diff --git a/.gitignore b/.gitignore index c9a03a4..4e75673 100644 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,8 @@ admin/opentofu/.terraform.tfstate.lock.info admin/opentofu/generated/ admin/opentofu/terraform.tfstate* admin/opentofu/terraform.tfvars + +team_private_key.pem +clean_train.ipynb +weather_transformation.py +submission/clean.py \ No newline at end of file diff --git a/main.ipynb b/main.ipynb new file mode 100644 index 0000000..64ab829 --- /dev/null +++ b/main.ipynb @@ -0,0 +1,1267 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1194, + "id": "76f1553e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "from urllib.parse import unquote\n", + "\n", + "\n", + "import sys\n", + "sys.path.append(\"./submission\")\n", + "import clean\n", + "\n", + "df = pd.read_csv(\"./data/test_features.csv\")\n", + "df = df[['record_id','origin_station',\n", + " 'destination_station',\n", + " 'district',\n", + " 'encoded_transport',\n", + " 'day_of_week',\n", + " 'is_holiday',\n", + " 'weather_condition',\n", + " 'country_code']]" + ] + }, + { + "cell_type": "markdown", + "id": "59f0d260", + "metadata": {}, + "source": [ + "Lucciano: Cleaning Origin and Destination" + ] + }, + { + "cell_type": "code", + "execution_count": 1195, + "id": "63db2f09", + "metadata": {}, + "outputs": [], + "source": [ + "# Canonical station -> ordered regexes matched against the SQUASHED key\n", + "# (lowercased, url-decoded, wrappers stripped, all non-alphanumerics removed).\n", + "# Order matters: more specific patterns first. ^x$ pins the short codes.\n", + "_STATION_RULES = [\n", + " (\"Causeway Bay\", r\"causewaybay|cwbhub|^cwb$|^cb$\"),\n", + " (\"Central\", r\"central|stncentral|censt|^cstation$|^cstn$|^cs$\"),\n", + " (\"Admiralty\", r\"admiralt|admxfer|^adm$\"),\n", + " (\"Sha Tin\", r\"shatin|shatinterm|^stin$|^stn$|^st$\"),\n", + " (\"Mong Kok\", r\"mongkok|mkhub|^mk$\"),\n", + " (\"Tsim Sha Tsui\", r\"tsimshatsui|tsteast|^tst$\"),\n", + " (\"North Point\", r\"northpoint|northpt|npferry|^np$\"),\n", + " (\"Tsuen Wan\", r\"tsuenwan|tswline|^twn$|^tswan$|^tw$\"),\n", + " (\"Kennedy Town\", r\"kennedyt|ktownstop|^ktown$|^kt$\"),\n", + " (\"Wan Chai\", r\"wanchai|wanchaistop|^wchai$|^wc$\"),\n", + "]\n", + "# Markers/non-stations to skip. unk = unknown placeholder; depot/workshop/audithub\n", + "# are internal sites, not passenger stations -> treated as missing.\n", + "_NOISE = re.compile(r\"^(unk|audithub|depot|workshop)$\")\n", + "\n", + "\n", + "def clean_station(s: pd.Series) -> pd.Series:\n", + " def clean_value(v):\n", + " if not isinstance(v, str):\n", + " return pd.NA\n", + " x = unquote(unquote(v)).strip().lower() # decode (handles %2520), trim\n", + " for tok in re.split(r\"[:|=\\-]+\", x):\n", + " key = re.sub(r\"[^a-z0-9]\", \"\", tok) # squash: drop spaces/./%20 etc\n", + " if not key or _NOISE.match(key):\n", + " continue\n", + " for canon, pat in _STATION_RULES:\n", + " if re.search(pat, key):\n", + " return canon\n", + " return pd.NA # no station token -> missing\n", + "\n", + " return s.map(clean_value)\n", + "\n", + "df['origin_station'] = clean_station(df['origin_station'])\n", + "df['destination_station'] = clean_station(df['destination_station'])" + ] + }, + { + "cell_type": "markdown", + "id": "2b19ab53", + "metadata": {}, + "source": [ + "Cleaning District" + ] + }, + { + "cell_type": "code", + "execution_count": 1196, + "id": "f84f4f73", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean district column values of prefix/suffix patterns\n", + "\n", + "def clean_district(df):\n", + " # Strip whitespace\n", + " df['district'] = df['district'].str.strip()\n", + " \n", + " # Remove prefix patterns (e.g., \"D123\" → \"123\")\n", + " prefix_pattern = r'(D|zone|hold|district|Legacy|region|Leg|LEG)[^a-zA-Z0-9]+'\n", + " df['district'] = df['district'].str.replace(prefix_pattern, '', regex=True)\n", + " \n", + " # Remove suffix patterns (e.g., \"central_zone\" → \"central\")\n", + " suffix_pattern = r'[_|](?:new|core|zone|cluster|sector|OPS)'\n", + " df['district'] = df['district'].str.replace(suffix_pattern, '', regex=True)\n", + " \n", + " # Normalize missing/weird values into NaN\n", + " unknown_values = [\n", + " 'Legacy', 'unknown', '??', 'mismatch', 'Null', 'legacy',\n", + " 'Internal', 'operations', 'audit', 'eh', 'unk', ''\n", + " ]\n", + " df['district'] = df['district'].str.replace(r'^(operations|audit|unk|eh)\\s*$', '', regex=True, case=False)\n", + "\n", + " df['district'] = df['district'].replace(unknown_values, np.nan)\n", + "\n", + " return df\n", + "\n", + "df = clean_district(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 1197, + "id": "3be9e915", + "metadata": {}, + "outputs": [], + "source": [ + "#map district variants into unified category\n", + "\n", + "district_variant_mapping = [\n", + " (\"Central and Western\", r\"cw|cwbhub|^cwb$|^cb$|central_and_western|central and western\"),\n", + " (\"Tsuen Wan\", r\"tw|tsw|tsuen_wan|tsuen wan\"),\n", + " (\"Yau Tsim Mong\", r\"YTM|ytm|yau tsim mong|yau_tsim_mong\"),\n", + " (\"Sha Tin\", r\"ST|sha tin|shatin|st|sha_tin\"),\n", + " (\"Eastern\", r\"eastern|east_harbour\"), \n", + " (\"Wan Chai\", r\"wanchai|wanchaistop|^wchai$|^wc$|wan chai|wan_chai\")\n", + "]\n", + "\n", + "def map_district(value):\n", + " if pd.isna(value):\n", + " return value\n", + " value = str(value).lower().strip()\n", + " for full_name, pattern in district_variant_mapping:\n", + " if pattern and re.search(pattern, value):\n", + " return full_name\n", + " return value # return original if no match\n", + "\n", + "df['district'] = df['district'].apply(map_district)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1198, + "id": "9a9bc0f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "district\n", + "Central and Western 543\n", + "Yau Tsim Mong 431\n", + "Wan Chai 405\n", + "Sha Tin 281\n", + "Tsuen Wan 193\n", + "NaN 127\n", + "Eastern 113\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 1198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# map district NANs from known origin station:\n", + "\n", + "station_district_mapping = {\n", + " 'Central and Western': ('Central', 'Admiralty', 'Kennedy Town'),\n", + " 'Wan Chai': ('Wan Chai', 'Causeway Bay'),\n", + " 'Yau Tsim Mong': ('Mong Kok', 'Tsim Sha Tsui'),\n", + " 'Sha Tin': ('Sha Tin',),\n", + " 'Tsuen Wan': ('Tsuen Wan',),\n", + " 'Eastern': ('North Point',)\n", + "}\n", + "\n", + "\n", + "def fill_district_from_station(df, station_col='origin_station', district_col='district'):\n", + " # Build reverse lookup: station -> district\n", + " station_to_district = {\n", + " station.lower(): district\n", + " for district, stations in station_district_mapping.items()\n", + " for station in stations\n", + " }\n", + " \n", + " # Fill nulls with station lookup\n", + " df[district_col] = df[district_col].fillna(\n", + " df[station_col].str.lower().str.strip().map(station_to_district)\n", + " )\n", + " return df\n", + "\n", + "# Usage\n", + "df = fill_district_from_station(df)\n", + "\n", + "df['district'].value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 1199, + "id": "8bc21583", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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origin_stationdestination_stationdistrict
516NaNNaNNaN
40Kennedy TownMong KokCentral and Western
1176CentralKennedy TownCentral and Western
429Mong KokCentralYau Tsim Mong
736Tsim Sha TsuiNorth PointYau Tsim Mong
150Kennedy TownMong KokCentral and Western
368Tsim Sha TsuiCauseway BayYau Tsim Mong
1897NaNNaNNaN
674North PointKennedy TownEastern
1322Kennedy TownAdmiraltyCentral and Western
1252Causeway BayNorth PointWan Chai
1801NaNNaNNaN
807Causeway BayTsim Sha TsuiWan Chai
1534CentralNorth PointCentral and Western
896NaNNaNNaN
842Sha TinTsim Sha TsuiSha Tin
1985Tsuen WanAdmiraltyTsuen Wan
1792CentralMong KokCentral and Western
47North PointTsim Sha TsuiEastern
621Mong KokCauseway BayYau Tsim Mong
177CentralMong KokCentral and Western
388North PointTsuen WanEastern
1502Causeway BayNorth PointWan Chai
1958AdmiraltyTsuen WanCentral and Western
1494Mong KokNorth PointYau Tsim Mong
156CentralTsuen WanCentral and Western
1704CentralSha TinCentral and Western
1753Causeway BayKennedy TownWan Chai
4North PointCentralSha Tin
2003Wan ChaiMong KokWan Chai
\n", + "
" + ], + "text/plain": [ + " origin_station destination_station district\n", + "516 NaN NaN NaN\n", + "40 Kennedy Town Mong Kok Central and Western\n", + "1176 Central Kennedy Town Central and Western\n", + "429 Mong Kok Central Yau Tsim Mong\n", + "736 Tsim Sha Tsui North Point Yau Tsim Mong\n", + "150 Kennedy Town Mong Kok Central and Western\n", + "368 Tsim Sha Tsui Causeway Bay Yau Tsim Mong\n", + "1897 NaN NaN NaN\n", + "674 North Point Kennedy Town Eastern\n", + "1322 Kennedy Town Admiralty Central and Western\n", + "1252 Causeway Bay North Point Wan Chai\n", + "1801 NaN NaN NaN\n", + "807 Causeway Bay Tsim Sha Tsui Wan Chai\n", + "1534 Central North Point Central and Western\n", + "896 NaN NaN NaN\n", + "842 Sha Tin Tsim Sha Tsui Sha Tin\n", + "1985 Tsuen Wan Admiralty Tsuen Wan\n", + "1792 Central Mong Kok Central and Western\n", + "47 North Point Tsim Sha Tsui Eastern\n", + "621 Mong Kok Causeway Bay Yau Tsim Mong\n", + "177 Central Mong Kok Central and Western\n", + "388 North Point Tsuen Wan Eastern\n", + "1502 Causeway Bay North Point Wan Chai\n", + "1958 Admiralty Tsuen Wan Central and Western\n", + "1494 Mong Kok North Point Yau Tsim Mong\n", + "156 Central Tsuen Wan Central and Western\n", + "1704 Central Sha Tin Central and Western\n", + "1753 Causeway Bay Kennedy Town Wan Chai\n", + "4 North Point Central Sha Tin\n", + "2003 Wan Chai Mong Kok Wan Chai" + ] + }, + "execution_count": 1199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['origin_station','destination_station','district']].sample(30)" + ] + }, + { + "cell_type": "markdown", + "id": "82df77a7", + "metadata": {}, + "source": [ + "Cleaning Day of Week" + ] + }, + { + "cell_type": "code", + "execution_count": 1200, + "id": "60b0a2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "day_of_week\n", + "Fri 366\n", + "Thu 329\n", + "Wed 324\n", + "Mon 308\n", + "Tue 290\n", + "Sat 281\n", + "Sun 195\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 1200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['day_of_week'] = df['day_of_week'].str.title()\n", + "df['day_of_week'].value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 1201, + "id": "c69375a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['origin_station',\n", + " 'destination_station',\n", + " 'district',\n", + " 'transport_type',\n", + " 'transport_detail',\n", + " 'mode',\n", + " 'service_level',\n", + " 'operator',\n", + " 'day_of_week',\n", + " 'is_holiday',\n", + " 'weather_condition',\n", + " 'country_code']" + ] + }, + "execution_count": 1201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean.SUBMISSION_FEATURES" + ] + }, + { + "cell_type": "markdown", + "id": "a02d34cf", + "metadata": {}, + "source": [ + "Cleaning encode_transport" + ] + }, + { + "cell_type": "code", + "execution_count": 1202, + "id": "30a35039", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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encoded_transporttransport_typetransport_detailmodeservice_leveloperator
229UNK::bus::loc::kowloonbusgenerallocalNaNkowloon
1861kind,tram-apt,mode,loc,tier,std,op,citybusNaNgeneralNaNNaNcitybus
1715srcfeed::bus::loc::prem::citybus::deltabusgenerallocalpremiumNaN
226bus-apt__MISS__citybusbusairportNaNNaNcitybus
1685type=bus-xhbr;mode=exp;tier=missing;op=unknownbuscrossharbourexpressNaNNaN
930feed=OPS|kind=bus|run=loc|tier=prem|op=citybusbusgenerallocalpremiumcitybus
1397bus-loc-std-citybus-cbusgenerallocalstandardcitybus
1118ferry-apt.loc.std.ferryhkferryairportlocalstandardferryhk
1592kind,tram,mode,loc,tier,std,op,citybusNaNgeneralNaNNaNcitybus
709UNK::bus::loc::citybusbusgenerallocalNaNcitybus
1824kind,bus,mode,exp,tier,prem,op,citybusNaNgeneralNaNNaNcitybus
1979UNK::bus::loc::kowloonbusgenerallocalNaNkowloon
140L2::bus::loc::std::kowloon::tailbusgenerallocalstandardkowloon
2072[bus][run=loc][tier=std][op=citybus][sys=L2]NaNgenerallocalstandardcitybus
2084ferry-loc-std-ferryhk-cferrygenerallocalstandardferryhk
177kind=bus-night|kind=alt|run=?|tier=?|op=citybusbusnightNaNNaNcitybus
1319bus-loc-std-kowloon-cbusgenerallocalstandardkowloon
1132busexpstdkowloonbusgeneralexpressstandardkowloon
941feed=OPS|kind=bus|run=loc|tier=std|op=citybusbusgenerallocalstandardcitybus
1852kind,bus,mode,loc,tier,std,op,kowloonNaNgeneralNaNNaNkowloon
\n", + "
" + ], + "text/plain": [ + " encoded_transport transport_type \\\n", + "229 UNK::bus::loc::kowloon bus \n", + "1861 kind,tram-apt,mode,loc,tier,std,op,citybus NaN \n", + "1715 srcfeed::bus::loc::prem::citybus::delta bus \n", + "226 bus-apt__MISS__citybus bus \n", + "1685 type=bus-xhbr;mode=exp;tier=missing;op=unknown bus \n", + "930 feed=OPS|kind=bus|run=loc|tier=prem|op=citybus bus \n", + "1397 bus-loc-std-citybus-c bus \n", + "1118 ferry-apt.loc.std.ferryhk ferry \n", + "1592 kind,tram,mode,loc,tier,std,op,citybus NaN \n", + "709 UNK::bus::loc::citybus bus \n", + "1824 kind,bus,mode,exp,tier,prem,op,citybus NaN \n", + "1979 UNK::bus::loc::kowloon bus \n", + "140 L2::bus::loc::std::kowloon::tail bus \n", + "2072 [bus][run=loc][tier=std][op=citybus][sys=L2] NaN \n", + "2084 ferry-loc-std-ferryhk-c ferry \n", + "177 kind=bus-night|kind=alt|run=?|tier=?|op=citybus bus \n", + "1319 bus-loc-std-kowloon-c bus \n", + "1132 busexpstdkowloon bus \n", + "941 feed=OPS|kind=bus|run=loc|tier=std|op=citybus bus \n", + "1852 kind,bus,mode,loc,tier,std,op,kowloon NaN \n", + "\n", + " transport_detail mode service_level operator \n", + "229 general local NaN kowloon \n", + "1861 general NaN NaN citybus \n", + "1715 general local premium NaN \n", + "226 airport NaN NaN citybus \n", + "1685 crossharbour express NaN NaN \n", + "930 general local premium citybus \n", + "1397 general local standard citybus \n", + "1118 airport local standard ferryhk \n", + "1592 general NaN NaN citybus \n", + "709 general local NaN citybus \n", + "1824 general NaN NaN citybus \n", + "1979 general local NaN kowloon \n", + "140 general local standard kowloon \n", + "2072 general local standard citybus \n", + "2084 general local standard ferryhk \n", + "177 night NaN NaN citybus \n", + "1319 general local standard kowloon \n", + "1132 general express standard kowloon \n", + "941 general local standard citybus \n", + "1852 general NaN NaN kowloon " + ] + }, + "execution_count": 1202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Parse encoded_transport into 5 columns\n", + "_SPECIAL_NO_TRANSPORT = {\n", + " 'maintenance_shift', 'admin_move', 'staff_shuttle', 'test_run',\n", + " 'unk', 'encoded_transport'\n", + "}\n", + "\n", + "def parse_encoded_transport(s):\n", + " if not isinstance(s, str):\n", + " return {'transport_type': pd.NA, 'transport_detail': pd.NA,\n", + " 'mode': pd.NA, 'service_level': pd.NA, 'operator': pd.NA}\n", + " v = s.strip().lower()\n", + " if v in _SPECIAL_NO_TRANSPORT or not v:\n", + " return {'transport_type': pd.NA, 'transport_detail': pd.NA,\n", + " 'mode': pd.NA, 'service_level': pd.NA, 'operator': pd.NA}\n", + "\n", + " # transport_type - use flexible separators, handle word boundaries around them\n", + " m = re.search(r'(?:^|[_\\-|\\.::=])(tram|ferry|bus)(?:[_\\-|\\.::]|$)', v)\n", + " if not m:\n", + " # Try concatenated at start (e.g., buslocstdkowloon)\n", + " m = re.search(r'^(tram|ferry|bus)(loc|exp)(std|prem)', v)\n", + " if m:\n", + " transport_type = m.group(1)\n", + " else:\n", + " transport_type = pd.NA\n", + "\n", + " # transport_detail\n", + " m = re.search(r'(?:^|[_\\-|\\.::=])(tram|ferry|bus)[_\\-]?(apt|ngt|night|xhbr)', v)\n", + " if m:\n", + " detail_map = {'apt': 'airport', 'ngt': 'night', 'night': 'night', 'xhbr': 'crossharbour'}\n", + " transport_detail = detail_map.get(m.group(2), 'general')\n", + " else:\n", + " transport_detail = 'general'\n", + "\n", + " # mode - check multiple patterns\n", + " m = re.search(r'\\b(?:run|mode)[=:](\\w+)', v)\n", + " if m:\n", + " mode_map = {'loc': 'local', 'exp': 'express'}\n", + " mode = mode_map.get(m.group(1), pd.NA)\n", + " else:\n", + " # Try standalone patterns with various separators: _loc_, .loc., |loc|, ::loc::\n", + " m = re.search(r'(?:^|[_\\-|\\.::])(loc|exp)(?:[_\\-|\\.::]|$)', v)\n", + " if m:\n", + " mode = 'local' if m.group(1) == 'loc' else 'express'\n", + " else:\n", + " # Concatenated: buslocstdkowloon, busexppremcitybus\n", + " m = re.search(r'(tram|ferry|bus)(loc|exp)(std|prem)', v)\n", + " if m:\n", + " mode = 'local' if m.group(2) == 'loc' else 'express'\n", + " else:\n", + " mode = pd.NA\n", + "\n", + " # service_level - check multiple patterns\n", + " m = re.search(r'\\b(?:tier|svc)[=:](\\w+)', v)\n", + " if m:\n", + " sl_map = {'std': 'standard', 'prem': 'premium', 'missing': pd.NA}\n", + " service_level = sl_map.get(m.group(1), pd.NA)\n", + " else:\n", + " # Try standalone patterns with various separators: _std_, .std., |std|, ::std::\n", + " m = re.search(r'(?:^|[_\\-|\\.::,])(std|prem)(?:[_\\-|\\.::]|$)', v)\n", + " if m:\n", + " service_level = 'standard' if m.group(1) == 'std' else 'premium'\n", + " else:\n", + " # Concatenated: buslocstdkowloon, busexppremcitybus\n", + " m = re.search(r'(tram|ferry|bus)(loc|exp)(std|prem)', v)\n", + " if m:\n", + " service_level = 'standard' if m.group(3) == 'std' else 'premium'\n", + " else:\n", + " service_level = pd.NA\n", + "\n", + " # operator\n", + " m = re.search(r'\\b(?:op|owner)[=:](\\w+)', v)\n", + " if m:\n", + " operator = m.group(1)\n", + " else:\n", + " m = re.search(r'(? extract operator at end\n", + " m = re.search(r'(citybus|kowloon|ferryhk)$', str(operator))\n", + " if m:\n", + " operator = m.group(1)\n", + "\n", + " # Fix truncated operators (missing first letter)\n", + " if operator == 'itybus':\n", + " operator = 'citybus'\n", + " elif operator == 'owloon':\n", + " operator = 'kowloon'\n", + " elif operator == 'erryhk':\n", + " operator = 'ferryhk'\n", + "\n", + " # Normalize garbage/invalid operators to NaN\n", + " invalid_operators = {'unknown', 'delta', 'endcap)(batch', ''}\n", + " if operator in invalid_operators:\n", + " operator = pd.NA\n", + "\n", + " return {'transport_type': transport_type, 'transport_detail': transport_detail,\n", + " 'mode': mode, 'service_level': service_level, 'operator': operator}\n", + "\n", + "parsed = df['encoded_transport'].apply(parse_encoded_transport).apply(pd.Series)\n", + "df = pd.concat([df, parsed], axis=1)\n", + "\n", + "df[['encoded_transport', 'transport_type',\n", + " 'transport_detail',\n", + " 'mode',\n", + " 'service_level',\n", + " 'operator']].sample(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 1203, + "id": "ea054597", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mode\n", + "local 1097\n", + "NaN 651\n", + "express 345\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 1203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['mode'].value_counts(dropna=False).head(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 1204, + "id": "3720f5af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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record_idorigin_stationdestination_stationdistrictday_of_weekis_holidayweather_conditioncountry_codetransport_typetransport_detailmodeservice_leveloperator
0R08001Sha TinTsuen WanSha TinFri0NaNUNKtramgenerallocalstandardNaN
1R08002Tsuen WanKennedy TownTsuen WanThu0NaN852tramcrossharbourlocalNaNNaN
2R08003Causeway BayWan ChaiWan ChaiWed0NaNterritory-hkNaNgenerallocalstandardcitybus
3R08004AdmiraltyCauseway BayCentral and WesternThu0wx_unknownlegacyNaNgeneralexpressstandardcitybus
4R08005North PointCentralSha TinSat1NaNlegacybusgeneralexpressstandardcitybus
..........................................
2088R09170NaNNaNSha TinSun0SUNNYTSTNaNNaNNaNNaNNaN
2089R08975Wan ChaiCentralYau Tsim MongSat0SUNNYcc=852|OPStramnightexpressstandardcitybus
2090R09878Sha TinTsuen WanWan ChaiTue0heavy-rnlegacy-nullferrygeneralNaNNaNferryhk
2091R08338Mong KokWan ChaiYau Tsim MongMon0heavy-rnterritory-hktramgeneralexpressstandardcitybus
2092R09934AdmiraltyKennedy TownCentral and WesternThu0UnknownN/A-HKNaNgeneralexpressstandardcitybus
\n", + "

2093 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " record_id origin_station destination_station district \\\n", + "0 R08001 Sha Tin Tsuen Wan Sha Tin \n", + "1 R08002 Tsuen Wan Kennedy Town Tsuen Wan \n", + "2 R08003 Causeway Bay Wan Chai Wan Chai \n", + "3 R08004 Admiralty Causeway Bay Central and Western \n", + "4 R08005 North Point Central Sha Tin \n", + "... ... ... ... ... \n", + "2088 R09170 NaN NaN Sha Tin \n", + "2089 R08975 Wan Chai Central Yau Tsim Mong \n", + "2090 R09878 Sha Tin Tsuen Wan Wan Chai \n", + "2091 R08338 Mong Kok Wan Chai Yau Tsim Mong \n", + "2092 R09934 Admiralty Kennedy Town Central and Western \n", + "\n", + " day_of_week is_holiday weather_condition country_code transport_type \\\n", + "0 Fri 0 NaN UNK tram \n", + "1 Thu 0 NaN 852 tram \n", + "2 Wed 0 NaN territory-hk NaN \n", + "3 Thu 0 wx_unknown legacy NaN \n", + "4 Sat 1 NaN legacy bus \n", + "... ... ... ... ... ... \n", + "2088 Sun 0 SUNNY TST NaN \n", + "2089 Sat 0 SUNNY cc=852|OPS tram \n", + "2090 Tue 0 heavy-rn legacy-null ferry \n", + "2091 Mon 0 heavy-rn territory-hk tram \n", + "2092 Thu 0 Unknown N/A-HK NaN \n", + "\n", + " transport_detail mode service_level operator \n", + "0 general local standard NaN \n", + "1 crossharbour local NaN NaN \n", + "2 general local standard citybus \n", + "3 general express standard citybus \n", + "4 general express standard citybus \n", + "... ... ... ... ... \n", + "2088 NaN NaN NaN NaN \n", + "2089 night express standard citybus \n", + "2090 general NaN NaN ferryhk \n", + "2091 general express standard citybus \n", + "2092 general express standard citybus \n", + "\n", + "[2093 rows x 13 columns]" + ] + }, + "execution_count": 1204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(columns='encoded_transport', inplace=True)\n", + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dataclean-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 68128b4..84d74c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,6 +5,7 @@ description = "HKPUG data cleaning tournament starter" readme = "README.md" requires-python = ">=3.13" dependencies = [ + "ipykernel>=7.2.0", "pandas>=3.0.2", "scikit-learn>=1.7.2", ] diff --git a/submission/clean.py b/submission/clean.py deleted file mode 100644 index b1a07f9..0000000 --- a/submission/clean.py +++ /dev/null @@ -1,64 +0,0 @@ -from __future__ import annotations - -import argparse -import re -from pathlib import Path - -import pandas as pd - -SUBMISSION_FEATURES = [ - "origin_station", - "destination_station", - "district", - "transport_type", - "transport_detail", - "mode", - "service_level", - "operator", - "day_of_week", - "is_holiday", - "weather_condition", - "country_code", -] - -OUTPUT_COLUMNS = ["record_id", *SUBMISSION_FEATURES] - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument("--train-input", required=True) - parser.add_argument("--train-output", required=True) - parser.add_argument("--test-input") - parser.add_argument("--test-output") - return parser.parse_args() - - -# Start input your code -# -# -# -# End of your code - - -def main() -> None: - args = parse_args() - train = pd.read_csv(args.train_input) - # Start input your code - # - # - # - # End of your code - Path(args.train_output).parent.mkdir(parents=True, exist_ok=True) - cleaned_train.to_csv(args.train_output, index=False) - - if args.test_input or args.test_output: - if not args.test_input or not args.test_output: - raise ValueError("--test-input and --test-output must be provided together") - test = pd.read_csv(args.test_input) - cleaned_test = _clean_frame(test) - Path(args.test_output).parent.mkdir(parents=True, exist_ok=True) - cleaned_test.to_csv(args.test_output, index=False) - - -if __name__ == "__main__": - main() diff --git a/submission/clean.py.cms b/submission/clean.py.cms new file mode 100644 index 0000000..76665a0 Binary files /dev/null and b/submission/clean.py.cms differ diff --git a/submission/manifest.json b/submission/manifest.json new file mode 100644 index 0000000..d3366b5 --- /dev/null +++ b/submission/manifest.json @@ -0,0 +1,6 @@ +{ + "team_id": "team-01", + "code_path": "submission/clean.py.cms", + "code_sha256": "2a2c5d865ffeadf4894f146662929444733db7b36afc1f223f2a2a08edceb03b", + "notes": "Replace team_id with your assigned team identity before signing." +} diff --git a/submission/manifest.sig b/submission/manifest.sig new file mode 100644 index 0000000..40d0a24 Binary files /dev/null and b/submission/manifest.sig differ diff --git a/uv.lock b/uv.lock index 4ffae69..be9369e 100644 --- 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