +accepted = false +errors = ["Stage 4: event horizons must match Stage 1.", "Stage 4: train and test event horizons must be purged."] + +[[cases]] +name = "backtest_fit_leak" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "folds", "0", "fit_ids"], "value" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}}] +accepted = false +errors = ["Stage 6: every fold must refit on its complete training membership only."] + +[[cases]] +name = "backtest_duplicate_prediction" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "record", "type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "walk-fold"}, "path_id" = {"kind" 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"type" = "FoldEvidence", "fields" = {"fold_id" = {"kind" = "scalar", "value" = "extra-fold"}, "path_id" = {"kind" = "scalar", "value" = "walk-path"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-4"}, {"kind" = "scalar", "value" = "obs-5"}]}, "refit_id" = {"kind" = "scalar", "value" = "walk-refit"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}]}}] +accepted = false +errors = ["Stage 6: a path cannot contain duplicate test contributions.", "Stage 6: ordinary temporal validation cannot duplicate test predictions."] + +[[cases]] +name = "backtest_embargo_violation" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "embargo"], "value" = {"kind" = "scalar", "value" = 100.0}}] +accepted = true +errors = [] + +[[cases]] +name = "backtest_overlap_violation" +base = "backdoor" +changes = [{"path" = ["backtest", "validation", "event_horizons", "0", "end"], "value" = {"kind" = "scalar", "value" = 100.0}}] +accepted = false +errors = ["Stage 6: train and test event horizons must be purged.", "Stage 6: walk-forward training must precede the test interval."] + +[[cases]] +name = "cpcv_missing_path" +base = "cpcv" +changes = [{"path" = ["backtest", "validation", "folds"], "value" = {"kind" = "tuple", "items" = [{"kind" = "record", 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"path-1"}, "train_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-4"}]}, "test_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-5"}]}, "fit_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-0"}, {"kind" = "scalar", "value" = "obs-1"}, {"kind" = "scalar", "value" = "obs-2"}, {"kind" = "scalar", "value" = "obs-4"}]}, "prediction_ids" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "obs-3"}, {"kind" = "scalar", "value" = "obs-5"}]}, "refit_id" = {"kind" = "scalar", "value" = "refit-1-2"}, "refit_components" = {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "VARIABLE_SELECTION"}, {"kind" = "scalar", "value" = "CAUSAL_ESTIMATION"}, {"kind" = "scalar", "value" = "PORTFOLIO_CONSTRUCTION"}]}}}]}}] +accepted = false +errors = ["Stage 6: a refit identifier cannot represent different training sets."] + +[[cases]] +name = "missing_trial" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "group"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}]}]}]}}] +accepted = false +errors = ["Stage 7: family partitions must cover declared trials exactly."] + +[[cases]] +name = "duplicate_trial" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "first"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}, {"kind" = "scalar", "value" = "trial-b"}]}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "second"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}]}]}}] +accepted = false +errors = ["Stage 7: family partitions must be disjoint."] + +[[cases]] +name = "duplicate_group" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "group"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}]}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "group"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}]}]}}] +accepted = false +errors = ["Stage 7: family partition identifiers must be unique."] + +[[cases]] +name = "valid_split" +base = "backdoor" +changes = [{"path" = ["multiple_testing_adjustments", "family_partitions"], "value" = {"kind" = "tuple", "items" = [{"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "first"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-a"}]}]}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "second"}, {"kind" = "tuple", "items" = [{"kind" = "scalar", "value" = "trial-b"}, {"kind" = "scalar", "value" = "trial-c"}]}]}]}}] +accepted = true +errors = [] diff --git a/test/fixtures/risk_formulas_python_320.toml b/test/fixtures/risk_formulas_python_320.toml new file mode 100644 index 0000000..129c070 --- /dev/null +++ b/test/fixtures/risk_formulas_python_320.toml @@ -0,0 +1,4335 @@ +reference = "RiskLabAI 3.2.0 multiple-testing and binary strategy formulas" + +[[adjustment]] +values = [] +holm = [] +bhy = [] + +[[adjustment]] +values = [0.02] +holm = [0.02] +bhy = [0.02] + +[[adjustment]] +values = [0.02, 0.02, 0.02, 0.02] +holm = [0.08, 0.08, 0.08, 0.08] +bhy = [0.041666666666666664, 0.041666666666666664, 0.041666666666666664, 0.041666666666666664] + +[[adjustment]] +values = [1.0, 0.0, 0.0001, 0.05, 1.0] +holm = [1.0, 0.0, 0.0004, 0.15000000000000002, 1.0] +bhy = [1.0, 0.0, 0.0005708333333333333, 0.19027777777777777, 1.0] + +[[adjustment]] +values = [0.9752894816394936, 0.7812592805872466, 0.4668496665234234, 0.15733824094585058, 0.4999467373763222, 0.3874884287057674, 0.7755362365217268, 0.34790861292398434, 0.24715284010377014, 0.6077330063135159, 0.7378606340945031, 0.17451007282926956, 0.44906739896662295, 0.34660899838241765, 0.4752987847684643, 0.039144245696473456, 0.7950988841079832, 0.3745753058674205, 0.8057381163267177] +holm = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.7437406682329957, 1.0, 1.0, 1.0] +bhy = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + +[[adjustment]] +values = [0.0, 0.0, 0.0] +holm = [0.0, 0.0, 0.0] +bhy = [0.0, 0.0, 0.0] + +[[adjustment]] +values = [0.0, 0.0, 0.001] +holm = [0.0, 0.0, 0.001] +bhy = [0.0, 0.0, 0.0018333333333333333] + +[[adjustment]] +values = [0.0, 0.0, 0.05] +holm = [0.0, 0.0, 0.05] +bhy = [0.0, 0.0, 0.09166666666666667] + +[[adjustment]] +values = [0.0, 0.0, 0.3] +holm = [0.0, 0.0, 0.3] +bhy = [0.0, 0.0, 0.5499999999999999] + +[[adjustment]] +values = [0.0, 0.0, 1.0] +holm = [0.0, 0.0, 1.0] +bhy = [0.0, 0.0, 1.0] + +[[adjustment]] +values = [0.0, 0.001, 0.0] +holm = [0.0, 0.001, 0.0] +bhy = [0.0, 0.0018333333333333333, 0.0] + +[[adjustment]] +values = [0.0, 0.001, 0.001] +holm = [0.0, 0.002, 0.002] +bhy = [0.0, 0.0018333333333333333, 0.0018333333333333333] + +[[adjustment]] +values = [0.0, 0.001, 0.05] +holm = [0.0, 0.002, 0.05] +bhy = [0.0, 0.00275, 0.09166666666666667] + +[[adjustment]] +values = [0.0, 0.001, 0.3] +holm = [0.0, 0.002, 0.3] +bhy = [0.0, 0.00275, 0.5499999999999999] + +[[adjustment]] +values = [0.0, 0.001, 1.0] +holm = [0.0, 0.002, 1.0] +bhy = [0.0, 0.00275, 1.0] + +[[adjustment]] +values = [0.0, 0.05, 0.0] +holm = [0.0, 0.05, 0.0] +bhy = [0.0, 0.09166666666666667, 0.0] + +[[adjustment]] +values = [0.0, 0.05, 0.001] +holm = [0.0, 0.05, 0.002] +bhy = [0.0, 0.09166666666666667, 0.00275] + +[[adjustment]] +values = [0.0, 0.05, 0.05] +holm = [0.0, 0.1, 0.1] +bhy = [0.0, 0.09166666666666667, 0.09166666666666667] + +[[adjustment]] +values = [0.0, 0.05, 0.3] +holm = [0.0, 0.1, 0.3] +bhy = [0.0, 0.1375, 0.5499999999999999] + +[[adjustment]] +values = [0.0, 0.05, 1.0] +holm = [0.0, 0.1, 1.0] +bhy = [0.0, 0.1375, 1.0] + +[[adjustment]] +values = [0.0, 0.3, 0.0] +holm = [0.0, 0.3, 0.0] +bhy = [0.0, 0.5499999999999999, 0.0] + +[[adjustment]] +values = [0.0, 0.3, 0.001] +holm = [0.0, 0.3, 0.002] +bhy = [0.0, 0.5499999999999999, 0.00275] + +[[adjustment]] +values = [0.0, 0.3, 0.05] +holm = [0.0, 0.3, 0.1] +bhy = [0.0, 0.5499999999999999, 0.1375] + +[[adjustment]] +values = [0.0, 0.3, 0.3] +holm = [0.0, 0.6, 0.6] +bhy = [0.0, 0.5499999999999999, 0.5499999999999999] + +[[adjustment]] +values = [0.0, 0.3, 1.0] +holm = [0.0, 0.6, 1.0] +bhy = [0.0, 0.825, 1.0] + +[[adjustment]] +values = [0.0, 1.0, 0.0] +holm = [0.0, 1.0, 0.0] +bhy = [0.0, 1.0, 0.0] + +[[adjustment]] +values = [0.0, 1.0, 0.001] +holm = [0.0, 1.0, 0.002] +bhy = [0.0, 1.0, 0.00275] + +[[adjustment]] +values = [0.0, 1.0, 0.05] +holm = [0.0, 1.0, 0.1] +bhy = [0.0, 1.0, 0.1375] + +[[adjustment]] +values = [0.0, 1.0, 0.3] +holm = [0.0, 1.0, 0.6] +bhy = [0.0, 1.0, 0.825] + +[[adjustment]] +values = [0.0, 1.0, 1.0] +holm = [0.0, 1.0, 1.0] +bhy = [0.0, 1.0, 1.0] + +[[adjustment]] +values = [0.001, 0.0, 0.0] +holm = [0.001, 0.0, 0.0] +bhy = [0.0018333333333333333, 0.0, 0.0] + +[[adjustment]] +values = [0.001, 0.0, 0.001] +holm = [0.002, 0.0, 0.002] +bhy = [0.0018333333333333333, 0.0, 0.0018333333333333333] + +[[adjustment]] +values = [0.001, 0.0, 0.05] +holm = [0.002, 0.0, 0.05] +bhy = [0.00275, 0.0, 0.09166666666666667] + +[[adjustment]] +values = [0.001, 0.0, 0.3] +holm = [0.002, 0.0, 0.3] +bhy = [0.00275, 0.0, 0.5499999999999999] + +[[adjustment]] +values = [0.001, 0.0, 1.0] +holm = [0.002, 0.0, 1.0] +bhy = [0.00275, 0.0, 1.0] + +[[adjustment]] +values = [0.001, 0.001, 0.0] +holm = [0.002, 0.002, 0.0] +bhy = [0.0018333333333333333, 0.0018333333333333333, 0.0] + +[[adjustment]] +values = [0.001, 0.001, 0.001] +holm = [0.003, 0.003, 0.003] +bhy = [0.0018333333333333333, 0.0018333333333333333, 0.0018333333333333333] + +[[adjustment]] +values = [0.001, 0.001, 0.05] +holm = [0.003, 0.003, 0.05] +bhy = [0.00275, 0.00275, 0.09166666666666667] + +[[adjustment]] +values = [0.001, 0.001, 0.3] +holm = [0.003, 0.003, 0.3] +bhy = [0.00275, 0.00275, 0.5499999999999999] + +[[adjustment]] +values = [0.001, 0.001, 1.0] +holm = [0.003, 0.003, 1.0] +bhy = [0.00275, 0.00275, 1.0] + +[[adjustment]] +values = [0.001, 0.05, 0.0] +holm = [0.002, 0.05, 0.0] +bhy = [0.00275, 0.09166666666666667, 0.0] + +[[adjustment]] +values = [0.001, 0.05, 0.001] +holm = [0.003, 0.05, 0.003] +bhy = [0.00275, 0.09166666666666667, 0.00275] + +[[adjustment]] +values = [0.001, 0.05, 0.05] +holm = [0.003, 0.1, 0.1] +bhy = [0.0055, 0.09166666666666667, 0.09166666666666667] + +[[adjustment]] +values = [0.001, 0.05, 0.3] +holm = [0.003, 0.1, 0.3] +bhy = [0.0055, 0.1375, 0.5499999999999999] + +[[adjustment]] +values = [0.001, 0.05, 1.0] +holm = [0.003, 0.1, 1.0] +bhy = [0.0055, 0.1375, 1.0] + +[[adjustment]] +values = [0.001, 0.3, 0.0] +holm = [0.002, 0.3, 0.0] +bhy = [0.00275, 0.5499999999999999, 0.0] + +[[adjustment]] +values = [0.001, 0.3, 0.001] +holm = [0.003, 0.3, 0.003] +bhy = [0.00275, 0.5499999999999999, 0.00275] + +[[adjustment]] +values = [0.001, 0.3, 0.05] +holm = [0.003, 0.3, 0.1] +bhy = [0.0055, 0.5499999999999999, 0.1375] + +[[adjustment]] +values = [0.001, 0.3, 0.3] +holm = [0.003, 0.6, 0.6] +bhy = [0.0055, 0.5499999999999999, 0.5499999999999999] + +[[adjustment]] +values = [0.001, 0.3, 1.0] +holm = [0.003, 0.6, 1.0] +bhy = [0.0055, 0.825, 1.0] + +[[adjustment]] +values = [0.001, 1.0, 0.0] +holm = [0.002, 1.0, 0.0] +bhy = [0.00275, 1.0, 0.0] + +[[adjustment]] +values = [0.001, 1.0, 0.001] +holm = [0.003, 1.0, 0.003] +bhy = [0.00275, 1.0, 0.00275] + +[[adjustment]] +values = [0.001, 1.0, 0.05] +holm = [0.003, 1.0, 0.1] +bhy = [0.0055, 1.0, 0.1375] + +[[adjustment]] +values = [0.001, 1.0, 0.3] +holm = [0.003, 1.0, 0.6] +bhy = [0.0055, 1.0, 0.825] + +[[adjustment]] +values = [0.001, 1.0, 1.0] +holm = [0.003, 1.0, 1.0] +bhy = [0.0055, 1.0, 1.0] + +[[adjustment]] +values = [0.05, 0.0, 0.0] +holm = [0.05, 0.0, 0.0] +bhy = [0.09166666666666667, 0.0, 0.0] + +[[adjustment]] +values = [0.05, 0.0, 0.001] +holm = [0.05, 0.0, 0.002] +bhy = [0.09166666666666667, 0.0, 0.00275] + +[[adjustment]] +values = [0.05, 0.0, 0.05] +holm = [0.1, 0.0, 0.1] +bhy = [0.09166666666666667, 0.0, 0.09166666666666667] + +[[adjustment]] +values = [0.05, 0.0, 0.3] +holm = [0.1, 0.0, 0.3] +bhy = [0.1375, 0.0, 0.5499999999999999] + +[[adjustment]] +values = [0.05, 0.0, 1.0] +holm = [0.1, 0.0, 1.0] +bhy = [0.1375, 0.0, 1.0] + +[[adjustment]] +values = [0.05, 0.001, 0.0] +holm = [0.05, 0.002, 0.0] +bhy = [0.09166666666666667, 0.00275, 0.0] + +[[adjustment]] +values = [0.05, 0.001, 0.001] +holm = [0.05, 0.003, 0.003] +bhy = [0.09166666666666667, 0.00275, 0.00275] + +[[adjustment]] +values = [0.05, 0.001, 0.05] +holm = [0.1, 0.003, 0.1] +bhy = [0.09166666666666667, 0.0055, 0.09166666666666667] + +[[adjustment]] +values = [0.05, 0.001, 0.3] +holm = [0.1, 0.003, 0.3] +bhy = [0.1375, 0.0055, 0.5499999999999999] + +[[adjustment]] +values = [0.05, 0.001, 1.0] +holm = [0.1, 0.003, 1.0] +bhy = [0.1375, 0.0055, 1.0] + +[[adjustment]] +values = [0.05, 0.05, 0.0] +holm = [0.1, 0.1, 0.0] +bhy = [0.09166666666666667, 0.09166666666666667, 0.0] + +[[adjustment]] +values = [0.05, 0.05, 0.001] +holm = [0.1, 0.1, 0.003] +bhy = [0.09166666666666667, 0.09166666666666667, 0.0055] + +[[adjustment]] +values = [0.05, 0.05, 0.05] +holm = [0.15000000000000002, 0.15000000000000002, 0.15000000000000002] +bhy = [0.09166666666666667, 0.09166666666666667, 0.09166666666666667] + +[[adjustment]] +values = [0.05, 0.05, 0.3] +holm = [0.15000000000000002, 0.15000000000000002, 0.3] +bhy = [0.1375, 0.1375, 0.5499999999999999] + +[[adjustment]] +values = [0.05, 0.05, 1.0] +holm = [0.15000000000000002, 0.15000000000000002, 1.0] +bhy = [0.1375, 0.1375, 1.0] + +[[adjustment]] +values = [0.05, 0.3, 0.0] +holm = [0.1, 0.3, 0.0] +bhy = [0.1375, 0.5499999999999999, 0.0] + +[[adjustment]] +values = [0.05, 0.3, 0.001] +holm = [0.1, 0.3, 0.003] +bhy = [0.1375, 0.5499999999999999, 0.0055] + +[[adjustment]] +values = [0.05, 0.3, 0.05] +holm = [0.15000000000000002, 0.3, 0.15000000000000002] +bhy = [0.1375, 0.5499999999999999, 0.1375] + +[[adjustment]] +values = [0.05, 0.3, 0.3] +holm = [0.15000000000000002, 0.6, 0.6] +bhy = [0.275, 0.5499999999999999, 0.5499999999999999] + +[[adjustment]] +values = [0.05, 0.3, 1.0] +holm = [0.15000000000000002, 0.6, 1.0] +bhy = [0.275, 0.825, 1.0] + +[[adjustment]] +values = [0.05, 1.0, 0.0] +holm = [0.1, 1.0, 0.0] +bhy = [0.1375, 1.0, 0.0] + +[[adjustment]] +values = [0.05, 1.0, 0.001] +holm = [0.1, 1.0, 0.003] +bhy = [0.1375, 1.0, 0.0055] + 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0.1 +output = 0.13867744613533126 + +[[maximum]] +trials = 7 +mean = 0.0 +std = 0.5 +output = 0.6933872306766562 + +[[maximum]] +trials = 7 +mean = 0.2 +std = 0.0 +output = 0.2 + +[[maximum]] +trials = 7 +mean = 0.2 +std = 0.1 +output = 0.33867744613533124 + +[[maximum]] +trials = 7 +mean = 0.2 +std = 0.5 +output = 0.8933872306766562 + +[[maximum]] +trials = 100 +mean = -0.1 +std = 0.0 +output = -0.1 + +[[maximum]] +trials = 100 +mean = -0.1 +std = 0.1 +output = 0.15306028932011426 + +[[maximum]] +trials = 100 +mean = -0.1 +std = 0.5 +output = 1.1653014466005711 + +[[maximum]] +trials = 100 +mean = 0.0 +std = 0.0 +output = 0.0 + +[[maximum]] +trials = 100 +mean = 0.0 +std = 0.1 +output = 0.25306028932011426 + +[[maximum]] +trials = 100 +mean = 0.0 +std = 0.5 +output = 1.2653014466005712 + +[[maximum]] +trials = 100 +mean = 0.2 +std = 0.0 +output = 0.2 + +[[maximum]] +trials = 100 +mean = 0.2 +std = 0.1 +output = 0.4530602893201143 + +[[maximum]] +trials = 100 +mean = 0.2 +std = 0.5 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0.0 +count = 1 +theta = 0.0 +alpha = 0.5 +beta = 0.5 + +[[error_rates]] +z = 0.0 +count = 1 +theta = 2.0 +alpha = 0.5 +beta = 0.022750131948179195 + +[[error_rates]] +z = 0.0 +count = 2 +theta = -1.0 +alpha = 0.75 +beta = 0.8413447460685429 + +[[error_rates]] +z = 0.0 +count = 2 +theta = 0.0 +alpha = 0.75 +beta = 0.5 + +[[error_rates]] +z = 0.0 +count = 2 +theta = 2.0 +alpha = 0.75 +beta = 0.022750131948179195 + +[[error_rates]] +z = 0.0 +count = 10 +theta = -1.0 +alpha = 0.9990234375 +beta = 0.8413447460685429 + +[[error_rates]] +z = 0.0 +count = 10 +theta = 0.0 +alpha = 0.9990234375 +beta = 0.5 + +[[error_rates]] +z = 0.0 +count = 10 +theta = 2.0 +alpha = 0.9990234375 +beta = 0.022750131948179195 + +[[error_rates]] +z = 0.0 +count = 100 +theta = -1.0 +alpha = 1.0 +beta = 0.0 + +[[error_rates]] +z = 0.0 +count = 100 +theta = 0.0 +alpha = 1.0 +beta = 0.0 + +[[error_rates]] +z = 0.0 +count = 100 +theta = 2.0 +alpha = 1.0 +beta = 0.0 + +[[error_rates]] +z = 1.0 +count = 1 +theta = -1.0 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0.9999999685660683 +beta = 0.9772498680548276 + +[[error_rates]] +z = 1.0 +count = 100 +theta = 0.0 +alpha = 0.9999999685660683 +beta = 0.8413447460820185 + +[[error_rates]] +z = 1.0 +count = 100 +theta = 2.0 +alpha = 0.9999999685660683 +beta = 0.15865525394493263 + +[[error_rates]] +z = 3.0 +count = 1 +theta = -1.0 +alpha = 0.0013498980316301035 +beta = 0.9999683287581669 + +[[error_rates]] +z = 3.0 +count = 1 +theta = 0.0 +alpha = 0.0013498980316301035 +beta = 0.9986501019683699 + +[[error_rates]] +z = 3.0 +count = 1 +theta = 2.0 +alpha = 0.0013498980316301035 +beta = 0.8413447460685426 + +[[error_rates]] +z = 3.0 +count = 2 +theta = -1.0 +alpha = 0.0026979738385644048 +beta = 0.9999683287581669 + +[[error_rates]] +z = 3.0 +count = 2 +theta = 0.0 +alpha = 0.0026979738385644048 +beta = 0.9986501019683699 + +[[error_rates]] +z = 3.0 +count = 2 +theta = 2.0 +alpha = 0.0026979738385644048 +beta = 0.8413447460685426 + +[[error_rates]] +z = 3.0 +count = 10 +theta = -1.0 +alpha = 0.013417274686916336 +beta = 0.9999683287581669 + +[[error_rates]] +z = 3.0 +count = 10 +theta = 0.0 +alpha = 0.013417274686916336 +beta = 0.9986501019683699 + +[[error_rates]] +z = 3.0 +count = 10 +theta = 2.0 +alpha = 0.013417274686916336 +beta = 0.8413447460685426 + +[[error_rates]] +z = 3.0 +count = 100 +theta = -1.0 +alpha = 0.12635485335490848 +beta = 0.9999683287581669 + +[[error_rates]] +z = 3.0 +count = 100 +theta = 0.0 +alpha = 0.12635485335490848 +beta = 0.9986501019683699 + +[[error_rates]] +z = 3.0 +count = 100 +theta = 2.0 +alpha = 0.12635485335490848 +beta = 0.8413447460685426 + +[[error_rates]] +z = 8.0 +count = 1 +theta = -1.0 +alpha = 6.661338147750939e-16 +beta = 1.0 + +[[error_rates]] +z = 8.0 +count = 1 +theta = 0.0 +alpha = 6.661338147750939e-16 +beta = 0.9999999999999993 + +[[error_rates]] +z = 8.0 +count = 1 +theta = 2.0 +alpha = 6.661338147750939e-16 +beta = 0.9999999989609011 + +[[error_rates]] +z = 8.0 +count = 2 +theta = -1.0 +alpha = 1.3322676295501878e-15 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-0.7874007874011809 +theta = -1.5748015748023623 + +[[z_statistics]] +observed = -0.3 +true = -0.2 +count = 63 +skew = 0.0 +kurtosis = 3.0 +z = -0.7702606282335209 +theta = -1.5405212564670423 + +[[z_statistics]] +observed = -0.3 +true = -0.2 +count = 63 +skew = 4.0 +kurtosis = 1.0 +z = -0.5308655025693246 +theta = -1.0617310051386497 + +[[z_statistics]] +observed = -0.3 +true = -0.2 +count = 63 +skew = 4.0 +kurtosis = 3.0 +z = -0.5255180920134029 +theta = -1.051036184026806 + +[[z_statistics]] +observed = -0.3 +true = 0.2 +count = 1 +skew = 0.0 +kurtosis = 1.0 +z = -0.0 +theta = 0.0 + +[[z_statistics]] +observed = -0.3 +true = 0.2 +count = 1 +skew = 0.0 +kurtosis = 3.0 +z = -0.0 +theta = 0.0 + +[[z_statistics]] +observed = -0.3 +true = 0.2 +count = 1 +skew = 4.0 +kurtosis = 1.0 +z = -0.0 +theta = 0.0 + +[[z_statistics]] +observed = -0.3 +true = 0.2 +count = 1 +skew = 4.0 +kurtosis = 3.0 +z = -0.0 +theta = 0.0 + +[[z_statistics]] +observed = -0.3 +true = 0.2 +count = 2 +skew = 0.0 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-1.5748015748023623 + +[[z_statistics]] +observed = 1.0 +true = -0.2 +count = 63 +skew = 0.0 +kurtosis = 3.0 +z = 7.714920608794364 +theta = -1.2858201014657276 + +[[z_statistics]] +observed = 1.0 +true = -0.2 +count = 63 +skew = 4.0 +kurtosis = 1.0 +z = nan +theta = nan + +[[z_statistics]] +observed = 1.0 +true = -0.2 +count = 63 +skew = 4.0 +kurtosis = 3.0 +z = nan +theta = nan + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 1 +skew = 0.0 +kurtosis = 1.0 +z = 0.0 +theta = 0.0 + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 1 +skew = 0.0 +kurtosis = 3.0 +z = 0.0 +theta = 0.0 + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 1 +skew = 4.0 +kurtosis = 1.0 +z = nan +theta = nan + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 1 +skew = 4.0 +kurtosis = 3.0 +z = nan +theta = nan + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 2 +skew = 0.0 +kurtosis = 1.0 +z = 0.8 +theta = 0.2 + +[[z_statistics]] +observed = 1.0 +true = 0.2 +count = 2 +skew = 0.0 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64.0 +probability = 1.0 +output = 0.0 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 0.0 +probability = 0.0 +output = -inf + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 0.0 +probability = 0.25 +output = -0.0 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 0.0 +probability = 0.5 +output = 0.0 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 0.0 +probability = 0.75 +output = 0.0 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 0.0 +probability = 1.0 +output = inf + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 1.0 +probability = 0.0 +output = -inf + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 1.0 +probability = 0.25 +output = -0.5773502691896258 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 1.0 +probability = 0.5 +output = 0.0 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 1.0 +probability = 0.75 +output = 0.5773502691896258 + +[[binomial]] +loss = -0.5 +profit = 0.5 +frequency = 1.0 +probability = 1.0 +output 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= inf + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 0.0 +probability = 0.0 +output = -inf + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 0.0 +probability = 0.25 +output = -0.0 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 0.0 +probability = 0.5 +output = -0.0 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 0.0 +probability = 0.75 +output = -0.0 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 0.0 +probability = 1.0 +output = 0.0 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 1.0 +probability = 0.0 +output = -inf + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 1.0 +probability = 0.25 +output = -1.7320508075688774 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 1.0 +probability = 0.5 +output = -1.0 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 1.0 +probability = 0.75 +output = -0.5773502691896258 + +[[binomial]] +loss = -0.25 +profit = 0.0 +frequency = 1.0 +probability = 1.0 +output = 0.0 + 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-0.05066310848655693, -0.10807725432756192, 0.045484908782168286, 0.02, -0.0004032403235481683, 0.02] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.01 +sigma2 = 0.02 +probability = 1.0 +count = 7 +normal_draws = [[-0.6782791351453962, 0.2604815695310676, -1.8042743723964831, 0.4584555283907739, -1.2494209113169956, -1.0894799349071937, -0.8958679349918922], []] +permutation = [4, 5, 3, 6, 2, 7, 1] +output = [0.02458455528390774, 0.0075057908868300435, 0.001957256276035168, 0.009105200650928062, 0.022604815695310677, 0.011041320650081078, 0.013217208648546037] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.0 +sigma2 = 0.1 +probability = 1.0 +count = 7 +normal_draws = [[1.1564439397110948, 0.15857814569508266, 0.3333833364917277, -0.919620188199495, -0.46171967064066394, 0.09877165724157787, 0.8162767778926145], []] +permutation = [3, 4, 6, 7, 5, 2, 1] +output = [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.01 +sigma2 = 0.02 +probability = 0.0 +count = 19 +normal_draws = [[], [1.7130573608359325, 1.724325309197954, -0.6490736226434173, 1.8175920305893032, 0.4315477633746298, -0.06617309251961902, -2.1043299669326334, 0.20287202014174524, 1.43355513830792, -0.14904497010027348, 0.6855958077963685, -0.6959901771222748, 0.6409157283654283, 1.6696496088005457, 1.088838763032681, -0.6465634484251668, 2.213343630519779, 1.1263153991508033, -0.880885008097484]] +permutation = [7, 15, 2, 6, 14, 19, 12, 8, 11, 3, 4, 9, 1, 17, 13, 5, 10, 18, 16] +output = [-0.05208659933865267, 0.011776775260653622, 0.024486506183959082, -0.011323461850392381, 0.023392992176010916, -0.027617700161949685, -0.023919803542445498, -0.005942559597165095, 0.003711916155927371, -0.022981472452868346, 0.02635184061178606, 0.018671102766158396, 0.024261147216718647, 0.03426687261039558, 0.002818314567308567, -0.0013690447325074035, -0.01298089940200547, 0.012526307983016067, -0.022931268968503338] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.0 +sigma2 = 0.1 +probability = 0.0 +count = 19 +normal_draws = [[], [-0.14968637544056326, 0.35183494702903206, 0.23749592012301335, -0.6214709833063072, 2.3060787451082327, 1.1429291337685272, 0.11923232560023916, 0.07155285265445736, -1.6229750035775932, -0.17478311997399607, 0.5042419327111376, -1.6762577139194246, -2.05467909759047, -0.6489322015857052, -0.24346665241641527, 0.5611874388560645, -0.14474964816277022, 0.7709923618228639, -0.10464220830151927]] +permutation = [17, 9, 13, 10, 5, 3, 15, 1, 19, 6, 4, 8, 7, 2, 11, 12, 18, 14, 16] +output = [-0.024474964816277023, -0.17229750035775934, -0.21546790975904703, -0.027478311997399606, 0.22060787451082328, 0.013749592012301337, -0.03434666524164153, -0.02496863754405633, -0.020464220830151928, 0.10429291337685273, -0.07214709833063072, -0.0028447147345542637, 0.0019232325600239165, 0.02518349470290321, 0.04042419327111376, -0.17762577139194247, 0.0670992361822864, -0.07489322015857051, 0.046118743885606446] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.01 +sigma2 = 0.02 +probability = 0.3 +count = 19 +normal_draws = [[-0.9751431151473144, 0.05951862861867, -0.3375008835305649, -0.38026472847149695, 0.514153447037877], [1.7869658562867157, -1.5299606599560638, 0.5970349750390697, -0.03607464664724949, 0.549892304342568, -0.7686991928144217, -0.17369099962605847, 1.435604690363596, -1.3058306149421992, -0.8475541941767581, 0.3558999753652948, 0.013156429822287295, -0.6002949542623006, 0.5074018714632934]] +permutation = [16, 2, 3, 10, 14, 9, 6, 18, 11, 4, 15, 8, 12, 7, 17, 19, 13, 5, 1] +output = [-0.0028820004926941043, 0.0205951862861867, 0.01662499116469435, 0.0009978460868513596, -0.036116612298843984, -0.01072149293294499, 0.02573931712573431, -0.022005899085246014, -0.025373983856288432, 0.01619735271528503, -0.026951083883535162, 0.0019406995007813926, -0.01347381999252117, -0.04059921319912128, -0.009736871403554254, 0.0001480374292658678, 0.018712093807271922, 0.02514153447037877, 0.010248568848526856] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.0 +sigma2 = 0.1 +probability = 0.3 +count = 19 +normal_draws = [[0.3855867749506492, 0.8042818123201656, -0.49223721840978546, 0.01573312548330054, 1.0421883907886536], [-1.1028650523260914, -1.3277097158187234, 1.479961629531749, -1.0629220295967259, -0.3210228529858646, 0.16094299898283682, 0.10417813399080844, 0.20838163861984654, -0.3061254299786608, -0.9655595307595356, 0.17217437087085746, 0.24430048235470994, -2.199359007090062, -1.245320056683847]] +permutation = [11, 1, 16, 10, 2, 3, 9, 14, 5, 13, 18, 7, 6, 17, 12, 19, 8, 15, 4] +output = [0.006094299898283683, 0.02, 0.007217437087085747, -0.042102285298586464, 0.02, 0.02, -0.11629220295967259, -0.04061254299786608, 0.02, 0.010838163861984657, -0.22993590070900624, -0.14277097158187235, -0.12028650523260914, 0.014430048235470996, 0.0004178133990808443, -0.1345320056683847, 0.1379961629531749, -0.10655595307595356, 0.02] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.01 +sigma2 = 0.02 +probability = 1.0 +count = 19 +normal_draws = [[-0.5045679931049948, 0.2805278756738115, -0.17500063098078422, -1.7066705206024544, 1.2583851147561287, -0.7736513476822453, -0.6261931466398599, -0.28797005693449423, 0.47570605370921326, -0.3498941135282855, -1.9227769378464816, -0.2517259224225535, 0.29233060846273456, 1.9588197381017152, -0.5457236569141063, -0.27870454042755644, 0.1459379460610743, 0.635347173792985, 1.594308493120991], []] +permutation = [18, 1, 2, 6, 13, 4, 5, 17, 14, 16, 9, 3, 11, 15, 12, 19, 7, 8, 10] +output = [0.026353471737929852, 0.014954320068950052, 0.022805278756738115, 0.012263486523177547, 0.022923306084627344, 0.0029332947939754578, 0.03258385114756129, 0.021459379460610745, 0.039588197381017154, 0.017212954595724438, 0.024757060537092132, 0.01824999369019216, 0.0007722306215351832, 0.014542763430858937, 0.017482740775774466, 0.035943084931209915, 0.013738068533601402, 0.01712029943065506, 0.016501058864717144] + +[[mixture]] +mu1 = 0.02 +mu2 = -0.01 +sigma1 = 0.0 +sigma2 = 0.1 +probability = 1.0 +count = 19 +normal_draws = [[-0.07872277691988445, -0.012911422322856775, -0.6631915866222049, -0.6413555415507207, 0.015052411504842768, -0.26353388305354714, 0.4655011549725122, -1.1743137785325592, -0.6413824733404067, 0.5058004957193809, -1.0600323636243012, -1.000811057651505, 0.2664015779912432, -0.2947664580680895, -2.3115009128589934, 0.00011861456121733657, -0.465772679311222, 0.4296003556215937, 0.44938474288778124], []] +permutation = [5, 18, 13, 14, 1, 11, 10, 7, 19, 8, 6, 3, 2, 16, 12, 9, 15, 17, 4] +output = [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02] diff --git a/test/fixtures/sample_weight_python_320.toml b/test/fixtures/sample_weight_python_320.toml new file mode 100644 index 0000000..6c150db --- /dev/null +++ b/test/fixtures/sample_weight_python_320.toml @@ -0,0 +1,468 @@ +reference = "RiskLabAI.py 3.2.0" +rng_contract = "Identical supplied uniforms; index outputs converted from zero-based to one-based." +rtol = 1e-10 +atol = 1e-12 + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 2] +ends = [3, 2, 3] +molecule = [1, 2] +prices = [1.0111337237407318, 1.0290753938377895, 1.0665212720076531, 1.0852988886246595, 1.0425886032131966, 1.0234699960543523, 1.0433081140808045, 1.0326257217355443, 1.0277114478790221, 1.025708530142366, 1.0495604820205164, 1.0210878843151852, 0.9936589426609664] +expected_index = [1, 2, 3] +expected_counts = [2, 3, 2] +expected_weights = [1.0016361531582156, 0.9983638468417847] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 2] +ends = [1, 1, 2] +molecule = [1] +prices = [1.0347249755096417, 0.9914328825321127, 0.9377399887250544, 0.9235133034155402, 0.9454266203609223, 1.0306592416876643, 1.0002245857626104, 1.0454641666291098, 1.0537971847740184, 1.0523292750412052, 1.0922530786014508, 1.1086484069895777, 1.1220895004197051] +expected_index = [1] +expected_counts = [2] +expected_weights = [0.9999999999999999] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 2, 4, 8] +ends = [10, 5, 4, 12] +molecule = [2, 4] +prices = [0.9602380632619427, 0.9663376323374246, 0.9774420868908175, 0.9790244173015994, 0.975527712324244, 0.9838500831710725, 1.023905192090494, 1.0384481561575207, 0.9950432251412491, 0.9582079389546502, 0.954704937318843, 0.9616849770057905, 0.925301034902463] +expected_index = [2, 3, 4, 5] +expected_counts = [2, 2, 3, 2] +expected_weights = [1.8186667237415906, 0.1813332762584095] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 2] +ends = [3, 2, 3] +molecule = [0, 1, 2] +prices = [0.967463324896168, 0.9803960945232753, 0.9849740172631837, 1.0166769298659057, 1.0400624092913882, 1.0531138926646706, 0.9858375694266851, 0.9799026345460475, 0.9567551575506013, 0.9917802050771868, 0.9959455647926151, 0.9570889210311315, 0.9506971981417001] +expected_index = [0, 1, 2, 3] +expected_counts = [1, 2, 3, 2] +expected_weights = [1.4530546041060377, 0.49533914140690644, 1.0516062544870555] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [2, 3, 4, 6, 9, 10, 11] +ends = [4, 3, 9, 9, 9, 10, 11] +molecule = [2, 4, 10] +prices = [1.0604568186532162, 1.102416964689345, 1.129315573845659, 1.0938827867195087, 1.0807652780306662, 1.0432971059699843, 1.127488972742161, 1.1029263025340301, 1.047688131082937, 1.0300533479485199, 1.0309183084782794, 1.0172106613800282, 1.0196703830091869] +expected_index = [2, 3, 4, 5, 6, 7, 8, 9, 10] +expected_counts = [1, 2, 2, 1, 2, 2, 2, 3, 1] +expected_weights = [0.8160285180561495, 2.169106426100378, 0.014865055843472277] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [2, 4, 5, 6, 7, 10, 11] +ends = [6, 6, 7, 9, 8, 12, 12] +molecule = [4, 7, 10] +prices = [0.9889890618917654, 1.0047176805172573, 1.0006406950214466, 1.0290802256126663, 0.9900255556185291, 0.9708643949696985, 0.9553205913771419, 0.994791969164477, 1.0007500454604337, 0.9862625689715909, 0.9762750423794543, 1.0253692120429754, 1.008483717863231] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 4, 3, 2, 1, 1, 2, 2] +expected_weights = [1.0033045041738735, 0.5531343344037568, 1.4435611614223698] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [1, 3, 4, 5, 6, 9, 11] +ends = [6, 4, 4, 9, 6, 11, 12] +molecule = [1, 9, 11] +prices = [0.9469829910453555, 1.0002903307347162, 0.9977062003714832, 0.9799803969807698, 0.9786844777776599, 0.9550490094370411, 0.9900209335089225, 0.9579007794662823, 0.9570609011145664, 0.9643156587266882, 0.9579685308664033, 0.9167736844225595, 0.9431852463405067] +expected_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 1, 2, 3, 2, 3, 1, 1, 2, 1, 2, 1] +expected_weights = [1.5710747751636402, 0.5588303115154832, 0.8700949133208761] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 4, 5, 8, 10, 11] +ends = [1, 4, 5, 6, 10, 12, 12] +molecule = [1, 5, 8] +prices = [1.0071761989207522, 1.0143048653321483, 1.0708168823198794, 1.0506617011527273, 1.0743508322875917, 1.1112919957798608, 1.123034987558023, 1.102677107982798, 1.1476487669058943, 1.2022128842803859, 1.193483025947348, 1.2427355235943405, 1.2715035999081694] +expected_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +expected_counts = [2, 1, 1, 2, 2, 1, 0, 1, 1, 2] +expected_weights = [1.2839049430487273, 0.4004579695599652, 1.3156370873913077] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 2, 3, 4, 6, 7, 11] +ends = [0, 7, 4, 7, 7, 12, 12] +molecule = [3, 4, 7] +prices = [1.0209976840834758, 1.0814493557849956, 1.1040285597453339, 1.1285750634680414, 1.161571014699614, 1.2026470582232074, 1.214372169836163, 1.177733438944822, 1.185083108094556, 1.234692398865225, 1.321074185762477, 1.3342958524757689, 1.3278178098260238] +expected_index = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 2, 3, 4, 1, 1, 1, 2, 2] +expected_weights = [0.32803859152158227, 0.6030971948787731, 2.0688642135996447] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [1, 3, 4, 5, 8, 9, 10] +ends = [1, 8, 9, 5, 12, 12, 12] +molecule = [4, 5, 8] +prices = [0.9909741732135608, 1.0128801508767573, 0.9999143856199415, 1.0255371911972915, 1.0133265250978214, 1.024787610099936, 1.0063036930362808, 1.0573315145756124, 1.0309977756453481, 0.9972222236803414, 0.957498828466214, 0.9987096050214478, 0.9874707224561245] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 2, 2, 3, 3, 3, 3, 3] +expected_weights = [1.6077236881161325, 0.09554855313064733, 1.2967277587532202] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 3, 4, 5, 6, 8, 9] +ends = [1, 8, 7, 5, 9, 12, 12] +molecule = [4, 6, 8] +prices = [1.048242208844041, 0.9618422795441683, 0.9534541419628788, 0.9574285656141692, 0.958256549995899, 0.9628219546290426, 0.9475844060295573, 0.971637256422133, 0.8759602889105514, 0.8227064973144154, 0.8572300594422194, 0.851787666516315, 0.8705237897377218] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 3, 3, 3, 3, 2, 2, 2] +expected_weights = [0.26911519810206114, 1.1858226323102523, 1.5450621695876867] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 4, 5, 6, 9, 11] +ends = [0, 1, 8, 10, 7, 11, 12] +molecule = [0, 1, 5] +prices = [1.0402839113561526, 1.032197011863358, 0.9841221840110901, 0.9498874497229658, 0.9819390127263484, 0.9843653265581022, 0.9746809973339613, 0.998339464465252, 0.964193869917291, 0.9779708869695742, 0.9249518993868346, 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0.9962949676830055, 1.0299981030784298, 1.0011903323335898, 0.9885176811460828, 0.9992539003488821, 0.9845440133110388, 0.9915358862194821, 0.957141615870737, 0.9741865775969393, 1.0646998693196363, 1.0349236384794915] +expected_index = [7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 4, 5, 3, 2] +expected_weights = [0.5285365648829239, 1.3411267680993686, 1.1303366670177073] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [1, 4, 5, 6, 7, 10, 11] +ends = [2, 9, 6, 9, 7, 10, 12] +molecule = [5, 6, 10] +prices = [0.9998067751418631, 1.031045426532336, 1.0315425183701925, 1.084751125411136, 1.04653963961379, 1.0000612701614442, 1.015081797794592, 1.02910142204834, 1.0361090026059128, 1.069767471388101, 1.0718968979534593, 1.0984048485982518, 1.0552786427733027] +expected_index = [5, 6, 7, 8, 9, 10] +expected_counts = [2, 3, 3, 2, 2, 1] +expected_weights = [1.4174475814485266, 1.480732817892994, 0.10181960065847967] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 2, 3, 4, 6, 8, 9] +ends = [1, 3, 6, 9, 9, 11, 10] +molecule = [3, 4, 9] +prices = [1.0340281931590787, 1.0771821952774059, 1.0361283595356572, 1.0248531108143215, 1.008696883546491, 0.9875313180484545, 1.00869539087092, 1.0389185541708612, 1.0356257157960198, 1.0329650547801839, 0.9877552301405853, 0.9302279450579057, 0.9074136323189563] +expected_index = [3, 4, 5, 6, 7, 8, 9, 10] +expected_counts = [2, 2, 2, 3, 2, 3, 4, 2] +expected_weights = [0.9695975800409633, 1.3124219444053757, 0.7179804755536608] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 2, 3, 4, 5, 9, 11] +ends = [2, 5, 6, 4, 6, 12, 12] +molecule = [2, 4, 5] +prices = [1.017630440422018, 0.9841829797566433, 1.0113419389465739, 1.0283305581949727, 1.0210159842500293, 1.0248233383774377, 1.0482238052724526, 1.0599584752962232, 1.0603945758459625, 1.0109961342651437, 0.9770578115499582, 0.9910263305183252, 0.9797951828799141] +expected_index = [2, 3, 4, 5, 6] +expected_counts = [2, 2, 3, 3, 2] +expected_weights = [1.8948081888079082, 0.1763943504855124, 0.928797460706579] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [1, 2, 3, 5, 7, 8, 9] +ends = [5, 7, 3, 6, 9, 9, 11] +molecule = [3, 5, 9] +prices = [0.975038260915395, 1.0177424913510058, 1.0300406612292674, 1.0125652654672144, 0.993333297194659, 0.9931346362933546, 1.0251563136331128, 1.0572315831766026, 1.0967640464856951, 1.0748475259057833, 1.1018525514637871, 1.1109208099962233, 1.1041422080053724] +expected_index = [3, 4, 5, 6, 7, 8, 9, 10, 11] +expected_counts = [3, 2, 3, 2, 2, 2, 3, 1, 1] +expected_weights = [0.27879310470349983, 0.7788216829525911, 1.9423852123439087] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 4, 5, 7, 9, 10] +ends = [2, 6, 7, 8, 12, 12, 12] +molecule = [4, 9, 10] +prices = [1.031231326924991, 1.0109232923934952, 1.0092274457186392, 1.0408930323452181, 0.9780671685479811, 0.9646559455169137, 0.9548484720438817, 0.9495695917488853, 0.9917517886606001, 0.9961036999558771, 1.0290249498029758, 1.068089021341571, 1.0710483777191624] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 3, 3, 3, 2, 2, 3, 3, 3] +expected_weights = [1.3432400952398362, 0.864255623957051, 0.7925042808031132] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 4, 5, 6, 9, 10] +ends = [3, 1, 6, 6, 8, 10, 12] +molecule = [0, 6, 10] +prices = [1.029845568084474, 1.017544721035797, 0.9941462620094942, 1.0241512284466858, 1.1002351113767423, 1.1295042661465289, 1.0624255605100474, 1.0738474155427418, 1.076909615948332, 1.0257847975612335, 1.0152180114326406, 1.0246821760100981, 0.9742926367443483] +expected_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 2, 1, 1, 1, 2, 3, 1, 1, 1, 2, 1, 1] +expected_weights = [1.1215301953930101, 0.6452618229622368, 1.2332079816447532] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [4, 5, 7, 8, 9, 10, 11] +ends = [5, 9, 12, 8, 9, 12, 12] +molecule = [4, 5, 10] +prices = [0.943826097259108, 0.9280966955879795, 0.9315794460784799, 0.9707371678084489, 0.9610831047112006, 0.9755727902201432, 0.9490891749156837, 0.9466099886308866, 0.9767460377178634, 0.9595630234065499, 0.9409863819144224, 0.9857691619129691, 0.9773890305649713] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 2, 1, 2, 3, 3, 2, 3, 3] +expected_weights = [0.5335365720033401, 1.60805998686375, 0.8584034411329099] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [4, 5, 6, 8, 9, 10, 11] +ends = [8, 7, 7, 12, 12, 12, 11] +molecule = [4, 9, 10] +prices = [1.0403711569970768, 1.0498489383588328, 1.0541226335882397, 1.0244281142648288, 1.049861282893304, 1.0460691445087291, 1.0235893258350421, 1.0784255557925264, 1.0598982117353248, 1.0177625712018024, 0.9794619878368973, 0.9947839820945575, 0.970856921989406] +expected_index = [4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 2, 3, 3, 2, 2, 3, 4, 3] +expected_weights = [1.3816845309969417, 1.04412998311019, 0.5741854858928682] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 3, 5, 6, 9, 10] +ends = [1, 3, 7, 10, 9, 10, 12] +molecule = [0, 1, 6] +prices = [1.000754584388367, 0.9844018663236375, 1.05501882016625, 1.0791741401849229, 1.0894352163454715, 1.0831458884761285, 1.1274507776947296, 1.1152319880682255, 1.12412278568329, 1.1426948097785579, 1.177536906726954, 1.1111296592478415, 1.1285615525293027] +expected_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] +expected_counts = [1, 2, 1, 2, 1, 2, 3, 3, 2, 3] +expected_weights = [0.2001031464006427, 2.1579345965188463, 0.6419622570805107] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 2, 5, 6, 8, 9, 10] +ends = [1, 5, 9, 10, 11, 12, 12] +molecule = [0, 8, 9] +prices = [1.0249264488378649, 1.0341115131311218, 1.0386987435552175, 1.065376286952909, 1.0755262299340862, 1.0670153640458424, 1.1329455837585802, 1.1465676334636268, 1.1377017383483983, 1.1227682228059948, 1.0709621423255455, 1.0563302983664804, 1.0514894330951883] +expected_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 3, 2] +expected_weights = [0.5030755556633345, 1.2566640222741188, 1.240260422062547] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [0, 1, 4, 6, 8, 9, 11] +ends = [1, 6, 5, 10, 12, 12, 11] +molecule = [1, 6, 9] +prices = [0.9903715315438845, 1.0529215089961423, 1.0339321301910924, 1.033964498778849, 1.0356870225591182, 1.1180070743799195, 1.1672663758964166, 1.1220219500979236, 1.0683372829157152, 1.143424986570873, 1.1394154528032052, 1.0854807464694591, 1.0672209208684085] +expected_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [2, 1, 1, 2, 2, 2, 1, 2, 3, 3, 3, 2] +expected_weights = [1.2285035016512145, 1.2277485552576146, 0.5437479430911709] + +[[concurrency]] +close_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +starts = [1, 2, 3, 4, 5, 10, 11] +ends = [1, 6, 8, 9, 7, 11, 12] +molecule = [1, 5, 11] +prices = [0.9441489556610774, 0.9365768950407279, 0.9252723272172566, 0.903426241278458, 0.953841039613575, 0.9386322089598013, 0.9365266926820781, 0.969200345302653, 0.9775090766289996, 0.9708204194966886, 0.9380854617352278, 0.9493870073198188, 0.984557965150128] +expected_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] +expected_counts = [1, 1, 2, 3, 4, 4, 3, 2, 1, 1, 2, 1] +expected_weights = [0.36366079041446764, 0.7230863852670448, 1.9132528243184876] + +[[bootstrap]] +indicator_rows = [[1, 0, 0], [1, 1, 0], [1, 1, 1], [0, 0, 1]] +uniforms = [0.8166969171904775, 0.6069632519451551, 0.7036863802110003, 0.2784546243910929, 0.5227914920203833, 0.541415795900445, 0.4747709939389868, 0.17647061421751697, 0.19235731017041824, 0.3640855127042725, 0.23761719401699188, 0.7654445236748262, 0.7662693746257473, 0.1833284770424527, 0.44204797778809846, 0.6084109760208424, 0.5300058658808205, 0.8797204634027556, 0.30046021660276523, 0.46794478299944664, 0.8124929375168645, 0.9480757046851606, 0.605575206518438, 0.5378218908211168] +expected_one_based = [3, 2, 2, 1, 2, 2, 2, 1, 1, 2, 1, 3, 3, 1, 2, 3, 2, 3, 1, 2, 3, 3, 2, 2] +expected_probabilities = [[0.3333333333333333, 0.3333333333333333, 0.3333333333333333], [0.39999999999999997, 0.36, 0.24], [0.42307692307692313, 0.2884615384615385, 0.2884615384615385], [0.4418604651162791, 0.24418604651162787, 0.313953488372093], [0.35514018691588783, 0.25233644859813087, 0.3925233644859813], [0.35862068965517235, 0.22758620689655173, 0.4137931034482758], [0.36170212765957444, 0.2074468085106383, 0.43085106382978716], [0.3644067796610169, 0.19067796610169493, 0.44491525423728817], [0.30943396226415093, 0.19245283018867923, 0.49811320754716987], [0.27483443708609273, 0.18874172185430466, 0.5364238410596026], [0.27323943661971833, 0.17746478873239438, 0.5492957746478874], [0.24774322968906717, 0.17301905717151456, 0.5792377131394183], [0.29784824462061155, 0.2038505096262741, 0.4983012457531144], [0.33303491495076093, 0.22381378692927487, 0.4431512981199642], [0.31147540983606553, 0.22131147540983603, 0.4672131147540983], [0.31107491856677527, 0.21254071661237786, 0.4763843648208469], [0.3381683251813121, 0.22769438353853938, 0.4341372912801484], [0.3385178408051235, 0.21957913998170175, 0.44190301921317476], [0.3608087091757387, 0.23094867807153963, 0.40824261275272156], [0.341692789968652, 0.2304075235109718, 0.4278996865203762], [0.34205231388329976, 0.22334004024144868, 0.43460764587525147], [0.36077057793345013, 0.2329246935201401, 0.4063047285464098], [0.3767264178665883, 0.24066999706141637, 0.38260358507199527], [0.37782005979885835, 0.23403098668116334, 0.3881489535199782]] + +[[bootstrap]] +indicator_rows = [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]] +uniforms = [0.04697603317313648, 0.18312705316428646, 0.0008404017489450633, 0.900358158527464, 0.797728189195639, 0.5988752356017826, 0.17972344690471742, 0.711112978591776, 0.24867021869748163, 0.5736399498617163, 0.1357977811904033, 0.7826871594229275, 0.2054361626034923, 0.2530204208517679, 0.31581074124527286, 0.28796268718987583, 0.6595945473810448, 0.2526278735041867, 0.2720147137529595, 0.6032344944725334, 0.630910609267346, 0.7614829705702162, 0.9335317640788212, 0.20829249769008373] +expected_one_based = [1, 1, 1, 4, 4, 3, 1, 3, 1, 3, 1, 4, 1, 2, 2, 2, 3, 2, 2, 3, 3, 4, 4, 1] +expected_probabilities = [[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]] + +[[bootstrap]] +indicator_rows = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]] +uniforms = [0.5244396845345107, 0.1334446642045899, 0.5255876564154038, 0.16527640619485973, 0.8641951459289559, 0.7166382355974138, 0.9290550341546074, 0.12959565902730386, 0.95632930561732, 0.9636522770209468, 0.7651924539177936, 0.455383047533068, 0.8417200957621583, 0.43412489317516434, 0.1215705008622423, 0.19894007259965107, 0.7262552030317667, 0.9612147152389652, 0.880746553938085, 0.39815379271645024, 0.530661298158059, 0.15025106976444502, 0.2604515896947367, 0.6534572406512142] +expected_one_based = [3, 1, 3, 1, 4, 3, 4, 1, 4, 4, 3, 2, 4, 2, 1, 1, 3, 4, 4, 2, 2, 1, 2, 3] +expected_probabilities = [[0.25, 0.25, 0.25, 0.25], [0.2857142857142857, 0.2857142857142857, 0.14285714285714285, 0.2857142857142857], [0.16666666666666666, 0.3333333333333333, 0.16666666666666666, 0.3333333333333333], [0.17647058823529413, 0.35294117647058826, 0.11764705882352942, 0.35294117647058826], [0.125, 0.375, 0.125, 0.375], [0.15384615384615385, 0.46153846153846156, 0.15384615384615385, 0.23076923076923078], [0.16, 0.4800000000000001, 0.12000000000000002, 0.24000000000000005], [0.17391304347826086, 0.5217391304347826, 0.13043478260869565, 0.17391304347826086], [0.13636363636363638, 0.5454545454545455, 0.13636363636363638, 0.18181818181818182], [0.14285714285714285, 0.5714285714285714, 0.14285714285714285, 0.14285714285714285], [0.14705882352941177, 0.5882352941176471, 0.14705882352941177, 0.11764705882352942], [0.15151515151515152, 0.6060606060606061, 0.12121212121212123, 0.12121212121212123], [0.2173913043478261, 0.4347826086956522, 0.1739130434782609, 0.1739130434782609], [0.22388059701492538, 0.44776119402985076, 0.17910447761194032, 0.14925373134328357], [0.26315789473684215, 0.3508771929824562, 0.21052631578947373, 0.1754385964912281], [0.22222222222222224, 0.37037037037037035, 0.22222222222222224, 0.18518518518518517], [0.19230769230769232, 0.38461538461538464, 0.2307692307692308, 0.19230769230769232], [0.2, 0.4, 0.2, 0.2], [0.20588235294117646, 0.4117647058823529, 0.20588235294117646, 0.1764705882352941], [0.21052631578947367, 0.42105263157894735, 0.21052631578947367, 0.15789473684210528], [0.23529411764705885, 0.35294117647058826, 0.23529411764705885, 0.17647058823529413], [0.2531645569620253, 0.3037974683544304, 0.2531645569620253, 0.189873417721519], [0.225140712945591, 0.3151969981238274, 0.2626641651031895, 0.19699812382739212], [0.2376237623762376, 0.27722772277227725, 0.27722772277227725, 0.20792079207920794]] + +[[bootstrap]] +indicator_rows = [[1], [0], [1]] +uniforms = [0.33680661795385014, 0.09289563415833602, 0.7604490331791322, 0.2968972575635779, 0.3148358062949188, 0.6368594549869107, 0.7939395951982724, 0.43904825444389517, 0.9644325742413203, 0.4753829997613569, 0.7873333425338774, 0.16822584870682555, 0.1938312172327391, 0.0544724895899219, 0.2778766296476196, 0.3339729053899382, 0.599640134640604, 0.620644207407638, 0.2822754766619442, 0.8676657041417389, 0.11468078902298495, 0.8032570311712494, 0.7056066951750721, 0.14638062262231066] +expected_one_based = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +expected_probabilities = [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]] diff --git a/test/fixtures/search_fdr_python_320.toml b/test/fixtures/search_fdr_python_320.toml new file mode 100644 index 0000000..9340033 --- /dev/null +++ b/test/fixtures/search_fdr_python_320.toml @@ -0,0 +1,4615 @@ +[[single]] +alpha = 0.0 +beta = 0.0 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.0 +probability = 0.01 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.0 +probability = 0.5 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.01 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.01 +probability = 0.01 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.01 +probability = 0.5 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.5 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.5 +probability = 0.01 +expected = 0.0 + +[[single]] +alpha = 0.0 +beta = 0.5 +probability = 0.5 +expected = 0.0 + +[[single]] +alpha = 0.01 +beta = 0.0 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.01 +beta = 0.0 +probability = 0.01 +expected = 0.00010099989900010116 + +[[single]] +alpha = 0.01 +beta = 0.0 +probability = 0.5 +expected = 0.009900990099009908 + +[[single]] +alpha = 0.01 +beta = 0.0 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.01 +beta = 0.01 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.01 +beta = 0.01 +probability = 0.01 +expected = 0.00010201999591920019 + +[[single]] +alpha = 0.01 +beta = 0.01 +probability = 0.5 +expected = 0.010000000000000004 + +[[single]] +alpha = 0.01 +beta = 0.01 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.01 +beta = 0.5 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.01 +beta = 0.5 +probability = 0.01 +expected = 0.00020197939810139378 + +[[single]] +alpha = 0.01 +beta = 0.5 +probability = 0.5 +expected = 0.019607843137254912 + +[[single]] +alpha = 0.01 +beta = 0.5 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.01 +beta = 1.0 +probability = 0.01 +expected = 1.0 + +[[single]] +alpha = 0.01 +beta = 1.0 +probability = 0.5 +expected = 1.0 + +[[single]] +alpha = 0.01 +beta = 1.0 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 0.0 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.5 +beta = 0.0 +probability = 0.01 +expected = 0.0050251256281407045 + +[[single]] +alpha = 0.5 +beta = 0.0 +probability = 0.5 +expected = 0.33333333333333337 + +[[single]] +alpha = 0.5 +beta = 0.0 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 0.01 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.5 +beta = 0.01 +probability = 0.01 +expected = 0.00507562683991473 + +[[single]] +alpha = 0.5 +beta = 0.01 +probability = 0.5 +expected = 0.33557046979865773 + +[[single]] +alpha = 0.5 +beta = 0.01 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 0.5 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 0.5 +beta = 0.5 +probability = 0.01 +expected = 0.010000000000000004 + +[[single]] +alpha = 0.5 +beta = 0.5 +probability = 0.5 +expected = 0.5 + +[[single]] +alpha = 0.5 +beta = 0.5 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 1.0 +probability = 0.01 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 1.0 +probability = 0.5 +expected = 1.0 + +[[single]] +alpha = 0.5 +beta = 1.0 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 0.0 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 1.0 +beta = 0.0 +probability = 0.01 +expected = 0.010000000000000004 + +[[single]] +alpha = 1.0 +beta = 0.0 +probability = 0.5 +expected = 0.5 + +[[single]] +alpha = 1.0 +beta = 0.0 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 0.01 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 1.0 +beta = 0.01 +probability = 0.01 +expected = 0.010099989900010101 + +[[single]] +alpha = 1.0 +beta = 0.01 +probability = 0.5 +expected = 0.5025125628140703 + +[[single]] +alpha = 1.0 +beta = 0.01 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 0.5 +probability = 0.0 +expected = 0.0 + +[[single]] +alpha = 1.0 +beta = 0.5 +probability = 0.01 +expected = 0.01980198019801981 + +[[single]] +alpha = 1.0 +beta = 0.5 +probability = 0.5 +expected = 0.6666666666666666 + +[[single]] +alpha = 1.0 +beta = 0.5 +probability = 1.0 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 1.0 +probability = 0.01 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 1.0 +probability = 0.5 +expected = 1.0 + +[[single]] +alpha = 1.0 +beta = 1.0 +probability = 1.0 +expected = 1.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.01 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.01 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.01 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.5 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.5 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.5 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.0 +probability = 0.5 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.01 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.01 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.01 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.5 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.5 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.5 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.01 +probability = 0.5 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.01 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.01 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.01 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.5 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.5 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.5 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.0 +beta = 0.5 +probability = 0.5 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.01 +trials = 1 +expected = 0.00010099989900010116 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.01 +trials = 2 +expected = 1.0000990098019709e-06 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.01 +trials = 17 +expected = 1.000000000000017e-36 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.5 +trials = 1 +expected = 0.009900990099009908 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.5 +trials = 2 +expected = 0.0033222591362126255 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.5 +trials = 17 +expected = 7.629452157269584e-08 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 0.5 +trials = 1000 +expected = 9.332636185033007e-304 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.01 +trials = 1 +expected = 0.00010201999591920019 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.01 +trials = 2 +expected = 1.0102010097969297e-06 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.01 +trials = 17 +expected = 1.0101010101010324e-36 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.5 +trials = 1 +expected = 0.010000000000000004 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.5 +trials = 2 +expected = 0.0033557046979865767 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.5 +trials = 17 +expected = 7.706517324636286e-08 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 0.5 +trials = 1000 +expected = 9.426905237407257e-304 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.01 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.01 +trials = 1 +expected = 0.00020197939810139378 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.01 +trials = 2 +expected = 2.0001960192098816e-06 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.01 +trials = 17 +expected = 2.000000000000038e-36 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.5 +trials = 1 +expected = 0.019607843137254912 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.5 +trials = 2 +expected = 0.006622516556291391 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.5 +trials = 17 +expected = 1.525890315036842e-07 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 0.5 +trials = 1000 +expected = 1.8665272370064987e-303 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 0.5 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.01 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.01 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.01 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.01 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.5 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.5 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.5 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 0.5 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.01 +beta = 1.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.01 +trials = 1 +expected = 0.0050251256281407045 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.01 +trials = 2 +expected = 5.0002500125006296e-05 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.01 +trials = 17 +expected = 5.000000000000051e-35 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.5 +trials = 1 +expected = 0.33333333333333337 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.5 +trials = 2 +expected = 0.14285714285714288 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.5 +trials = 17 +expected = 3.81471181759574e-06 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 0.5 +trials = 1000 +expected = 4.66631809251667e-302 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.01 +trials = 1 +expected = 0.00507562683991473 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.01 +trials = 2 +expected = 5.05075503737054e-05 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.01 +trials = 17 +expected = 5.050505050505128e-35 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.5 +trials = 1 +expected = 0.33557046979865773 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.5 +trials = 2 +expected = 0.14409221902017294 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.5 +trials = 17 +expected = 3.853244111722812e-06 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 0.5 +trials = 1000 +expected = 4.713452618703798e-302 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.01 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.01 +trials = 1 +expected = 0.010000000000000004 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.01 +trials = 2 +expected = 0.00010000000000000009 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.01 +trials = 17 +expected = 1.0000000000000121e-34 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.5 +trials = 1 +expected = 0.5 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.5 +trials = 2 +expected = 0.25 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.5 +trials = 17 +expected = 7.629394531250002e-06 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 0.5 +trials = 1000 +expected = 9.332636185032827e-302 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 0.5 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.01 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.01 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.01 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.01 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.5 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.5 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.5 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 0.5 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 0.5 +beta = 1.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.01 +trials = 1 +expected = 0.010000000000000004 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.01 +trials = 2 +expected = 0.00010000000000000009 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.01 +trials = 17 +expected = 1.0000000000000121e-34 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.5 +trials = 1 +expected = 0.5 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.5 +trials = 2 +expected = 0.25 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.5 +trials = 17 +expected = 7.629394531250002e-06 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 0.5 +trials = 1000 +expected = 9.332636185032827e-302 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.01 +trials = 1 +expected = 0.010099989900010101 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.01 +trials = 2 +expected = 0.00010100999897979915 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.01 +trials = 17 +expected = 1.0101010101010275e-34 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.5 +trials = 1 +expected = 0.5025125628140703 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.5 +trials = 2 +expected = 0.2518891687657431 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.5 +trials = 17 +expected = 7.706458528579676e-06 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 0.5 +trials = 1000 +expected = 9.426905237407079e-302 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.01 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.0 +trials = 2 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.0 +trials = 17 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.01 +trials = 1 +expected = 0.01980198019801981 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.01 +trials = 2 +expected = 0.00019998000199980022 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.01 +trials = 17 +expected = 2.000000000000028e-34 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.01 +trials = 1000 +expected = 0.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.5 +trials = 1 +expected = 0.6666666666666666 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.5 +trials = 2 +expected = 0.39999999999999997 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.5 +trials = 17 +expected = 1.5258672648066353e-05 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 0.5 +trials = 1000 +expected = 1.866527237006463e-301 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 0.5 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.01 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.01 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.01 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.01 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.5 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.5 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.5 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 0.5 +trials = 1000 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 1.0 +trials = 2 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 1.0 +trials = 17 +expected = 1.0 + +[[family]] +alpha = 1.0 +beta = 1.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.19999999999999998} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.05523809523809523} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.0, "family_type_ii_error" = 4.019988717840501e-13} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.3, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.19999999999999998} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.1138461538461538} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.0, "family_type_ii_error" = 7.393477636002704e-08} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.9, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.20000000000000032} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.81, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.19157894736842132} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.07640584112771333} + +[[errors]] +alpha = 0.0 +beta = 0.2 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.0, "family_type_ii_error" = 6.13518051448395e-37} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.6552380952380954} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.014780882941434611} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 3.0550539125987036e-92} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.3, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.7999999999999999} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.7138461538461536} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.048974388785174926} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 3.1510206083891404e-66} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.9, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.7999999999999997} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.81, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.7915789473684207} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.0, "family_type_ii_error" = 0.6216038314760649} + +[[errors]] +alpha = 0.0 +beta = 0.8 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.6829673572159275e-09} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.0 +beta = 1.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.2} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.055047619047619054} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 3.855763208579199e-13} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.3, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.19999999999999998} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.11292307692307686} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 6.448173349961299e-08} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.9, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.20000000000000015} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.81, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.18968421052631596} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.06326608162201589} + +[[errors]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.2, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 3.29895941628808e-41} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.6544761904761904} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.014599469051146534} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 1.647604955041141e-92} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.3, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.7101538461538461} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.04566846750074507} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 9.568016667753333e-68} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.9, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.7999999999999997} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.81, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.7839999999999998} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.5187461188610349} + +[[errors]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 0.8, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 1.6566467315699085e-13} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.9999999999999999} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.999047619047619} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.9900473578023274} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.6064548228400276} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 0.9999999999999998} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.9953846153846151} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.9416796087099288} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.04956308282314992} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.010000000000000009, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.01990000000000002, "family_type_ii_error" = 0.9905263157894734} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.18209306240276926, "family_type_ii_error" = 0.8368987998610151} + +[[errors]] +alpha = 0.01 +beta = 1.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.01, "trial_type_ii_error" = 1.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 0.9999568287525893, "family_type_ii_error" = 0.00011848257529216657} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.2} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.047619047619047616} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 7.054294986864034e-14} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.3, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.19999999999999987} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.07692307692307686} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 1.2676378520362053e-10} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.9, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.20000000000000032} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.81, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.1157894736842107} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 5.4124094930650975e-06} + +[[errors]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.2, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.5417963251463286e-252} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.6247619047619049} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.00896482519768437} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 4.237320268597899e-103} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.3, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.5661538461538461} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.0024245681434085543} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.7049817353925232e-131} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.9, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.81, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.48842105263157887} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 7.513154993163264e-05} + +[[errors]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 0.8, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 2.4642768560314816e-208} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 0.4, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.9619047619047618} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.6676079717550942} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.6829673572159275e-09} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.3, "family_type_i_error" = 0.4, "family_type_ii_error" = 0.9999999999999998} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.8153846153846152} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.07756279364089252} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 3.038590321326928e-56} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.9, "family_type_i_error" = 0.4, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.81, "family_type_i_error" = 0.64, "family_type_ii_error" = 0.6210526315789475} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 0.9999634384155993, "family_type_ii_error" = 0.00014625950588852937} + +[[errors]] +alpha = 0.4 +beta = 1.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 0.4, "trial_type_ii_error" = 1.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.513470582304313e-194} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.3, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.9, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.81, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.2} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.03619047619047619} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 1.0, "family_type_ii_error" = 3.758997345754602e-15} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.3, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.19999999999999998} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.021538461538461527} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 1.0, "family_type_ii_error" = 8.366825542820168e-18} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.9, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.19999999999999998} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.81, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.002105263157894737} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.1937023366691845e-34} + +[[errors]] +alpha = 1.0 +beta = 0.2 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.2, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.579047619047619} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.004133061290436298} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 6.51016927069412e-120} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.3, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.7999999999999999} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.34461538461538455} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 1.0, "family_type_ii_error" = 9.19942197190405e-06} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.541796325145978e-252} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.9, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.8} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.81, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.03368421052631578} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.312489599271145e-22} + +[[errors]] +alpha = 1.0 +beta = 0.8 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 0.8, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.05 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.05000000000000001, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.05 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0025000000000000005, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.9047619047619047} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.05 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 9.536743164062544e-27, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.3584859224085419} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.05 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.05, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 5.291822747744794e-23} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.3 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.3, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.3 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.08999999999999998, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.5384615384615383} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.3 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 3.486784400999994e-11, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.000797922663003941} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.3 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.3, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.0, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.2532566399656417e-155} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.9 +trials = 1 +expected = {"n_trials" = 1, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.9, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.0} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.9 +trials = 2 +expected = {"n_trials" = 2, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.81, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.05263157894736841} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.9 +trials = 20 +expected = {"n_trials" = 20, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 0.12157665459056931, "family_type_i_error" = 1.0, "family_type_ii_error" = 1.1384032599155186e-20} + +[[errors]] +alpha = 1.0 +beta = 1.0 +probability = 0.9 +trials = 1000 +expected = {"n_trials" = 1000, "trial_null_probability" = 0.9, "trial_type_i_error" = 1.0, "trial_type_ii_error" = 1.0, "family_null_probability" = 1.7478712517226825e-46, "family_type_i_error" = 1.0, "family_type_ii_error" = 0.0} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.2 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.0006574621959237345, "family_level_false_discovery_rate" = 0.0006574621959237345, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = -6.938893903907228e-18, "equation_13_log_gap" = 6.938893903907228e-18, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.055047619047619054 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.0006574621959237345, "family_level_false_discovery_rate" = 5.277732028593118e-05, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 3.211045245753012, "equation_13_log_gap" = -2.5229106070166107, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.05 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 3.855763208579199e-13 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.0006574621959237345, "family_level_false_discovery_rate" = 1.736574768093494e-27, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 57.1933500432272, "equation_13_log_gap" = -54.291417247946704, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.19999999999999998 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.005328596802841924, "family_level_false_discovery_rate" = 0.005328596802841924, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 5.551115123125783e-17, "equation_13_log_gap" = -5.551115123125783e-17, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.11292307692307686 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.005328596802841924, "family_level_false_discovery_rate" = 0.0022137594233101105, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 1.569657042422256, "equation_13_log_gap" = -0.8815224036858547, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.3 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 6.448173349961299e-08 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.005328596802841924, "family_level_false_discovery_rate" = 6.349192904750963e-12, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 23.455301712929124, "equation_13_log_gap" = -20.553368917648626, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.20000000000000015 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.10112359550561806, "family_level_false_discovery_rate" = 0.1011235955056181, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 0.0, "equation_13_log_gap" = 0.0, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.18968421052631596 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.10112359550561806, "family_level_false_discovery_rate" = 0.09477360520699216, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 0.7600267093994899, "equation_13_log_gap" = -0.07189207066308856, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 0.06326608162201589 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.10112359550561806, "family_level_false_discovery_rate" = 0.026199527223105708, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 4.332595801503621, "equation_13_log_gap" = -1.4306630062231247, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.2 +probability = 0.9 +trials = 1000 +family_alpha = 0.9999568287525893 +family_beta = 3.29895941628808e-41 +expected = {"n_trials" = 1000, "single_trial_false_discovery_rate" = 0.10112359550561806, "family_level_false_discovery_rate" = 1.747795793940429e-46, "log_observed_type_i_inflation" = 4.605127013808775, "log_required_type_i_inflation" = 107.78088378647672, "equation_13_log_gap" = -103.17575677266795, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.8 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.0026246719160105004, "family_level_false_discovery_rate" = 0.0026246719160105004, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = -6.938893903907228e-18, "equation_13_log_gap" = 6.938893903907228e-18, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.6544761904761904 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.0026246719160105004, "family_level_false_discovery_rate" = 0.00014432431922798837, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 3.591266625246936, "equation_13_log_gap" = -2.9031319865105347, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 0.014599469051146534 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.0026246719160105004, "family_level_false_discovery_rate" = 1.7623034629590272e-27, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 58.56493731429062, "equation_13_log_gap" = -55.66300451901012, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.05 +trials = 1000 +family_alpha = 0.9999568287525893 +family_beta = 1.647604955041141e-92 +expected = {"n_trials" = 1000, "single_trial_false_discovery_rate" = 0.0026246719160105004, "family_level_false_discovery_rate" = 0.0, "log_observed_type_i_inflation" = 4.605127013808775, "log_required_type_i_inflation" = 2994.3972724872588, "equation_13_log_gap" = -2989.79214547345, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.8 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.020979020979020994, "family_level_false_discovery_rate" = 0.020979020979020994, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 0.0, "equation_13_log_gap" = 0.0, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.7101538461538461 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.020979020979020994, "family_level_false_discovery_rate" = 0.006744467164499483, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 1.8373699804801076, "equation_13_log_gap" = -1.1492353417437062, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 0.04566846750074507 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.020979020979020994, "family_level_false_discovery_rate" = 6.653025996861344e-12, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 24.79485198896942, "equation_13_log_gap" = -21.892919193688922, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.3 +trials = 1000 +family_alpha = 0.9999568287525893 +family_beta = 9.568016667753333e-68 +expected = {"n_trials" = 1000, "single_trial_false_discovery_rate" = 0.020979020979020994, "family_level_false_discovery_rate" = 0.0, "log_observed_type_i_inflation" = 4.605127013808775, "log_required_type_i_inflation" = 1204.7349443779829, "equation_13_log_gap" = -1200.1298173641742, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 1 +family_alpha = 0.010000000000000009 +family_beta = 0.7999999999999997 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.3103448275862072, "family_level_false_discovery_rate" = 0.3103448275862068, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 1.7763568394002505e-15, "equation_13_log_gap" = -1.7763568394002505e-15, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 2 +family_alpha = 0.01990000000000002 +family_beta = 0.7839999999999998 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.3103448275862072, "family_level_false_discovery_rate" = 0.28200283419933864, "log_observed_type_i_inflation" = 0.6881346387364013, "log_required_type_i_inflation" = 0.8241754429663506, "equation_13_log_gap" = -0.13604080422994924, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 20 +family_alpha = 0.18209306240276926 +family_beta = 0.5187461188610349 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.3103448275862072, "family_level_false_discovery_rate" = 0.04976200450218708, "log_observed_type_i_inflation" = 2.9019327952804965, "log_required_type_i_inflation" = 5.052885842835017, "equation_13_log_gap" = -2.15095304755452, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.01 +beta = 0.8 +probability = 0.9 +trials = 1000 +family_alpha = 0.9999568287525893 +family_beta = 1.6566467315699085e-13 +expected = {"n_trials" = 1000, "single_trial_false_discovery_rate" = 0.3103448275862072, "family_level_false_discovery_rate" = 1.747795793940727e-46, "log_observed_type_i_inflation" = 4.605127013808775, "log_required_type_i_inflation" = 109.16717814759645, "equation_13_log_gap" = -104.56205113378768, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 1 +family_alpha = 0.4 +family_beta = 0.2 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.025641025641025644, "family_level_false_discovery_rate" = 0.025641025641025644, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = -6.938893903907228e-18, "equation_13_log_gap" = 6.938893903907228e-18, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 2 +family_alpha = 0.64 +family_beta = 0.047619047619047616 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.025641025641025644, "family_level_false_discovery_rate" = 0.0016813787305590589, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 3.2188758248682006, "equation_13_log_gap" = -2.748872195622465, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.05 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 7.054294986864034e-14 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.025641025641025644, "family_level_false_discovery_rate" = 9.536394485623088e-27, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 57.19335004322751, "equation_13_log_gap" = -56.27709587360615, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 1 +family_alpha = 0.4 +family_beta = 0.19999999999999987 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.17647058823529413, "family_level_false_discovery_rate" = 0.17647058823529407, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 1.6653345369377348e-16, "equation_13_log_gap" = -1.6653345369377348e-16, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 2 +family_alpha = 0.64 +family_beta = 0.07692307692307686 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.17647058823529413, "family_level_false_discovery_rate" = 0.0641711229946524, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 1.6094379124341007, "equation_13_log_gap" = -1.1394342831883653, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.3 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 1.2676378520362053e-10 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.17647058823529413, "family_level_false_discovery_rate" = 3.486656919079815e-11, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 23.455301777284095, "equation_13_log_gap" = -22.53904760766273, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 1 +family_alpha = 0.4 +family_beta = 0.20000000000000032 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.8181818181818182, "family_level_false_discovery_rate" = 0.8181818181818183, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = -4.440892098500626e-16, "equation_13_log_gap" = 4.440892098500626e-16, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 2 +family_alpha = 0.64 +family_beta = 0.1157894736842107 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.8181818181818182, "family_level_false_discovery_rate" = 0.7552447552447553, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 0.8472978603872034, "equation_13_log_gap" = -0.37729423114146793, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.2 +probability = 0.9 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 5.4124094930650975e-06 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.8181818181818182, "family_level_false_discovery_rate" = 0.12157332795979572, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 4.397946397999306, "equation_13_log_gap" = -3.481692228377942, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 1 +family_alpha = 0.4 +family_beta = 0.8 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.09523809523809529, "family_level_false_discovery_rate" = 0.09523809523809529, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = -6.938893903907228e-18, "equation_13_log_gap" = 6.938893903907228e-18, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 2 +family_alpha = 0.64 +family_beta = 0.6247619047619049 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.09523809523809529, "family_level_false_discovery_rate" = 0.004256451183825489, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 3.673765816303888, "equation_13_log_gap" = -3.2037621870581523, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.05 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 0.00896482519768437 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.09523809523809529, "family_level_false_discovery_rate" = 9.622659950010994e-27, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 58.57063915331599, "equation_13_log_gap" = -57.65438498369463, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 1 +family_alpha = 0.4 +family_beta = 0.8 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.4615384615384615, "family_level_false_discovery_rate" = 0.4615384615384615, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 0.0, "equation_13_log_gap" = 0.0, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 2 +family_alpha = 0.64 +family_beta = 0.5661538461538461 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.4615384615384615, "family_level_false_discovery_rate" = 0.12732095490716175, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 2.240709689275959, "equation_13_log_gap" = -1.7707060600302233, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.3 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 0.0024245681434085543 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.4615384615384615, "family_level_false_discovery_rate" = 3.495131102164888e-11, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 24.83916862636238, "equation_13_log_gap" = -23.922914456741015, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 1 +family_alpha = 0.4 +family_beta = 0.8 +expected = {"n_trials" = 1, "single_trial_false_discovery_rate" = 0.9473684210526316, "family_level_false_discovery_rate" = 0.9473684210526316, "log_observed_type_i_inflation" = 0.0, "log_required_type_i_inflation" = 0.0, "equation_13_log_gap" = 0.0, "identification_condition_holds" = true, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 2 +family_alpha = 0.64 +family_beta = 0.48842105263157887 +expected = {"n_trials" = 2, "single_trial_false_discovery_rate" = 0.9473684210526316, "family_level_false_discovery_rate" = 0.8421052631578947, "log_observed_type_i_inflation" = 0.4700036292457355, "log_required_type_i_inflation" = 1.686398953570229, "equation_13_log_gap" = -1.2163953243244936, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[comparison]] +alpha = 0.4 +beta = 0.8 +probability = 0.9 +trials = 20 +family_alpha = 0.9999634384155993 +family_beta = 7.513154993163264e-05 +expected = {"n_trials" = 20, "single_trial_false_discovery_rate" = 0.9473684210526316, "family_level_false_discovery_rate" = 0.12158077398998082, "log_observed_type_i_inflation" = 0.9162541696213633, "log_required_type_i_inflation" = 5.784171037170888, "equation_13_log_gap" = -4.867916867549525, "identification_condition_holds" = false, "single_trial_upper_bounds_family" = true} + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1 +expected = 0.8 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 3 +expected = 0.5120000000000001 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1000 +expected = 1.230231922161183e-97 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.7 +trials = 1 +expected = 0.24000000000000005 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.7 +trials = 3 +expected = 0.013824000000000006 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 1.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 1.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 0.8 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.0 +trials = 1 +expected = 1.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.0 +trials = 3 +expected = 1.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.0 +trials = 1000 +expected = 1.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.7 +trials = 1 +expected = 0.30000000000000004 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.7 +trials = 3 +expected = 0.027000000000000007 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1 +expected = 0.13999999999999999 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.7 +trials = 3 +expected = 0.002743999999999998 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1 +expected = 0.2 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 1.0 +trials = 3 +expected = 0.008000000000000004 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1 +expected = 0.8 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.0 +trials = 3 +expected = 0.5120000000000001 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1000 +expected = 1.230231922161183e-97 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.7 +trials = 1 +expected = 0.38 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.7 +trials = 3 +expected = 0.05487200000000001 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 1.0 +trials = 1 +expected = 0.2 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 1.0 +trials = 3 +expected = 0.008000000000000004 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 0.8 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.0 +trials = 1 +expected = 1.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.0 +trials = 3 +expected = 1.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.0 +trials = 1000 +expected = 1.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.7 +trials = 1 +expected = 0.44000000000000006 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.7 +trials = 3 +expected = 0.08518400000000004 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 0.7 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1 +expected = 0.2 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 1.0 +trials = 3 +expected = 0.008000000000000004 + +[[mixture]] +null_cdf = 0.2 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1 +expected = 0.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 3 +expected = 0.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.0 +trials = 1000 +expected = 0.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1 +expected = 0.7 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 3 +expected = 0.3429999999999999 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 0.7 +trials = 1000 +expected = 1.2532566399656417e-155 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 3 +expected = 1.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1 +expected = 0.8 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 3 +expected = 0.5120000000000001 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 +probability = 0.0 +trials = 1000 +expected = 1.230231922161183e-97 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 +probability = 0.7 +trials = 1 +expected = 0.94 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 +probability = 0.7 +trials = 3 +expected = 0.8305839999999999 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 0.8 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+[[mixture]] +null_cdf = 1.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1 +expected = 1.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 3 +expected = 1.0 + +[[mixture]] +null_cdf = 1.0 +alternative_cdf = 1.0 +probability = 1.0 +trials = 1000 +expected = 1.0 + +[[conditional]] +value = 0.0 +threshold = 0.0 +expected = 1.0 + +[[conditional]] +value = 0.5 +threshold = 0.0 +expected = 0.5 + +[[conditional]] +value = 1.0 +threshold = 0.0 +expected = 0.0 + +[[conditional]] +value = 0.2 +threshold = 0.2 +expected = 1.0 + +[[conditional]] +value = 0.6 +threshold = 0.2 +expected = 0.5 + +[[conditional]] +value = 1.0 +threshold = 0.2 +expected = 0.0 + +[[conditional]] +value = 0.95 +threshold = 0.95 +expected = 1.0 + +[[conditional]] +value = 0.975 +threshold = 0.95 +expected = 0.5 + +[[conditional]] +value = 1.0 +threshold = 0.95 +expected = 0.0 + +[[conditional]] +value = 0.9999999999999999 +threshold = 0.9999999999999999 +expected = 1.0 + 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0.0, 0.5, 1.2, 8.0, 40.0] +expected = {"trial_cdf" = [2.7536241186061556e-89, 3.167124183311986e-05, 0.15865525393145707, 0.5, 0.5987063256829237, 0.7257468822499265, 0.9999683287581669, 1.0], "maximum_cdf" = [2.087920559592253e-266, 3.176839524402604e-14, 0.003993589074329777, 0.12500000000000003, 0.2146058420403026, 0.3822570786514705, 0.9999049892836716, 1.0], "log_density" = [-608.3477841672911, -29.23367639815109, -4.695516715115035, -1.8997677862163989, -1.5706915759153692, -1.3345813690715462, -8.513536768583263, -200.51347342509652], "log_likelihood" = -856.1090282054404} + +[[gaussian]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +trials = 50 +observations = [-40.0, -8.0, -2.0, 0.0, 0.5, 1.2, 8.0, 40.0] +expected = {"trial_cdf" = [2.7536241186061556e-89, 3.167124183311986e-05, 0.15865525393145707, 0.5, 0.5987063256829237, 0.7257468822499265, 0.9999683287581669, 1.0], "maximum_cdf" = [0.0, 1.0795794825414816e-225, 1.0537144249096151e-40, 8.881784197001244e-16, 7.255917518935037e-12, 1.0946088135956346e-07, 0.9984176660436752, 1.0], "log_density" = [-10189.640675892104, -513.3450355481738, -88.41012331379038, -31.66427455577379, -22.867532403398563, -13.587207335724898, -5.7016146237619685, -197.70006270833647], "log_likelihood" = -11062.916526381065} + +[[aggregate]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +trials = 1 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 1, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [0.022750131948179195, 0.15865525393145707, 0.3820885778110473, 0.8413447460685429, 0.9986501019683699], "family_type_i_errors" = [0.8413447460685429, 0.5, 0.241963652223073, 0.02275013194817921, 3.167124183311998e-05], "family_type_ii_errors" = [0.022750131948179212, 0.15865525393145702, 0.3820885778110474, 0.841344746068543, 0.9986501019683699], "mean_family_type_i_error" = 0.32121804029632567, "mean_family_type_ii_error" = 0.4806977623455193, "family_null_probability" = 0.2, "false_discovery_rate" = 0.13392862968652702} + +[[aggregate]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +trials = 3 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 3, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [0.022750131948179195, 0.15865525393145707, 0.3820885778110473, 0.8413447460685429, 0.9986501019683699], "family_type_i_errors" = [0.9960064109256702, 0.875, 0.5644178326233253, 0.06670946508530941, 9.501071632845058e-05], "family_type_ii_errors" = [9.328192599059492e-05, 0.010771542039941934, 0.09287671813195748, 0.6529181572203536, 0.996719294977433], "mean_family_type_i_error" = 0.5004457438701266, "mean_family_type_ii_error" = 0.3506757988591353, "family_null_probability" = 0.008000000000000004, "false_discovery_rate" = 0.006177073038011817} + +[[aggregate]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +trials = 50 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 50, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [0.022750131948179195, 0.15865525393145707, 0.3820885778110473, 0.8413447460685429, 0.9986501019683699], "family_type_i_errors" = [1.0, 0.9999999999999991, 0.9999990350572983, 0.6835675430863251, 0.0015823339563248145], "family_type_ii_errors" = [8.290509000189871e-66, 6.223589705354836e-33, 1.0209903583708415e-17, 0.000869231501860583, 0.9471079945667636], "mean_family_type_i_error" = 0.7370297824199895, "mean_family_type_ii_error" = 0.18959544521372484, "family_null_probability" = 1.1258999068426267e-35, "false_discovery_rate" = 1.0239598956668602e-35} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +trials = 1 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 1, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [9.107648574484005e-06, 0.0021373669800862794, 0.031645416116672626, 0.5, 0.9978626330199137], "family_type_i_errors" = [0.8413447460685429, 0.5, 0.241963652223073, 0.02275013194817921, 3.167124183311998e-05], "family_type_ii_errors" = [9.107648574608503e-06, 0.002137366980086204, 0.03164541611667254, 0.49999999999999983, 0.9978626330199141], "mean_family_type_i_error" = 0.32121804029632567, "mean_family_type_ii_error" = 0.30633090475304947, "family_null_probability" = 0.8, "false_discovery_rate" = 0.6494037751339141} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +trials = 3 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 3, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [9.107648574484005e-06, 0.0021373669800862794, 0.031645416116672626, 0.5, 0.9978626330199137], "family_type_i_errors" = [0.9960064109256702, 0.875, 0.5644178326233253, 0.06670946508530941, 9.501071632845058e-05], "family_type_ii_errors" = [1.8039845624382877e-07, 0.00042091514017858695, 0.014458624074760383, 0.42585516253656863, 0.9973172732319927], "mean_family_type_i_error" = 0.5004457438701266, "mean_family_type_ii_error" = 0.2876104310763913, "family_null_probability" = 0.5120000000000001, "false_discovery_rate" = 0.424307190953046} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +trials = 50 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 50, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.8413447460685429, 0.5, 0.24196365222307303, 0.022750131948179195, 3.167124183311986e-05], "trial_type_ii_errors" = [9.107648574484005e-06, 0.0021373669800862794, 0.031645416116672626, 0.5, 0.9978626330199137], "family_type_i_errors" = [1.0, 0.9999999999999991, 0.9999990350572983, 0.6835675430863251, 0.0015823339563248145], "family_type_ii_errors" = [1.0795505972833084e-48, 6.954107649287412e-22, 9.37290664160154e-12, 0.001851186035667369, 0.9776085440768563], "mean_family_type_i_error" = 0.7370297824199895, "mean_family_type_ii_error" = 0.19589194602437932, "family_null_probability" = 1.4272476927059631e-05, "false_discovery_rate" = 1.3081889972853506e-05} + +[[aggregate]] +model = {"trial_null_probability" = 0.5, "null_standard_deviation" = 0.5, "alternative_mean" = -0.3, "alternative_standard_deviation" = 1.8} +trials = 1 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 1, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.9772498680518208, 0.5, 0.08075665923377107, 3.167124183311986e-05, 6.22096057427174e-16], "trial_type_ii_errors" = [0.348679170938738, 0.5661838326109037, 0.7107426392460281, 0.8993361040084994, 0.9915502937877492], "family_type_i_errors" = [0.9772498680518208, 0.5, 0.08075665923377107, 3.167124183311998e-05, 6.661338147750939e-16], "family_type_ii_errors" = [0.34867917093873796, 0.5661838326109037, 0.7107426392460281, 0.8993361040084994, 0.9915502937877491], "mean_family_type_i_error" = 0.3116076397054851, "mean_family_type_ii_error" = 0.7032984081183836, "family_null_probability" = 0.5, "false_discovery_rate" = 0.5122520315736278} + +[[aggregate]] +model = {"trial_null_probability" = 0.5, "null_standard_deviation" = 0.5, "alternative_mean" = -0.3, "alternative_standard_deviation" = 1.8} +trials = 3 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 3, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.9772498680518208, 0.5, 0.08075665923377107, 3.167124183311986e-05, 6.22096057427174e-16], "trial_type_ii_errors" = [0.348679170938738, 0.5661838326109037, 0.7107426392460281, 0.8993361040084994, 0.9915502937877492], "family_type_i_errors" = [0.9999882252482495, 0.875, 0.2232317293679829, 9.501071632845058e-05, 1.99840144432528e-15], "family_type_ii_errors" = [0.0073186443557604584, 0.15528318599314062, 0.5076952830304081, 0.835937824997332, 0.985575901053604], "mean_family_type_i_error" = 0.4196629930665126, "mean_family_type_ii_error" = 0.4983621678860491, "family_null_probability" = 0.12500000000000003, "false_discovery_rate" = 0.10675384075771943} + +[[aggregate]] +model = {"trial_null_probability" = 0.5, "null_standard_deviation" = 0.5, "alternative_mean" = -0.3, "alternative_standard_deviation" = 1.8} +trials = 50 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 50, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.9772498680518208, 0.5, 0.08075665923377107, 3.167124183311986e-05, 6.22096057427174e-16], "trial_type_ii_errors" = [0.348679170938738, 0.5661838326109037, 0.7107426392460281, 0.8993361040084994, 0.9915502937877492], "family_type_i_errors" = [1.0, 0.9999999999999991, 0.9851568997525498, 0.0015823339563248145, 3.3306690738754153e-14], "family_type_ii_errors" = [2.7689070535660514e-37, 2.1882898764053227e-14, 3.611550260057229e-05, 0.07554910237159067, 0.8092153935107804], "mean_family_type_i_error" = 0.5973478467417814, "mean_family_type_ii_error" = 0.1769601222769987, "family_null_probability" = 8.881784197001244e-16, "false_discovery_rate" = 6.446242531992437e-16} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +trials = 1 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 1, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.6914624612740131, 0.5, 0.3631693488243809, 0.15865525393145707, 0.022750131948179195], "trial_type_ii_errors" = [0.3085375387259869, 0.5, 0.6368306511756191, 0.8413447460685429, 0.9772498680518208], "family_type_i_errors" = [0.6914624612740132, 0.5, 0.3631693488243809, 0.15865525393145705, 0.02275013194817921], "family_type_ii_errors" = [0.3085375387259868, 0.5000000000000001, 0.636830651175619, 0.8413447460685431, 0.9772498680518208], "mean_family_type_i_error" = 0.34720743919560604, "mean_family_type_ii_error" = 0.652792560804394, "family_null_probability" = 0.8, "false_discovery_rate" = 0.8} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +trials = 3 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 3, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.6914624612740131, 0.5, 0.3631693488243809, 0.15865525393145707, 0.022750131948179195], "trial_type_ii_errors" = [0.3085375387259869, 0.5, 0.6368306511756191, 0.8413447460685429, 0.9772498680518208], "family_type_i_errors" = [0.9706286416357304, 0.875, 0.7417312417086019, 0.40444488206853557, 0.06670946508530941], "family_type_ii_errors" = [0.02937135836426963, 0.12500000000000003, 0.25826875829139806, 0.5955551179314647, 0.9332905349146904], "mean_family_type_i_error" = 0.6117028460996353, "mean_family_type_ii_error" = 0.38829715390036457, "family_null_probability" = 0.5120000000000001, "false_discovery_rate" = 0.5120000000000001} + +[[aggregate]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +trials = 50 +thresholds = [-1.0, 0.0, 0.7, 2.0, 4.0] +expected = {"n_trials" = 50, "thresholds" = [-1.0, 0.0, 0.7, 2.0, 4.0], "trial_type_i_errors" = [0.6914624612740131, 0.5, 0.3631693488243809, 0.15865525393145707, 0.022750131948179195], "trial_type_ii_errors" = [0.3085375387259869, 0.5, 0.6368306511756191, 0.8413447460685429, 0.9772498680518208], "family_type_i_errors" = [1.0, 0.9999999999999991, 0.9999999998410729, 0.9998227038307536, 0.6835675430863251], "family_type_ii_errors" = [2.920118910322559e-26, 8.881784197001244e-16, 1.5892708176695259e-10, 0.00017729616924645506, 0.3164324569136749], "mean_family_type_i_error" = 0.9366780493516302, "mean_family_type_ii_error" = 0.06332195064836986, "family_null_probability" = 1.4272476927059631e-05, "false_discovery_rate" = 1.4272476927059631e-05} + +[[witness]] +observable = 1e-08 +target = 1e-08 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 1e-08, "trial_null_probability" = 0.0001, "observable_cdf_at_threshold" = 1e-08, "latent_trial_cdf_at_threshold" = 0.0001, "family_null_probability" = 1e-08, "family_type_i_error" = 0.99999999, "family_type_ii_error" = 1e-08, "family_false_discovery_rate" = 1.0000000000000018e-08, "reconstructed_observable_cdf_at_threshold" = 1e-08} + +[[witness]] +observable = 1e-08 +target = 0.1 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.1, "trial_null_probability" = 0.31622776601683794, "observable_cdf_at_threshold" = 1e-08, "latent_trial_cdf_at_threshold" = 0.0001, "family_null_probability" = 0.1, "family_type_i_error" = 0.99999999, "family_type_ii_error" = 1e-08, "family_false_discovery_rate" = 0.10000000000000002, "reconstructed_observable_cdf_at_threshold" = 1e-08} + +[[witness]] +observable = 1e-08 +target = 0.7 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.7, "trial_null_probability" = 0.8366600265340756, "observable_cdf_at_threshold" = 1e-08, "latent_trial_cdf_at_threshold" = 0.0001, "family_null_probability" = 0.7000000000000001, "family_type_i_error" = 0.99999999, "family_type_ii_error" = 1e-08, "family_false_discovery_rate" = 0.7000000000000001, "reconstructed_observable_cdf_at_threshold" = 1e-08} + +[[witness]] +observable = 1e-08 +target = 0.99999999 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.99999999, "trial_null_probability" = 0.9999999949999999, "observable_cdf_at_threshold" = 1e-08, "latent_trial_cdf_at_threshold" = 0.0001, "family_null_probability" = 0.9999999899999998, "family_type_i_error" = 0.99999999, "family_type_ii_error" = 1e-08, "family_false_discovery_rate" = 0.9999999899999998, "reconstructed_observable_cdf_at_threshold" = 1e-08} + +[[witness]] +observable = 0.2 +target = 1e-08 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 1e-08, "trial_null_probability" = 0.0001, "observable_cdf_at_threshold" = 0.2, "latent_trial_cdf_at_threshold" = 0.4472135954999579, "family_null_probability" = 1e-08, "family_type_i_error" = 0.8, "family_type_ii_error" = 0.2, "family_false_discovery_rate" = 1.0000000000000018e-08, "reconstructed_observable_cdf_at_threshold" = 0.19999999999999998} + +[[witness]] +observable = 0.2 +target = 0.1 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.1, "trial_null_probability" = 0.31622776601683794, "observable_cdf_at_threshold" = 0.2, "latent_trial_cdf_at_threshold" = 0.4472135954999579, "family_null_probability" = 0.1, "family_type_i_error" = 0.8, "family_type_ii_error" = 0.2, "family_false_discovery_rate" = 0.10000000000000002, "reconstructed_observable_cdf_at_threshold" = 0.19999999999999998} + +[[witness]] +observable = 0.2 +target = 0.7 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.7, "trial_null_probability" = 0.8366600265340756, "observable_cdf_at_threshold" = 0.2, "latent_trial_cdf_at_threshold" = 0.4472135954999579, "family_null_probability" = 0.7000000000000001, "family_type_i_error" = 0.8, "family_type_ii_error" = 0.2, "family_false_discovery_rate" = 0.7000000000000001, "reconstructed_observable_cdf_at_threshold" = 0.19999999999999998} + +[[witness]] +observable = 0.2 +target = 0.99999999 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.99999999, "trial_null_probability" = 0.9999999949999999, "observable_cdf_at_threshold" = 0.2, "latent_trial_cdf_at_threshold" = 0.4472135954999579, "family_null_probability" = 0.9999999899999998, "family_type_i_error" = 0.8, "family_type_ii_error" = 0.2, "family_false_discovery_rate" = 0.9999999899999998, "reconstructed_observable_cdf_at_threshold" = 0.19999999999999998} + +[[witness]] +observable = 0.8 +target = 1e-08 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 1e-08, "trial_null_probability" = 0.0001, "observable_cdf_at_threshold" = 0.8, "latent_trial_cdf_at_threshold" = 0.8944271909999159, "family_null_probability" = 1e-08, "family_type_i_error" = 0.19999999999999996, "family_type_ii_error" = 0.8, "family_false_discovery_rate" = 1.0000000000000018e-08, "reconstructed_observable_cdf_at_threshold" = 0.7999999999999999} + +[[witness]] +observable = 0.8 +target = 0.1 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.1, "trial_null_probability" = 0.31622776601683794, "observable_cdf_at_threshold" = 0.8, "latent_trial_cdf_at_threshold" = 0.8944271909999159, "family_null_probability" = 0.1, "family_type_i_error" = 0.19999999999999996, "family_type_ii_error" = 0.8, "family_false_discovery_rate" = 0.10000000000000002, "reconstructed_observable_cdf_at_threshold" = 0.7999999999999999} + +[[witness]] +observable = 0.8 +target = 0.7 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.7, "trial_null_probability" = 0.8366600265340756, "observable_cdf_at_threshold" = 0.8, "latent_trial_cdf_at_threshold" = 0.8944271909999159, "family_null_probability" = 0.7000000000000001, "family_type_i_error" = 0.19999999999999996, "family_type_ii_error" = 0.8, "family_false_discovery_rate" = 0.7, "reconstructed_observable_cdf_at_threshold" = 0.7999999999999999} + +[[witness]] +observable = 0.8 +target = 0.99999999 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.99999999, "trial_null_probability" = 0.9999999949999999, "observable_cdf_at_threshold" = 0.8, "latent_trial_cdf_at_threshold" = 0.8944271909999159, "family_null_probability" = 0.9999999899999998, "family_type_i_error" = 0.19999999999999996, "family_type_ii_error" = 0.8, "family_false_discovery_rate" = 0.9999999899999998, "reconstructed_observable_cdf_at_threshold" = 0.7999999999999999} + +[[witness]] +observable = 0.99999999 +target = 1e-08 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 1e-08, "trial_null_probability" = 0.0001, "observable_cdf_at_threshold" = 0.99999999, "latent_trial_cdf_at_threshold" = 0.9999999949999999, "family_null_probability" = 1e-08, "family_type_i_error" = 1.0000000050247593e-08, "family_type_ii_error" = 0.99999999, "family_false_discovery_rate" = 9.999999999999982e-09, "reconstructed_observable_cdf_at_threshold" = 0.9999999899999998} + +[[witness]] +observable = 0.99999999 +target = 0.1 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.1, "trial_null_probability" = 0.31622776601683794, "observable_cdf_at_threshold" = 0.99999999, "latent_trial_cdf_at_threshold" = 0.9999999949999999, "family_null_probability" = 0.1, "family_type_i_error" = 1.0000000050247593e-08, "family_type_ii_error" = 0.99999999, "family_false_discovery_rate" = 0.0999999999999999, "reconstructed_observable_cdf_at_threshold" = 0.9999999899999998} + +[[witness]] +observable = 0.99999999 +target = 0.7 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.7, "trial_null_probability" = 0.8366600265340756, "observable_cdf_at_threshold" = 0.99999999, "latent_trial_cdf_at_threshold" = 0.9999999949999999, "family_null_probability" = 0.7000000000000001, "family_type_i_error" = 1.0000000050247593e-08, "family_type_ii_error" = 0.99999999, "family_false_discovery_rate" = 0.7000000000000006, "reconstructed_observable_cdf_at_threshold" = 0.9999999899999998} + +[[witness]] +observable = 0.99999999 +target = 0.99999999 +expected = {"n_trials" = 2, "target_false_discovery_rate" = 0.99999999, "trial_null_probability" = 0.9999999949999999, "observable_cdf_at_threshold" = 0.99999999, "latent_trial_cdf_at_threshold" = 0.9999999949999999, "family_null_probability" = 0.9999999899999998, "family_type_i_error" = 1.0000000050247593e-08, "family_type_ii_error" = 0.99999999, "family_false_discovery_rate" = 0.9999999899999992, "reconstructed_observable_cdf_at_threshold" = 0.9999999899999998} + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = -2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.19544997361036417, "selection_probability" = 0.99437005518506, "conditional_null_probability" = 0.19655657628787845, "quadrature_absolute_error" = 2.710474408645365e-12} +independent_joint = 0.19544997361036417 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = -2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.07102758166738454, "selection_probability" = 0.999999998995346, "conditional_null_probability" = 0.07102758173874268, "quadrature_absolute_error" = 2.2852102848520958e-12} +independent_joint = 0.07102758166673258 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = -2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.023521691976437477, "selection_probability" = 1.0, "conditional_null_probability" = 0.023521691976437477, "quadrature_absolute_error" = 1.8621823157098927e-12} +independent_joint = 0.02352169193512988 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 0.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.1, "selection_probability" = 0.7730757968548343, "conditional_null_probability" = 0.12935342227351826, "quadrature_absolute_error" = 2.4850969138277515e-12} +independent_joint = 0.1 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 0.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.07014255306485133, "selection_probability" = 0.9973483067915488, "conditional_null_probability" = 0.07032904411348391, "quadrature_absolute_error" = 3.008141275231635e-12} +independent_joint = 0.07014255306465096 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 0.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.02352169197643745, "selection_probability" = 1.0, "conditional_null_probability" = 0.02352169197643745, "quadrature_absolute_error" = 1.3836079556130744e-12} +independent_joint = 0.023521691964292952 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.004550026389635842, "selection_probability" = 0.1314742295348014, "conditional_null_probability" = 0.03460774332532935, "quadrature_absolute_error" = 3.779235114158127e-14} +independent_joint = 0.004550026389635842 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.01429981241482731, "selection_probability" = 0.4309756693666416, "conditional_null_probability" = 0.033180092128732463, "quadrature_absolute_error" = 3.0254705209075016e-13} +independent_joint = 0.014299812414826564 + +[[selection]] +model = {"trial_null_probability" = 0.2, "null_standard_deviation" = 1.0, "alternative_mean" = 1.0, "alternative_standard_deviation" = 1.0} +threshold = 2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.0226527008770539, "selection_probability" = 0.9854292712188051, "conditional_null_probability" = 0.022987647656372574, "quadrature_absolute_error" = 2.6953962080014823e-13} +independent_joint = 0.02265270087700237 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = -2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.7817998944414567, "selection_probability" = 0.9817998933397989, "conditional_null_probability" = 0.7962925029274549, "quadrature_absolute_error" = 1.084189763458146e-11} +independent_joint = 0.7817998944414567 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = -2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.4688119687123874, "selection_probability" = 0.9999998902774904, "conditional_null_probability" = 0.46881202015161877, "quadrature_absolute_error" = 1.9247078046593848e-11} +independent_joint = 0.46881196871225833 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = -2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.10714442164960833, "selection_probability" = 1.0, "conditional_null_probability" = 0.10714442164960833, "quadrature_absolute_error" = 2.064783362823433e-12} +independent_joint = 0.10714442166023068 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 0.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.4, "selection_probability" = 0.5995725266039827, "conditional_null_probability" = 0.6671419757433278, "quadrature_absolute_error" = 9.940387655311006e-12} +independent_joint = 0.4 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 0.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.44317285683995067, "selection_probability" = 0.9742903912614401, "conditional_null_probability" = 0.45486731760349475, "quadrature_absolute_error" = 7.694475210792195e-12} +independent_joint = 0.443172856839875 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 0.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.107144421648424, "selection_probability" = 0.9999999999988095, "conditional_null_probability" = 0.10714442164855155, "quadrature_absolute_error" = 1.3247610666241467e-11} +independent_joint = 0.1071444216499267 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.01820010555854337, "selection_probability" = 0.11820010555854332, "conditional_null_probability" = 0.15397706687773674, "quadrature_absolute_error" = 1.5116940456632507e-13} +independent_joint = 0.01820010555854337 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.05820058594359309, "selection_probability" = 0.395383256234873, "conditional_null_probability" = 0.14720043154538562, "quadrature_absolute_error" = 4.0016251767888534e-13} +independent_joint = 0.058200585943592224 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 1.0, "alternative_mean" = 2.0, "alternative_standard_deviation" = 0.7} +threshold = 2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.09897680773746044, "selection_probability" = 0.977033158403825, "conditional_null_probability" = 0.10130342751023767, "quadrature_absolute_error" = 2.6114362681852215e-12} +independent_joint = 0.09897680773741019 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = -2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.6730757968548345, "selection_probability" = 0.8413447460685429, "conditional_null_probability" = 0.8000000000000003, "quadrature_absolute_error" = 4.807952605631328e-11} +independent_joint = 0.6730757968548344 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = -2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.7994931168890522, "selection_probability" = 0.9993663961113143, "conditional_null_probability" = 0.8000000000000008, "quadrature_absolute_error" = 7.830502625910594e-11} +independent_joint = 0.7994931168888896 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = -2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.8000000000000005, "selection_probability" = 1.0, "conditional_null_probability" = 0.8000000000000005, "quadrature_absolute_error" = 5.260952450368444e-11} +independent_joint = 0.7999999999972506 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 0.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.4, "selection_probability" = 0.5, "conditional_null_probability" = 0.8, "quadrature_absolute_error" = 3.352788969159358e-11} +independent_joint = 0.4 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 0.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.7500000000000001, "selection_probability" = 0.9375, "conditional_null_probability" = 0.8000000000000002, "quadrature_absolute_error" = 2.5635176864707506e-13} +independent_joint = 0.749999999999956 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 0.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.7999999992549423, "selection_probability" = 0.9999999990686774, "conditional_null_probability" = 0.8000000000000003, "quadrature_absolute_error" = 7.492254263964359e-12} +independent_joint = 0.7999999992534081 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 2.0 +trials = 1 +expected = {"joint_null_and_selection_probability" = 0.12692420314516567, "selection_probability" = 0.15865525393145696, "conditional_null_probability" = 0.8000000000000006, "quadrature_absolute_error" = 5.62589514459447e-12} +independent_joint = 0.12692420314516567 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 2.0 +trials = 4 +expected = {"joint_null_and_selection_probability" = 0.39914626442730483, "selection_probability" = 0.4989328305341308, "conditional_null_probability" = 0.8000000000000004, "quadrature_absolute_error" = 2.526737050534621e-11} +independent_joint = 0.39914626442730317 + +[[selection]] +model = {"trial_null_probability" = 0.8, "null_standard_deviation" = 2.0, "alternative_mean" = 0.0, "alternative_standard_deviation" = 2.0} +threshold = 2.0 +trials = 30 +expected = {"joint_null_and_selection_probability" = 0.7955093459604752, "selection_probability" = 0.9943866824505936, "conditional_null_probability" = 0.8000000000000003, "quadrature_absolute_error" = 4.957133815983086e-11} +independent_joint = 0.7955093459601454 diff --git a/test/fixtures/signal_sizing_python_320.toml b/test/fixtures/signal_sizing_python_320.toml new file mode 100644 index 0000000..43a81a4 --- /dev/null +++ b/test/fixtures/signal_sizing_python_320.toml @@ -0,0 +1,3081 @@ +reference = "RiskLabAI 3.2.0 probability, interval and sigmoid bet sizing" + +[[probability]] +probabilities = [] +sides = [] +output = [] + +[[probability]] +probabilities = [0.0] +sides = [-1.0] +output = [-0.0] + +[[probability]] +probabilities = [0.0, 0.5, 1.0] +sides = [-1.0, 0.0, 1.0] +output = [-0.0, 0.0, 0.6826894921370859] + +[[probability]] +probabilities = [0.0, 0.14285714285714285, 0.2857142857142857, 0.42857142857142855, 0.5714285714285714, 0.7142857142857142, 0.8571428571428571, 1.0] +sides = [-1.0, 0.0, 1.0, -1.0, 0.0, 1.0, -1.0, 0.0] +output = [-0.0, 0.0, 0.22490303780401533, -0.33176485820475077, 0.0, 0.5249494759460469, -0.6086340616924479, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 2, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 1.0, 0.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.6318797493064809, 0.0, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 2, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 1.0, 1.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.6318797493064809, -0.6318797493064809, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 2, 6] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 1.0, 1.0, 1.0, 1.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 3, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 0.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, 0.0, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 3, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 1.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, -0.6318797493064809, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 3, 6] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 1.0, 1.0, 1.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 5, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 0.25, 0.25, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, 0.4514937644998529, 0.4514937644998529, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 5, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 0.625, 0.25, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, -0.09019299240331402, 0.4514937644998529, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [1, 5, 6] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, 0.25, 0.625, 0.625, 0.625, 1.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.4514937644998529, -0.09019299240331402, -0.09019299240331402, -0.09019299240331402, -0.6318797493064809, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 2, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 1.0, 0.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.6318797493064809, 0.0, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 2, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 1.0, 1.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.6318797493064809, -0.6318797493064809, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 2, 6] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 1.0, 1.0, 1.0, 1.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 3, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 0.625, 0.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.09019299240331402, 0.0, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 3, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 0.625, 1.0, 0.0, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.09019299240331402, -0.6318797493064809, 0.0, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 3, 6] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 0.625, 1.0, 1.0, 1.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.09019299240331402, -0.6318797493064809, -0.6318797493064809, -0.6318797493064809, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 5, 3] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 0.625, 0.25, 0.25, 0.0, 0.0] +strategy = [0.0, -0.07965567455405798, 0.18591904497289746, -0.09019299240331402, 0.4514937644998529, 0.4514937644998529, 0.0, 0.0] + +[[inclusive]] +prices = [0, 1, 2, 3, 4, 5, 6, 7] +starts = [1, 2, 3] +ends = [2, 5, 4] +signals = [-0.5, 0.25, 1.0] +probabilities = [0.1, 0.6, 0.9] +sides = [-1.0, 1.0, -1.0] +output = [0.0, -0.5, -0.125, 0.625, 0.625, 0.25, 0.0, 0.0] 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100.0 +width = 0.25 +size = 1.0 +output = 100.0 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = -1.0 +output = 100.0 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = -0.75 +output = 102.26778683805536 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = -0.2 +output = 100.40824829046386 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = 0.0 +output = 100.0 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = 0.2 +output = 99.59175170953614 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = 0.75 +output = 97.73221316194464 + +[[inverse]] +forecast = 100.0 +width = 4.0 +size = 1.0 +output = 100.0 + +[[width]] +divergence = -2.0 +size = -1.0 +output = inf + +[[width]] +divergence = -2.0 +size = -0.75 +output = 3.1111111111111107 + +[[width]] +divergence = -2.0 +size = -0.2 +output = 95.99999999999999 + +[[width]] +divergence = -2.0 +size = 0.0 +output = inf + +[[width]] +divergence = -2.0 +size = 0.2 +output = 95.99999999999999 + +[[width]] +divergence = -2.0 +size = 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+[[limit]] +current = -1 +target = -2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 98.84529946162075 + +[[limit]] +current = -1 +target = -1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 + +[[limit]] +current = -1 +target = 0 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 + +[[limit]] +current = -1 +target = 1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.74180111025284 + +[[limit]] +current = -1 +target = 2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.44296722737549 + +[[limit]] +current = -1 +target = 3 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.01527871101777 + +[[limit]] +current = 0 +target = -3 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 98.68703828135703 + +[[limit]] +current = 0 +target = -2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.16445084106321 + +[[limit]] +current = 0 +target = -1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.48360222050567 + +[[limit]] +current = 0 +target = 0 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 + +[[limit]] +current = 0 +target = 1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.48360222050567 + +[[limit]] +current = 0 +target = 2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.16445084106321 + +[[limit]] +current = 0 +target = 3 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 98.68703828135703 + +[[limit]] +current = 1 +target = -3 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.01527871101777 + +[[limit]] +current = 1 +target = -2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.44296722737549 + +[[limit]] +current = 1 +target = -1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 99.74180111025284 + +[[limit]] +current = 1 +target = 0 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 + +[[limit]] +current = 1 +target = 1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 + +[[limit]] +current = 1 +target = 2 +forecast = 100.0 +width = 4.0 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+[[limit]] +current = 3 +target = -2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 19.76905989232415 + +[[limit]] +current = 3 +target = -1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 0.0 + +[[limit]] +current = 3 +target = 0 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 0.0 + +[[limit]] +current = 3 +target = 1 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 0.0 + +[[limit]] +current = 3 +target = 2 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 0.0 + +[[limit]] +current = 3 +target = 3 +forecast = 100.0 +width = 4.0 +maximum = 4 +output = 100.0 diff --git a/test/fixtures/structural_breaks_python_320.toml b/test/fixtures/structural_breaks_python_320.toml new file mode 100644 index 0000000..b24c3d6 --- /dev/null +++ b/test/fixtures/structural_breaks_python_320.toml @@ -0,0 +1,8279 @@ +reference = "RiskLabAI 3.2.0 ADF, PSY, bootstrap and PELT contracts" + +[[design]] +values = [] +constant = "nc" +lags = 0 +y = [] +x = [] +columns = 1 +index = [] + +[[design]] +values = [] +constant = "nc" +lags = 0 +y = [] +x = [] +columns = 1 +index = [] + +[[design]] +values = [] +constant = "nc" +lags = 1 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [] +constant = "nc" +lags = 1 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [] +constant = "nc" +lags = 2 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "nc" +lags = 2 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 0 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 0 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 1 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 1 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 2 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "c" +lags = 2 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 0 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 0 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 1 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 1 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 2 +y = [] +x = [] +columns = 5 +index = [] + +[[design]] +values = [] +constant = "ct" +lags = 2 +y = [] +x = [] +columns = 5 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 0 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 0 +y = [] +x = [] +columns = 4 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 1 +y = [] +x = [] +columns = 5 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 1 +y = [] +x = [] +columns = 5 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 2 +y = [] +x = [] +columns = 6 +index = [] + +[[design]] +values = [] +constant = "ctt" +lags = 2 +y = [] +x = [] +columns = 6 +index = [] + +[[design]] +values = [-0.44097382492184395, 1.0680527616278617] +constant = "nc" +lags = 0 +y = [1.5090265865497057] +x = [[-0.44097382492184395]] +columns = 1 +index = [2] + +[[design]] +values = [-0.6186611258375527, -0.4564934008074213] +constant = "nc" +lags = 0 +y = [0.16216772503013144] +x = [[-0.6186611258375527]] +columns = 1 +index = [2] + +[[design]] +values = [-0.6520767765951949, -0.4266444999640626] +constant = "nc" +lags = 1 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [1.5314162636462116, 1.7228796742492034] +constant = "nc" +lags = 1 +y = [] +x = [] +columns = 2 +index = [] + +[[design]] +values = [0.1342924998355478, -0.11382355555202317] +constant = "nc" +lags = 2 +y = [] +x = [] +columns = 3 +index = [] + +[[design]] +values = [0.14418661424937948, 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+[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [-1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [0.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[10, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[10, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[10, 20]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [1.0, nan, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, -1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[40, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 0.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 0.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [[20, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, 1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40], [70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[20, 40]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, 1.0, nan, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [[70, 70]] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, 1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, -1.0, nan] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, 0.0, -1.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 3 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 1 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = [nan, nan, 0.0, 0.0] +critical = [0.0, 0.0, 0.0, 0.0] +minimum = 2 +output = [] + +[[episodes]] +index = [10, 20, 40, 70] +values = 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-1.1327783615418665, 0.2518833996378933, -1.966697239971782, -3.1474773178803863, -2.5476240025176993, -1.237818572924495, -0.8114208628918265, -2.185700213007178, -2.1919130899838075, -1.7297227694080097, -4.462532887527172, -4.5752550357917965] +minimum = 8 +bootstrap = 0 +signs = [] +output = {"sadf" = -1.784988762249381, "gsadf" = -0.9361889154966864, "sadf_pvalue" = 1.0, "gsadf_pvalue" = 1.0, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [1.1289670371919185, 1.1660951422431511, 1.5124850781376042, 1.8441999912421811, 2.1458444018910354, 2.5523090073594803, 2.9652997063262334, 3.39837109161441, 3.9929434327747066, 4.680818897392696, 5.513105562307414, 6.406726318755398, 7.2649972286716435, 8.36533624998165, 9.564787745766349, 11.226074531816757, 12.999725570992291] +minimum = 8 +bootstrap = 0 +signs = [] +output = {"sadf" = 19.195164401220023, "gsadf" = 19.891899734127218, "sadf_pvalue" = 1.0, "gsadf_pvalue" = 1.0, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [-0.5726491534157012, 0.9120000054674651, 1.5583380228617911, 2.6203121176152058, 2.5994524877959875, 2.460984332592704, 1.341266617475296, 1.5659243150089999, 2.1446557400451987, 3.335878616573934, 5.824364603934694, 6.151682651979864, 6.391198134089899, 5.75610781225324, 4.474749922844435, 3.833287312019854, 5.41863570088444] +minimum = 8 +bootstrap = 7 +signs = [[1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1], [1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1], [-1, -1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, 1], [1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1], [-1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, -1], [-1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1], [1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1]] +output = {"sadf" = -0.3471119851539704, "gsadf" = 0.5619893915763683, "sadf_pvalue" = 0.875, "gsadf_pvalue" = 0.625, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [0.16324667593375888, 0.1164299567984126, 0.15391663587865115, 0.03394843500469427, -0.09064779064849526, -0.17295557394487682, -0.24720121398336498, -0.395862297076453, -0.504863567741291, -0.5517660728172115, -0.6909230068238621, -0.567472103813706, -0.6318057792442991, -0.6579240816706506, -0.6850649291040105, -0.7960248311986561, -0.9712118770341431] +minimum = 8 +bootstrap = 7 +signs = [[1, -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1], [-1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1], [1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, 1], [1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1], [-1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1], [1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1], [1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1]] +output = {"sadf" = 1.2172950245088612, "gsadf" = 1.2172950245088612, "sadf_pvalue" = 0.125, "gsadf_pvalue" = 0.125, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [1.8289528296279345, 2.3827655951645856, 3.3503702619627136, 3.314826531041144, 2.8572565689327125, 3.935284586736689, 4.980163559749005, 4.562795036473904, 5.7154408136481845, 5.659367648598458, 5.028390912730168, 5.826173047358161, 5.094262361531769, 5.461145512308816, 6.75530402397309, 7.439113393313606, 7.864229596160657] +minimum = 8 +bootstrap = 39 +signs = [[-1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1], [1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1], [-1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, 1], [-1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1], [-1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1], [-1, 1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, -1], [1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1], [-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1], [-1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1], [1, -1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1], [1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1], [-1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1], [-1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1], [1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1], [-1, 1, 1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1], [1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1], [1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1], [-1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1], [1, 1, 1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1], [1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1], [-1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 1], [-1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1], [-1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1], [1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1], [-1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, 1], [-1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1], [1, -1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1], [-1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1], [-1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1], [1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1], [1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1], [-1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1], [-1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1], [-1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1], [1, -1, -1, -1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1], [1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, -1, 1], [1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1], [1, -1, -1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, 1], [1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1]] +output = {"sadf" = -0.5702030462021181, "gsadf" = -0.02662321732134859, "sadf_pvalue" = 0.625, "gsadf_pvalue" = 0.5, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [-1.0046202207890218, -0.9274837822574357, -1.068651718689529, -1.276673727976467, -1.5103214446223474, -1.8285455410610802, -2.13245852245299, -2.5567118673236564, -2.9681037701593724, -3.439493703310616, -3.831187930682717, -4.395307261402685, -4.978771831152042, -5.750530064666139, -6.620187837042232, -7.575932757708506, -8.945195954329964] +minimum = 8 +bootstrap = 39 +signs = [[-1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1], [-1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, 1], [1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1], [-1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1], [-1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1], [1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1], [-1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1], [-1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1], [1, 1, -1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1], [1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1], [1, 1, 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1], [1, -1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1], [1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1], [-1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1], [-1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1], [-1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1], [-1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1, 1], [-1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1], [-1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, 1, 1, 1], [-1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1], [-1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1], [-1, 1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1], [-1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1], [-1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1], [1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1], [-1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1], [1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1], [1, 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1], [-1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1], [-1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1], [-1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, 1, 1, 1, 1], [-1, -1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1], [-1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1], [1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1], [1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1], [-1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, 1], [-1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1], [-1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1], [-1, -1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1]] +output = {"sadf" = 13.068907925371446, "gsadf" = 15.969246246100216, "sadf_pvalue" = 0.025, "gsadf_pvalue" = 0.025, "reject_sadf" = true, "reject_gsadf" = true} + +[[robust]] +values = [-0.9923758497917048, -2.030824457121539, -2.313009799098744, -1.1637581371115235, -2.2697776753730627, -1.5888857945159418, -1.3982286447895556, -1.1340320627974327, 0.030291418780400425, -0.7488431498443077, -3.368804185055665, -3.1343580606134323, -3.4275305751097758, -1.8328451364423526, -1.491630858085208, -1.6365944485857127, -2.5390606547633903, -1.9996599243488808, -2.572004972134663, -3.680681763252972, -4.103874258550509, -4.818164712365551, -5.814139608020927, -4.291750445661903, -4.910425089815459, -3.771696582431579, -4.265097267206811, -3.9082830928354957, -4.359340306885581] +minimum = 8 +bootstrap = 0 +signs = [] +output = {"sadf" = -0.8196412143223254, "gsadf" = 1.0641997228182172, "sadf_pvalue" = 1.0, "gsadf_pvalue" = 1.0, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [0.4305240315759838, 0.46494707992989426, 0.39914828351466325, 0.5401745576305745, 0.7323097871002902, 0.8083613991045859, 0.8845299455236068, 1.1325141046170097, 1.1724950093268192, 1.260491607214926, 1.3291932420089305, 1.3736889572451414, 1.4962371519412163, 1.6427092561149517, 2.039194056218807, 2.3991505576080328, 2.8174999119219617, 3.2564236452215183, 3.6979675682144557, 4.431424260641742, 5.062127787189592, 5.915910596816688, 6.809996750563735, 7.785577322941391, 8.989440442950123, 10.358858981225604, 12.057791577563949, 13.849381022532327, 16.212506084851206] +minimum = 8 +bootstrap = 0 +signs = [] +output = {"sadf" = 33.01573515165322, "gsadf" = 33.01573515165322, "sadf_pvalue" = 1.0, "gsadf_pvalue" = 1.0, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [0.3671383833602262, -0.20751052995400276, -0.0035449103807438653, -0.6643292518464993, -1.525403557219678, -1.945521164243469, -3.259868287892809, -1.126614696834538, -1.8003173129252228, -0.8355658737616932, -2.7256240232343645, -3.8171230714324267, -4.957601683468183, -6.480320139396321, -4.9674588862372016, -4.39504512902685, -3.8461169181632227, -5.245563752247991, -7.208964370712387, -5.83672220168013, -4.966896455604425, -4.1212689784059835, -5.424243781987967, -6.014504040678231, -4.771674520547434, -4.809199413098165, -3.814972840325477, -3.202345570309733, -3.604830560080483] +minimum = 8 +bootstrap = 7 +signs = [[1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1], [-1, 1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, 1, 1], [1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1], [-1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1], [-1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1], [-1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1], [1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1]] +output = {"sadf" = 0.030685003699857005, "gsadf" = 0.05004086302313143, "sadf_pvalue" = 1.0, "gsadf_pvalue" = 1.0, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [-0.6261654344143217, -0.6653880813328851, -0.8112082916514446, -0.957601176640013, -1.271740047259263, -1.4303141418293914, -1.6086749752225549, -1.8598694974677181, -2.3439955959811587, -2.6745099801797267, -2.9586576273569483, -3.372213956644235, -3.878824026129875, -4.4589388798483025, -5.300150695137896, -6.176616870660643, -7.247603159736608, -8.610550131497067, -10.170557386744568, -11.764351580934713, -13.642275069690566, -15.760700623024658, -18.297498160519368, -21.279667930753238, -24.846093630878247, -28.934246006585024, -33.57202472866879, -38.954901403796505, -45.1681257864008] +minimum = 8 +bootstrap = 7 +signs = [[1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1], [1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1], [1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1], [1, -1, -1, 1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1], [-1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1], [1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1], [1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1]] +output = {"sadf" = 97.62792879625734, "gsadf" = 97.62792879625734, "sadf_pvalue" = 0.125, "gsadf_pvalue" = 0.125, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [0.3639656639049367, -1.0154034887080015, 0.4532077751920436, -0.5119732280608077, 0.9974877262780858, 2.259052461536778, -0.77144583737131, -1.8874203250992627, -1.5775861631598644, -1.5034430306727435, -3.4264548841683973, -2.935741129393548, -4.474298587490949, -4.184447558967046, -3.0431138976165846, -2.9700611165023934, -4.043061513612934, -4.499495802544657, -4.914704190297567, -3.8566879167352157, -5.399637021648269, -6.621213274554354, -6.747338780010016, -7.117240021007272, -8.733131460098209, -8.05271338054942, -6.9725008856161335, -6.176406069624746, -7.84743896514064] +minimum = 8 +bootstrap = 39 +signs = [[-1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1], [1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1], [1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1], [-1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1], [1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1, -1], [1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1], [-1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1], [1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1], [1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1], [1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, 1], [1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1], [1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1], [-1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1], [1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1], [1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1], [1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1], [1, -1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1], [1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1], [1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1], [-1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1], [1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, -1], [1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1], [-1, 1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1], [-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1], [1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1], [-1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1], [-1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1], [-1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, 1, -1, 1], [1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1], [1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1], [-1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1], [1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, 1], [-1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1], [1, -1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1], [-1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1], [1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1], [1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1], [1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, 1]] +output = {"sadf" = -0.38288980690619095, "gsadf" = 0.5145391012752448, "sadf_pvalue" = 0.3, "gsadf_pvalue" = 0.25, "reject_sadf" = false, "reject_gsadf" = false} + +[[robust]] +values = [-0.5930901535940851, -0.8421080049678099, -1.1597822534750224, -1.1352697426164444, -1.3176422224280442, -1.6683502983547365, -2.129089798553528, -2.363343404686254, -2.856225433419209, -3.170229329554763, -3.8314000500822565, -4.440363706375841, -5.219816402333767, -6.10814934537737, -7.0896058509075, -8.240160877030972, -9.496610549664657, -11.088917248824496, -13.037090815864218, -15.021022671338413, -17.453090543510065, -20.15727114202945, -23.31218315854269, -27.034056558512052, -31.51522337469016, -36.39652340695896, -42.330970419182286, -49.01670687013717, -56.9182767812263] +minimum = 8 +bootstrap = 39 +signs = [[1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1], [-1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1], [1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, 1], [1, -1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1], [1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1], [-1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1], [1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1], [-1, -1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1], [1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1], [1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1], [-1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1], [1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, -1], [1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1], [1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1], [1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1], [1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1], [-1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1], [-1, -1, 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1], [-1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1], [-1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1], [1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1], [-1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1], [-1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1], [-1, 1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1], [1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1], [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1], [-1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1], [1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1], [1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, 1], [1, -1, -1, 1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1], [-1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, -1], [1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1], [1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1], [-1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1], [1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1], [-1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1], [1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1], [-1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1]] +output = {"sadf" = 97.1316894323255, "gsadf" = 102.36312072206553, "sadf_pvalue" = 0.025, "gsadf_pvalue" = 0.025, "reject_sadf" = true, "reject_gsadf" = true} + +[[pelt]] +values = [-0.42090220910543913, -0.04151349846337182, -0.391755643221674] +model = "normal" +multiplier = 0.5 +minimum = 2 +jump = 1 +output = [] +objective = 0.0 +reference_objective = 0.0 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= "normal" +multiplier = 8.0 +minimum = 4 +jump = 5 +output = [] +objective = 0.0 +reference_objective = 0.0 +unpruned_output = [] + +[[pelt]] +values = [-0.08418044182108783, -0.08302699692674365, -0.783511286443348] +model = "l2" +multiplier = 0.5 +minimum = 2 +jump = 1 +output = [] +objective = 0.0 +reference_objective = 0.0 +unpruned_output = [] + +[[pelt]] +values = [-0.08418044182108783, -0.08302699692674365, -0.783511286443348] +model = "l2" +multiplier = 3.0 +minimum = 3 +jump = 3 +output = [] +objective = 0.0 +reference_objective = 0.0 +unpruned_output = [] + +[[pelt]] +values = [-0.08418044182108783, -0.08302699692674365, -0.783511286443348] +model = "l2" +multiplier = 8.0 +minimum = 4 +jump = 5 +output = [] +objective = 0.0 +reference_objective = 0.0 +unpruned_output = [] + +[[pelt]] +values = [0.9299429757609596, 2.158848988621069, -1.567823506851155, -0.9965895127420419, 1.4654179050130516, -2.125495986797038, -0.5665815422973947, -0.13107388781469126] +model = "normal" 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1000000000.0336423, 1000000000.1824011, 1000000001.8457817, 1000000001.8144747, 1000000001.8839688, 1000000001.9458114, 1000000002.1907434, 1000000002.033271, 1000000002.0259078, 1000000001.9499757, 1000000002.0202267, 1000000001.8889081] +model = "l2" +multiplier = 0.5 +minimum = 2 +jump = 1 +output = [10] +objective = 3.2023361208431926 +reference_objective = 3.2023361208431926 +unpruned_output = [10] + +[[pelt]] +values = [1000000000.0582571, 999999999.9699368, 1000000000.1221689, 999999999.8898013, 1000000000.1210483, 999999999.8967837, 1000000000.0809661, 999999999.9287181, 1000000000.0336423, 1000000000.1824011, 1000000001.8457817, 1000000001.8144747, 1000000001.8839688, 1000000001.9458114, 1000000002.1907434, 1000000002.033271, 1000000002.0259078, 1000000001.9499757, 1000000002.0202267, 1000000001.8889081] +model = "l2" +multiplier = 3.0 +minimum = 3 +jump = 3 +output = [9] +objective = 21.026933953149996 +reference_objective = 21.026933953149996 +unpruned_output = [9] + +[[pelt]] +values = [1000000000.0582571, 999999999.9699368, 1000000000.1221689, 999999999.8898013, 1000000000.1210483, 999999999.8967837, 1000000000.0809661, 999999999.9287181, 1000000000.0336423, 1000000000.1824011, 1000000001.8457817, 1000000001.8144747, 1000000001.8839688, 1000000001.9458114, 1000000002.1907434, 1000000002.033271, 1000000002.0259078, 1000000001.9499757, 1000000002.0202267, 1000000001.8889081] +model = "l2" +multiplier = 8.0 +minimum = 4 +jump = 5 +output = [] +objective = 42.82659090491724 +reference_objective = 42.82659090491724 +unpruned_output = [] diff --git a/test/fixtures/synthetic_python_320.toml b/test/fixtures/synthetic_python_320.toml new file mode 100644 index 0000000..1904175 --- /dev/null +++ b/test/fixtures/synthetic_python_320.toml @@ -0,0 +1,1314 @@ +reference = "RiskLabAI Python 3.2.0" +contract = "Captured draws; no cross-language equal-seed claim" + +[[blocks]] +blocks = 1 +size = 1 +correlation = -0.2 +output = [[1.0]] + +[[blocks]] +blocks = 1 +size = 1 +correlation = 0.0 +output = [[1.0]] + +[[blocks]] +blocks = 1 +size = 1 +correlation = 0.6 +output = [[1.0]] + +[[blocks]] +blocks = 1 +size = 1 +correlation = 1.0 +output = [[1.0]] + +[[blocks]] +blocks = 1 +size = 2 +correlation = -0.2 +output = [[1.0, -0.2], [-0.2, 1.0]] + +[[blocks]] +blocks = 1 +size = 2 +correlation = 0.0 +output = [[1.0, 0.0], [0.0, 1.0]] + +[[blocks]] +blocks = 1 +size = 2 +correlation = 0.6 +output = [[1.0, 0.6], [0.6, 1.0]] + +[[blocks]] +blocks = 1 +size = 2 +correlation = 1.0 +output = [[1.0, 1.0], [1.0, 1.0]] + +[[blocks]] +blocks = 1 +size = 5 +correlation = -0.2 +output = [[1.0, -0.2, -0.2, -0.2, -0.2], [-0.2, 1.0, -0.2, -0.2, -0.2], [-0.2, -0.2, 1.0, -0.2, -0.2], [-0.2, -0.2, -0.2, 1.0, -0.2], [-0.2, -0.2, -0.2, -0.2, 1.0]] + +[[blocks]] +blocks = 1 +size = 5 +correlation = 0.0 +output = [[1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 1 +size = 5 +correlation = 0.6 +output = [[1.0, 0.6, 0.6, 0.6, 0.6], [0.6, 1.0, 0.6, 0.6, 0.6], [0.6, 0.6, 1.0, 0.6, 0.6], [0.6, 0.6, 0.6, 1.0, 0.6], [0.6, 0.6, 0.6, 0.6, 1.0]] + +[[blocks]] +blocks = 1 +size = 5 +correlation = 1.0 +output = [[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 1 +correlation = -0.2 +output = [[1.0, 0.0], [0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 1 +correlation = 0.0 +output = [[1.0, 0.0], [0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 1 +correlation = 0.6 +output = [[1.0, 0.0], [0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 1 +correlation = 1.0 +output = [[1.0, 0.0], [0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 2 +correlation = -0.2 +output = [[1.0, -0.2, 0.0, 0.0], [-0.2, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, -0.2], [0.0, 0.0, -0.2, 1.0]] + +[[blocks]] +blocks = 2 +size = 2 +correlation = 0.0 +output = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 2 +correlation = 0.6 +output = [[1.0, 0.6, 0.0, 0.0], [0.6, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.6], [0.0, 0.0, 0.6, 1.0]] + +[[blocks]] +blocks = 2 +size = 2 +correlation = 1.0 +output = [[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 5 +correlation = -0.2 +output = [[1.0, -0.2, -0.2, -0.2, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.2, 1.0, -0.2, -0.2, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.2, -0.2, 1.0, -0.2, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.2, -0.2, -0.2, 1.0, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.2, -0.2, -0.2, -0.2, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -0.2, -0.2, -0.2, -0.2], [0.0, 0.0, 0.0, 0.0, 0.0, -0.2, 1.0, -0.2, -0.2, -0.2], [0.0, 0.0, 0.0, 0.0, 0.0, -0.2, -0.2, 1.0, -0.2, -0.2], [0.0, 0.0, 0.0, 0.0, 0.0, -0.2, -0.2, -0.2, 1.0, -0.2], [0.0, 0.0, 0.0, 0.0, 0.0, -0.2, -0.2, -0.2, -0.2, 1.0]] + +[[blocks]] +blocks = 2 +size = 5 +correlation = 0.0 +output = [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 2 +size = 5 +correlation = 0.6 +output = [[1.0, 0.6, 0.6, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 1.0, 0.6, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 0.6, 1.0, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 0.6, 0.6, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 0.6, 0.6, 0.6, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.6, 0.6, 0.6, 0.6], [0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 1.0, 0.6, 0.6, 0.6], [0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.6, 1.0, 0.6, 0.6], [0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.6, 0.6, 1.0, 0.6], [0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.0]] + +[[blocks]] +blocks = 2 +size = 5 +correlation = 1.0 +output = [[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 1 +correlation = -0.2 +output = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 1 +correlation = 0.0 +output = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 1 +correlation = 0.6 +output = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 1 +correlation = 1.0 +output = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 2 +correlation = -0.2 +output = [[1.0, -0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.2, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, -0.2, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, -0.2, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, -0.2, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, -0.2, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -0.2], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.2, 1.0]] + +[[blocks]] +blocks = 4 +size = 2 +correlation = 0.0 +output = [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] + +[[blocks]] +blocks = 4 +size = 2 +correlation = 0.6 +output = [[1.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.6, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.6, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.6, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.6, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.6], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 1.0]] + +[[blocks]] +blocks = 4 +size = 2 +correlation = 1.0 +output = [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0]] + 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= [0.0, 1.0, 0.0] +uniform = 0.25 +output = 2 + +[[categorical]] +probabilities = [0.0, 1.0, 0.0] +uniform = 0.5 +output = 2 + +[[categorical]] +probabilities = [0.0, 1.0, 0.0] +uniform = 0.9999999999999999 +output = 2 + +[[categorical]] +probabilities = [0.25, 0.25, 0.5] +uniform = 0.0 +output = 1 + +[[categorical]] +probabilities = [0.25, 0.25, 0.5] +uniform = 0.24999999999999997 +output = 1 + +[[categorical]] +probabilities = [0.25, 0.25, 0.5] +uniform = 0.25 +output = 2 + +[[categorical]] +probabilities = [0.25, 0.25, 0.5] +uniform = 0.5 +output = 3 + +[[categorical]] +probabilities = [0.25, 0.25, 0.5] +uniform = 0.9999999999999999 +output = 3 + +[[categorical]] +probabilities = [0.5, 0.5, 0.0] +uniform = 0.0 +output = 1 + +[[categorical]] +probabilities = [0.5, 0.5, 0.0] +uniform = 0.24999999999999997 +output = 1 + +[[categorical]] +probabilities = [0.5, 0.5, 0.0] +uniform = 0.25 +output = 1 + +[[categorical]] +probabilities = [0.5, 0.5, 0.0] +uniform = 0.5 +output = 2 + +[[categorical]] +probabilities = [0.5, 0.5, 0.0] +uniform = 0.9999999999999999 +output = 2 + +[[categorical]] +probabilities = [0.0, 0.5, 0.5] +uniform = 0.0 +output = 2 + +[[categorical]] +probabilities = [0.0, 0.5, 0.5] +uniform = 0.24999999999999997 +output = 2 + +[[categorical]] +probabilities = [0.0, 0.5, 0.5] +uniform = 0.25 +output = 2 + +[[categorical]] +probabilities = [0.0, 0.5, 0.5] +uniform = 0.5 +output = 3 + +[[categorical]] +probabilities = [0.0, 0.5, 0.5] +uniform = 0.9999999999999999 +output = 3 diff --git a/test/fixtures/timegan_python_320.toml b/test/fixtures/timegan_python_320.toml new file mode 100644 index 0000000..b24a0b9 --- /dev/null +++ b/test/fixtures/timegan_python_320.toml @@ -0,0 +1,217 @@ +reference = "RiskLabAI 3.2.0 public temporal-only TimeGAN" + +[[splits]] +series = [[-0.10697717789553764], [0.5354856039942887], [2.338859850187703], [0.7771649417944926], [1.5740796511090733], [1.261313904090438], [0.5059537149264751], [-0.9197313631296736], 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[0.7899999999999999, 0.82, 0.82, 1.0] + +[[decay]] +values = [0.3, 0.1, 0.0, 0.6] +last_weight = 1.0 +output = [1.0, 1.0, 1.0, 1.0] + +[[decay]] +values = [1.0, nan, 2.0, 3.0] +last_weight = 0.0 +output = [0.16666666666666666, nan, 0.5, 1.0] + +[[decay]] +values = [1.0, nan, 2.0, 3.0] +last_weight = 0.2 +output = [0.33333333333333326, nan, 0.6, 1.0] + +[[decay]] +values = [1.0, nan, 2.0, 3.0] +last_weight = 0.7 +output = [0.75, nan, 0.85, 1.0] + +[[decay]] +values = [1.0, nan, 2.0, 3.0] +last_weight = 1.0 +output = [1.0, nan, 1.0, 1.0] + +[[decay]] +values = [nan, 1.0, 2.0] +last_weight = 0.0 +output = [nan, 0.3333333333333333, 1.0] + +[[decay]] +values = [nan, 1.0, 2.0] +last_weight = 0.2 +output = [nan, 0.4666666666666666, 1.0] + +[[decay]] +values = [nan, 1.0, 2.0] +last_weight = 0.7 +output = [nan, 0.7999999999999999, 1.0] + +[[decay]] +values = [nan, 1.0, 2.0] +last_weight = 1.0 +output = [nan, 1.0, 1.0] + +[[decay]] +values = [1.0, 2.0, nan] +last_weight = 0.0 +output = [nan, nan, nan] + +[[decay]] +values = [1.0, 2.0, nan] +last_weight = 0.2 +output = [nan, nan, nan] + +[[decay]] +values = [1.0, 2.0, nan] +last_weight = 0.7 +output = [nan, nan, nan] + +[[decay]] +values = [1.0, 2.0, nan] +last_weight = 1.0 +output = [1.0, 1.0, nan] diff --git a/test/fixtures/validation_python_320.toml b/test/fixtures/validation_python_320.toml new file mode 100644 index 0000000..9787a2e --- /dev/null +++ b/test/fixtures/validation_python_320.toml @@ -0,0 +1,14737 @@ +reference = "RiskLabAI 3.2.0 purging, adaptive boundaries and estimator-driven paths" + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.0 +continuous = false +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.0 +continuous = true +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.125 +continuous = false +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.125 +continuous = true +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.375 +continuous = false +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [] +embargo = 0.375 +continuous = true +train = [1, 2, 3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.0 +continuous = false +train = [3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.0 +continuous = true +train = [3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.125 +continuous = false +train = [4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.125 +continuous = true +train = [4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.375 +continuous = false +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2] +embargo = 0.375 +continuous = true +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.0 +continuous = true +train = [3, 4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.125 +continuous = true +train = [4, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [2, 1] +embargo = 0.375 +continuous = true +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.0 +continuous = false +train = [2, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.0 +continuous = true +train = [2, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.125 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.125 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.0 +continuous = true +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.0 +continuous = false +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.0 +continuous = true +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.125 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.0 +continuous = true +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.0 +continuous = false +train = [2, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.0 +continuous = true +train = [2, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.125 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.125 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.0 +continuous = false +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.125 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.0 +continuous = false +train = [2, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.0 +continuous = true +train = [2, 5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.125 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.125 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.0 +continuous = false +train = [6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.125 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [5, 6, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.0 +continuous = false +train = [2, 4, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.0 +continuous = true +train = [2, 4, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.0 +continuous = false +train = [4, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.0 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.0 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.0 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.0 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.0 +continuous = false +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.0 +continuous = true +train = [2, 7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [7, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.0 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.0 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.125 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.125 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.375 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6] +embargo = 0.375 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.0 +continuous = false +train = [3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.0 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.0 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.0 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.0 +continuous = false +train = [1, 2, 3, 4, 5, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.0 +continuous = true +train = [1, 2, 3, 4, 5, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.125 +continuous = false +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.125 +continuous = true +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.375 +continuous = false +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7] +embargo = 0.375 +continuous = true +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.0 +continuous = false +train = [3, 4, 5, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.125 +continuous = false +train = [4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.0 +continuous = false +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.0 +continuous = false +train = [2, 5, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.0 +continuous = false +train = [5, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.0 +continuous = false +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.0 +continuous = false +train = [2, 4, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.0 +continuous = true +train = [2, 4, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.0 +continuous = false +train = [4, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.0 +continuous = false +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.0 +continuous = false +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.0 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.0 +continuous = true +train = [2, 3, 4, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.125 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.125 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.375 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6] +embargo = 0.375 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.0 +continuous = false +train = [3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.0 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.0 +continuous = true +train = [2, 4, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.0 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2, 8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [8] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [7, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.0 +continuous = false +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.0 +continuous = true +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.125 +continuous = false +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.125 +continuous = true +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.375 +continuous = false +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8] +embargo = 0.375 +continuous = true +train = [1, 2, 3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.0 +continuous = false +train = [3, 4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.125 +continuous = false +train = [4, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.375 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.0 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.125 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.125 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.0 +continuous = false +train = [2, 5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.125 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.0 +continuous = false +train = [5, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.125 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.0 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.125 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.0 +continuous = false +train = [2, 4, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.0 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.0 +continuous = false +train = [4, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.0 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.0 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2, 7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [7] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.0 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.0 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.125 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.125 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.375 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6] +embargo = 0.375 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.0 +continuous = false +train = [3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.0 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.0 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.0 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.0 +continuous = false +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.0 +continuous = true +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.125 +continuous = false +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.125 +continuous = true +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.375 +continuous = false +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7] +embargo = 0.375 +continuous = true +train = [1, 2, 3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.0 +continuous = false +train = [3, 4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.125 +continuous = false +train = [4, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.0 +continuous = false +train = [2, 5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.0 +continuous = false +train = [5] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.0 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.0 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.0 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.0 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.0 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.125 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.125 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.375 +continuous = false +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6] +embargo = 0.375 +continuous = true +train = [2, 3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.0 +continuous = false +train = [3, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.0 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.0 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.125 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.125 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.375 +continuous = false +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5] +embargo = 0.375 +continuous = true +train = [2, 4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.0 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.125 +continuous = false +train = [4] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.0 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.0 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.125 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.125 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.375 +continuous = false +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3] +embargo = 0.375 +continuous = true +train = [2] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 1] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2] +embargo = 0.375 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.0 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.125 +continuous = true +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = false +train = [] + +[[purge]] +starts = [0, 1, 2, 4, 6, 9, 13, 15] +ends = [12, 1, 6, 5, 10, 16, 14, 19] +test = [8, 7, 6, 5, 4, 3, 2, 1] +embargo = 0.375 +continuous = true +train = [] + +[[geometry]] +kind = "combinatorial" +groups = 2 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11]] +locations = {"1" = [[1, 1], [2, 2]]} +splits = [{"train" = [9, 10, 11], "test" = [1, 2, 3, 4, 5, 6]}, {"train" = [1, 2, 3], "test" = [7, 8, 9, 10, 11]}] +paths = {"1" = [{"train" = [9, 10, 11], "test" = [1, 2, 3, 4, 5, 6]}, {"train" = [1, 2, 3], "test" = [7, 8, 9, 10, 11]}]} + +[[geometry]] +kind = "combinatorial" +groups = 2 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25] +embargo = 0.075 +segments = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11]] +locations = {"1" = [[1, 1], [2, 2]]} +splits = [{"train" = [9, 10, 11], "test" = [1, 2, 3, 4, 5, 6]}, {"train" = [1, 2, 3], "test" = [7, 8, 9, 10, 11]}] +paths = {"1" = [{"train" = [9, 10, 11], "test" = [1, 2, 3, 4, 5, 6]}, {"train" = [1, 2, 3], "test" = [7, 8, 9, 10, 11]}]} + +[[geometry]] +kind = "combinatorial" +groups = 3 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]] +locations = {"1" = [[1, 1], [2, 2], [3, 3]]} +splits = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 9], "test" = [11, 12, 13, 14]}] +paths = {"1" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 9], "test" = [11, 12, 13, 14]}]} + +[[geometry]] +kind = "combinatorial" +groups = 3 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30] +embargo = 0.075 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]] +locations = {"1" = [[1, 1], [2, 2], [3, 3]]} +splits = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 9], "test" = [11, 12, 13, 14]}] +paths = {"1" = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 9], "test" = [11, 12, 13, 14]}]} + +[[geometry]] +kind = "combinatorial" +groups = 3 +test_groups = 2 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]] +locations = {"1" = [[1, 1], [2, 1], [3, 2]], "2" = [[1, 2], [2, 3], [3, 3]]} +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, {"train" = [9], "test" = [1, 2, 3, 4, 5, 11, 12, 13, 14]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10, 11, 12, 13, 14]}] +paths = {"1" = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [], "test" = [6, 7, 8, 9, 10]}, {"train" = [9], "test" = [11, 12, 13, 14]}], "2" = [{"train" = [9], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3], "test" = [11, 12, 13, 14]}]} + +[[geometry]] +kind = "combinatorial" +groups = 3 +test_groups = 2 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30] +embargo = 0.075 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]] +locations = {"1" = [[1, 1], [2, 1], [3, 2]], "2" = [[1, 2], [2, 3], [3, 3]]} +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 11, 12, 13, 14]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10, 11, 12, 13, 14]}] +paths = {"1" = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [], "test" = [6, 7, 8, 9, 10]}, {"train" = [], "test" = [11, 12, 13, 14]}], "2" = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3], "test" = [11, 12, 13, 14]}]} + +[[geometry]] +kind = "combinatorial" +groups = 4 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16, 17]] +locations = {"1" = [[1, 1], [2, 2], [3, 3], [4, 4]]} +splits = [{"train" = [9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 12, 13, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 17], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12], "test" = [14, 15, 16, 17]}] +paths = {"1" = [{"train" = [9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 12, 13, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 17], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12], "test" = [14, 15, 16, 17]}]} + +[[geometry]] +kind = "combinatorial" +groups = 4 +test_groups = 1 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35] +embargo = 0.075 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16, 17]] +locations = {"1" = [[1, 1], [2, 2], [3, 3], [4, 4]]} +splits = [{"train" = [11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12], "test" = [14, 15, 16, 17]}] +paths = {"1" = [{"train" = [11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12], "test" = [14, 15, 16, 17]}]} + +[[geometry]] +kind = "combinatorial" +groups = 4 +test_groups = 2 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16, 17]] +locations = {"1" = [[1, 1], [2, 1], [3, 2], [4, 3]], "2" = [[1, 2], [2, 4], [3, 4], [4, 5]], "3" = [[1, 3], [2, 5], [3, 6], [4, 6]]} +splits = [{"train" = [12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [17], "test" = [1, 2, 3, 4, 5, 10, 11, 12, 13]}, {"train" = [9, 11, 12], "test" = [1, 2, 3, 4, 5, 14, 15, 16, 17]}, {"train" = [1, 2, 3, 17], "test" = [6, 7, 8, 9, 10, 11, 12, 13]}, {"train" = [1, 2, 3, 12], "test" = [6, 7, 8, 9, 14, 15, 16, 17]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] +paths = {"1" = [{"train" = [12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [12, 13, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [17], "test" = [10, 11, 12, 13]}, {"train" = [9, 11, 12], "test" = [14, 15, 16, 17]}], "2" = [{"train" = [17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 17], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 12], "test" = [14, 15, 16, 17]}], "3" = [{"train" = [9, 11, 12], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 12], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [14, 15, 16, 17]}]} + +[[geometry]] +kind = "combinatorial" +groups = 4 +test_groups = 2 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35] +embargo = 0.075 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16, 17]] +locations = {"1" = [[1, 1], [2, 1], [3, 2], [4, 3]], "2" = [[1, 2], [2, 4], [3, 4], [4, 5]], "3" = [[1, 3], [2, 5], [3, 6], [4, 6]]} +splits = [{"train" = [14, 15, 16, 17], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 10, 11, 12, 13]}, {"train" = [11, 12], "test" = [1, 2, 3, 4, 5, 14, 15, 16, 17]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10, 11, 12, 13]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 14, 15, 16, 17]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] +paths = {"1" = [{"train" = [14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [], "test" = [10, 11, 12, 13]}, {"train" = [11, 12], "test" = [14, 15, 16, 17]}], "2" = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3], "test" = [14, 15, 16, 17]}], "3" = [{"train" = [11, 12], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [14, 15, 16, 17]}]} + +[[geometry]] +kind = "combinatorial" +groups = 4 +test_groups = 3 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35] +embargo = 0.0 +segments = [[1, 2, 3, 4, 5], [6, 7, 8, 9], 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"test" = [9, 10, 11, 12]}, {"train" = [1, 23], "test" = [13, 14, 15, 16]}, {"train" = [1, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}], "3" = [{"train" = [12, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 16, 17, 20], "test" = [21, 22, 23]}], "4" = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}], "5" = [{"train" = [11, 12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 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16, 17, 18, 19, 20]}, {"train" = [9, 12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 21, 22, 23], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [1, 12], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1, 12], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}] +paths = {"1" = [{"train" = [15, 16, 17, 18, 19, 20, 21, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [15, 16, 17, 18, 19, 20, 21, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 18, 19, 20, 21, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [12, 21, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}], "2" = [{"train" = [12, 21, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [12, 21, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [21, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [21, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [15, 16, 17, 20], "test" = [21, 22, 23]}], "3" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 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= [1, 12, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [1, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 12, 13, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}], "10" = [{"train" = [9, 12, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 22, 23], "test" = [24, 25, 26]}], "11" = [{"train" = [9, 12, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "12" = [{"train" = [9, 12, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 15, 16, 17, 20], "test" = [24, 25, 26]}], "13" = [{"train" = [9, 10, 11, 12, 13, 14, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 26], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}], "14" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}], "15" = [{"train" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}]} + +[[geometry]] +kind = "combinatorial" +groups = 7 +test_groups = 3 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 2], [5, 3], [6, 4], [7, 5]], "2" = [[1, 2], [2, 2], [3, 6], [4, 6], [5, 7], [6, 8], [7, 9]], "3" = [[1, 3], [2, 3], [3, 7], [4, 10], [5, 10], [6, 11], [7, 12]], "4" = [[1, 4], [2, 4], [3, 8], [4, 11], [5, 13], [6, 13], [7, 14]], "5" = [[1, 5], [2, 5], [3, 9], [4, 12], [5, 14], [6, 15], [7, 15]], "6" = [[1, 6], [2, 16], [3, 16], [4, 16], [5, 17], [6, 18], [7, 19]], "7" = [[1, 7], [2, 17], [3, 17], [4, 20], [5, 20], [6, 21], [7, 22]], "8" = [[1, 8], [2, 18], [3, 18], [4, 21], [5, 23], [6, 23], [7, 24]], "9" = [[1, 9], [2, 19], [3, 19], [4, 22], [5, 24], [6, 25], [7, 25]], "10" = [[1, 10], [2, 20], [3, 26], [4, 26], [5, 26], [6, 27], [7, 28]], "11" = [[1, 11], [2, 21], [3, 27], [4, 27], [5, 29], [6, 29], [7, 30]], "12" = [[1, 12], [2, 22], [3, 28], [4, 28], [5, 30], [6, 31], [7, 31]], "13" = [[1, 13], [2, 23], [3, 29], [4, 32], [5, 32], [6, 32], [7, 33]], "14" = [[1, 14], [2, 24], [3, 30], [4, 33], [5, 33], [6, 34], [7, 34]], "15" = [[1, 15], [2, 25], [3, 31], [4, 34], [5, 35], [6, 35], [7, 35]]} +splits = [{"train" = [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}, {"train" = [23, 24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16]}, {"train" = [14, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20]}, {"train" = [14, 15, 16, 17, 20], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 21, 22, 23]}, {"train" = [14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 24, 25, 26]}, {"train" = [23, 24, 25, 26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [25, 26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [16, 17, 20], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [12, 25, 26], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [12, 23], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [11, 12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 23, 24, 25, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 25, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [1, 16, 17, 20], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [1, 16, 17, 18, 19, 20, 22, 23], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [1, 25, 26], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 23], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [1, 14], "test" = [5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 14], "test" = [5, 6, 7, 8, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 14, 15, 16, 17, 20], "test" = [5, 6, 7, 8, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 25, 26], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 16, 17, 20], "test" = [9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}] +paths = {"1" = [{"train" = [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [23, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [14, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [24, 25, 26]}], "2" = [{"train" = [23, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [23, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [23, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [23, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [25, 26], "test" = [17, 18, 19, 20]}, {"train" = [16, 17, 20], "test" = [21, 22, 23]}, {"train" = [16, 17, 18, 19, 20, 22, 23], "test" = [24, 25, 26]}], "3" = [{"train" = [14, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [14, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [25, 26], "test" = [9, 10, 11, 12]}, {"train" = [12, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [12, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [12], "test" = [21, 22, 23]}, {"train" = [12, 23], "test" = [24, 25, 26]}], "4" = [{"train" = [14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [14, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [12], "test" = [13, 14, 15, 16]}, {"train" = [11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [11, 12, 13, 14], "test" = [24, 25, 26]}], "5" = [{"train" = [14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [16, 17, 18, 19, 20, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [12, 23], "test" = [13, 14, 15, 16]}, {"train" = [11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [11, 12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [11, 12, 13, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}], "6" = [{"train" = [23, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 23, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 23, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 23, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 16, 17, 18, 19, 20, 22, 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= [17, 18, 19, 20]}, {"train" = [1, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}], "10" = [{"train" = [12, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [24, 25, 26]}], "11" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "12" = [{"train" = [12, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 23], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 16, 17, 20], "test" = [24, 25, 26]}], "13" = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}], "14" = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}], "15" = [{"train" = [11, 12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 14, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}]} + +[[geometry]] +kind = "combinatorial" +groups = 7 +test_groups = 4 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61] +embargo = 0.0 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 2], [6, 3], [7, 4]], "2" = [[1, 2], [2, 2], [3, 2], [4, 5], [5, 5], [6, 6], [7, 7]], "3" = [[1, 3], [2, 3], [3, 3], [4, 6], [5, 8], [6, 8], [7, 9]], "4" = [[1, 4], [2, 4], [3, 4], [4, 7], [5, 9], [6, 10], [7, 10]], "5" = [[1, 5], [2, 5], [3, 11], [4, 11], [5, 11], [6, 12], [7, 13]], "6" = [[1, 6], [2, 6], [3, 12], [4, 12], [5, 14], [6, 14], [7, 15]], "7" = [[1, 7], [2, 7], [3, 13], [4, 13], [5, 15], [6, 16], [7, 16]], "8" = [[1, 8], [2, 8], [3, 14], [4, 17], [5, 17], [6, 17], [7, 18]], "9" = [[1, 9], [2, 9], [3, 15], [4, 18], [5, 18], [6, 19], [7, 19]], "10" = [[1, 10], [2, 10], [3, 16], [4, 19], [5, 20], [6, 20], [7, 20]], "11" = [[1, 11], [2, 21], [3, 21], [4, 21], [5, 21], [6, 22], [7, 23]], "12" = [[1, 12], [2, 22], [3, 22], [4, 22], [5, 24], [6, 24], [7, 25]], "13" = [[1, 13], [2, 23], [3, 23], [4, 23], [5, 25], [6, 26], [7, 26]], "14" = [[1, 14], [2, 24], [3, 24], [4, 27], [5, 27], [6, 27], [7, 28]], "15" = [[1, 15], [2, 25], [3, 25], [4, 28], [5, 28], [6, 29], [7, 29]], "16" = [[1, 16], [2, 26], [3, 26], [4, 29], [5, 30], [6, 30], [7, 30]], "17" = [[1, 17], [2, 27], [3, 31], [4, 31], [5, 31], [6, 31], [7, 32]], "18" = [[1, 18], [2, 28], [3, 32], [4, 32], [5, 32], [6, 33], [7, 33]], "19" = [[1, 19], [2, 29], [3, 33], [4, 33], [5, 34], [6, 34], [7, 34]], "20" = [[1, 20], [2, 30], [3, 34], [4, 35], [5, 35], [6, 35], [7, 35]]} +splits = [{"train" = [21, 22, 23, 24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [15, 16, 17, 20, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [12, 24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [12, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [12, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [12, 13, 14, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [12, 13, 14], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 21, 22, 23, 24, 25, 26]}, {"train" = [24, 25, 26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [22, 23], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [9, 12, 26], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [9, 12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [9, 12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 24, 25, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 22, 23], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [1, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 12, 26], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 12], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 12], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}] +paths = {"1" = [{"train" = [21, 22, 23, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [21, 22, 23, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [21, 22, 23, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [21, 22, 23, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [15, 16, 17, 20, 26], "test" = [21, 22, 23]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [24, 25, 26]}], "2" = [{"train" = [24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [12, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [12, 24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [12, 26], "test" = [21, 22, 23]}, {"train" = [12, 22, 23], "test" = [24, 25, 26]}], "3" = [{"train" = [15, 16, 17, 20, 26], "test" = [1, 2, 3, 4]}, {"train" = [15, 16, 17, 20, 26], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 20, 26], "test" = [9, 10, 11, 12]}, {"train" = [12, 26], "test" = [13, 14, 15, 16]}, {"train" = [12, 13, 14, 26], "test" = [17, 18, 19, 20]}, {"train" = [12, 13, 14, 26], "test" = [21, 22, 23]}, {"train" = [12, 13, 14], "test" = [24, 25, 26]}], "4" = [{"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [12, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}], "5" = [{"train" = [12, 24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [12, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [26], "test" = [21, 22, 23]}, {"train" = [22, 23], "test" = [24, 25, 26]}], "6" = [{"train" = [12, 26], "test" = [1, 2, 3, 4]}, {"train" = [12, 26], "test" = [5, 6, 7, 8]}, {"train" = [26], "test" = [9, 10, 11, 12]}, {"train" = [26], "test" = [13, 14, 15, 16]}, {"train" = [26], "test" = [17, 18, 19, 20]}, {"train" = [26], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}], "7" = [{"train" = [12, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [12, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [22, 23], "test" = [9, 10, 11, 12]}, {"train" = [22, 23], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [15, 16, 17, 20], "test" = [24, 25, 26]}], "8" = [{"train" = [12, 13, 14, 26], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14, 26], "test" = [5, 6, 7, 8]}, {"train" = [26], "test" = [9, 10, 11, 12]}, {"train" = [9, 12, 26], "test" = [13, 14, 15, 16]}, {"train" = [9, 12, 26], "test" = [17, 18, 19, 20]}, {"train" = [9, 12, 26], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}], "9" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 12], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}], "10" = [{"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}], "11" = [{"train" = [24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 26], "test" = [21, 22, 23]}, {"train" = [1, 22, 23], "test" = [24, 25, 26]}], "12" = [{"train" = [26], "test" = [1, 2, 3, 4]}, {"train" = [1, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 26], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "13" = [{"train" = [22, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 15, 16, 17, 20], "test" = [24, 25, 26]}], "14" = [{"train" = [26], "test" = [1, 2, 3, 4]}, {"train" = [1, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 12, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 12, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 12, 26], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}], "15" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}], "16" = [{"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 12, 13, 14], "test" = [24, 25, 26]}], "17" = [{"train" = [9, 12, 26], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 26], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "18" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "19" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "20" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}]} + +[[geometry]] +kind = "combinatorial" +groups = 7 +test_groups = 4 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 2], [6, 3], [7, 4]], "2" = [[1, 2], [2, 2], [3, 2], [4, 5], [5, 5], [6, 6], [7, 7]], "3" = [[1, 3], [2, 3], [3, 3], [4, 6], [5, 8], [6, 8], [7, 9]], "4" = [[1, 4], [2, 4], [3, 4], [4, 7], [5, 9], [6, 10], [7, 10]], "5" = [[1, 5], [2, 5], [3, 11], [4, 11], [5, 11], [6, 12], [7, 13]], "6" = [[1, 6], [2, 6], [3, 12], [4, 12], [5, 14], [6, 14], [7, 15]], "7" = [[1, 7], [2, 7], [3, 13], [4, 13], [5, 15], [6, 16], [7, 16]], "8" = [[1, 8], [2, 8], [3, 14], [4, 17], [5, 17], [6, 17], [7, 18]], "9" = [[1, 9], [2, 9], [3, 15], [4, 18], [5, 18], [6, 19], [7, 19]], "10" = [[1, 10], [2, 10], [3, 16], [4, 19], [5, 20], [6, 20], [7, 20]], "11" = [[1, 11], [2, 21], [3, 21], [4, 21], [5, 21], [6, 22], [7, 23]], "12" = [[1, 12], [2, 22], [3, 22], [4, 22], [5, 24], [6, 24], [7, 25]], "13" = [[1, 13], [2, 23], [3, 23], [4, 23], [5, 25], [6, 26], [7, 26]], "14" = [[1, 14], [2, 24], [3, 24], [4, 27], [5, 27], [6, 27], [7, 28]], "15" = [[1, 15], [2, 25], [3, 25], [4, 28], [5, 28], [6, 29], [7, 29]], "16" = [[1, 16], [2, 26], [3, 26], [4, 29], [5, 30], [6, 30], [7, 30]], "17" = [[1, 17], [2, 27], [3, 31], [4, 31], [5, 31], [6, 31], [7, 32]], "18" = [[1, 18], [2, 28], [3, 32], [4, 32], [5, 32], [6, 33], [7, 33]], "19" = [[1, 19], [2, 29], [3, 33], [4, 33], [5, 34], [6, 34], [7, 34]], "20" = [[1, 20], [2, 30], [3, 34], [4, 35], [5, 35], [6, 35], [7, 35]]} +splits = [{"train" = [23, 24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [16, 17, 20], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [14], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [14], "test" = [1, 2, 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{"train" = [1, 25, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [1, 23], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1, 16, 17, 20], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 14], "test" = [5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 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[17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1, 23], "test" = [24, 25, 26]}], "12" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "13" = [{"train" = [23], "test" = [1, 2, 3, 4]}, {"train" = [1, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 23], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 16, 17, 20], "test" = [24, 25, 26]}], "14" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "15" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "16" = [{"train" = [16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 14], "test" = [21, 22, 23]}, {"train" = [1, 14], "test" = [24, 25, 26]}], "17" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "18" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "19" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}], "20" = [{"train" = [11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}]} + +[[geometry]] +kind = "combinatorial" +groups = 7 +test_groups = 5 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61] +embargo = 0.0 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 2], [7, 3]], "2" = [[1, 2], [2, 2], [3, 2], [4, 2], [5, 4], [6, 4], [7, 5]], "3" = [[1, 3], [2, 3], [3, 3], [4, 3], [5, 5], [6, 6], [7, 6]], "4" = [[1, 4], [2, 4], [3, 4], [4, 7], [5, 7], [6, 7], [7, 8]], "5" = [[1, 5], [2, 5], [3, 5], [4, 8], [5, 8], [6, 9], [7, 9]], "6" = [[1, 6], [2, 6], [3, 6], [4, 9], [5, 10], [6, 10], [7, 10]], "7" = [[1, 7], [2, 7], [3, 11], [4, 11], [5, 11], [6, 11], [7, 12]], "8" = [[1, 8], [2, 8], [3, 12], [4, 12], [5, 12], [6, 13], [7, 13]], "9" = [[1, 9], [2, 9], [3, 13], [4, 13], [5, 14], [6, 14], [7, 14]], "10" = [[1, 10], [2, 10], [3, 14], [4, 15], [5, 15], [6, 15], [7, 15]], "11" = [[1, 11], [2, 16], [3, 16], [4, 16], [5, 16], [6, 16], [7, 17]], "12" = [[1, 12], [2, 17], [3, 17], [4, 17], [5, 17], [6, 18], [7, 18]], "13" = [[1, 13], [2, 18], [3, 18], [4, 18], [5, 19], [6, 19], [7, 19]], "14" = [[1, 14], [2, 19], [3, 19], [4, 20], [5, 20], [6, 20], [7, 20]], "15" = [[1, 15], [2, 20], [3, 21], [4, 21], [5, 21], [6, 21], [7, 21]]} +splits = [{"train" = [24, 25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [12, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [12], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [12], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [12, 13, 14], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [26], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [9, 12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 26], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 12], "test" = [5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}] +paths = {"1" = [{"train" = [24, 25, 26], "test" = [1, 2, 3, 4]}, {"train" = [24, 25, 26], "test" = [5, 6, 7, 8]}, {"train" = [24, 25, 26], "test" = [9, 10, 11, 12]}, {"train" = [24, 25, 26], "test" = [13, 14, 15, 16]}, {"train" = [24, 25, 26], "test" = [17, 18, 19, 20]}, {"train" = [26], "test" = [21, 22, 23]}, {"train" = [22, 23], "test" = [24, 25, 26]}], "2" = [{"train" = [26], "test" = [1, 2, 3, 4]}, {"train" = [26], "test" = [5, 6, 7, 8]}, {"train" = [26], "test" = [9, 10, 11, 12]}, {"train" = [26], "test" = [13, 14, 15, 16]}, {"train" = [26], "test" = [17, 18, 19, 20]}, {"train" = [26], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}], "3" = [{"train" = [22, 23], "test" = [1, 2, 3, 4]}, {"train" = [22, 23], "test" = [5, 6, 7, 8]}, {"train" = [22, 23], "test" = [9, 10, 11, 12]}, {"train" = [22, 23], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [15, 16, 17, 20], "test" = [24, 25, 26]}], "4" = [{"train" = [26], "test" = [1, 2, 3, 4]}, {"train" = [26], "test" = [5, 6, 7, 8]}, {"train" = [26], "test" = [9, 10, 11, 12]}, {"train" = [12, 26], "test" = [13, 14, 15, 16]}, {"train" = [12, 26], "test" = [17, 18, 19, 20]}, {"train" = [12, 26], "test" = [21, 22, 23]}, {"train" = [12], "test" = [24, 25, 26]}], "5" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [12], "test" = [13, 14, 15, 16]}, {"train" = [12], "test" = [17, 18, 19, 20]}, {"train" = [12], "test" = [21, 22, 23]}, {"train" = [12], "test" = [24, 25, 26]}], "6" = [{"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [12], "test" = [13, 14, 15, 16]}, {"train" = [12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [12, 13, 14], "test" = [21, 22, 23]}, {"train" = [12, 13, 14], "test" = [24, 25, 26]}], "7" = [{"train" = [12, 26], "test" = [1, 2, 3, 4]}, {"train" = [12, 26], "test" = [5, 6, 7, 8]}, {"train" = [26], "test" = [9, 10, 11, 12]}, {"train" = [26], "test" = [13, 14, 15, 16]}, 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23]}, {"train" = [9, 12], "test" = [24, 25, 26]}], "11" = [{"train" = [26], "test" = [1, 2, 3, 4]}, {"train" = [1, 26], "test" = [5, 6, 7, 8]}, {"train" = [1, 26], "test" = [9, 10, 11, 12]}, {"train" = [1, 26], "test" = [13, 14, 15, 16]}, {"train" = [1, 26], "test" = [17, 18, 19, 20]}, {"train" = [1, 26], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "12" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "13" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}], "14" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}], "15" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}]} + +[[geometry]] +kind = "combinatorial" +groups = 7 +test_groups = 5 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61] +embargo = 0.075 +segments = [[1, 2, 3, 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= [[1, 14], [2, 19], [3, 19], [4, 20], [5, 20], [6, 20], [7, 20]], "15" = [[1, 15], [2, 20], [3, 21], [4, 21], [5, 21], [6, 21], [7, 21]]} +splits = [{"train" = [25, 26], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [16, 17, 20], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [14], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23, 24, 25, 26]}, {"train" = [1], "test" = [5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]}, {"train" = [1], "test" = 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[27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 3 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 2], [5, 3], [6, 4], [7, 5], [8, 6]], "2" = [[1, 2], [2, 2], [3, 7], [4, 7], [5, 8], [6, 9], [7, 10], [8, 11]], "3" = [[1, 3], [2, 3], [3, 8], [4, 12], [5, 12], [6, 13], [7, 14], [8, 15]], "4" = [[1, 4], [2, 4], [3, 9], [4, 13], [5, 16], [6, 16], [7, 17], [8, 18]], "5" = [[1, 5], [2, 5], [3, 10], [4, 14], [5, 17], [6, 19], [7, 19], [8, 20]], "6" = [[1, 6], [2, 6], [3, 11], [4, 15], [5, 18], [6, 20], [7, 21], [8, 21]], "7" = [[1, 7], [2, 22], [3, 22], [4, 22], [5, 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"20" = [[1, 20], [2, 35], [3, 45], [4, 51], [5, 54], [6, 54], [7, 55], [8, 55]], "21" = [[1, 21], [2, 36], [3, 46], [4, 52], [5, 55], [6, 56], [7, 56], [8, 56]]} +splits = [{"train" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]}, {"train" = [24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16]}, {"train" = [26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20]}, {"train" = [15, 16, 17, 20, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 21, 22, 23]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 24, 25, 26]}, {"train" = [15, 16, 17, 18, 19, 20, 21, 22, 23, 25], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 27, 28, 29]}, {"train" = [24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [26, 27, 28, 29], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [17, 20, 29], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [17, 18, 19, 20, 21, 22, 23, 25], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 27, 28, 29]}, {"train" = [12, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [12, 29], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [12, 25], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 27, 28, 29]}, {"train" = [12, 13, 14, 29], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [12, 13, 14], "test" = [1, 2, 3, 4, 17, 18, 19, 20, 27, 28, 29]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4, 21, 22, 23, 24, 25, 26]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4, 21, 22, 23, 27, 28, 29]}, {"train" = [12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 24, 25, 26, 27, 28, 29]}, 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[1, 2, 3, 4, 5, 6, 25], "test" = [27, 28, 29]}], "13" = [{"train" = [12, 29], "test" = [1, 2, 3, 4]}, {"train" = [1, 29], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 29], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 29], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 29], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 29], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}], "14" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 20], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 20], "test" = [27, 28, 29]}], "15" = [{"train" = [12, 25], "test" = [1, 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[{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [27, 28, 29]}], "18" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [27, 28, 29]}], "19" = [{"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [27, 28, 29]}], "20" = [{"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [27, 28, 29]}], "21" = [{"train" = [12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 15, 16, 17, 18, 19, 20, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 17, 18, 19, 20, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "test" = [27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 4 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.0 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 2], [6, 3], [7, 4], [8, 5]], "2" = [[1, 2], [2, 2], [3, 2], [4, 6], [5, 6], [6, 7], [7, 8], [8, 9]], "3" = [[1, 3], [2, 3], [3, 3], [4, 7], [5, 10], [6, 10], [7, 11], [8, 12]], "4" = [[1, 4], [2, 4], [3, 4], [4, 8], [5, 11], [6, 13], [7, 13], [8, 14]], "5" = [[1, 5], [2, 5], [3, 5], [4, 9], [5, 12], [6, 14], [7, 15], [8, 15]], "6" = [[1, 6], [2, 6], [3, 16], [4, 16], [5, 16], [6, 17], [7, 18], [8, 19]], "7" = [[1, 7], [2, 7], [3, 17], [4, 17], [5, 20], [6, 20], [7, 21], [8, 22]], "8" = [[1, 8], [2, 8], [3, 18], [4, 18], [5, 21], [6, 23], [7, 23], [8, 24]], "9" = [[1, 9], [2, 9], [3, 19], [4, 19], [5, 22], [6, 24], [7, 25], [8, 25]], "10" = [[1, 10], [2, 10], [3, 20], [4, 26], [5, 26], [6, 26], [7, 27], [8, 28]], "11" = [[1, 11], [2, 11], [3, 21], [4, 27], [5, 27], [6, 29], [7, 29], [8, 30]], "12" = [[1, 12], [2, 12], [3, 22], [4, 28], [5, 28], [6, 30], [7, 31], [8, 31]], "13" = [[1, 13], [2, 13], [3, 23], [4, 29], [5, 32], [6, 32], [7, 32], [8, 33]], "14" = [[1, 14], [2, 14], [3, 24], [4, 30], [5, 33], [6, 33], [7, 34], [8, 34]], "15" = [[1, 15], [2, 15], [3, 25], [4, 31], [5, 34], [6, 35], [7, 35], [8, 35]], "16" = [[1, 16], [2, 36], [3, 36], [4, 36], [5, 36], [6, 37], [7, 38], [8, 39]], "17" = [[1, 17], [2, 37], [3, 37], [4, 37], [5, 40], [6, 40], [7, 41], [8, 42]], "18" = [[1, 18], [2, 38], [3, 38], [4, 38], [5, 41], [6, 43], [7, 43], [8, 44]], "19" = [[1, 19], [2, 39], [3, 39], [4, 39], [5, 42], [6, 44], [7, 45], [8, 45]], "20" = [[1, 20], [2, 40], [3, 40], [4, 46], [5, 46], [6, 46], [7, 47], [8, 48]], "21" = [[1, 21], [2, 41], [3, 41], [4, 47], [5, 47], [6, 49], [7, 49], [8, 50]], "22" = [[1, 22], [2, 42], [3, 42], [4, 48], [5, 48], [6, 50], [7, 51], [8, 51]], "23" = [[1, 23], [2, 43], [3, 43], [4, 49], [5, 52], [6, 52], [7, 52], [8, 53]], "24" = [[1, 24], [2, 44], [3, 44], [4, 50], [5, 53], [6, 53], [7, 54], [8, 54]], "25" = [[1, 25], [2, 45], [3, 45], [4, 51], [5, 54], [6, 55], [7, 55], [8, 55]], "26" = [[1, 26], [2, 46], [3, 56], [4, 56], [5, 56], [6, 56], [7, 57], [8, 58]], "27" = [[1, 27], [2, 47], [3, 57], [4, 57], [5, 57], [6, 59], [7, 59], [8, 60]], "28" = [[1, 28], [2, 48], [3, 58], [4, 58], [5, 58], [6, 60], [7, 61], [8, 61]], "29" = [[1, 29], [2, 49], [3, 59], [4, 59], [5, 62], [6, 62], [7, 62], [8, 63]], "30" = [[1, 30], [2, 50], [3, 60], [4, 60], [5, 63], [6, 63], [7, 64], [8, 64]], "31" = [[1, 31], [2, 51], [3, 61], [4, 61], [5, 64], [6, 65], [7, 65], [8, 65]], "32" = [[1, 32], [2, 52], [3, 62], [4, 66], [5, 66], [6, 66], [7, 66], [8, 67]], "33" = [[1, 33], [2, 53], [3, 63], [4, 67], [5, 67], [6, 67], [7, 68], [8, 68]], "34" = [[1, 34], [2, 54], [3, 64], [4, 68], [5, 68], [6, 69], [7, 69], [8, 69]], "35" = [[1, 35], [2, 55], [3, 65], [4, 69], [5, 70], [6, 70], [7, 70], [8, 70]]} +splits = [{"train" = [21, 22, 23, 24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20]}, {"train" = [15, 16, 17, 20, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 21, 22, 23]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 24, 25, 26]}, {"train" = [15, 16, 17, 18, 19, 20, 21, 22, 23, 25], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 27, 28, 29]}, {"train" = [12, 24, 25, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [12, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [12, 22, 23], "test" = [1, 2, 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10, 11, 12, 13, 14, 15, 16, 24, 25, 26]}, {"train" = [21, 22, 23, 25], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 16, 27, 28, 29]}, {"train" = [26, 27, 28, 29], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [25], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 17, 18, 19, 20, 27, 28, 29]}, {"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 21, 22, 23, 24, 25, 26]}, {"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 21, 22, 23, 27, 28, 29]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4, 9, 10, 11, 12, 24, 25, 26, 27, 28, 29]}, {"train" = [9, 12, 26, 27, 28, 29], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}, {"train" = [9, 12], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 26]}, {"train" = [9, 12, 25], "test" = [1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20, 27, 28, 29]}, {"train" = [9, 12], 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7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [27, 28, 29]}], "33" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [27, 28, 29]}], "34" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], 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[27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 4 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 2], [6, 3], [7, 4], [8, 5]], "2" = [[1, 2], [2, 2], [3, 2], [4, 6], [5, 6], [6, 7], [7, 8], [8, 9]], "3" = [[1, 3], [2, 3], [3, 3], [4, 7], [5, 10], [6, 10], [7, 11], [8, 12]], "4" = [[1, 4], [2, 4], [3, 4], [4, 8], [5, 11], [6, 13], [7, 13], [8, 14]], "5" = [[1, 5], [2, 5], [3, 5], [4, 9], [5, 12], [6, 14], [7, 15], [8, 15]], "6" = [[1, 6], [2, 6], [3, 16], [4, 16], [5, 16], [6, 17], [7, 18], [8, 19]], "7" = [[1, 7], [2, 7], [3, 17], [4, 17], [5, 20], [6, 20], [7, 21], [8, 22]], "8" = [[1, 8], [2, 8], [3, 18], [4, 18], [5, 21], [6, 23], [7, 23], [8, 24]], "9" = [[1, 9], [2, 9], [3, 19], [4, 19], [5, 22], [6, 24], [7, 25], [8, 25]], "10" = [[1, 10], [2, 10], [3, 20], [4, 26], [5, 26], [6, 26], [7, 27], [8, 28]], "11" = [[1, 11], [2, 11], [3, 21], [4, 27], [5, 27], [6, 29], [7, 29], [8, 30]], "12" = [[1, 12], [2, 12], [3, 22], [4, 28], [5, 28], [6, 30], [7, 31], [8, 31]], "13" = [[1, 13], [2, 13], [3, 23], [4, 29], [5, 32], [6, 32], [7, 32], [8, 33]], "14" = [[1, 14], [2, 14], [3, 24], [4, 30], [5, 33], [6, 33], [7, 34], [8, 34]], "15" = [[1, 15], [2, 15], [3, 25], [4, 31], [5, 34], [6, 35], [7, 35], [8, 35]], "16" = [[1, 16], [2, 36], [3, 36], [4, 36], [5, 36], [6, 37], [7, 38], [8, 39]], "17" = [[1, 17], [2, 37], [3, 37], [4, 37], [5, 40], [6, 40], [7, 41], [8, 42]], "18" = [[1, 18], [2, 38], [3, 38], [4, 38], [5, 41], [6, 43], [7, 43], [8, 44]], "19" = [[1, 19], [2, 39], [3, 39], [4, 39], [5, 42], [6, 44], [7, 45], [8, 45]], "20" = 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"test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 5 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.0 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 2], [7, 3], [8, 4]], "2" = [[1, 2], [2, 2], [3, 2], [4, 2], [5, 5], [6, 5], [7, 6], [8, 7]], "3" = [[1, 3], [2, 3], [3, 3], [4, 3], [5, 6], [6, 8], [7, 8], [8, 9]], "4" = [[1, 4], [2, 4], [3, 4], [4, 4], [5, 7], [6, 9], [7, 10], [8, 10]], "5" = [[1, 5], [2, 5], [3, 5], [4, 11], [5, 11], [6, 11], [7, 12], [8, 13]], "6" = [[1, 6], [2, 6], [3, 6], [4, 12], [5, 12], [6, 14], [7, 14], [8, 15]], "7" = [[1, 7], [2, 7], [3, 7], [4, 13], [5, 13], [6, 15], [7, 16], [8, 16]], "8" = [[1, 8], [2, 8], [3, 8], [4, 14], [5, 17], [6, 17], [7, 17], [8, 18]], "9" = [[1, 9], [2, 9], [3, 9], [4, 15], [5, 18], [6, 18], [7, 19], [8, 19]], "10" = [[1, 10], [2, 10], [3, 10], [4, 16], [5, 19], [6, 20], [7, 20], [8, 20]], "11" = [[1, 11], [2, 11], [3, 21], [4, 21], [5, 21], [6, 21], [7, 22], [8, 23]], "12" = [[1, 12], [2, 12], [3, 22], [4, 22], [5, 22], [6, 24], [7, 24], [8, 25]], "13" = [[1, 13], [2, 13], [3, 23], [4, 23], [5, 23], [6, 25], [7, 26], [8, 26]], "14" = [[1, 14], [2, 14], [3, 24], [4, 24], [5, 27], [6, 27], [7, 27], [8, 28]], "15" = [[1, 15], [2, 15], [3, 25], [4, 25], [5, 28], [6, 28], [7, 29], [8, 29]], "16" = [[1, 16], [2, 16], [3, 26], [4, 26], [5, 29], [6, 30], [7, 30], [8, 30]], "17" = [[1, 17], [2, 17], [3, 27], [4, 31], [5, 31], [6, 31], [7, 31], [8, 32]], "18" = [[1, 18], [2, 18], [3, 28], [4, 32], [5, 32], [6, 32], [7, 33], [8, 33]], "19" = [[1, 19], [2, 19], [3, 29], [4, 33], [5, 33], [6, 34], [7, 34], [8, 34]], "20" = [[1, 20], [2, 20], [3, 30], [4, 34], [5, 35], [6, 35], [7, 35], [8, 35]], "21" = [[1, 21], [2, 36], [3, 36], [4, 36], [5, 36], [6, 36], [7, 37], [8, 38]], "22" = [[1, 22], [2, 37], [3, 37], [4, 37], [5, 37], [6, 39], [7, 39], [8, 40]], "23" = [[1, 23], [2, 38], [3, 38], [4, 38], [5, 38], [6, 40], [7, 41], [8, 41]], "24" = [[1, 24], [2, 39], [3, 39], [4, 39], [5, 42], [6, 42], [7, 42], [8, 43]], "25" = [[1, 25], [2, 40], [3, 40], [4, 40], [5, 43], [6, 43], [7, 44], [8, 44]], "26" = [[1, 26], [2, 41], [3, 41], [4, 41], [5, 44], [6, 45], [7, 45], [8, 45]], "27" = [[1, 27], [2, 42], [3, 42], [4, 46], [5, 46], [6, 46], [7, 46], [8, 47]], "28" = [[1, 28], [2, 43], [3, 43], [4, 47], [5, 47], [6, 47], [7, 48], [8, 48]], "29" = [[1, 29], [2, 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= [24, 25, 26]}, {"train" = [12, 13, 14], "test" = [27, 28, 29]}], "10" = [{"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 18, 19, 20, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [12, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [24, 25, 26]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [27, 28, 29]}], "11" = [{"train" = [12, 26, 27, 28, 29], "test" = [1, 2, 3, 4]}, {"train" = [12, 26, 27, 28, 29], "test" = [5, 6, 7, 8]}, {"train" = [26, 27, 28, 29], "test" = [9, 10, 11, 12]}, {"train" = [26, 27, 28, 29], "test" = [13, 14, 15, 16]}, {"train" = [26, 27, 28, 29], "test" = [17, 18, 19, 20]}, {"train" = [26, 27, 28, 29], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}, {"train" = [25], "test" = [27, 28, 29]}], "12" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [12], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}, {"train" = [], "test" = [27, 28, 29]}], "13" = [{"train" = [12, 25], "test" = [1, 2, 3, 4]}, {"train" = [12, 25], "test" = [5, 6, 7, 8]}, {"train" = [25], "test" = [9, 10, 11, 12]}, {"train" = [25], "test" = [13, 14, 15, 16]}, {"train" = [25], "test" = [17, 18, 19, 20]}, {"train" = [], "test" = [21, 22, 23]}, {"train" = [22, 23], "test" = [24, 25, 26]}, {"train" = [22, 23], "test" = [27, 28, 29]}], "14" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [12], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}, {"train" = [], "test" = [27, 28, 29]}], "15" = [{"train" = [12], "test" = [1, 2, 3, 4]}, {"train" = [12], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}, {"train" = [], "test" = [27, 28, 29]}], "16" = [{"train" = [12, 22, 23], "test" = [1, 2, 3, 4]}, {"train" = [12, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [22, 23], "test" = [9, 10, 11, 12]}, {"train" = [22, 23], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [15, 16, 17, 20], "test" = [24, 25, 26]}, {"train" = [15, 16, 17, 20], "test" = [27, 28, 29]}], "17" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 12], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}, {"train" = [9, 12], "test" = [27, 28, 29]}], "18" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 12], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}, {"train" = [9, 12], "test" = [27, 28, 29]}], "19" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 12], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}, {"train" = [9, 12], "test" = [27, 28, 29]}], "20" = [{"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [24, 25, 26]}, {"train" = [9, 10, 11, 12, 13, 14], "test" = [27, 28, 29]}], "21" = [{"train" = [26, 27, 28, 29], "test" = [1, 2, 3, 4]}, {"train" = [1, 26, 27, 28, 29], "test" = [5, 6, 7, 8]}, {"train" = [1, 26, 27, 28, 29], "test" = [9, 10, 11, 12]}, {"train" = [1, 26, 27, 28, 29], "test" = [13, 14, 15, 16]}, {"train" = [1, 26, 27, 28, 29], "test" = [17, 18, 19, 20]}, {"train" = [1, 26, 27, 28, 29], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1, 25], "test" = [27, 28, 29]}], "22" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "23" = [{"train" = [25], "test" = [1, 2, 3, 4]}, {"train" = [1, 25], "test" = [5, 6, 7, 8]}, {"train" = [1, 25], "test" = [9, 10, 11, 12]}, {"train" = [1, 25], "test" = [13, 14, 15, 16]}, {"train" = [1, 25], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1, 22, 23], "test" = [24, 25, 26]}, {"train" = [1, 22, 23], "test" = [27, 28, 29]}], "24" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "25" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "26" = [{"train" = [22, 23], "test" = [1, 2, 3, 4]}, {"train" = [1, 22, 23], "test" = [5, 6, 7, 8]}, {"train" = [1, 22, 23], "test" = [9, 10, 11, 12]}, {"train" = [1, 22, 23], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1, 15, 16, 17, 20], "test" = [21, 22, 23]}, {"train" = [1, 15, 16, 17, 20], "test" = [24, 25, 26]}, {"train" = [1, 15, 16, 17, 20], "test" = [27, 28, 29]}], "27" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}, {"train" = [1, 12], "test" = [27, 28, 29]}], "28" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}, {"train" = [1, 12], "test" = [27, 28, 29]}], "29" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}, {"train" = [1, 12], "test" = [27, 28, 29]}], "30" = [{"train" = [15, 16, 17, 20], "test" = [1, 2, 3, 4]}, {"train" = [1, 15, 16, 17, 20], "test" = [5, 6, 7, 8]}, {"train" = [1, 15, 16, 17, 20], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12, 13, 14], "test" = [17, 18, 19, 20]}, {"train" = [1, 12, 13, 14], "test" = [21, 22, 23]}, {"train" = [1, 12, 13, 14], "test" = [24, 25, 26]}, {"train" = [1, 12, 13, 14], "test" = [27, 28, 29]}], "31" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}], "32" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}], "33" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}], "34" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}], "35" = [{"train" = [9, 10, 11, 12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [1, 12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12], "test" = [27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 5 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 2], [7, 3], [8, 4]], "2" = [[1, 2], [2, 2], [3, 2], [4, 2], [5, 5], [6, 5], [7, 6], [8, 7]], "3" = [[1, 3], [2, 3], [3, 3], [4, 3], [5, 6], [6, 8], [7, 8], [8, 9]], "4" = [[1, 4], [2, 4], [3, 4], [4, 4], [5, 7], [6, 9], [7, 10], [8, 10]], "5" = [[1, 5], [2, 5], [3, 5], [4, 11], [5, 11], [6, 11], [7, 12], [8, 13]], "6" = [[1, 6], [2, 6], [3, 6], [4, 12], [5, 12], [6, 14], [7, 14], [8, 15]], "7" = [[1, 7], [2, 7], [3, 7], [4, 13], [5, 13], [6, 15], [7, 16], [8, 16]], "8" = [[1, 8], [2, 8], [3, 8], [4, 14], [5, 17], [6, 17], [7, 17], [8, 18]], "9" = [[1, 9], [2, 9], [3, 9], [4, 15], [5, 18], [6, 18], [7, 19], [8, 19]], "10" = [[1, 10], [2, 10], [3, 10], [4, 16], [5, 19], [6, 20], [7, 20], [8, 20]], "11" = [[1, 11], [2, 11], [3, 21], [4, 21], [5, 21], [6, 21], [7, 22], [8, 23]], "12" = [[1, 12], [2, 12], [3, 22], [4, 22], [5, 22], [6, 24], [7, 24], [8, 25]], "13" = [[1, 13], [2, 13], [3, 23], [4, 23], [5, 23], [6, 25], [7, 26], [8, 26]], "14" = [[1, 14], [2, 14], [3, 24], [4, 24], [5, 27], [6, 27], [7, 27], [8, 28]], "15" = [[1, 15], [2, 15], [3, 25], [4, 25], [5, 28], [6, 28], [7, 29], [8, 29]], "16" = [[1, 16], [2, 16], [3, 26], [4, 26], [5, 29], [6, 30], [7, 30], [8, 30]], "17" = [[1, 17], [2, 17], [3, 27], [4, 31], [5, 31], [6, 31], [7, 31], [8, 32]], "18" = [[1, 18], [2, 18], [3, 28], [4, 32], [5, 32], [6, 32], [7, 33], [8, 33]], "19" = [[1, 19], [2, 19], [3, 29], [4, 33], [5, 33], [6, 34], [7, 34], [8, 34]], "20" = [[1, 20], [2, 20], [3, 30], [4, 34], [5, 35], [6, 35], [7, 35], [8, 35]], "21" = [[1, 21], [2, 36], [3, 36], [4, 36], [5, 36], [6, 36], [7, 37], [8, 38]], "22" = [[1, 22], [2, 37], [3, 37], [4, 37], [5, 37], [6, 39], [7, 39], [8, 40]], "23" = [[1, 23], [2, 38], [3, 38], [4, 38], [5, 38], [6, 40], [7, 41], [8, 41]], "24" = [[1, 24], [2, 39], [3, 39], [4, 39], [5, 42], [6, 42], [7, 42], [8, 43]], "25" = [[1, 25], [2, 40], [3, 40], [4, 40], [5, 43], [6, 43], [7, 44], [8, 44]], "26" = [[1, 26], [2, 41], [3, 41], [4, 41], [5, 44], [6, 45], [7, 45], [8, 45]], "27" = [[1, 27], [2, 42], [3, 42], [4, 46], [5, 46], [6, 46], [7, 46], [8, 47]], "28" = [[1, 28], [2, 43], [3, 43], [4, 47], [5, 47], [6, 47], [7, 48], [8, 48]], "29" = [[1, 29], [2, 44], [3, 44], [4, 48], [5, 48], [6, 49], [7, 49], [8, 49]], "30" = [[1, 30], [2, 45], [3, 45], [4, 49], [5, 50], [6, 50], [7, 50], [8, 50]], "31" = [[1, 31], [2, 46], [3, 51], [4, 51], [5, 51], [6, 51], [7, 51], [8, 52]], "32" = [[1, 32], [2, 47], [3, 52], [4, 52], [5, 52], [6, 52], [7, 53], [8, 53]], "33" = [[1, 33], [2, 48], [3, 53], [4, 53], [5, 53], [6, 54], [7, 54], [8, 54]], "34" = [[1, 34], [2, 49], [3, 54], [4, 54], [5, 55], [6, 55], [7, 55], [8, 55]], "35" = [[1, 35], [2, 50], [3, 55], [4, 56], [5, 56], [6, 56], [7, 56], [8, 56]]} +splits = [{"train" = [26, 27, 28, 29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]}, {"train" = [29], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 21, 22, 23]}, {"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 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11, 12]}, {"train" = [], "test" = [13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20]}, {"train" = [], "test" = [21, 22, 23]}, {"train" = [], "test" = [24, 25, 26]}, {"train" = [], "test" = [27, 28, 29]}], "15" = [{"train" = [12, 13, 14], "test" = [1, 2, 3, 4]}, {"train" = [12, 13, 14], "test" = [5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12]}, {"train" = [9, 12], "test" = [13, 14, 15, 16]}, {"train" = [9, 12], "test" = [17, 18, 19, 20]}, {"train" = [9, 12], "test" = [21, 22, 23]}, {"train" = [9, 12], "test" = [24, 25, 26]}, {"train" = [9, 12], "test" = [27, 28, 29]}], "16" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "17" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "18" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "19" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1], "test" = [13, 14, 15, 16]}, {"train" = [1], "test" = [17, 18, 19, 20]}, {"train" = [1], "test" = [21, 22, 23]}, {"train" = [1], "test" = [24, 25, 26]}, {"train" = [1], "test" = [27, 28, 29]}], "20" = [{"train" = [], "test" = [1, 2, 3, 4]}, {"train" = [1], "test" = [5, 6, 7, 8]}, {"train" = [1], "test" = [9, 10, 11, 12]}, {"train" = [1, 12], "test" = [13, 14, 15, 16]}, {"train" = [1, 12], "test" = [17, 18, 19, 20]}, {"train" = [1, 12], "test" = [21, 22, 23]}, {"train" = [1, 12], "test" = [24, 25, 26]}, {"train" = [1, 12], "test" = [27, 28, 29]}], "21" = [{"train" = [9, 12], "test" = [1, 2, 3, 4]}, {"train" = [1, 12], "test" = [5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [17, 18, 19, 20]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [24, 25, 26]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [27, 28, 29]}]} + +[[geometry]] +kind = "combinatorial" +groups = 8 +test_groups = 6 +starts = [1, 3, 6, 7, 9, 12, 13, 15, 18, 19, 21, 24, 25, 27, 30, 31, 33, 36, 37, 39, 42, 43, 45, 48, 49, 51, 54, 55, 57] +ends = [1, 10, 9, 17, 15, 14, 22, 20, 19, 27, 25, 24, 32, 30, 40, 37, 35, 45, 42, 40, 50, 47, 45, 55, 52, 61, 60, 57, 66] +embargo = 0.075 +segments = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]] +locations = {"1" = [[1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 1], [7, 2], [8, 3]], "2" = [[1, 2], [2, 2], [3, 2], [4, 2], [5, 2], [6, 4], [7, 4], [8, 5]], "3" = [[1, 3], [2, 3], [3, 3], [4, 3], [5, 3], [6, 5], [7, 6], [8, 6]], "4" = [[1, 4], [2, 4], [3, 4], [4, 4], [5, 7], [6, 7], [7, 7], [8, 8]], "5" = [[1, 5], [2, 5], [3, 5], [4, 5], [5, 8], [6, 8], [7, 9], [8, 9]], "6" = [[1, 6], [2, 6], [3, 6], [4, 6], [5, 9], [6, 10], [7, 10], [8, 10]], "7" = [[1, 7], [2, 7], [3, 7], [4, 11], [5, 11], [6, 11], [7, 11], [8, 12]], "8" = [[1, 8], [2, 8], [3, 8], [4, 12], [5, 12], [6, 12], [7, 13], [8, 13]], "9" = [[1, 9], [2, 9], [3, 9], [4, 13], [5, 13], [6, 14], [7, 14], [8, 14]], "10" = [[1, 10], [2, 10], [3, 10], [4, 14], [5, 15], [6, 15], [7, 15], [8, 15]], "11" = [[1, 11], [2, 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31, 32, 33, 34, 35, 36, 37, 38, 41], "test" = [55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]}]} + +[[geometry]] +kind = "adaptive" +groups = 5 +test_groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66] +ends = [0, 2, 4, 6, 8, 5, 7, 9, 11, 13, 10, 12, 14, 16, 18, 15, 17, 19, 21, 23, 20, 22, 24, 26, 28, 25, 27, 29, 31, 33, 30, 32, 34, 36, 38, 35, 37, 39, 41, 43, 40, 42, 44, 46, 48, 45, 47, 49, 51, 53, 50, 52, 54, 56, 58, 55, 57, 59, 61, 63, 60, 62, 64, 66, 68, 65, 67] +embargo = 0.03 +subsplits = 1 +feature = [0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3] +lower = 0.4 +upper = 0.6 +subtract = true +segments 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10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [4, 5, 6, 7], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 4 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 100 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9]}, {"train" = [], "test" = [10, 11, 12, 13, 14, 15, 16, 17]}] + +[[basic]] +kind = "kfold" +shuffle = false +seed = 0 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 5, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 14, 15, 16, 17], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "kfold" +shuffle = true +seed = 812 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [12, 13, 4, 2, 8, 14, 3, 11, 1, 10, 15, 17, 5, 16, 6, 7, 9] +splits = [{"train" = [1, 3, 5, 6, 7, 9, 10, 11, 14, 15, 16, 17], "test" = [12, 13, 4, 2, 8]}, {"train" = [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17], "test" = [14, 3, 11, 1]}, {"train" = [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16], "test" = [10, 15, 17, 5]}, {"train" = [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 17], "test" = [16, 6, 7, 9]}] + +[[basic]] +kind = "purged" +embargo = 0.06 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 13, 14, 15, 16, 17], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 15, 16, 17], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [2, 3, 4, 5], "test" = [6, 7, 8, 9]}, {"train" = [6, 7, 8, 9], "test" = [10, 11, 12, 13]}, {"train" = [10, 11, 12, 13], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 5], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9]}, {"train" = [4, 5, 6, 7], "test" = [10, 11, 12, 13]}, {"train" = [8, 9, 10, 11], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7], "test" = [10, 11, 12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [], "test" = [6, 7, 8, 9]}, {"train" = [], "test" = [10, 11, 12, 13]}, {"train" = [], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [], "test" = [6, 7, 8, 9]}, {"train" = [], "test" = [10, 11, 12, 13]}, {"train" = [], "test" = [14, 15, 16, 17]}] + +[[basic]] +kind = "kfold" +shuffle = false +seed = 0 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3]}, {"train" = [1, 2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [4, 5, 6]}, {"train" = [1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17], "test" = [10, 11]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17], "test" = [12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17], "test" = [14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "test" = [16, 17]}] + +[[basic]] +kind = "kfold" +shuffle = true +seed = 812 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [12, 13, 4, 2, 8, 14, 3, 11, 1, 10, 15, 17, 5, 16, 6, 7, 9] +splits = [{"train" = [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17], "test" = [12, 13, 4]}, {"train" = [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17], "test" = [2, 8, 14]}, {"train" = [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17], "test" = [3, 11, 1]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17], "test" = [10, 15]}, {"train" = [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "test" = [17, 5]}, {"train" = [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17], "test" = [16, 6]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [7, 9]}] + +[[basic]] +kind = "purged" +embargo = 0.06 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [1, 2, 3]}, {"train" = [1, 2, 10, 11, 12, 13, 14, 15, 16, 17], "test" = [4, 5, 6]}, {"train" = [1, 2, 3, 4, 5, 13, 14, 15, 16, 17], "test" = [7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 14, 15, 16, 17], "test" = [10, 11]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17], "test" = [12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13], "test" = [14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 4 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [1, 2, 3], "test" = [4, 5, 6]}, {"train" = [3, 4, 5, 6], "test" = [7, 8, 9]}, {"train" = [6, 7, 8, 9], "test" = [10, 11]}, {"train" = [8, 9, 10, 11], "test" = [12, 13]}, {"train" = [10, 11, 12, 13], "test" = [14, 15]}, {"train" = [12, 13, 14, 15], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 100 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [1, 2, 3], "test" = [4, 5, 6]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9], "test" = [10, 11]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], "test" = [12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "test" = [14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [1], "test" = [4, 5, 6]}, {"train" = [1, 2, 3, 4], "test" = [7, 8, 9]}, {"train" = [4, 5, 6, 7], "test" = [10, 11]}, {"train" = [6, 7, 8, 9], "test" = [12, 13]}, {"train" = [8, 9, 10, 11], "test" = [14, 15]}, {"train" = [10, 11, 12, 13], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [1], "test" = [4, 5, 6]}, {"train" = [1, 2, 3, 4], "test" = [7, 8, 9]}, {"train" = [1, 2, 3, 4, 5, 6, 7], "test" = [10, 11]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9], "test" = [12, 13]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], "test" = [14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 4 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [], "test" = [4, 5, 6]}, {"train" = [], "test" = [7, 8, 9]}, {"train" = [], "test" = [10, 11]}, {"train" = [], "test" = [12, 13]}, {"train" = [], "test" = [14, 15]}, {"train" = [], "test" = [16, 17]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 100 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] +splits = [{"train" = [], "test" = [1, 2, 3]}, {"train" = [], "test" = [4, 5, 6]}, {"train" = [], "test" = [7, 8, 9]}, {"train" = [], "test" = [10, 11]}, {"train" = [], "test" = [12, 13]}, {"train" = [], "test" = [14, 15]}, {"train" = [], "test" = [16, 17]}] + +[[basic]] +kind = "kfold" +shuffle = false +seed = 0 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "kfold" +shuffle = true +seed = 812 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 15, 13, 22, 4, 5, 19, 12, 3, 29, 25, 16, 18, 20, 8, 2, 11, 14, 17, 10, 24, 27, 7, 6, 23, 9, 28, 30, 31, 21, 26] +splits = [{"train" = [6, 7, 9, 10, 11, 14, 17, 21, 23, 24, 26, 27, 28, 30, 31], "test" = [1, 15, 13, 22, 4, 5, 19, 12, 3, 29, 25, 16, 18, 20, 8, 2]}, {"train" = [1, 2, 3, 4, 5, 8, 12, 13, 15, 16, 18, 19, 20, 22, 25, 29], "test" = [11, 14, 17, 10, 24, 27, 7, 6, 23, 9, 28, 30, 31, 21, 26]}] + +[[basic]] +kind = "purged" +embargo = 0.06 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 4 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [13, 14, 15, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 100 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [11, 12, 13, 14], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 4 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 100 +groups = 2 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "kfold" +shuffle = false +seed = 0 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 25, 26, 27, 28, 29, 30, 31], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "kfold" +shuffle = true +seed = 812 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 15, 13, 22, 4, 5, 19, 12, 3, 29, 25, 16, 18, 20, 8, 2, 11, 14, 17, 10, 24, 27, 7, 6, 23, 9, 28, 30, 31, 21, 26] +splits = [{"train" = [2, 3, 6, 7, 8, 9, 10, 11, 14, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 15, 13, 22, 4, 5, 19, 12]}, {"train" = [1, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 23, 24, 26, 27, 28, 30, 31], "test" = [3, 29, 25, 16, 18, 20, 8, 2]}, {"train" = [1, 2, 3, 4, 5, 8, 9, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31], "test" = [11, 14, 17, 10, 24, 27, 7, 6]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 25, 27, 29], "test" = [23, 9, 28, 30, 31, 21, 26]}] + +[[basic]] +kind = "purged" +embargo = 0.06 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 28, 29, 30, 31], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [5, 6, 7, 8], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [13, 14, 15, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [21, 22, 23, 24], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [11, 12, 13, 14], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [19, 20, 21, 22], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [1, 2, 3, 4, 5, 6], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 4 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 50 +max_train_size = 100 +groups = 4 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5, 6, 7, 8]}, {"train" = [], "test" = [9, 10, 11, 12, 13, 14, 15, 16]}, {"train" = [], "test" = [17, 18, 19, 20, 21, 22, 23, 24]}, {"train" = [], "test" = [25, 26, 27, 28, 29, 30, 31]}] + +[[basic]] +kind = "kfold" +shuffle = false +seed = 0 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [11, 12, 13, 14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [16, 17, 18, 19]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 28, 29, 30, 31], "test" = [24, 25, 26, 27]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], "test" = [28, 29, 30, 31]}] + +[[basic]] +kind = "kfold" +shuffle = true +seed = 812 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 15, 13, 22, 4, 5, 19, 12, 3, 29, 25, 16, 18, 20, 8, 2, 11, 14, 17, 10, 24, 27, 7, 6, 23, 9, 28, 30, 31, 21, 26] +splits = [{"train" = [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 15, 13, 22, 4]}, {"train" = [1, 2, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31], "test" = [5, 19, 12, 3, 29]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31], "test" = [25, 16, 18, 20, 8]}, {"train" = [1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [2, 11, 14, 17]}, {"train" = [1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31], "test" = [10, 24, 27, 7]}, {"train" = [1, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31], "test" = [6, 23, 9, 28]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 28, 29], "test" = [30, 31, 21, 26]}] + +[[basic]] +kind = "purged" +embargo = 0.06 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 10, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [11, 12, 13, 14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "test" = [16, 17, 18, 19]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 26, 27, 28, 29, 30, 31], "test" = [20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 31], "test" = [24, 25, 26, 27]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], "test" = [28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 4 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [2, 3, 4, 5], "test" = [6, 7, 8, 9, 10]}, {"train" = [7, 8, 9, 10], "test" = [11, 12, 13, 14, 15]}, {"train" = [12, 13, 14, 15], "test" = [16, 17, 18, 19]}, {"train" = [16, 17, 18, 19], "test" = [20, 21, 22, 23]}, {"train" = [20, 21, 22, 23], "test" = [24, 25, 26, 27]}, {"train" = [24, 25, 26, 27], "test" = [28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 0 +max_train_size = 100 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3, 4, 5], "test" = [6, 7, 8, 9, 10]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "test" = [11, 12, 13, 14, 15]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "test" = [16, 17, 18, 19]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "test" = [20, 21, 22, 23]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "test" = [24, 25, 26, 27]}, {"train" = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], "test" = [28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 4 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] +splits = [{"train" = [], "test" = [1, 2, 3, 4, 5]}, {"train" = [1, 2, 3], "test" = [6, 7, 8, 9, 10]}, {"train" = [5, 6, 7, 8], "test" = [11, 12, 13, 14, 15]}, {"train" = [10, 11, 12, 13], "test" = [16, 17, 18, 19]}, {"train" = [14, 15, 16, 17], "test" = [20, 21, 22, 23]}, {"train" = [18, 19, 20, 21], "test" = [24, 25, 26, 27]}, {"train" = [22, 23, 24, 25], "test" = [28, 29, 30, 31]}] + +[[basic]] +kind = "walk" +gap = 2 +max_train_size = 100 +groups = 7 +starts = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] +ends = [0, 2, 4, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12, 14, 16, 15, 17, 19, 18, 20, 22, 21, 23, 25, 24, 26, 28, 27, 29, 31, 30] +permutation = [1, 2, 3, 4, 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0.4764211024544373], [0.47178625693843, 0.52821374306157], [0.42085020253340377, 0.5791497974665962], [0.37610515014752754, 0.6238948498524725], [0.3424495780670984, 0.6575504219329016], [0.32007449002832833, 0.6799255099716717], [0.31092254404441566, 0.6890774559555843], [0.31523606624286393, 0.6847639337571361], [0.3329108963812506, 0.6670891036187494], [0.36333739779823837, 0.6366626022017616], [0.4048462394544796, 0.5951537605455204], [0.4541423305737107, 0.5458576694262893], [0.506299474813344, 0.49370052518665597], [0.5556301370449863, 0.4443698629550137], [0.5970245919202433, 0.4029754080797567], [0.6269039668045722, 0.37309603319542783], [0.6445452084853729, 0.35545479151462706], [0.6467259066228395, 0.3532740933771606], [0.6346044670402692, 0.3653955329597308], [0.6087069154123862, 0.3912930845876138], [0.5705492861100683, 0.4294507138899318], [0.5231718359595899, 0.47682816404041006], [0.4712705294595162, 0.5287294705404838], [0.4204952744410648, 0.5795047255589352], [0.3761410890319561, 0.6238589109680439], [0.34205325162448164, 0.6579467483755184], [0.3203648870999367, 0.6796351129000633], [0.3119486157061926, 0.6880513842938074], [0.3170268855636478, 0.6829731144363522], [0.3354736203747575, 0.6645263796252425], [0.3666280605224188, 0.6333719394775812], [0.40872785030851844, 0.5912721496914816], [0.4583626434019158, 0.5416373565980842], [0.5105215811664122, 0.4894784188335878], [0.559513269253175, 0.44048673074682493], [0.6003062400098045, 0.3996937599901956], [0.6294337711055187, 0.3705662288944814], [0.6450468257708224, 0.35495317422917766], [0.6464230669621559, 0.3535769330378441], [0.6335087688268355, 0.36649123117316457]]} diff --git a/test/graph_regressions.jl b/test/graph_regressions.jl new file mode 100644 index 0000000..1f4c936 --- /dev/null +++ b/test/graph_regressions.jl @@ -0,0 +1,131 @@ +using Test +using TOML +using RiskLabAI + +const GraphUnderTest = RiskLabAI.CausalFactorAnalysis +const graph_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "graphs_python_320.toml")) +function graph_plain(value) + value isa GraphUnderTest.TreatmentOutcomeRole && return string(value) + parentmodule(typeof(value)) === GraphUnderTest || return value + return Dict(string(name) => graph_plain(getproperty(value, name)) for name in propertynames(value)) +end +graph_plain(value::NamedTuple) = Dict(string(key) => graph_plain(item) for (key, item) in pairs(value)) +graph_plain(value::Tuple) = [graph_plain(item) for item in value] +graph_plain(value::AbstractVector) = [graph_plain(item) for item in value] +graph_plain(value::AbstractMatrix) = [graph_plain(collect(row)) for row in eachrow(value)] +graph_fixture_matrix(rows) = reduce(vcat, permutedims.(rows)) + +@testset "Public DAG evidence against independently checked Python 3.2.0 graphs" begin + dags = Dict{Int, GraphUnderTest.CausalDAG}() + for row in graph_fixtures["graphs"] + dag = GraphUnderTest.CausalDAG(row["nodes"], row["directed_edges"], row["observed_nodes"]) + @test dag.nodes == Tuple(row["nodes"]) + @test dag.directed_edges == Tuple(Tuple(edge) for edge in row["directed_edges"]) + @test dag.observed_nodes == Tuple(row["observed_nodes"]) + dags[row["id"]] = dag + end + for row in graph_fixtures["separation"] + result = GraphUnderTest.d_separation(dags[row["graph"]], row["left"], row["right"], row["conditioned"]) + @test graph_plain(result) == row["expected"] + end + for (group, method) in (("backdoor", GraphUnderTest.check_backdoor_adjustment_set), ("frontdoor", GraphUnderTest.check_frontdoor_adjustment_set)) + for row in graph_fixtures[group] + result = method(dags[row["graph"]], row["treatment"], row["outcome"], row["candidate"]) + @test graph_plain(result) == row["expected"] + end + end + for row in graph_fixtures["minimal"] + @test graph_plain(GraphUnderTest.minimal_backdoor_adjustment_sets(dags[row["graph"]], row["treatment"], row["outcome"])) == row["expected"] + end + for row in graph_fixtures["instrument"] + result = GraphUnderTest.check_instrument(dags[row["graph"]], row["instrument"], row["treatment"], row["outcome"], row["conditioned"]) + @test graph_plain(result) == row["expected"] + end + for (group, method) in (("path_roles", GraphUnderTest.causal_role_evidence), ("one_hop", GraphUnderTest.classify_treatment_outcome_role)) + for row in graph_fixtures[group] + @test graph_plain(method(dags[row["graph"]], row["treatment"], row["outcome"], row["node"])) == row["expected"] + end + end +end + +@testset "Immutable DAG domains and complete-search limits" begin + nodes, edges, observed = ["B", "A", "D", "C"], [["A", "B"], ["A", "C"], ["B", "D"], ["C", "D"]], ["A", "B", "C", "D"] + diamond = GraphUnderTest.CausalDAG(nodes, edges, observed) + nodes[1], edges[1][1], observed[1] = "Z", "Z", "Z" + @test diamond.nodes == ("A", "B", "C", "D") + @test diamond.directed_edges[1] == ("A", "B") + @test diamond.observed_nodes == ("A", "B", "C", "D") + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("D",); max_paths = 1) + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("D",); max_path_states = 1) + @test length(GraphUnderTest.d_separation(diamond, ("A",), ("D",); max_paths = nothing, max_path_states = nothing).paths) == 2 + @test_throws ArgumentError GraphUnderTest.check_frontdoor_adjustment_set(diamond, "A", "D", ("B", "C"); max_paths = 1) + fork = GraphUnderTest.CausalDAG(("X", "Y", "Z"), (("Z", "X"), ("Z", "Y")), ("X", "Y", "Z")) + @test_throws ArgumentError GraphUnderTest.minimal_backdoor_adjustment_sets(fork, "X", "Y"; max_candidates = 1) + @test GraphUnderTest.minimal_backdoor_adjustment_sets(fork, "X", "Y"; max_candidates = nothing) == (("Z",),) + @test GraphUnderTest.minimal_backdoor_adjustment_sets(diamond, "A", "D"; max_candidates = 1) == ((),) + collider = GraphUnderTest.CausalDAG(("X", "Y", "M", "D"), (("X", "M"), ("Y", "M"), ("M", "D")), ("X", "Y", "D")) + @test GraphUnderTest.d_separation(collider, ("X",), ("Y",)).separated + @test !GraphUnderTest.d_separation(collider, ("X",), ("Y",), ("D",)).separated + multi = GraphUnderTest.d_separation(diamond, ("A", "B"), ("D",), ("C",)) + @test !multi.separated && !isempty(multi.paths) + @test_throws ArgumentError GraphUnderTest.CausalDAG([], [], []) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A", "A"), (), ("A",)) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A",), (("A", "A"),), ("A",)) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A", "B"), (("A", "B"), ("B", "A")), ("A", "B")) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A", "B"), (("A", "B"), ("A", "B")), ("A", "B")) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A",), (("A", "B"),), ("A",)) + @test_throws ArgumentError GraphUnderTest.CausalDAG(("A",), (), ("B",)) + @test_throws ArgumentError GraphUnderTest.CausalDAG("A", (), ("A",)) + @test_throws ArgumentError GraphUnderTest.CausalDAG((" ",), (), ()) + for tolerance in (0, -1, true, 1.2) + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("D",); max_paths = tolerance) + @test_throws ArgumentError GraphUnderTest.minimal_backdoor_adjustment_sets(diamond, "A", "D"; max_candidates = tolerance) + end + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, "A", ("D",)) + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("A",)) + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("D",), ("D",)) + @test_throws ArgumentError GraphUnderTest.d_separation(diamond, ("A",), ("Z",)) + @test_throws ArgumentError GraphUnderTest.check_backdoor_adjustment_set(collider, "X", "M") + @test_throws ArgumentError GraphUnderTest.check_instrument(diamond, "A", "A", "D") + @test_throws ArgumentError GraphUnderTest.causal_role_evidence(diamond, "A", "D", "A") + reversed = GraphUnderTest.CausalDAG(("T", "Y", "Z"), (("Y", "Z"), ("Z", "T")), ("T", "Y", "Z")) + @test_throws ArgumentError GraphUnderTest.classify_treatment_outcome_role(reversed, "T", "Y", "Z") +end + +@testset "Supplied structural mechanisms and independent snapshots" begin + for row in graph_fixtures["structural"] + graph_row = graph_fixtures["graphs"][row["graph"]] + dag = GraphUnderTest.CausalDAG(graph_row["nodes"], graph_row["directed_edges"], graph_row["observed_nodes"]) + coefficients = Dict((entry["source"], entry["target"]) => entry["value"] for entry in row["coefficients"]) + parent_names = Dict(node => sort!([source for (source, target) in dag.directed_edges if target == node]) for node in dag.nodes) + mechanisms = Dict(node => ((parents, noise) -> parents * [coefficients[(parent, node)] for parent in parent_names[node]] + noise) for node in dag.nodes) + exogenous = Dict(node => Vector{Float64}(row["noise"][node]) for node in dag.nodes) + result = GraphUnderTest.evaluate_structural_causal_model(dag, mechanisms, exogenous) + @test result isa GraphUnderTest.StructuralCausalModelResult + @test result.node_order == Tuple(row["expected"]["node_order"]) + @test result.values ≈ graph_fixture_matrix(row["expected"]["values"]) rtol = 1e-10 atol = 1e-12 + @test result.values isa Matrix{Float64} + exogenous[first(dag.nodes)] .= 1000 + @test result.values ≈ graph_fixture_matrix(row["expected"]["values"]) rtol = 1e-10 atol = 1e-12 + end + dag = GraphUnderTest.CausalDAG(("A", "B"), (("A", "B"),), ("A", "B")) + inputs = Dict("A" => [1.0, 2.0], "B" => [3.0, 4.0]) + clean = Dict("A" => ((parents, noise) -> noise), "B" => ((parents, noise) -> parents[:, 1] + noise)) + result = GraphUnderTest.evaluate_structural_causal_model(dag, clean, inputs) + @test result.values == [1.0 4.0; 2.0 6.0] + mutation = Dict("A" => ((parents, noise) -> (noise[1] = 90.0; noise)), "B" => clean["B"]) + GraphUnderTest.evaluate_structural_causal_model(dag, mutation, inputs) + @test inputs == Dict("A" => [1.0, 2.0], "B" => [3.0, 4.0]) + mutation = Dict("A" => clean["A"], "B" => ((parents, noise) -> (parents[1, 1] = 90.0; noise))) + GraphUnderTest.evaluate_structural_causal_model(dag, mutation, inputs) + @test inputs == Dict("A" => [1.0, 2.0], "B" => [3.0, 4.0]) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, Dict("A" => clean["A"]), inputs) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, Dict("A" => 3.0, "B" => clean["B"]), inputs) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, clean, Dict("A" => [1.0], "B" => [1.0, 2.0])) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, clean, Dict("A" => [NaN], "B" => [1.0])) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, clean, Dict("A" => [true], "B" => [false])) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, clean, Dict("A" => Float64[], "B" => Float64[])) + for wrong in ([1.0], [NaN, 0.0], [true, false], 1.0) + @test_throws ArgumentError GraphUnderTest.evaluate_structural_causal_model(dag, Dict("A" => ((parents, noise) -> wrong), "B" => clean["B"]), inputs) + end +end diff --git a/test/hedging_regressions.jl b/test/hedging_regressions.jl new file mode 100644 index 0000000..0566f31 --- /dev/null +++ b/test/hedging_regressions.jl @@ -0,0 +1,157 @@ +using Test +using TOML +using Random +using Zygote +using RiskLabAI +using RiskLabAI.Backtest: gbm_hedging_paths, hedging_pnl, entropic_risk, + black_scholes_call_delta, DeepHedge, train_deep_hedge + +hedging_fixture_matrix(rows, type = Float64) = type.(permutedims(hcat(rows...))) + +function hedging_fixture_parameters(encoded) + names = (:weight1, :bias1, :weight2, :bias2, :weight3, :bias3) + return NamedTuple{names}(Tuple(startswith(string(name), "weight") ? hedging_fixture_matrix(encoded[string(name)]) : Float64.(encoded[string(name)]) for name in names)) +end + +@testset "Supplied-draw GBM and zero-rate call deltas" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hedging_python_320.toml")) + for case in fixture["gbm"] + output = gbm_hedging_paths(; (Symbol(key) => case[key] for key in ("spot", "maturity", "volatility", "drift", "steps", "paths", "seed"))..., + normal_draws = hedging_fixture_matrix(case["draws"])) + @test isapprox(output, hedging_fixture_matrix(case["expected"]); rtol = 1e-10, atol = 1e-12) + @test output[:, 1] == fill(case["spot"], case["paths"]) + @test eltype(output) == Float64 + end + for case in fixture["delta"] + prices = hedging_fixture_matrix(case["prices"]) + settings = (strike = case["strike"], maturity = case["maturity"], volatility = case["volatility"]) + delta = black_scholes_call_delta(prices; settings...) + @test isapprox(delta, hedging_fixture_matrix(case["expected"]); rtol = 1e-10, atol = 1e-12) + @test all(value -> 0 <= value <= 1, delta) + delta32 = black_scholes_call_delta(Float32.(prices); settings...) + @test eltype(delta32) == Float32 + @test maximum(abs.(delta32 .- delta)) <= 8eps(Float32) + end +end + +@testset "Self-financing hedge cash flow and entropic value gradients" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hedging_python_320.toml")) + for case in fixture["pnl"] + type = case["dtype"] == "float32" ? Float32 : Float64 + prices, positions = hedging_fixture_matrix(case["prices"], type), hedging_fixture_matrix(case["positions"], type) + settings = (strike = case["strike"], cost_rate = case["cost_rate"], premium = case["premium"]) + pnl = hedging_pnl(prices, positions; settings...) + gradient = only(Zygote.gradient(value -> sum(hedging_pnl(prices, value; settings...)), positions)) + absolute = type == Float64 ? 1e-12 : case["roundoff_bound"] + relative = type == Float64 ? 1e-10 : 0.0 + @test all(isapprox.(pnl, case["expected"]; rtol = relative, atol = absolute)) + @test all(isapprox.(gradient, hedging_fixture_matrix(case["gradient"]); rtol = relative, atol = absolute)) + @test eltype(pnl) == type + @test eltype(gradient) == type + end + for case in fixture["risk"] + pnl = Float64.(case["pnl"]) + risk = entropic_risk(pnl, case["risk_aversion"]) + gradient = only(Zygote.gradient(value -> entropic_risk(value, case["risk_aversion"]), pnl)) + bound = max(1e-12, case["roundoff_bound"]) + @test abs(risk - case["expected"]) <= bound + @test abs(risk - case["high_precision"]) <= bound + @test isapprox(gradient, case["gradient"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(sum(gradient), -1; rtol = 1e-10, atol = 1e-12) + @test abs(entropic_risk(pnl .+ 7, case["risk_aversion"]) - (risk - 7)) <= 2bound + end + @test isapprox(hedging_pnl([100.0 120.0], reshape([0.5], 1, 1); strike = 110, cost_rate = 0.01, premium = 5), [3.9]; rtol = 1e-10, atol = 1e-12) + @test hedging_pnl([100.0 120.0], zeros(1, 1); strike = 110, cost_rate = 0.01) == [-10.0] + @test only(Zygote.gradient(value -> sum(hedging_pnl([100.0 120.0], value; strike = 110, cost_rate = 0.01)), zeros(1, 1))) == reshape([20.0], 1, 1) +end + +@testset "Shared-weight adapted neural hedge, gradients and Adam training" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hedging_python_320.toml")) + for case in fixture["networks"] + parameters = hedging_fixture_parameters(case["parameters"]) + model = DeepHedge(case["hidden_size"]; parameters) + prices = hedging_fixture_matrix(case["prices"]) + settings = (strike = case["strike"], maturity = case["maturity"]) + positions = model(prices; settings...) + @test isapprox(positions, hedging_fixture_matrix(case["positions"]); rtol = 1e-10, atol = 1e-12) + objective(values) = entropic_risk(hedging_pnl(prices, + RiskLabAI.Backtest._deep_hedge_positions(values, prices, settings.strike, settings.maturity); + strike = settings.strike, cost_rate = case["cost_rate"]), case["risk_aversion"]) + @test isapprox(objective(model.parameters), case["risk"]; rtol = 1e-10, atol = 1e-12) + gradients = only(Zygote.gradient(objective, model.parameters)) + expected_gradients = hedging_fixture_parameters(case["gradients"]) + for name in keys(parameters) + @test isapprox(getproperty(gradients, name), getproperty(expected_gradients, name); rtol = 1e-10, atol = 1e-12) + end + for time in 1:size(positions, 2) + modified = copy(prices) + modified[:, time+1:end] .*= 1.7 + @test model(modified; settings...)[:, 1:time] == positions[:, 1:time] + end + history = train_deep_hedge(model, prices; settings..., cost_rate = case["cost_rate"], + risk_aversion = case["risk_aversion"], iterations = case["iterations"], learning_rate = case["learning_rate"]) + @test isapprox(history, case["history"]; rtol = 1e-10, atol = 1e-12) + trained = hedging_fixture_parameters(case["trained_parameters"]) + for name in keys(parameters) + @test isapprox(getproperty(model.parameters, name), getproperty(trained, name); rtol = 1e-10, atol = 1e-12) + end + evaluation = hedging_fixture_matrix(case["evaluation_prices"]) + before = map(copy, model.parameters) + @test isapprox(model(evaluation; settings...), hedging_fixture_matrix(case["evaluation_positions"]); rtol = 1e-10, atol = 1e-12) + @test model.parameters == before + @test parameters == hedging_fixture_parameters(case["parameters"]) + end +end + +@testset "Hedging domains, snapshots, local RNGs and numerical range" begin + Random.seed!(751) + expected = rand(5) + Random.seed!(751) + model = DeepHedge(3; seed = 15) + paths = gbm_hedging_paths(; spot = 100, maturity = 1, volatility = 0.2, drift = 0, steps = 4, paths = 5, seed = 16) + @test rand(5) == expected + @test model.parameters == DeepHedge(3; seed = 15).parameters + @test paths == gbm_hedging_paths(; spot = 100, maturity = 1, volatility = 0.2, drift = 0, steps = 4, paths = 5, seed = 16) + @test all(value -> value >= 0, black_scholes_call_delta(paths; strike = 100, maturity = 1, volatility = 0.2)) + supplied = map(copy, model.parameters) + snapshot = DeepHedge(3; parameters = supplied) + supplied.weight1[1] = 100 + @test snapshot.parameters.weight1[1] != 100 + @test all(abs.(snapshot.parameters.weight1) .<= Float32(1 / sqrt(3))) + @test all(abs.(snapshot.parameters.weight2) .<= Float32(1 / sqrt(3))) + @test Float64.(Float32.(snapshot.parameters.bias3)) == snapshot.parameters.bias3 + for bad in (0, -1, true, 1.5) + @test_throws ArgumentError DeepHedge(bad) + end + for bad in (true, 1.5, big(2)^65) + @test_throws ArgumentError DeepHedge(3; seed = bad) + end + @test DeepHedge(1; seed = -1).hidden_size == 1 + @test_throws ArgumentError DeepHedge(3; parameters = merge(model.parameters, (weight1 = ones(2, 3),))) + @test_throws ArgumentError DeepHedge(3; parameters = map(value -> Float32.(value), model.parameters)) + settings = (spot = 100., maturity = 1., volatility = 0.2, drift = 0., steps = 4, paths = 5, seed = 0) + for bad in (0, -1, NaN, Inf, true) + @test_throws ArgumentError gbm_hedging_paths(; settings..., spot = bad) + @test_throws ArgumentError gbm_hedging_paths(; settings..., maturity = bad) + end + @test_throws ArgumentError gbm_hedging_paths(; settings..., volatility = -1) + @test_throws ArgumentError gbm_hedging_paths(; settings..., drift = Inf) + @test_throws ArgumentError gbm_hedging_paths(; settings..., normal_draws = ones(5, 3)) + @test_throws ArgumentError gbm_hedging_paths(; settings..., drift = 1000) + @test_throws ArgumentError gbm_hedging_paths(; settings..., drift = -1000) + @test_throws ArgumentError hedging_pnl(paths, ones(5, 3); strike = 100, cost_rate = 0) + @test_throws ArgumentError hedging_pnl(paths, ones(Float32, 5, 4); strike = 100, cost_rate = 0) + @test_throws ArgumentError hedging_pnl(zeros(5, 5), ones(5, 4); strike = 100, cost_rate = 0) + @test_throws ArgumentError hedging_pnl(paths, ones(5, 4); strike = 100, cost_rate = -0.1) + @test_throws ArgumentError hedging_pnl(paths, ones(5, 4); strike = 100, cost_rate = 0, premium = Inf) + @test_throws ArgumentError entropic_risk(Int[1, 2], 1.0) + @test_throws ArgumentError entropic_risk(Float64[], 1.0) + for bad in (0, -1, true, NaN, Inf) + @test_throws ArgumentError entropic_risk([1., 2.], bad) + end + @test_throws ArgumentError black_scholes_call_delta(paths; strike = 100, maturity = 1, volatility = 0) + @test_throws ArgumentError model(Float32.(paths); strike = 100, maturity = 1) + @test_throws ArgumentError model(paths[:, 1:1]; strike = 100, maturity = 1) + @test_throws ArgumentError train_deep_hedge(model, paths; strike = 100, maturity = 1, cost_rate = 0, risk_aversion = 1, iterations = 0) + @test_throws ArgumentError train_deep_hedge(model, paths; strike = 100, maturity = 1, cost_rate = 0, risk_aversion = 1, iterations = 1, learning_rate = 0) +end diff --git a/test/hierarchy_regressions.jl b/test/hierarchy_regressions.jl new file mode 100644 index 0000000..0561709 --- /dev/null +++ b/test/hierarchy_regressions.jl @@ -0,0 +1,67 @@ +using Test +using TOML +using Random +using LinearAlgebra +using RiskLabAI + +hierarchy_matrix(rows) = reduce(vcat, permutedims.([Float64[row...] for row in rows])) +hierarchy_equal(left, right) = isapprox(left, right; rtol = 1e-10, atol = 1e-12) + +@testset "Shared inverse variance, cluster variance and tree leaves" begin + O = RiskLabAI.Optimization + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hierarchy_python_320.toml")) + for case in fixture["ties"] + distance = hierarchy_matrix(case["distance"]) + original = copy(distance) + @test O._single_linkage_order(distance) == case["order"] + @test distance == original + end + for case in fixture["hrp"] + covariance, correlation, tree = hierarchy_matrix(case["covariance"]), hierarchy_matrix(case["correlation"]), hierarchy_matrix(case["linkage"]) + original = deepcopy((covariance, correlation, tree)) + @test hierarchy_equal(O.inverse_variance_weights(covariance), case["inverse"]) + @test hierarchy_equal(O.cluster_variance(covariance, case["selected"]), case["variance"]) + @test hierarchy_equal(O.distance_corr(correlation), hierarchy_matrix(case["distance"])) + @test O.quasi_diagonal(tree) == case["order"] + @test isequal((covariance, correlation, tree), original) + end +end + +@testset "Shared recursive allocation and complete single linkage portfolios" begin + O = RiskLabAI.Optimization + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hierarchy_python_320.toml")) + for case in fixture["bisection"] + covariance, order = hierarchy_matrix(case["covariance"]), case["order"] + original = deepcopy((covariance, order)) + actual = O.recursive_bisection(covariance, order) + @test hierarchy_equal(actual, case["output"]) + @test isapprox(sum(actual), 1; atol = 1e-12) + @test all(>(0), actual) + @test isequal((covariance, order), original) + end + for case in fixture["hrp"] + covariance, correlation = hierarchy_matrix(case["covariance"]), hierarchy_matrix(case["correlation"]) + original = deepcopy((covariance, correlation)) + actual = O.hrp(covariance, correlation) + @test hierarchy_equal(actual, case["output"]) + @test isapprox(sum(actual), 1; atol = 1e-12) + @test all(>(0), actual) + @test isequal((covariance, correlation), original) + end +end + +@testset "Shared supplied cluster NCO allocation" begin + O = RiskLabAI.Optimization + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hierarchy_python_320.toml")) + for case in fixture["nco"] + covariance = hierarchy_matrix(case["covariance"]) + clusters = Dict(index => Int[items...] for (index, items) in enumerate(case["clusters"])) + target = case["with_mean"] ? case["mu"] : nothing + original = deepcopy((covariance, clusters, target)) + actual = O.get_optimal_portfolio_weights_nco(covariance; mu = target, clusters = clusters) + @test hierarchy_equal(actual, case["output"]) + @test isapprox(sum(actual), 1; atol = 1e-12) + @test isequal((covariance, clusters, target), original) + @test_throws ArgumentError O.get_optimal_portfolio_weights_nco(covariance; clusters = Dict(1 => [1, 1])) + end +end diff --git a/test/hpo_regressions.jl b/test/hpo_regressions.jl new file mode 100644 index 0000000..e515264 --- /dev/null +++ b/test/hpo_regressions.jl @@ -0,0 +1,129 @@ +using Test +using TOML +using Statistics +using RiskLabAI + +hpo_matrix(rows) = reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) +hpo_close(left, right) = isapprox(left, right; rtol = 1e-10, atol = 2e-13, nans = true) + +mutable struct HpoDiagnosticEstimator + params::Dict + threshold::Float64 + fits::Int +end + +function hpo_callbacks(records) + calls = Ref(0) + factory(params) = HpoDiagnosticEstimator(deepcopy(params), NaN, 0) + fit = function (model, x, y) + calls[] += 1 + record = records[calls[]] + @test model.fits == 0 + @test Int.(x[:, 1]) == record["train"] + model.fits += 1 + model.threshold = model.params["cut"]+.15*mean(x[:, 2])+.05*mean(y) + @test hpo_close(model.threshold, record["threshold"]) + return model + end + predict = function (model, x) + @test model.fits == 1 + @test Int.(x[:, 1]) == records[calls[]]["test"] + return Int.(model.params["slope"].*x[:, 2] .> model.threshold) + end + score = (model, x, y) -> mean(predict(model, x) .== y)+.001*mean(x[:, 2]) + return factory, fit, predict, score, calls +end + +@testset "HPO: public fold skips and caller-estimator scoring" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hpo_python_320.toml")) + for case in fixtures["objective"] + x, y = hpo_matrix(case["x"]), Vector{Int}(case["y"]) + cv = V.PurgedKFoldCV(case["splits"], case["starts"], case["ends"]; embargo = case["embargo"]) + factory, fit, predict, score, calls = hpo_callbacks(case["records"]) + actual = V._purged_cv_score(factory, case["params"], x, y, cv; + fit_estimator = fit, predict = predict, scoring = case["custom"] ? score : nothing) + @test hpo_close(actual, case["score"]) + @test calls[] == length(case["records"]) + end +end + +@testset "HPO: recorded TPE trials and sampler feedback" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hpo_python_320.toml")) + for case in fixtures["study"] + proposals = case["proposed"] + records = reduce(vcat, [proposal["records"] for proposal in proposals]) + factory, fit, predict, score, calls = hpo_callbacks(records) + asked, told = Ref(0), Ref(0) + ask = function (number, history, rng) + asked[] += 1 + @test asked[] == number && length(history) == number-1 + for (previous, proposal) in zip(history, proposals) + @test previous.params == proposal["parameters"] + @test hpo_close(previous.score, proposal["score"]) + @test previous.state == :complete + end + return proposals[number] + end + tell = function (trial, value) + told[] += 1 + @test trial === proposals[told[]] + @test hpo_close(value, trial["score"]) + end + actual = V.leakage_aware_hpo(trial -> trial["configuration"], factory, + hpo_matrix(case["x"]), Vector{Int}(case["y"]); + event_starts = case["starts"], event_ends = case["ends"], + ask_trial = ask, fit_estimator = fit, predict = predict, + scoring = case["custom"] ? score : nothing, + trial_parameters = trial -> trial["parameters"], tell_trial! = tell, + n_trials = length(proposals), n_splits = case["splits"], embargo = case["embargo"]) + expected = case["output"] + @test actual.best_params == expected["best_params"] + @test !haskey(actual.best_params, "fixed") + @test hpo_close(actual.best_score, expected["best_score"]) + @test hpo_close(actual.trial_scores, Vector{Float64}(expected["trial_scores"])) + @test hpo_close(actual.mean_trial_score, expected["mean_trial_score"]) + @test hpo_close(actual.std_trial_score, expected["std_trial_score"]) + @test actual.n_trials == length(proposals) == asked[] == told[] + @test calls[] == length(records) + end +end + +@testset "HPO: failed, infinite and singleton trial summaries" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hpo_python_320.toml")) + for case in fixtures["boundary"] + history = [(number = index, params = proposal["parameters"], score = proposal["score"], state = isnan(proposal["score"]) ? :failed : :complete) for (index, proposal) in enumerate(case["proposals"])] + if !isempty(case["error"]) + @test_throws ArgumentError V._hpo_trial_summary(history, length(history)) + else + actual = V._hpo_trial_summary(history, length(history)) + @test actual.best_params == case["best_params"] + @test hpo_close(actual.best_score, case["best_score"]) + @test hpo_close(actual.trial_scores, Vector{Float64}(case["scores"])) + @test hpo_close(actual.mean_trial_score, case["mean"]) + @test hpo_close(actual.std_trial_score, case["deviation"]) + end + end + x, y = zeros(40, 2), repeat([0, 1], 20) + cv = V.PurgedKFoldCV(4, collect(1:40), collect(1:40)) + @test_throws DimensionMismatch V._purged_cv_score(identity, Dict(), x, y[1:end-1], cv; fit_estimator = identity, predict = identity) + @test_throws ArgumentError V._purged_cv_score(identity, Dict(), x, y, cv; fit_estimator = identity) + @test_throws ErrorException V._purged_cv_score(identity, Dict(), x, y, cv; fit_estimator = (model, x, y) -> error("fit failure"), predict = identity) +end + +@testset "HPO: DSR gate with independently checked biased moments" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "hpo_python_320.toml")) + for case in fixtures["gate"] + actual = V.deflated_sharpe_gate(case["returns"], case["trials"], case["dispersion"]; threshold = case["threshold"]) + for name in (:observed_sharpe, :benchmark_sharpe, :deflated_sharpe) + @test hpo_close(getproperty(actual, name), case["output"][String(name)]) + end + @test actual.n_trials == case["output"]["n_trials"] + @test actual.passes == case["output"]["passes"] + equal_threshold = V.deflated_sharpe_gate(case["returns"], case["trials"], case["dispersion"]; threshold = actual.deflated_sharpe) + @test !equal_threshold.passes + end +end diff --git a/test/importance_regressions.jl b/test/importance_regressions.jl new file mode 100644 index 0000000..da0d9ed --- /dev/null +++ b/test/importance_regressions.jl @@ -0,0 +1,164 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +importance_matrix(values) = permutedims(reduce(hcat, [Float64.(row) for row in values])) +importance_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isapprox(left, right; rtol = 1e-11, atol = 1e-13) +importance_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(importance_equal(a, b) for (a, b) in zip(left, right)) + +function importance_probability(model, data) + signal = .05 .* (data * collect(1:size(data, 2))) .+ .01*model.feature .+ .002*model.visits + scores = log.(model.prior)' .+ signal .* (model.classes .+ 1)' + exponential = exp.(scores .- maximum(scores; dims = 2)) + return exponential ./ sum(exponential; dims = 2) +end + +@testset "Shared PCA column filtering and invariant eigenspaces" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "importance_python_320.toml")) + for case in fixture["pca"] + x = importance_matrix(case["x"]) + original = copy(x) + transformed, values, vectors, cumulative = orthogonal_features(x; variance_threshold = case["threshold"]) + normalized = importance_matrix(case["normalized"]) + if case["threshold"] == 1.0 && size(normalized, 2) > size(normalized, 1)-1 + retained_rank = rank(normalized) + @test retained_rank <= length(values) <= size(normalized, 2) + @test isapprox(values[1:retained_rank], Float64.(case["eigenvalues"][1:retained_rank]); rtol = 1e-10, atol = 1e-10) + @test isapprox(cumulative[1:retained_rank], Float64.(case["cumulative"][1:retained_rank]); rtol = 1e-10, atol = 1e-10) + @test all(abs.(values[(retained_rank+1):end]) .< 1e-10) + @test all(abs.(case["eigenvalues"][(retained_rank+1):end]) .< 1e-10) + else + @test length(values) == length(case["eigenvalues"]) + @test isapprox(values, Float64.(case["eigenvalues"]); rtol = 1e-10, atol = 1e-10) + @test isapprox(cumulative, Float64.(case["cumulative"]); rtol = 1e-10, atol = 1e-10) + end + @test size(vectors, 1) == length(case["retained_columns"]) + @test isapprox(vectors' * vectors, I; atol = 1e-10) + @test isapprox(normalized' * normalized * vectors, vectors * Diagonal(values); rtol = 1e-10, atol = 1e-10) + @test isapprox(transformed, normalized * vectors; rtol = 1e-10, atol = 1e-10) + @test isapprox(transformed * transformed', importance_matrix(case["output_gram"]); rtol = 1e-10, atol = 1e-10) + if case["separated"] + @test isapprox(vectors * vectors', importance_matrix(case["projector"]); rtol = 1e-10, atol = 1e-10) + end + @test isequal(x, original) + end + @test_throws ArgumentError orthogonal_features(ones(12, 3)) + @test_throws ArgumentError orthogonal_features(ones(1, 3)) +end + +@testset "Shared weighted ranks with ties and missing scores" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "importance_python_320.toml")) + for case in fixture["tau"] + actual = calculate_weighted_tau(Float64[case["values"]...], Float64[case["ranks"]...]) + @test importance_equal(actual, case["result"]) + end + @test_throws DimensionMismatch calculate_weighted_tau([1., 2.], [1.]) +end + +@testset "Shared per-tree and clustered impurity aggregation" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "importance_python_320.toml")) + for case in fixture["mdi"] + per_tree = importance_matrix(case["per_tree"]) + original = copy(per_tree) + actual = feature_importance_mdi(per_tree) + @test importance_equal(actual.mean, Float64.(case["mean"])) + @test importance_equal(actual.std, Float64.(case["std"])) + clusters = [name => Int[group...] for (name, group) in zip(case["cluster_names"], case["clusters"])] + grouped = clustered_feature_importance_mdi(per_tree, clusters) + @test grouped.names == "C_" .* case["cluster_names"] + @test importance_equal(grouped.mean, Float64.(case["clustered_mean"])) + @test importance_equal(grouped.std, Float64.(case["clustered_std"])) + calls = [] + fit_estimator = function(model, x, y, weights) + push!(calls, (x = copy(x), y = copy(y), weights = copy(weights))) + return (per_tree = model.per_tree,) + end + x, y, weights = importance_matrix(case["x"]), Int.(case["y"]), Float64.(case["weights"]) + arguments = (fit_estimator = fit_estimator, tree_importances = model -> model.per_tree, sample_weights = weights) + fitted = feature_importance_mdi((per_tree = per_tree,), x, y; arguments...) + fitted_grouped = clustered_feature_importance_mdi((per_tree = per_tree,), x, y, clusters; arguments...) + @test importance_equal(fitted.mean, actual.mean) + @test importance_equal(fitted.std, actual.std) + @test importance_equal(fitted_grouped.mean, grouped.mean) + @test importance_equal(fitted_grouped.std, grouped.std) + @test length(calls) == 2 + @test all(call.x == x && call.y == y && call.weights == weights for call in calls) + @test fitted.estimator.per_tree == per_tree + @test per_tree == original + end +end + +@testset "Shared weighted permutation and single-feature importance" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "importance_python_320.toml")) + for case in fixture["importance"] + x, y = importance_matrix(case["x"]), Int.(case["y"]) + originals = deepcopy((x, y, case["train_weights"], case["score_weights"])) + calls = [] + fit_estimator = function(model, data, labels, weights) + visits = model.visits + 1 + classes = sort(unique(labels)) + prior = [1 + sum(weights[labels .== label]) for label in classes] + feature = sum(weights .* (data * collect(1:size(data, 2)))) / sum(weights) + push!(calls, (x = copy(data), y = copy(labels), weights = copy(weights), visits = visits)) + return (classes = classes, prior = prior, feature = feature, visits = visits) + end + predict = (model, data) -> [model.classes[argmax(row)] for row in eachrow(importance_probability(model, data))] + folds = [(Int.(fold["train"]), Int.(fold["test"])) for fold in case["folds"]] + weights = (train_sample_weights = case["weight_mode"] == "omitted" ? nothing : Float64.(case["train_weights"]), score_sample_weights = case["weight_mode"] == "omitted" ? nothing : Float64.(case["score_weights"])) + arguments = (fit_estimator = fit_estimator, predict_proba = importance_probability, estimator_classes = model -> model.classes, folds = folds) + prototype = (visits = 0,) + if case["method"] == "mda" + actual = feature_importance_mda(prototype, x, y; arguments..., weights..., permutations = case["permutations"]) + elseif case["method"] == "clustered_mda" + clusters = [name => Int[group...] for (name, group) in zip(case["cluster_names"], case["clusters"])] + actual = clustered_feature_importance_mda(prototype, x, y, clusters; arguments..., weights..., permutations = case["permutations"]) + @test actual.names == "C_" .* case["cluster_names"] + else + scoring = case["method"] == "sfi_accuracy" ? :accuracy : :log_loss + actual = feature_importance_sfi(prototype, x, y; arguments..., weights..., scoring, predict) + end + @test importance_equal(actual.mean, Float64.(case["mean"])) + @test importance_equal(actual.std, Float64.(case["std"])) + @test length(calls) == length(case["traces"]) + for (observed, expected) in zip(calls, case["traces"]) + @test observed.x == importance_matrix(expected["x"]) + @test observed.y == expected["y"] + @test observed.weights == expected["weights"] + @test observed.visits == expected["visits"] + end + @test actual.estimator.visits == length(calls) + @test (x, y, case["train_weights"], case["score_weights"]) == originals + end +end + +@testset "Importance clipping, weighting and input contracts" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "importance_python_320.toml")) + for case in fixture["loss"] + actual = F._importance_log_loss(case["labels"], importance_matrix(case["probabilities"]), case["weights"], case["classes"]) + @test importance_equal(actual, case["output"]) + end + @test_throws ArgumentError F._importance_log_loss([0, 1], [.2 .8; .1 .9], [0., 0.], [0, 1]) + @test_throws ArgumentError F._importance_log_loss([0, 1], [.2 .8; -.1 1.1], [1., 1.], [0, 1]) + @test_throws ArgumentError F._importance_log_loss([0, 2], [.2 .8; .1 .9], [1., 1.], [0, 1]) + @test_throws DimensionMismatch F._importance_log_loss([0, 1], ones(2, 3)./3, [1., 1.], [0, 1]) + @test_throws ArgumentError feature_importance_mdi(zeros(0, 3)) + @test_throws ArgumentError clustered_feature_importance_mdi(ones(2, 3), [[4]]) + @test_throws ArgumentError F._importance_folds(12, 1, nothing, true, 0) + @test_throws ArgumentError F._importance_folds(4, 2, [([1, 2], [2, 3]), ([3, 4], [1, 4])], false, 0) + @test_throws ArgumentError F._importance_permutations([[[1, 1]], [[1, 2]]], [([3, 4], [1, 2]), ([1, 2], [3, 4])], [[1]]) + @test_throws ArgumentError F._importance_weights([-1., 1.], 2) + @test_throws DimensionMismatch F._importance_weights([1.], 2) + x, y = reshape(collect(1.:24.), 12, 2), repeat([0, 1], 6) + failures = Ref(0) + fit_estimator = function(model, x, y, weights) + failures[] += 1 + throw(ArgumentError("caller fit failed")) + end + @test_throws ArgumentError feature_importance_mda(nothing, x, y; fit_estimator, predict_proba = identity, estimator_classes = identity, n_splits = 3) + @test failures[] == 1 + @test_throws ArgumentError feature_importance_sfi(nothing, x, y; fit_estimator, scoring = :f1) + @test_throws ArgumentError feature_importance_sfi(nothing, x, y; fit_estimator, scoring = :accuracy) + @test_throws ArgumentError feature_importance_sfi(nothing, x, y; fit_estimator) +end diff --git a/test/infrastructure_regressions.jl b/test/infrastructure_regressions.jl new file mode 100644 index 0000000..e4a21d0 --- /dev/null +++ b/test/infrastructure_regressions.jl @@ -0,0 +1,160 @@ +using Test +using TOML +using Dates +using DataFrames +using RiskLabAI + +@testset "Infrastructure: work partitions and submitted-order mapping" begin + P = RiskLabAI.ParallelWork + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "infrastructure_python_320.toml")) + for case in fixture["partitions"] + @test P.linear_partitions(case["count"], case["workers"]) == case["linear"] + @test P.nested_partitions(case["count"], case["workers"]) == case["nested"] + @test P.nested_partitions(case["count"], case["workers"]; upper_triangle = true) == case["upper"] + boundaries = case["linear"] + flattened = reduce(vcat, [collect(boundaries[index]+1:boundaries[index+1]) for index in 1:length(boundaries)-1]; init = Int[]) + @test flattened == collect(1:case["count"]) + end + for case in fixture["parallel"] + values = Vector{Int}(case["values"]) + callback = case["chunked"] ? (positions -> [[index, values[index]^2] for index in positions]) : (value -> value^2) + result = P.parallel_run(callback, values; num_cpus = case["workers"], lin_partition = case["chunked"]) + @test result == case["output"] + end + @test P.parallel_run((value; scale) -> value*scale, (index for index in 1:5); num_cpus = 2, scale = 3) == [3,6,9,12,15] + @test_throws ArgumentError P.linear_partitions(5, 0) + @test_throws ArgumentError P.nested_partitions(-1, 2) + @test_throws ArgumentError P.parallel_run(identity, 1:3; num_cpus = 0) +end + +@testset "Infrastructure: completion collection and explicit table indices" begin + P = RiskLabAI.ParallelWork + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "infrastructure_python_320.toml")) + for case in fixture["indexed"] + lookup = Dict(zip(case["indices"], case["values"])) + callback = function (; molecule, values) + selected = reverse(molecule) + return DataFrame(index = selected, value = [values[index]^2 for index in selected]) + end + result = P.parallel_indexed(callback, case["indices"]; num_threads = 2, + mp_batches = case["batches"], linear_partition = case["linear"], values = lookup) + @test result.index == case["output_indices"] + @test result.value == case["output_values"] + end + callback = (; key, delay, value) -> begin + sleep(delay) + (key, value^2) + end + jobs = [(func = callback, key = index, delay = index == 1 ? .035 : 0., value = index+1) for index in 1:6] + expected = [(index, (index+1)^2) for index in 1:6] + @test P.process_jobs_sequential(jobs) == expected + @test P.process_jobs(jobs; num_threads = 2, ordered = true) == expected + completion = P.process_jobs(jobs; num_threads = 2) + @test sort(completion; by = first) == expected + @test length(unique(first.(completion))) == length(jobs) + @test all(job.func === callback for job in jobs) + dictionaries = [Dict("func" => ((; value) -> value^2), "value" => index) for index in 1:4] + @test P.process_jobs(dictionaries; num_threads = 2, ordered = true) == [1,4,9,16] + @test all(haskey(job, "func") for job in dictionaries) + @test isempty(P.process_jobs(NamedTuple[]; num_threads = 2)) + @test_throws Exception P.process_jobs([(func = (; value) -> error("job failed"), value = 1)]; num_threads = 2) +end + +@testset "Infrastructure: registry aliases, replacement and lazy factories" begin + C = RiskLabAI.Components + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "infrastructure_python_320.toml")) + registry = C.ComponentRegistry("local contract") + scalar = (; factor = 1.) -> 4*factor + for case in fixture["registry"] + plan = case["plan"] + action, key = plan["action"], plan["key"] + if action in ("register", "override") + C.register_component!(registry, key, scalar; aliases = plan["aliases"], metadata = plan["metadata"], override = action == "override", accepted_keywords = [:factor]) + @test C.component_metadata(registry, key) == plan["metadata"] + copied = C.component_metadata(registry, key) + copied["family"] = "changed locally" + @test C.component_metadata(registry, key) == plan["metadata"] + elseif action == "create" + @test C.create_component(registry, key; factor = plan["factor"], ignored = 99, filter_unknown_kwargs = plan["filter"]) == plan["output"] + elseif action == "duplicate" + @test_throws KeyError C.register_component!(registry, key, scalar) + else + C.unregister_component!(registry, key) + end + @test C.available_components(registry) == case["state"]["available"] + @test C.component_aliases(registry) == case["state"]["aliases"] + @test length(registry) == case["state"]["length"] + for name in C.available_components(registry) + @test uppercase(name) in registry + @test registry[name] === scalar + end + end + loads = Ref(0) + C.register_lazy!(registry, "counter", () -> begin + loads[] += 1 + text -> Dict(character => count(==(character), text) for character in unique(text)) + end) + @test C.is_lazy(registry, "counter") + @test C.create_component(registry, "COUNTER", "abracadabra")['a'] == 5 + @test !C.is_lazy(registry, "counter") + @test C.create_component(registry, "counter", "abaca")['a'] == 3 + @test loads[] == 1 + @test !(42 in registry) + @test_throws KeyError C.create_component(registry, "absent") + @test_throws ArgumentError C.register_component!(registry, "", scalar) + @test C.list_components() == fixture["catalogue"] + for (family, names) in fixture["catalogue"] + for name in names + @test C.component_metadata(C.get_registry(family), name) == fixture["catalogue_metadata"][family][name] + end + end + @test C.create_component(C.CROSS_VALIDATORS, "KFOLD", 3) isa RiskLabAI.Validation.KFoldCV + @test C.create_component(C.FEATURE_IMPORTANCE, "mdi").function_value === RiskLabAI.Features.feature_importance_mdi + @test C.create_component(C.BARS, "standard_bars"; metric = :tick, threshold = 7.) isa RiskLabAI.Data.StandardBars{RiskLabAI.Data.Tick} + @test C.create_component(C.BARS, "time_bars"; resolution_type = "S", resolution_units = 1) isa RiskLabAI.Data.TimeBars +end + +struct InfrastructureDirectionLabeler <: RiskLabAI.Components.AbstractLabeler + horizon::Int +end + +function RiskLabAI.Components.label(model::InfrastructureDirectionLabeler, prices) + forward = [index+model.horizon <= length(prices) ? prices[index+model.horizon]/value-1 : NaN for (index, value) in enumerate(prices)] + return (forward = forward, direction = [isnan(value) ? 0 : Int(sign(value)) for value in forward]) +end + +@testset "Infrastructure: labeler dispatch and complete bar-input status" begin + C = RiskLabAI.Components + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "infrastructure_python_320.toml")) + for case in fixture["labels"] + actual = C.label(InfrastructureDirectionLabeler(case["horizon"]), case["prices"]) + @test isequal(actual.forward, case["forward"]) + @test actual.direction == case["direction"] + end + @test C.BAR_COLUMNS == fixture["bar_columns"] + @test length(unique(C.bar_methods())) == 16 + for case in fixture["bars"] + rows = case["rows"] + data = Matrix{Any}(undef, length(rows), 3) + for (index, row) in enumerate(rows) + data[index, :] = [DateTime(1970)+Millisecond(Int(row[1])), row[2], row[3]] + end + result = C.process_bars(C.BarController(), case["method"], case["arguments"], data; batch_size = case["batch"]) + @test result.completed_input + @test result.processed_rows == length(rows) + @test result.error === nothing + @test names(result.bars) == case["columns"] + @test nrow(result.bars) == length(case["output"]) + for (actual, expected) in zip(eachrow(result.bars), case["output"]) + @test Dates.value(actual[1]-DateTime(1970)) == expected[1] + @test collect(Float64, actual[2:end]) ≈ Vector{Float64}(expected[2:end]) rtol = 1e-10 atol = 2e-10 nans = true + end + end + failed = C.process_bars(C.BarController(), "unknown", Dict(), zeros(3,3)) + @test !failed.completed_input && failed.processed_rows == 0 + @test failed.error !== nothing && nrow(failed.bars) == 0 + @test names(failed.bars) == C.BAR_COLUMNS + empty = C.process_bars(C.BarController(), "tick_standard_bars", Dict(), Matrix{Any}(undef,0,3)) + @test empty.completed_input && empty.processed_rows == 0 && nrow(empty.bars) == 0 + @test_throws ArgumentError C.process_bars(C.BarController(), "tick_standard_bars", Dict(), zeros(3,3); batch_size = 0) +end diff --git a/test/labeling_regressions.jl b/test/labeling_regressions.jl new file mode 100644 index 0000000..c6595ff --- /dev/null +++ b/test/labeling_regressions.jl @@ -0,0 +1,78 @@ +using Test +using TOML +using Dates +using DataFrames +using RiskLabAI + +label_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-10, atol = 1e-12) +label_equal(left::AbstractVector, right::AbstractVector) = length(left) == length(right) && all(label_equal(a, b) for (a, b) in zip(left, right)) +label_date(offset) = DateTime(2020, 1, 1)+Minute(offset) +label_ends(values) = Union{Missing,DateTime}[value == -99999 ? missing : label_date(value) for value in values] + +@testset "Shared strict CUSUM boundaries and missing differences" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "labeling_python_320.toml")) + for case in fixture["cusum"] + values, thresholds = Float64[case["values"]...], Float64[case["dynamic"]...] + index = label_date.(collect(0:(length(values)-1))) + original = deepcopy((values, thresholds, index)) + @test D.symmetric_cusum_filter(index, values, case["threshold"]) == label_date.(Int[case["events"]...]) + @test D.cusum_filter_events_dynamic_threshold(index, values, thresholds) == label_date.(Int[case["dynamic_events"]...]) + @test isequal((values, thresholds, index), original) + end +end + +@testset "Shared daily lookbacks, debiased EWM and vertical dates" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "labeling_python_320.toml")) + for case in fixture["volatility"] + index, prices = label_date.(Int[case["minutes"]...]), Float64[case["prices"]...] + original = deepcopy((index, prices)) + actual = D.daily_volatility_with_log_returns(index, prices; span = case["span"]) + @test actual.index == label_date.(Int[case["output_index"]...]) + @test label_equal(actual.volatility, case["output"]) + @test isequal((index, prices), original) + end + for case in fixture["vertical"] + actual = D.vertical_barrier(label_date.(case["minutes"]), label_date.(case["events"]), case["days"]) + @test actual.event == label_date.(Int[case["output_events"]...]) + @test actual.barrier == label_date.(Int[case["output_barriers"]...]) + end + @test_throws ArgumentError D._ewm_std([.1, .2], 0) +end + +@testset "Shared first-touch barriers and next-price meta labels" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "labeling_python_320.toml")) + for case in fixture["triple"] + events = DataFrame(event_start = label_date.(case["starts"]), end_time = label_ends(case["ends"]), base_width = Float64.(case["widths"])) + case["with_side"] && (events.side = Float64.(case["sides"])) + index, prices = label_date.(case["minutes"]), Float64.(case["prices"]) + original = deepcopy((events, index, prices)) + actual = D.triple_barrier(index, prices, events, Tuple(Float64.(case["multipliers"]))) + @test isequal(actual, label_ends(case["output"])) + @test isequal((events, index, prices), original) + end + for case in fixture["meta"] + events = DataFrame(event_start = label_date.(case["starts"]), end_time = label_ends(case["ends"])) + case["with_side"] && (events.side = Float64.(case["sides"])) + index, prices = label_date.(case["minutes"]), Float64.(case["prices"]) + original = deepcopy((events, index, prices)) + actual = D.meta_labeling(events, index, prices) + @test actual.event_start == label_date.(case["output_starts"]) + @test actual.end_time == label_date.(case["output_ends"]) + @test label_equal(actual.ret, case["returns"]) + @test label_equal(actual.label, case["labels"]) + @test ("side" in names(actual)) == case["with_side"] + if case["with_side"] + @test label_equal(actual.side, case["output_sides"]) + end + @test isequal((events, index, prices), original) + end + index = label_date.([0, 10, 20]) + prices = [100., 102., 105.] + events = D.meta_events(index, prices, index[1:2], (1., 1.), Dict(index[1] => .01, index[2] => .01), 0.; side = Dict(index[1] => 1.)) + @test events.end_time[1] == index[2] + @test ismissing(events.end_time[2]) + @test isnan(events.side[2]) +end diff --git a/test/microstructure_regressions.jl b/test/microstructure_regressions.jl new file mode 100644 index 0000000..1d22ac6 --- /dev/null +++ b/test/microstructure_regressions.jl @@ -0,0 +1,41 @@ +using Test +using TOML +using RiskLabAI + +micro_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-9, atol = 2e-12) +micro_equal(left::AbstractVector, right::AbstractVector) = length(left) == length(right) && all(micro_equal(a, b) for (a, b) in zip(left, right)) + +@testset "Shared rolling OHLC spread and volatility conventions" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "microstructure_python_320.toml")) + for case in fixture["rolling"] + high, low = Float64[case["high"]...], Float64[case["low"]...] + original = deepcopy((high, low)) + beta, gamma = F.beta_estimates(high, low, case["width"]), F.gamma_estimates(high, low) + @test micro_equal(beta, case["beta"]) + @test micro_equal(gamma, case["gamma"]) + @test micro_equal(F.alpha_estimates(beta, gamma), case["alpha"]) + @test micro_equal(F.sigma_estimates(beta, gamma), case["sigma"]) + @test micro_equal(F.corwin_schultz_estimator(high, low, case["width"]), case["spread"]) + @test micro_equal(F.bekker_parkinson_volatility_estimates(high, low, case["width"]), case["volatility"]) + @test isequal((high, low), original) + end + for case in fixture["moments"] + @test micro_equal(only(F.alpha_estimates([case["beta"]], [case["gamma"]])), case["alpha"]) + @test micro_equal(only(F.sigma_estimates([case["beta"]], [case["gamma"]])), case["sigma"]) + end +end + +@testset "Shared EDGE trade indicators, signed estimates and missing observations" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "microstructure_python_320.toml")) + for case in fixture["edge"] + inputs = Tuple(Float64[case[key]...] for key in ("open", "high", "low", "close")) + original = deepcopy(inputs) + @test micro_equal(F.edge_estimator(inputs...; sign = true), case["signed"]) + @test micro_equal(F.edge_estimator(inputs...), case["unsigned"]) + @test micro_equal(F.edge_estimator((values .* 8 for values in inputs)...; sign = true), case["signed"]) + @test isequal(inputs, original) + end + @test_throws ArgumentError F.edge_estimator([1., 2., 3.], [1., 2.], [1., 2., 3.], [1., 2., 3.]) +end diff --git a/test/mp_fit_regressions.jl b/test/mp_fit_regressions.jl new file mode 100644 index 0000000..08e17e7 --- /dev/null +++ b/test/mp_fit_regressions.jl @@ -0,0 +1,64 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +mp_fit_matrix(rows) = reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) + +@testset "MP fitting: extrapolated line-search intervals" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "mp_line_search_book_cases.toml")) + for case in fixture["fit"] + values = Float64.(case["eigenvalues"]) + cutoff, variance = RiskLabAI.Data.find_max_eval(values, case["q"], case["bandwidth"]) + @test variance ≈ case["variance"] atol = 1e-5 rtol = 1e-6 + @test cutoff ≈ case["variance"]*(1+sqrt(1/case["q"]))^2 atol = 3e-5 rtol = 1e-6 + @test count(>(cutoff), values) == case["factors"] + end +end + +@testset "MP fitting: reference local optima and strict factor counts" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "mp_fit_python_320.toml")) + for case in fixture["fit"] + values = Vector{Float64}(case["eigenvalues"]) + cutoff, variance = D.find_max_eval(values, case["q"], case["bandwidth"]) + @test variance ≈ case["variance"] atol = 1e-5 rtol = 1e-6 + @test cutoff ≈ case["cutoff"] atol = 1e-5*(1+sqrt(1/case["q"]))^2 rtol = 1e-6 + @test count(>(cutoff), values) == case["factors"] + @test D._mp_pdf_fit_error(case["variance"], case["q"], values, case["bandwidth"]) ≈ case["objective"] rtol = 2e-10 atol = 2e-11 + @test D.find_max_eval(Matrix(Diagonal(values)), case["q"], case["bandwidth"]) == (cutoff, variance) + end +end + +@testset "MP fitting: independent spectral reconstruction and portfolios" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "mp_fit_python_320.toml")) + for case in fixture["covariance"] + covariance = mp_fit_matrix(case["covariance"]) + actual = D.denoise_cov(covariance, case["q"]; bandwidth = case["bandwidth"]) + @test actual ≈ mp_fit_matrix(case["output"]) rtol = 2e-10 atol = 2e-11 + @test diag(actual) ≈ diag(covariance) rtol = 2e-14 atol = 2e-14 + @test isposdef(Symmetric(actual)) + @test D.optimal_portfolio_denoised(covariance, case["q"]; mu = case["mu"], bandwidth = case["bandwidth"]) ≈ Vector{Float64}(case["portfolio"]) rtol = 2e-10 atol = 2e-11 + @test D.optimal_portfolio_denoised(covariance, case["q"]; bandwidth = case["bandwidth"]) ≈ Vector{Float64}(case["minimum"]) rtol = 2e-10 atol = 2e-11 + eigenvalues, _ = D.pca(D.cov_to_corr(covariance)) + cutoff, _ = D.find_max_eval(eigenvalues, case["q"], case["bandwidth"]) + @test count(>(cutoff), eigenvalues) == case["factors"] + end +end + +@testset "MP fitting: bounded local convergence and explicit failures" begin + D = RiskLabAI.Data + for (objective, expected) in ((value -> (value-.23)^2, .23), (value -> (value+2)^2, 1e-5), (value -> (value-2)^2, 1-1e-5), (value -> 3., .5)) + actual = D._mp_bounded_local_min(objective) + @test actual.converged + @test actual.variance ≈ expected atol = 2e-7 + end + @test !D._mp_bounded_local_min(value -> (value-.23)^2; max_iter = 0).converged + @test !D._mp_bounded_local_min(value -> (value-.23)^2; max_evals = 2).converged + @test !D._mp_bounded_local_min(value -> NaN).converged + @test_throws ArgumentError D.find_max_eval(Float64[], 2., .1) + @test_throws ArgumentError D.find_max_eval([NaN], 2., .1) + @test_throws ArgumentError D.find_max_eval([1.], 0., .1) + @test_throws ArgumentError D.find_max_eval([1.], 2., 0.) +end diff --git a/test/mp_gradient_regressions.jl b/test/mp_gradient_regressions.jl new file mode 100644 index 0000000..c0d998b --- /dev/null +++ b/test/mp_gradient_regressions.jl @@ -0,0 +1,29 @@ +using Test, TOML, LinearAlgebra + +@testset "MP analytic gradient and preserved failure rules" begin + D=RiskLabAI.Data + fixture=TOML.parsefile(joinpath(@__DIR__,"fixtures","mp_gradient_reference.toml")) + for case in fixture["case"] + values=Float64.(case["eigenvalues"]) + actual=D._mp_pdf_fit_gradient(case["variance"],case["q"],values,case["bandwidth"]) + @test actual ≈ case["gradient"] rtol=fixture["gradient_rtol"] atol=fixture["gradient_atol"] + @test D._mp_pdf_fit_error(case["variance"],case["q"],values,case["bandwidth"]) ≈ case["objective"] rtol=2e-10 atol=2e-11 + end + for (objective,derivative,expected) in [(v->(v-.23)^2,v->2*(v-.23),.23),(v->(v+2)^2,v->2*(v+2),1e-5),(v->(v-2)^2,v->2*(v-2),1-1e-5),(v->3.,v->0.,.5)] + result=D._mp_bounded_local_min(objective;derivative) + @test result.converged + @test result.variance ≈ expected atol=2e-7 + end + @test !D._mp_bounded_local_min(v->(v-.23)^2;derivative=v->2*(v-.23),max_iter=0).converged + @test !D._mp_bounded_local_min(v->(v-.23)^2;derivative=v->2*(v-.23),max_evals=1).converged + @test !D._mp_bounded_local_min(v->NaN;derivative=v->0.).converged + cutoff,variance=D.find_max_eval([1.,2.],1.,nextfloat(0.)) + @test variance==1. + @test cutoff==4. + case=only(filter(row->row["name"]=="chapter23_0.42385584339082083",fixture["case"])) + values=Float64.(case["eigenvalues"]) + cutoff,variance=D.find_max_eval(values,case["q"],case["bandwidth"]) + @test variance ≈ .4238558433793075 rtol=1e-8 atol=2e-9 + @test count(>(cutoff),values)==5 + @test D.find_max_eval(Matrix(Diagonal(values)),case["q"],case["bandwidth"])==(cutoff,variance) +end diff --git a/test/neural_sde_regressions.jl b/test/neural_sde_regressions.jl new file mode 100644 index 0000000..1004b4c --- /dev/null +++ b/test/neural_sde_regressions.jl @@ -0,0 +1,242 @@ +using Test +using TOML +using Random +using LinearAlgebra +using Zygote +using RiskLabAI +using RiskLabAI.Pde: normalized_call_grid_constraints, normalized_call_grid_witness, + affine_price_state_interval, IntervalNeuralSDE, PolytopeNeuralSDE, + negative_log_likelihood, fit_interval_neural_sde, fit_polytope_neural_sde, + simulate_interval_neural_sde, simulate_polytope_neural_sde, hjm_drift_residual + +nse_fixture_matrix(rows) = Float64.(permutedims(hcat(rows...))) +nse_fixture_array(windows) = cat((reshape(nse_fixture_matrix(window), 1, length(window), length(first(window))) for window in windows)...; dims = 1) + +function nse_fixture_parameters(encoded) + return (weight1 = nse_fixture_matrix(encoded["network.0.weight"]), bias1 = Float64.(encoded["network.0.bias"]), + weight2 = nse_fixture_matrix(encoded["network.2.weight"]), bias2 = Float64.(encoded["network.2.bias"])) +end + +function nse_test_parameters(actual, expected) + @test keys(actual) == keys(expected) + for name in keys(expected) + @test isapprox(getproperty(actual, name), getproperty(expected, name); rtol = 1e-10, atol = 1e-12) + end +end + +@testset "Normalized call-grid inequalities and probability witnesses" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "neural_sde_python_320.toml")) + for case in fixture["grids"] + strikes, maturities, prices = Float64.(case["strikes"]), Float64.(case["maturities"]), nse_fixture_matrix(case["prices"]) + matrix, bounds = normalized_call_grid_constraints(strikes, maturities) + @test isapprox(matrix, nse_fixture_matrix(case["matrix"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(bounds, case["bounds"]; rtol = 1e-10, atol = 1e-12) + @test minimum(matrix * vec(permutedims(prices)) - bounds) >= -1e-12 + witness = normalized_call_grid_witness(strikes, maturities, prices) + @test isapprox(witness.support, case["support"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(witness.probabilities, nse_fixture_matrix(case["probabilities"]); rtol = 1e-10, atol = 1e-12) + @test witness.maximum_mean_error <= 1e-10 + @test witness.maximum_price_error <= 1e-10 + @test all(>=(0), witness.probabilities) + @test isapprox(vec(sum(witness.probabilities; dims = 2)), ones(length(maturities)); rtol = 1e-10, atol = 1e-12) + @test isapprox(witness.probabilities * witness.support, ones(length(maturities)); rtol = 1e-10, atol = 1e-12) + @test isapprox(witness.probabilities * max.(witness.support .- strikes', 0), prices; rtol = 1e-10, atol = 1e-12) + fine_strikes = collect(range(0, witness.support[end]; length = 301)) + calls = witness.probabilities * max.(witness.support .- fine_strikes', 0) + @test all(diff(calls; dims = 1) .>= -1e-12) + end + matrix, bounds = normalized_call_grid_constraints([0.5, 1.5], [1.0]) + @test minimum(matrix * [1.0, 1.0] - bounds) >= -1e-12 + @test_throws ArgumentError normalized_call_grid_witness([0.5, 1.5], [1.0], [1.0 1.0]) + zero_tail = normalized_call_grid_witness([1.0, 2.0], [1.0], [0.0 0.0]) + @test zero_tail.probabilities == [0.0 1.0 0.0 0.0] + for (strikes, maturities) in (([1.0], [1.0]), ([0.0, 1.0], [1.0]), ([1.0, 0.5], [1.0]), ([1.0, 1.0], [1.0]), ([0.5, 1.0], [0.0]), ([0.5, 1.0], [1.0, 1.0]), ([0.5, NaN], [1.0]), (Float64[], [1.0])) + @test_throws ArgumentError normalized_call_grid_constraints(strikes, maturities) + end + @test_throws ArgumentError normalized_call_grid_witness([0.5, 1.5], [1.0], [0.2 0.4]) + @test_throws ArgumentError normalized_call_grid_witness([0.5, 1.5], [1.0], ones(2, 2)) +end + +@testset "Supplied affine state bounds and HJM residuals" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "neural_sde_python_320.toml")) + for case in fixture["affine"] + offset, loading = Float64.(case["offset"]), Float64.(case["loading"]) + matrix, bounds = nse_fixture_matrix(case["matrix"]), Float64.(case["bounds"]) + interval = affine_price_state_interval(offset, loading, matrix, bounds) + @test isapprox(collect(interval), [case["lower"], case["upper"]]; rtol = 1e-10, atol = 1e-12) + @test all(matrix * (offset + interval[1] * loading) .>= bounds .- 1e-12) + @test all(matrix * (offset + interval[2] * loading) .>= bounds .- 1e-12) + @test any(matrix * (offset + (interval[1] - 1e-5) * loading) .< bounds) + @test any(matrix * (offset + (interval[2] + 1e-5) * loading) .< bounds) + end + for case in fixture["hjm"] + basis, diffusion = nse_fixture_matrix(case["price_basis"]), nse_fixture_matrix(case["diffusion"]) + drift, required = Float64.(case["drift"]), Float64.(case["required_price_drift"]) + actual = hjm_drift_residual(basis, drift, diffusion, required) + for name in (:market_price_of_risk, :residual, :squared_residual, :singular_values) + @test isapprox(getproperty(actual, name), case["expected"][string(name)]; rtol = 1e-10, atol = 1e-12) + end + @test actual.rank == case["expected"]["rank"] + @test norm((basis * diffusion)' * actual.residual) <= 1e-10 + @test isapprox(actual.residual, basis * drift - required - basis * diffusion * actual.market_price_of_risk; rtol = 1e-10, atol = 1e-12) + end + @test_throws ArgumentError affine_price_state_interval([0.0], [0.0], ones(1, 1), [1.0]) + @test_throws ArgumentError affine_price_state_interval([0.0], [1.0], ones(1, 1), [0.0]) + @test_throws ArgumentError affine_price_state_interval([0.0], [1.0], reshape([1.0, -1.0], 2, 1), [0.0, 0.0]) + @test_throws ArgumentError affine_price_state_interval([0.0], [1.0], reshape([1.0, -1.0], 2, 1), [1.0, 0.0]) + @test_throws ArgumentError affine_price_state_interval([0.0], [1.0, 2.0], ones(1, 1), [0.0]) + @test_throws ArgumentError hjm_drift_residual(ones(2, 2), ones(3), ones(2, 2), ones(2)) + @test_throws ArgumentError hjm_drift_residual(ones(2, 2), ones(2), fill(NaN, 2, 2), ones(2)) +end + +@testset "Scalar neural-SDE boundaries, likelihood and Adam parity" begin + P = RiskLabAI.Pde + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "neural_sde_python_320.toml")) + for case in fixture["intervals"] + parameters = nse_fixture_parameters(case["parameters"]) + model = IntervalNeuralSDE(case["lower"], case["upper"]; drift_bound = case["drift_bound"], diffusion_scale = case["diffusion_scale"], hidden_size = case["hidden_size"], parameters) + states, following, boundary = Float64.(case["states"]), Float64.(case["next_states"]), Float64.(case["boundary_states"]) + drift, diffusion = model(boundary) + @test isapprox(drift, case["drift"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(diffusion, case["diffusion"]; rtol = 1e-10, atol = 1e-12) + @test diffusion[1] == diffusion[end] == 0 + @test drift[1] >= 0 && drift[end] <= 0 + @test all(>(0), diffusion[2:end-1]) + @test isapprox(negative_log_likelihood(model, states, following, case["dt"]), case["nll"]; rtol = 1e-10, atol = 1e-12) + gradient = only(Zygote.gradient(value -> P._nse_likelihood(model, value, states, following, case["dt"]), model.parameters)) + nse_test_parameters(gradient, nse_fixture_parameters(case["gradients"])) + state_gradient = only(Zygote.gradient(states) do values + drift, diffusion = model(values) + sum(drift) + 0.3sum(diffusion) + end) + @test isapprox(state_gradient, case["state_gradients"]; rtol = 1e-10, atol = 1e-12) + original = deepcopy((states, following, parameters)) + history = fit_interval_neural_sde(model, states, following; dt = case["dt"], iterations = case["iterations"], learning_rate = case["learning_rate"]) + @test isapprox(history, case["history"]; rtol = 1e-10, atol = 1e-12) + nse_test_parameters(model.parameters, nse_fixture_parameters(case["trained_parameters"])) + @test (states, following, parameters) == original + simulation = simulate_interval_neural_sde(model, Float64.(case["initial"]), nse_fixture_matrix(case["increments"]); dt = case["dt"]) + @test isapprox(simulation, nse_fixture_matrix(case["simulation"]); rtol = 1e-10, atol = 1e-12) + @test all(value -> model.lower <= value <= model.upper, simulation) + fitted = deepcopy(model.parameters) + drift, diffusion = model(Float64.(case["evaluation_states"])) + @test isapprox(drift, case["evaluation_drift"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(diffusion, case["evaluation_diffusion"]; rtol = 1e-10, atol = 1e-12) + @test model.parameters == fitted + @test_throws ArgumentError negative_log_likelihood(model, [model.lower], [states[1]], case["dt"]) + failure = try + simulate_interval_neural_sde(model, states[1:1], fill(1e6, 1, 2); dt = case["dt"]) + catch exception + exception + end + @test failure isa ErrorException + @test occursin("Euler step 1 exits", sprint(showerror, failure)) + end +end + +@testset "Fixed-coordinate polytope diffusion, likelihood and Adam parity" begin + P = RiskLabAI.Pde + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "neural_sde_python_320.toml")) + for case in fixture["polytopes"] + normals, bounds, interior = nse_fixture_matrix(case["normals"]), Float64.(case["bounds"]), Float64.(case["interior"]) + parameters = nse_fixture_parameters(case["parameters"]) + settings = (drift_bound = case["drift_bound"], diffusion_scale = case["diffusion_scale"], hidden_size = case["hidden_size"]) + model = PolytopeNeuralSDE(normals, bounds, interior; settings..., parameters) + @test isapprox(model.normals, nse_fixture_matrix(case["normalized_normals"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(model.bounds, case["normalized_bounds"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(model.rho, case["rho"]; rtol = 1e-10, atol = 1e-12) + states, following, boundary = nse_fixture_matrix(case["states"]), nse_fixture_matrix(case["next_states"]), nse_fixture_matrix(case["boundary_states"]) + drift, diffusion = model(boundary) + @test isapprox(drift, nse_fixture_matrix(case["drift"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(diffusion, nse_fixture_array(case["diffusion"]); rtol = 1e-10, atol = 1e-12) + distances = boundary * model.normals' .- model.bounds' + for index in findall(abs.(distances) .< 1e-14) + row, face = Tuple(index) + @test dot(model.normals[face, :], drift[row, :]) >= -1e-12 + @test norm(model.normals[face, :]' * diffusion[row, :, :]) <= 1e-12 + end + @test isapprox(negative_log_likelihood(model, states, following, case["dt"]), case["nll"]; rtol = 1e-10, atol = 1e-12) + gradient = only(Zygote.gradient(value -> P._nse_likelihood(model, value, states, following, case["dt"]), model.parameters)) + nse_test_parameters(gradient, nse_fixture_parameters(case["gradients"])) + state_gradient = only(Zygote.gradient(states) do values + drift, diffusion = model(values) + sum(drift) + 0.3sum(diffusion) + end) + @test isapprox(state_gradient, nse_fixture_matrix(case["state_gradients"]); rtol = 1e-10, atol = 1e-12) + rescaling = Float64.(1:length(bounds)) + reversed = length(bounds):-1:1 + equivalent = PolytopeNeuralSDE((normals .* rescaling)[reversed, :], (bounds .* rescaling)[reversed], interior; settings..., parameters) + other_drift, other_diffusion = equivalent(states) + baseline_drift, baseline_diffusion = model(states) + @test isapprox(other_drift, baseline_drift; rtol = 1e-10, atol = 1e-12) + @test isapprox(other_diffusion, baseline_diffusion; rtol = 1e-10, atol = 1e-12) + original = deepcopy((states, following, parameters, normals, bounds, interior)) + history = fit_polytope_neural_sde(model, states, following; dt = case["dt"], iterations = case["iterations"], learning_rate = case["learning_rate"]) + @test isapprox(history, case["history"]; rtol = 1e-10, atol = 1e-12) + nse_test_parameters(model.parameters, nse_fixture_parameters(case["trained_parameters"])) + @test (states, following, parameters, normals, bounds, interior) == original + initial = nse_fixture_matrix(case["initial"]) + simulation = simulate_polytope_neural_sde(model, initial, nse_fixture_array(case["increments"]); dt = case["dt"]) + @test isapprox(simulation, nse_fixture_array(case["simulation"]); rtol = 1e-10, atol = 1e-12) + fitted = deepcopy(model.parameters) + drift, diffusion = model(nse_fixture_matrix(case["evaluation_states"])) + @test isapprox(drift, nse_fixture_matrix(case["evaluation_drift"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(diffusion, nse_fixture_array(case["evaluation_diffusion"]); rtol = 1e-10, atol = 1e-12) + @test model.parameters == fitted + @test_throws ArgumentError negative_log_likelihood(model, boundary[1:1, :], states[1:1, :], case["dt"]) + failure = try + simulate_polytope_neural_sde(model, initial[1:1, :], fill(1e6, 1, 2, model.dimension); dt = case["dt"]) + catch exception + exception + end + @test failure isa ErrorException + @test occursin("Euler step 1 exits", sprint(showerror, failure)) + end +end + +@testset "Neural-SDE domain checks, roundoff and local RNG" begin + P = RiskLabAI.Pde + settings = (drift_bound = 0.3, diffusion_scale = 0.2, hidden_size = 2) + Random.seed!(193) + expected = rand(5) + Random.seed!(193) + model = IntervalNeuralSDE(-1, 1; settings..., seed = 14) + @test rand(5) == expected + @test model.parameters == IntervalNeuralSDE(-1, 1; settings..., seed = 14).parameters + @test model.parameters != IntervalNeuralSDE(-1, 1; settings..., seed = 15).parameters + polytope = PolytopeNeuralSDE(reshape([1.0, -1.0], 2, 1), [-1.0, -1.0], [0.0]; settings...) + @test P._nse_distances(polytope, reshape([-1.0 - 5e-13], 1, 1)) == zeros(1, 2) .+ [0.0 2.0+5e-13] + @test polytope(reshape([-1.0 - 5e-13], 1, 1))[2][1, 1, 1] == 0 + @test polytope(reshape([-1.0 + 1e-13], 1, 1))[2][1, 1, 1] > 0 + @test_throws ArgumentError polytope(reshape([-1.0 - 2e-12], 1, 1)) + @test P._nse_softplus(30.0) == 30.0 + @test only(Zygote.gradient(P._nse_softplus, 0.0)) == 0.5 + zero_tangent = only(Zygote.gradient(P._nse_relu, 0.0)) + @test isnothing(zero_tangent) || iszero(zero_tangent) + for (lower, upper) in ((1, 1), (2, 1), (-Inf, 1), (-1e308, 1e308), (true, 2)) + @test_throws ArgumentError IntervalNeuralSDE(lower, upper; settings...) + end + for invalid in ((drift_bound = 0.0,), (diffusion_scale = -1.0,), (hidden_size = true,), (seed = 1.5,), (parameters = (;),)) + @test_throws ArgumentError IntervalNeuralSDE(-1, 1; merge(settings, invalid)...) + end + @test_throws ArgumentError PolytopeNeuralSDE([1.0 0.0; 0.0 1.0], [0.0, 0.0], [1.0, 1.0]; settings...) + @test_throws ArgumentError PolytopeNeuralSDE([1.0 0.0; -1.0 0.0], [-1.0, -1.0], [0.0, 0.0]; settings...) + @test_throws ArgumentError PolytopeNeuralSDE(reshape([1.0, -1.0], 2, 1), [-1.0, -1.0], [-1.0]; settings...) + for values in (Float64[], [1.1], [NaN], [true], ones(Float32, 2), ones(2, 1)) + @test_throws ArgumentError model(values) + end + for values in (zeros(0, 1), ones(2, 2), fill(NaN, 1, 1), ones(Float32, 2, 1), [0.0]) + @test_throws ArgumentError polytope(values) + end + @test_throws ArgumentError negative_log_likelihood(model, [0.0], [0.0, 0.1], 0.01) + @test_throws ArgumentError negative_log_likelihood(model, [0.0], [2.0], 0.01) + @test_throws ArgumentError negative_log_likelihood(model, [0.0], [0.1], 0.0) + @test_throws ArgumentError simulate_interval_neural_sde(model, [0.0], zeros(1, 0); dt = 0.01) + @test_throws ArgumentError simulate_interval_neural_sde(model, [0.0], ones(Float32, 1, 2); dt = 0.01) + @test_throws ArgumentError simulate_polytope_neural_sde(polytope, zeros(1, 1), zeros(1, 1, 2); dt = 0.01) + initial = deepcopy(model.parameters) + @test_throws ArgumentError fit_interval_neural_sde(model, [0.0], [0.1]; dt = 0.01, iterations = 0) + @test_throws ArgumentError fit_interval_neural_sde(model, [0.0], [0.1]; dt = 0.01, iterations = 1, learning_rate = 0) + @test_throws ArgumentError fit_interval_neural_sde(model, [-1.0], [0.1]; dt = 0.01, iterations = 1) + @test model.parameters == initial +end diff --git a/test/ou_boundary_regressions.jl b/test/ou_boundary_regressions.jl new file mode 100644 index 0000000..16311ed --- /dev/null +++ b/test/ou_boundary_regressions.jl @@ -0,0 +1,19 @@ +using Test, TOML, RiskLabAI + +@testset "OU saved stopping boundaries and independent moments" begin + B = RiskLabAI.Backtest + reference = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ou_boundary_reference.toml"))["cases"] + contacts = Dict("python_fit"=>0, "julia_fit"=>0) + for row in reference + actual = B.optimal_ou_trading_rule(row["theta"], row["sigma"], row["entry_gap"]; cost=row["cost"], bounds=(row["lower"],row["upper"])) + for (key, expected) in row["expected"] + @test isapprox(getproperty(actual,Symbol(key)), expected; rtol=1e-9, atol=2e-11) + end + at_upper = abs(actual.stop_loss-row["upper"]) <= 1e-4 + 1e-5 * abs(row["upper"]) + @test at_upper + startswith(row["case"], "bound_") && (contacts[row["variant"]] += at_upper) + end + @test contacts["python_fit"] == 4 + @test contacts["julia_fit"] == 4 + @test isnan(B.mean_exit_time(1e16,.1,.1,.1,1.)) +end diff --git a/test/ou_regressions.jl b/test/ou_regressions.jl new file mode 100644 index 0000000..5ab657d --- /dev/null +++ b/test/ou_regressions.jl @@ -0,0 +1,70 @@ +using Test +using TOML +using RiskLabAI + +ou_equal(left, right) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-9, atol = 2e-11) + +@testset "Shared OU scale, first-passage and gain moments" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ou_python_320.toml")) + for case in fixture["scales"] + @test ou_equal(B.theta_from_half_life(case["half_life"]), case["theta"]) + @test ou_equal(B.stationary_std(case["theta"], case["sigma"]), case["stationary_std"]) + end + for case in fixture["metrics"] + theta, sigma, gap, pt, sl = (case[key] for key in ("theta", "sigma", "gap", "pt", "sl")) + actual = B.ou_rule_metrics(pt, sl, theta, sigma, gap, case["cost"]) + for (key, value) in case["output"] + @test ou_equal(getproperty(actual, Symbol(key)), value) + end + probability = B.hit_upper_probability(gap, pt, sl, theta, sigma) + @test ou_equal(probability, case["output"]["hit_probability"]) + @test ou_equal(B.mean_exit_time(gap, pt, sl, theta, sigma), case["output"]["expected_holding_time"]) + @test ou_equal(probability+B.hit_upper_probability(-gap, sl, pt, theta, sigma), 1.) + @test ou_equal(B.mean_exit_time(-gap, sl, pt, theta, sigma), actual.expected_holding_time) + end + for case in fixture["boundary"] + actual = B.mean_exit_time(case["gap"], case["pt"], case["sl"], case["theta"], case["sigma"]) + @test isapprox(actual, case["time"]; rtol = 1e-4) + end + for case in fixture["invalid"] + actual = B.ou_rule_metrics(case["pt"], case["sl"], .1, 1., .5) + for (key, value) in case["output"] + @test ou_equal(getproperty(actual, Symbol(key)), value) + end + end + @test isnan(B.hit_upper_probability(1., -1., -1., .1, 1.)) + @test isnan(B.mean_exit_time(1., -1., -1., .1, 1.)) +end + +@testset "Shared OU least squares, finite filtering and persistence clipping" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ou_python_320.toml")) + for case in fixture["fit"] + values = Float64.(case["values"]) + original = copy(values) + actual = B.fit_ornstein_uhlenbeck(values; dt = case["dt"]) + for (key, value) in case["output"] + @test isapprox(getproperty(actual, Symbol(key)), value; rtol = 1e-8, atol = 2e-9) + end + @test isequal(values, original) + @test 1e-6 <= actual.rho <= .999999 + @test isapprox(exp(-actual.theta*case["dt"]), actual.rho; rtol = 1e-12) + end +end + +@testset "OU optimizer objective and bound compatibility" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "ou_python_320.toml")) + for case in fixture["optimum"] + lower, upper = case["bounds"] + actual = B.optimal_ou_trading_rule(case["theta"], case["sigma"], case["gap"]; cost = case["cost"], bounds = (lower, upper)) + @test lower <= actual.profit_take <= upper + @test lower <= actual.stop_loss <= upper + recomputed = B.ou_rule_metrics(actual.profit_take, actual.stop_loss, case["theta"], case["sigma"], case["gap"], case["cost"]) + @test all(ou_equal(getproperty(actual, key), getproperty(recomputed, key)) for key in keys(recomputed)) + reference_rate = case["output"]["return_rate"] + @test actual.return_rate >= reference_rate-1e-3*max(abs(reference_rate), 1e-3) + println("OU numerical comparison: ", (theta = case["theta"], gap = case["gap"], cost = case["cost"], julia = (actual.profit_take, actual.stop_loss, actual.return_rate), python = (case["output"]["profit_take"], case["output"]["stop_loss"], reference_rate))) + end +end diff --git a/test/pbo_regressions.jl b/test/pbo_regressions.jl new file mode 100644 index 0000000..2facfb8 --- /dev/null +++ b/test/pbo_regressions.jl @@ -0,0 +1,95 @@ +using Test +using TOML +using Random +using RiskLabAI + +pbo_matrix(rows) = reduce(vcat, [permutedims(Vector{Float64}(row)) for row in rows]) +pbo_mean(values, risk_free) = sum(values) / length(values) - risk_free +pbo_same(left, right) = length(left) == length(right) && all(isequal(a, b) || isapprox(a, b; rtol = 1e-10, atol = 2e-12, nans = true) for (a, b) in zip(left, right)) + +function pbo_rank_provider(records) + position = Ref(0) + provider = function (scores) + position[] += 1 + record = records[position[]] + @test pbo_same(scores, record["testing"]) + return Vector{Int}(record["order"]) + end + return provider, position +end + +@testset "PBO: recorded ordinal ranks and combinatorial paths" begin + B = RiskLabAI.Backtest + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pbo_python_320.toml")) + for case in fixtures["evaluation"] + train, test = pbo_matrix(case["train"]), pbo_matrix(case["test"]) + provider, calls = pbo_rank_provider([case["record"]]) + actual = B.performance_evaluation(train, test, size(train, 2), (values, rf) -> first(values)-rf, 0.; rank_order = provider) + @test actual[1] == case["record"]["is_overfit"] + @test actual[2] ≈ case["record"]["output"] atol = 1e-13 + @test calls[] == 1 + end + for case in fixtures["plain"] + values = pbo_matrix(case["values"]) + provider, calls = pbo_rank_provider(case["records"]) + result = B.probability_of_backtest_overfitting(values; n_partitions = case["partitions"], risk_free_return = .001, metric = pbo_mean, rank_order = provider) + @test result[1] ≈ case["pbo"] atol = 1e-14 + @test result[2] ≈ Vector{Float64}(case["logits"]) atol = 1e-13 + @test calls[] == length(case["records"]) + if all(length(unique(record["testing"])) == length(record["testing"]) for record in case["records"]) + native = B.probability_of_backtest_overfitting(values; n_partitions = case["partitions"], risk_free_return = .001, metric = pbo_mean) + @test native[1] == result[1] + @test native[2] == result[2] + end + end + counterexample = first(case for case in fixtures["evaluation"] if case["record"]["is_overfit"] != case["record"]["stable_overfit"]) + actual = B.performance_evaluation(pbo_matrix(counterexample["train"]), pbo_matrix(counterexample["test"]), 10, (values, rf) -> first(values), 0.) + @test actual[1] == counterexample["record"]["stable_overfit"] + @test actual[1] != counterexample["record"]["is_overfit"] +end + +@testset "PBO: observed-sample regimes and adaptive weights" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pbo_python_320.toml")) + for case in fixtures["regimes"] + window = case["window"] == -1 ? nothing : case["window"] + actual = V.estimate_volatility_regimes(pbo_matrix(case["values"]); n_regimes = case["n_regimes"], window = window) + @test actual == Vector{Int}(case["output"]) + end + for case in fixtures["adaptive"] + provider, calls = pbo_rank_provider(case["records"]) + actual = V.adaptive_probability_of_backtest_overfitting(pbo_matrix(case["values"]); n_partitions = case["partitions"], target_fraction = case["fraction"], n_regimes = case["n_regimes"], risk_free_return = .001, metric = pbo_mean, rank_order = provider) + @test actual[1] ≈ case["output"] atol = 2e-13 + @test actual[2] == case["selected"] + @test calls[] == length(case["records"]) + end +end + +@testset "PBO: supplied circular blocks and rank replay" begin + V = RiskLabAI.Validation + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pbo_python_320.toml")) + for case in fixtures["bagged"] + provider, calls = pbo_rank_provider(case["records"]) + size = case["block_size"] == -1 ? nothing : case["block_size"] + rows = [Vector{Int}(row) for row in case["rows"]] + actual = V.bagged_probability_of_backtest_overfitting(pbo_matrix(case["values"]); n_partitions = case["partitions"], n_bag = case["repetitions"], block_size = size, risk_free_return = .001, metric = pbo_mean, bootstrap_rows = rows, rank_order = provider) + @test isapprox(actual[1], case["output"]; atol = 1e-14, nans = true) + @test actual[2] ≈ Vector{Float64}(case["pbos"]) atol = 1e-14 + @test calls[] == length(case["records"]) + end + for case in fixtures["blocks"] + actual = V.moving_block_bootstrap_indices(case["observations"], case["block_size"], MersenneTwister(1); starts = Vector{Int}(case["starts"])) + @test actual == Vector{Int}(case["output"]) + end +end + +@testset "PBO: rank and bootstrap contract boundaries" begin + B, V = RiskLabAI.Backtest, RiskLabAI.Validation + x = [1. 2. 3.; 2. 1. 4.] + @test_throws ArgumentError B.performance_evaluation(x, x, 3, pbo_mean, 0.; rank_order = values -> [1, 1, 3]) + @test_throws ArgumentError B.performance_evaluation(x, x, 3, pbo_mean, 0.; rank_order = values -> [3, 2, 1]) + @test_throws ArgumentError B.probability_of_backtest_overfitting(x; n_partitions = 3) + @test_throws ArgumentError V.moving_block_bootstrap_indices(5, 2, MersenneTwister(1); starts = [0, 2, 3]) + @test_throws DimensionMismatch V.bagged_probability_of_backtest_overfitting(x; n_bag = 2, bootstrap_rows = [[1, 2]]) + @test_throws ArgumentError V.bagged_probability_of_backtest_overfitting(x; n_bag = 1, bootstrap_rows = [[0, 2]]) +end diff --git a/test/pde_equation_regressions.jl b/test/pde_equation_regressions.jl new file mode 100644 index 0000000..6deb1bd --- /dev/null +++ b/test/pde_equation_regressions.jl @@ -0,0 +1,71 @@ +using Test +using TOML +using Random +using RiskLabAI +using RiskLabAI.Pde + +function pde_equation_array(values) + dimensions = Int[] + cursor = values + while cursor isa AbstractVector + push!(dimensions, length(cursor)) + cursor = first(cursor) + end + flatten(value::Number) = [Float64(value)] + flatten(value::AbstractVector) = reduce(vcat, flatten.(value)) + return permutedims(reshape(flatten(values), reverse(dimensions)...), reverse(Tuple(1:length(dimensions)))) +end + +@testset "Public PDE equation formulas and supplied-normal forward paths" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pde_equations_python_320.toml")) + for case in fixture["equations"] + config = case["configuration"] + equation = getproperty(RiskLabAI.Pde, Symbol(case["kind"]))(config["dim"], config["total_time"], config["num_time_interval"]) + @test equation isa Equation + @test equation.delta_t == config["total_time"] / config["num_time_interval"] + @test equation.sqrt_delta_t == sqrt(equation.delta_t) + normal = pde_equation_array(case["normal_draws"]) + original = copy(normal) + increments, paths = pde_sample(equation, size(normal, 1); normal_draws = normal) + @test isapprox(increments, pde_equation_array(case["increments"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(paths, pde_equation_array(case["paths"]); rtol = 1e-10, atol = 1e-12) + @test normal == original + x, y, z = (pde_equation_array(case[name]) for name in ("x", "y", "z")) + @test isapprox(pde_driver(equation, 0.37, x, y, z), pde_equation_array(case["rate"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(pde_hamiltonian(equation, 0.37, x, y, z), pde_equation_array(case["hamiltonian"]); rtol = 1e-10, atol = 1e-12) + @test isapprox(pde_terminal(equation, equation.total_time, x), pde_equation_array(case["terminal"]); rtol = 1e-10, atol = 1e-12) + sigma = case["sigma"] isa Number ? case["sigma"] : pde_equation_array(case["sigma"]) + @test isapprox(pde_sigma(equation, x), sigma; rtol = 1e-10, atol = 1e-12) + @test isapprox(pde_terminal_for_sample(equation, pde_equation_array(case["sample_states"])), pde_equation_array(case["sample_terminal"]); rtol = 1e-10, atol = 1e-12) + rng = MersenneTwister(734) + expected = rand(rng, 5) + rng = MersenneTwister(734) + pde_sample(equation, size(normal, 1); rng, normal_draws = normal) + @test rand(rng, 5) == expected + end + @test isabstracttype(Equation) + @test_throws MethodError Equation() +end + +@testset "PDE equation domains and separate terminal helper contracts" begin + for constructor in (HJBLQ, BlackScholesBarenblatt, PricingDefaultRisk, PricingDiffRate) + @test_throws ArgumentError constructor(0, 1., 3) + @test_throws ArgumentError constructor(2, -1., 3) + @test_throws ArgumentError constructor(2, Inf, 3) + @test_throws ArgumentError constructor(2, 1., 0) + @test_throws ArgumentError constructor(true, 1., 3) + equation = constructor(2, 0., 3) + dw, path = pde_sample(equation, 2; normal_draws = ones(2, 2, 3)) + @test all(iszero, dw) + @test all(path[:, :, index] == path[:, :, 1] for index in 2:4) + end + equation = PricingDiffRate(1, 1., 2) + @test pde_terminal(equation, 1., fill(175., 1, 1)) == fill(5., 1, 1) + @test pde_terminal_for_sample(equation, fill(175., 1, 1, 1)) == fill(55., 1, 1, 1) + @test_throws ArgumentError pde_sample(equation, 0) + @test_throws ArgumentError pde_sample(equation, true) + @test_throws ArgumentError pde_sample(equation, 2; normal_draws = ones(2, 1, 3)) + @test_throws ArgumentError pde_sample(equation, 2; normal_draws = ones(Float32, 2, 1, 2)) + @test_throws ArgumentError pde_sample(equation, 2; normal_draws = fill(NaN, 2, 1, 2)) + @test_throws ArgumentError pde_terminal_for_sample(equation, ones(2, 2, 2)) +end diff --git a/test/pde_network_regressions.jl b/test/pde_network_regressions.jl new file mode 100644 index 0000000..8740390 --- /dev/null +++ b/test/pde_network_regressions.jl @@ -0,0 +1,200 @@ +using Test +using TOML +using Random +using LinearAlgebra +using Zygote +using RiskLabAI +using RiskLabAI.Pde + +function pde_fixture_array(values) + dimensions = Int[] + item = values + while item isa AbstractVector + push!(dimensions, length(item)) + item = first(item) + end + flatten(value::Number) = [Float64(value)] + flatten(value::AbstractVector) = reduce(vcat, flatten.(value)) + array = reshape(flatten(values), reverse(dimensions)...) + return permutedims(array, reverse(Tuple(1:length(dimensions)))) +end + +pde_row_major(array) = vec(permutedims(array, reverse(Tuple(1:ndims(array))))) + +function pde_shared_values(shape, shift, scale, offset) + indices = 0:prod(shape)-1 + integers = mod.(indices .* 37 .+ shift * 101, 9973) .- 4986 + flat = offset .+ scale .* Float64.(integers) ./ 4986 + return permutedims(reshape(flat, reverse(Tuple(shape))...), reverse(Tuple(1:length(shape)))) +end + +function pde_fixture_tree(template::NamedTuple, specifications, path = ()) + return NamedTuple{keys(template)}(Tuple(pde_fixture_tree(getproperty(template, name), specifications, (path..., string(name))) for name in keys(template))) +end + +function pde_fixture_tree(template::Tuple, specifications, path = ()) + return Tuple(pde_fixture_tree(value, specifications, (path..., string(index))) for (index, value) in enumerate(template)) +end + +function pde_fixture_tree(template::AbstractArray, specifications, path = ()) + specification = specifications[join(path, "/")] + @test Tuple(specification["shape"]) == size(template) + return pde_shared_values(specification["shape"], specification["shift"], specification["scale"], specification["offset"]) +end + +function pde_get_path(value, path) + for name in path + isnothing(value) && return nothing + value = value isa NamedTuple ? getproperty(value, Symbol(name)) : value[parse(Int, name)] + end + return value +end + +function pde_fixture_model(kind, settings; parameters = nothing) + kind == "Net1" && return Net1(settings["input_dim"], settings["output_dim"]; parameters) + kind == "TimeNet" && return TimeNet(settings["output_dim"]; parameters) + kind == "FBSNNNetwork" && return FBSNNNetwork(settings["layer_sizes"]; parameters) + kind == "DeepBSDE" && return DeepBSDE(settings["layer_sizes"]; parameters) + kind == "TimeNetForSet" && return TimeNetForSet(settings["input_dim"], settings["output_dim"]; parameters) + kind == "DeepTimeSetTransformer" && return DeepTimeSetTransformer(settings["input_dim"]; parameters) + kind == "TimeDependentNetwork" && return TimeDependentNetwork(settings["input_dim"], settings["hidden_sizes"], settings["output_dim"]; parameters) + kind == "TimeDependentNetworkMonteCarlo" && return TimeDependentNetworkMonteCarlo(settings["input_dim"], settings["hidden_sizes"], settings["output_dim"], settings["sigma"]; parameters) + kind == "MAB" && return MAB(settings["dim_q"], settings["dim_k"], settings["dim_v"], settings["num_heads"]; ln = settings["ln"], parameters) + kind == "SAB" && return SAB(settings["dim_in"], settings["dim_out"], settings["num_heads"]; ln = settings["ln"], parameters) + kind == "ISAB" && return ISAB(settings["dim_in"], settings["dim_out"], settings["num_heads"], settings["num_inds"]; ln = settings["ln"], parameters) + kind == "PMA" && return PMA(settings["dim"], settings["num_heads"], settings["num_seeds"]; ln = settings["ln"], parameters) + error("Unknown shared network fixture") +end + +function pde_parameter_forward(model, parameters, inputs...) + P = RiskLabAI.Pde + model isa Net1 && return P._pde_dense(parameters.layer, only(inputs)) + model isa TimeNet && return P._pde_time_forward(parameters, only(inputs)) + model isa FBSNNNetwork && return P._pde_hidden_forward(parameters, only(inputs), sin) + model isa DeepBSDE && return P._pde_hidden_forward(parameters, only(inputs), P._pde_relu) + model isa TimeNetForSet && return P._pde_time_set_forward(parameters, inputs...) + model isa DeepTimeSetTransformer && return P._pde_deep_set_forward(parameters, inputs...) + model isa TimeDependentNetwork && return P._pde_dtnn_forward(model, parameters, inputs...; update_state = false) + model isa TimeDependentNetworkMonteCarlo && return P._pde_monte_carlo_forward(model, parameters, inputs...) + model isa MAB && return P._pde_attention(parameters, inputs..., model.num_heads, model.ln) + model isa SAB && return P._pde_attention(parameters.mab, only(inputs), only(inputs), model.num_heads, model.ln) + model isa ISAB && return P._pde_isab_forward(parameters, only(inputs), model.num_heads, model.ln) + model isa PMA && return P._pde_pma_forward(parameters, only(inputs), model.num_heads, model.ln) + error("Unknown native network type") +end + +@testset "Shared PDE dense, attention and time-network values and gradients" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pde_networks_python_320.toml")) + for case in fixture["networks"] + template = pde_fixture_model(case["kind"], case["settings"]) + specifications = Dict(join(value["native_path"], "/") => value for value in values(case["parameters"])) + @test length(specifications) == length(case["parameters"]) + parameters = pde_fixture_tree(template.parameters, specifications) + model = pde_fixture_model(case["kind"], case["settings"]; parameters) + inputs = Tuple(pde_fixture_array(value) for value in case["inputs"]) + original = deepcopy((inputs, parameters)) + expected = pde_fixture_array(case["output"]) + output = model(inputs...) + @test size(output) == size(expected) + @test isapprox(output, expected; rtol = 1e-10, atol = 1e-12) + weights = pde_fixture_array(case["objective_weights"]) + objective = supplied -> sum(pde_parameter_forward(model, supplied, inputs...) .* weights) + gradients = only(Zygote.gradient(objective, model.parameters)) + for (name, encoded) in case["parameters"] + gradient = pde_get_path(gradients, encoded["native_path"]) + gradient = isnothing(gradient) ? zeros(Tuple(encoded["shape"])) : gradient + independent = case["parameter_gradients"][name] + probe = pde_shared_values(encoded["shape"], encoded["shift"] + 71, 1.0, 0.0) + probe ./= norm(probe) + @test isapprox(sum(gradient .* probe), independent["projection"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(norm(gradient), independent["norm"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(pde_row_major(gradient)[Int.(independent["indices"])], independent["values"]; rtol = 1e-10, atol = 1e-12) + end + input_gradients = Zygote.gradient((values...) -> sum(pde_parameter_forward(model, model.parameters, values...) .* weights), inputs...) + for (gradient, encoded) in zip(input_gradients, case["input_gradients"]) + reference = pde_fixture_array(encoded) + actual = isnothing(gradient) ? zeros(size(reference)) : gradient + @test isapprox(actual, reference; rtol = 1e-10, atol = 1e-12) + end + @test (inputs, parameters) == original + if model isa TimeDependentNetwork + for (actual, encoded) in zip(model.state, case["normalization_state"]) + @test isapprox(actual.mean, encoded["mean"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(actual.variance, encoded["variance"]; rtol = 1e-10, atol = 1e-12) + @test actual.batches == encoded["batches"] + end + model.training = false + before = deepcopy(model.state) + evaluation = Tuple(pde_fixture_array(value) for value in case["evaluation_inputs"]) + @test isapprox(model(evaluation...), pde_fixture_array(case["evaluation_output"]); rtol = 1e-10, atol = 1e-12) + @test model.state == before + end + if model isa Union{SAB, ISAB} + permuted = inputs[1][:, end:-1:1, :] + @test isapprox(model(permuted), output[:, end:-1:1, :]; rtol = 1e-10, atol = 1e-12) + elseif model isa PMA + @test isapprox(model(inputs[1][:, end:-1:1, :]), output; rtol = 1e-10, atol = 1e-12) + elseif model isa MAB + @test isapprox(model(inputs[1], inputs[2][:, end:-1:1, :]), output; rtol = 1e-10, atol = 1e-12) + @test isapprox(model(inputs[1][:, end:-1:1, :], inputs[2]), output[:, end:-1:1, :]; rtol = 1e-10, atol = 1e-12) + elseif model isa DeepTimeSetTransformer + @test isapprox(model(inputs[1], inputs[2][:, end:-1:1, :]), output; rtol = 1e-10, atol = 1e-12) + end + end +end + +@testset "PDE dense initialization, normalization modes and argument domains" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pde_networks_python_320.toml")) + for case in fixture["initializers"] + model = Net1(case["input_dim"], case["output_dim"]) + draws = pde_fixture_array(case["normal_draws"]) + @test isnothing(initialize_weights(model.parameters.layer; normal_draws = draws)) + @test model.parameters.layer.weight == pde_fixture_array(case["weight"]) + @test model.parameters.layer.bias == case["bias"] + @test draws == pde_fixture_array(case["normal_draws"]) + end + model = Net1(2, 3) + original = deepcopy(model.parameters) + @test isnothing(initialize_weights(model.parameters)) + @test model.parameters == original + Random.seed!(613) + expected = rand(5) + Random.seed!(613) + initialize_weights(model.parameters.layer; seed = 25) + @test rand(5) == expected + second = Net1(2, 3; seed = 7) + initialize_weights(second.parameters.layer; seed = 25) + @test model.parameters == second.parameters + @test_throws ArgumentError initialize_weights((weight = ones(3, 2),)) + @test_throws ArgumentError initialize_weights(model.parameters.layer; normal_draws = ones(2, 3)) + @test_throws ArgumentError Net1(0, 2) + @test_throws ArgumentError FBSNNNetwork([2]) + @test_throws ArgumentError DeepBSDE([2, -1, 1]) + @test_throws ArgumentError TimeNet(true) + @test_throws ArgumentError MAB(2, 2, 3, 2) + @test_throws ArgumentError SAB(2, 4, 2; ln = 1) + @test_throws ArgumentError ISAB(2, 4, 2, 0) + @test_throws ArgumentError PMA(4, 2, 0) + @test_throws ArgumentError TimeDependentNetwork(2, [], 2) + @test_throws ArgumentError TimeDependentNetworkMonteCarlo(2, [3], 3, 0.4) + @test_throws ArgumentError Net1(2, 2; parameters = (;)) + @test_throws ArgumentError model(ones(Float32, 3, 2)) + @test_throws ArgumentError model(fill(NaN, 3, 2)) + @test_throws ArgumentError MAB(2, 2, 4, 2)(ones(2, 3, 2), ones(3, 3, 2)) + @test_throws ArgumentError TimeNetForSet(2, 3)(ones(2, 1), ones(3, 2)) + dependent = TimeDependentNetwork(2, [3], 2; seed = 81) + @test_throws ArgumentError dependent(ones(1, 1), ones(1, 2)) + @test all(state -> state.batches == 0, dependent.state) + dependent(reshape([0.1, 0.2, 0.3], :, 1), [0.1 0.2; 0.4 0.3; 0.8 0.2]) + @test all(state -> state.batches == 1, dependent.state) + dependent.training = false + state = deepcopy(dependent.state) + @test all(isfinite, dependent(ones(1, 1), ones(1, 2))) + @test dependent.state == state + Random.seed!(614) + expected = rand(5) + Random.seed!(614) + SAB(2, 4, 2; seed = 12) + @test rand(5) == expected + @test SAB(2, 4, 2; seed = 12).parameters == SAB(2, 4, 2; seed = 12).parameters +end diff --git a/test/pde_solver_regressions.jl b/test/pde_solver_regressions.jl new file mode 100644 index 0000000..58ba8d1 --- /dev/null +++ b/test/pde_solver_regressions.jl @@ -0,0 +1,172 @@ +using Test +using TOML +using Random +using LinearAlgebra +using Zygote +using RiskLabAI +using RiskLabAI.Pde + +function pdes_array(values) + dimensions = Int[] + current = values + while current isa AbstractVector + push!(dimensions, length(current)) + current = first(current) + end + flatten(value::Number) = [Float64(value)] + flatten(value::AbstractVector) = reduce(vcat, flatten.(value)) + return permutedims(reshape(flatten(values), reverse(dimensions)...), reverse(Tuple(1:length(dimensions)))) +end + +function pdes_pattern(shape, shift, scale = 1.0, offset = 0.0) + integers = mod.((0:prod(shape)-1) .* 37 .+ shift * 101, 9973) .- 4986 + values = offset .+ scale .* Float64.(integers) ./ 4986 + return permutedims(reshape(values, reverse(Tuple(shape))...), reverse(Tuple(1:length(shape)))) +end + +function pdes_parameters(template::NamedTuple, specifications, path = ()) + return NamedTuple{keys(template)}(Tuple(pdes_parameters(getproperty(template, key), specifications, (path..., string(key))) for key in keys(template))) +end + +pdes_parameters(template::Tuple, specifications, path = ()) = Tuple(pdes_parameters(value, specifications, (path..., string(index))) for (index, value) in enumerate(template)) + +function pdes_parameters(template::AbstractArray, specifications, path = ()) + specification = specifications[join(path, "/")] + @test size(template) == Tuple(specification["shape"]) + return pdes_pattern(specification["shape"], specification["shift"], specification["scale"], specification["offset"]) +end + +function pdes_path(value, path) + for key in path + isnothing(value) && return nothing + value = value isa NamedTuple ? getproperty(value, Symbol(key)) : value[parse(Int, key)] + end + return value +end + +function pdes_check_summary(value, specification, expected) + value = isnothing(value) ? zeros(Tuple(specification["shape"])) : value + probe = pdes_pattern(specification["shape"], specification["shift"] + 71) + probe ./= norm(probe) + flattened = vec(permutedims(value, reverse(Tuple(1:ndims(value))))) + @test isapprox(norm(value), expected["norm"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(sum(value .* probe), expected["projection"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(flattened[Int.(expected["indices"])], expected["values"]; rtol = 1e-10, atol = 1e-12) +end + +function pdes_model(case; parameters = nothing) + config = case["configuration"] + equation = getproperty(RiskLabAI.Pde, Symbol(case["kind"]))(config["dim"], config["total_time"], config["num_time_interval"]) + return case["method"] == "FBSNN" ? FBSNNSolver(equation, case["layer_sizes"], case["learning_rate"]; parameters) : FBSDESolver(equation, case["layer_sizes"], case["learning_rate"], case["method"]; parameters) +end + +function pdes_batch(encoded) + return (paths = pdes_array(encoded["paths"]), increments = pdes_array(encoded["increments"]), monte_carlo_gradients = haskey(encoded, "monte_carlo_gradients") ? pdes_array(encoded["monte_carlo_gradients"]) : nothing) +end + +function pdes_loss(model, parameters, paths, increments, initial, gradient, monte_carlo) + time = ones(size(paths, 1), 1) + return model isa FBSNNSolver ? RiskLabAI.Pde._pde_fbsnn_loss(model, parameters, paths, increments, time) : first(RiskLabAI.Pde._pde_bsde_loss(model, parameters, paths, increments, time, initial, gradient, monte_carlo)) +end + +@testset "Shared PDE solver objectives, path derivatives and optimizer replay" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pde_solvers_python_320.toml")) + for case in fixture["solvers"] + @testset "$(case["method"]) $(case["kind"])" begin + template = pdes_model(case) + specifications = Dict(join(encoded["native_path"], "/") => encoded for encoded in values(case["parameters"])) + parameters = pdes_parameters(template.parameters, specifications) + model = pdes_model(case; parameters) + batch = pdes_batch(case["batch"]) + initial, gradient = [case["initial_value"]], pdes_array(case["initial_gradient"]) + preserved = deepcopy((parameters, batch, initial, gradient)) + time = ones(size(batch.paths, 1), 1) + if model isa FBSNNSolver + value = compute_pde_loss(model, batch.paths, batch.increments, time, initial) + else + value, components... = compute_pde_loss(model, batch.paths, batch.increments, time, initial, gradient; monte_carlo_gradients = batch.monte_carlo_gradients) + for (actual, encoded) in zip(components, case["components"]) + @test isapprox(actual, pdes_array(encoded); rtol = 1e-10, atol = 1e-12) + end + end + @test isapprox(value, case["loss"]; rtol = 1e-10, atol = 1e-12) + derivatives = only(Zygote.gradient(p -> pdes_loss(model, p, batch.paths, batch.increments, initial, gradient, batch.monte_carlo_gradients), model.parameters)) + for (name, encoded) in case["parameters"] + pdes_check_summary(pdes_path(derivatives, encoded["native_path"]), encoded, case["parameter_gradients"][name]) + end + input_derivatives = Zygote.gradient((x, dw, y, z) -> pdes_loss(model, model.parameters, x, dw, y, z, batch.monte_carlo_gradients), batch.paths, batch.increments, initial, gradient) + for (actual, encoded) in zip(input_derivatives, case["argument_gradients"]) + expected = pdes_array(encoded) + actual = isnothing(actual) ? zeros(size(expected)) : actual + @test isapprox(actual, expected; rtol = 1e-10, atol = 1e-12) + end + @test (parameters, batch, initial, gradient) == preserved + trained = pdes_model(case; parameters) + batches = pdes_batch.(case["training_batches"]) + validation = pdes_batch(case["validation_batch"]) + original_batches = deepcopy((batches, validation)) + losses, estimates = solve_pde(trained; iterations = length(batches), batch_size = 4, init_y = case["initial_value"], training_batches = batches, validation_batch = validation) + @test isapprox(losses, [row["validation_loss"] for row in case["history"]]; rtol = 1e-10, atol = 1e-12) + @test isapprox(estimates, [row["estimate"] for row in case["history"]]; rtol = 1e-10, atol = 1e-12) + for (name, encoded) in case["parameters"] + pdes_check_summary(pdes_path(trained.parameters, encoded["native_path"]), encoded, case["trained_parameters"][name]) + end + if case["method"] == "DTNN" + for (actual, encoded) in zip(trained.solver.state, case["normalization_state"]) + @test isapprox(actual.mean, encoded["mean"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(actual.variance, encoded["variance"]; rtol = 1e-10, atol = 1e-12) + @test actual.batches == encoded["batches"] + end + end + @test (batches, validation) == original_batches + @test parameters == preserved[1] + end + end +end + +@testset "PDE solver native derivatives, input boundaries and continuation" begin + equation = HJBLQ(1, 0.2, 2) + @test_throws ArgumentError FBSDESolver(equation, [1, 2, 1], 0.01, "unknown") + @test_throws ArgumentError FBSDESolver(equation, [2, 2, 1], 0.01, "DeepBSDE") + @test_throws ArgumentError FBSDESolver(equation, [2], 0., "DTNN") + @test_throws ArgumentError FBSDESolver(HJBLQ(2, 0.2, 2), [2], 0.01, "Deep-Time-SetTransformer") + @test_throws ArgumentError FBSNNSolver(equation, [1, 2, 1], 0.01) + @test_throws ArgumentError FBSNNSolver(equation, [2, 2, 2], 0.01) + @test_throws ArgumentError FBSNNSolver(equation, [2, 2, 1], -0.01) + monte_carlo = FBSDESolver(equation, [2], 0.01, "Monte-Carlo") + @test_throws ArgumentError solve_pde(monte_carlo) + increments, paths = pde_sample(equation, 4; rng = MersenneTwister(812)) + batch = (paths = paths, increments = increments) + @test_throws ArgumentError compute_pde_loss(monte_carlo, paths, increments, ones(4, 1), [0.], zeros(1, 1)) + sine = FBSNNSolver(equation, [2, 3, 2, 1], 0.001; seed = 832) + inputs = [0.1 0.2; 0.2 -0.4; 0.3 0.1] + output, derivative = RiskLabAI.Pde._pde_sine_value_gradient(sine.parameters.layers, inputs) + @test output ≈ sine.solver(inputs) + @test derivative ≈ only(Zygote.gradient(values -> sum(sine.solver(values)), inputs)) + original = deepcopy(sine.parameters) + @test_throws ArgumentError solve_pde(sine; iterations = 2, batch_size = 4, training_batches = [batch], validation_batch = batch) + @test_throws ArgumentError solve_pde(sine; iterations = 1, batch_size = 5, training_batches = [batch], validation_batch = batch) + @test sine.parameters == original + continuous = FBSNNSolver(equation, [2, 3, 2, 1], 0.001; seed = 832) + whole = solve_pde(continuous; iterations = 2, batch_size = 4, training_batches = [batch, batch], validation_batch = batch) + first_result = solve_pde(sine; iterations = 1, batch_size = 4, init_y = -35., training_batches = [batch], validation_batch = batch) + second_result = solve_pde(sine; iterations = 1, batch_size = 4, init_y = 95., training_batches = [batch], validation_batch = batch) + @test vcat(first_result[1], second_result[1]) == whole[1] + @test vcat(first_result[2], second_result[2]) == whole[2] + @test sine.parameters == continuous.parameters + single = FBSDESolver(HJBLQ(1, 0.1, 1), [1, 2, 1], 0.001, "DeepBSDE") + @test isempty(single.parameters) + @test all(isfinite, first(solve_pde(single; iterations = 2, batch_size = 4, seed = 381))) + Random.seed!(749) + expected = rand(5) + Random.seed!(749) + native = FBSNNSolver(equation, [2, 3, 1], 0.001; seed = 64) + result = solve_pde(native; iterations = 1, batch_size = 4, seed = 65) + @test rand(5) == expected + repeated = FBSNNSolver(equation, [2, 3, 1], 0.001; seed = 64) + @test solve_pde(repeated; iterations = 1, batch_size = 4, seed = 65) == result + wrapper_rng = MersenneTwister(614) + direct = FBSDESolver(equation, [1, 3, 1], 0.003, "DeepBSDE"; seed = rand(wrapper_rng, UInt32)) + expected_wrapper = solve_pde(direct; iterations = 2, batch_size = 4, init_y = 0.2, rng = wrapper_rng) + @test solve_deep_bsde(equation; hidden_sizes = [3], iterations = 2, batch_size = 4, init_y = 0.2, learning_rate = 0.003, rng = MersenneTwister(614)) == expected_wrapper +end diff --git a/test/pipeline_regressions.jl b/test/pipeline_regressions.jl new file mode 100644 index 0000000..6326324 --- /dev/null +++ b/test/pipeline_regressions.jl @@ -0,0 +1,210 @@ +using Test +using TOML +using Statistics +using Random +using Distributions: Uniform +using RiskLabAI + +pipeline_matrix(values) = permutedims(reduce(hcat, [Float64.(row) for row in values])) +pipeline_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isapprox(left, right; rtol = 1e-11, atol = 1e-13) +pipeline_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(pipeline_equal(a, b) for (a, b) in zip(left, right)) + +function pipeline_diagnostic_probability(model, data) + get(model, "score_fail", false) && throw(ArgumentError("requested diagnostic scoring failure")) + signal = get(model, "strength", 1.) .* (.45 .* data[:, 1] .+ .15 .* vec(sum(data; dims = 2))) .+ get(model, "bias", 0.) .+ .01*model["feature"] .+ (get(model, "random_state", 0)%97)*1e-5 + scores = log.(model["prior"])' .+ signal .* collect(0:length(model["classes"])-1)' + exponential = exp.(scores .- maximum(scores; dims = 2)) + return exponential ./ sum(exponential; dims = 2) +end + +function pipeline_diagnostic(traces = Any[]; fit_transform = false, probability = true) + clone_step = function(name, model, seed = nothing) + options = Dict{String,Any}(key => deepcopy(value) for (key, value) in model if key in ("strength", "bias", "fail", "score_fail", "with_std", "random_state")) + name == "model" && seed !== nothing && (options["random_state"] = seed) + return options + end + transform = (name, model, data) -> (data .- model["mean"]') ./ model["scale"]' + fit_step = function(name, model, data, labels, params) + weights = Float64.(get(params, "sample_weight", ones(length(labels)))) + if name == "scale" + center = vec(sum(data .* weights; dims = 1)) ./ sum(weights) + deviation = get(model, "with_std", true) ? sqrt.(vec(sum((data .- center').^2 .* weights; dims = 1)) ./ sum(weights)) : ones(size(data, 2)) + deviation[deviation .== 0] .= 1 + merge!(model, Dict("mean" => center, "scale" => deviation)) + push!(traces, Dict("step" => name, "x" => copy(data), "weights" => weights, "weighted" => haskey(params, "sample_weight"), "mean" => center, "scale" => deviation)) + else + get(model, "fail", false) && throw(ArgumentError("requested diagnostic fit failure")) + !isempty(labels) && sum(weights) > 0 || throw(ArgumentError("empty diagnostic fit")) + classes = sort(unique(labels)) + prior = [1+sum(weights[labels .== code]) for code in classes] + feature = sum(weights .* vec(sum(data; dims = 2))) / sum(weights) + visits = get(model, "visits", 0)+1 + merge!(model, Dict("classes" => classes, "prior" => prior, "feature" => feature, "visits" => visits)) + push!(traces, Dict("step" => name, "x" => copy(data), "y" => copy(labels), "weights" => weights, "weighted" => haskey(params, "sample_weight"), "visits" => visits, "row_tag" => get(params, "row_tag", Int[]), "token" => get(params, "token", "fixed"), "seed" => get(model, "random_state", 0))) + end + return model + end + combined = fit_transform ? (name, model, data, labels, options) -> begin + fitted = fit_step(name, model, data, labels, options) + return fitted, transform(name, fitted, data) + end : nothing + prediction = (name, model, data) -> [model["classes"][argmax(row)] for row in eachrow(pipeline_diagnostic_probability(model, data))] + return SampleWeightedPipeline(["scale" => Dict{String,Any}(), "model" => Dict{String,Any}()]; clone_step, fit_step, transform_step = transform, predict_step = prediction, predict_proba_step = probability ? (_, model, data) -> pipeline_diagnostic_probability(model, data) : nothing, classes_step = (_, model) -> model["classes"], configure_step = (_, model, params) -> merge(model, params), fit_transform_step = combined) +end + +function pipeline_check_traces(actual, expected) + @test length(actual) == length(expected) + for (left, right) in zip(actual, expected) + @test Set(keys(left)) == Set(keys(right)) + for key in keys(right) + if key == "x" + @test pipeline_equal(left[key], pipeline_matrix(right[key])) + elseif right[key] isa AbstractVector + @test pipeline_equal(left[key], right[key]) + else + @test left[key] == right[key] + end + end + end +end + +@testset "Shared pipeline routing and estimator state" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pipeline_python_320.toml")) + for case in fixture["pipeline"], combined in (false, true) + traces = Any[] + model = pipeline_diagnostic(traces; fit_transform = combined) + x, y = pipeline_matrix(case["x"]), Int.(case["y"]) + original = deepcopy((x, y, case["fit_params"])) + @test fit_pipeline!(model, x, y; fit_params = case["fit_params"]) === model + @test fit_pipeline!(model, x, y; fit_params = case["fit_params"]) === model + @test pipeline_equal(predict_pipeline_proba(model, x), pipeline_matrix(case["probabilities"])) + @test predict_pipeline(model, x) == case["predictions"] + @test pipeline_classes(model) == sort(unique(y)) + pipeline_check_traces(traces, case["traces"]) + cloned = clone_pipeline(model) + @test !cloned.fitted + @test !haskey(last(cloned.steps[end]), "visits") + @test last(model.steps[end])["visits"] == 2 + @test (x, y, case["fit_params"]) == original + end +end + +@testset "Shared weighted F1 and log-loss pipeline search" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pipeline_python_320.toml")) + for case in fixture["search"] + traces = Any[] + prototype = pipeline_diagnostic(traces) + x, y = pipeline_matrix(case["x"]), Int.(case["y"]) + cv = PurgedKFoldCV(case["n_splits"], Int.(case["starts"]), Int.(case["ends"]); embargo = case["embargo"]) + original = deepcopy((x, y, case["fit_params"], case["grid"])) + result = clf_hyper_fit(cv, x, y, prototype, case["grid"]; fit_params = case["fit_params"], rnd_search_iter = case["iterations"], candidate_indices = case["iterations"] == 0 ? nothing : Int.(case["candidate_indices"]), return_search = true) + @test result.candidates == case["candidates"] + @test result.best_index == case["best_index"] + @test result.best_params == case["candidates"][case["best_index"]] + @test result.scoring == (Set(y) == Set([0, 1]) ? :f1 : :neg_log_loss) + @test isempty(result.failures) + @test pipeline_equal(result.fold_scores, pipeline_matrix(case["scores"])) + @test pipeline_equal(result.mean_scores, Float64.(case["mean_scores"])) + @test pipeline_equal(predict_pipeline_proba(result.estimator, x), pipeline_matrix(case["probabilities"])) + @test predict_pipeline(result.estimator, x) == case["predictions"] + pipeline_check_traces(traces, case["traces"]) + @test all(trace["visits"] == 1 for trace in traces if trace["step"] == "model") + @test size(traces[end]["x"], 1) == length(y) + @test !prototype.fitted + @test all(isempty(last(step)) for step in prototype.steps) + @test (x, y, case["fit_params"], case["grid"]) == original + end +end + +@testset "Shared post-search weighted bagging" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pipeline_python_320.toml")) + for case in fixture["bagging"] + traces = Any[] + prototype = pipeline_diagnostic(traces) + x, y = pipeline_matrix(case["x"]), Int.(case["y"]) + cv = PurgedKFoldCV(3, Int.(case["starts"]), Int.(case["ends"]); embargo = 0.) + plans = case["plans"] + result = clf_hyper_fit(cv, x, y, prototype, case["grid"]; fit_params = case["fit_params"], rnd_search_iter = case["iterations"], candidate_indices = Int.(case["candidate_indices"]), bagging = case["bagging"], bagging_sample_indices = [Int.(plan["samples"]) for plan in plans], bagging_feature_indices = [Int.(plan["features"]) for plan in plans], bagging_estimator_seeds = [plan["seed"] for plan in plans], return_search = true) + @test result.best_index == case["best_index"] + @test result.best_params == case["best_params"] + @test first.(result.estimator.steps) == ["bag"] + @test length(last(result.estimator.steps[end]).members) == case["bagging"][1] + @test pipeline_equal(predict_pipeline_proba(result.estimator, x), pipeline_matrix(case["probabilities"])) + @test predict_pipeline(result.estimator, x) == case["predictions"] + @test pipeline_classes(result.estimator) == sort(unique(y)) + pipeline_check_traces(traces, case["traces"]) + @test !prototype.fitted + end +end + +@testset "Search failures, first ties and random candidate contracts" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "pipeline_python_320.toml")) + source = fixture["search"][1] + x, y = pipeline_matrix(source["x"]), Int.(source["y"]) + cv = PurgedKFoldCV(3, Int.(source["starts"]), Int.(source["ends"]); embargo = 0.) + for case in fixture["errors"] + if case["raises"] + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), case["grid"]) + else + result = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), case["grid"]; return_search = true) + @test result.best_index == case["best_index"] + @test pipeline_equal(result.fold_scores, pipeline_matrix(case["scores"])) + @test pipeline_equal(result.mean_scores, Float64.(case["mean_scores"])) + @test all(failure.phase == (case["name"] == "partial_fit" ? :fit : :score) for failure in result.failures) + @test result.estimator.fitted + end + end + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), Dict("model__fail" => [true, false]); error_score = :raise) + draw_case = only(fixture["distribution"]) + result = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), Dict("model__strength" => Uniform(-2, 2), "model__bias" => [-.4, .3]); rnd_search_iter = 4, candidate_draws = draw_case["candidates"], return_search = true) + @test result.best_index == draw_case["best_index"] + @test pipeline_equal(result.fold_scores, pipeline_matrix(draw_case["scores"])) + result_a = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; rnd_search_iter = 3, rng = MersenneTwister(46), return_search = true) + result_b = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; rnd_search_iter = 3, rng = MersenneTwister(46), return_search = true) + @test result_a.candidates == result_b.candidates + @test result_a.fold_scores == result_b.fold_scores + @test allunique(result_a.candidates) + result_all = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; rnd_search_iter = 20, rng = MersenneTwister(46), return_search = true) + @test length(result_all.candidates) == 8 + @test allunique(result_all.candidates) + continuous = Dict("model__strength" => Uniform(-2, 2), "model__bias" => [-.4, .3]) + native_a = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), continuous; rnd_search_iter = 7, rng = MersenneTwister(4), return_search = true) + native_b = clf_hyper_fit(cv, x, y, pipeline_diagnostic(), continuous; rnd_search_iter = 7, rng = MersenneTwister(4), return_search = true) + @test native_a.candidates == native_b.candidates + @test length(native_a.candidates) == 7 + @test all(-2 <= row["model__strength"] <= 2 && row["model__bias"] in (-.4, .3) for row in native_a.candidates) + @test pipeline_parameter_grid([Dict("b" => [1, 2], "a" => [4, 5]), Dict()]) == [Dict("a" => 4, "b" => 1), Dict("a" => 4, "b" => 2), Dict("a" => 5, "b" => 1), Dict("a" => 5, "b" => 2), Dict()] + @test_throws ArgumentError pipeline_parameter_grid(Dict("a" => [])) + @test_throws ArgumentError pipeline_parameter_grid(Dict("a" => 1)) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; rnd_search_iter = 3, candidate_indices = [1, 1, 2]) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; candidate_indices = [1]) + @test_throws DimensionMismatch clf_hyper_fit(cv, x, y, pipeline_diagnostic(), continuous; rnd_search_iter = 3, candidate_draws = [Dict()]) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), continuous; rnd_search_iter = 3, candidate_indices = [1, 2, 3]) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), source["grid"]; sample_weight = -ones(length(y))) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), Dict(); folds = [([1, 2], [2, 3])]) + @test_throws ArgumentError clf_hyper_fit(cv, x, y, pipeline_diagnostic(), Dict(); fit_params = Dict("model__sample_weight" => ones(length(y))), bagging = [2, .8, 1.]) + @test clf_hyper_fit(x, y, Int.(source["starts"]), Int.(source["ends"]), pipeline_diagnostic(), Dict()) isa SampleWeightedPipeline + @test clf_hyper_fit(cv, x, y, pipeline_diagnostic(; probability = false), Dict(); bagging = [2, .8, 1.], rng = MersenneTwister(6)) isa SampleWeightedPipeline +end + +@testset "Pipeline boundary and passthrough contracts" begin + prototype = pipeline_diagnostic() + @test_throws ArgumentError predict_pipeline(prototype, ones(3, 2)) + @test_throws ArgumentError pipeline_classes(prototype) + @test_throws ArgumentError configure_pipeline!(prototype, Dict("unknown__option" => 1)) + @test_throws ArgumentError fit_pipeline!(prototype, ones(3, 2), [0, 1, 0]; fit_params = Dict("unknown__sample_weight" => ones(3))) + @test_throws ArgumentError fit_pipeline!(prototype, ones(3, 2), [0, 1, 0]; fit_params = Dict("row_tag" => ones(3))) + @test_throws DimensionMismatch fit_pipeline!(prototype, ones(3, 2), [0, 1]) + @test_throws DimensionMismatch fit_pipeline!(prototype, ones(3, 2), [0, 1, 0]; sample_weight = [1.]) + @test_throws ArgumentError SampleWeightedPipeline(["same" => nothing, "same" => nothing]; prototype.callbacks...) + @test_throws ArgumentError SampleWeightedPipeline(["bad__name" => nothing]; prototype.callbacks...) + passthrough = SampleWeightedPipeline(["scale" => :passthrough, "model" => Dict{String,Any}()]; prototype.callbacks...) + fit_pipeline!(passthrough, [1. 2.; 2. 3.; 4. 1.], [0, 1, 0]) + @test predict_pipeline(passthrough, [1. 2.; 2. 3.]) == [1, 1] + @test predict_pipeline_proba(passthrough, [1. 2.])[1, 2] ≈ 2exp(.9+13/300) / (3+2exp(.9+13/300)) + @test sum(predict_pipeline_proba(passthrough, [1. 2.])) ≈ 1. + @test first(clone_pipeline(passthrough).steps) == ("scale" => :passthrough) + unicode = SampleWeightedPipeline(["modèle" => Dict{String,Any}()]; prototype.callbacks...) + configure_pipeline!(unicode, Dict("modèle__bias" => 1.)) + @test last(unicode.steps[end])["bias"] == 1. +end diff --git a/test/portfolio_regressions.jl b/test/portfolio_regressions.jl new file mode 100644 index 0000000..ee516ee --- /dev/null +++ b/test/portfolio_regressions.jl @@ -0,0 +1,105 @@ +using Test +using TOML +using LinearAlgebra +using RiskLabAI + +const PortfolioUnderTest = RiskLabAI.Optimization +portfolio_fixture_matrix(values) = reduce(vcat, permutedims.(values)) + +@testset "Constrained portfolios against Python 3.2.0 and scalar calculus" begin + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "portfolio_python_320.toml")) + for row in fixtures["cases"] + covariance = portfolio_fixture_matrix(row["covariance"]) + kwargs = Dict{Symbol, Any}(Symbol(name) => row[name] for name in ("caps", "return_floor") if haskey(row, name)) + result = if row["method"] == "constrained_minimum_variance" + haskey(row, "expected_returns") && (kwargs[:expected_returns] = row["expected_returns"]) + PortfolioUnderTest.constrained_minimum_variance(covariance; kwargs...) + else + PortfolioUnderTest.robust_mean_variance(row["expected_returns"], covariance, + portfolio_fixture_matrix(row["uncertainty_loading"]); + radius = row["radius"], risk_aversion = row["risk_aversion"], kwargs...) + end + covariance_scale = maximum(abs, covariance) + objective_scale = row["method"] == "constrained_minimum_variance" ? covariance_scale : + max(covariance_scale * row["risk_aversion"], maximum(abs, row["expected_returns"]), row["radius"] * norm(portfolio_fixture_matrix(row["uncertainty_loading"]))) + @test result.solver_status == row["expected_solver_status"] == "optimal" + @test all(abs.(result.weights - row["expected_weights"]) .<= 1e-6 .+ 1e-6 .* abs.(row["expected_weights"])) + @test all(abs.(result.weights - row["independent_weights"]) .<= 1e-6 .+ 1e-6 .* abs.(row["independent_weights"])) + mean = get(row, "expected_returns", zeros(length(result.weights))) + loading = haskey(row, "uncertainty_loading") ? portfolio_fixture_matrix(row["uncertainty_loading"]) : zeros(length(result.weights), 1) + radius = get(row, "radius", 0.0) + weight_error_bound = 1e-6 .+ 1e-6 .* abs.(row["expected_weights"]) + variance_error_bound = opnorm(covariance, 2) * norm(weight_error_bound) * norm(result.weights + row["expected_weights"]) + mean_error_bound = dot(abs.(mean), weight_error_bound) + worst_mean_error_bound = mean_error_bound + radius * opnorm(loading, 2) * norm(weight_error_bound) + @test abs(result.variance - row["expected_variance"]) <= variance_error_bound + 1e-10 * covariance_scale + @test abs(result.expected_return - row["expected_expected_return"]) <= mean_error_bound + 1e-10 + @test abs(result.worst_case_expected_return - row["expected_worst_case_expected_return"]) <= worst_mean_error_bound + 1e-10 + @test result.variance ≈ dot(result.weights, covariance * result.weights) rtol = 1e-12 atol = 1e-14 * covariance_scale + @test result.expected_return ≈ dot(mean, result.weights) rtol = 1e-12 atol = 1e-14 + @test result.worst_case_expected_return ≈ dot(mean, result.weights) - radius * norm(loading' * result.weights) rtol = 1e-12 atol = 1e-14 + @test result.objective ≈ row["expected_objective"] rtol = 1e-8 atol = 1e-10 * objective_scale + @test result.objective ≈ row["independent_objective"] rtol = 1e-8 atol = 1e-10 * objective_scale + @test abs(sum(result.weights) - 1) <= 1e-7 + @test all(result.weights .>= -1e-7) + @test all(result.weights .<= get(row, "caps", ones(length(result.weights))) .+ 1e-7) + if haskey(row, "return_floor") + @test dot(row["expected_returns"], result.weights) >= row["return_floor"] - 1e-7 + end + end +end + +@testset "Public-book robust portfolio conic formulation" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "portfolio_conic_book_cases.toml")) + covariance = portfolio_fixture_matrix(fixture["covariance"]) + expected = fixture["expected_returns"] + base_loading = portfolio_fixture_matrix(fixture["uncertainty_loading"]) + caps = fixture["caps"] + for row in fixture["cases"] + loading = row["zero_loading"] ? zero(base_loading) : base_loading + floor = get(row, "return_floor", nothing) + result = PortfolioUnderTest.robust_mean_variance(expected, covariance, loading; + radius = row["radius"], risk_aversion = row["risk_aversion"], caps, return_floor = floor) + @test result.solver_status == row["solver_status"] + @test all(isapprox.(result.weights, row["expected_weights"]; rtol = 1e-9, atol = 1e-11)) + @test result.variance ≈ row["variance"] rtol = 1e-9 atol = 1e-11 + @test result.expected_return ≈ row["expected_return"] rtol = 1e-9 atol = 1e-11 + @test result.worst_case_expected_return ≈ row["worst_case_expected_return"] rtol = 1e-9 atol = 1e-11 + @test result.objective ≈ row["objective"] rtol = 1e-9 atol = 1e-11 + @test abs(sum(result.weights) - 1) <= 1e-7 + @test all(result.weights .>= -1e-7) + @test all(result.weights .<= caps .+ 1e-7) + @test isnothing(floor) || result.expected_return >= floor - 1e-7 + end +end + +@testset "Constrained portfolio domains and nonunique optima" begin + for covariance in (zeros(3, 3), ones(3, 3), [1.0 -1.0 0.0; -1.0 1.0 0.0; 0.0 0.0 0.0]) + result = PortfolioUnderTest.constrained_minimum_variance(covariance; caps = [0.4, 0.4, 0.4]) + @test result.solver_status == "optimal" + @test abs(sum(result.weights) - 1) <= 1e-7 + @test all(-1e-7 .<= result.weights .<= 0.4 + 1e-7) + @test result.variance ≈ (all(==(1.0), covariance) ? 1.0 : 0.0) atol = 1e-10 + end + result = PortfolioUnderTest.constrained_minimum_variance(reshape([0.04], 1, 1); expected_returns = [0.08], return_floor = 0.07) + @test result.weights ≈ [1.0] atol = 1e-8 + @test result.variance ≈ 0.04 atol = 1e-10 + diagonal = Diagonal([1.0, 2.0, 4.0]) + result = PortfolioUnderTest.constrained_minimum_variance(diagonal) + @test result.weights ≈ [4 / 7, 2 / 7, 1 / 7] atol = 1e-7 + @test result.variance ≈ 4 / 7 atol = 1e-10 + identity_covariance = Matrix{Float64}(I, 2, 2) + for covariance in (zeros(0, 0), ones(2, 3), [1.0 0.1; 0.2 1.0], [1.0 0.0; 0.0 -1e-8], [1.0 NaN; NaN 1.0], fill(true, 2, 2), ones(ComplexF64, 2, 2)) + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(covariance) + end + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(identity_covariance; caps = [0.4, 0.4]) + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(identity_covariance; caps = [-0.1, 1.1]) + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(identity_covariance; expected_returns = [0.1]) + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(identity_covariance; return_floor = 0.1) + @test_throws ArgumentError PortfolioUnderTest.constrained_minimum_variance(identity_covariance; expected_returns = [0.1, 0.15], return_floor = 0.2) + @test_throws ArgumentError PortfolioUnderTest.robust_mean_variance([0.1, 0.2], identity_covariance, ones(1, 2); radius = 0.1, risk_aversion = 1.0) + @test_throws ArgumentError PortfolioUnderTest.robust_mean_variance([0.1, 0.2], identity_covariance, zeros(2, 0); radius = 0.1, risk_aversion = 1.0) + @test_throws ArgumentError PortfolioUnderTest.robust_mean_variance([0.1, 0.2], identity_covariance, identity_covariance; radius = -0.1, risk_aversion = 1.0) + @test_throws ArgumentError PortfolioUnderTest.robust_mean_variance([0.1, 0.2], identity_covariance, identity_covariance; radius = 0.1, risk_aversion = -1.0) + @test_throws ArgumentError PortfolioUnderTest.robust_mean_variance([0.1, 0.2], identity_covariance, identity_covariance; radius = true, risk_aversion = 1.0) +end diff --git a/test/preserved_runtests.jl b/test/preserved_runtests.jl index bd2a532..8e9a5c8 100644 --- a/test/preserved_runtests.jl +++ b/test/preserved_runtests.jl @@ -5,14 +5,6 @@ using LinearAlgebra using Random using Statistics: mean, cov -const RISKLABAI_TEST_DEEP_BSDE = get(ENV, "RISKLABAI_TEST_DEEP_BSDE", "0") == "1" - -if RISKLABAI_TEST_DEEP_BSDE - using Lux - using Optimisers - using Zygote -end - using RiskLabAI @testset "RiskLabAI smoke tests" begin @@ -270,12 +262,12 @@ end # Deterministic anchors (R/S Hurst blend) — exact parity with Python's R/S path. @test D.rescaled_range_hurst(incr) ≈ 0.945557656342456 atol = 1e-9 - @test D.adaptive_differencing_order(incr) ≈ 0.44555765634245603 atol = 1e-9 + @test D.adaptive_differencing_order(incr) ≈ 0.8610667486651158 atol = 1e-9 # Wavelet component is an optional dependency (not wired) -> NaN, R/S-only blend. - @test isnan(D.wavelet_variance_hurst(incr)) + @test D.wavelet_variance_hurst(incr) ≈ 1.7765758409877757 atol = 1e-9 res = D.adaptive_fractional_difference(price) - @test res.d_hat ≈ 0.4458675284494259 atol = 1e-8 + @test res.d_hat ≈ 0.99 atol = 1e-8 # Order/threshold/p-value are behavioural (ADF impl differs from statsmodels): # structural checks. @test 0.0 <= res.order <= 1.2 @@ -667,7 +659,7 @@ end @test B.mean_exit_time(eg, 2.0, 1.0, th, sg) ≈ 130958.42974177317 rtol = 1e-9 m = B.ou_rule_metrics(2.0, 1.0, th, sg, eg, 0.0) @test m.return_rate ≈ 1.5272021846493343e-5 rtol = 1e-9 - @test m.time_scaled_sharpe ≈ 2941.8908064682373 rtol = 1e-9 + @test m.time_scaled_sharpe ≈ 2942.57349001657 rtol = 1e-9 @test isnan(B.ou_rule_metrics(-1.0, 1.0, th, sg, eg).hit_probability) # invalid -> bad # Optimizer (grid search vs SciPy L-BFGS-B): the objective surface is flat along @@ -1900,37 +1892,20 @@ end @test xs[:, :, 1] == repeat([1.0 0.5], 16, 1) end -@testset "Pde — optional Deep BSDE extension" begin +@testset "Pde — native Deep BSDE solver" begin P = RiskLabAI.Pde eq = P.HJBLQ(1, 0.5, 4) - extension = Base.get_extension(RiskLabAI, :RiskLabAIDeepBSDEExt) - - if RISKLABAI_TEST_DEEP_BSDE - extension === nothing && error("Deep-BSDE test extension did not load") - losses, inits = P.solve_deep_bsde( - eq; - hidden_sizes = [8], - iterations = 12, - batch_size = 64, - init_y = 3.0, - learning_rate = 0.02, - rng = MersenneTwister(1), - ) - @test length(losses) == 12 - @test all(isfinite, losses) - @test all(isfinite, inits) - @test minimum(losses) <= losses[1] # training improves on the initial loss - else - @test extension === nothing - failure = try - P.solve_deep_bsde(eq) - nothing - catch caught - caught - end - @test failure isa ArgumentError - message = sprint(showerror, failure) - @test occursin("deep_bsde extension", message) - @test all(occursin(dependency, message) for dependency in ("Lux", "Optimisers", "Zygote")) - end + losses, inits = P.solve_deep_bsde( + eq; + hidden_sizes = [8], + iterations = 12, + batch_size = 64, + init_y = 3.0, + learning_rate = 0.02, + rng = MersenneTwister(1), + ) + @test length(losses) == 12 + @test all(isfinite, losses) + @test all(isfinite, inits) + @test minimum(losses) <= losses[1] end diff --git a/test/protocol_regressions.jl b/test/protocol_regressions.jl new file mode 100644 index 0000000..09e1fd4 --- /dev/null +++ b/test/protocol_regressions.jl @@ -0,0 +1,144 @@ +using Test +using TOML +using RiskLabAI +using RiskLabAI.CausalFactorAnalysis + +function protocol_fixture_value(encoded) + kind = encoded["kind"] + kind == "none" && return nothing + kind == "scalar" && return encoded["value"] + kind == "float" && return encoded["value"] == "nan" ? NaN : encoded["value"] == "inf" ? Inf : -Inf + if kind in ("tuple", "list") + items = map(protocol_fixture_value, encoded["items"]) + return kind == "tuple" ? Tuple(items) : items + end + kind == "record" || error("Unexpected protocol fixture value") + types = (EventHorizon, FoldEvidence, ValidationEvidence, VariableSelectionStage, + CausalDiscoveryStage, CausalAdjustmentSetStage, CausalExplanatoryAndPredictivePowerStage, + CausalPortfolioConstructionStage, BacktestStage, MultipleTestingAdjustmentsStage, CausalFactorProtocolReport) + index = findfirst(type -> string(nameof(type)) == encoded["type"], types) + isnothing(index) && error("Unexpected protocol fixture record type") + return types[index](; ((key == "end" ? :stop : Symbol(key)) => protocol_fixture_value(value) for (key, value) in encoded["fields"])...) +end + +function protocol_fixture_change(value, path, replacement) + isempty(path) && return replacement + key, rest = first(path), path[2:end] + if value isa Tuple || value isa AbstractVector + index = parse(Int, key) + 1 + items = [position == index ? protocol_fixture_change(item, rest, replacement) : item for (position, item) in enumerate(value)] + return value isa Tuple ? Tuple(items) : items + end + field = key == "end" ? :stop : Symbol(key) + index = findfirst(==(field), fieldnames(typeof(value))) + isnothing(index) && error("Unexpected protocol fixture path") + updated = protocol_fixture_change(getfield(value, field), rest, replacement) + declared = fieldtype(typeof(value), index) + bool_as_number = updated isa Bool && declared != Bool && declared != Union{Nothing,Bool} + updated isa declared && !bool_as_number || throw(ArgumentError("Protocol fixture value is rejected by the released Julia record type")) + fields = value isa CausalFactorProtocolReport ? fieldnames(typeof(value))[1:9] : fieldnames(typeof(value)) + return typeof(value)(; (name => (name == field ? updated : getfield(value, name)) for name in fields)...) +end + +function protocol_case_result(base, changes) + report = base + try + for change in changes + report = protocol_fixture_change(report, change["path"], protocol_fixture_value(change["value"])) + end + output = protocol_validation_result(report) + return (report = report, result = output.result, error = output.error, constructor_rejection = false) + catch error + return (report = report, result = nothing, error = error, constructor_rejection = true) + end +end + +function protocol_validation_result(report) + try + return (result = validate_causal_factor_protocol(report), error = nothing) + catch error + return (result = nothing, error = error) + end +end + +function protocol_diagnostics(error) + error isa ArgumentError || return String[] + return [String(item.match) for item in eachmatch(r"(?:Protocol|Stage [1-7]): .*?(?= (?:Protocol|Stage [1-7]): |$)", error.msg)] +end + +@testset "Seven-stage protocol records against public Python 3.2.0" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "protocol_python_320.toml")) + bases = Dict(name => protocol_fixture_value(value["value"]) for (name, value) in fixture["bases"]) + for case in fixture["cases"] + output = protocol_case_result(bases[case["base"]], case["changes"]) + @testset "$(case["name"])" begin + @test isnothing(output.error) == case["accepted"] + if case["accepted"] + @test output.result === output.report + @test isempty(case["errors"]) + elseif output.constructor_rejection + @test !case["accepted"] + else + @test output.error isa ArgumentError + messages = protocol_diagnostics(output.error) + @test !isempty(messages) + @test all(startswith(message, "Protocol:") || startswith(message, "Stage ") for message in messages) + @test length(messages) == length(unique(messages)) + end + end + end +end + +@testset "Protocol tuple snapshots, defaults and fixed source properties" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "protocol_python_320.toml")) + report = protocol_fixture_value(fixture["bases"]["backdoor"]["value"]) + @test report.stage_names == ("Variable Selection", "Causal Discovery", "Causal Adjustment Set", "Causal Explanatory and Predictive Power", "Causal Portfolio Construction", "Backtest", "Multiple Testing Adjustments") + @test first.(report.stages) == report.stage_names + @test last.(report.stages) == Tuple(getfield(report, field) for field in fieldnames(typeof(report))[3:9]) + @test report.source_oracle == "PROTOCOL-01" + @test report.source_locator == "PDF pages 18-22 (journal pages 27-31), Exhibit 10" + @test all(hasproperty(report, name) for name in (:stages, :stage_names, :source_oracle, :source_locator)) + @test_throws ErrorException setproperty!(report, :trial_family_id, "changed") + @test_throws ErrorException setproperty!(report, :source_oracle, "changed") + train, test = ["a", "b"], ["c"] + components = ["VARIABLE_SELECTION"] + fold = FoldEvidence("fold", "path", train, test, train, test, "refit", components) + train[1], test[1], components[1] = "changed", "changed", "changed" + @test fold.train_ids == fold.fit_ids == ("a", "b") + @test fold.test_ids == fold.prediction_ids == ("c",) + @test fold.refit_components == ("VARIABLE_SELECTION",) + @test_throws ErrorException setproperty!(fold, :fold_id, "changed") + edges = [["x", "y"]] + nodes = ["x", "y"] + discovery = CausalDiscoveryStage(["PC"], "graph", nodes, edges, "DAG", [], ["assumption"]) + edges[1][1], nodes[1] = "changed", "changed" + @test discovery.directed_edges == (("x", "y"),) + @test discovery.graph_nodes == ("x", "y") + trials = ["a", "b"] + partitions = [["group", trials]] + adjustment = MultipleTestingAdjustmentsStage(["HOLM"], "family", trials, partitions, 0.05, false) + trials[1] = "changed" + partitions[1][1] = "changed" + @test adjustment.declared_trial_ids == ("a", "b") + @test adjustment.family_partitions == (("group", ("a", "b")),) + @test isnothing(adjustment.sample_length) + @test isnothing(adjustment.p_value_estimator) + horizon = EventHorizon("a", 0, 1) + @test getproperty(horizon, :stop) == 1 + @test EventHorizon(; observation_id = "a", start = 0, stop = 1).stop == 1 + horizons, folds, methods = [horizon], [fold], ["RESAMPLING"] + evidence = ValidationEvidence(methods, horizons, folds) + empty!(horizons) + empty!(folds) + empty!(methods) + @test evidence.event_horizons == (horizon,) + @test evidence.folds == (fold,) + @test evidence.method_labels == ("RESAMPLING",) + @test evidence.embargo == 0.0 + for (_, stage) in report.stages + @test !ismutable(stage) + end + touching = Dict("a" => (0.0, 1.0), "b" => (1.0, 2.0)) + @test !RiskLabAI.CausalFactorAnalysis._has_any_overlap(touching) + @test RiskLabAI.CausalFactorAnalysis._has_any_overlap(Dict("a" => (0.0, nextfloat(1.0)), "b" => (1.0, 2.0))) +end diff --git a/test/risk_formula_regressions.jl b/test/risk_formula_regressions.jl new file mode 100644 index 0000000..1bc6466 --- /dev/null +++ b/test/risk_formula_regressions.jl @@ -0,0 +1,85 @@ +using Test +using TOML +using Random +using RiskLabAI + +riskformula_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-11, atol = 1e-13) +riskformula_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(riskformula_equal(a, b) for (a, b) in zip(left, right)) + +@testset "Shared family p-value corrections and extreme-tail haircuts" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "risk_formulas_python_320.toml")) + for case in fixture["adjustment"] + p = Float64[case["values"]...] + original = copy(p) + holm = holm_adjusted_p_values(p) + bhy = benjamini_hochberg_yekutieli_adjusted_p_values(p) + @test riskformula_equal(holm, Float64[case["holm"]...]) + @test riskformula_equal(bhy, Float64[case["bhy"]...]) + @test all(holm .>= p .- 1e-15) + @test all(bhy .>= p .- 1e-15) + @test issorted(holm[sortperm(p)]) + @test issorted(bhy[sortperm(p)]) + @test p == original + end + for case in fixture["haircut"] + values = Float64[case["values"]...] + actual = haircut_sharpe_ratios(values, case["count"]; method = case["method"], significance_level = case["level"]) + @test riskformula_equal(sharpe_ratio_p_values(values, case["count"]), Float64[case["output"]["p_values"]...]) + for (key, value) in pairs(actual) + @test riskformula_equal(value, case["output"][string(key)]) + end + end + @test_throws ArgumentError haircut_sharpe_ratios([.1], 100; method = "unknown") +end + +@testset "Shared expected maxima and test-set error formulas" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "risk_formulas_python_320.toml")) + for case in fixture["maximum"] + @test riskformula_equal(expected_max_sharpe_ratio(case["trials"], case["mean"], case["std"]), case["output"]) + end + for case in fixture["error_rates"] + @test riskformula_equal(strategy_type1_error_probability(case["z"]; k = case["count"]), case["alpha"]) + @test riskformula_equal(strategy_type2_error_probability(case["alpha"], case["count"], case["theta"]), case["beta"]) + end + for case in fixture["z_statistics"] + @test riskformula_equal(estimated_sharpe_ratio_z_statistics(case["observed"], case["count"]; true_sharpe_ratio = case["true"], skew = case["skew"], kurt = case["kurtosis"]), case["z"]) + @test riskformula_equal(theta_for_type2_error(case["observed"], case["count"], case["true"]; skew = case["skew"], kurt = case["kurtosis"]), case["theta"]) + end +end + +@testset "Shared binary strategy moments and inverse requirements" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "risk_formulas_python_320.toml")) + for case in fixture["variance"] + @test riskformula_equal(target_sharpe_ratio_symbolic(case["probability"], case["up"], case["down"]), case["output"]) + end + for case in fixture["binomial"] + @test riskformula_equal(binomial_sharpe_ratio(case["loss"], case["profit"], case["frequency"], case["probability"]), case["output"]) + end + for case in fixture["precision"] + actual = implied_precision(case["loss"], case["profit"], case["frequency"], case["target"]) + @test riskformula_equal(actual, case["output"]) + @test 0 <= actual <= 1 + @test isapprox(binomial_sharpe_ratio(case["loss"], case["profit"], case["frequency"], actual), case["target"]; rtol = 1e-10, atol = 1e-12) + end + for case in fixture["frequency"] + @test riskformula_equal(bin_frequency(case["loss"], case["profit"], case["probability"], case["target"]), case["output"]) + end +end + +@testset "Shared Gaussian mixture primitives and permutations" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "risk_formulas_python_320.toml")) + for case in fixture["mixture"] + draws = [Float64[row...] for row in case["normal_draws"]] + order = Int[case["permutation"]...] + original = deepcopy((draws, order)) + actual = mix_gaussians(case["mu1"], case["mu2"], case["sigma1"], case["sigma2"], case["probability"], case["count"]; normal_draws = draws, permutation = order) + @test riskformula_equal(actual, Float64[case["output"]...]) + @test length(actual) == case["count"] + @test (draws, order) == original + end + @test mix_gaussians(.1, -.1, .2, .3, .4, 20; rng = MersenneTwister(4)) == mix_gaussians(.1, -.1, .2, .3, .4, 20; rng = MersenneTwister(4)) + @test_throws ArgumentError mix_gaussians(.1, -.1, .2, .3, 1.2, 10) + @test_throws ArgumentError mix_gaussians(.1, -.1, -.2, .3, .4, 10) + @test_throws DimensionMismatch mix_gaussians(.1, -.1, .2, .3, .4, 10; normal_draws = [ones(3), ones(7)]) + @test_throws ArgumentError mix_gaussians(.1, -.1, .2, .3, .4, 10; permutation = ones(Int, 10)) +end diff --git a/test/runtests.jl b/test/runtests.jl index 504d1ae..b8aa652 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -79,6 +79,8 @@ const EXPECTED_CAUSAL_FACTOR_EXPORTS = Set(( :compare_single_and_family_fdr, :conditional_upper_tail_probability, :evaluate_structural_causal_model, + :evaluate_policy, + :exposure_mean_contrast, :factor_control_roles, :family_level_false_discovery_rate, :fdr_nonidentification_witness, @@ -91,8 +93,11 @@ const EXPECTED_CAUSAL_FACTOR_EXPORTS = Set(( :generalized_confounder_undercontrolled_coefficient, :max_selection_family_errors, :max_selection_null_probability, + :manski_ate_bounds, :maximum_mixture_cdf, :single_trial_false_discovery_rate, + :select_policy, + :transported_ate, )) @testset "CausalFactorAnalysis" begin @@ -111,3 +116,53 @@ const EXPECTED_CAUSAL_FACTOR_EXPORTS = Set(( include("causal_factor_analysis/test_false_discovery_rates.jl") include("causal_factor_analysis/test_additive_numeric_parity.jl") end + +include("sample_weight_regressions.jl") +include("causal_estimator_regressions.jl") +include("portfolio_regressions.jl") +include("convex_regressions.jl") +include("covariance_regressions.jl") +include("feature_contract_regressions.jl") +include("graph_regressions.jl") +include("density_tail_regressions.jl") +include("factor_diagnostic_regressions.jl") +include("allocation_regressions.jl") +include("search_fdr_regressions.jl") +include("bet_sizing_regressions.jl") +include("protocol_regressions.jl") +include("adaptation_regressions.jl") +include("hedging_regressions.jl") +include("timegan_regressions.jl") +include("neural_sde_regressions.jl") +include("pde_network_regressions.jl") +include("pde_equation_regressions.jl") +include("pde_solver_regressions.jl") +include("differencing_regressions.jl") +include("validation_regressions.jl") +include("bagged_validation_regressions.jl") +include("importance_regressions.jl") +include("pipeline_regressions.jl") +include("backtest_statistics_regressions.jl") +include("risk_formula_regressions.jl") +include("signal_sizing_regressions.jl") +include("ou_regressions.jl") +include("ou_boundary_regressions.jl") +include("entropy_regressions.jl") +include("microstructure_regressions.jl") +include("distance_regressions.jl") +include("trend_weight_regressions.jl") +include("labeling_regressions.jl") +include("structural_break_regressions.jl") +include("synthetic_regressions.jl") +include("constants_regressions.jl") +include("covariance_algebra_regressions.jl") +include("hierarchy_regressions.jl") +include("clustering_regressions.jl") +include("bar_stream_regressions.jl") +include("debiased_importance_regressions.jl") +include("ensemble_contract_regressions.jl") +include("pbo_regressions.jl") +include("hpo_regressions.jl") +include("mp_fit_regressions.jl") +include("infrastructure_regressions.jl") +include("mp_gradient_regressions.jl") diff --git a/test/sample_weight_regressions.jl b/test/sample_weight_regressions.jl new file mode 100644 index 0000000..62cfba7 --- /dev/null +++ b/test/sample_weight_regressions.jl @@ -0,0 +1,76 @@ +using Test +using Dates +using Random +using TOML +using RiskLabAI + +mutable struct SampleWeightUniformRNG <: AbstractRNG + values::Vector{Float64} + next::Int +end + +function Random.rand(generator::SampleWeightUniformRNG) + value = generator.values[generator.next] + generator.next += 1 + return value +end + +@testset "Sample weights against Python 3.2.0" begin + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "sample_weight_python_320.toml")) + for fixture in fixtures["concurrency"] + result = RiskLabAI.Data.expand_label_for_meta_labeling( + fixture["close_index"], fixture["starts"], fixture["ends"], fixture["molecule"], + ) + @test result.index == fixture["expected_index"] + @test result.concurrency == fixture["expected_counts"] + weights = RiskLabAI.Data.sample_weight_absolute_return_meta_labeling( + fixture["starts"], fixture["ends"], fixture["close_index"], fixture["prices"], fixture["molecule"], + ) + @test isapprox(weights, fixture["expected_weights"]; rtol = fixtures["rtol"], atol = fixtures["atol"]) + end + calendar = Date(2026, 1, 1) .+ Day.(0:4) + result = RiskLabAI.Data.expand_label_for_meta_labeling(calendar, calendar[1:3], calendar[[4, 3, 4]], calendar[2:3]) + @test result.index == calendar[2:4] + @test result.concurrency == [2, 3, 2] + result = RiskLabAI.Data.expand_label_for_meta_labeling(collect(0:4), [0, 1], [missing, 2], [1]) + @test result.index == [1, 2] + @test result.concurrency == [2, 2] + result = RiskLabAI.Data.expand_label_for_meta_labeling(collect(0:4), [0, 1], [NaN, 2.0], [1]) + @test result.concurrency == [2, 2] +end + +@testset "Sequential bootstrap shared draws and domain checks" begin + fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "sample_weight_python_320.toml")) + bootstrap = RiskLabAI.Data.sequential_bootstrap + for fixture in fixtures["bootstrap"] + indicator = reduce(vcat, permutedims.(fixture["indicator_rows"])) + original = copy(indicator) + generator = SampleWeightUniformRNG(fixture["uniforms"], 1) + result = bootstrap(indicator; sample_length = length(fixture["uniforms"]), random_state = generator) + @test result == fixture["expected_one_based"] + @test generator.next == length(fixture["uniforms"]) + 1 + @test indicator == original + end + indicator = [1 0 0; 1 1 0; 0 1 1; 0 0 1] + @test bootstrap(indicator; random_state = 19) == bootstrap(indicator; random_state = 19) + @test length(bootstrap(indicator; random_state = 19)) == size(indicator, 2) + @test bootstrap(ones(2, 1); sample_length = 4, random_state = 0) == ones(Int, 4) + generator = Xoshiro(29) + untouched = copy(generator) + @test isempty(bootstrap(indicator; sample_length = 0, random_state = generator)) + @test rand(generator) == rand(untouched) + @test isempty(bootstrap(zeros(3, 0); random_state = 0)) + @test isempty(bootstrap(zeros(0, 0); sample_length = 0, random_state = 0)) + @test_throws ArgumentError bootstrap(zeros(0, 1)) + @test_throws ArgumentError bootstrap(zeros(2, 1)) + @test_throws ArgumentError bootstrap(zeros(3, 0); sample_length = 1) + @test_throws ArgumentError bootstrap([1.0 NaN; 0.0 1.0]) + @test_throws ArgumentError bootstrap([1 2; 0 1]) + @test_throws ArgumentError bootstrap(ComplexF64[1 0; 0 1]) + for length in [-1, 0.5, true] + @test_throws ArgumentError bootstrap(indicator; sample_length = length) + end + for seed in [-1, 0.5, true, "seed"] + @test_throws ArgumentError bootstrap(indicator; random_state = seed) + end +end diff --git a/test/search_fdr_regressions.jl b/test/search_fdr_regressions.jl new file mode 100644 index 0000000..ca4156d --- /dev/null +++ b/test/search_fdr_regressions.jl @@ -0,0 +1,176 @@ +using Test +using TOML +using Distributions: Normal, cdf, pdf +using RiskLabAI + +const SearchFDR = RiskLabAI.CausalFactorAnalysis +const search_fdr_fixtures = TOML.parsefile(joinpath(@__DIR__, "fixtures", "search_fdr_python_320.toml")) + +search_fdr_model(row) = SearchFDR.GaussianTrialMixture(row["trial_null_probability"], row["null_standard_deviation"], row["alternative_mean"], row["alternative_standard_deviation"]) + +function check_search_fields(result, expected) + for name in propertynames(result) + actual, reference = getproperty(result, name), expected[string(name)] + if actual isa Bool || actual isa Integer + @test actual == reference + else + @test actual ≈ reference rtol = 1e-10 atol = 1e-12 + end + end +end + +@testset "Single and family search posteriors against Python 3.2.0" begin + for row in search_fdr_fixtures["single"] + @test SearchFDR.single_trial_false_discovery_rate(row["alpha"], row["beta"], row["probability"]) ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + end + for row in search_fdr_fixtures["family"] + @test SearchFDR.family_level_false_discovery_rate(row["alpha"], row["beta"], row["probability"], row["trials"]) ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + end + for row in search_fdr_fixtures["errors"] + result = SearchFDR.max_selection_family_errors(row["alpha"], row["beta"], row["probability"], row["trials"]) + @test result isa SearchFDR.MaxSelectionFamilyErrors + check_search_fields(result, row["expected"]) + end + for row in search_fdr_fixtures["comparison"] + result = SearchFDR.compare_single_and_family_fdr(row["alpha"], row["beta"], row["probability"], row["trials"]; family_type_i_error = row["family_alpha"], family_type_ii_error = row["family_beta"]) + @test result isa SearchFDR.FDRComparisonEvidence + check_search_fields(result, row["expected"]) + end + for row in search_fdr_fixtures["mixture"] + @test SearchFDR.maximum_mixture_cdf(row["null_cdf"], row["alternative_cdf"], row["probability"], row["trials"]) ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + end + for row in search_fdr_fixtures["conditional"] + @test SearchFDR.conditional_upper_tail_probability(row["value"], row["threshold"]) ≈ row["expected"] rtol = 1e-10 atol = 1e-12 + end + @test SearchFDR.single_trial_false_discovery_rate(1e-300, 0, 0.5) ≈ 1e-300 rtol = 1e-12 atol = 0.0 + @test SearchFDR.family_level_false_discovery_rate(0.1, 1, 0.1, 1000) == 1.0 +end + +@testset "Gaussian selected maxima and nonidentification witness" begin + for row in search_fdr_fixtures["gaussian"] + model = search_fdr_model(row["model"]) + @test !ismutabletype(typeof(model)) + check_search_fields(model, row["model"]) + for (index, observation) in enumerate(row["observations"]) + @test SearchFDR.gaussian_trial_mixture_cdf(model, observation) ≈ row["expected"]["trial_cdf"][index] rtol = 1e-10 atol = 1e-12 + @test SearchFDR.gaussian_max_selection_cdf(model, observation, row["trials"]) ≈ row["expected"]["maximum_cdf"][index] rtol = 1e-10 atol = 1e-12 + @test SearchFDR.gaussian_max_selection_log_density(model, observation, row["trials"]) ≈ row["expected"]["log_density"][index] rtol = 1e-10 atol = 1e-12 + end + @test SearchFDR.gaussian_max_selection_log_likelihood(model, row["observations"], row["trials"]) ≈ row["expected"]["log_likelihood"] rtol = 1e-10 atol = 1e-12 + end + for row in search_fdr_fixtures["aggregate"] + model, thresholds = search_fdr_model(row["model"]), copy(row["thresholds"]) + result = SearchFDR.gaussian_search_adjusted_false_discovery_rate(model, thresholds, row["trials"]) + @test result isa SearchFDR.GaussianSearchAdjustedFDR + check_search_fields(result, row["expected"]) + thresholds .= 999.0 + @test collect(result.thresholds) == row["thresholds"] + @test result.thresholds isa Vector{Float64} + end + for row in search_fdr_fixtures["witness"] + result = SearchFDR.fdr_nonidentification_witness(row["observable"], row["target"]) + @test result isa SearchFDR.FDRNonIdentificationWitness + check_search_fields(result, row["expected"]) + @test result.family_false_discovery_rate ≈ row["target"] rtol = 1e-10 atol = 1e-12 + @test result.reconstructed_observable_cdf_at_threshold ≈ row["observable"] rtol = 1e-10 atol = 1e-12 + end +end + +@testset "Selected-winner quadrature and independent identities" begin + if all(isdefined(SearchFDR, name) for name in (:_SELECTION_KRONROD_WEIGHTS, :_SELECTION_KRONROD_POINTS, :_SELECTION_GAUSS_WEIGHTS)) + for degree in 0:22 + value = sum(SearchFDR._SELECTION_KRONROD_WEIGHTS[index] * (SearchFDR._SELECTION_KRONROD_POINTS[index]^degree + (-SearchFDR._SELECTION_KRONROD_POINTS[index])^degree) for index in 1:7) + SearchFDR._SELECTION_KRONROD_WEIGHTS[8] * SearchFDR._SELECTION_KRONROD_POINTS[8]^degree + @test value ≈ (iseven(degree) ? 2 / (degree + 1) : 0.0) rtol = 2e-14 atol = 2e-14 + end + for degree in 0:13 + value = sum(SearchFDR._SELECTION_GAUSS_WEIGHTS[index] * (SearchFDR._SELECTION_KRONROD_POINTS[2index]^degree + (-SearchFDR._SELECTION_KRONROD_POINTS[2index])^degree) for index in 1:3) + SearchFDR._SELECTION_GAUSS_WEIGHTS[4] * SearchFDR._SELECTION_KRONROD_POINTS[8]^degree + @test value ≈ (iseven(degree) ? 2 / (degree + 1) : 0.0) rtol = 2e-14 atol = 2e-14 + end + end + for row in search_fdr_fixtures["selection"] + model = search_fdr_model(row["model"]) + result = SearchFDR.max_selection_null_probability(row["threshold"], model.trial_null_probability, row["trials"]; + null_density = value -> pdf(Normal(0, model.null_standard_deviation), value), + mixture_cdf = value -> SearchFDR.gaussian_trial_mixture_cdf(model, value)) + @test result isa SearchFDR.SelectionLevelProbabilityEvidence + for name in (:joint_null_and_selection_probability, :selection_probability, :conditional_null_probability) + @test getproperty(result, name) ≈ row["expected"][string(name)] rtol = 1e-10 atol = 1e-12 + end + @test result.joint_null_and_selection_probability ≈ row["independent_joint"] rtol = 1e-8 atol = 1e-10 + @test 0 <= result.conditional_null_probability <= 1 + @test 0 <= result.quadrature_absolute_error <= max(1e-12 * row["trials"] * model.trial_null_probability, 1e-10 * result.joint_null_and_selection_probability) * (1 + 1e-12) + end + for probability in (0.0, 0.1, 0.9, 1.0), trials in (1, 5, 100) + result = SearchFDR.max_selection_null_probability(0.0, probability, trials; null_density = value -> pdf(Normal(), value), mixture_cdf = value -> cdf(Normal(), value)) + @test result.conditional_null_probability ≈ probability rtol = 1e-10 atol = 1e-12 + @test result.selection_probability ≈ 1 - 0.5^trials rtol = 1e-10 atol = 1e-12 + end + skipped = SearchFDR.max_selection_null_probability(0.0, 0.0, 2; null_density = value -> error("density must not be evaluated"), mixture_cdf = value -> 0.5) + @test skipped.joint_null_and_selection_probability == skipped.quadrature_absolute_error == 0 + if isdefined(SearchFDR, :_SELECTION_KRONROD_WEIGHTS) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(-2.0, 0.5, 10; null_density = value -> pdf(Normal(), value), mixture_cdf = value -> cdf(Normal(), value), absolute_tolerance = 1e-14, relative_tolerance = 1e-14, max_subintervals = 1) + end + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0.0, 1.0, 1; null_density = value -> 10pdf(Normal(), value), mixture_cdf = value -> cdf(Normal(), value)) +end + +@testset "Selected-winner reported quadrature diagnostics" begin + model = SearchFDR.GaussianTrialMixture(0.95, 1.0, 0.3, 1.0) + expected_errors = [3.444780539307629e-14, 2.425603797050804e-13, 7.234731622609446e-13, 2.7334009146924937e-12, 2.1909677950044404e-11] + expected_probabilities = [0.907421416202264, 0.9071719137893014, 0.9068565103644982, 0.9062157995070059, 0.9042505451539194] + for (index, trials) in enumerate([1, 5, 10, 20, 50]) + result = SearchFDR.max_selection_null_probability(1.96, 0.95, trials; + null_density = value -> exp(-0.5 * value^2) / sqrt(2pi), + mixture_cdf = value -> SearchFDR.gaussian_trial_mixture_cdf(model, value)) + if isdefined(SearchFDR, :_SELECTION_KRONROD_WEIGHTS) + @test result.quadrature_absolute_error ≈ expected_errors[index] rtol = 1e-9 atol = 1e-11 + else + @test 0 <= result.quadrature_absolute_error <= 1e-9 + end + @test result.conditional_null_probability ≈ expected_probabilities[index] rtol = 1e-10 atol = 1e-12 + end +end + +@testset "Search probability domains and undefined posteriors" begin + for invalid in (-0.1, 1.1, true, NaN, Inf) + @test_throws ArgumentError SearchFDR.single_trial_false_discovery_rate(invalid, 0.2, 0.5) + @test_throws ArgumentError SearchFDR.single_trial_false_discovery_rate(0.1, invalid, 0.5) + @test_throws ArgumentError SearchFDR.single_trial_false_discovery_rate(0.1, 0.2, invalid) + @test_throws ArgumentError SearchFDR.GaussianTrialMixture(invalid, 1, 0, 1) + end + @test_throws ArgumentError SearchFDR.single_trial_false_discovery_rate(0, 1, 0.5) + @test_throws ArgumentError SearchFDR.family_level_false_discovery_rate(0, 1, 0.5, 5) + @test_throws ArgumentError SearchFDR.single_trial_false_discovery_rate(0, 0, 1) + for invalid in (0, -1, true, NaN, Inf) + @test_throws ArgumentError SearchFDR.GaussianTrialMixture(0.5, invalid, 0, 1) + @test_throws ArgumentError SearchFDR.GaussianTrialMixture(0.5, 1, 0, invalid) + end + @test_throws ArgumentError SearchFDR.GaussianTrialMixture(0.5, 1, NaN, 1) + for trials in (0, -1, true, 1.0) + @test_throws ArgumentError SearchFDR.family_level_false_discovery_rate(0.1, 0.2, 0.5, trials) + @test_throws ArgumentError SearchFDR.max_selection_family_errors(0.1, 0.2, 0.5, trials) + @test_throws ArgumentError SearchFDR.maximum_mixture_cdf(0.1, 0.2, 0.5, trials) + end + for probability in (0, 1) + @test_throws ArgumentError SearchFDR.max_selection_family_errors(0.1, 0.2, probability, 2) + @test_throws ArgumentError SearchFDR.fdr_nonidentification_witness(probability, 0.5) + @test_throws ArgumentError SearchFDR.fdr_nonidentification_witness(0.5, probability) + end + @test_throws ArgumentError SearchFDR.conditional_upper_tail_probability(0.4, 0.5) + @test_throws ArgumentError SearchFDR.conditional_upper_tail_probability(1, 1) + @test_throws ArgumentError SearchFDR.compare_single_and_family_fdr(0, 0.2, 0.5, 2; family_type_i_error = 0.2, family_type_ii_error = 0.1) + @test_throws ArgumentError SearchFDR.compare_single_and_family_fdr(0.1, 0.2, 0.5, 2; family_type_i_error = 0.2, family_type_ii_error = 0.1, relative_tolerance = -1) + model = SearchFDR.GaussianTrialMixture(0.5, 1, 1, 1) + for values in (Float64[], [true], [NaN], [Inf], [1im], ones(1, 2)) + @test_throws ArgumentError SearchFDR.gaussian_max_selection_log_likelihood(model, values, 2) + @test_throws ArgumentError SearchFDR.gaussian_search_adjusted_false_discovery_rate(model, values, 2) + end + @test SearchFDR.gaussian_max_selection_log_likelihood(model, [-1e200], 2) == -Inf + @test_throws ArgumentError SearchFDR.gaussian_trial_mixture_cdf(model, NaN) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = 1, mixture_cdf = value -> 0.5) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> -1, mixture_cdf = value -> 0.5) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> NaN, mixture_cdf = value -> 0.5) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> 1, mixture_cdf = value -> 1.1) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> 1, mixture_cdf = value -> 1.0) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> 1, mixture_cdf = value -> 0.5, max_subintervals = 0) + @test_throws ArgumentError SearchFDR.max_selection_null_probability(0, 0.5, 2; null_density = value -> 1, mixture_cdf = value -> 0.5, absolute_tolerance = 0) +end diff --git a/test/signal_sizing_regressions.jl b/test/signal_sizing_regressions.jl new file mode 100644 index 0000000..1ad474a --- /dev/null +++ b/test/signal_sizing_regressions.jl @@ -0,0 +1,76 @@ +using Test +using TOML +using RiskLabAI + +signal_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-11, atol = 1e-13) +signal_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(signal_equal(a, b) for (a, b) in zip(left, right)) +signal_ends(values) = Union{Int,Missing}[value == -1 ? missing : Int(value) for value in values] + +@testset "Shared probability sizes and inclusive bet intervals" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "signal_sizing_python_320.toml")) + for case in fixture["probability"] + @test signal_equal(B.probability_bet_size(Float64[case["probabilities"]...], Float64[case["sides"]...]), Float64[case["output"]...]) + end + for case in fixture["inclusive"] + prices, starts, ends = Int.(case["prices"]), Int.(case["starts"]), Int.(case["ends"]) + signals, sides, probabilities = Float64.(case["signals"]), Float64.(case["sides"]), Float64.(case["probabilities"]) + original = deepcopy((prices, starts, ends, signals, sides, probabilities)) + @test signal_equal(B.average_bet_sizes(prices, starts, ends, signals), Float64.(case["output"])) + @test signal_equal(B.strategy_bet_sizing(prices, starts, ends, sides, probabilities), Float64.(case["strategy"])) + @test (prices, starts, ends, signals, sides, probabilities) == original + end + @test B.average_bet_sizes(0:3, Int[], Int[], Float64[]) == zeros(4) +end + +@testset "Shared half-open signal averaging and discretized meta-signals" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "signal_sizing_python_320.toml")) + for case in fixture["active"] + starts, ends, values = Int.(case["starts"]), signal_ends(case["ends"]), Float64.(case["signals"]) + original = deepcopy((starts, ends, values)) + points, actual = B.avg_active_signals(starts, ends, values) + @test points == case["points"] + @test signal_equal(actual, Float64.(case["output"])) + @test signal_equal(B.mp_avg_active_signals(starts, ends, values, reverse(points)), reverse(Float64.(case["output"]))) + @test isequal((starts, ends, values), original) + end + for case in fixture["generated"] + starts, ends = Int.(case["starts"]), signal_ends(case["ends"]) + sides = case["meta"] ? Float64.(case["sides"]) : nothing + probability, prediction = Float64.(case["probabilities"]), Float64.(case["predictions"]) + original = deepcopy((starts, ends, sides, probability, prediction)) + points, actual = B.generate_signal(starts, ends, sides, probability, prediction, case["classes"], case["step"]) + @test points == case["points"] + @test signal_equal(actual, Float64.(case["output"])) + @test all(-1 .<= actual .<= 1) + @test isequal((starts, ends, sides, probability, prediction), original) + end + for case in fixture["discrete"] + @test signal_equal(B.discrete_signal(Float64.(case["values"]), case["step"]), Float64.(case["output"])) + end + @test B.generate_signal(Int[], Union{Int,Missing}[], nothing, Float64[], Float64[], 2, .1) == (Int[], Float64[]) + @test B.avg_active_signals(Int[], Union{Int,Missing}[], Float64[]) == (Int[], Float64[]) +end + +@testset "Shared sigmoid positions, inverses and public limit-price ranges" begin + B = RiskLabAI.Backtest + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "signal_sizing_python_320.toml")) + for case in fixture["sigmoid"] + @test signal_equal(B.bet_size_sigmoid(case["width"], case["divergence"]), case["output"]) + @test B.target_position(case["width"], 100. + case["divergence"], 100., case["maximum"]) == case["position"] + end + for case in fixture["inverse"] + value = B.inverse_price(case["forecast"], case["width"], case["size"]) + @test signal_equal(value, case["output"]) + if abs(case["size"]) < 1 + @test isapprox(B.bet_size_sigmoid(case["width"], case["forecast"]-value), case["size"]; rtol = 1e-10, atol = 1e-12) + end + end + for case in fixture["width"] + @test signal_equal(B.compute_sigmoid_width(case["divergence"], case["size"]), case["output"]) + end + for case in fixture["limit"] + @test signal_equal(B.limit_price(case["target"], case["current"], case["forecast"], case["width"], case["maximum"]), case["output"]) + end +end diff --git a/test/structural_break_regressions.jl b/test/structural_break_regressions.jl new file mode 100644 index 0000000..0d51319 --- /dev/null +++ b/test/structural_break_regressions.jl @@ -0,0 +1,130 @@ +using Test +using TOML +using Statistics +using RiskLabAI + +break_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 5e-8, atol = 5e-9) +break_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(break_equal(a, b) for (a, b) in zip(left, right)) +break_matrix(rows, columns) = isempty(rows) ? zeros(0, columns) : permutedims(reduce(hcat, Float64.(row) for row in rows)) + +@testset "Shared compressed-difference ADF designs and QR-checked OLS" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + for case in fixture["design"] + values = Float64[case["values"]...] + original = copy(values) + actual = F.prepare_data(values, case["constant"], case["lags"]) + @test break_equal(actual.y, Float64[case["y"]...]) + @test break_equal(actual.x, break_matrix(case["x"], case["columns"])) + @test actual.index == case["index"] + @test isequal(values, original) + end + for case in fixture["ols"] + x, y = break_matrix(case["x"], length(case["mean"])), Float64.(case["y"]) + for dependent in (y, reshape(y, :, 1)) + coefficients, covariance = F.compute_beta(dependent, x) + @test break_equal(vec(coefficients), Float64.(case["mean"])) + @test break_equal(covariance, break_matrix(case["covariance"], length(case["mean"]))) + end + end +end + +@testset "Shared expanding, forward and backward ADF windows" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + for case in fixture["expanding"] + actual = F.get_expanding_window_adf(Float64.(case["values"]), case["minimum"], case["constant"], case["lags"]) + @test actual.index == case["index"] + @test break_equal(actual.statistics, Float64.(case["output"])) + end + for case in fixture["sequences"] + values = Float64.(case["values"]) + original = copy(values) + arguments = (values, case["minimum"], case["constant"], case["lags"]) + sadf, bsadf = F.get_sadf_sequence(arguments...), F.get_bsadf_sequence(arguments...) + @test sadf.index == bsadf.index == case["index"] + @test break_equal(sadf.values, Float64.(case["sadf"])) + @test break_equal(bsadf.values, Float64.(case["bsadf"])) + @test break_equal(F.get_gsadf_statistic(arguments...), case["gsadf"]) + @test isequal(values, original) + end + @test isempty(F.get_sadf_sequence([1., 2.], 8).values) + @test isnan(F.get_gsadf_statistic(fill(1., 20), 8)) +end + +@testset "Shared PSY minimum windows and exhaustive episode dates" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + for case in fixture["minimum"] + @test F.psy_minimum_window(case["length"]) == case["output"] + end + for case in fixture["episodes"] + expected = Tuple{Int,Int}[Tuple(Int.(row)) for row in case["output"]] + @test F.get_bubble_episodes(Int.(case["index"]), Float64.(case["values"]), Float64.(case["critical"]); min_duration = case["minimum"]) == expected + @test F.get_bubble_episodes(Int.(case["index"]), Float64.(case["values"]), 0.; min_duration = case["minimum"]) == expected + end +end + +@testset "Shared normal null paths and PSY finite-sample quantiles" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + for case in fixture["critical"] + draws = break_matrix(case["draws"], case["length"]) + original = copy(draws) + actual = F.simulate_psy_critical_values(case["length"], case["minimum"]; constant = case["constant"], lags = case["lags"], n_simulations = case["simulations"], level = case["level"], normal_draws = draws) + for key in ("sample_length", "min_sample_length", "level") + @test getproperty(actual, Symbol(key)) == case["output"][key] + end + for key in ("sadf_global_cv", "gsadf_global_cv", "sadf_sequence_cv", "bsadf_sequence_cv") + @test break_equal(getproperty(actual, Symbol(key)), case["output"][key]) + end + @test draws == original + end + @test_throws DimensionMismatch F.simulate_psy_critical_values(17, 8; n_simulations = 2, normal_draws = zeros(2, 16)) + @test_throws ArgumentError F.simulate_psy_critical_values(17, 8; n_simulations = 0) +end + +@testset "Shared wild-bootstrap paths, corrected p-values and decisions" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + for case in fixture["robust"] + values = Float64.(case["values"]) + signs = break_matrix(case["signs"], length(values)-1) + original = deepcopy((values, signs)) + actual = F.volatility_robust_sadf(values; min_length = case["minimum"], n_bootstrap = case["bootstrap"], sign_draws = signs) + @test break_equal(actual.sadf, case["output"]["sadf"]) + @test break_equal(actual.gsadf, case["output"]["gsadf"]) + for key in ("sadf_pvalue", "gsadf_pvalue", "reject_sadf", "reject_gsadf") + @test getproperty(actual, Symbol(key)) == case["output"][key] + end + @test isequal((values, signs), original) + end + @test_throws ArgumentError F.volatility_robust_sadf(collect(1.:7.)) + @test_throws ArgumentError F.volatility_robust_sadf(collect(1.:17.); n_bootstrap = 2, sign_draws = zeros(2, 16)) +end + +@testset "Shared PELT supplier pruning and stable segment costs" begin + F = RiskLabAI.Features + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "structural_breaks_python_320.toml")) + disagreements = 0 + for case in fixture["pelt"] + values = Float64.(case["values"]) + original = copy(values) + actual = F.pelt_change_points(values; model = case["model"], penalty_multiplier = case["multiplier"], min_size = case["minimum"], jump = case["jump"]) + @test actual == case["output"] + @test values == original + if length(values) >= 2*case["minimum"] + endpoints = [0; actual; length(values)] + objective = 0. + for index in 1:(length(endpoints)-1) + segment = values[(endpoints[index]+1):endpoints[index+1]] + cost = case["model"] == "normal" ? length(segment)*log(var(segment; corrected = false)+1e-6) : length(segment)*var(segment; corrected = false) + objective += cost+case["multiplier"]*log(length(values)) + end + @test isapprox(objective, case["reference_objective"]; rtol = 1e-8, atol = 1e-7) + @test objective >= case["objective"]-1e-7*max(1., abs(case["objective"])) + disagreements += actual != case["unpruned_output"] + end + end + @test disagreements == 7 +end diff --git a/test/synthetic_regressions.jl b/test/synthetic_regressions.jl new file mode 100644 index 0000000..3af7b2d --- /dev/null +++ b/test/synthetic_regressions.jl @@ -0,0 +1,119 @@ +using Test +using TOML +using Random +using Statistics +using LinearAlgebra +using RiskLabAI + +synthetic_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 1e-11, atol = 1e-12) +synthetic_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(synthetic_equal(a, b) for (a, b) in zip(left, right)) +synthetic_matrix(rows, columns) = isempty(rows) ? zeros(0, columns) : reduce(vcat, permutedims.([Float64[row...] for row in rows])) + +@testset "Shared block covariances and supplied covariance draws" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "synthetic_python_320.toml")) + for case in fixture["blocks"] + actual = D.form_block_matrix(case["blocks"], case["size"], case["correlation"]) + @test synthetic_equal(actual, synthetic_matrix(case["output"], size(actual, 2))) + @test issymmetric(actual) + @test diag(actual) == ones(size(actual, 1)) + end + for case in fixture["covariance"] + normal, uniform = synthetic_matrix(case["normal"], case["factors"]), Float64.(case["uniform"]) + original = deepcopy((normal, uniform)) + actual = D.random_cov(case["columns"], case["factors"]; normal_draws = normal, uniform_draws = uniform) + @test synthetic_equal(actual, synthetic_matrix(case["output"], case["columns"])) + @test isposdef(actual) + @test isequal((normal, uniform), original) + end + for case in fixture["true_matrix"] + order, deviation, normal = case["order"], case["deviation"], case["normal"] + original = deepcopy((order, deviation, normal)) + mu, covariance = D.form_true_matrix(case["blocks"], case["size"], case["correlation"]; permutation = order, standard_deviation_draws = deviation, normal_draws = normal) + @test synthetic_equal(mu, case["mean"]) + @test synthetic_equal(covariance, synthetic_matrix(case["covariance"], length(mu))) + @test isequal((order, deviation, normal), original) + end + @test_throws ArgumentError D.random_cov(2, 1; normal_draws = zeros(1, 2)) + @test_throws ArgumentError D.random_cov(2, 1; uniform_draws = [0.1, -1.]) + @test_throws ArgumentError D.form_true_matrix(2, 2, .5; permutation = [1, 1, 3, 4]) + @test_throws ArgumentError D.form_true_matrix(1, 2, .5; standard_deviation_draws = [.1, .4]) +end + +@testset "Shared drift profiles and parameter padding" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "synthetic_python_320.toml")) + for case in fixture["burst"] + drift, volatility = D.drift_volatility_burst(case["length"], .02, -.03, .1, .15, case["alpha"], case["beta"]; explosion_filter_width = case["width"]) + @test synthetic_equal(drift, Float64[case["drift"]...]) + @test synthetic_equal(volatility, Float64[case["volatility"]...]) + end + for case in fixture["alignment"] + parameters = deepcopy(case["parameters"]) + aligned, length = D.align_params_length(parameters) + @test length == case["length"] + @test Set(keys(aligned)) == Set(keys(case["output"])) + for key in keys(aligned) + @test synthetic_equal(aligned[key], Float64[case["output"][key]...]) + end + @test isequal(parameters, case["parameters"]) + end +end + +@testset "Shared Heston Merton draws and semidefinite Gaussian law" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "synthetic_python_320.toml")) + negative_variances = 0 + for case in fixture["heston"] + p = case["parameters"] + gaussian, poisson, changes = synthetic_matrix(case["gaussian"], 3), case["poisson"], Bool.(case["changes"]) + original = deepcopy((p, gaussian, poisson, changes)) + args = (case["total_time"], case["length"], (p[key] for key in ("mu", "kappa", "theta", "xi", "rho", "lam", "m", "v"))..., changes) + actual = D.heston_merton_log_returns(args...; gaussian_draws = gaussian, poisson_draws = poisson) + @test synthetic_equal(actual, case["output"]) + direct = D.compute_log_returns(case["length"], p["mu"], p["kappa"], p["theta"], p["xi"], gaussian[:, 1], gaussian[:, 2], gaussian[:, 3], poisson, case["total_time"] / case["length"], sqrt(case["total_time"] / case["length"]), p["lam"], p["m"], p["v"], changes) + @test synthetic_equal(direct, case["output"]) + native = D.heston_merton_log_returns(args...; random_state = 82) + @test all(isfinite, native) + @test isequal(native, D.heston_merton_log_returns(args...; random_state = 82)) + @test isequal((p, gaussian, poisson, changes), original) + @test_throws ArgumentError D.heston_merton_log_returns(args...; gaussian_draws = zeros(case["length"], 2)) + @test_throws ArgumentError D.heston_merton_log_returns(args...; poisson_draws = fill(.5, case["length"])) + negative_variances += count(<(0), case["variance"]) + end + @test negative_variances > 0 + primitives = sqrt(3.) .* vcat(Matrix{Float64}(I, 3, 3), -Matrix{Float64}(I, 3, 3)) + for rho in (-1., -.4, 0., .8, 1.), dispersion in (0., -.2, .3) + outcomes = reduce(vcat, [permutedims(collect(D._heston_merton_gaussian(rho, .07, dispersion, row))) for row in eachrow(primitives)]) + @test isapprox(vec(mean(outcomes; dims = 1)), [0., 0., .07]; atol = 1e-14) + @test isapprox(cov(outcomes; corrected = false), [1. rho 0.; rho 1. 0.; 0. 0. dispersion^2]; atol = 1e-14) + end +end + +@testset "Shared Markov boundaries, regime expansion and missing prices" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "synthetic_python_320.toml")) + for case in fixture["categorical"] + @test D._sample_categorical(MersenneTwister(1), case["probabilities"]; uniform = case["uniform"]) == case["output"] + end + missing_prices, missing_drifts = 0, 0 + for case in fixture["regimes"] + transition = synthetic_matrix(case["transition"], 3) + gaussian = synthetic_matrix(case["gaussian"], 3) + original = deepcopy(case) + states = D._markov_simulate(transition, case["length"], 3; rng = MersenneTwister(1), initial_state = case["initial"], transition_uniforms = case["uniforms"]) + @test states == case["states"] + prices, path = D.generate_prices_from_regimes(case["regimes"], transition, .8, case["length"]; regime_order = case["order"], initial_state = case["initial"], transition_uniforms = case["uniforms"], gaussian_draws = gaussian, poisson_draws = case["poisson"]) + @test path == case["path"] + @test synthetic_equal(prices, case["prices"]) + @test isequal(case, original) + @test_throws ArgumentError D.generate_prices_from_regimes(case["regimes"], transition, .8, case["length"]; regime_order = ["growth", "stress", "growth"]) + missing_prices += count(isnan, prices) + missing_drifts += count(isnan, case["parameters"]["mu"]) + end + @test missing_prices > 0 + @test missing_drifts > missing_prices + @test_throws ArgumentError D._markov_simulate([.2 .2; .5 .5], 3, 2; rng = MersenneTwister(1)) + @test_throws ArgumentError D._markov_simulate([.5 .5; .5 .5], 3, 2; rng = MersenneTwister(1), initial_state = 0) + @test_throws ArgumentError D._markov_simulate([.5 .5; .5 .5], 3, 2; rng = MersenneTwister(1), transition_uniforms = [1., .2]) +end diff --git a/test/timegan_regressions.jl b/test/timegan_regressions.jl new file mode 100644 index 0000000..37ab194 --- /dev/null +++ b/test/timegan_regressions.jl @@ -0,0 +1,224 @@ +using Test +using TOML +using Random +using Zygote +using Statistics +using RiskLabAI +using RiskLabAI.Data: chronological_timegan_data, TimeGAN, latent_sequence, + timegan_losses, sample_timegan, fit_timegan, timegan_quality_report + +timegan_fixture_matrix(rows) = Float64.(permutedims(hcat(rows...))) +timegan_fixture_array(windows) = cat((reshape(timegan_fixture_matrix(window), 1, length(window), length(first(window))) for window in windows)...; dims = 1) + +function timegan_fixture_parameters(encoded) + recurrent(prefix, suffix = "") = (weight_ih = timegan_fixture_matrix(encoded[prefix * "weight_ih" * suffix]), + weight_hh = timegan_fixture_matrix(encoded[prefix * "weight_hh" * suffix]), + bias_ih = Float64.(encoded[prefix * "bias_ih" * suffix]), bias_hh = Float64.(encoded[prefix * "bias_hh" * suffix])) + output(prefix) = (weight = timegan_fixture_matrix(encoded[prefix * "weight"]), bias = Float64.(encoded[prefix * "bias"])) + return (embedding = (forward = recurrent("embedding.recurrent.", "_l0"), output = output("embedding.output.")), + recovery = (forward = recurrent("recovery.recurrent.", "_l0"), output = output("recovery.output.")), + generator_cell = recurrent("generator_cell."), generator_output = output("generator_output."), + discriminator = (forward = recurrent("discriminator.recurrent.", "_l0"), backward = recurrent("discriminator.recurrent.", "_l0_reverse"), output = output("discriminator.output."))) +end + +function timegan_test_parameters(actual::NamedTuple, expected::NamedTuple) + @test keys(actual) == keys(expected) + for name in keys(actual) + timegan_test_parameters(getproperty(actual, name), getproperty(expected, name)) + end +end + +timegan_test_parameters(actual, expected) = @test isapprox(actual, expected; rtol = 1e-10, atol = 1e-12) + +@testset "Chronological TimeGAN windows and training-only scaling" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "timegan_python_320.toml")) + for case in fixture["splits"] + data = timegan_fixture_matrix(case["series"]) + original = copy(data) + actual = chronological_timegan_data(data; training_end = case["training_end"], window_length = case["window_length"]) + @test actual.training == timegan_fixture_array(case["training"]) + @test actual.validation == timegan_fixture_array(case["validation"]) + @test actual.minimum == case["minimum"] + @test actual.scale == case["scale"] + @test actual.training_end == case["training_end"] + @test data == original + @test all(value -> 0 <= value <= 1, actual.training) + @test maximum(actual.validation) > 1 + changed = copy(data) + changed[case["training_end"]+1:end, :] .+= 100 + repeated = chronological_timegan_data(changed; training_end = case["training_end"], window_length = case["window_length"]) + @test actual.training == repeated.training + @test actual.minimum == repeated.minimum + @test actual.scale == repeated.scale + @test repeated.validation != actual.validation + @test isapprox(actual.training[1, :, :] .* actual.scale' .+ actual.minimum', data[1:case["window_length"], :]; rtol = 1e-10, atol = 1e-12) + end + for data in (ones(6), ones(2, 3, 1), zeros(0, 2), zeros(4, 0), fill(NaN, 6, 1), fill(Inf, 6, 1), fill(true, 6, 1), fill(1im, 6, 1)) + @test_throws ArgumentError chronological_timegan_data(data; training_end = 3, window_length = 2) + end + for (split, length) in ((0, 2), (1, 2), (5, 2), (3, 1), (3, 0), (3.0, 2), (3, true), (true, 2)) + @test_throws ArgumentError chronological_timegan_data(ones(6, 1); training_end = split, window_length = length) + end +end + +@testset "Shared-weight TimeGAN GRUs, teacher forcing and loss gradients" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "timegan_python_320.toml")) + for case in fixture["networks"] + parameters = timegan_fixture_parameters(case["parameters"]) + model = TimeGAN(case["features"], case["hidden_size"]; parameters) + data, noise = timegan_fixture_array(case["data"]), timegan_fixture_array(case["noise"]) + embedded = D._timegan_sequence_map(model.parameters.embedding, data) + reconstructed = D._timegan_sequence_map(model.parameters.recovery, embedded) + free = latent_sequence(model, noise) + forced = latent_sequence(model, noise; teacher = embedded) + real_logits = D._timegan_sequence_map(model.parameters.discriminator, embedded; sigmoid = false) + fake_logits = D._timegan_sequence_map(model.parameters.discriminator, free; sigmoid = false) + sampled = sample_timegan(model, size(noise, 1), size(noise, 2); seed = 52, normal_draws = noise) + for (name, output) in pairs((; embedded, reconstructed, free, forced, real_logits, fake_logits, sampled)) + @test isapprox(output, timegan_fixture_array(case["outputs"][string(name)]); rtol = 1e-10, atol = 1e-12) + end + losses = timegan_losses(model, data, noise) + for name in keys(losses) + @test isapprox(getproperty(losses, name), case["losses"][string(name)]; rtol = 1e-10, atol = 1e-12) + end + objective = value -> begin + losses = D._timegan_losses(value, data, noise) + losses.reconstruction + 0.7losses.supervised + 0.3losses.discriminator + 0.2losses.generator_adversarial + end + gradient = only(Zygote.gradient(objective, model.parameters)) + timegan_test_parameters(gradient, timegan_fixture_parameters(case["gradients"])) + changed = copy(data) + changed[:, end, :] .= 1 .- changed[:, end, :] + changed_embedding = D._timegan_sequence_map(model.parameters.embedding, changed) + @test embedded[:, 1:end-1, :] == changed_embedding[:, 1:end-1, :] + @test reconstructed[:, 1:end-1, :] == D._timegan_sequence_map(model.parameters.recovery, changed_embedding)[:, 1:end-1, :] + changed_noise = copy(noise) + changed_noise[:, end, :] .+= 3 + @test free[:, 1:end-1, :] == latent_sequence(model, changed_noise)[:, 1:end-1, :] + @test free[:, 1, :] == forced[:, 1, :] + @test forced == latent_sequence(model, noise; teacher = changed_embedding) + changed_teacher = copy(embedded) + changed_teacher[:, 1, :] .+= 1 + @test forced[:, 1, :] == latent_sequence(model, noise; teacher = changed_teacher)[:, 1, :] + @test forced[:, 2:end, :] != latent_sequence(model, noise; teacher = changed_teacher)[:, 2:end, :] + @test real_logits[:, 1, :] != D._timegan_sequence_map(model.parameters.discriminator, changed_embedding; sigmoid = false)[:, 1, :] + @test all(value -> 0 < value < 1, sampled) + @test model.parameters.embedding.forward.weight_ih !== parameters.embedding.forward.weight_ih + end + @test only(Zygote.gradient(D._timegan_logsigmoid, 0.0)) == 0.5 + @test isfinite(D._timegan_logsigmoid(-1000.0)) + @test D._timegan_logsigmoid(1000.0) == 0.0 +end + +@testset "TimeGAN fixed phases and shared-minibatch Adam updates" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "timegan_python_320.toml")) + for case in fixture["networks"] + model = TimeGAN(case["features"], case["hidden_size"]; parameters = timegan_fixture_parameters(case["parameters"])) + data = timegan_fixture_array(case["data"]) + draws = [(indices = Int.(draw["indices"]), noise = timegan_fixture_array(draw["noise"])) for draw in case["draws"]] + original_data, original_draws = copy(data), deepcopy(draws) + history = fit_timegan(model, data; embedding_steps = 2, supervised_steps = 2, joint_steps = 2, batch_size = 3, + learning_rate = case["learning_rate"], embedding_weight = case["embedding_weight"], generator_weight = case["generator_weight"], seed = 19, training_draws = draws) + @test length(history) == 6 + @test getproperty.(history, :phase) == ["embedding", "embedding", "supervised", "supervised", "joint", "joint"] + for (actual, expected) in zip(history, case["history"]) + for name in (:reconstruction, :supervised, :discriminator, :generator_adversarial) + @test isapprox(getproperty(actual, name), expected[string(name)]; rtol = 1e-10, atol = 1e-12) + end + end + timegan_test_parameters(model.parameters, timegan_fixture_parameters(case["trained_parameters"])) + evaluation_noise = timegan_fixture_array(case["evaluation_noise"]) + sample = sample_timegan(model, size(evaluation_noise, 1), size(evaluation_noise, 2); seed = 53, normal_draws = evaluation_noise) + @test isapprox(sample, timegan_fixture_array(case["evaluation_sample"]); rtol = 1e-10, atol = 1e-12) + @test data == original_data + @test draws == original_draws + end +end + +@testset "TimeGAN held-out descriptive quality contracts" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "timegan_python_320.toml")) + for case in fixture["quality"] + arrays = Tuple(timegan_fixture_array(case[name]) for name in ("real_training", "real_validation", "synthetic_training", "synthetic_validation")) + original = deepcopy(arrays) + actual = timegan_quality_report(arrays...) + for name in keys(actual) + result, expected = getproperty(actual, name), case["expected"][string(name)] + if result isa AbstractVector + @test length(result) == length(expected) + @test all(isapprox.(result, expected; rtol = 1e-10, atol = 1e-12, nans = true)) + else + @test isapprox(result, expected; rtol = 1e-10, atol = 1e-12) + end + end + rt, rv, st, sv = arrays + train = vcat(D._timegan_flatten(rt), D._timegan_flatten(st)) + center, scale = D._timegan_scaler(train) + @test isapprox(center, case["logistic"]["center"]; rtol = 1e-10, atol = 1e-12) + @test isapprox(scale, case["logistic"]["scale"]; rtol = 1e-10, atol = 1e-12) + features = (train .- center') ./ scale' + labels = [ones(size(rt, 1)); zeros(size(st, 1))] + weights = [fill(length(labels) / (2size(rt, 1)), size(rt, 1)); fill(length(labels) / (2size(st, 1)), size(st, 1))] + parameters = Float64.(case["logistic"]["parameters"]) + gradient = zeros(length(parameters)) + @test isfinite(D._timegan_logistic_loss!(gradient, parameters, features, labels, weights)) + @test maximum(abs, gradient) <= 1e-9 + @test arrays == original + @test 0 <= actual.balanced_discriminator_accuracy <= 1 + end + @test D._timegan_flatten(reshape(collect(1.0:12.0), 2, 3, 2)) == [1 7 3 9 5 11; 2 8 4 10 6 12] + @test D._timegan_scaler([1.0 1e16; 1.0 1e16+2])[2] == [1.0, 1.0] + for arrays in ((ones(1, 2, 1), ones(2, 2, 1), ones(2, 2, 1), ones(2, 2, 1)), + (ones(2, 2, 1), ones(2, 3, 1), ones(2, 2, 1), ones(2, 2, 1)), + ntuple(_ -> ones(2, 1, 1), 4), ntuple(_ -> fill(NaN, 2, 2, 1), 4)) + @test_throws ArgumentError timegan_quality_report(arrays...) + end +end + +@testset "TimeGAN local randomness and invalid training boundaries" begin + Random.seed!(710) + expected_global = rand(5) + Random.seed!(710) + first = TimeGAN(1, 2; seed = 73) + @test rand(5) == expected_global + second = TimeGAN(1, 2; seed = 73) + @test first.parameters == second.parameters + @test first.parameters != TimeGAN(1, 2; seed = 74).parameters + @test sample_timegan(first, 3, 3; seed = 7) == sample_timegan(second, 3, 3; seed = 7) + @test sample_timegan(first, 3, 3; seed = 7) != sample_timegan(first, 3, 3; seed = 8) + data = fill(0.5, 4, 2, 1) + settings = (embedding_steps = 1, supervised_steps = 1, joint_steps = 1, batch_size = 2, seed = 82) + Random.seed!(711) + expected_global = rand(5) + Random.seed!(711) + sample_timegan(first, 2, 2; seed = 1) + history = fit_timegan(first, data; settings...) + @test rand(5) == expected_global + @test history == fit_timegan(second, data; settings...) + @test first.parameters == second.parameters + for (features, hidden) in ((0, 2), (1, 0), (true, 2), (1, 2.0), (-1, 2)) + @test_throws ArgumentError TimeGAN(features, hidden) + end + for seed in (true, 1.5, big(1) << 65) + @test_throws ArgumentError TimeGAN(1, 2; seed) + end + @test_throws ArgumentError TimeGAN(1, 2; parameters = (;)) + for noise in (ones(2, 2), ones(2, 1, 2), ones(0, 2, 2), ones(2, 2, 3), ones(Float32, 2, 2, 2), fill(NaN, 2, 2, 2)) + @test_throws ArgumentError latent_sequence(first, noise) + end + @test_throws ArgumentError latent_sequence(first, ones(2, 2, 2); teacher = ones(3, 2, 2)) + @test_throws ArgumentError timegan_losses(first, fill(1.1, 2, 2, 1), ones(2, 2, 2)) + @test_throws ArgumentError timegan_losses(first, fill(0.5, 2, 2, 1), ones(3, 2, 2)) + @test_throws ArgumentError sample_timegan(first, 2, 1; seed = 1) + @test_throws ArgumentError sample_timegan(first, 2, 2; seed = 1, normal_draws = ones(3, 2, 2)) + initial = deepcopy(first.parameters) + for invalid in ((embedding_steps = 0,), (supervised_steps = 0,), (joint_steps = 0,), (batch_size = true,), (learning_rate = 0.0,), (embedding_weight = -1.0,), (generator_weight = Inf,)) + @test_throws ArgumentError fit_timegan(first, data; merge(settings, invalid)...) + end + @test_throws ArgumentError fit_timegan(first, fill(-0.1, 4, 2, 1); settings...) + @test_throws ArgumentError fit_timegan(first, data; settings..., training_draws = []) + @test_throws ArgumentError fit_timegan(first, data; settings..., training_draws = fill((indices = [0, 1], noise = ones(2, 2, 2)), 3)) + @test_throws ArgumentError fit_timegan(first, data; settings..., training_draws = fill((indices = [1, 1], noise = ones(2, 2, 3)), 3)) + @test first.parameters == initial +end diff --git a/test/trend_weight_regressions.jl b/test/trend_weight_regressions.jl new file mode 100644 index 0000000..91087bc --- /dev/null +++ b/test/trend_weight_regressions.jl @@ -0,0 +1,51 @@ +using Test +using TOML +using Dates +using DataFrames +using RiskLabAI + +trend_weight_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isinf(right) ? left == right : isapprox(left, right; rtol = 2e-8, atol = 2e-9) +trend_weight_equal(left::AbstractVector, right::AbstractVector) = length(left) == length(right) && all(trend_weight_equal(a, b) for (a, b) in zip(left, right)) + +@testset "Shared trend regression and exact forward-window selection" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "trend_weights_python_320.toml")) + for case in fixture["regression"] + @test trend_weight_equal(D.calculate_t_value_linear_regression(Float64[case["values"]...]), case["output"]) + end + date(offset) = DateTime(2020, 1, 1)+Minute(offset) + for case in fixture["scanning"] + values = Float64.(case["values"]) + index, molecule = date.(case["offsets"]), date.(case["molecule"]) + original = deepcopy((values, index, molecule)) + result = D.find_trend_using_trend_scanning(molecule, index, values, Tuple(Int.(case["span"]))) + @test result.event_start == date.(Int[case["starts"]...]) + @test result.end_time == date.(Int[case["ends"]...]) + @test trend_weight_equal(result.t_value, case["t"]) + @test result.trend == case["trend"] + @test all(!isnan(value) for value in result.trend) + @test isequal((values, index, molecule), original) + end +end + +@testset "Shared exhaustive uniqueness and chronological time-decay weights" begin + D = RiskLabAI.Data + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "trend_weights_python_320.toml")) + for case in fixture["uniqueness"] + matrix = zeros(case["rows"], case["columns"]) + for row in axes(matrix, 1), column in axes(matrix, 2) + matrix[row, column] = case["matrix"][row][column] + end + original = copy(matrix) + @test trend_weight_equal(D.calculate_average_uniqueness(matrix), case["output"]) + @test isequal(matrix, original) + end + for case in fixture["decay"] + values = Float64[case["values"]...] + original = copy(values) + @test trend_weight_equal(D.calculate_time_decay(values; clf_last_weight = case["last_weight"]), case["output"]) + @test isequal(values, original) + end + @test_throws ArgumentError D.calculate_time_decay([1., 2.]; clf_last_weight = -.01) + @test_throws ArgumentError D.calculate_time_decay([1., 2.]; clf_last_weight = 1.01) +end diff --git a/test/validation_regressions.jl b/test/validation_regressions.jl new file mode 100644 index 0000000..ca52f0a --- /dev/null +++ b/test/validation_regressions.jl @@ -0,0 +1,177 @@ +using Test +using TOML +using Dates +using RiskLabAI + +validation_matrix(values) = permutedims(reduce(hcat, [Float64.(row) for row in values])) +validation_splits(values) = [(Int[item["train"]...], Int[item["test"]...]) for item in values] +validation_paths(values) = Dict(parse(Int, key) => validation_splits(parts) for (key, parts) in values) +validation_equal(left::Number, right::Number) = isnan(right) ? isnan(left) : isapprox(left, right; rtol = 1e-12, atol = 1e-13) +validation_equal(left::AbstractArray, right::AbstractArray) = size(left) == size(right) && all(validation_equal(a, b) for (a, b) in zip(left, right)) + +function validation_validator(case) + V = RiskLabAI.Validation + groups = case["groups"] + kind = case["kind"] + if kind == "kfold" + return V.KFoldCV(groups; shuffle = case["shuffle"], random_seed = case["seed"]) + elseif kind == "walk" + return V.WalkForwardCV(groups; gap = case["gap"], max_train_size = case["max_train_size"]) + elseif kind == "purged" + return V.PurgedKFoldCV(groups, case["starts"], case["ends"]; embargo = case["embargo"]) + elseif kind == "combinatorial" + return V.CombinatorialPurgedCV(groups, case["test_groups"], case["starts"], case["ends"]; embargo = case["embargo"]) + end + return V.AdaptiveCombinatorialPurgedCV(groups, case["test_groups"], case["starts"], case["ends"]; embargo = case["embargo"], external_feature = case["feature"], n_subsplits = case["subsplits"], lower_quantile = case["lower"], upper_quantile = case["upper"], subtract_border_adjustments = case["subtract"]) +end + +function validation_predict_probability(model, x) + probability = 1.0 ./ (1.0 .+ exp.(-(model.alpha .* x[:, 1] .+ 0.2 * model.score))) + return hcat(1.0 .- probability, probability) +end + +validation_predict(model, x) = Int.(validation_predict_probability(model, x)[:, 2] .> 0.5) + +@testset "Shared exhaustive inclusive purge and observation embargo" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "validation_python_320.toml")) + for case in fixture["purge"] + actual = filtered_training_indices_with_embargo(case["starts"], case["ends"], Int[case["test"]...]; embargo = case["embargo"], continuous = case["continuous"]) + @test actual == case["train"] + end +end + +@testset "Shared combinatorial occurrence paths and adaptive boundaries" begin + V = RiskLabAI.Validation + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "validation_python_320.toml")) + for case in fixture["geometry"] + validator = validation_validator(case) + @test V._validation_segments(validator) == case["segments"] + combinations = collect(V.combinations(1:case["groups"], case["test_groups"])) + expected_locations = Dict(parse(Int, path) => [(pair[1], pair[2]) for pair in locations] for (path, locations) in case["locations"]) + @test V._path_locations(case["groups"], combinations) == expected_locations + @test cv_split(validator) == validation_splits(case["splits"]) + @test get_n_splits(validator) == length(case["splits"]) + @test backtest_paths(validator) == validation_paths(case["paths"]) + @test length(backtest_paths(validator)) == binomial(case["groups"]-1, case["test_groups"]-1) + if case["kind"] == "combinatorial" || case["subsplits"] >= 3 + @test all(reduce(vcat, [test for (_, test) in path]) == collect(1:length(case["starts"])) for path in values(backtest_paths(validator))) + end + end + ordering = V._path_locations(4, collect(V.combinations(1:4, 2))) + @test ordering[2] == [(1, 2), (2, 4), (3, 4), (4, 5)] + reversed = V._path_locations(4, reverse(collect(V.combinations(1:4, 2)))) + @test reversed[1] == [(1, 4), (2, 2), (3, 1), (4, 1)] +end + +@testset "Shared K-fold, purged and walk-forward index contracts" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "validation_python_320.toml")) + for case in fixture["basic"] + validator = validation_validator(case) + count = length(case["starts"]) + expected = validation_splits(case["splits"]) + if case["kind"] == "kfold" + permutation = case["shuffle"] ? case["permutation"] : nothing + @test cv_split(validator, count; permutation) == expected + @test backtest_paths(validator, count; permutation) == Dict(1 => expected) + elseif case["kind"] == "walk" + @test cv_split(validator, count) == expected + @test backtest_paths(validator, count) == Dict(1 => expected) + else + @test cv_split(validator) == expected + @test backtest_paths(validator) == Dict(1 => expected) + end + @test get_n_splits(validator) == case["groups"] + end +end + +@testset "Shared caller-estimator predictions preserve rows, weights and model reuse" begin + fixture = TOML.parsefile(joinpath(@__DIR__, "fixtures", "validation_python_320.toml")) + for case in fixture["predictions"] + validator = validation_validator(case) + x, y, weights = validation_matrix(case["x"]), Int.(case["y"]), Float64.(case["weights"]) + originals = deepcopy((x, y, weights)) + prototype = (alpha = 0.7, visits = 7) + calls = [] + fit_estimator = function(model, train_x, train_y, train_weights) + push!(calls, (train = Int.(train_x[:, 3]), weights = copy(train_weights))) + visits = model.visits + 1 + score = sum(train_weights .* (train_y .+ 0.1 .* train_x[:, 2])) / sum(train_weights) + 0.01 * visits + return (alpha = model.alpha, visits = visits, score = score) + end + clone_estimator = case["kind"] == "adaptive" ? model -> (alpha = model.alpha, visits = 0) : deepcopy + permutation = case["kind"] == "kfold" && case["shuffle"] ? case["permutation"] : nothing + arguments = (clone_estimator = clone_estimator, fit_estimator = fit_estimator, predict = validation_predict, predict_proba = validation_predict_probability, sample_weights = case["weight_mode"] == "omitted" ? nothing : weights, permutation = permutation) + actual = backtest_predictions(validator, prototype, x, y; arguments...) + @test length(calls) == length(case["calls"]) + for (observed, expected) in zip(calls, case["calls"]) + @test observed.train == expected["train"] + @test observed.weights == expected["weights"] + end + probabilities = backtest_predictions(validator, prototype, x, y; arguments..., predict_probability = true) + @test Set(keys(actual)) == Set(parse.(Int, collect(keys(case["predictions"])))) + for key in keys(actual) + @test validation_equal(actual[key], Float64.(case["predictions"][string(key)])) + @test validation_equal(probabilities[key], validation_matrix(case["probabilities"][string(key)])) + end + @test prototype == (alpha = 0.7, visits = 7) + @test (x, y, weights) == originals + end +end + +@testset "Validator callback failures, snapshots and multiple datasets" begin + starts = collect(1:24) + ends = starts .+ 1 + feature = sin.(starts) + adaptive = AdaptiveCombinatorialPurgedCV(4, 2, starts, ends; external_feature = feature, n_subsplits = 3) + original = cv_split(adaptive) + feature .= 100 + starts[1] = -100 + ends[1] = 100 + @test cv_split(adaptive) == original + dates = Date(2020, 1, 1) .+ Day.(0:23) + @test filtered_training_indices_with_embargo(dates, dates .+ Day(2), [5, 6, 7]; embargo = .1) == filtered_training_indices_with_embargo(collect(0:23), collect(2:25), [5, 6, 7]; embargo = .1) + x = hcat(sin.(1:24), cos.(1:24), collect(1:24)) + y = Int.(iseven.(1:24)) + purged = PurgedKFoldCV(4, collect(1:24), collect(2:25)) + prototype = (alpha = .7, visits = 0) + fit_estimator = (model, x, y, weights) -> (alpha = model.alpha, score = sum(weights .* y) / sum(weights)) + arguments = (clone_estimator = deepcopy, fit_estimator = fit_estimator, predict = validation_predict, predict_proba = validation_predict_probability) + validators = Dict("purged" => purged, "adaptive" => adaptive) + datasets = Dict(key => copy(x) for key in keys(validators)) + labels = Dict(key => copy(y) for key in keys(validators)) + estimators = Dict(key => prototype for key in keys(validators)) + weights = Dict(key => collect(1.0:24.0) for key in keys(validators)) + predictions = backtest_predictions(validators, estimators, datasets, labels; sample_weights = weights, arguments...) + @test Set(keys(predictions)) == Set(keys(validators)) + @test all(predictions[key] == backtest_predictions(validators[key], prototype, x, y; sample_weights = weights[key], arguments...) for key in keys(validators)) + @test backtest_paths(validators) == Dict(key => backtest_paths(value) for (key, value) in validators) + @test backtest_paths(validators, datasets) == backtest_paths(validators) + @test backtest_paths(Dict("fold" => KFoldCV(4)), Dict("fold" => x))["fold"] == backtest_paths(KFoldCV(4), 24) + @test_throws ArgumentError backtest_predictions(validators, Dict("wrong" => prototype), datasets, labels; arguments...) + @test_throws ArgumentError backtest_predictions(validators, estimators, datasets, labels; sample_weights = Dict("wrong" => ones(24)), arguments...) + @test_throws DimensionMismatch backtest_predictions(purged, prototype, x, y[1:23]; arguments...) + @test_throws DimensionMismatch backtest_predictions(purged, prototype, x, y; sample_weights = ones(23), arguments...) + @test_throws ArgumentError backtest_predictions(purged, prototype, x, y; sample_weights = fill(-1.0, 24), arguments...) + @test_throws ArgumentError backtest_predictions(purged, prototype, x, y; predict_probability = true, clone_estimator = deepcopy, fit_estimator = fit_estimator, predict = validation_predict) + @test_throws DimensionMismatch backtest_predictions(purged, prototype, x, y; clone_estimator = deepcopy, fit_estimator = fit_estimator, predict = (model, data) -> zeros(size(data, 1), 1)) + @test_throws DimensionMismatch backtest_predictions(purged, prototype, x, y; clone_estimator = deepcopy, fit_estimator = fit_estimator, predict = (model, data) -> zeros(size(data, 1)+1)) + failures = Ref(0) + failing_fit = function(model, x, y, weights) + failures[] += 1 + throw(ArgumentError("estimator does not support these weights")) + end + @test_throws ArgumentError backtest_predictions(purged, prototype, x, y; clone_estimator = deepcopy, fit_estimator = failing_fit, predict = validation_predict) + @test failures[] == 1 + @test_throws ArgumentError cv_split(KFoldCV(4; shuffle = true), 24; permutation = fill(1, 24)) + @test_throws ArgumentError cv_split(KFoldCV(4), 24; permutation = collect(1:24)) + @test_throws ArgumentError cv_split(KFoldCV(0), 24) + @test_throws ArgumentError cv_split(WalkForwardCV(4; gap = -1), 24) + @test_throws ArgumentError cv_split(WalkForwardCV(4; max_train_size = 0), 24) + @test_throws DimensionMismatch filtered_training_indices_with_embargo([1, 2], [1], [1]) + @test_throws ArgumentError filtered_training_indices_with_embargo([2, 1], [2, 1], [1]) + @test_throws ArgumentError filtered_training_indices_with_embargo([1, 2], [1, 2], [3]) + @test_throws ArgumentError filtered_training_indices_with_embargo([1, 2], [1, 2], [1]; embargo = -0.1) + @test_throws ArgumentError AdaptiveCombinatorialPurgedCV(4, 2, collect(1:8), collect(1:8); external_feature = ones(8)) + @test_throws ArgumentError AdaptiveCombinatorialPurgedCV(4, 2, collect(1:24), collect(1:24); external_feature = fill(NaN, 24)) + @test_throws ArgumentError AdaptiveCombinatorialPurgedCV(4, 2, collect(1:24), collect(1:24); external_feature = ones(24), lower_quantile = .8, upper_quantile = .2) +end