diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb new file mode 100644 index 0000000..2087a76 --- /dev/null +++ b/notebooks/project_starter.ipynb @@ -0,0 +1,4839 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Projet du cours de Machine Learning : analyse du dataset d'OpenFoodFact" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Chargement des données " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### A partir du format csv" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# from pathlib import Path\n", + "# import os\n", + "# import sys\n", + "\n", + "# project_root = Path().resolve().parent \n", + "# sys.path.append(str(project_root))\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# import Data_filter_Jess as dfj\n", + "# import encoding_func \n", + "# import Imputing\n", + "# import Scaling\n", + "# from encoding_func import *\n", + "# from Scaling import *\n", + "# from Imputing import *\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\acomi\\AppData\\Local\\Temp\\ipykernel_11304\\1145578495.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_train = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "int64", + "type": "integer" + }, + { + "name": "url", + "rawType": "object", + "type": "string" + }, + { + "name": "creator", + "rawType": "object", + "type": "string" + }, + { + "name": "created_t", + "rawType": "int64", + "type": "integer" + }, + { + "name": "created_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "last_modified_t", + "rawType": "int64", + "type": "integer" + }, + { + "name": "last_modified_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "last_modified_by", + "rawType": "object", + "type": "unknown" + }, + { + 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}, + { + "name": "phylloquinone_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "beta-glucan_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "inositol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carnitine_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sulphate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nitrate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "acidity_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates-total_100g", + "rawType": "float64", + "type": "float" + } + ], + "ref": "806e6e57-9d7b-4c02-ba35-41fe9c57d066", + "rows": [ + [ + "0", + "54", + "http://world-en.openfoodfacts.org/product/000000000054/limonade-artisanale-a-la-rose", + "kiliweb", + "1582569031", + "2020-02-24T18:30:31Z", + "1733085204", + "2024-12-01T20:33:24Z", + null, + "1740205422", + "2025-02-22T06:23:42Z", + "Limonade artisanale a la rose", 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en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + null, + null, + null, + null, + null, + "0.1625", + "1733085204.0", + "2024-12-01T20:33:24Z", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/front_en.6.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_en.6.200.jpg", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ], + [ + "1", + "63", + "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy", + "kiliweb", + "1673620307", + "2023-01-13T14:31:47Z", + "1750061386", + "2025-06-16T08:09:46Z", + "bodysupport", + "1750061386", + "2025-06-16T08:09:46Z", + "M&M white", + null, + null, + "80 gram", + null, + null, + null, + null, + "Fitpiggy", + "xx:fitpiggy", + "fitpiggy", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "en:fr", + "en:france", + "France", + "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (", + "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal", + "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", + null, + null, + null, + null, + null, + "80 gram", + "80.0", + null, + "0.0", + null, + null, + null, + null, + "unknown", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "80.0", + null, + null, + "1.0", + "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024", + "0.6625", + "1746257766.0", + "2025-05-03T07:36:06Z", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/front_en.12.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_en.12.200.jpg", + "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.200.jpg", + null, + null, + null, + "359.0", + "1502.0", + null, + "19.0", + "17.3", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "26.0", + "1.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "17.0", + null, + null, + null, + "1.2", + null, + "0.48", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ], + [ + "2", + "114", + "http://world-en.openfoodfacts.org/product/000000000114/chocolate-n3-jeff-de-bruges", + "kiliweb", + "1580066482", + "2020-01-26T19:21:22Z", + "1751035658", + "2025-06-27T14:47:38Z", + "teolemon", + "1751035658", + "2025-06-27T14:47:38Z", + "Chocolate n3", + null, + null, + "80 g", + null, + null, + null, + null, + "Jeff de Bruges", + "xx:jeff-de-bruges", + "jeff-de-bruges", + null, + null, + null, + null, + null, + null, + null, + null, + "Green Dot,Made in France", + "en:green-dot,en:made-in-france", + "Green Dot,Made in France", + null, + null, + null, + null, + null, + null, + null, + "France", + "en:france", + "France", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "unknown", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "80.0", + null, + null, + "1.0", + "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", + "0.475", + "1737247860.0", + "2025-01-19T00:51:00Z", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.200.jpg", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg", + "2415.0", + null, + "2415.0", + null, + "44.0", + "28.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "30.0", + "27.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "7.1", + null, + null, + null, + "0.025", + null, + "0.01", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ], + [ + "3", + "105", + "http://world-en.openfoodfacts.org/product/0000000105/paleta-gran-reserva-sierra-nevada-advocare", + "kiliweb", + "1572117743", + "2019-10-26T19:22:23Z", + "1738073570", + "2025-01-28T14:12:50Z", + null, + "1743653496", + "2025-04-03T04:11:36Z", + "Paleta gran reserva - Sierra nevada-", + null, + null, + "750ml", + null, + null, + null, + null, + "AdvoCare", + "xx:advocare", + "advocare", + "Bebidas y preparaciones de bebidas, Bebidas", + "en:beverages-and-beverages-preparations,en:beverages", + "Beverages and beverages preparations,Beverages", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "Spanien, Germany", + "en:germany,en:spain", + "Germany,Spain", + "Thiamin, Biotin, Chromium, Garcinia cambogia fruit extract, Taurine, Green coffee fruit extract, Caffeine, Inositol, Citric Acid, Natural , Artificial Flavors, Sucralose, Spirulina Extract, Beta Carotene", + "en:thiamin,en:biotin,en:vitamins,en:chromium,en:minerals,en:garcinia-cambogia-fruit-extract,en:taurine,en:green-coffee-fruit-extract,en:caffeine,en:inositol,en:e330,en:natural,en:artificial-flavouring,en:flavouring,en:e955,en:spirulina-concentrate,en:algae,en:spirulina,en:e160ai,en:e160a", + "en:may-contain-palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown", + null, + null, + null, + null, + null, + "5g", + "5.0", + null, + "2.0", + null, + "en:e330,en:e955", + "E330 - Citric acid,E955 - Sucralose", + null, + "unknown", + "4.0", + "Beverages", + "Artificially sweetened beverages", + "en:artificially-sweetened-beverages", + "en:beverages,en:artificially-sweetened-beverages", + "Beverages,Artificially sweetened beverages", + "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "750.0", + null, + null, + "1.0", + "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-az-scans-2024,top-1000-az-scans-2024,top-5000-az-scans-2024,top-10000-az-scans-2024,top-50000-az-scans-2024,top-100000-az-scans-2024,top-country-az-scans-2024", + "0.675", + "1738073557.0", + "2025-01-28T14:12:37Z", + "en:beverages", + "Beverages", + "https://images.openfoodfacts.org/images/products/invalid/front_es.21.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_es.21.200.jpg", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.200.jpg", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.0113351004464306", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ], + [ + "4", + "2", + "http://world-en.openfoodfacts.org/product/00000002/filets-de-poulet-blanc-x2-solo", + "kiliweb", + "1722606455", + "2024-08-02T13:47:35Z", + "1749171851", + "2025-06-06T01:04:11Z", + "altroconsumo", + "1749171851", + "2025-06-06T01:04:11Z", + "Filets de poulet blanc x2", + null, + null, + "240-400 g", + null, + null, + null, + null, + "SoLo, selbstgemacht 2 Liter", + "xx:solo,xx:selbstgemacht-2-liter", + "solo,selbstgemacht-2-liter", + "Protein powders", + "en:dietary-supplements,en:bodybuilding-supplements,en:protein-powders", + "Dietary supplements,Bodybuilding supplements,Protein powders", + "île d’Orléans,Québec,Canada", + "en:canada,en:quebec,fr:ile-d-orleans", + "Canada,Québec,fr:ile-d-orleans", + "Ancenis", + "ancenis", + "Organic, EU Organic, French meat, Bee Friendly, French poultry, AB Agriculture Biologique, en:no-additives", + "en:organic,en:eu-organic,en:french-meat,en:bee-friendly,en:french-poultry,en:no-additives,fr:ab-agriculture-biologique", + "Organic,EU Organic,French meat,Bee Friendly,French poultry,No additives,AB Agriculture Biologique", + null, + null, + null, + null, + null, + null, + null, + "Brasilien, Germany", + "en:brazil,en:germany", + "Brazil,Germany", + "48% Tomatenpulver, Stärke, Zucker, jodiertes Speisesalz, WEIZENMEHL, Würze, Maiskeimöl, Kaliumchlorid, Kräuter (Basilikum, Thymian, Oregano), Hefeextrakt, Zwiebeln, Gewürze (Knoblauch, Pfeffer), Rote-Bete-Pulver, Aromen, Speisesalz. Kann ROGGEN, GERSTE, HAFER, EI, SOJA, MILCH, SELLERIE, SENF enthalten. Kochsalzersatz, gewonnen aus natürlichen Kaliummineralien. NUTRI-SCORE berechnet pro 100 g zubereitetes Gericht. Unilever Deutschland GmbH Konsumenten - Postfach 57 05 50 22774 Hamburg Ene Fett da Fe Kol da Ball Elw Salz 196 Erw service Tel.: 0800 5858 555 E (gebührenfrei) ww Unilever www.knorr.de", + "en:tomatenpulver,en:starke,en:zucker,en:jodiertes-speisesalz,en:weizenmehl,en:wurze,en:maiskeimol,en:kaliumchlorid,en:krauter,en:hefeextrakt,en:zwiebeln,en:gewurze,en:rote-bete-pulver,en:aromen,en:speisesalz,en:kann-roggen,en:gerste,en:hafer,en:ei,en:soya,en:milch,en:sellerie,en:senf-enthalten,en:kochsalzersatz,en:gewonnen-aus-naturlichen-kaliummineralien,en:nutri-score-berechnet-pro-100-g-zubereitetes-gericht,en:unilever-deutschland-gmbh-konsumenten,en:postfach-57-05-50-22774-hamburg-ene-fett-da-fe-kol-da-ball-elw-salz-196-erw-service-tel,en:basilikum,en:thymian,en:oregano,en:herb,en:knoblauch,en:pfeffer,en:0800-5858-555-e,en:ww-unilever-www-knorr-de,en:gebuhrenfrei", + "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", + null, + null, + null, + null, + null, + "35gm", + "35.0", + null, + "0.0", + null, + null, + null, + null, + "not-applicable", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-moderate-quantity,en:salt-in-moderate-quantity", + "400.0", + "org-le-picoreur-bodin-bio", + "en:energy-value-in-kcal-does-not-match-value-in-kj,en:nutrition-sugars-plus-starch-greater-than-carbohydrates,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients", + "1.0", + "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-in-scans-2024,top-50000-in-scans-2024,top-100000-in-scans-2024,top-country-in-scans-2024", + "0.8", + "1749171849.0", + "2025-06-06T01:04:09Z", + "en:protein-powders", + "Protein powders", + "https://images.openfoodfacts.org/images/products/invalid/front_en.120.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_en.120.200.jpg", + "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.122.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.122.200.jpg", + "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.200.jpg", + "392.0", + "141.0", + "392.0", + null, + "2.7", + "0.6", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.0", + null, + "0.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.0", + "0.0", + "0.9", + "6.2", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "2.2", + null, + null, + "30.0", + null, + null, + null, + "0.4", + null, + "0.16", + null, + "0.0", + null, + null, + null, + null, + "0.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.0", + null, + null, + null, + "0.0001", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "0.052506232193732", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ] + ], + "shape": { + "columns": 214, + "rows": 5 + } + }, + "text/html": [ + "
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codeurlcreatorcreated_tcreated_datetimelast_modified_tlast_modified_datetimelast_modified_bylast_updated_tlast_updated_datetime...water-hardness_100gcholine_100gphylloquinone_100gbeta-glucan_100ginositol_100gcarnitine_100gsulphate_100gnitrate_100gacidity_100gcarbohydrates-total_100g
054http://world-en.openfoodfacts.org/product/0000...kiliweb15825690312020-02-24T18:30:31Z17330852042024-12-01T20:33:24ZNaN17402054222025-02-22T06:23:42Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
163http://world-en.openfoodfacts.org/product/0000...kiliweb16736203072023-01-13T14:31:47Z17500613862025-06-16T08:09:46Zbodysupport17500613862025-06-16T08:09:46Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2114http://world-en.openfoodfacts.org/product/0000...kiliweb15800664822020-01-26T19:21:22Z17510356582025-06-27T14:47:38Zteolemon17510356582025-06-27T14:47:38Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3105http://world-en.openfoodfacts.org/product/0000...kiliweb15721177432019-10-26T19:22:23Z17380735702025-01-28T14:12:50ZNaN17436534962025-04-03T04:11:36Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " code url creator \\\n", + "0 54 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "1 63 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "2 114 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "3 105 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "4 2 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "\n", + " created_t created_datetime last_modified_t last_modified_datetime \\\n", + "0 1582569031 2020-02-24T18:30:31Z 1733085204 2024-12-01T20:33:24Z \n", + "1 1673620307 2023-01-13T14:31:47Z 1750061386 2025-06-16T08:09:46Z \n", + "2 1580066482 2020-01-26T19:21:22Z 1751035658 2025-06-27T14:47:38Z \n", + "3 1572117743 2019-10-26T19:22:23Z 1738073570 2025-01-28T14:12:50Z \n", + "4 1722606455 2024-08-02T13:47:35Z 1749171851 2025-06-06T01:04:11Z \n", + "\n", + " last_modified_by last_updated_t last_updated_datetime ... \\\n", + "0 NaN 1740205422 2025-02-22T06:23:42Z ... \n", + "1 bodysupport 1750061386 2025-06-16T08:09:46Z ... \n", + "2 teolemon 1751035658 2025-06-27T14:47:38Z ... \n", + "3 NaN 1743653496 2025-04-03T04:11:36Z ... \n", + "4 altroconsumo 1749171851 2025-06-06T01:04:11Z ... \n", + "\n", + " water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " carbohydrates-total_100g \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 214 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", + "df_train = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n", + "\n", + "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=10000) # skip rows 1–5000, keep header (row 0)\n", + "df_train.head()\n", + " # read 5000 rows (5001 → 10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Pre-processing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.1 Data curation " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def filter_nutriscore_data(df, df_test):\n", + " \"\"\"\n", + " Filters the DataFrame to include only rows with a valid nutriscore_score.\n", + " \n", + " Parameters:\n", + " df (DataFrame): The input train DataFrame containing food data.\n", + " df_test (DataFrame): The input test DataFrame containing food data.\n", + " \n", + " Returns:\n", + " DataFrame: 2 filtered DataFrame with only valid nutriscore_score entries.\n", + " \"\"\"\n", + " # Check if 'nutriscore_score' column exists\n", + " if 'nutriscore_score' not in df.columns:\n", + " raise ValueError(\"The DataFrame does not contain a 'nutriscore_score' column.\")\n", + " \n", + " # Filter out rows where 'nutriscore_score' is NaN\n", + " filtered_df = df[df['nutriscore_score'].notna()]\n", + " filtered_df_test = df_test[df_test['nutriscore_score'].notna()]\n", + " \n", + " return filtered_df, filtered_df_test\n", + "\n", + "\n", + "def categorical_filter(df, df_test, cat_keep):\n", + " \"\"\"\"\n", + " Filters the DataFrame to include only rows with specified categories.\n", + " \n", + " Parameters:\n", + " df (DataFrame): The input DataFrame containing food data.\n", + " cat_keep (list): A list of categories to keep in the DataFrame.\n", + " \n", + " Returns:\n", + " DataFrame: A categorical only - filtered datafreme\n", + " \"\"\"\n", + " categorical = df.select_dtypes(exclude=['float64','int64']) \n", + " cat_keep = ['categories','pnns_groups_1', 'pnns_groups_2', 'brands_tags', 'ingredients_analysis_tags']\n", + " df_cat = categorical[cat_keep]\n", + "\n", + " categorical = df_test.select_dtypes(exclude=['float64','int64']) \n", + " df_cat_test = categorical[cat_keep]\n", + " \n", + " return df_cat, df_cat_test\n", + "\n", + "def numerical_filter(df, df_test, num_drop):\n", + " \"\"\"\"\n", + " Filters the DataFrame to include only relevant numerical values.\n", + " \n", + " Parameters:\n", + " df (DataFrame): The input DataFrame containing food data.\n", + " num_keep (list): A list of categories to keep in the DataFrame.\n", + " \n", + " Returns:\n", + " DataFrame: A categorical only - filtered datafreme\n", + " \"\"\"\n", + " num_drop = ['created_t', 'last_updated_t', 'last_modified_t', 'last_image_t','serving_quantity', 'nova_group', 'product_quantity', 'unique_scans_n', 'completeness', 'energy-kj_100g', 'energy-kcal_100g']\n", + " numerical = df.select_dtypes(include=['float64','int64'])\n", + " numerical_test = df_test.select_dtypes(include=['float64','int64'])\n", + " percent_to_keep = numerical.isnull().sum()*100 /len(df)\n", + " percent_to_keep = percent_to_keep[percent_to_keep.values < 80] \n", + " num_keep = numerical[percent_to_keep.index]\n", + " num_keep_test = numerical_test[percent_to_keep.index]\n", + " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n", + " num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n", + " \n", + " return num_keep, num_keep_test\n", + " \n", + "def final_df(df_cat, df_num, df_cat_test, df_num_test):\n", + " \"\"\"\n", + " Combines categorical and numerical filtered DataFrames.\n", + " \n", + " Parameters:\n", + " df_cat (DataFrame): The categorical filtered DataFrame.\n", + " df_num (DataFrame): The numerical filtered DataFrame.\n", + " \n", + " Returns:\n", + " DataFrame: A combined DataFrame with both categorical and numerical data.\n", + " \"\"\"\n", + " filtered_df = pd.concat([df_cat, df_num], axis=1)\n", + " filtered_df_test = pd.concat([df_cat_test, df_num_test], axis=1)\n", + "\n", + " return filtered_df, filtered_df_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\acomi\\AppData\\Local\\Temp\\ipykernel_11304\\2007311091.py:61: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n", + "C:\\Users\\acomi\\AppData\\Local\\Temp\\ipykernel_11304\\2007311091.py:62: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df, filtered_df_test = filter_nutriscore_data(df_train, df_test)\n", + "\n", + "### Cleaning the variables : get rid of vars with too many missing values etc\n", + "cat_df, cat_df_test = categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n", + "num_df, num_df_test = numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n", + "final_df, final_df_test = final_df(cat_df, num_df, cat_df_test, num_df_test)\n", + "\n", + "### Droping some additionnal columns \n", + "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n", + "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n", + "\n", + "final_df.columns.equals(final_df_test.columns)\n", + "final_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 15. 4. 6. -11. 3. 20. 9. 7. -1. -4. 45. 17. 0. 13.\n", + " 18. -5. 22. 11. 14. 8. -3. 27. 12. 2. 5. 31. -2. -7.\n", + " 28. 21. 19. -6. 23. 38. 30. 1. 24. 25. -16. 35. 33. 34.\n", + " 40. -12. 29. 26. 16. 50. 10. -8. -10. -9. 44. 37. 32. 39.\n", + " 43. 36. 41. 49.]\n" + ] + } + ], + "source": [ + "final_df['nutriscore_score'].dropna(inplace=True)\n", + "final_df_test['nutriscore_score'].dropna(inplace=True)\n", + "print(final_df['nutriscore_score'].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.2 Enconding " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pnns_groups_1 PNNS_pro\n", + "6 unknown NA\n", + "9 unknown NA\n", + "11 Fruits and vegetables Plant_based\n", + "12 unknown NA\n", + "14 Composite foods Processed\n", + "... ... ...\n", + "9946 Composite foods Processed\n", + "9948 Composite foods Processed\n", + "9957 Sugary snacks Snacks\n", + "9963 Composite foods Processed\n", + "9977 Cereals and potatoes Plant_based\n", + "\n", + "[1207 rows x 2 columns]\n", + " pnns_groups_1 PNNS_pro\n", + "0 Sugary snacks Snacks\n", + "14 Composite foods Processed\n", + "23 Milk and dairy products Animal_based\n", + "29 Sugary snacks Snacks\n", + "38 Sugary snacks Snacks\n", + "... ... ...\n", + "9947 Fat and sauces Processed\n", + "9961 Fruits and vegetables Plant_based\n", + "9972 Fat and sauces Processed\n", + "9976 Composite foods Processed\n", + "9995 Sugary snacks Snacks\n", + "\n", + "[3566 rows x 2 columns]\n" + ] + } + ], + "source": [ + "pnns_mapping = {\"unknown\" : 'NA', \n", + " 'Beverages' : 'Drinks',\n", + " 'nan' : 'NA',\n", + " 'Fruits and vegetables' : 'Plant_based',\n", + " 'Composite foods' : 'Processed',\n", + " 'Sugary snacks' : 'Snacks',\n", + " 'Salty snacks' : 'Snacks',\n", + " 'Cereals and potatoes' : 'Plant_based',\n", + " 'Fat and sauces' : 'Processed', \n", + " 'Milk and dairy products' : 'Animal_based',\n", + " 'Fish Meat Eggs' : 'Animal_based',\n", + " 'Alcoholic beverages' : 'Drinks'\n", + "}\n", + "\n", + "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n", + "print(final_df_test[['pnns_groups_1', 'PNNS_pro']])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "import numpy as np \n", + "import pandas as pd\n", + "\n", + "\n", + "def one_hot_encode_column(df, column_name):\n", + " \"\"\"\n", + " One-hot encodes a specified column in the DataFrame.\n", + " \n", + " Parameters:\n", + " df (pd.DataFrame): The DataFrame containing the column to encode.\n", + " column_name (str): The name of the column to one-hot encode.\n", + " \n", + " Returns:\n", + " pd.DataFrame: The DataFrame with the one-hot encoded columns added.\n", + " \"\"\"\n", + " # Replace NaN values with 'unknown' for consistency\n", + " df[[column_name]] = df[[column_name]].replace('', np.nan)\n", + " df[[column_name]]= df[[column_name]].fillna('unknown')\n", + "\n", + " enc = OneHotEncoder(handle_unknown='ignore')\n", + " enc.fit(df[[column_name]])\n", + " encoded_array = enc.transform(df[[column_name]]).toarray()\n", + " encoded_df = pd.DataFrame(encoded_array, columns=enc.get_feature_names_out([column_name]))\n", + " \n", + " return pd.concat([df, encoded_df], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "categories", + "rawType": "object", + "type": "string" + }, + { + "name": "pnns_groups_1", + "rawType": "object", + "type": "string" + }, + { + "name": "pnns_groups_2", + "rawType": 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"fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro", + "rawType": "object", + "type": "string" + }, + { + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_NA", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Plant_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Processed", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Snacks", + "rawType": "float64", + "type": "float" + } + ], + "ref": "a284f120-a337-450e-bfb7-0808473cca3a", + "rows": [ + [ + "0", + "Snacks, Sweet snacks, Biscuits and cakes, Gingerbreads, Gingerbread blocks", + "Sugary snacks", + "Biscuits and cakes", + null, + null, + "1234570383.0", + null, + "20.0", + "1289.0", + "0.7", + "0.3", + null, + null, + "70.0", + "46.0", + null, + "5.4", + "0.84", + "0.336", + null, + null, + null, + null, + null, + "Snacks", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0" + ], + [ + "14", + "Sandwichs, Sandwichs au poisson, Sandwichs au thon", + "Composite foods", + "Sandwiches", + "xx:crous", + "en:may-contain-palm-oil,en:non-vegan,en:non-vegetarian", + "12409.0", + "9.0", + "0.0", + "891.0", + "10.2", + "1.1", + null, + 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categoriespnns_groups_1pnns_groups_2brands_tagsingredients_analysis_tagscodeadditives_nnutriscore_scoreenergy_100gfat_100g...calcium_100giron_100gfruits-vegetables-nuts-estimate-from-ingredients_100gPNNS_proPNNS_pro_Animal_basedPNNS_pro_DrinksPNNS_pro_NAPNNS_pro_Plant_basedPNNS_pro_ProcessedPNNS_pro_Snacks
0Snacks, Sweet snacks, Biscuits and cakes, Ging...Sugary snacksBiscuits and cakesNaNNaN1.234570e+09NaN20.01289.00.7...NaNNaNNaNSnacks0.00.00.00.00.01.0
14Sandwichs, Sandwichs au poisson, Sandwichs au ...Composite foodsSandwichesxx:crousen:may-contain-palm-oil,en:non-vegan,en:non-ve...1.240900e+049.00.0891.010.2...NaNNaN180.375Processed0.00.00.00.00.01.0
23Efterrätter, Fryst mat, Frysta efterrätter, Gl...Milk and dairy productsIce creamxx:mcdonald-sen:may-contain-palm-oil,en:non-vegan,en:maybe-...1.244400e+043.013.0NaNNaN...NaNNaN0.000Animal_based0.00.00.00.00.01.0
29Breakfasts, Spreads, Sweet spreads, Bee produc...Sugary snacksSweetsmoon-shine-trading-coen:palm-oil-content-unknown,en:vegan-status-un...1.245309e+090.018.01276.00.0...NaNNaN0.000Snacks0.00.00.00.01.00.0
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" + ], + "text/plain": [ + " categories \\\n", + "0 Snacks, Sweet snacks, Biscuits and cakes, Ging... \n", + "14 Sandwichs, Sandwichs au poisson, Sandwichs au ... \n", + "23 Efterrätter, Fryst mat, Frysta efterrätter, Gl... \n", + "29 Breakfasts, Spreads, Sweet spreads, Bee produc... \n", + "38 Botanas, Snacks dulces, Cacao y sus productos,... \n", + "\n", + " pnns_groups_1 pnns_groups_2 brands_tags \\\n", + "0 Sugary snacks Biscuits and cakes NaN \n", + "14 Composite foods Sandwiches xx:crous \n", + "23 Milk and dairy products Ice cream xx:mcdonald-s \n", + "29 Sugary snacks Sweets moon-shine-trading-co \n", + "38 Sugary snacks Chocolate products xx:hacendado \n", + "\n", + " ingredients_analysis_tags code \\\n", + "0 NaN 1.234570e+09 \n", + "14 en:may-contain-palm-oil,en:non-vegan,en:non-ve... 1.240900e+04 \n", + "23 en:may-contain-palm-oil,en:non-vegan,en:maybe-... 1.244400e+04 \n", + "29 en:palm-oil-content-unknown,en:vegan-status-un... 1.245309e+09 \n", + "38 en:palm-oil-free,en:vegan-status-unknown,en:ve... 1.248000e+04 \n", + "\n", + " additives_n nutriscore_score energy_100g fat_100g ... calcium_100g \\\n", + "0 NaN 20.0 1289.0 0.7 ... NaN \n", + "14 9.0 0.0 891.0 10.2 ... NaN \n", + "23 3.0 13.0 NaN NaN ... NaN \n", + "29 0.0 18.0 1276.0 0.0 ... NaN \n", + "38 1.0 18.0 2450.0 47.0 ... NaN \n", + "\n", + " iron_100g fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "0 NaN NaN \n", + "14 NaN 180.375 \n", + "23 NaN 0.000 \n", + "29 NaN 0.000 \n", + "38 NaN 25.000 \n", + "\n", + " PNNS_pro PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA \\\n", + "0 Snacks 0.0 0.0 0.0 \n", + "14 Processed 0.0 0.0 0.0 \n", + "23 Animal_based 0.0 0.0 0.0 \n", + "29 Snacks 0.0 0.0 0.0 \n", + "38 Snacks 0.0 0.0 0.0 \n", + "\n", + " PNNS_pro_Plant_based PNNS_pro_Processed PNNS_pro_Snacks \n", + "0 0.0 0.0 1.0 \n", + "14 0.0 0.0 1.0 \n", + "23 0.0 0.0 1.0 \n", + "29 0.0 1.0 0.0 \n", + "38 0.0 1.0 0.0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", + "filtered_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n", + "filtered_df.head()\n", + "filtered_df_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n", + "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.3 Imputing" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numeric features for imputation: 25 columns\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.impute import KNNImputer\n", + "#this function imputes missing values from any df with KNNImputer\n", + "def knn_impute_numeric(df_train, df_test, n_neighbors=5):\n", + " \n", + " #select only the numerical variables to do the imputer\n", + " numeric_cols = df_train.select_dtypes(include=['float', 'int']).columns\n", + " print(f\"Numeric features for imputation: {len(numeric_cols)} columns\")\n", + "\n", + " # Fit imputer on training data\n", + " imputer = KNNImputer(n_neighbors=n_neighbors, missing_values=np.nan, keep_empty_features=True)\n", + " imputed_train = imputer.fit_transform(df_train[numeric_cols])\n", + " imputed_train_df = pd.DataFrame(imputed_train, columns=numeric_cols, index=df_train.index)\n", + "\n", + " # Transform test data\n", + " imputed_test = imputer.transform(df_test[numeric_cols])\n", + " imputed_test_df = pd.DataFrame(imputed_test, columns=numeric_cols, index=df_test.index)\n", + "\n", + " # Keep non-numeric columns unchanged\n", + " non_numeric_cols = df_train.select_dtypes(exclude=['float', 'int']).columns\n", + " imputed_train_df = pd.concat([imputed_train_df, df_train[non_numeric_cols]], axis=1)[df_train.columns]\n", + " imputed_test_df = pd.concat([imputed_test_df, df_test[non_numeric_cols]], axis=1)[df_test.columns]\n", + "\n", + " return imputed_train_df, imputed_test_df, imputer\n", + "\n", + "#How to use\n", + "imputed_df, imputed_df_test, imputer = knn_impute_numeric(filtered_df, filtered_df_test, n_neighbors=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_NA", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Plant_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Processed", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Snacks", + "rawType": "float64", + "type": "float" + } + ], + "ref": "6c3af938-b6c6-4e7c-8e14-16aa5afc5105", + "rows": [ + [ + "6", + "4.0", + "0.0", + "15.0", + "2401.0", + "12.0", + "10.5", + "0.0", + "0.0", + "13.0", + "9.0", + "36.0", + "23.0", + "0.3", + "0.12", + "0.030258428792000004", + "0.058185714", + "0.176374286", + "0.010164708552", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0" + ], + [ + "9", + "6.0", + "1.8", + "4.0", + "1520.0", + "11.0", + "2.0", + "0.0", + "0.01", + "25.0", + "0.98", + "9.0", + "22.0", + "0.95", + "0.38", + "0.030258428792000004", + "0.058185713999999986", + "0.171798016", + "0.008664708552", + "20.400223270165018", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0" + ], + [ + "11", + "7.0", + "0.0", + "4.0", + "4.0", + "1.0", + "1.0", + "0.129619454", + "0.011300083200000002", + "1.0", + "1.0", + "1.0", + "1.0", + "1.0", + "0.4", + "0.030258428792000004", + "0.058185714", + "0.164718016", + "0.015298708552", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0" + ], + [ + "12", + "8.0", + "1.0", + "6.0", + "1510.0", + "2.0", + "0.5", + "0.11299435", + "0.0167446326", + "6.7", + "1.7", + "10.714286", + "76.0", + "1.5", + "0.6", + "0.00023214286", + "0.07142857", + "0.17857143", + "0.008928571", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0", + "0.0" + ], + [ + "14", + "9.0", + "0.6", + "-11.0", + "293.0", + "0.5", + "0.06", + "0.129619454", + "0.0097602602", + "2.0", + "0.24", + "88.0", + "18.0", + "0.275", + "0.11", + "0.030258428792000004", + "0.09", + "0.164718016", + "0.006798708552", + "20.00029130415483", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0", + "0.0" + ] + ], + "shape": { + "columns": 25, + "rows": 5 + } + }, + "text/html": [ + "
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X_train = df_train.drop([target_col], axis=1)\n", + " X_test = df_test.drop([target_col], axis=1)\n", + " y_train = df_train[target_col]\n", + " y_test = df_test[target_col]\n", + " \n", + " # Select numeric columns\n", + " numeric_cols = X_train.select_dtypes(include=['float','int']).columns\n", + " non_numeric_cols = X_train.select_dtypes(exclude=['float','int']).columns\n", + "\n", + " # Fit the scaler on the numeric columns of the train dataset\n", + " scaler = RobustScaler()\n", + " X_train_scaled = scaler.fit_transform(X_train[numeric_cols])\n", + " X_train_scaled_df = pd.DataFrame(X_train_scaled, columns=numeric_cols, index=X_train.index)\n", + "\n", + " # Apply the scaling on the numeric columns of the test datset\n", + " X_test_scaled = scaler.transform(X_test[numeric_cols])\n", + " X_test_scaled_df = pd.DataFrame(X_test_scaled, columns=numeric_cols, index=X_test.index)\n", + "\n", + " # Combine the numeric with the non-numeric columns for both datasets\n", + " \n", + " scaled_train_df = pd.concat([X_train_scaled_df, X_train[non_numeric_cols]], axis=1)[X_train.columns]\n", + " scaled_test_df = pd.concat([X_test_scaled_df, X_test[non_numeric_cols]], axis=1)[X_test.columns]\n", + "\n", + " scaled_train_df[target_col] = y_train\n", + " scaled_test_df[target_col] = y_test\n", + " # Ensure original column order\n", + " scaled_train_df = scaled_train_df[df_train.columns]\n", + " scaled_test_df = scaled_test_df[df_test.columns]\n", + "\n", + " return scaled_train_df, scaled_test_df, scaler\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": 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codeadditives_nnutriscore_scoreenergy_100gfat_100gsaturated-fat_100gtrans-fat_100gcholesterol_100gcarbohydrates_100gsugars_100g...vitamin-c_100gcalcium_100giron_100gfruits-vegetables-nuts-estimate-from-ingredients_100gPNNS_pro_Animal_basedPNNS_pro_DrinksPNNS_pro_NAPNNS_pro_Plant_basedPNNS_pro_ProcessedPNNS_pro_Snacks
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" + ], + "text/plain": [ + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 -0.052708 -0.375 15.0 1.511376 -0.085857 \n", + "9 -0.052708 0.750 4.0 0.009885 -0.151901 \n", + "11 -0.052708 -0.375 4.0 -2.573839 -0.812339 \n", + "12 -0.052708 0.250 6.0 -0.007158 -0.746295 \n", + "14 -0.052708 0.000 -11.0 -2.081295 -0.845361 \n", + "\n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 1.453208 0.00000 -0.808143 -0.313454 \n", + "9 -0.150566 0.00000 0.292175 -0.065827 \n", + "11 -0.339245 7.79661 0.435225 -0.561081 \n", + "12 -0.433585 6.79661 1.034299 -0.443459 \n", + "14 -0.516604 7.79661 0.265796 -0.540446 \n", + "\n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 0.112138 ... -0.100514 1.373132 0.000000 \n", + "9 -0.293887 ... -0.100514 1.284096 -0.191017 \n", + "11 -0.292874 ... -0.100514 1.146348 0.653787 \n", + "12 -0.257436 ... 0.099131 1.415880 -0.157415 \n", + "14 -0.331350 ... 0.379107 1.146348 -0.428642 \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 -0.121432 \n", + "9 0.286572 \n", + "11 -0.121432 \n", + "12 -0.121432 \n", + "14 0.278574 \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 1.0 \n", + "12 0.0 0.0 2.5 0.0 \n", + "14 0.0 0.0 2.5 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 -0.333333 5.0 \n", + "9 1.333333 0.0 \n", + "11 -0.333333 0.0 \n", + "12 -0.333333 0.0 \n", + "14 -0.333333 0.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaled_df, scaled_df_test, scaler = robust_scaler(imputed_df, imputed_df_test, target_col='nutriscore_score') \n", + "scaled_df.head() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Predicting the nutriscore" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "X = scaled_df.drop(\"nutriscore_score\", axis=1) \n", + "y = scaled_df[\"nutriscore_score\"]\n", + "X_test = scaled_df_test.drop(\"nutriscore_score\", axis=1) \n", + "y_test = scaled_df_test[\"nutriscore_score\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree - no feature selection" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'max_depth': 20, 'min_samples_leaf': 0.01, 'min_samples_split': 0.01}\n", + "RMSE: 3.8918\n", + "MSE: 15.1462\n", + "MAE: 2.2235\n", + "R2: 0.7953\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.experimental import enable_halving_search_cv\n", + "from sklearn.model_selection import train_test_split, HalvingGridSearchCV, learning_curve\n", + "from sklearn.feature_selection import SelectFromModel\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", + " \n", + "def decision_tree(X_train, y_train, X_test, y_test, test_size=0.3, random_state=42):\n", + " \n", + " # Let's define the model (so our DecisionTreeRegressor) and do a HalvingGridSearchCV to find the best hyperparameters\n", + " dt = DecisionTreeRegressor(random_state=random_state)\n", + "\n", + " param_grid = {\n", + " \"max_depth\": [None, 5, 10, 20, 30],\n", + " \"min_samples_split\": [0.001, 0.01, 0.1],\n", + " \"min_samples_leaf\": [0.001, 0.01, 0.1],\n", + " }\n", + "\n", + " halving_search = HalvingGridSearchCV(\n", + " dt,\n", + " param_grid,\n", + " cv=5,\n", + " factor=2,\n", + " scoring=\"neg_root_mean_squared_error\"\n", + " )\n", + "\n", + " halving_search.fit(X_train, y_train)\n", + " best_dt = halving_search.best_estimator_\n", + "\n", + " #Visualization\n", + "\n", + " train_sizes, train_scores, val_scores = learning_curve(best_dt, X_train, y_train, cv=5, scoring=\"neg_root_mean_squared_error\", train_sizes=np.linspace(0.1,1.0,20))\n", + "\n", + " # Average scores across folds\n", + " train_rmse = -train_scores.mean(axis=1)\n", + " val_rmse = -val_scores.mean(axis=1)\n", + "\n", + " # Let's visualize the loss curve\n", + " plt.figure(figsize=(6, 4))\n", + " plt.plot(train_sizes, train_rmse, \"o-\", label=\"Training RMSE\")\n", + " plt.plot(train_sizes, val_rmse, \"o-\", label=\"Validation RMSE\")\n", + " plt.xlabel(\"Training set size\")\n", + " plt.ylabel(\"RMSE\")\n", + " plt.title(\"Learning Curve (Decision Tree)\")\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + " #predict on the test dataset\n", + " y_pred = best_dt.predict(X_test)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " r2 = r2_score(y_test, y_pred)\n", + "\n", + " metrics = {\n", + " \"RMSE\": rmse,\n", + " \"MSE\": mse,\n", + " \"MAE\": mae,\n", + " \"R2\": r2,\n", + " }\n", + "\n", + " print(f\"Best parameters: {halving_search.best_params_}\")\n", + " for k, v in metrics.items():\n", + " print(f\"{k}: {v:.4f}\")\n", + "\n", + " return best_dt, y_pred, metrics\n", + "\n", + "# #How to use this\n", + "best_dt, y_pred, metrics = decision_tree(X, y, X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import shap\n", + "X_train_df = pd.DataFrame(X) #,columns=selected_features)\n", + "\n", + "# --- Create the SHAP explainer ---\n", + "explainer = shap.TreeExplainer(best_dt) # your trained DecisionTreeRegressor\n", + "shap_values = explainer.shap_values(X_train_df)\n", + "\n", + "# --- Summary plot (feature importance as mean absolute SHAP value) ---\n", + "shap.summary_plot(shap_values, X_train_df, plot_type=\"bar\")\n", + "\n", + "# --- Summary plot (beeswarm) showing how feature values affect predictions ---\n", + "shap.summary_plot(shap_values, X_train_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree - with feature selection" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\acomi\\anaconda3\\envs\\formation\\Lib\\site-packages\\sklearn\\utils\\validation.py:2732: UserWarning: X has feature names, but SelectFromModel was fitted without feature names\n", + " warnings.warn(\n", + "c:\\Users\\acomi\\anaconda3\\envs\\formation\\Lib\\site-packages\\sklearn\\utils\\validation.py:2732: UserWarning: X has feature names, but SelectFromModel was fitted without feature names\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected 12 features out of 24\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10}\n", + "RMSE: 5.5717\n", + "MSE: 31.0437\n", + "MAE: 2.4963\n", + "R2: 0.5804\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.experimental import enable_halving_search_cv\n", + "from sklearn.model_selection import HalvingGridSearchCV, learning_curve\n", + "from sklearn.feature_selection import SelectFromModel\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def decision_tree_FS(X_train, y_train, X_test, y_test, random_state=42):\n", + " #Feature selection with RandomForest\n", + " rf = RandomForestRegressor(random_state=random_state)\n", + " rf.fit(X_train, y_train)\n", + "\n", + " selector = SelectFromModel(rf, threshold=\"median\") # keep features above median importance\n", + " X_train_sel = selector.transform(X_train)\n", + " X_test_sel = selector.transform(X_test)\n", + "\n", + " print(f\"Selected {X_train_sel.shape[1]} features out of {X_train.shape[1]}\")\n", + "\n", + " # DecisionTree with Halving Grid Search\n", + " dt = DecisionTreeRegressor(random_state=random_state)\n", + "\n", + " param_grid = {\n", + " \"max_depth\": [None, 5, 10, 20, 30],\n", + " \"min_samples_split\": [2, 5, 10], # changed to valid integers\n", + " \"min_samples_leaf\": [1, 5, 10],\n", + " }\n", + "\n", + " halving_search = HalvingGridSearchCV(\n", + " dt,\n", + " param_grid,\n", + " cv=5,\n", + " factor=2,\n", + " scoring=\"neg_root_mean_squared_error\"\n", + " )\n", + "\n", + " halving_search.fit(X_train_sel, y_train)\n", + " best_dt_FS = halving_search.best_estimator_\n", + "\n", + " # Learning Curve\n", + " train_sizes, train_scores, val_scores = learning_curve(\n", + " best_dt_FS, X_train_sel, y_train, cv=5,\n", + " scoring=\"neg_root_mean_squared_error\",\n", + " train_sizes=np.linspace(0.1, 1.0, 20)\n", + " )\n", + "\n", + " train_rmse = -train_scores.mean(axis=1)\n", + " val_rmse = -val_scores.mean(axis=1)\n", + "\n", + " plt.figure(figsize=(6, 4))\n", + " plt.plot(train_sizes, train_rmse, \"o-\", label=\"Training RMSE\")\n", + " plt.plot(train_sizes, val_rmse, \"o-\", label=\"Validation RMSE\")\n", + " plt.xlabel(\"Training set size\")\n", + " plt.ylabel(\"RMSE\")\n", + " plt.title(\"Learning Curve (Decision Tree with Feature Selection)\")\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + " # Evaluate on test set\n", + " y_pred = best_dt_FS.predict(X_test_sel)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " r2 = r2_score(y_test, y_pred)\n", + "\n", + " metrics = {\n", + " \"RMSE\": rmse,\n", + " \"MSE\": mse,\n", + " \"MAE\": mae,\n", + " \"R2\": r2,\n", + " }\n", + "\n", + " print(f\"Best parameters: {halving_search.best_params_}\")\n", + " for k, v in metrics.items():\n", + " print(f\"{k}: {v:.4f}\")\n", + "\n", + " return best_dt_FS, y_pred, metrics, selector\n", + "best_dt_FS, y_pred, metrics, selector = decision_tree_FS(X, y, X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\acomi\\anaconda3\\envs\\formation\\Lib\\site-packages\\sklearn\\utils\\validation.py:2732: UserWarning: X has feature names, but SelectFromModel was fitted without feature names\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Get the selected feature names\n", + "selected_features = X.columns[selector.get_support()]\n", + "\n", + "# Convert selected training features into a DataFrame\n", + "X_train_sel = selector.transform(X) # or selector.transform(X_train) if you passed split data\n", + "X_train_df = pd.DataFrame(X_train_sel, columns=selected_features)\n", + "\n", + "# --- Create the SHAP explainer ---\n", + "explainer = shap.TreeExplainer(best_dt_FS) # your trained DecisionTreeRegressor\n", + "shap_values = explainer.shap_values(X_train_df)\n", + "\n", + "# --- Summary plot (feature importance as mean absolute SHAP value) ---\n", + "shap.summary_plot(shap_values, X_train_df, plot_type=\"bar\")\n", + "\n", + "# --- Summary plot (beeswarm) showing how feature values affect predictions ---\n", + "shap.summary_plot(shap_values, X_train_df)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "formation", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/DecisionTree.py b/scripts/DecisionTree.py index 20d345c..ce88430 100644 --- a/scripts/DecisionTree.py +++ b/scripts/DecisionTree.py @@ -68,3 +68,17 @@ def decision_tree(X_train, y_train, X_test, y_test, test_size=0.3, random_state= # #How to use this #best_dt, y_pred, metrics = decision_tree(X, y, X_test, y_test) + +#import shap +#X_train_df = pd.DataFrame(X) #,columns=selected_features) + +# --- Create the SHAP explainer --- +#explainer = shap.TreeExplainer(best_dt) # your trained DecisionTreeRegressor +#shap_values = explainer.shap_values(X_train_df) + +# --- Summary plot (feature importance as mean absolute SHAP value) --- +#shap.summary_plot(shap_values, X_train_df, plot_type="bar") + +# --- Summary plot (beeswarm) showing how feature values affect predictions --- +#shap.summary_plot(shap_values, X_train_df) +======= diff --git a/scripts/Scaling.py b/scripts/Scaling.py index 7426096..c32df40 100644 --- a/scripts/Scaling.py +++ b/scripts/Scaling.py @@ -35,7 +35,12 @@ def robust_scaler(df_train, df_test, target_col): scaled_test_df = scaled_test_df[df_test.columns] return scaled_train_df, scaled_test_df, scaler + +#scaled_df, scaled_df_test, scaler = robust_scaler(imputed_df, imputed_df_test, target_col='nutriscore_score') +#scaled_df.head() +======= #scaled_df, scaled_df_test, scaler = robust_scaler(imputed_df, imputed_df_test, target_col='nutriscore_score') #scaled_df.head() +