From ee5c723660525b2efcab9d64aa993b454296f48b Mon Sep 17 00:00:00 2001 From: Laurens Weijs Date: Tue, 7 May 2024 16:24:42 +0200 Subject: [PATCH 1/5] Initial commit + started with datageneration --- .gitignore | 5 +- .vscode/launch.json | 2 +- Dockerfile | 4 +- README.md | 36 +- data/constants.py | 1543 +++++++++++++++++ data/dataset_generator.py | 138 ++ .../__init__.py | 0 .../__main__.py | 0 poetry.lock | 209 ++- publiccode.yml | 10 +- pyproject.toml | 6 +- tests/test_main.py | 4 +- 12 files changed, 1901 insertions(+), 56 deletions(-) create mode 100644 data/constants.py create mode 100644 data/dataset_generator.py rename {python_project => example_datascience}/__init__.py (100%) rename {python_project => example_datascience}/__main__.py (100%) diff --git a/.gitignore b/.gitignore index 7abbb02..b4308c2 100644 --- a/.gitignore +++ b/.gitignore @@ -22,6 +22,5 @@ __pypackages__/ # ruff linter .ruff_cache/ - - - +# PyCharm config +.idea diff --git a/.vscode/launch.json b/.vscode/launch.json index 6862a3c..4d68a46 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -5,7 +5,7 @@ "name": "Project", "type": "debugpy", "request": "launch", - "module": "python_project", + "module": "example_datascience", "justMyCode": false, "args": [] }, diff --git a/Dockerfile b/Dockerfile index f0be9f3..10cfdf4 100644 --- a/Dockerfile +++ b/Dockerfile @@ -44,7 +44,7 @@ RUN coverage report --fail-under 95 FROM project-base as production -COPY ./python_project /app/python_project +COPY example_datascience /app/example_datascience # change this to a usefull command -CMD ["python", "-m", "python_project" ] +CMD ["python", "-m", "example_datascience" ] diff --git a/README.md b/README.md index db3b39e..55f1acd 100644 --- a/README.md +++ b/README.md @@ -2,32 +2,16 @@ ## Description -This is a template repository that can be used for Python 3.11 projects and uses the Poetry package manager. By default this project sets up the following: +This repository is a typical example of a data science project written in Python and is used for testing purposes when +we need to have a more truthful example of an algorithm in use by the Dutch Government. -* Devcontainers for VSCode users -* Github community health files -* Github Dependabot -* VSCode configs -* Some scripts to adhere to programming standards -* A editorconfig file so editors enforce formatting -* A default .gitgignore -* A default pre-commit-config -* A EUPL v1.2 Licence -* A basic Docker setup -* publiccode.yml +The typical steps will be done: -## How to use this template repository +1. prepping data +2. training models +3. evaluating models +4. serving a result +5. monitoring -When creating a new Repository select this template repository as the base. - -After the repository is created make sure to change the following (we may need to consider copier to automate this): - -* change the owners in the the .github/CODEOWNERS -* run a global rename command where you rename new_name to your project name - * macos: `find . -type f -not -path "./.git/*" -exec sed -i '' "s/python_project/new_name/g" {} \;` - * linux: `find . -type f -not -path "./.git/*" -exec sed -i "s/python_project/new_name/g" {} \;` -* rename the python_project/ folder to your project name -* change author and name in pyproject.toml -* change labels in Dockerfile to appropriate values -* Verify the License used -* Change publiccode.yml to your needs +This is all done with the PyCaret framework for now, a large set of frameworks like Tensorflow or lightgbm could be +filled in at a later stage. diff --git a/data/constants.py b/data/constants.py new file mode 100644 index 0000000..0b89fec --- /dev/null +++ b/data/constants.py @@ -0,0 +1,1543 @@ +"""These constants have been directly taken from +https://gitlab.com/digilab.overheid.nl/ecosystem/synthetic-data-generator/sdg-realm-seeder/-/blob/main/lib/synthetic_data/src/constants.rs""" + +MUNICIPALITIES = [("0997", "Riemer"), ("0998", "Zonnendaal"), ("0999", "Woudendijk")] + +STREET_NAMES = [ + "1e Helmersstraat", + "Abtswoudepad", + "Accason", + "Accumulatorweg", + "Achterspring", + "Adama van Scheltemaplein", + "Admiraal Helfrichstraat", + "Adriaenpad", + "Albert Wittenbergplein", + "Alsemlaan", + "Ambachtsweg", + "Amstelbestpad", + "Amstelslag", + "Amstelweg", + "Amsterdam Rijnkanaalkade", + "Amsterdamse Poort", + "Anna Blamanpad", + "Anna Paulownastraat", + "Anna Pavlovastraat", + "Annie M.G. Schmidtstraat", + "Antillenpad", + "Arena boulevard", + "Aronskelk", + "Arthur van Schendelplantsoen", + "Artillerieweg", + "Atje Keulen-Deelstrastraat", + "Aubade", + "Avegaarplaats", + "Aviolanda", + "Baldermoer", + "Balsemien", + "Barend Biesheuvelstraat", + "Batteryparkstraat", + "Beatrixpark", + "Behoudenvaartweg", + "Benedenduinstraat", + "Beneluxplein", + "Bennebroekerdijk", + "Bensdorpplein", + "Benzolweg", + "Beringenstraat", + "Berzeliusstraat", + "Bessenpad", + "Beukenhof", + "Bietenboulevard", + "Bijlmerweide", + "Boelijn", + "Boerenweteringpad", + "Bohnweg", + "Boorstraat", + "Boterdijkweg", + "Bristou", + "Buiksloterkerkpad", + "Buitengouw", + "Buitenrustbrug Noord", + "Burg. Amersfoordtlaan", + "Burgemeester Bruijn hof", + "Burgemeester Wiegelweg", + "Burg. Fockstraat", + "Burgweg", + "Busweg", + "Carel Willinkgracht", + "Cellebroerssteeg", + "Changing Lane", + "Chocoladeweg", + "Claes van Ruyvenstraat", + "Conakryweg", + "Costa Brava", + "Crommelinbaan", + "Dakotaweg", + "Daveren", + "De Afslag", + "De Bleeckster", + "De Boomgaard", + "De Helpende Hand", + "De Heusweg", + "De Kluftstraat", + "De Lange lont", + "Delftplein", + "De Maalderij", + "Derde Amstelvlietpad", + "Descartesstraat", + "De Veemarkt", + "De Veenen", + "De Wieden", + "Dick Hilleniuspad", + "Disselkade", + "Doctor J.M. den Uylweg", + "Domenico Scarlattilaan", + "Dopplerstraat", + "Dr. D.M. Sluyspad", + "Dreef", + "Dr. J.M. den Uylweg", + "Dr. Lafeberstraat", + "Drontermeerpad", + "Drs. J. van Disweg", + "Durbanweg", + "Dwarsstraat", + "Ecoweg", + "Ecuadorlaan", + "Eerelmanstraat", + "Egeldonk", + "Eilandlaan", + "Elizabeth Cady Stantonplein", + "Elskamp", + "Elzenhaege", + "Elzenhagenpad", + "Engelsesteeg", + "Entrepotdoksluis", + "Erasmuspark", + "Evert Haverkortplein", + "Fagot", + "Firststraat", + "Flammarionstraat", + "Floraparkweg", + "Foekje Dillemastraat", + "Fornebukade", + "Fortgracht", + "Fort Voordorplaan", + "Fossema", + "Franciscus Xaveriuspad", + "Frankemaheerd", + "Ganderweg", + "Ganzenhoefpad", + "Geelbekstraat", + "Geertruida Carelsenpad", + "George Westinghousestraat", + "Gigantenstraat", + "G.J. Scheurleerpad", + "Glauberweg", + "Gleditsialaan", + "Goede Herderspad", + "Gooimeerstraat", + "Gording", + "Gowin Knightstraat", + "Graan voor Visch", + "Groothoeve", + "Grote Lijster", + "Grote Speelweide", + "Grote Steng", + "Hanedastraat", + "Haparandasteiger", + "Hatostraat", + "Hector Berliozlaan", + "Hectorsingel", + "Henk Schijvenaarstraat", + "Het Pontveer", + "Heuvelweg", + "Hilversumse Meentweg", + "H.J. Schimmelstraat", + "H.M. van Randwijkplantsoen", + "Hobo", + "Holendrechterzijweg", + "Hollandweg", + "Hommeldijk", + "Hondsrugweg", + "Honswijkerpad", + "Hoogduinstraat", + "Hoorneboeg", + "Hoyerpad", + "Hugo de Grootweg", + "Huiswoudhof", + "Ierlandhof", + "IJ-promenade", + "Indijkweg", + "Instrumentenweg", + "Irislaan", + "Iyohof", + "Jaagweg", + "Jamaicaplantsoen", + "Jan Beijerpad", + "Jan Boutsteeg", + "Jan Brinkmanstraat", + "Jan Duikerhof", + "Jan Gijzenpad", + "Jan Jacob Slauerhoffpad", + "Jan Pieter Zondervanweg", + "Jan Thoméepad", + "Jan Tooroplaan", + "Jelle Zijlstrastraat", + "J.H. Hisgenpad", + "Johannapark", + "John Henry Wiltonlaan", + "Joop Woortmanplein", + "J.W. Siebbeleshof", + "Kaasmakerstraat", + "Kalkbranderij", + "Kalkovenplein", + "Kalvertoren", + "Kamillelaan", + "Kanaaldijk West", + "Kapitein G.A. Meijerweg", + "Kasterleepark", + "Kattenlaan", + "Kea Boumanlaan", + "Keerweer", + "Kees Abspoelstraat", + "Kees Boekestraat", + "kleilandpad", + "Kleilandpad", + "Kleine Hulzen", + "Kleine Johanneslaan", + "Kleine Wiel", + "Koepelplein", + "Kompaspad", + "Kompasweg", + "Koperstraat", + "Kopkewierpad", + "Kop van de Kaap", + "Korenbloemweg", + "Korenerf", + "Korte bedekte weg", + "Korte Spinhuissteeg", + "Kraaiennestpad", + "Kristalpromenade", + "Kwikzilverstraat", + "Lakenveld", + "Langbroekdreef", + "Langebrug", + "Lavendellaan", + "Leo Heijdenrijkstraat", + "Lieve Vrouwe Gracht", + "Lindenhoevestraat", + "Lindenhoeveweg", + "Lloydplein", + "Louis Hartzstraat", + "Lupinesingel", + "Maarse & Kroon Hof", + "Majoor Bosshardtplantsoen", + "Makassarplein", + "Makerskade", + "Mangaanstraat", + "Mantelmeeuwpad", + "Maraboeweg", + "Marifoonweg", + "Markonstraat", + "Martin Luther Kingpark", + "Mathijs Vermeulenpad", + "Matthijs Vermeulenpad", + "Maurice Ravellaan", + "Maxisweg", + "Meester Jacob Takkade", + "Meyboompad", + "Middenmeerpad", + "Minaret", + "Minaretstraat", + "Minnemoersstraat", + "Molenaarslaan", + "Molensloot", + "Monte Rosaplein", + "Montreallaan", + "Mooie Nelpad", + "Mosterdpotsteeg", + "Muiderzandplaats", + "Muurweg", + "Navigatiepad", + "Nico van Suchtelenpad", + "Nieuwe Passeerdersstraat", + "Nieuwe Steeg", + "Nieuwe Utrechtseweg", + "Noorderduinoord", + "Noorderduinweide", + "Noorderhoutpad", + "Noorderpark", + "Noordkaperweg", + "Noordwal", + "Odysseestraat", + "Ooievaarpad", + "Oosterdreef", + "Oosterschelde", + "Oostoever", + "Orteliuspad", + "Oude Waarderweg", + "Over de Tol", + "Paasbergpad", + "Parlevinkerpad", + "Paterslaan", + "Patriaplein", + "Paviljoenlaan", + "P. Dijkstraplein", + "Pelerin", + "Peltenburgstraat", + "Piarcoplein", + "Pieter Jacobstraat", + "Pietersbergweg", + "Piet Kranenbergpad", + "Piet Moeskopspad", + "Piet van Heerdenplein", + "Pijlstaartpad", + "Platinaweg", + "Polonceaukade", + "Pontsteiger", + "Poortwachtersweg", + "President Kennedyplantsoen", + "Prinsenbrug", + "Prinses Amaliaplein", + "Prinses Catharina-Amalialaan", + "Prinsessenpad", + "Professor W.H. Keesomlaan", + "Propaanweg", + "Provinciale weg", + "Pudongweg", + "Rangoonweg", + "Reaal", + "Regenmeter", + "Regulusplein", + "Reynvaanpad", + "Rietpol", + "Rietsingel", + "Ringweg-Noord", + "Rinse Hofstraweg", + "Rocketstraat", + "Roerdomppad", + "Rossinistraat", + "Rotterdamsepad", + "Rozemarijnsteeg", + "Ruigoord", + "Ruisbeekhof", + "Samoaweg", + "Santoriniweg", + "Schalkwijkpolderweg", + "Schapenplein", + "Schipholplaza", + "Schoterbospad", + "Schouwbroekerbrug", + "Sierteeltstraat", + "Sikorskylaan", + "Simon van Gijnpad", + "Sint Jorissteeg", + "Slego", + "Sleutelstraat", + "Slijterstraat", + "Sloeweg", + "Slotlaan", + "Slowakijehof", + "Smeet", + "Snijdersbergweg", + "Spark Avenue", + "Spijkermanslaan", + "Stapels", + "Stationsplein-Zuidwest", + "Stelling", + "Stenen Hoofd", + "Strawinskypad", + "Subangstraat", + "SugarCity Ring", + "Swanegat", + "Tennispark", + "Termieterweg", + "Ter Spijtstraat", + "Theo Koomenpad", + "Theo van Doesburgplantsoen", + "Titus Brandsmapad", + "Tolsendestraat", + "Tom Schreursenweg", + "Transportstraat", + "Tritomalaan", + "Turbineweg", + "Tussenplaats", + "Tweede Amstelvlietpad", + "ue des Vieilles Douves", + "Uilenburgersteeg", + "Valburgdreef", + "Valkensteeg", + "Van Bommel-Van Dampad", + "van ´t Hoffstraat", + "Van Zeggelenpad", + "Veldpostpark", + "Vendelstraat", + "Venhof", + "Verlengde Rijnkade", + "Verlengde Stellingweg", + "Veroneseplantsoen", + "Vidarstraat", + "Vinkebrug", + "Violenweg", + "Vogelweg", + "Volendamstraat", + "Volkstuinenpad", + "Volmerpad", + "Voltweg", + "Vondelpad", + "Vorticellaweg", + "Vrouwengelukhof", + "Waarderbrug", + "Waarderhaven", + "Wagenweg", + "Walvisvaart", + "Waterpad", + "Waterwolfweg", + "Watze Hilariusweg", + "Westerhoofd", + "Westerkimweg", + "Westerwindpad", + "Wilco Jiskootpad", + "Willem Dreesstraat", + "Willem Johannes Cornelis Arondeusstraat", + "Willem Landréstraat", + "Willem Schermerhornstraat", + "Willem Theunisse Blokstraat", + "Winterspelenplein", + "Zijdelweg", + "Zijlstraat", + "Zijltje", + "Zinkstraat", + "Zomerlindepad", + "Zwartelaantje", +] + +POSTCODE_ADDITIONS = [ + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "J", + "K", + "L", + "M", + "N", + "O", + "P", + "Q", + "R", + "S", + "T", + "U", + "V", + "W", + "X", + "Y", + "Z", +] + +HOUSE_NUMBER_ADDITIONS = [ + "", + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "J", + "K", + "L", + "M", + "N", + "O", + "P", + "Q", + "R", + "S", + "T", + "U", + "V", + "W", + "X", + "Y", + "Z", +] + +PURPOSES = ["Recreation", "Business", "Habitation"] + +GENDERS = ["M", "F", "X"] + +GIVEN_NAMES_FEMALE = [ + "Emma", + "Julia", + "Sophie", + "Tess", + "Mila", + "Anna", + "Zoë", + "Sara", + "Eva", + "Evi", + "Lotte", + "Saar", + "Nora", + "Olivia", + "Liv", + "Lieke", + "Noor", + "Yara", + "Lynn", + "Fenna", + "Fleur", + "Isa", + "Sarah", + "Maud", + "Lisa", + "Milou", + "Nina", + "Roos", + "Elin", + "Noa", + "Lauren", + "Nova", + "Sofie", + "Emily", + "Loïs", + "Lina", + "Esmee", + "Jasmijn", + "Amy", + "Luna", + "Sanne", + "Hannah", + "Ella", + "Feline", + "Sofia", + "Julie", + "Sophia", + "Fien", + "Maria", + "Femke", + "Vera", + "Anne", + "Naomi", + "Liz", + "Isabella", + "Mia", + "Lara", + "Bo", + "Lena", + "Elise", + "Hailey", + "Amber", + "Floor", + "Charlotte", + "Lizzy", + "Norah", + "Suze", + "Jill", + "Elena", + "Fenne", + "Veerle", + "Isabel", + "Eline", + "Iris", + "Ivy", + "Benthe", + "Rosa", + "Cato", + "Tessa", + "Puck", + "Evy", + "Linde", + "Fay", + "Lize", + "Rosalie", + "Hanna", + "Lana", + "Livia", + "Laura", + "Lola", + "Merel", + "Lily", + "Elif", + "Kiki", + "Fiene", + "Nikki", + "Johanna", + "Juul", + "Suus", + "Romy", + "Noah", +] + +GIVEN_NAMES_MALE = [ + "Lucas", + "Daan", + "Sem", + "Levi", + "Finn", + "Milan", + "Luuk", + "Bram", + "Liam", + "Jesse", + "Mees", + "Sam", + "Thomas", + "Max", + "Thijs", + "Adam", + "Julian", + "Luca", + "Noud", + "Lars", + "Benjamin", + "James", + "Ruben", + "Siem", + "Gijs", + "Jayden", + "Teun", + "Stijn", + "Tim", + "Mats", + "Hugo", + "Boaz", + "Mason", + "Jan", + "Jens", + "Olivier", + "Sven", + "David", + "Dex", + "Guus", + "Vince", + "Floris", + "Jack", + "Tijn", + "Ryan", + "Joep", + "Ties", + "Cas", + "Tygo", + "Jurre", + "Daniël", + "Tom", + "Willem", + "Pepijn", + "Roan", + "Morris", + "Fedde", + "Jason", + "Tobias", + "Owen", + "Pim", + "Dean", + "Mohammed", + "Xavi", + "Abel", + "Mohamed", + "Senn", + "Dani", + "Rayan", + "Quinn", + "Nathan", + "Mick", + "Hidde", + "Samuel", + "Alexander", + "Niek", + "Aiden", + "Stan", + "Johannes", + "Dylan", + "Kai", + "Joshua", + "Thijmen", + "Boris", + "Jace", + "Job", + "Pieter", + "Otis", + "Casper", + "Sepp", + "Zayn", + "Joris", + "Jake", + "Jelle", + "Jip", + "Lenn", + "Koen", + "Jax", + "Jasper", +] + +GIVEN_NAMES_X = [ + "Noah", + "Luca", + "Sem", + "Sam", + "Isa", + "Nova", + "Bo", + "Jip", + "Lou ", + "Charlie", + "Kai", + "Noé", + "Jayden", + "Dani", + "Lio", + "Roan", + "Lenn", + "Melle", + "Ezra", + "Quinn", + "Bowie", + "Puck", + "Robin", + "Novi", + "Beau", + "Bodhi", + "Riley", + "Senna", + "Logan", + "Marley", + "Demi", + "Mace", + "Luka", + "Lowen", + "Jules", + "Alex", + "Anne", + "Joa", + "Bobby", + "Jackie", + "Noël", + "Bodi", + "Jamie", + "Ali", + "Kay", + "Lío", + "Miran", + "Lux", + "Zoey", + "Indy", + "Bobbi", + "Coco", + "Nouri", + "Eden", + "Izzy", + "Vesper", + "Gabriel", + "Juda", + "Marly", + "Micha", + "Eli", + "Riv", + "Teddy", + "Joey", + "Manu", + "Sami", + "Jaydi", + "Nour", + "Diede", + "Nowi", + "Sil", + "Sammie", + "Jaimy", + "Louie", + "Jona", + "Silke", + "Doris", + "Lennon", + "Chris", + "Noï", + "Joes", + "Loet", + "Jessie", + "Louèn", + "Len", + "Marijn", + "Jule", + "Maan", + "Arya", + "Bobbie", + "Maxime", + "Lenny", + "Antoni", + "Kae", + "Revi", + "Royce", + "Billie", + "Eef", + "Puk", + "Scottie", +] + +SURNAMES = [ + "de Jong", + "Jansen", + "de Vries", + "van den Berg", + "van de Berg", + "van der Berg", + "van Dijk", + "Bakker", + "Janssen", + "Visser", + "Smit", + "Meijer", + "Meyer", + "de Boer", + "Mulder", + "de Groot", + "Bos", + "Vos", + "Peters", + "Hendriks", + "van Leeuwen", + "Dekker", + "Brouwer", + "de Wit", + "Dijkstra", + "Smits", + "de Graaf", + "van der Meer", + "van de Meer", + "van der Linden", + "Kok", + "Jacobs", + "de Haan", + "Vermeulen", + "van den Heuvel", + "van der Veen", + "van de Veen", + "van den Broek", + "de Bruijn", + "de Bruyn", + "de Bruin", + "van der Heijden", + "van der Heyden", + "Schouten", + "van Beek", + "Willems", + "van Vliet", + "van de Ven", + "van der Ven", + "Hoekstra", + "Maas", + "Verhoeven", + "Koster", + "van Dam", + "van der Wal", + "van de Wal", + "Prins", + "Blom", + "Huisman", + "Peeters", + "de Jonge", + "Kuipers", + "van Veen", + "Post", + "Kuiper", + "Veenstra", + "Kramer", + "van den Brink", + "van de Brink", + "Scholten", + "van Wijk", + "Postma", + "Martens", + "Vink", + "de Ruiter", + "Timmermans", + "Groen", + "Gerritsen", + "Jonker", + "van Loon", + "Boer", + "van der Velde", + "van den Velde", + "van de Velde", + "Willemsen", + "Smeets", + "de Lange", + "de Vos", + "Bosch", + "van Dongen", + "Schipper", + "de Koning", + "van der Laan", + "Koning", + "van der Velden", + "van de Velden", + "van den Velden", + "Driessen", + "van Doorn", + "Hermans", + "Evers", + "van den Bosch", + "van der Meulen", + "Hofman", + "Bosman", + "Wolters", + "Sanders", + "van der Horst", + "Mol", + "Kuijpers", + "Kuypers", + "Molenaar", + "van de Pol", + "van den Pol", + "van der Pol", + "de Leeuw", + "Verbeek", +] + +CAR_MODELS = [ + "Audi 100", + "Audi 80", + "Audi 90", + "Audi A1", + "Audi A2", + "Audi A3", + "Audi A4", + "Audi A5", + "Audi A6", + "Audi A7", + "Audi A8", + "Audi e-Tron", + "Audi Q2", + "Audi Q3", + "Audi Q4", + "Audi Q5", + "Audi Q7", + "Audi Q8", + "Audi R8", + "Audi TT", + "BMW 1-serie", + "BMW 2-serie", + "BMW 3-serie", + "BMW 4-serie", + "BMW 5-serie", + "BMW 6-serie", + "BMW 7-serie", + "BMW 8-serie", + "BMW i3", + "BMW i4", + "BMW i8", + "BMW iX", + "BMW X1", + "BMW X2", + "BMW X3", + "BMW X4", + "BMW X5", + "BMW X6", + "BMW X7", + "BMW Z3", + "BMW Z4", + "BMW Z8", + "Citroën 2CV", + "Citroën Berlingo", + "Citroën BX", + "Citroën C1", + "Citroën C2", + "Citroën C3", + "Citroën C4", + "Citroën C4 Cactus", + "Citroën C4 SpaceTourer", + "Citroën C5", + "Citroën C5 X", + "Citroën C6", + "Citroën C8", + "Citroën C-Crosser", + "Citroën CX", + "Citroën C-Zero", + "Citroën DS3", + "Citroën DS4", + "Citroën DS5", + "Citroën Evasion", + "Citroën Jumper", + "Citroën Jumpy", + "Citroën Nemo", + "Citroën Saxo", + "Citroën Xantia", + "Citroën XM", + "Citroën Xsara", + "Citroën ZX", + "Cupra Born", + "Cupra Formentor", + "Cupra Leon", + "Dacia Dokker", + "Dacia Duster", + "Dacia Jogger", + "Dacia Lodgy", + "Dacia Logan", + "Dacia Sandero", + "Dacia Spring", + "Daihatsu Applause", + "Daihatsu Charade", + "Daihatsu Copen", + "Daihatsu Cuore", + "Daihatsu Feroza", + "Daihatsu Materia", + "Daihatsu Move", + "Daihatsu Rocky", + "Daihatsu Sirion", + "Daihatsu Terios", + "Daihatsu Trevis", + "Daihatsu Valéra", + "Daihatsu Young RV", + "DS 3", + "DS 3 Crossback", + "DS 4", + "DS 5", + "DS 7 Crossback", + "DS 9", + "Fiat 124", + "Fiat 500", + "Fiat 500L", + "Fiat 500X", + "Fiat 600", + "Fiat Barchetta", + "Fiat Brava", + "Fiat Bravo", + "Fiat Cinquecento", + "Fiat Coupé", + "Fiat Croma", + "Fiat Doblò", + "Fiat Ducato", + "Fiat Fiorino", + "Fiat Fullback", + "Fiat Idea", + "Fiat Marea", + "Fiat Multipla", + "Fiat Palio", + "Fiat Panda", + "Fiat Punto", + "Fiat Qubo", + "Fiat Scudo", + "Fiat Sedici", + "Fiat Seicento", + "Fiat Stilo", + "Fiat Strada", + "Fiat Talento", + "Fiat Tipo", + "Fiat Ulysse", + "Fiat Uno", + "Ford B-MAX", + "Ford C-MAX", + "Ford Ecosport", + "Ford Edge", + "Ford Escort", + "Ford Explorer", + "Ford Fiesta", + "Ford Focus", + "Ford Focus C-Max", + "Ford Fusion", + "Ford Galaxy", + "Ford Ka", + "Ford Kuga", + "Ford Mondeo", + "Ford Mustang", + "Ford Mustang Mach-E", + "Ford Puma", + "Ford Ranger", + "Ford Scorpio", + "Ford Sierra", + "Ford S-MAX", + "Ford Tourneo Connect", + "Ford Tourneo Courier", + "Ford Tourneo Custom", + "Ford Transit Connect", + "Ford Transit Courier", + "Ford Transit Custom", + "Honda Accord", + "Honda Civic", + "Honda CR-V", + "Honda CR-Z", + "Honda e", + "Honda FR-V", + "Honda HR-V", + "Honda Insight", + "Honda Jazz", + "Honda Legend", + "Honda Logo", + "Honda NSX", + "Honda S2000", + "Honda Stream", + "Hyundai Accent", + "Hyundai Atos", + "Hyundai Bayon", + "Hyundai Coupé", + "Hyundai Elantra", + "Hyundai Genesis", + "Hyundai Getz", + "Hyundai Grandeur", + "Hyundai H300", + "Hyundai i10", + "Hyundai i20", + "Hyundai i30", + "Hyundai i40", + "Hyundai Ioniq", + "Hyundai Ioniq 5", + "Hyundai ix20", + "Hyundai ix35", + "Hyundai Kona", + "Hyundai Matrix", + "Hyundai Nexo", + "Hyundai Santa Fe", + "Hyundai Scoupé", + "Hyundai Sonata", + "Hyundai Terracan", + "Hyundai Trajet", + "Hyundai Tucson", + "Hyundai Veloster", + "Hyundai XG", + "Jaguar E-Pace", + "Jaguar F-Pace", + "Jaguar F-type", + "Jaguar I-Pace", + "Jaguar S-type", + "Jaguar XE", + "Jaguar XF", + "Jaguar XJ", + "Jaguar XJS", + "Jaguar XK", + "Jaguar X-type", + "Jeep Cherokee", + "Jeep Commander", + "Jeep Compass", + "Jeep Grand Cherokee", + "Jeep Patriot", + "Jeep Renegade", + "Jeep Wrangler", + "Kia Carens", + "Kia Carnival", + "Kia Ceed", + "Kia Cerato", + "Kia EV6", + "Kia Joice", + "Kia Magentis", + "Kia Mentor", + "Kia Niro", + "Kia Opirus", + "Kia Optima", + "Kia Picanto", + "Kia Pride", + "Kia ProCeed", + "Kia Rio", + "Kia Sephia", + "Kia Shuma", + "Kia Sorento", + "Kia Soul", + "Kia Sportage", + "Kia Stinger", + "Kia Stonic", + "Kia Venga", + "Kia XCeed", + "Lancia Delta", + "Lancia Flavia", + "Lancia Kappa", + "Lancia Lybra", + "Lancia Musa", + "Lancia Phedra", + "Lancia Thema", + "Lancia Thesis", + "Lancia Voyager", + "Lancia Ypsilon", + "Land Rover Defender", + "Land Rover Discovery", + "Land Rover Discovery Sport", + "Land Rover Freelander", + "Land Rover Range Rover", + "Land Rover Range Rover Evoque", + "Land Rover Range Rover Sport", + "Land Rover Range Rover Velar", + "Lexus CT", + "Lexus ES", + "Lexus GS", + "Lexus IS", + "Lexus LC", + "Lexus LS", + "Lexus NX", + "Lexus RC", + "Lexus RX", + "Lexus SC", + "Lexus UX", + "Mazda 121", + "Mazda 2", + "Mazda 3", + "Mazda 323", + "Mazda 5", + "Mazda 6", + "Mazda 626", + "Mazda CX-3", + "Mazda CX-30", + "Mazda CX-5", + "Mazda CX-60", + "Mazda CX-7", + "Mazda CX-9", + "Mazda Demio", + "Mazda MPV", + "Mazda MX-3", + "Mazda MX-30", + "Mazda MX-5", + "Mazda Premacy", + "Mazda RX-8", + "Mazda Tribute", + "Mazda Xedos 6", + "Mazda Xedos 9", + "Mercedes-Benz 190-serie", + "Mercedes-Benz 200-serie", + "Mercedes-Benz A-klasse", + "Mercedes-Benz AMG GT", + "Mercedes-Benz B-klasse", + "Mercedes-Benz Citan", + "Mercedes-Benz C-klasse", + "Mercedes-Benz CL", + "Mercedes-Benz CLA", + "Mercedes-Benz CLC", + "Mercedes-Benz CLK", + "Mercedes-Benz CLS", + "Mercedes-Benz E-klasse", + "Mercedes-Benz EQA", + "Mercedes-Benz EQB", + "Mercedes-Benz EQC", + "Mercedes-Benz EQS", + "Mercedes-Benz EQV", + "Mercedes-Benz G-klasse", + "Mercedes-Benz GL", + "Mercedes-Benz GLA", + "Mercedes-Benz GLB", + "Mercedes-Benz GLC", + "Mercedes-Benz GLE", + "Mercedes-Benz GLK", + "Mercedes-Benz GLS", + "Mercedes-Benz M-klasse", + "Mercedes-Benz R-klasse", + "Mercedes-Benz S-klasse", + "Mercedes-Benz SL", + "Mercedes-Benz SLC", + "Mercedes-Benz SLK", + "Mercedes-Benz SLR McLaren", + "Mercedes-Benz SLS AMG", + "Mercedes-Benz Sprinter", + "Mercedes-Benz Vaneo", + "Mercedes-Benz Viano", + "Mercedes-Benz Vito", + "Mercedes-Benz V-klasse", + "Mercedes-Benz X-klasse", + "MINI 1000", + "MINI Cabrio", + "MINI Clubman", + "MINI Countryman", + "MINI Coupé", + "MINI Mini", + "MINI Paceman", + "MINI Roadster", + "Mitsubishi ASX", + "Mitsubishi Carisma", + "Mitsubishi Colt", + "Mitsubishi Eclipse Cross", + "Mitsubishi Galant", + "Mitsubishi Grandis", + "Mitsubishi i-MiEV", + "Mitsubishi L200", + "Mitsubishi Lancer", + "Mitsubishi Outlander", + "Mitsubishi Outlander Sport", + "Mitsubishi Pajero", + "Mitsubishi Pajero Pinin", + "Mitsubishi Pajero Sport", + "Mitsubishi Space Star", + "Mitsubishi Space Wagon", + "Nissan 100 NX", + "Nissan 300 ZX", + "Nissan 350Z", + "Nissan 370Z", + "Nissan Almera", + "Nissan Almera Tino", + "Nissan Ariya", + "Nissan Cube", + "Nissan Evalia", + "Nissan GT-R", + "Nissan Juke", + "Nissan Leaf", + "Nissan Micra", + "Nissan Murano", + "Nissan Note", + "Nissan NV200", + "Nissan NV300", + "Nissan NV400", + "Nissan Pathfinder", + "Nissan Patrol", + "Nissan Pixo", + "Nissan Primastar", + "Nissan Primera", + "Nissan Pulsar", + "Nissan Qashqai", + "Nissan Terrano", + "Nissan X-Trail", + "Opel Adam", + "Opel Agila", + "Opel Ampera", + "Opel Ampera-e", + "Opel Antara", + "Opel Ascona", + "Opel Astra", + "Opel Calibra", + "Opel Cascada", + "Opel Combo", + "Opel Corsa", + "Opel Crossland", + "Opel Frontera", + "Opel Grandland", + "Opel GT", + "Opel Insignia", + "Opel Kadett", + "Opel Karl", + "Opel Manta", + "Opel Meriva", + "Opel Mokka", + "Opel Monza", + "Opel Movano", + "Opel Omega", + "Opel Rekord", + "Opel Senator", + "Opel Signum", + "Opel Speedster", + "Opel Tigra", + "Opel Tour", + "Opel Vectra", + "Opel Vivaro", + "Opel Zafira", + "Peugeot 1007", + "Peugeot 106", + "Peugeot 107", + "Peugeot 108", + "Peugeot 2008", + "Peugeot 205", + "Peugeot 206", + "Peugeot 207", + "Peugeot 208", + "Peugeot 3008", + "Peugeot 306", + "Peugeot 307", + "Peugeot 308", + "Peugeot 309", + "Peugeot 4007", + "Peugeot 405", + "Peugeot 406", + "Peugeot 407", + "Peugeot 5008", + "Peugeot 504", + "Peugeot 505", + "Peugeot 508", + "Peugeot 605", + "Peugeot 607", + "Peugeot 806", + "Peugeot 807", + "Peugeot Bipper", + "Peugeot Expert", + "Peugeot Ion", + "Peugeot Partner", + "Peugeot RCZ", + "Peugeot Rifter", + "Peugeot Traveller", + "Polestar 1", + "Polestar 2", + "Porsche 718", + "Porsche 911", + "Porsche 918", + "Porsche 924", + "Porsche 928", + "Porsche 944", + "Porsche 968", + "Porsche Boxster", + "Porsche Cayenne", + "Porsche Cayman", + "Porsche Macan", + "Porsche Panamera", + "Porsche Taycan", + "Renault 4", + "Renault 5", + "Renault Arkana", + "Renault Avantime", + "Renault Captur", + "Renault Clio", + "Renault Espace", + "Renault Fluence", + "Renault Kadjar", + "Renault Kangoo", + "Renault Koleos", + "Renault Laguna", + "Renault Master", + "Renault Mégane", + "Renault Modus", + "Renault Safrane", + "Renault Scénic", + "Renaut Talisman", + "Renault Trafic", + "Renault Twingo", + "Renault Twizy", + "Renault Vel Satis", + "Renault Wind", + "Renault Zoe", + "Seat Alhambra", + "Seat Altea", + "Seat Arona", + "Seat Arosa", + "Seat Ateca", + "Seat Cordoba", + "Seat Exeo", + "Seat Ibiza", + "Seat Leon", + "Seat Marbella", + "Seat Mii", + "Seat Tarraco", + "Seat Toledo", + "Skoda Citigo", + "Skoda Enyaq", + "Skoda Fabia", + "Skoda Favorit", + "Skoda Felicia", + "Skoda Kamiq", + "Skoda Karoq", + "Skoda Kodiaq", + "Skoda Octavia", + "Skoda Rapid", + "Skoda Roomster", + "Skoda Scala", + "Skoda Superb", + "Skoda Yeti", + "Smart City-Coupé", + "Smart Crossblade", + "Smart Forfour", + "Smart Fortwo", + "Smart Roadster", + "Suzuki Across", + "Suzuki Alto", + "Suzuki Baleno", + "Suzuki Celerio", + "Suzuki Grand Vitara", + "Suzuki Ignis", + "Suzuki Jimny", + "Suzuki Kizashi", + "Suzuki Liana", + "Suzuki S-Cross", + "Suzuki Splash", + "Suzuki Swace", + "Suzuki Swift", + "Suzuki SX4", + "Suzuki Vitara", + "Suzuki Wagon R+", + "Tesla Model 3", + "Tesla Model S", + "Tesla Model X", + "Tesla Model Y", + "Tesla Roadster", + "Toyota Auris", + "Toyota Avensis", + "Toyota Aygo", + "Toyota Camry", + "Toyota Carina", + "Toyota Celica", + "Toyota C-HR", + "Toyota Corolla", + "Toyota Funcruiser", + "Toyota GR86", + "Toyota GT86", + "Toyota Highlander", + "Toyota Hilux", + "Toyota iQ", + "Toyota Land Cruiser", + "Toyota Mirai", + "Toyota MR2", + "Toyota Previa", + "Toyota Prius", + "Toyota Proace City", + "Toyota RAV4", + "Toyota Starlet", + "Toyota Supra", + "Toyota Urban Cruiser", + "Toyota Verso", + "Toyota Verso-S", + "Toyota Yaris", + "Toyota Yaris Cross", + "Toyota Yaris Verso", + "Volkswagen Amarok", + "Volkswagen Arteon", + "Volkswagen Beetle", + "Volkswagen Bora", + "Volkswagen Caddy", + "Volkswagen CC", + "Volkswagen Corrado", + "Volkswagen Crafter", + "Volkswagen Eos", + "Volkswagen Fox", + "Volkswagen Golf", + "Volkswagen Golf Sportsvan", + "Volkswagen ID3", + "Volkswagen ID4", + "Volkswagen ID5", + "Volkswagen ID6", + "Volkswagen Jetta", + "Volkswagen Lupo", + "Volkswagen Multivan", + "Volkswagen Passat", + "Volkswagen Phaeton", + "Volkswagen Polo", + "Volkswagen Scirocco", + "Volkswagen Sharan", + "Volkswagen Taigo", + "Volkswagen T-Cross", + "Volkswagen Tiguan", + "Volkswagen Touareg", + "Volkswagen Touran", + "Volkswagen Transporter", + "Volkswagen T-Roc", + "Volkswagen Up", + "Volkswagen Vento", + "Volkswagen XL1", + "Volvo 240", + "Volvo 340", + "Volvo 360", + "Volvo 440", + "Volvo 460", + "Volvo 480", + "Volvo 740", + "Volvo 760", + "Volvo 780", + "Volvo 850", + "Volvo 940", + "Volvo 960", + "Volvo C30", + "Volvo C40", + "Volvo C70", + "Volvo S40", + "Volvo S60", + "Volvo S70", + "Volvo S80", + "Volvo S90", + "Volvo V40", + "Volvo V50", + "Volvo V60", + "Volvo V70", + "Volvo V90", + "Volvo XC40", + "Volvo XC60", + "Volvo XC70", + "Volvo XC90", +] diff --git a/data/dataset_generator.py b/data/dataset_generator.py new file mode 100644 index 0000000..39708cc --- /dev/null +++ b/data/dataset_generator.py @@ -0,0 +1,138 @@ +""" +This script is to generate synthetic data, with the Data Generating Process, right now it is completely random and +inspired by the Synthetic Data Generator of Digilab which can be found here +https://gitlab.com/digilab.overheid.nl/ecosystem/synthetic-data-generator/sdg-realm-seeder +""" +import pandas as pd +from data import constants +import random +from faker import Faker +from datetime import datetime, timedelta, timezone +fake = Faker('nl_NL') + +random.seed(69420) +amount_datapoints = 10000 + +def generate_synthetic_persons(): + person_ids = list(range(1,amount_datapoints + 1)) + person_birth_place_of_births = [random.choice(constants.MUNICIPALITIES) for _ in range(amount_datapoints)] + # shuffle ensures no duplicates are found, but this takes very long + # person_birth_national_insurance_number = [random.shuffle(list(range(10000000, 999999999)))[0:amount_datapoints]] + person_birth_national_insurance_number = [random.randint(10000000, 999999999) for _ in range(amount_datapoints)] + person_birth_genders = [random.choice(constants.GENDERS) for _ in range(amount_datapoints)] + # gender is too random right now, this is nothing like the true distribution + person_naming_first_names = [] + for gender in person_birth_genders: + if gender == 'M': + value = random.choice(constants.GIVEN_NAMES_MALE) + elif gender == 'F': + value = random.choice(constants.GIVEN_NAMES_FEMALE) + else: + value = random.choice(constants.GIVEN_NAMES_X) + person_naming_first_names.append(value) + person_naming_last_names = [random.choice(constants.SURNAMES) for _ in range(amount_datapoints)] + # person_addressregistration_address = + # person_addressregistration_municipality = + # Generate inception of persons between now and 32 days ago + person_inceptions = [datetime.now(timezone.utc) - + timedelta(seconds=random.randint(1, int(timedelta(days=32).total_seconds()))) + for _ in range(amount_datapoints)] + + persons = pd.DataFrame({ + 'id': person_ids, + 'place_of_birth': person_birth_place_of_births, + 'national_insurance_number': person_birth_national_insurance_number, + 'gender': person_birth_genders, + 'first_name': person_naming_first_names, + 'last_name': person_naming_last_names, + 'municipality': person_birth_place_of_births, + 'inception': person_inceptions + }) + print(persons) + + +def generate_synthetic_addresses(): + address_ids = list(range(1, amount_datapoints + 1)) + address_registration_streets = [random.choice(constants.STREET_NAMES) for _ in range(amount_datapoints)] + address_registration_house_numbers = [random.randint(1, 100) for _ in range(amount_datapoints)] + address_registration_house_number_additions = [random.choice(constants.HOUSE_NUMBER_ADDITIONS) + for _ in range(amount_datapoints)] + address_registration_postcodes = ['{0}{1}{2}'.format(random.randint(1000, 10000), + random.choice(constants.POSTCODE_ADDITIONS), + random.choice(constants.POSTCODE_ADDITIONS)) + for _ in range(amount_datapoints)] + address_registration_municipalities = [random.choice(constants.MUNICIPALITIES) for _ in range(amount_datapoints)] + # municipalities does not have the same postal code as postcodes + address_registration_purposes = [random.choice(constants.PURPOSES) for _ in range(amount_datapoints)] + address_registration_surfaces = [random.randint(20, 200) for _ in range(amount_datapoints)] + + addresses = pd.DataFrame({ + 'id': address_ids, + 'street': address_registration_streets, + 'house_number': address_registration_house_numbers, + 'house_number_additions': address_registration_house_number_additions, + 'postcode': address_registration_postcodes, + 'municipality': address_registration_municipalities, + 'purpose': address_registration_purposes, + 'surface': address_registration_surfaces, + }) + print(addresses) + + +def generate_synthetic_buildings(): + building_ids = list(range(1, amount_datapoints + 1)) + # for now there is just 1 building on 1 address + building_address_assignment = [random.shuffle(list(range(1, amount_datapoints + 1)))] + # Generate inception of buildings between now and 32 days ago + building_registration_constructed_ats = [datetime.now(timezone.utc) - + timedelta(seconds=random.randint(1, + int(timedelta(days=32).total_seconds()))) + for _ in range(amount_datapoints)] + building_registration_surfaces = [random.randint(20, 200) for _ in range(amount_datapoints)] + building_types = [random.choice(["Residential", "NonResidential"]) for _ in range(amount_datapoints)] + + building_valuation_values = [random.randint(100000, 1000000) for _ in range(amount_datapoints)] + building_valuation_valuation_ats = [datetime.now(timezone.utc) - + timedelta(seconds=random.randint(1, + int(timedelta(days=6).total_seconds()))) + for _ in range(amount_datapoints)] + building_valuation_effective_ats = [datetime.now(timezone.utc) - + timedelta(seconds=random.randint(1, + int(timedelta(days=8).total_seconds()))) + for _ in range(amount_datapoints)] + # + # building_subject_ninos = + # building_subject_rsins = + # + # building_valuedetermination_ids = + # building_valuedetermination_addressable_object_ids = + # building_valuedetermination_owners = + # building_valuedetermination_occupants = + # building_valuedetermination_registered_persons = + # building_valuedetermination_type = + # building_valuedetermination_values = + # building_valuedetermination_municipality = + + buildings = pd.DataFrame({ + 'id': building_ids, + 'address_assignment': building_address_assignment, + 'registration_constructed_at': building_registration_constructed_ats, + 'registration_surface': building_registration_surfaces, + 'value_determination' + 'type': building_types, + + }) + + print(buildings) + +def generate_synthetic_vehicles(): + vehicle_ids = list(range(1, amount_datapoints + 1)) + vehicle_registration_car_models = [random.choice(constants.CAR_MODELS) for _ in range(amount_datapoints)] + vehicle_registration_number_plates = [fake.license_plate() for _ in range(amount_datapoints)] + + vehicles = pd.DataFrame({ + 'id': vehicle_ids, + 'car_model': vehicle_registration_car_models, + 'number_plate': vehicle_registration_number_plates, + }) + print(vehicles) diff --git a/python_project/__init__.py b/example_datascience/__init__.py similarity index 100% rename from python_project/__init__.py rename to example_datascience/__init__.py diff --git a/python_project/__main__.py b/example_datascience/__main__.py similarity index 100% rename from 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["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"] +xml = ["lxml (>=4.9.2)"] + [[package]] name = "platformdirs" -version = "4.2.0" -description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +version = "4.2.1" +description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`." optional = false python-versions = ">=3.8" files = [ - {file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"}, - {file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"}, + {file = "platformdirs-4.2.1-py3-none-any.whl", hash = "sha256:17d5a1161b3fd67b390023cb2d3b026bbd40abde6fdb052dfbd3a29c3ba22ee1"}, + {file = "platformdirs-4.2.1.tar.gz", hash = "sha256:031cd18d4ec63ec53e82dceaac0417d218a6863f7745dfcc9efe7793b7039bdf"}, ] [package.extras] docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"] test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"] +type = ["mypy (>=1.8)"] [[package]] name = "pluggy" @@ -249,6 +381,31 @@ pluggy = ">=1.5,<2.0" [package.extras] dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +description = "Extensions to the standard Python datetime module" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +files = [ + {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, + {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, +] + +[package.dependencies] +six = ">=1.5" + +[[package]] +name = "pytz" +version = "2024.1" +description = "World timezone definitions, modern and historical" +optional = false +python-versions = "*" +files = [ + {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"}, + {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, +] + [[package]] name = "pyyaml" version = "6.0.1" @@ -351,15 +508,37 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +[[package]] +name = "six" +version = "1.16.0" +description = "Python 2 and 3 compatibility utilities" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +files = [ + {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"}, + {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, +] + +[[package]] +name = "tzdata" +version = "2024.1" +description = "Provider of IANA time zone data" +optional = false +python-versions = ">=2" +files = [ + {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, + {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, +] + [[package]] name = "virtualenv" -version = "20.25.3" +version = "20.26.1" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.25.3-py3-none-any.whl", hash = "sha256:8aac4332f2ea6ef519c648d0bc48a5b1d324994753519919bddbb1aff25a104e"}, - {file = "virtualenv-20.25.3.tar.gz", hash = "sha256:7bb554bbdfeaacc3349fa614ea5bff6ac300fc7c335e9facf3a3bcfc703f45be"}, + {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"}, + {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"}, ] [package.dependencies] @@ -374,4 +553,4 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "58823b751315aa7cad2de9c0e2adeab07fabeb883cec726ab1fc2181a8b25c16" +content-hash = "239389780ec5bc979a65dd248916b04fda7f43d85ff02e784b177fabb2e070d5" diff --git a/publiccode.yml b/publiccode.yml index 3c2de7d..1e8cee1 100644 --- a/publiccode.yml +++ b/publiccode.yml @@ -9,10 +9,10 @@ description: en: features: - Python Template - genericName: python_project + genericName: example_datascience longDescription: | - This a an example python template project. - shortDescription: Python Template project + This a an example data science project + shortDescription: Example Data Science project developmentStatus: concept legal: license: EUPL-1.2 @@ -24,9 +24,9 @@ maintenance: contacts: - name: AI Validatie type: community -name: python_project +name: example_datascience platforms: - web releaseDate: '2024-04-25' softwareType: standalone/web -url: 'https://github.com/MinBZK/python-project-template' +url: 'https://github.com/MinBZK/example-data-science-project' diff --git a/pyproject.toml b/pyproject.toml index 729b94a..ea34a1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [tool.poetry] -name = "python_project" +name = "example_datascience" version = "0.1.0" description = "" authors = ["ai-validation-team "] @@ -7,6 +7,8 @@ readme = "README.md" [tool.poetry.dependencies] python = "^3.11" +pandas = "^2.2.2" +faker = "^25.0.1" [tool.poetry.group.test.dependencies] pytest = "^8.2.0" @@ -46,7 +48,7 @@ fail_under = 95 [tool.coverage.html] directory = "htmlcov" -title = "python_project" +title = "example_datascience" [tool.pytest.ini_options] diff --git a/tests/test_main.py b/tests/test_main.py index c4fe859..c1cad82 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -1,10 +1,10 @@ import subprocess -from python_project import __main__ +from example_datascience import __main__ def test_main_process(): - completed_process = subprocess.run(["python", "-m", "python_project"], capture_output=True, text=True) + completed_process = subprocess.run(["python", "-m", "example_datascience"], capture_output=True, text=True) assert completed_process.returncode == 0 From 0ca1ba160cbe2df3f4239123452ab33256bda78a Mon Sep 17 00:00:00 2001 From: Laurens Weijs Date: Wed, 15 May 2024 15:09:31 +0200 Subject: [PATCH 2/5] Better structure of the project --- .gitignore | 9 +- README.md | 17 +- data/constants.py | 1543 ------------- data/dataset_generator.py | 138 -- data/download_dataset.py | 18 + example_datascience/__main__.py | 291 ++- poetry.lock | 3803 ++++++++++++++++++++++++++++--- pyproject.toml | 16 +- 8 files changed, 3838 insertions(+), 1997 deletions(-) delete mode 100644 data/constants.py delete mode 100644 data/dataset_generator.py create mode 100644 data/download_dataset.py diff --git a/.gitignore b/.gitignore index b4308c2..634bf8f 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,6 @@ __pycache__/ *.py[cod] *$py.class - # Unit test / coverage reports .coverage .coverage.* @@ -15,6 +14,7 @@ __pypackages__/ # Environments .venv +.python-version # pytest .pytest_cache/ @@ -24,3 +24,10 @@ __pypackages__/ # PyCharm config .idea + +# Data files +*.csv +*.pkl + +# log files +*.log diff --git a/README.md b/README.md index 55f1acd..dd7f494 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Python Project Template +# Explame Data Science Project ## Description @@ -7,11 +7,22 @@ we need to have a more truthful example of an algorithm in use by the Dutch Gove The typical steps will be done: +0. exploratory data analysis 1. prepping data 2. training models 3. evaluating models 4. serving a result 5. monitoring -This is all done with the PyCaret framework for now, a large set of frameworks like Tensorflow or lightgbm could be -filled in at a later stage. +The steps does not follow a linear path, but actually loops back whenever one notices models do not have wanted +features. + +## Tools used + +Different frameworks are used for different stages in the project, for the exploratory data analyses `PyCaret` is used to +very quickly check a bunch of models and in which direction to build the "production" model later. For the "production" +model, `scikitlearn` is used to generate a not deep machine learning model. For the analysis on fairness and mitigation +of bias both `FairLearn` and `AIF360` are used. Possible extensions are to use experiment tracking with `MLflow`. + +This repository is inspired by the +[Thesis of Guusje Juijn](https://studenttheses.uu.nl/bitstream/handle/20.500.12932/43868/Thesis%20Guusje%20Final%20Version.pdf?sequence=1&isAllowed=y) diff --git a/data/constants.py b/data/constants.py deleted file mode 100644 index 0b89fec..0000000 --- a/data/constants.py +++ /dev/null @@ -1,1543 +0,0 @@ -"""These constants have been directly taken from -https://gitlab.com/digilab.overheid.nl/ecosystem/synthetic-data-generator/sdg-realm-seeder/-/blob/main/lib/synthetic_data/src/constants.rs""" - -MUNICIPALITIES = [("0997", "Riemer"), ("0998", "Zonnendaal"), ("0999", "Woudendijk")] - -STREET_NAMES = [ - "1e Helmersstraat", - "Abtswoudepad", - "Accason", - "Accumulatorweg", - "Achterspring", - "Adama van Scheltemaplein", - "Admiraal Helfrichstraat", - "Adriaenpad", - "Albert Wittenbergplein", - "Alsemlaan", - "Ambachtsweg", - "Amstelbestpad", - "Amstelslag", - "Amstelweg", - "Amsterdam Rijnkanaalkade", - "Amsterdamse Poort", - "Anna Blamanpad", - "Anna Paulownastraat", - "Anna Pavlovastraat", - "Annie M.G. Schmidtstraat", - "Antillenpad", - "Arena boulevard", - "Aronskelk", - "Arthur van Schendelplantsoen", - "Artillerieweg", - "Atje Keulen-Deelstrastraat", - "Aubade", - "Avegaarplaats", - "Aviolanda", - "Baldermoer", - "Balsemien", - "Barend Biesheuvelstraat", - "Batteryparkstraat", - "Beatrixpark", - "Behoudenvaartweg", - "Benedenduinstraat", - "Beneluxplein", - "Bennebroekerdijk", - "Bensdorpplein", - "Benzolweg", - "Beringenstraat", - "Berzeliusstraat", - "Bessenpad", - "Beukenhof", - "Bietenboulevard", - "Bijlmerweide", - "Boelijn", - "Boerenweteringpad", - "Bohnweg", - "Boorstraat", - "Boterdijkweg", - "Bristou", - "Buiksloterkerkpad", - "Buitengouw", - "Buitenrustbrug Noord", - "Burg. Amersfoordtlaan", - "Burgemeester Bruijn hof", - "Burgemeester Wiegelweg", - "Burg. Fockstraat", - "Burgweg", - "Busweg", - "Carel Willinkgracht", - "Cellebroerssteeg", - "Changing Lane", - "Chocoladeweg", - "Claes van Ruyvenstraat", - "Conakryweg", - "Costa Brava", - "Crommelinbaan", - "Dakotaweg", - "Daveren", - "De Afslag", - "De Bleeckster", - "De Boomgaard", - "De Helpende Hand", - "De Heusweg", - "De Kluftstraat", - "De Lange lont", - "Delftplein", - "De Maalderij", - "Derde Amstelvlietpad", - "Descartesstraat", - "De Veemarkt", - "De Veenen", - "De Wieden", - "Dick Hilleniuspad", - "Disselkade", - "Doctor J.M. den Uylweg", - "Domenico Scarlattilaan", - "Dopplerstraat", - "Dr. D.M. Sluyspad", - "Dreef", - "Dr. J.M. den Uylweg", - "Dr. Lafeberstraat", - "Drontermeerpad", - "Drs. J. van Disweg", - "Durbanweg", - "Dwarsstraat", - "Ecoweg", - "Ecuadorlaan", - "Eerelmanstraat", - "Egeldonk", - "Eilandlaan", - "Elizabeth Cady Stantonplein", - "Elskamp", - "Elzenhaege", - "Elzenhagenpad", - "Engelsesteeg", - "Entrepotdoksluis", - "Erasmuspark", - "Evert Haverkortplein", - "Fagot", - "Firststraat", - "Flammarionstraat", - "Floraparkweg", - "Foekje Dillemastraat", - "Fornebukade", - "Fortgracht", - "Fort Voordorplaan", - "Fossema", - "Franciscus Xaveriuspad", - "Frankemaheerd", - "Ganderweg", - "Ganzenhoefpad", - "Geelbekstraat", - "Geertruida Carelsenpad", - "George Westinghousestraat", - "Gigantenstraat", - "G.J. Scheurleerpad", - "Glauberweg", - "Gleditsialaan", - "Goede Herderspad", - "Gooimeerstraat", - "Gording", - "Gowin Knightstraat", - "Graan voor Visch", - "Groothoeve", - "Grote Lijster", - "Grote Speelweide", - "Grote Steng", - "Hanedastraat", - "Haparandasteiger", - "Hatostraat", - "Hector Berliozlaan", - "Hectorsingel", - "Henk Schijvenaarstraat", - "Het Pontveer", - "Heuvelweg", - "Hilversumse Meentweg", - "H.J. Schimmelstraat", - "H.M. van Randwijkplantsoen", - "Hobo", - "Holendrechterzijweg", - "Hollandweg", - "Hommeldijk", - "Hondsrugweg", - "Honswijkerpad", - "Hoogduinstraat", - "Hoorneboeg", - "Hoyerpad", - "Hugo de Grootweg", - "Huiswoudhof", - "Ierlandhof", - "IJ-promenade", - "Indijkweg", - "Instrumentenweg", - "Irislaan", - "Iyohof", - "Jaagweg", - "Jamaicaplantsoen", - "Jan Beijerpad", - "Jan Boutsteeg", - "Jan Brinkmanstraat", - "Jan Duikerhof", - "Jan Gijzenpad", - "Jan Jacob Slauerhoffpad", - "Jan Pieter Zondervanweg", - "Jan Thoméepad", - "Jan Tooroplaan", - "Jelle Zijlstrastraat", - "J.H. Hisgenpad", - "Johannapark", - "John Henry Wiltonlaan", - "Joop Woortmanplein", - "J.W. Siebbeleshof", - "Kaasmakerstraat", - "Kalkbranderij", - "Kalkovenplein", - "Kalvertoren", - "Kamillelaan", - "Kanaaldijk West", - "Kapitein G.A. Meijerweg", - "Kasterleepark", - "Kattenlaan", - "Kea Boumanlaan", - "Keerweer", - "Kees Abspoelstraat", - "Kees Boekestraat", - "kleilandpad", - "Kleilandpad", - "Kleine Hulzen", - "Kleine Johanneslaan", - "Kleine Wiel", - "Koepelplein", - "Kompaspad", - "Kompasweg", - "Koperstraat", - "Kopkewierpad", - "Kop van de Kaap", - "Korenbloemweg", - "Korenerf", - "Korte bedekte weg", - "Korte Spinhuissteeg", - "Kraaiennestpad", - "Kristalpromenade", - "Kwikzilverstraat", - "Lakenveld", - "Langbroekdreef", - "Langebrug", - "Lavendellaan", - "Leo Heijdenrijkstraat", - "Lieve Vrouwe Gracht", - "Lindenhoevestraat", - "Lindenhoeveweg", - "Lloydplein", - "Louis Hartzstraat", - "Lupinesingel", - "Maarse & Kroon Hof", - "Majoor Bosshardtplantsoen", - "Makassarplein", - "Makerskade", - "Mangaanstraat", - "Mantelmeeuwpad", - "Maraboeweg", - "Marifoonweg", - "Markonstraat", - "Martin Luther Kingpark", - "Mathijs Vermeulenpad", - "Matthijs Vermeulenpad", - "Maurice Ravellaan", - "Maxisweg", - "Meester Jacob Takkade", - "Meyboompad", - "Middenmeerpad", - "Minaret", - "Minaretstraat", - "Minnemoersstraat", - "Molenaarslaan", - "Molensloot", - "Monte Rosaplein", - "Montreallaan", - "Mooie Nelpad", - "Mosterdpotsteeg", - "Muiderzandplaats", - "Muurweg", - "Navigatiepad", - "Nico van Suchtelenpad", - "Nieuwe Passeerdersstraat", - "Nieuwe Steeg", - "Nieuwe Utrechtseweg", - "Noorderduinoord", - "Noorderduinweide", - "Noorderhoutpad", - "Noorderpark", - "Noordkaperweg", - "Noordwal", - "Odysseestraat", - "Ooievaarpad", - "Oosterdreef", - "Oosterschelde", - "Oostoever", - "Orteliuspad", - "Oude Waarderweg", - "Over de Tol", - "Paasbergpad", - "Parlevinkerpad", - "Paterslaan", - "Patriaplein", - "Paviljoenlaan", - "P. Dijkstraplein", - "Pelerin", - "Peltenburgstraat", - "Piarcoplein", - "Pieter Jacobstraat", - "Pietersbergweg", - "Piet Kranenbergpad", - "Piet Moeskopspad", - "Piet van Heerdenplein", - "Pijlstaartpad", - "Platinaweg", - "Polonceaukade", - "Pontsteiger", - "Poortwachtersweg", - "President Kennedyplantsoen", - "Prinsenbrug", - "Prinses Amaliaplein", - "Prinses Catharina-Amalialaan", - "Prinsessenpad", - "Professor W.H. Keesomlaan", - "Propaanweg", - "Provinciale weg", - "Pudongweg", - "Rangoonweg", - "Reaal", - "Regenmeter", - "Regulusplein", - "Reynvaanpad", - "Rietpol", - "Rietsingel", - "Ringweg-Noord", - "Rinse Hofstraweg", - "Rocketstraat", - "Roerdomppad", - "Rossinistraat", - "Rotterdamsepad", - "Rozemarijnsteeg", - "Ruigoord", - "Ruisbeekhof", - "Samoaweg", - "Santoriniweg", - "Schalkwijkpolderweg", - "Schapenplein", - "Schipholplaza", - "Schoterbospad", - "Schouwbroekerbrug", - "Sierteeltstraat", - "Sikorskylaan", - "Simon van Gijnpad", - "Sint Jorissteeg", - "Slego", - "Sleutelstraat", - "Slijterstraat", - "Sloeweg", - "Slotlaan", - "Slowakijehof", - "Smeet", - "Snijdersbergweg", - "Spark Avenue", - "Spijkermanslaan", - "Stapels", - "Stationsplein-Zuidwest", - "Stelling", - "Stenen Hoofd", - "Strawinskypad", - "Subangstraat", - "SugarCity Ring", - "Swanegat", - "Tennispark", - "Termieterweg", - "Ter Spijtstraat", - "Theo Koomenpad", - "Theo van Doesburgplantsoen", - "Titus Brandsmapad", - "Tolsendestraat", - "Tom Schreursenweg", - "Transportstraat", - "Tritomalaan", - "Turbineweg", - "Tussenplaats", - "Tweede Amstelvlietpad", - "ue des Vieilles Douves", - "Uilenburgersteeg", - "Valburgdreef", - "Valkensteeg", - "Van Bommel-Van Dampad", - "van ´t Hoffstraat", - "Van Zeggelenpad", - "Veldpostpark", - "Vendelstraat", - "Venhof", - "Verlengde Rijnkade", - "Verlengde Stellingweg", - "Veroneseplantsoen", - "Vidarstraat", - "Vinkebrug", - "Violenweg", - "Vogelweg", - "Volendamstraat", - "Volkstuinenpad", - "Volmerpad", - "Voltweg", - "Vondelpad", - "Vorticellaweg", - "Vrouwengelukhof", - "Waarderbrug", - "Waarderhaven", - "Wagenweg", - "Walvisvaart", - "Waterpad", - "Waterwolfweg", - "Watze Hilariusweg", - "Westerhoofd", - "Westerkimweg", - "Westerwindpad", - "Wilco Jiskootpad", - "Willem Dreesstraat", - "Willem Johannes Cornelis Arondeusstraat", - "Willem Landréstraat", - "Willem Schermerhornstraat", - "Willem Theunisse Blokstraat", - "Winterspelenplein", - "Zijdelweg", - "Zijlstraat", - "Zijltje", - "Zinkstraat", - "Zomerlindepad", - "Zwartelaantje", -] - -POSTCODE_ADDITIONS = [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "J", - "K", - "L", - "M", - "N", - "O", - "P", - "Q", - "R", - "S", - "T", - "U", - "V", - "W", - "X", - "Y", - "Z", -] - -HOUSE_NUMBER_ADDITIONS = [ - "", - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "J", - "K", - "L", - "M", - "N", - "O", - "P", - "Q", - "R", - "S", - "T", - "U", - "V", - "W", - "X", - "Y", - "Z", -] - -PURPOSES = ["Recreation", "Business", "Habitation"] - -GENDERS = ["M", "F", "X"] - -GIVEN_NAMES_FEMALE = [ - "Emma", - "Julia", - "Sophie", - "Tess", - "Mila", - "Anna", - "Zoë", - "Sara", - "Eva", - "Evi", - "Lotte", - "Saar", - "Nora", - "Olivia", - "Liv", - "Lieke", - "Noor", - "Yara", - "Lynn", - "Fenna", - "Fleur", - "Isa", - "Sarah", - "Maud", - "Lisa", - "Milou", - "Nina", - "Roos", - "Elin", - "Noa", - "Lauren", - "Nova", - "Sofie", - "Emily", - "Loïs", - "Lina", - "Esmee", - "Jasmijn", - "Amy", - "Luna", - "Sanne", - "Hannah", - "Ella", - "Feline", - "Sofia", - "Julie", - "Sophia", - "Fien", - "Maria", - "Femke", - "Vera", - "Anne", - "Naomi", - "Liz", - "Isabella", - "Mia", - "Lara", - "Bo", - "Lena", - "Elise", - "Hailey", - "Amber", - "Floor", - "Charlotte", - "Lizzy", - "Norah", - "Suze", - "Jill", - "Elena", - "Fenne", - "Veerle", - "Isabel", - "Eline", - "Iris", - "Ivy", - "Benthe", - "Rosa", - "Cato", - "Tessa", - "Puck", - "Evy", - "Linde", - "Fay", - "Lize", - "Rosalie", - "Hanna", - "Lana", - "Livia", - "Laura", - "Lola", - "Merel", - "Lily", - "Elif", - "Kiki", - "Fiene", - "Nikki", - "Johanna", - "Juul", - "Suus", - "Romy", - "Noah", -] - -GIVEN_NAMES_MALE = [ - "Lucas", - "Daan", - "Sem", - "Levi", - "Finn", - "Milan", - "Luuk", - "Bram", - "Liam", - "Jesse", - "Mees", - "Sam", - "Thomas", - "Max", - "Thijs", - "Adam", - "Julian", - "Luca", - "Noud", - "Lars", - "Benjamin", - "James", - "Ruben", - "Siem", - "Gijs", - "Jayden", - "Teun", - "Stijn", - "Tim", - "Mats", - "Hugo", - "Boaz", - "Mason", - "Jan", - "Jens", - "Olivier", - "Sven", - "David", - "Dex", - "Guus", - "Vince", - "Floris", - "Jack", - "Tijn", - "Ryan", - "Joep", - "Ties", - "Cas", - "Tygo", - "Jurre", - "Daniël", - "Tom", - "Willem", - "Pepijn", - "Roan", - "Morris", - "Fedde", - "Jason", - "Tobias", - "Owen", - "Pim", - "Dean", - "Mohammed", - "Xavi", - "Abel", - "Mohamed", - "Senn", - "Dani", - "Rayan", - "Quinn", - "Nathan", - "Mick", - "Hidde", - "Samuel", - "Alexander", - "Niek", - "Aiden", - "Stan", - "Johannes", - "Dylan", - "Kai", - "Joshua", - "Thijmen", - "Boris", - "Jace", - "Job", - "Pieter", - "Otis", - "Casper", - "Sepp", - "Zayn", - "Joris", - "Jake", - "Jelle", - "Jip", - "Lenn", - "Koen", - "Jax", - "Jasper", -] - -GIVEN_NAMES_X = [ - "Noah", - "Luca", - "Sem", - "Sam", - "Isa", - "Nova", - "Bo", - "Jip", - "Lou ", - "Charlie", - "Kai", - "Noé", - "Jayden", - "Dani", - "Lio", - "Roan", - "Lenn", - "Melle", - "Ezra", - "Quinn", - "Bowie", - "Puck", - "Robin", - "Novi", - "Beau", - "Bodhi", - "Riley", - "Senna", - "Logan", - "Marley", - "Demi", - "Mace", - "Luka", - "Lowen", - "Jules", - "Alex", - "Anne", - "Joa", - "Bobby", - "Jackie", - "Noël", - "Bodi", - "Jamie", - "Ali", - "Kay", - "Lío", - "Miran", - "Lux", - "Zoey", - "Indy", - "Bobbi", - "Coco", - "Nouri", - "Eden", - "Izzy", - "Vesper", - "Gabriel", - "Juda", - "Marly", - "Micha", - "Eli", - "Riv", - "Teddy", - "Joey", - "Manu", - "Sami", - "Jaydi", - "Nour", - "Diede", - "Nowi", - "Sil", - "Sammie", - "Jaimy", - "Louie", - "Jona", - "Silke", - "Doris", - "Lennon", - "Chris", - "Noï", - "Joes", - "Loet", - "Jessie", - "Louèn", - "Len", - "Marijn", - "Jule", - "Maan", - "Arya", - "Bobbie", - "Maxime", - "Lenny", - "Antoni", - "Kae", - "Revi", - "Royce", - "Billie", - "Eef", - "Puk", - "Scottie", -] - -SURNAMES = [ - "de Jong", - "Jansen", - "de Vries", - "van den Berg", - "van de Berg", - "van der Berg", - "van Dijk", - "Bakker", - "Janssen", - "Visser", - "Smit", - "Meijer", - "Meyer", - "de Boer", - "Mulder", - "de Groot", - "Bos", - "Vos", - "Peters", - "Hendriks", - "van Leeuwen", - "Dekker", - "Brouwer", - "de Wit", - "Dijkstra", - "Smits", - "de Graaf", - "van der Meer", - "van de Meer", - "van der Linden", - "Kok", - "Jacobs", - "de Haan", - "Vermeulen", - "van den Heuvel", - "van der Veen", - "van de Veen", - "van den Broek", - "de Bruijn", - "de Bruyn", - "de Bruin", - "van der Heijden", - "van der Heyden", - "Schouten", - "van Beek", - "Willems", - "van Vliet", - "van de Ven", - "van der Ven", - "Hoekstra", - "Maas", - "Verhoeven", - "Koster", - "van Dam", - "van der Wal", - "van de Wal", - "Prins", - "Blom", - "Huisman", - "Peeters", - "de Jonge", - "Kuipers", - "van Veen", - "Post", - "Kuiper", - "Veenstra", - "Kramer", - "van den Brink", - "van de Brink", - "Scholten", - "van Wijk", - "Postma", - "Martens", - "Vink", - "de Ruiter", - "Timmermans", - "Groen", - "Gerritsen", - "Jonker", - "van Loon", - "Boer", - "van der Velde", - "van den Velde", - "van de Velde", - "Willemsen", - "Smeets", - "de Lange", - "de Vos", - "Bosch", - "van Dongen", - "Schipper", - "de Koning", - "van der Laan", - "Koning", - "van der Velden", - "van de Velden", - "van den Velden", - "Driessen", - "van Doorn", - "Hermans", - "Evers", - "van den Bosch", - "van der Meulen", - "Hofman", - "Bosman", - "Wolters", - "Sanders", - "van der Horst", - "Mol", - "Kuijpers", - "Kuypers", - "Molenaar", - "van de Pol", - "van den Pol", - "van der Pol", - "de Leeuw", - "Verbeek", -] - -CAR_MODELS = [ - "Audi 100", - "Audi 80", - "Audi 90", - "Audi A1", - "Audi A2", - "Audi A3", - "Audi A4", - "Audi A5", - "Audi A6", - "Audi A7", - "Audi A8", - "Audi e-Tron", - "Audi Q2", - "Audi Q3", - "Audi Q4", - "Audi Q5", - "Audi Q7", - "Audi Q8", - "Audi R8", - "Audi TT", - "BMW 1-serie", - "BMW 2-serie", - "BMW 3-serie", - "BMW 4-serie", - "BMW 5-serie", - "BMW 6-serie", - "BMW 7-serie", - "BMW 8-serie", - "BMW i3", - "BMW i4", - "BMW i8", - "BMW iX", - "BMW X1", - "BMW X2", - "BMW X3", - "BMW X4", - "BMW X5", - "BMW X6", - "BMW X7", - "BMW Z3", - "BMW Z4", - "BMW Z8", - "Citroën 2CV", - "Citroën Berlingo", - "Citroën BX", - "Citroën C1", - "Citroën C2", - "Citroën C3", - "Citroën C4", - "Citroën C4 Cactus", - "Citroën C4 SpaceTourer", - "Citroën C5", - "Citroën C5 X", - "Citroën C6", - "Citroën C8", - "Citroën C-Crosser", - "Citroën CX", - "Citroën C-Zero", - "Citroën DS3", - "Citroën DS4", - "Citroën DS5", - "Citroën Evasion", - "Citroën Jumper", - "Citroën Jumpy", - "Citroën Nemo", - "Citroën Saxo", - "Citroën Xantia", - "Citroën XM", - "Citroën Xsara", - "Citroën ZX", - "Cupra Born", - "Cupra Formentor", - "Cupra Leon", - "Dacia Dokker", - "Dacia Duster", - "Dacia Jogger", - "Dacia Lodgy", - "Dacia Logan", - "Dacia Sandero", - "Dacia Spring", - "Daihatsu Applause", - "Daihatsu Charade", - "Daihatsu Copen", - "Daihatsu Cuore", - "Daihatsu Feroza", - "Daihatsu Materia", - "Daihatsu Move", - "Daihatsu Rocky", - "Daihatsu Sirion", - "Daihatsu Terios", - "Daihatsu Trevis", - "Daihatsu Valéra", - "Daihatsu Young RV", - "DS 3", - "DS 3 Crossback", - "DS 4", - "DS 5", - "DS 7 Crossback", - "DS 9", - "Fiat 124", - "Fiat 500", - "Fiat 500L", - "Fiat 500X", - "Fiat 600", - "Fiat Barchetta", - "Fiat Brava", - "Fiat Bravo", - "Fiat Cinquecento", - "Fiat Coupé", - "Fiat Croma", - "Fiat Doblò", - "Fiat Ducato", - "Fiat Fiorino", - "Fiat Fullback", - "Fiat Idea", - "Fiat Marea", - "Fiat Multipla", - "Fiat Palio", - "Fiat Panda", - "Fiat Punto", - "Fiat Qubo", - "Fiat Scudo", - "Fiat Sedici", - "Fiat Seicento", - "Fiat Stilo", - "Fiat Strada", - "Fiat Talento", - "Fiat Tipo", - "Fiat Ulysse", - "Fiat Uno", - "Ford B-MAX", - "Ford C-MAX", - "Ford Ecosport", - "Ford Edge", - "Ford Escort", - "Ford Explorer", - "Ford Fiesta", - "Ford Focus", - "Ford Focus C-Max", - "Ford Fusion", - "Ford Galaxy", - "Ford Ka", - "Ford Kuga", - "Ford Mondeo", - "Ford Mustang", - "Ford Mustang Mach-E", - "Ford Puma", - "Ford Ranger", - "Ford Scorpio", - "Ford Sierra", - "Ford S-MAX", - "Ford Tourneo Connect", - "Ford Tourneo Courier", - "Ford Tourneo Custom", - "Ford Transit Connect", - "Ford Transit Courier", - "Ford Transit Custom", - "Honda Accord", - "Honda Civic", - "Honda CR-V", - "Honda CR-Z", - "Honda e", - "Honda FR-V", - "Honda HR-V", - "Honda Insight", - "Honda Jazz", - "Honda Legend", - "Honda Logo", - "Honda NSX", - "Honda S2000", - "Honda Stream", - "Hyundai Accent", - "Hyundai Atos", - "Hyundai Bayon", - "Hyundai Coupé", - "Hyundai Elantra", - "Hyundai Genesis", - "Hyundai Getz", - "Hyundai Grandeur", - "Hyundai H300", - "Hyundai i10", - "Hyundai i20", - "Hyundai i30", - "Hyundai i40", - "Hyundai Ioniq", - "Hyundai Ioniq 5", - "Hyundai ix20", - "Hyundai ix35", - "Hyundai Kona", - "Hyundai Matrix", - "Hyundai Nexo", - "Hyundai Santa Fe", - "Hyundai Scoupé", - "Hyundai Sonata", - "Hyundai Terracan", - "Hyundai Trajet", - "Hyundai Tucson", - "Hyundai Veloster", - "Hyundai XG", - "Jaguar E-Pace", - "Jaguar F-Pace", - "Jaguar F-type", - "Jaguar I-Pace", - "Jaguar S-type", - "Jaguar XE", - "Jaguar XF", - "Jaguar XJ", - "Jaguar XJS", - "Jaguar XK", - "Jaguar X-type", - "Jeep Cherokee", - "Jeep Commander", - "Jeep Compass", - "Jeep Grand Cherokee", - "Jeep Patriot", - "Jeep Renegade", - "Jeep Wrangler", - "Kia Carens", - "Kia Carnival", - "Kia Ceed", - "Kia Cerato", - "Kia EV6", - "Kia Joice", - "Kia Magentis", - "Kia Mentor", - "Kia Niro", - "Kia Opirus", - "Kia Optima", - "Kia Picanto", - "Kia Pride", - "Kia ProCeed", - "Kia Rio", - "Kia Sephia", - "Kia Shuma", - "Kia Sorento", - "Kia Soul", - "Kia Sportage", - "Kia Stinger", - "Kia Stonic", - "Kia Venga", - "Kia XCeed", - "Lancia Delta", - "Lancia Flavia", - "Lancia Kappa", - "Lancia Lybra", - "Lancia Musa", - "Lancia Phedra", - "Lancia Thema", - "Lancia Thesis", - "Lancia Voyager", - "Lancia Ypsilon", - "Land Rover Defender", - "Land Rover Discovery", - "Land Rover Discovery Sport", - "Land Rover Freelander", - "Land Rover Range Rover", - "Land Rover Range Rover Evoque", - "Land Rover Range Rover Sport", - "Land Rover Range Rover Velar", - "Lexus CT", - "Lexus ES", - "Lexus GS", - "Lexus IS", - "Lexus LC", - "Lexus LS", - "Lexus NX", - "Lexus RC", - "Lexus RX", - "Lexus SC", - "Lexus UX", - "Mazda 121", - "Mazda 2", - "Mazda 3", - "Mazda 323", - "Mazda 5", - "Mazda 6", - "Mazda 626", - "Mazda CX-3", - "Mazda CX-30", - "Mazda CX-5", - "Mazda CX-60", - "Mazda CX-7", - "Mazda CX-9", - "Mazda Demio", - "Mazda MPV", - "Mazda MX-3", - "Mazda MX-30", - "Mazda MX-5", - "Mazda Premacy", - "Mazda RX-8", - "Mazda Tribute", - "Mazda Xedos 6", - "Mazda Xedos 9", - "Mercedes-Benz 190-serie", - "Mercedes-Benz 200-serie", - "Mercedes-Benz A-klasse", - "Mercedes-Benz AMG GT", - "Mercedes-Benz B-klasse", - "Mercedes-Benz Citan", - "Mercedes-Benz C-klasse", - "Mercedes-Benz CL", - "Mercedes-Benz CLA", - "Mercedes-Benz CLC", - "Mercedes-Benz CLK", - "Mercedes-Benz CLS", - "Mercedes-Benz E-klasse", - "Mercedes-Benz EQA", - "Mercedes-Benz EQB", - "Mercedes-Benz EQC", - "Mercedes-Benz EQS", - "Mercedes-Benz EQV", - "Mercedes-Benz G-klasse", - "Mercedes-Benz GL", - "Mercedes-Benz GLA", - "Mercedes-Benz GLB", - "Mercedes-Benz GLC", - "Mercedes-Benz GLE", - "Mercedes-Benz GLK", - "Mercedes-Benz GLS", - "Mercedes-Benz M-klasse", - "Mercedes-Benz R-klasse", - "Mercedes-Benz S-klasse", - "Mercedes-Benz SL", - "Mercedes-Benz SLC", - "Mercedes-Benz SLK", - "Mercedes-Benz SLR McLaren", - "Mercedes-Benz SLS AMG", - "Mercedes-Benz Sprinter", - "Mercedes-Benz Vaneo", - "Mercedes-Benz Viano", - "Mercedes-Benz Vito", - "Mercedes-Benz V-klasse", - "Mercedes-Benz X-klasse", - "MINI 1000", - "MINI Cabrio", - "MINI Clubman", - "MINI Countryman", - "MINI Coupé", - "MINI Mini", - "MINI Paceman", - "MINI Roadster", - "Mitsubishi ASX", - "Mitsubishi Carisma", - "Mitsubishi Colt", - "Mitsubishi Eclipse Cross", - "Mitsubishi Galant", - "Mitsubishi Grandis", - "Mitsubishi i-MiEV", - "Mitsubishi L200", - "Mitsubishi Lancer", - "Mitsubishi Outlander", - "Mitsubishi Outlander Sport", - "Mitsubishi Pajero", - "Mitsubishi Pajero Pinin", - "Mitsubishi Pajero Sport", - "Mitsubishi Space Star", - "Mitsubishi Space Wagon", - "Nissan 100 NX", - "Nissan 300 ZX", - "Nissan 350Z", - "Nissan 370Z", - "Nissan Almera", - "Nissan Almera Tino", - "Nissan Ariya", - "Nissan Cube", - "Nissan Evalia", - "Nissan GT-R", - "Nissan Juke", - "Nissan Leaf", - "Nissan Micra", - "Nissan Murano", - "Nissan Note", - "Nissan NV200", - "Nissan NV300", - "Nissan NV400", - "Nissan Pathfinder", - "Nissan Patrol", - "Nissan Pixo", - "Nissan Primastar", - "Nissan Primera", - "Nissan Pulsar", - "Nissan Qashqai", - "Nissan Terrano", - "Nissan X-Trail", - "Opel Adam", - "Opel Agila", - "Opel Ampera", - "Opel Ampera-e", - "Opel Antara", - "Opel Ascona", - "Opel Astra", - "Opel Calibra", - "Opel Cascada", - "Opel Combo", - "Opel Corsa", - "Opel Crossland", - "Opel Frontera", - "Opel Grandland", - "Opel GT", - "Opel Insignia", - "Opel Kadett", - "Opel Karl", - "Opel Manta", - "Opel Meriva", - "Opel Mokka", - "Opel Monza", - "Opel Movano", - "Opel Omega", - "Opel Rekord", - "Opel Senator", - "Opel Signum", - "Opel Speedster", - "Opel Tigra", - "Opel Tour", - "Opel Vectra", - "Opel Vivaro", - "Opel Zafira", - "Peugeot 1007", - "Peugeot 106", - "Peugeot 107", - "Peugeot 108", - "Peugeot 2008", - "Peugeot 205", - "Peugeot 206", - "Peugeot 207", - "Peugeot 208", - "Peugeot 3008", - "Peugeot 306", - "Peugeot 307", - "Peugeot 308", - "Peugeot 309", - "Peugeot 4007", - "Peugeot 405", - "Peugeot 406", - "Peugeot 407", - "Peugeot 5008", - "Peugeot 504", - "Peugeot 505", - "Peugeot 508", - "Peugeot 605", - "Peugeot 607", - "Peugeot 806", - "Peugeot 807", - "Peugeot Bipper", - "Peugeot Expert", - "Peugeot Ion", - "Peugeot Partner", - "Peugeot RCZ", - "Peugeot Rifter", - "Peugeot Traveller", - "Polestar 1", - "Polestar 2", - "Porsche 718", - "Porsche 911", - "Porsche 918", - "Porsche 924", - "Porsche 928", - "Porsche 944", - "Porsche 968", - "Porsche Boxster", - "Porsche Cayenne", - "Porsche Cayman", - "Porsche Macan", - "Porsche Panamera", - "Porsche Taycan", - "Renault 4", - "Renault 5", - "Renault Arkana", - "Renault Avantime", - "Renault Captur", - "Renault Clio", - "Renault Espace", - "Renault Fluence", - "Renault Kadjar", - "Renault Kangoo", - "Renault Koleos", - "Renault Laguna", - "Renault Master", - "Renault Mégane", - "Renault Modus", - "Renault Safrane", - "Renault Scénic", - "Renaut Talisman", - "Renault Trafic", - "Renault Twingo", - "Renault Twizy", - "Renault Vel Satis", - "Renault Wind", - "Renault Zoe", - "Seat Alhambra", - "Seat Altea", - "Seat Arona", - "Seat Arosa", - "Seat Ateca", - "Seat Cordoba", - "Seat Exeo", - "Seat Ibiza", - "Seat Leon", - "Seat Marbella", - "Seat Mii", - "Seat Tarraco", - "Seat Toledo", - "Skoda Citigo", - "Skoda Enyaq", - "Skoda Fabia", - "Skoda Favorit", - "Skoda Felicia", - "Skoda Kamiq", - "Skoda Karoq", - "Skoda Kodiaq", - "Skoda Octavia", - "Skoda Rapid", - "Skoda Roomster", - "Skoda Scala", - "Skoda Superb", - "Skoda Yeti", - "Smart City-Coupé", - "Smart Crossblade", - "Smart Forfour", - "Smart Fortwo", - "Smart Roadster", - "Suzuki Across", - "Suzuki Alto", - "Suzuki Baleno", - "Suzuki Celerio", - "Suzuki Grand Vitara", - "Suzuki Ignis", - "Suzuki Jimny", - "Suzuki Kizashi", - "Suzuki Liana", - "Suzuki S-Cross", - "Suzuki Splash", - "Suzuki Swace", - "Suzuki Swift", - "Suzuki SX4", - "Suzuki Vitara", - "Suzuki Wagon R+", - "Tesla Model 3", - "Tesla Model S", - "Tesla Model X", - "Tesla Model Y", - "Tesla Roadster", - "Toyota Auris", - "Toyota Avensis", - "Toyota Aygo", - "Toyota Camry", - "Toyota Carina", - "Toyota Celica", - "Toyota C-HR", - "Toyota Corolla", - "Toyota Funcruiser", - "Toyota GR86", - "Toyota GT86", - "Toyota Highlander", - "Toyota Hilux", - "Toyota iQ", - "Toyota Land Cruiser", - "Toyota Mirai", - "Toyota MR2", - "Toyota Previa", - "Toyota Prius", - "Toyota Proace City", - "Toyota RAV4", - "Toyota Starlet", - "Toyota Supra", - "Toyota Urban Cruiser", - "Toyota Verso", - "Toyota Verso-S", - "Toyota Yaris", - "Toyota Yaris Cross", - "Toyota Yaris Verso", - "Volkswagen Amarok", - "Volkswagen Arteon", - "Volkswagen Beetle", - "Volkswagen Bora", - "Volkswagen Caddy", - "Volkswagen CC", - "Volkswagen Corrado", - "Volkswagen Crafter", - "Volkswagen Eos", - "Volkswagen Fox", - "Volkswagen Golf", - "Volkswagen Golf Sportsvan", - "Volkswagen ID3", - "Volkswagen ID4", - "Volkswagen ID5", - "Volkswagen ID6", - "Volkswagen Jetta", - "Volkswagen Lupo", - "Volkswagen Multivan", - "Volkswagen Passat", - "Volkswagen Phaeton", - "Volkswagen Polo", - "Volkswagen Scirocco", - "Volkswagen Sharan", - "Volkswagen Taigo", - "Volkswagen T-Cross", - "Volkswagen Tiguan", - "Volkswagen Touareg", - "Volkswagen Touran", - "Volkswagen Transporter", - "Volkswagen T-Roc", - "Volkswagen Up", - "Volkswagen Vento", - "Volkswagen XL1", - "Volvo 240", - "Volvo 340", - "Volvo 360", - "Volvo 440", - "Volvo 460", - "Volvo 480", - "Volvo 740", - "Volvo 760", - "Volvo 780", - "Volvo 850", - "Volvo 940", - "Volvo 960", - "Volvo C30", - "Volvo C40", - "Volvo C70", - "Volvo S40", - "Volvo S60", - "Volvo S70", - "Volvo S80", - "Volvo S90", - "Volvo V40", - "Volvo V50", - "Volvo V60", - "Volvo V70", - "Volvo V90", - "Volvo XC40", - "Volvo XC60", - "Volvo XC70", - "Volvo XC90", -] diff --git a/data/dataset_generator.py b/data/dataset_generator.py deleted file mode 100644 index 39708cc..0000000 --- a/data/dataset_generator.py +++ /dev/null @@ -1,138 +0,0 @@ -""" -This script is to generate synthetic data, with the Data Generating Process, right now it is completely random and -inspired by the Synthetic Data Generator of Digilab which can be found here -https://gitlab.com/digilab.overheid.nl/ecosystem/synthetic-data-generator/sdg-realm-seeder -""" -import pandas as pd -from data import constants -import random -from faker import Faker -from datetime import datetime, timedelta, timezone -fake = Faker('nl_NL') - -random.seed(69420) -amount_datapoints = 10000 - -def generate_synthetic_persons(): - person_ids = list(range(1,amount_datapoints + 1)) - person_birth_place_of_births = [random.choice(constants.MUNICIPALITIES) for _ in range(amount_datapoints)] - # shuffle ensures no duplicates are found, but this takes very long - # person_birth_national_insurance_number = [random.shuffle(list(range(10000000, 999999999)))[0:amount_datapoints]] - person_birth_national_insurance_number = [random.randint(10000000, 999999999) for _ in range(amount_datapoints)] - person_birth_genders = [random.choice(constants.GENDERS) for _ in range(amount_datapoints)] - # gender is too random right now, this is nothing like the true distribution - person_naming_first_names = [] - for gender in person_birth_genders: - if gender == 'M': - value = random.choice(constants.GIVEN_NAMES_MALE) - elif gender == 'F': - value = random.choice(constants.GIVEN_NAMES_FEMALE) - else: - value = random.choice(constants.GIVEN_NAMES_X) - person_naming_first_names.append(value) - person_naming_last_names = [random.choice(constants.SURNAMES) for _ in range(amount_datapoints)] - # person_addressregistration_address = - # person_addressregistration_municipality = - # Generate inception of persons between now and 32 days ago - person_inceptions = [datetime.now(timezone.utc) - - timedelta(seconds=random.randint(1, int(timedelta(days=32).total_seconds()))) - for _ in range(amount_datapoints)] - - persons = pd.DataFrame({ - 'id': person_ids, - 'place_of_birth': person_birth_place_of_births, - 'national_insurance_number': person_birth_national_insurance_number, - 'gender': person_birth_genders, - 'first_name': person_naming_first_names, - 'last_name': person_naming_last_names, - 'municipality': person_birth_place_of_births, - 'inception': person_inceptions - }) - print(persons) - - -def generate_synthetic_addresses(): - address_ids = list(range(1, amount_datapoints + 1)) - address_registration_streets = [random.choice(constants.STREET_NAMES) for _ in range(amount_datapoints)] - address_registration_house_numbers = [random.randint(1, 100) for _ in range(amount_datapoints)] - address_registration_house_number_additions = [random.choice(constants.HOUSE_NUMBER_ADDITIONS) - for _ in range(amount_datapoints)] - address_registration_postcodes = ['{0}{1}{2}'.format(random.randint(1000, 10000), - random.choice(constants.POSTCODE_ADDITIONS), - random.choice(constants.POSTCODE_ADDITIONS)) - for _ in range(amount_datapoints)] - address_registration_municipalities = [random.choice(constants.MUNICIPALITIES) for _ in range(amount_datapoints)] - # municipalities does not have the same postal code as postcodes - address_registration_purposes = [random.choice(constants.PURPOSES) for _ in range(amount_datapoints)] - address_registration_surfaces = [random.randint(20, 200) for _ in range(amount_datapoints)] - - addresses = pd.DataFrame({ - 'id': address_ids, - 'street': address_registration_streets, - 'house_number': address_registration_house_numbers, - 'house_number_additions': address_registration_house_number_additions, - 'postcode': address_registration_postcodes, - 'municipality': address_registration_municipalities, - 'purpose': address_registration_purposes, - 'surface': address_registration_surfaces, - }) - print(addresses) - - -def generate_synthetic_buildings(): - building_ids = list(range(1, amount_datapoints + 1)) - # for now there is just 1 building on 1 address - building_address_assignment = [random.shuffle(list(range(1, amount_datapoints + 1)))] - # Generate inception of buildings between now and 32 days ago - building_registration_constructed_ats = [datetime.now(timezone.utc) - - timedelta(seconds=random.randint(1, - int(timedelta(days=32).total_seconds()))) - for _ in range(amount_datapoints)] - building_registration_surfaces = [random.randint(20, 200) for _ in range(amount_datapoints)] - building_types = [random.choice(["Residential", "NonResidential"]) for _ in range(amount_datapoints)] - - building_valuation_values = [random.randint(100000, 1000000) for _ in range(amount_datapoints)] - building_valuation_valuation_ats = [datetime.now(timezone.utc) - - timedelta(seconds=random.randint(1, - int(timedelta(days=6).total_seconds()))) - for _ in range(amount_datapoints)] - building_valuation_effective_ats = [datetime.now(timezone.utc) - - timedelta(seconds=random.randint(1, - int(timedelta(days=8).total_seconds()))) - for _ in range(amount_datapoints)] - # - # building_subject_ninos = - # building_subject_rsins = - # - # building_valuedetermination_ids = - # building_valuedetermination_addressable_object_ids = - # building_valuedetermination_owners = - # building_valuedetermination_occupants = - # building_valuedetermination_registered_persons = - # building_valuedetermination_type = - # building_valuedetermination_values = - # building_valuedetermination_municipality = - - buildings = pd.DataFrame({ - 'id': building_ids, - 'address_assignment': building_address_assignment, - 'registration_constructed_at': building_registration_constructed_ats, - 'registration_surface': building_registration_surfaces, - 'value_determination' - 'type': building_types, - - }) - - print(buildings) - -def generate_synthetic_vehicles(): - vehicle_ids = list(range(1, amount_datapoints + 1)) - vehicle_registration_car_models = [random.choice(constants.CAR_MODELS) for _ in range(amount_datapoints)] - vehicle_registration_number_plates = [fake.license_plate() for _ in range(amount_datapoints)] - - vehicles = pd.DataFrame({ - 'id': vehicle_ids, - 'car_model': vehicle_registration_car_models, - 'number_plate': vehicle_registration_number_plates, - }) - print(vehicles) diff --git a/data/download_dataset.py b/data/download_dataset.py new file mode 100644 index 0000000..e8351b9 --- /dev/null +++ b/data/download_dataset.py @@ -0,0 +1,18 @@ +import kaggle +import os.path + + +def download_from_kaggle(datapath): + """ + Downloads the dataset from Kaggle, the authentication needs to be stored locally in the home directy as a + kaggle.json more info on the website of Kaggle + [here](https://www.kaggle.com/docs/api#getting-started-installation-&-authentication). + :param datapath: The path to the datafile + :return: 0 + """ + if not os.path.isfile(datapath): + kaggle.api.authenticate() + kaggle.api.dataset_download_files('ictinstitute/utrecht-fairness-recruitment-dataset', + path='./', + unzip=True) + return 0 diff --git a/example_datascience/__main__.py b/example_datascience/__main__.py index fbe04d3..aee81d0 100644 --- a/example_datascience/__main__.py +++ b/example_datascience/__main__.py @@ -1,15 +1,298 @@ import sys +import pandas as pd +import numpy as np +from pycaret.classification import ClassificationExperiment, create_model, check_fairness +from fairlearn.metrics import MetricFrame, selection_rate, false_positive_rate, false_negative_rate, count, demographic_parity_ratio, demographic_parity_difference +from sklearn.metrics import accuracy_score, precision_score +from fairlearn.reductions import DemographicParity, ExponentiatedGradient +from aif360.metrics import BinaryLabelDatasetMetric +from sklearn.preprocessing import OneHotEncoder +from sklearn.preprocessing import StandardScaler +from sklearn.compose import ColumnTransformer +from aif360.datasets import StandardDataset +from data.download_dataset import download_from_kaggle +import lightgbm as lgb +from sklearn.pipeline import make_pipeline +from sklearn.pipeline import Pipeline +from sklearn.model_selection import cross_val_predict +import joblib +np.random.seed(69) + + +def exploratory_data_analysis(): + """ + This function will do the exploratory data analysis (EDA) on the recruitment dataset of the University of Utrecht. + In most project this is done in a Jupyter Notebook as this makes interaction easier, but a slice of the EDA is still + implementated to give you an idea. + :return: pd.Dataframe containing the data + """ + datapath = './data/recruitmentdataset-2022-1.3.csv' + download_from_kaggle(datapath) + + df = pd.read_csv(datapath, index_col='Id') + + # print(df.head()) # The data is about whether based on some characteristic people are hired or not + # print(df.shape) # There are 4000 datapoints with 13 feature columns and 1 label column + # print(df[df.duplicated()]) # There are 39 duplicate values, but this is to be expected based on the categorical features + # print(df.isna().sum()) # There are no missing values + # print(df.describe()) # There are just 3 numerical features, which are the age, university grade, and languages. + # print(df.columns) + cat_cols = ['gender', 'nationality', 'sport', 'ind-debateclub', 'ind-programming_exp', 'ind-international_exp', + 'ind-entrepeneur_exp', 'ind-exact_study', 'company', 'decision'] + + for col in cat_cols: + categories = df.groupby(col).size() + # print(categories) + # From the categorical features we see that the non-binary gender is underrepresented although it is from the true + # distribution of society at this moment. The same holds for nationality where Dutch is overrepresented. almost 3/4 + # of the applicants got rejected which we need to take into account in our model as well. + return df + + +def preprocessing_data(data): + """ + Preprocessing of the dataset, on the one hand for the AIF360 we need to parse the dataset to a StandardDataset + which is an object needed to be able to use the features of AIF360. Also for the machine learning models it is very + beneficial to transform the features, like one-hot encodings for the categorical variables or scaling of numerical + features, like stated in this book [Machine Learning Design Patterns](https://g.co/kgs/tQhwiL5). + :param data: pandas Dataframe as the input data + :return: (preprocessor_pipeline, preprocessed_data ): Tuple of a Sklearn Pipeline to process the dataset and + the preprocessed data + """ + preprocessor_pipeline = ColumnTransformer( + [ + ("numerical", StandardScaler(), ["age", "ind-university_grade", "ind-languages"]), + ("categorical", OneHotEncoder(sparse_output=False), ["gender", + "nationality", + "sport", + "ind-degree", + "company", + "ind-debateclub", + "ind-programming_exp", + "ind-international_exp", + "ind-exact_study", + "ind-entrepeneur_exp"]), + ], + verbose_feature_names_out=False, + ) + preprocessor_pipeline.set_output(transform="pandas") + preprocessed_data = preprocessor_pipeline.fit_transform(data) + return preprocessor_pipeline, preprocessed_data -def main() -> int: - # implement python validation - # implement argument parsing +def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_analysis=False): + """ + This function will train a model on the dataframe provided + :param preprocessing_pipeline: sklearn pipeline with the preprocessing steps for the dataset + :param x_data: pandas Dataframe as the input data but only the features + :param y_data: pandas Dataframe as the input data but only the dependent variable + :param exploratory_model_analysis: Boolean value to indicate whether to run the pycaret model compare method + :return: 0: does not return anything as the trained model is stored locally + """ + # First let's do some exploratory training analysis, to find out what type of models would perform well on the + # dataset at hand. As the value to predict is a binary choice between True or False, getting hired or not, this + # is a binary classification problem. - # read config directory + if exploratory_model_analysis: + # This piece of code will run a couple of models with PyCaret, and will generate summary statistics for the + # models like accuracy, and F-Score. + exp = ClassificationExperiment() + x_data['decision'] = y_data + exp.setup(x_data, target='decision', system_log='./data/logs.log') + exp.compare_models() + # From this exploratory training analysis comes forward that boosting methods are the best w.r.t. Accuracy, so + # moving forward in the scikit learn pipeline the lightGBM Classifier will be used. + classifier = Pipeline(steps=[ + ('classifier', lgb.LGBMClassifier()) + ]) + classifier.fit(x_data, y_data) + # potential improvement to use a cross validation instead of fit (to overcome overfitting) like + from sklearn.model_selection import RandomizedSearchCV + # param_dist = { + # 'classifier__bagging_fraction': (0.5, 0.8), + # 'classifier__feature_fraction': (0.5, 0.8), + # 'classifier__max_depth': (10, 13), + # 'classifier__min_data_in_leaf': (90, 120), + # 'classifier__num_leaves': (1200, 1550) + # } + # search = RandomizedSearchCV(classifier, scoring='average_precision', cv=5, + # n_iter=10, verbose=True, param_distributions=param_dist) + # search.fit(x_data, y_data) + # classifier = search.best_estimator_ + + # store the preprocessing pipeline together with the classifier pipeline for later serving of the model + complete_pipeline = Pipeline(steps=[ + ('Preprocessing', preprocessing_pipeline), + ('classifier', classifier) + ]) + joblib.dump(complete_pipeline, './data/model/recruitment_lightgbm_model.pkl') + return 0 + +def evaluating_model(classifier, x_data, y_data): + """ + This function will evaluate a model whether it adheres to specific requirements we set. Specifically whether it + adheres to bias requirements. If it doesn't adhere we need to go back in the previous steps and fix the + preprocessing steps or the model hyperparameters. As this is an exmaple project, only suggestions will be done and + not implemented as this would worsen the flow of the script. + :param classifier: sklearn pipeline with the classifier model in it + :param x_data: + :param y_data: + :return: + """ return 0 +def serving_a_model(data): + """ + This function will 'serve' the model, normally when serving a model one would include the preprocessing steps also + within the model/pipeline. Therefore, this way of passing just the classifier and the preprocessed data would not + suffice. Also the serving of a model is generally that to an API/package you pass the input data and get the + predicted result back. + :param data: Pandas Dataframe containing the data + :return: prediction_results: a list of boolean values for each datapoint a prediction + """ + complete_pipeline = joblib.load('./data/model/recruitment_lightgbm_model.pkl') + return complete_pipeline.predict(data) + +def monitor_the_model(): + """ + If data drift occurs in the model we will see in the accuracy of the model (if we also have the true labels) + declining. But even without the true labels we can also investigate whether the data distribution changes over + time. + :return: + """ + return 0 + + +def convert_to_standard_dataset_for_aif360(df, target_label_name, scores_name=""): + protected_attributes = [] + + # columns from the dataset that we want to select for this Bias study + selected_features = ['gender', 'age'] + + privileged_classes = [[]] + + favorable_target_label = [1] + + # List of column names in the DataFrame which are to be expanded into one-hot vectors. + categorical_features = ['gender', 'nationality', 'sport', 'ind-degree', 'company'] + + # create the `StandardDataset` object + standard_dataset = StandardDataset(df=df, label_name=target_label_name, + favorable_classes=favorable_target_label, + scores_name=scores_name, + protected_attribute_names=protected_attributes, + privileged_classes=privileged_classes, + categorical_features=categorical_features, + features_to_keep=selected_features) + if scores_name == "": + standard_dataset.scores = standard_dataset.labels.copy() + + return standard_dataset + + +def main() -> int: + data = exploratory_data_analysis() + preprocessor_pipeline, preprocessed_data = preprocessing_data(data) + training_model(preprocessor_pipeline, preprocessed_data, data['decision'], exploratory_model_analysis=False) + serving_a_model(data) + + # # models = exp.compare_models(include=['lr', 'dt', 'knn', 'catboost']) + # models = exp.compare_models() + # + # # dt = exp.create_model('dt') + # # catboost = exp.create_model('catboost') + # + # # 3. Evaluating models + # # Experiment Tracking met mlflow (potentieel ook dataset tracking, maar voor nu doen we dat nog niet?) + # + # # save model + # # exp.save_model(catboost, 'catboost_pipeline') + # catboost = exp.load_model('catboost_pipeline') + # + # # 3.1 evaluating fairness-related metrics + # # y_pred = exp.predict_model(catboost, data=unseen_data).loc[:, "prediction_label"] + # y_pred = exp.predict_model(catboost, data=unseen_data) + # y_true = unseen_data.loc[:, "decision"] + # gender = unseen_data.loc[:, "gender"] + # # dp = demographic_parity_difference(y_true=y_true, y_pred=y_pred, sensitive_features=gender) + # # print(dp) + # # dp = demographic_parity_ratio(y_true=y_true, y_pred=y_pred, sensitive_features=gender) + # # Deze geeft een ratio van 0.67 aan wat betekent dat er vaker males worden geselecteerd dan females door het model + # # mfx = MetricFrame(metrics=accuracy_score, y_true=y_true, y_pred=y_pred, sensitive_features=gender) + # # sr = MetricFrame(metrics=selection_rate, y_true=y_true, y_pred=y_pred, sensitive_features=gender) + # # print(dp) + # # print(sr.by_group) + # + # # ## check fairness out of the box of pycaret + # # catboost_fairness = exp.check_fairness(catboost, sensitive_features=['gender']) + # # print(catboost_fairness) + # + # ## check mitigation of demographic disparity of gender via fairlearn + # # constraint = DemographicParity() + # # mitigator = ExponentiatedGradient(catboost, constraint) + # # exp.traiN_model + # X_train_transformed = exp.get_config(variable="X_train_transformed") + # train_gender_transformed = exp.get_config(variable="X_train_transformed").loc[:, ["gender_female", "gender_male", "gender_other"]] + # y_train_transformed = exp.get_config(variable="y_train_transformed") + # # mitigator.fit(X_train_transformed, y_train_transformed, sensitive_features=train_gender_transformed) + # # y_pred_mitigated = mitigator.predict(unseen_data) + # # sr_mitigated = MetricFrame(metrics=selection_rate, y_true=y_true, y_pred=y_pred_mitigated, sensitive_features=test_gender) + # # print(sr_mitigated.overall) + # # print(sr_mitigated.by_group) + # # + # + # + # ## plotting of the fairness metrics w.r.t. gender + # # metrics = { + # # "accuracy": accuracy_score, + # # "precision": precision_score, + # # "false positive rate": false_positive_rate, + # # "false negative rate": false_negative_rate, + # # "selection rate": selection_rate, + # # "count": count, + # # } + # # metric_frame = MetricFrame( + # # metrics=metrics, y_true=y_true, y_pred=y_pred, sensitive_features=gender + # # ) + # # fig = metric_frame.by_group.plot.bar( + # # subplots=True, + # # layout=[3, 3], + # # legend=False, + # # figsize=[12, 8], + # # title="Show all metrics", + # # ) + # # fig[0][0].figure.savefig("bias.png") + # # print(unseen_data.loc[unseen_data['gender'] == 'other']) + # + # # 4. Analysis & Interpretability + # # exp.plot_model(catboost, plot='confusion_matrix') + # # exp.plot_model(catboost, plot='auc') + # exp.plot_model(catboost, plot='feature', save=True) + # + # # 4. Serving a result + # + # # 5. Monitoring + # pred_dataset = exp.predict_model(catboost, data=unseen_data) + # X_train_transformed = exp.get_config(variable="X_train_transformed") + # print(X_train_transformed.columns) + # print(pred_dataset.columns) + ### TESTING WITH AIF360 packages + + # Metric for the original dataset + # standard_dataset_pred_aif360 = convert_to_standard_dataset_for_aif360(exp.predict_model(catboost, data=unseen_data), + # target_label_name='prediction_label', + # scores_name='prediction_score') + # # metric_orig_train = BinaryLabelDatasetMetric(X_train_transformed, + # # unprivileged_groups=[{'Gender': 'other'}], + # # privileged_groups=[{'Gender': 'male'}]) + # print(metric_orig_train) + + + + # bias mitigating results + return 0 if __name__ == "__main__": # pragma: no cover sys.exit(main()) diff --git a/poetry.lock b/poetry.lock index 22f4865..0466d8c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,215 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed 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"more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + [metadata] lock-version = "2.0" -python-versions = "^3.11" -content-hash = "239389780ec5bc979a65dd248916b04fda7f43d85ff02e784b177fabb2e070d5" +python-versions = ">=3.11,<3.13" +content-hash = "6644b67f69724e237e2513239154ad0c8e2c2429ded8314a4b2fe64fb5358218" diff --git a/pyproject.toml b/pyproject.toml index ea34a1f..0689c2f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,9 +6,17 @@ authors = ["ai-validation-team "] readme = "README.md" [tool.poetry.dependencies] -python = "^3.11" -pandas = "^2.2.2" -faker = "^25.0.1" +python = ">=3.11,<3.13" +kaleido = "0.2.1" +pycaret = "^3.3.2" +pandas = "2.1.4" +kaggle = "^1.6.12" +xgboost = "^2.0.3" +catboost = "^1.2.5" +mlflow = "^2.12.1" +fairlearn = "^0.10.0" +matplotlib = "3.7.5" +aif360 = "^0.6.1" [tool.poetry.group.test.dependencies] pytest = "^8.2.0" @@ -34,7 +42,7 @@ select = ["I", "SIM", "B", "UP", "F", "E"] fixable = ["ALL"] [tool.pyright] -pythonVersion = "3.11" +pythonVersion = ">=3.11,<3.13" typeCheckingMode = "strict" reportMissingImports = true reportMissingTypeStubs = true From 75ec80524467942a8f9c824a2edaa8af54248c27 Mon Sep 17 00:00:00 2001 From: Laurens Weijs Date: Wed, 15 May 2024 15:18:50 +0200 Subject: [PATCH 3/5] Minor additions --- README.md | 12 ++-- data/download_dataset.py | 4 +- example_datascience/__main__.py | 100 ++++++++++++++++++++------------ 3 files changed, 71 insertions(+), 45 deletions(-) diff --git a/README.md b/README.md index dd7f494..43a852f 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Explame Data Science Project +# Example Data Science Project ## Description @@ -14,7 +14,7 @@ The typical steps will be done: 4. serving a result 5. monitoring -The steps does not follow a linear path, but actually loops back whenever one notices models do not have wanted +The steps do not follow a linear path, but actually loops back whenever one notices models do not have wanted features. ## Tools used @@ -22,7 +22,11 @@ features. Different frameworks are used for different stages in the project, for the exploratory data analyses `PyCaret` is used to very quickly check a bunch of models and in which direction to build the "production" model later. For the "production" model, `scikitlearn` is used to generate a not deep machine learning model. For the analysis on fairness and mitigation -of bias both `FairLearn` and `AIF360` are used. Possible extensions are to use experiment tracking with `MLflow`. +of bias both `FairLearn` and `AIF360` are used. Possible extensitons are: -This repository is inspired by the +- For the analysis on data drift for the monitoring of the model `evidentlyai` + +- For experiment tracking with `MLflow`. + +This repository is inspired by the [Thesis of Guusje Juijn](https://studenttheses.uu.nl/bitstream/handle/20.500.12932/43868/Thesis%20Guusje%20Final%20Version.pdf?sequence=1&isAllowed=y) diff --git a/data/download_dataset.py b/data/download_dataset.py index e8351b9..8b76507 100644 --- a/data/download_dataset.py +++ b/data/download_dataset.py @@ -12,7 +12,5 @@ def download_from_kaggle(datapath): """ if not os.path.isfile(datapath): kaggle.api.authenticate() - kaggle.api.dataset_download_files('ictinstitute/utrecht-fairness-recruitment-dataset', - path='./', - unzip=True) + kaggle.api.dataset_download_files("ictinstitute/utrecht-fairness-recruitment-dataset", path="./", unzip=True) return 0 diff --git a/example_datascience/__main__.py b/example_datascience/__main__.py index aee81d0..eb04e3e 100644 --- a/example_datascience/__main__.py +++ b/example_datascience/__main__.py @@ -2,7 +2,15 @@ import pandas as pd import numpy as np from pycaret.classification import ClassificationExperiment, create_model, check_fairness -from fairlearn.metrics import MetricFrame, selection_rate, false_positive_rate, false_negative_rate, count, demographic_parity_ratio, demographic_parity_difference +from fairlearn.metrics import ( + MetricFrame, + selection_rate, + false_positive_rate, + false_negative_rate, + count, + demographic_parity_ratio, + demographic_parity_difference, +) from sklearn.metrics import accuracy_score, precision_score from fairlearn.reductions import DemographicParity, ExponentiatedGradient from aif360.metrics import BinaryLabelDatasetMetric @@ -27,10 +35,10 @@ def exploratory_data_analysis(): implementated to give you an idea. :return: pd.Dataframe containing the data """ - datapath = './data/recruitmentdataset-2022-1.3.csv' + datapath = "./data/recruitmentdataset-2022-1.3.csv" download_from_kaggle(datapath) - df = pd.read_csv(datapath, index_col='Id') + df = pd.read_csv(datapath, index_col="Id") # print(df.head()) # The data is about whether based on some characteristic people are hired or not # print(df.shape) # There are 4000 datapoints with 13 feature columns and 1 label column @@ -38,8 +46,18 @@ def exploratory_data_analysis(): # print(df.isna().sum()) # There are no missing values # print(df.describe()) # There are just 3 numerical features, which are the age, university grade, and languages. # print(df.columns) - cat_cols = ['gender', 'nationality', 'sport', 'ind-debateclub', 'ind-programming_exp', 'ind-international_exp', - 'ind-entrepeneur_exp', 'ind-exact_study', 'company', 'decision'] + cat_cols = [ + "gender", + "nationality", + "sport", + "ind-debateclub", + "ind-programming_exp", + "ind-international_exp", + "ind-entrepeneur_exp", + "ind-exact_study", + "company", + "decision", + ] for col in cat_cols: categories = df.groupby(col).size() @@ -63,16 +81,22 @@ def preprocessing_data(data): preprocessor_pipeline = ColumnTransformer( [ ("numerical", StandardScaler(), ["age", "ind-university_grade", "ind-languages"]), - ("categorical", OneHotEncoder(sparse_output=False), ["gender", - "nationality", - "sport", - "ind-degree", - "company", - "ind-debateclub", - "ind-programming_exp", - "ind-international_exp", - "ind-exact_study", - "ind-entrepeneur_exp"]), + ( + "categorical", + OneHotEncoder(sparse_output=False), + [ + "gender", + "nationality", + "sport", + "ind-degree", + "company", + "ind-debateclub", + "ind-programming_exp", + "ind-international_exp", + "ind-exact_study", + "ind-entrepeneur_exp", + ], + ), ], verbose_feature_names_out=False, ) @@ -98,15 +122,13 @@ def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_ana # This piece of code will run a couple of models with PyCaret, and will generate summary statistics for the # models like accuracy, and F-Score. exp = ClassificationExperiment() - x_data['decision'] = y_data - exp.setup(x_data, target='decision', system_log='./data/logs.log') + x_data["decision"] = y_data + exp.setup(x_data, target="decision", system_log="./data/logs.log") exp.compare_models() # From this exploratory training analysis comes forward that boosting methods are the best w.r.t. Accuracy, so # moving forward in the scikit learn pipeline the lightGBM Classifier will be used. - classifier = Pipeline(steps=[ - ('classifier', lgb.LGBMClassifier()) - ]) + classifier = Pipeline(steps=[("classifier", lgb.LGBMClassifier())]) classifier.fit(x_data, y_data) # potential improvement to use a cross validation instead of fit (to overcome overfitting) like from sklearn.model_selection import RandomizedSearchCV @@ -123,13 +145,11 @@ def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_ana # classifier = search.best_estimator_ # store the preprocessing pipeline together with the classifier pipeline for later serving of the model - complete_pipeline = Pipeline(steps=[ - ('Preprocessing', preprocessing_pipeline), - ('classifier', classifier) - ]) - joblib.dump(complete_pipeline, './data/model/recruitment_lightgbm_model.pkl') + complete_pipeline = Pipeline(steps=[("Preprocessing", preprocessing_pipeline), ("classifier", classifier)]) + joblib.dump(complete_pipeline, "./data/model/recruitment_lightgbm_model.pkl") return 0 + def evaluating_model(classifier, x_data, y_data): """ This function will evaluate a model whether it adheres to specific requirements we set. Specifically whether it @@ -143,6 +163,7 @@ def evaluating_model(classifier, x_data, y_data): """ return 0 + def serving_a_model(data): """ This function will 'serve' the model, normally when serving a model one would include the preprocessing steps also @@ -152,9 +173,10 @@ def serving_a_model(data): :param data: Pandas Dataframe containing the data :return: prediction_results: a list of boolean values for each datapoint a prediction """ - complete_pipeline = joblib.load('./data/model/recruitment_lightgbm_model.pkl') + complete_pipeline = joblib.load("./data/model/recruitment_lightgbm_model.pkl") return complete_pipeline.predict(data) + def monitor_the_model(): """ If data drift occurs in the model we will see in the accuracy of the model (if we also have the true labels) @@ -169,23 +191,26 @@ def convert_to_standard_dataset_for_aif360(df, target_label_name, scores_name="" protected_attributes = [] # columns from the dataset that we want to select for this Bias study - selected_features = ['gender', 'age'] + selected_features = ["gender", "age"] privileged_classes = [[]] favorable_target_label = [1] # List of column names in the DataFrame which are to be expanded into one-hot vectors. - categorical_features = ['gender', 'nationality', 'sport', 'ind-degree', 'company'] + categorical_features = ["gender", "nationality", "sport", "ind-degree", "company"] # create the `StandardDataset` object - standard_dataset = StandardDataset(df=df, label_name=target_label_name, - favorable_classes=favorable_target_label, - scores_name=scores_name, - protected_attribute_names=protected_attributes, - privileged_classes=privileged_classes, - categorical_features=categorical_features, - features_to_keep=selected_features) + standard_dataset = StandardDataset( + df=df, + label_name=target_label_name, + favorable_classes=favorable_target_label, + scores_name=scores_name, + protected_attribute_names=protected_attributes, + privileged_classes=privileged_classes, + categorical_features=categorical_features, + features_to_keep=selected_features, + ) if scores_name == "": standard_dataset.scores = standard_dataset.labels.copy() @@ -195,7 +220,7 @@ def convert_to_standard_dataset_for_aif360(df, target_label_name, scores_name="" def main() -> int: data = exploratory_data_analysis() preprocessor_pipeline, preprocessed_data = preprocessing_data(data) - training_model(preprocessor_pipeline, preprocessed_data, data['decision'], exploratory_model_analysis=False) + training_model(preprocessor_pipeline, preprocessed_data, data["decision"], exploratory_model_analysis=False) serving_a_model(data) # # models = exp.compare_models(include=['lr', 'dt', 'knn', 'catboost']) @@ -289,10 +314,9 @@ def main() -> int: # # privileged_groups=[{'Gender': 'male'}]) # print(metric_orig_train) - - # bias mitigating results return 0 + if __name__ == "__main__": # pragma: no cover sys.exit(main()) From 73e57b5161c8470c4c3cad959aabbe9243dde1f3 Mon Sep 17 00:00:00 2001 From: Laurens Weijs Date: Wed, 15 May 2024 16:33:47 +0200 Subject: [PATCH 4/5] Adding extra improvements to the model + monitoring on data drift and bias evaluation --- README.md | 4 +- example_datascience/__main__.py | 238 ++---- poetry.lock | 1265 ++++++++++++++++++++++++++++++- pyproject.toml | 2 + 4 files changed, 1339 insertions(+), 170 deletions(-) diff --git a/README.md b/README.md index 43a852f..420d76b 100644 --- a/README.md +++ b/README.md @@ -24,9 +24,9 @@ very quickly check a bunch of models and in which direction to build the "produc model, `scikitlearn` is used to generate a not deep machine learning model. For the analysis on fairness and mitigation of bias both `FairLearn` and `AIF360` are used. Possible extensitons are: -- For the analysis on data drift for the monitoring of the model `evidentlyai` +- For the analysis on data drift for the monitoring of the model `evidentlyai`, now just a very small part has been used -- For experiment tracking with `MLflow`. +- For experiment tracking and logging with `MLflow`. This repository is inspired by the [Thesis of Guusje Juijn](https://studenttheses.uu.nl/bitstream/handle/20.500.12932/43868/Thesis%20Guusje%20Final%20Version.pdf?sequence=1&isAllowed=y) diff --git a/example_datascience/__main__.py b/example_datascience/__main__.py index eb04e3e..83dd549 100644 --- a/example_datascience/__main__.py +++ b/example_datascience/__main__.py @@ -1,29 +1,18 @@ import sys -import pandas as pd + +import joblib +import lightgbm as lgb import numpy as np -from pycaret.classification import ClassificationExperiment, create_model, check_fairness -from fairlearn.metrics import ( - MetricFrame, - selection_rate, - false_positive_rate, - false_negative_rate, - count, - demographic_parity_ratio, - demographic_parity_difference, -) -from sklearn.metrics import accuracy_score, precision_score -from fairlearn.reductions import DemographicParity, ExponentiatedGradient -from aif360.metrics import BinaryLabelDatasetMetric -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import StandardScaler +import pandas as pd +from evidently.test_suite import TestSuite +from evidently.tests import TestColumnDrift +from fairlearn.metrics import MetricFrame, demographic_parity_difference, selection_rate +from pycaret.classification import ClassificationExperiment from sklearn.compose import ColumnTransformer -from aif360.datasets import StandardDataset -from data.download_dataset import download_from_kaggle -import lightgbm as lgb -from sklearn.pipeline import make_pipeline from sklearn.pipeline import Pipeline -from sklearn.model_selection import cross_val_predict -import joblib +from sklearn.preprocessing import OneHotEncoder, StandardScaler + +from data.download_dataset import download_from_kaggle np.random.seed(69) @@ -40,12 +29,11 @@ def exploratory_data_analysis(): df = pd.read_csv(datapath, index_col="Id") - # print(df.head()) # The data is about whether based on some characteristic people are hired or not - # print(df.shape) # There are 4000 datapoints with 13 feature columns and 1 label column - # print(df[df.duplicated()]) # There are 39 duplicate values, but this is to be expected based on the categorical features - # print(df.isna().sum()) # There are no missing values - # print(df.describe()) # There are just 3 numerical features, which are the age, university grade, and languages. - # print(df.columns) + df.head() # The data is about whether based on some characteristic people are hired or not + df.shape # There are 4000 datapoints with 13 feature columns and 1 label column + df[df.duplicated()] # There are 39 duplicate values, but this is to be expected based on the categorical features + df.isna().sum() # There are no missing values + df.describe() # There are just 3 numerical features, which are the age, university grade, and languages. cat_cols = [ "gender", "nationality", @@ -68,7 +56,7 @@ def exploratory_data_analysis(): return df -def preprocessing_data(data): +def preprocessing_data(data: pd.DataFrame) -> (Pipeline, pd.DataFrame): """ Preprocessing of the dataset, on the one hand for the AIF360 we need to parse the dataset to a StandardDataset which is an object needed to be able to use the features of AIF360. Also for the machine learning models it is very @@ -105,7 +93,13 @@ def preprocessing_data(data): return preprocessor_pipeline, preprocessed_data -def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_analysis=False): +def training_model( + preprocessing_pipeline: Pipeline, + x_data: pd.DataFrame, + y_data: pd.DataFrame, + raw_data: pd.DataFrame, + exploratory_model_analysis: bool = False, +) -> int: """ This function will train a model on the dataframe provided :param preprocessing_pipeline: sklearn pipeline with the preprocessing steps for the dataset @@ -130,8 +124,11 @@ def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_ana classifier = Pipeline(steps=[("classifier", lgb.LGBMClassifier())]) classifier.fit(x_data, y_data) + + ## Cross validation # potential improvement to use a cross validation instead of fit (to overcome overfitting) like from sklearn.model_selection import RandomizedSearchCV + # param_dist = { # 'classifier__bagging_fraction': (0.5, 0.8), # 'classifier__feature_fraction': (0.5, 0.8), @@ -143,32 +140,49 @@ def training_model(preprocessing_pipeline, x_data, y_data, exploratory_model_ana # n_iter=10, verbose=True, param_distributions=param_dist) # search.fit(x_data, y_data) # classifier = search.best_estimator_ - # store the preprocessing pipeline together with the classifier pipeline for later serving of the model complete_pipeline = Pipeline(steps=[("Preprocessing", preprocessing_pipeline), ("classifier", classifier)]) + + ## Mitigation with fairlearn for gender bias + # exponentiated_gradient = ExponentiatedGradient( + # estimator=complete_pipeline, + # constraints=DemographicParity(), + # sample_weight_name="classifier__classifier__sample_weight", + # ) + # exponentiated_gradient.fit(raw_data, y_data, sensitive_features=raw_data["gender"]) + # complete_pipeline = exponentiated_gradient + joblib.dump(complete_pipeline, "./data/model/recruitment_lightgbm_model.pkl") return 0 -def evaluating_model(classifier, x_data, y_data): +def evaluating_model(data: pd.DataFrame) -> int: """ This function will evaluate a model whether it adheres to specific requirements we set. Specifically whether it adheres to bias requirements. If it doesn't adhere we need to go back in the previous steps and fix the preprocessing steps or the model hyperparameters. As this is an exmaple project, only suggestions will be done and not implemented as this would worsen the flow of the script. - :param classifier: sklearn pipeline with the classifier model in it - :param x_data: - :param y_data: - :return: + :param data: The data to look at the bias metrics to + :return: 0 """ + y_pred = serving_a_model(data) + y_true = data.loc[:, "decision"] + gender = data.loc[:, "gender"] + dp = demographic_parity_difference(y_true=y_true, y_pred=y_pred, sensitive_features=gender) + sr = MetricFrame(metrics=selection_rate, y_true=y_true, y_pred=y_pred, sensitive_features=gender) + # print(dp) + # print(sr.by_group) + # The difference in demographic parirty is 0.25, this means that there is a 25% difference between the amount of + # times that the 'lowest' gender gets selected compared to the highest. In this case that is between 'female' and + # 'other'. This gives us reason to mitigate this bias for gender in the original model. return 0 -def serving_a_model(data): +def serving_a_model(data: pd.DataFrame) -> list: """ This function will 'serve' the model, normally when serving a model one would include the preprocessing steps also within the model/pipeline. Therefore, this way of passing just the classifier and the preprocessed data would not - suffice. Also the serving of a model is generally that to an API/package you pass the input data and get the + suffice. Also, the serving of a model is generally that to an API/package you pass the input data and get the predicted result back. :param data: Pandas Dataframe containing the data :return: prediction_results: a list of boolean values for each datapoint a prediction @@ -177,144 +191,34 @@ def serving_a_model(data): return complete_pipeline.predict(data) -def monitor_the_model(): +def monitor_the_model(data): """ If data drift occurs in the model we will see in the accuracy of the model (if we also have the true labels) declining. But even without the true labels we can also investigate whether the data distribution changes over - time. - :return: + time. More information on model monitoring [here](https://www.evidentlyai.com/ml-in-production/model-monitoring). + :return: Testsuite object from evidently from which data drift on a specific column is investigated. """ - return 0 - - -def convert_to_standard_dataset_for_aif360(df, target_label_name, scores_name=""): - protected_attributes = [] - - # columns from the dataset that we want to select for this Bias study - selected_features = ["gender", "age"] - - privileged_classes = [[]] - - favorable_target_label = [1] + data_drift_column_tests = TestSuite(tests=[TestColumnDrift(column_name="gender", stattest="psi")]) - # List of column names in the DataFrame which are to be expanded into one-hot vectors. - categorical_features = ["gender", "nationality", "sport", "ind-degree", "company"] - - # create the `StandardDataset` object - standard_dataset = StandardDataset( - df=df, - label_name=target_label_name, - favorable_classes=favorable_target_label, - scores_name=scores_name, - protected_attribute_names=protected_attributes, - privileged_classes=privileged_classes, - categorical_features=categorical_features, - features_to_keep=selected_features, - ) - if scores_name == "": - standard_dataset.scores = standard_dataset.labels.copy() - - return standard_dataset + data_drift_column_tests.run(reference_data=data[:100], current_data=data[100:]) + return data_drift_column_tests.json() def main() -> int: data = exploratory_data_analysis() - preprocessor_pipeline, preprocessed_data = preprocessing_data(data) - training_model(preprocessor_pipeline, preprocessed_data, data["decision"], exploratory_model_analysis=False) - serving_a_model(data) - - # # models = exp.compare_models(include=['lr', 'dt', 'knn', 'catboost']) - # models = exp.compare_models() - # - # # dt = exp.create_model('dt') - # # catboost = exp.create_model('catboost') - # - # # 3. Evaluating models - # # Experiment Tracking met mlflow (potentieel ook dataset tracking, maar voor nu doen we dat nog niet?) - # - # # save model - # # exp.save_model(catboost, 'catboost_pipeline') - # catboost = exp.load_model('catboost_pipeline') - # - # # 3.1 evaluating fairness-related metrics - # # y_pred = exp.predict_model(catboost, data=unseen_data).loc[:, "prediction_label"] - # y_pred = exp.predict_model(catboost, data=unseen_data) - # y_true = unseen_data.loc[:, "decision"] - # gender = unseen_data.loc[:, "gender"] - # # dp = demographic_parity_difference(y_true=y_true, y_pred=y_pred, sensitive_features=gender) - # # print(dp) - # # dp = demographic_parity_ratio(y_true=y_true, y_pred=y_pred, sensitive_features=gender) - # # Deze geeft een ratio van 0.67 aan wat betekent dat er vaker males worden geselecteerd dan females door het model - # # mfx = MetricFrame(metrics=accuracy_score, y_true=y_true, y_pred=y_pred, sensitive_features=gender) - # # sr = MetricFrame(metrics=selection_rate, y_true=y_true, y_pred=y_pred, sensitive_features=gender) - # # print(dp) - # # print(sr.by_group) - # - # # ## check fairness out of the box of pycaret - # # catboost_fairness = exp.check_fairness(catboost, sensitive_features=['gender']) - # # print(catboost_fairness) - # - # ## check mitigation of demographic disparity of gender via fairlearn - # # constraint = DemographicParity() - # # mitigator = ExponentiatedGradient(catboost, constraint) - # # exp.traiN_model - # X_train_transformed = exp.get_config(variable="X_train_transformed") - # train_gender_transformed = exp.get_config(variable="X_train_transformed").loc[:, ["gender_female", "gender_male", "gender_other"]] - # y_train_transformed = exp.get_config(variable="y_train_transformed") - # # mitigator.fit(X_train_transformed, y_train_transformed, sensitive_features=train_gender_transformed) - # # y_pred_mitigated = mitigator.predict(unseen_data) - # # sr_mitigated = MetricFrame(metrics=selection_rate, y_true=y_true, y_pred=y_pred_mitigated, sensitive_features=test_gender) - # # print(sr_mitigated.overall) - # # print(sr_mitigated.by_group) - # # - # - # - # ## plotting of the fairness metrics w.r.t. gender - # # metrics = { - # # "accuracy": accuracy_score, - # # "precision": precision_score, - # # "false positive rate": false_positive_rate, - # # "false negative rate": false_negative_rate, - # # "selection rate": selection_rate, - # # "count": count, - # # } - # # metric_frame = MetricFrame( - # # metrics=metrics, y_true=y_true, y_pred=y_pred, sensitive_features=gender - # # ) - # # fig = metric_frame.by_group.plot.bar( - # # subplots=True, - # # layout=[3, 3], - # # legend=False, - # # figsize=[12, 8], - # # title="Show all metrics", - # # ) - # # fig[0][0].figure.savefig("bias.png") - # # print(unseen_data.loc[unseen_data['gender'] == 'other']) - # - # # 4. Analysis & Interpretability - # # exp.plot_model(catboost, plot='confusion_matrix') - # # exp.plot_model(catboost, plot='auc') - # exp.plot_model(catboost, plot='feature', save=True) - # - # # 4. Serving a result - # - # # 5. Monitoring - # pred_dataset = exp.predict_model(catboost, data=unseen_data) - # X_train_transformed = exp.get_config(variable="X_train_transformed") - # print(X_train_transformed.columns) - # print(pred_dataset.columns) - ### TESTING WITH AIF360 packages - - # Metric for the original dataset - # standard_dataset_pred_aif360 = convert_to_standard_dataset_for_aif360(exp.predict_model(catboost, data=unseen_data), - # target_label_name='prediction_label', - # scores_name='prediction_score') - # # metric_orig_train = BinaryLabelDatasetMetric(X_train_transformed, - # # unprivileged_groups=[{'Gender': 'other'}], - # # privileged_groups=[{'Gender': 'male'}]) - # print(metric_orig_train) - - # bias mitigating results + train_data = data.sample(frac=0.8) + evaluate_data = data.drop(train_data.index) + preprocessor_pipeline, preprocessed_data = preprocessing_data(data=train_data) + training_model( + preprocessing_pipeline=preprocessor_pipeline, + x_data=preprocessed_data, + y_data=train_data["decision"], + raw_data=train_data, + exploratory_model_analysis=False, + ) + evaluating_model(data=evaluate_data) + serving_a_model(data=evaluate_data[0:10]) + monitor_the_model(data=evaluate_data) return 0 diff --git a/poetry.lock b/poetry.lock index 0466d8c..d743dd7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -68,6 +68,48 @@ files = [ [package.extras] dev = ["black", "coverage", "isort", "pre-commit", "pyenchant", "pylint"] +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + +[[package]] +name = "anyio" +version = "4.3.0" +description = "High level compatibility layer for 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name = "werkzeug" version = "3.0.3" @@ -3748,4 +5011,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.13" -content-hash = "6644b67f69724e237e2513239154ad0c8e2c2429ded8314a4b2fe64fb5358218" +content-hash = "26b40740d3db5c8c85d2a4a064603efc1a372f3031278372bb154749418c6ca0" diff --git a/pyproject.toml b/pyproject.toml index 0689c2f..c17240a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,8 @@ mlflow = "^2.12.1" fairlearn = "^0.10.0" matplotlib = "3.7.5" aif360 = "^0.6.1" +evidently = "^0.4.22" +isort = "^5.13.2" [tool.poetry.group.test.dependencies] pytest = "^8.2.0" From 66b0811d8bb471eebc938e6e16e4083f92fa17d4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 16 May 2024 11:23:49 +0000 Subject: [PATCH 5/5] Bump pyright from 1.1.361 to 1.1.363 Bumps [pyright](https://github.com/RobertCraigie/pyright-python) from 1.1.361 to 1.1.363. - [Release notes](https://github.com/RobertCraigie/pyright-python/releases) - [Commits](https://github.com/RobertCraigie/pyright-python/compare/v1.1.361...v1.1.363) --- updated-dependencies: - dependency-name: pyright dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- poetry.lock | 10 +++++----- pyproject.toml | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index d743dd7..255b63a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "aif360" @@ -3218,13 +3218,13 @@ diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "pyright" -version = "1.1.361" +version = "1.1.363" description = "Command line wrapper for pyright" optional = false python-versions = ">=3.7" files = [ - {file = "pyright-1.1.361-py3-none-any.whl", hash = "sha256:c50fc94ce92b5c958cfccbbe34142e7411d474da43d6c14a958667e35b9df7ea"}, - {file = "pyright-1.1.361.tar.gz", hash = "sha256:1d67933315666b05d230c85ea8fb97aaa2056e4092a13df87b7765bb9e8f1a8d"}, + {file = "pyright-1.1.363-py3-none-any.whl", hash = "sha256:d3b8d73c8d230e26cc3523862f3398032a0c39a00d7bb69dc0f595f8e888fd01"}, + {file = "pyright-1.1.363.tar.gz", hash = "sha256:00a8f0ae0e339473bb0488f8a2a2dcdf574e94a16cd7b4390d49d144714d8db2"}, ] [package.dependencies] @@ -5011,4 +5011,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.13" -content-hash = "26b40740d3db5c8c85d2a4a064603efc1a372f3031278372bb154749418c6ca0" +content-hash = "5bb0c17f2083670e12458d08d7e94e70cfc53ea699db80130f3ed43f85d5e7eb" diff --git a/pyproject.toml b/pyproject.toml index c17240a..fe9c519 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ coverage = "^7.5.1" [tool.poetry.group.dev.dependencies] ruff = "^0.4.3" pre-commit = "^3.7.0" -pyright = "^1.1.361" +pyright = "^1.1.363" [build-system] requires = ["poetry-core"]