Modélisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6c8cad7",
   "metadata": {},
   "source": [
    "# Réalisez une application de recommandation de contenu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9d22c",
   "metadata": {},
   "source": [
    "## Librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "738cc090",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\anaconda3\\envs\\OC_P09\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import zipfile\n",
    "import pickle\n",
    "from surprise import Reader, Dataset\n",
    "from surprise import dump, accuracy\n",
    "from surprise.model_selection import train_test_split, cross_validate\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise import NormalPredictor\n",
    "from surprise import KNNBasic\n",
    "from surprise import KNNWithMeans\n",
    "from surprise import KNNWithZScore\n",
    "from surprise import KNNBaseline\n",
    "from surprise import SVD\n",
    "from surprise import BaselineOnly\n",
    "from surprise import SVDpp\n",
    "from surprise import NMF\n",
    "from surprise import SlopeOne\n",
    "from surprise import CoClustering\n",
    "from surprise.accuracy import rmse\n",
    "from collections import defaultdict\n",
    "from operator import itemgetter\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.decomposition import PCA\n",
    "from math import floor\n",
    "from heapq import nlargest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb85fa8f",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e434c14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"dark_background\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b651ba",
   "metadata": {},
   "source": [
    "## Données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc5ff20",
   "metadata": {},
   "source": [
    "### Téléchargement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4bf4f991",
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_url = \"https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+9+-+R%C3%A9alisez+une+application+mobile+de+recommandation+de+contenu/news-portal-user-interactions-by-globocom.zip\"\n",
    "data_path = \"./data/\"\n",
    "local_file = data_path + \"news-portal-user-interactions-by-globocom.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "feefd1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = requests.get(remote_url)\n",
    "with open(local_file, 'wb')as file:\n",
    "    file.write(data.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63d26d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['news-portal-user-interactions-by-globocom.zip']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c494ff",
   "metadata": {},
   "source": [
    "### Décompression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af2ba98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(local_file, 'r') as zip_ref:\n",
    "    zip_ref.extractall(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f79d419a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['articles_embeddings.pickle',\n",
       " 'articles_metadata.csv',\n",
       " 'clicks.zip',\n",
       " 'clicks_sample.csv',\n",
       " 'news-portal-user-interactions-by-globocom.zip']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb999e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(data_path+\"clicks.zip\", 'r') as zip_ref:\n",
    "    zip_ref.extractall(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a814f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks_path = data_path + \"clicks/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e39756b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['clicks_hour_000.csv',\n",
       " 'clicks_hour_001.csv',\n",
       " 'clicks_hour_002.csv',\n",
       " 'clicks_hour_003.csv',\n",
       " 'clicks_hour_004.csv',\n",
       " 'clicks_hour_005.csv',\n",
       " 'clicks_hour_006.csv',\n",
       " 'clicks_hour_007.csv',\n",
       " 'clicks_hour_008.csv',\n",
       " 'clicks_hour_009.csv',\n",
       " 'clicks_hour_010.csv',\n",
       " 'clicks_hour_011.csv',\n",
       " 'clicks_hour_012.csv',\n",
       " 'clicks_hour_013.csv',\n",
       " 'clicks_hour_014.csv',\n",
       " 'clicks_hour_015.csv',\n",
       " 'clicks_hour_016.csv',\n",
       " 'clicks_hour_017.csv',\n",
       " 'clicks_hour_018.csv',\n",
       " 'clicks_hour_019.csv',\n",
       " 'clicks_hour_020.csv',\n",
       " 'clicks_hour_021.csv',\n",
       " 'clicks_hour_022.csv',\n",
       " 'clicks_hour_023.csv',\n",
       " 'clicks_hour_024.csv',\n",
       " 'clicks_hour_025.csv',\n",
       " 'clicks_hour_026.csv',\n",
       " 'clicks_hour_027.csv',\n",
       " 'clicks_hour_028.csv',\n",
       " 'clicks_hour_029.csv',\n",
       " 'clicks_hour_030.csv',\n",
       " 'clicks_hour_031.csv',\n",
       " 'clicks_hour_032.csv',\n",
       " 'clicks_hour_033.csv',\n",
       " 'clicks_hour_034.csv',\n",
       " 'clicks_hour_035.csv',\n",
       " 'clicks_hour_036.csv',\n",
       " 'clicks_hour_037.csv',\n",
       " 'clicks_hour_038.csv',\n",
       " 'clicks_hour_039.csv',\n",
       " 'clicks_hour_040.csv',\n",
       " 'clicks_hour_041.csv',\n",
       " 'clicks_hour_042.csv',\n",
       " 'clicks_hour_043.csv',\n",
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       " 'clicks_hour_045.csv',\n",
       " 'clicks_hour_046.csv',\n",
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       " 'clicks_hour_048.csv',\n",
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       " 'clicks_hour_050.csv',\n",
       " 'clicks_hour_051.csv',\n",
       " 'clicks_hour_052.csv',\n",
       " 'clicks_hour_053.csv',\n",
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       " 'clicks_hour_055.csv',\n",
       " 'clicks_hour_056.csv',\n",
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       " 'clicks_hour_061.csv',\n",
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       " 'clicks_hour_065.csv',\n",
       " 'clicks_hour_066.csv',\n",
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       " 'clicks_hour_094.csv',\n",
       " 'clicks_hour_095.csv',\n",
       " 'clicks_hour_096.csv',\n",
       " 'clicks_hour_097.csv',\n",
       " 'clicks_hour_098.csv',\n",
       " 'clicks_hour_099.csv',\n",
       " 'clicks_hour_100.csv',\n",
       " 'clicks_hour_101.csv',\n",
       " 'clicks_hour_102.csv',\n",
       " 'clicks_hour_103.csv',\n",
       " 'clicks_hour_104.csv',\n",
       " 'clicks_hour_105.csv',\n",
       " 'clicks_hour_106.csv',\n",
       " 'clicks_hour_107.csv',\n",
       " 'clicks_hour_108.csv',\n",
       " 'clicks_hour_109.csv',\n",
       " 'clicks_hour_110.csv',\n",
       " 'clicks_hour_111.csv',\n",
       " 'clicks_hour_112.csv',\n",
       " 'clicks_hour_113.csv',\n",
       " 'clicks_hour_114.csv',\n",
       " 'clicks_hour_115.csv',\n",
       " 'clicks_hour_116.csv',\n",
       " 'clicks_hour_117.csv',\n",
       " 'clicks_hour_118.csv',\n",
       " 'clicks_hour_119.csv',\n",
       " 'clicks_hour_120.csv',\n",
       " 'clicks_hour_121.csv',\n",
       " 'clicks_hour_122.csv',\n",
       " 'clicks_hour_123.csv',\n",
       " 'clicks_hour_124.csv',\n",
       " 'clicks_hour_125.csv',\n",
       " 'clicks_hour_126.csv',\n",
       " 'clicks_hour_127.csv',\n",
       " 'clicks_hour_128.csv',\n",
       " 'clicks_hour_129.csv',\n",
       " 'clicks_hour_130.csv',\n",
       " 'clicks_hour_131.csv',\n",
       " 'clicks_hour_132.csv',\n",
       " 'clicks_hour_133.csv',\n",
       " 'clicks_hour_134.csv',\n",
       " 'clicks_hour_135.csv',\n",
       " 'clicks_hour_136.csv',\n",
       " 'clicks_hour_137.csv',\n",
       " 'clicks_hour_138.csv',\n",
       " 'clicks_hour_139.csv',\n",
       " 'clicks_hour_140.csv',\n",
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       " 'clicks_hour_143.csv',\n",
       " 'clicks_hour_144.csv',\n",
       " 'clicks_hour_145.csv',\n",
       " 'clicks_hour_146.csv',\n",
       " 'clicks_hour_147.csv',\n",
       " 'clicks_hour_148.csv',\n",
       " 'clicks_hour_149.csv',\n",
       " 'clicks_hour_150.csv',\n",
       " 'clicks_hour_151.csv',\n",
       " 'clicks_hour_152.csv',\n",
       " 'clicks_hour_153.csv',\n",
       " 'clicks_hour_154.csv',\n",
       " 'clicks_hour_155.csv',\n",
       " 'clicks_hour_156.csv',\n",
       " 'clicks_hour_157.csv',\n",
       " 'clicks_hour_158.csv',\n",
       " 'clicks_hour_159.csv',\n",
       " 'clicks_hour_160.csv',\n",
       " 'clicks_hour_161.csv',\n",
       " 'clicks_hour_162.csv',\n",
       " 'clicks_hour_163.csv',\n",
       " 'clicks_hour_164.csv',\n",
       " 'clicks_hour_165.csv',\n",
       " 'clicks_hour_166.csv',\n",
       " 'clicks_hour_167.csv',\n",
       " 'clicks_hour_168.csv',\n",
       " 'clicks_hour_169.csv',\n",
       " 'clicks_hour_170.csv',\n",
       " 'clicks_hour_171.csv',\n",
       " 'clicks_hour_172.csv',\n",
       " 'clicks_hour_173.csv',\n",
       " 'clicks_hour_174.csv',\n",
       " 'clicks_hour_175.csv',\n",
       " 'clicks_hour_176.csv',\n",
       " 'clicks_hour_177.csv',\n",
       " 'clicks_hour_178.csv',\n",
       " 'clicks_hour_179.csv',\n",
       " 'clicks_hour_180.csv',\n",
       " 'clicks_hour_181.csv',\n",
       " 'clicks_hour_182.csv',\n",
       " 'clicks_hour_183.csv',\n",
       " 'clicks_hour_184.csv',\n",
       " 'clicks_hour_185.csv',\n",
       " 'clicks_hour_186.csv',\n",
       " 'clicks_hour_187.csv',\n",
       " 'clicks_hour_188.csv',\n",
       " 'clicks_hour_189.csv',\n",
       " 'clicks_hour_190.csv',\n",
       " 'clicks_hour_191.csv',\n",
       " 'clicks_hour_192.csv',\n",
       " 'clicks_hour_193.csv',\n",
       " 'clicks_hour_194.csv',\n",
       " 'clicks_hour_195.csv',\n",
       " 'clicks_hour_196.csv',\n",
       " 'clicks_hour_197.csv',\n",
       " 'clicks_hour_198.csv',\n",
       " 'clicks_hour_199.csv',\n",
       " 'clicks_hour_200.csv',\n",
       " 'clicks_hour_201.csv',\n",
       " 'clicks_hour_202.csv',\n",
       " 'clicks_hour_203.csv',\n",
       " 'clicks_hour_204.csv',\n",
       " 'clicks_hour_205.csv',\n",
       " 'clicks_hour_206.csv',\n",
       " 'clicks_hour_207.csv',\n",
       " 'clicks_hour_208.csv',\n",
       " 'clicks_hour_209.csv',\n",
       " 'clicks_hour_210.csv',\n",
       " 'clicks_hour_211.csv',\n",
       " 'clicks_hour_212.csv',\n",
       " 'clicks_hour_213.csv',\n",
       " 'clicks_hour_214.csv',\n",
       " 'clicks_hour_215.csv',\n",
       " 'clicks_hour_216.csv',\n",
       " 'clicks_hour_217.csv',\n",
       " 'clicks_hour_218.csv',\n",
       " 'clicks_hour_219.csv',\n",
       " 'clicks_hour_220.csv',\n",
       " 'clicks_hour_221.csv',\n",
       " 'clicks_hour_222.csv',\n",
       " 'clicks_hour_223.csv',\n",
       " 'clicks_hour_224.csv',\n",
       " 'clicks_hour_225.csv',\n",
       " 'clicks_hour_226.csv',\n",
       " 'clicks_hour_227.csv',\n",
       " 'clicks_hour_228.csv',\n",
       " 'clicks_hour_229.csv',\n",
       " 'clicks_hour_230.csv',\n",
       " 'clicks_hour_231.csv',\n",
       " 'clicks_hour_232.csv',\n",
       " 'clicks_hour_233.csv',\n",
       " 'clicks_hour_234.csv',\n",
       " 'clicks_hour_235.csv',\n",
       " 'clicks_hour_236.csv',\n",
       " 'clicks_hour_237.csv',\n",
       " 'clicks_hour_238.csv',\n",
       " 'clicks_hour_239.csv',\n",
       " 'clicks_hour_240.csv',\n",
       " 'clicks_hour_241.csv',\n",
       " 'clicks_hour_242.csv',\n",
       " 'clicks_hour_243.csv',\n",
       " 'clicks_hour_244.csv',\n",
       " 'clicks_hour_245.csv',\n",
       " 'clicks_hour_246.csv',\n",
       " 'clicks_hour_247.csv',\n",
       " 'clicks_hour_248.csv',\n",
       " 'clicks_hour_249.csv',\n",
       " 'clicks_hour_250.csv',\n",
       " 'clicks_hour_251.csv',\n",
       " 'clicks_hour_252.csv',\n",
       " 'clicks_hour_253.csv',\n",
       " 'clicks_hour_254.csv',\n",
       " 'clicks_hour_255.csv',\n",
       " 'clicks_hour_256.csv',\n",
       " 'clicks_hour_257.csv',\n",
       " 'clicks_hour_258.csv',\n",
       " 'clicks_hour_259.csv',\n",
       " 'clicks_hour_260.csv',\n",
       " 'clicks_hour_261.csv',\n",
       " 'clicks_hour_262.csv',\n",
       " 'clicks_hour_263.csv',\n",
       " 'clicks_hour_264.csv',\n",
       " 'clicks_hour_265.csv',\n",
       " 'clicks_hour_266.csv',\n",
       " 'clicks_hour_267.csv',\n",
       " 'clicks_hour_268.csv',\n",
       " 'clicks_hour_269.csv',\n",
       " 'clicks_hour_270.csv',\n",
       " 'clicks_hour_271.csv',\n",
       " 'clicks_hour_272.csv',\n",
       " 'clicks_hour_273.csv',\n",
       " 'clicks_hour_274.csv',\n",
       " 'clicks_hour_275.csv',\n",
       " 'clicks_hour_276.csv',\n",
       " 'clicks_hour_277.csv',\n",
       " 'clicks_hour_278.csv',\n",
       " 'clicks_hour_279.csv',\n",
       " 'clicks_hour_280.csv',\n",
       " 'clicks_hour_281.csv',\n",
       " 'clicks_hour_282.csv',\n",
       " 'clicks_hour_283.csv',\n",
       " 'clicks_hour_284.csv',\n",
       " 'clicks_hour_285.csv',\n",
       " 'clicks_hour_286.csv',\n",
       " 'clicks_hour_287.csv',\n",
       " 'clicks_hour_288.csv',\n",
       " 'clicks_hour_289.csv',\n",
       " 'clicks_hour_290.csv',\n",
       " 'clicks_hour_291.csv',\n",
       " 'clicks_hour_292.csv',\n",
       " 'clicks_hour_293.csv',\n",
       " 'clicks_hour_294.csv',\n",
       " 'clicks_hour_295.csv',\n",
       " 'clicks_hour_296.csv',\n",
       " 'clicks_hour_297.csv',\n",
       " 'clicks_hour_298.csv',\n",
       " 'clicks_hour_299.csv',\n",
       " 'clicks_hour_300.csv',\n",
       " 'clicks_hour_301.csv',\n",
       " 'clicks_hour_302.csv',\n",
       " 'clicks_hour_303.csv',\n",
       " 'clicks_hour_304.csv',\n",
       " 'clicks_hour_305.csv',\n",
       " 'clicks_hour_306.csv',\n",
       " 'clicks_hour_307.csv',\n",
       " 'clicks_hour_308.csv',\n",
       " 'clicks_hour_309.csv',\n",
       " 'clicks_hour_310.csv',\n",
       " 'clicks_hour_311.csv',\n",
       " 'clicks_hour_312.csv',\n",
       " 'clicks_hour_313.csv',\n",
       " 'clicks_hour_314.csv',\n",
       " 'clicks_hour_315.csv',\n",
       " 'clicks_hour_316.csv',\n",
       " 'clicks_hour_317.csv',\n",
       " 'clicks_hour_318.csv',\n",
       " 'clicks_hour_319.csv',\n",
       " 'clicks_hour_320.csv',\n",
       " 'clicks_hour_321.csv',\n",
       " 'clicks_hour_322.csv',\n",
       " 'clicks_hour_323.csv',\n",
       " 'clicks_hour_324.csv',\n",
       " 'clicks_hour_325.csv',\n",
       " 'clicks_hour_326.csv',\n",
       " 'clicks_hour_327.csv',\n",
       " 'clicks_hour_328.csv',\n",
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       " 'clicks_hour_330.csv',\n",
       " 'clicks_hour_331.csv',\n",
       " 'clicks_hour_332.csv',\n",
       " 'clicks_hour_333.csv',\n",
       " 'clicks_hour_334.csv',\n",
       " 'clicks_hour_335.csv',\n",
       " 'clicks_hour_336.csv',\n",
       " 'clicks_hour_337.csv',\n",
       " 'clicks_hour_338.csv',\n",
       " 'clicks_hour_339.csv',\n",
       " 'clicks_hour_340.csv',\n",
       " 'clicks_hour_341.csv',\n",
       " 'clicks_hour_342.csv',\n",
       " 'clicks_hour_343.csv',\n",
       " 'clicks_hour_344.csv',\n",
       " 'clicks_hour_345.csv',\n",
       " 'clicks_hour_346.csv',\n",
       " 'clicks_hour_347.csv',\n",
       " 'clicks_hour_348.csv',\n",
       " 'clicks_hour_349.csv',\n",
       " 'clicks_hour_350.csv',\n",
       " 'clicks_hour_351.csv',\n",
       " 'clicks_hour_352.csv',\n",
       " 'clicks_hour_353.csv',\n",
       " 'clicks_hour_354.csv',\n",
       " 'clicks_hour_355.csv',\n",
       " 'clicks_hour_356.csv',\n",
       " 'clicks_hour_357.csv',\n",
       " 'clicks_hour_358.csv',\n",
       " 'clicks_hour_359.csv',\n",
       " 'clicks_hour_360.csv',\n",
       " 'clicks_hour_361.csv',\n",
       " 'clicks_hour_362.csv',\n",
       " 'clicks_hour_363.csv',\n",
       " 'clicks_hour_364.csv',\n",
       " 'clicks_hour_365.csv',\n",
       " 'clicks_hour_366.csv',\n",
       " 'clicks_hour_367.csv',\n",
       " 'clicks_hour_368.csv',\n",
       " 'clicks_hour_369.csv',\n",
       " 'clicks_hour_370.csv',\n",
       " 'clicks_hour_371.csv',\n",
       " 'clicks_hour_372.csv',\n",
       " 'clicks_hour_373.csv',\n",
       " 'clicks_hour_374.csv',\n",
       " 'clicks_hour_375.csv',\n",
       " 'clicks_hour_376.csv',\n",
       " 'clicks_hour_377.csv',\n",
       " 'clicks_hour_378.csv',\n",
       " 'clicks_hour_379.csv',\n",
       " 'clicks_hour_380.csv',\n",
       " 'clicks_hour_381.csv',\n",
       " 'clicks_hour_382.csv',\n",
       " 'clicks_hour_383.csv',\n",
       " 'clicks_hour_384.csv']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(clicks_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e00e441",
   "metadata": {},
   "source": [
    "## Fonctions usuelles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25d73e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_limits(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lim_inf = Q1 - (1.5*IQR)\n",
    "    lim_sup = Q3 + (1.5*IQR)\n",
    "    return lim_inf, lim_sup\n",
    "\n",
    "def get_outliers(df, column):\n",
    "    lim_inf, lim_sup = get_limits(df, column)\n",
    "    return df[(df[column]<lim_inf) | (df[column]>lim_sup)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b0c560",
   "metadata": {},
   "source": [
    "## Analyse des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f59414a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c7d216",
   "metadata": {},
   "source": [
    "### articles_metadata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76c922eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>publisher_id</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1513144419000</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1405341936000</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1408667706000</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1408468313000</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1407071171000</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id  category_id  created_at_ts  publisher_id  words_count\n",
       "0           0            0  1513144419000             0          168\n",
       "1           1            1  1405341936000             0          189\n",
       "2           2            1  1408667706000             0          250\n",
       "3           3            1  1408468313000             0          230\n",
       "4           4            1  1407071171000             0          162"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"articles_metadata\"] = pd.read_csv(\n",
    "    data_path + \"articles_metadata.csv\"\n",
    ")\n",
    "df[\"articles_metadata\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2242dd7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364047, 5)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"articles_metadata\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dc4efcf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>created_at_ts</th>\n",
       "      <th>publisher_id</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364047.000000</td>\n",
       "      <td>364047.000000</td>\n",
       "      <td>3.640470e+05</td>\n",
       "      <td>364047.0</td>\n",
       "      <td>364047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>182023.000000</td>\n",
       "      <td>283.108239</td>\n",
       "      <td>1.474070e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.897727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105091.461061</td>\n",
       "      <td>136.723470</td>\n",
       "      <td>4.293038e+10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.502766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.159356e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>91011.500000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1.444925e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>182023.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>1.489422e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>273034.500000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>1.509891e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>364046.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>1.520943e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6690.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          article_id    category_id  created_at_ts  publisher_id  \\\n",
       "count  364047.000000  364047.000000   3.640470e+05      364047.0   \n",
       "mean   182023.000000     283.108239   1.474070e+12           0.0   \n",
       "std    105091.461061     136.723470   4.293038e+10           0.0   \n",
       "min         0.000000       0.000000   1.159356e+12           0.0   \n",
       "25%     91011.500000     199.000000   1.444925e+12           0.0   \n",
       "50%    182023.000000     301.000000   1.489422e+12           0.0   \n",
       "75%    273034.500000     399.000000   1.509891e+12           0.0   \n",
       "max    364046.000000     460.000000   1.520943e+12           0.0   \n",
       "\n",
       "         words_count  \n",
       "count  364047.000000  \n",
       "mean      190.897727  \n",
       "std        59.502766  \n",
       "min         0.000000  \n",
       "25%       159.000000  \n",
       "50%       186.000000  \n",
       "75%       218.000000  \n",
       "max      6690.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"articles_metadata\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "981311e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 364047 entries, 0 to 364046\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count   Dtype\n",
      "---  ------         --------------   -----\n",
      " 0   article_id     364047 non-null  int64\n",
      " 1   category_id    364047 non-null  int64\n",
      " 2   created_at_ts  364047 non-null  int64\n",
      " 3   publisher_id   364047 non-null  int64\n",
      " 4   words_count    364047 non-null  int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 13.9 MB\n"
     ]
    }
   ],
   "source": [
    "df[\"articles_metadata\"].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523ef2bd",
   "metadata": {},
   "source": [
    "#### Description des colonnes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb272e8",
   "metadata": {},
   "source": [
    "- **article_id** : identifiant de l'article\n",
    "- **category_id** : identifiant de la catégorie\n",
    "- **created_at_ts** : date de création\n",
    "- **publisher_id** : ne contient que la valeur 0\n",
    "- **words_count** : nombre de mots de l'article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ce9f03cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dans le jeu de données articles_metadata.csv, il y a :\n",
      "- 364047 articles\n",
      "- 461 catégories\n",
      "- 1 éditeurs\n"
     ]
    }
   ],
   "source": [
    "nb_articles = len(df[\"articles_metadata\"][\"article_id\"].unique())\n",
    "nb_categories = len(df[\"articles_metadata\"][\"category_id\"].unique())\n",
    "nb_publisher_id = len(df[\"articles_metadata\"][\"publisher_id\"].unique())\n",
    "\n",
    "print(\"Dans le jeu de données articles_metadata.csv, il y a :\")\n",
    "print(\"- {} articles\".format(nb_articles))\n",
    "print(\"- {} catégories\".format(nb_categories))\n",
    "print(\"- {} éditeurs\".format(nb_publisher_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b55d29a",
   "metadata": {},
   "source": [
    "#### Valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e34688f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "article_id       0\n",
       "category_id      0\n",
       "created_at_ts    0\n",
       "publisher_id     0\n",
       "words_count      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"articles_metadata\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3335aca3",
   "metadata": {},
   "source": [
    "Il n'y a pas de valeur manquante, excepté dans la colonne **publisher_id** où toutes les valeurs sont 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfad6565",
   "metadata": {},
   "source": [
    "#### Colonne words_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba69fec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"articles_metadata\"][\"words_count\"].hist(\n",
    "    figsize=(18,9),\n",
    "    bins=1000,\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Nombre de mots\")\n",
    "plt.ylabel(\"Nombre d'articles\")\n",
    "plt.title(\"Nombre de mots par article\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ccb5c8f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\anaconda3\\envs\\OC_P09\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,9))\n",
    "sns.boxplot(\n",
    "    df['articles_metadata'][\"words_count\"]\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fdc8d70e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La majorité des articles comprennent entre 70.5 et 306.5 mots\n"
     ]
    }
   ],
   "source": [
    "lim_inf, lim_sup = get_limits(df['articles_metadata'], \"words_count\")\n",
    "print(\"La majorité des articles comprennent entre {} et {} mots\".format(lim_inf, lim_sup))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219fb99a",
   "metadata": {},
   "source": [
    "#### Colonne category_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b83bdbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Il y a 461 catégories d'articles différentes\n"
     ]
    }
   ],
   "source": [
    "nb_cat = len(df['articles_metadata'][\"category_id\"].unique())\n",
    "print(\"Il y a {} catégories d'articles différentes\".format(nb_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4ebaf527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['articles_metadata']['category_id'].hist(figsize=(18,9), bins=100)\n",
    "plt.xlabel(\"Identifiant de catégorie\")\n",
    "plt.ylabel(\"Nombre\")\n",
    "plt.title(\"Distribution des catégories\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a70cab1",
   "metadata": {},
   "source": [
    "### clicks_sample.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8efcb9",
   "metadata": {},
   "source": [
    "Ce fichier est un échantillon des fichiers csv contenus dans clicks.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41ef9403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_size</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1506825423271737</td>\n",
       "      <td>1506825423000</td>\n",
       "      <td>2</td>\n",
       "      <td>157541</td>\n",
       "      <td>1506826828020</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1506825423271737</td>\n",
       "      <td>1506825423000</td>\n",
       "      <td>2</td>\n",
       "      <td>68866</td>\n",
       "      <td>1506826858020</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>235840</td>\n",
       "      <td>1506827017951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>96663</td>\n",
       "      <td>1506827047951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1506825435299739</td>\n",
       "      <td>1506825435000</td>\n",
       "      <td>2</td>\n",
       "      <td>119592</td>\n",
       "      <td>1506827090575</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id        session_id  session_start  session_size  click_article_id  \\\n",
       "0        0  1506825423271737  1506825423000             2            157541   \n",
       "1        0  1506825423271737  1506825423000             2             68866   \n",
       "2        1  1506825426267738  1506825426000             2            235840   \n",
       "3        1  1506825426267738  1506825426000             2             96663   \n",
       "4        2  1506825435299739  1506825435000             2            119592   \n",
       "\n",
       "   click_timestamp  click_environment  click_deviceGroup  click_os  \\\n",
       "0    1506826828020                  4                  3        20   \n",
       "1    1506826858020                  4                  3        20   \n",
       "2    1506827017951                  4                  1        17   \n",
       "3    1506827047951                  4                  1        17   \n",
       "4    1506827090575                  4                  1        17   \n",
       "\n",
       "   click_country  click_region  click_referrer_type  \n",
       "0              1            20                    2  \n",
       "1              1            20                    2  \n",
       "2              1            16                    2  \n",
       "3              1            16                    2  \n",
       "4              1            24                    2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks_sample\"] = pd.read_csv(\n",
    "    data_path + \"clicks_sample.csv\"\n",
    ")\n",
    "df[\"clicks_sample\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4cb340",
   "metadata": {},
   "source": [
    "### clicks.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7dfcab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"clicks\"] = pd.DataFrame()\n",
    "clicks_path\n",
    "for f in os.listdir(clicks_path):\n",
    "    tmp_df = pd.read_csv(\n",
    "        clicks_path + f, \n",
    "        index_col=None,\n",
    "        header=0\n",
    "    )\n",
    "    df[\"clicks\"] = pd.concat(\n",
    "        [df[\"clicks\"], tmp_df], \n",
    "        axis=0,\n",
    "        ignore_index=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "5f73ce11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_size</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1506825423271737</td>\n",
       "      <td>1506825423000</td>\n",
       "      <td>2</td>\n",
       "      <td>157541</td>\n",
       "      <td>1506826828020</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>68866</td>\n",
       "      <td>1506826858020</td>\n",
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       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>235840</td>\n",
       "      <td>1506827017951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>96663</td>\n",
       "      <td>1506827047951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1506825435299739</td>\n",
       "      <td>1506825435000</td>\n",
       "      <td>2</td>\n",
       "      <td>119592</td>\n",
       "      <td>1506827090575</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id        session_id  session_start session_size click_article_id  \\\n",
       "0       0  1506825423271737  1506825423000            2           157541   \n",
       "1       0  1506825423271737  1506825423000            2            68866   \n",
       "2       1  1506825426267738  1506825426000            2           235840   \n",
       "3       1  1506825426267738  1506825426000            2            96663   \n",
       "4       2  1506825435299739  1506825435000            2           119592   \n",
       "\n",
       "  click_timestamp click_environment click_deviceGroup click_os click_country  \\\n",
       "0   1506826828020                 4                 3       20             1   \n",
       "1   1506826858020                 4                 3       20             1   \n",
       "2   1506827017951                 4                 1       17             1   \n",
       "3   1506827047951                 4                 1       17             1   \n",
       "4   1506827090575                 4                 1       17             1   \n",
       "\n",
       "  click_region click_referrer_type  \n",
       "0           20                   2  \n",
       "1           20                   2  \n",
       "2           16                   2  \n",
       "3           16                   2  \n",
       "4           24                   2  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d8e4fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_size</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "      <td>2988181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>322897</td>\n",
       "      <td>1048594</td>\n",
       "      <td>646874</td>\n",
       "      <td>72</td>\n",
       "      <td>46033</td>\n",
       "      <td>2983198</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>5890</td>\n",
       "      <td>1507563657895091</td>\n",
       "      <td>1507563657000</td>\n",
       "      <td>2</td>\n",
       "      <td>160974</td>\n",
       "      <td>1506961009961</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1232</td>\n",
       "      <td>124</td>\n",
       "      <td>127</td>\n",
       "      <td>1260372</td>\n",
       "      <td>37213</td>\n",
       "      <td>3</td>\n",
       "      <td>2904478</td>\n",
       "      <td>1823162</td>\n",
       "      <td>1738138</td>\n",
       "      <td>2852406</td>\n",
       "      <td>804985</td>\n",
       "      <td>1602601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id        session_id  session_start  session_size  \\\n",
       "count   2988181           2988181        2988181       2988181   \n",
       "unique   322897           1048594         646874            72   \n",
       "top        5890  1507563657895091  1507563657000             2   \n",
       "freq       1232               124            127       1260372   \n",
       "\n",
       "        click_article_id  click_timestamp  click_environment  \\\n",
       "count            2988181          2988181            2988181   \n",
       "unique             46033          2983198                  3   \n",
       "top               160974    1506961009961                  4   \n",
       "freq               37213                3            2904478   \n",
       "\n",
       "        click_deviceGroup  click_os  click_country  click_region  \\\n",
       "count             2988181   2988181        2988181       2988181   \n",
       "unique                  5         8             11            28   \n",
       "top                     1        17              1            25   \n",
       "freq              1823162   1738138        2852406        804985   \n",
       "\n",
       "        click_referrer_type  \n",
       "count               2988181  \n",
       "unique                    7  \n",
       "top                       2  \n",
       "freq                1602601  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54b30031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre total de sessions est de  1048594\n",
      "Le nombre total d'utilisateurs est de  322897\n",
      "Le nombre total de clics est de  2983198\n"
     ]
    }
   ],
   "source": [
    "print(\"Le nombre total de sessions est de \", len(df[\"clicks\"][\"session_id\"].unique()))\n",
    "print(\"Le nombre total d'utilisateurs est de \", len(df[\"clicks\"][\"user_id\"].unique()))\n",
    "print(\"Le nombre total de clics est de \", len(df[\"clicks\"][\"click_timestamp\"].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1b67872",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"clicks\"].to_csv(data_path+\"clicks.csv\", sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a88b81f",
   "metadata": {},
   "source": [
    "Nous allons regrouper chaque clic par utilisateur :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4d468e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIST_click_article_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[157541, 68866, 96755, 313996, 160158, 233470,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[235840, 96663, 59758, 160474, 285719, 156723,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[119592, 30970, 30760, 209122]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[236065, 236294, 234686, 233769, 235665, 23513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[48915, 44488, 195887, 195084, 63307, 336499, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>322892</th>\n",
       "      <td>[42567, 39894]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322893</th>\n",
       "      <td>[50644, 36162]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322894</th>\n",
       "      <td>[36162, 168401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322895</th>\n",
       "      <td>[289197, 63746]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322896</th>\n",
       "      <td>[30760, 157507]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322897 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     LIST_click_article_id\n",
       "user_id                                                   \n",
       "0        [157541, 68866, 96755, 313996, 160158, 233470,...\n",
       "1        [235840, 96663, 59758, 160474, 285719, 156723,...\n",
       "2                           [119592, 30970, 30760, 209122]\n",
       "3        [236065, 236294, 234686, 233769, 235665, 23513...\n",
       "4        [48915, 44488, 195887, 195084, 63307, 336499, ...\n",
       "...                                                    ...\n",
       "322892                                      [42567, 39894]\n",
       "322893                                      [50644, 36162]\n",
       "322894                                     [36162, 168401]\n",
       "322895                                     [289197, 63746]\n",
       "322896                                     [30760, 157507]\n",
       "\n",
       "[322897 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks_by_user\"] = df[\"clicks\"].groupby('user_id').agg(\n",
    "    LIST_click_article_id = ('click_article_id', lambda x: list(x)),\n",
    ")\n",
    "df[\"clicks_by_user\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15024c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = data_path + \"clicks_by_user.sav\"\n",
    "\n",
    "if not os.path.exists(file_path):\n",
    "    \n",
    "    df[\"clicks_by_user\"][\"categories\"] = \"\"\n",
    "    \n",
    "    for index, row in df[\"clicks_by_user\"].iterrows():\n",
    "        ls_row = []\n",
    "        for article in row[\"LIST_click_article_id\"]:\n",
    "            ls_row.append(\n",
    "                df[\"articles_metadata\"][df[\"articles_metadata\"][\"article_id\"] == article][\"category_id\"].values[0]\n",
    "            )\n",
    "        df[\"clicks_by_user\"].loc[index][\"categories\"] = ls_row\n",
    "    pickle.dump(df[\"clicks_by_user\"], open(file_path, \"wb\"))\n",
    "else:\n",
    "    df[\"clicks_by_user\"] = pickle.load(open(file_path, \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33b57ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(df, open(\"df.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a61d7ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIST_click_article_id</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[157541, 68866, 96755, 313996, 160158, 233470,...</td>\n",
       "      <td>[281, 136, 209, 431, 281, 375, 186, 186]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[235840, 96663, 59758, 160474, 285719, 156723,...</td>\n",
       "      <td>[375, 209, 123, 281, 412, 281, 331, 412, 435, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[119592, 30970, 30760, 209122]</td>\n",
       "      <td>[247, 26, 26, 332]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[236065, 236294, 234686, 233769, 235665, 23513...</td>\n",
       "      <td>[375, 375, 375, 375, 375, 375, 281, 375, 375, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[48915, 44488, 195887, 195084, 63307, 336499, ...</td>\n",
       "      <td>[92, 81, 317, 317, 132, 437, 399]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322892</th>\n",
       "      <td>[42567, 39894]</td>\n",
       "      <td>[67, 66]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322893</th>\n",
       "      <td>[50644, 36162]</td>\n",
       "      <td>[99, 43]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322894</th>\n",
       "      <td>[36162, 168401]</td>\n",
       "      <td>[43, 297]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322895</th>\n",
       "      <td>[289197, 63746]</td>\n",
       "      <td>[418, 133]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322896</th>\n",
       "      <td>[30760, 157507]</td>\n",
       "      <td>[26, 281]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322897 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     LIST_click_article_id  \\\n",
       "user_id                                                      \n",
       "0        [157541, 68866, 96755, 313996, 160158, 233470,...   \n",
       "1        [235840, 96663, 59758, 160474, 285719, 156723,...   \n",
       "2                           [119592, 30970, 30760, 209122]   \n",
       "3        [236065, 236294, 234686, 233769, 235665, 23513...   \n",
       "4        [48915, 44488, 195887, 195084, 63307, 336499, ...   \n",
       "...                                                    ...   \n",
       "322892                                      [42567, 39894]   \n",
       "322893                                      [50644, 36162]   \n",
       "322894                                     [36162, 168401]   \n",
       "322895                                     [289197, 63746]   \n",
       "322896                                     [30760, 157507]   \n",
       "\n",
       "                                                categories  \n",
       "user_id                                                     \n",
       "0                 [281, 136, 209, 431, 281, 375, 186, 186]  \n",
       "1        [375, 209, 123, 281, 412, 281, 331, 412, 435, ...  \n",
       "2                                       [247, 26, 26, 332]  \n",
       "3        [375, 375, 375, 375, 375, 375, 281, 375, 375, ...  \n",
       "4                        [92, 81, 317, 317, 132, 437, 399]  \n",
       "...                                                    ...  \n",
       "322892                                            [67, 66]  \n",
       "322893                                            [99, 43]  \n",
       "322894                                           [43, 297]  \n",
       "322895                                          [418, 133]  \n",
       "322896                                           [26, 281]  \n",
       "\n",
       "[322897 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks_by_user\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b41ab6",
   "metadata": {},
   "source": [
    "### Articles Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200708db",
   "metadata": {},
   "source": [
    "Il s'agit d'une matrice numpy contenant les plongements (embeddings) des articles entraînés sur les textes et les données des articles. Il s'agit de vecteurs de dimension 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "70631d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "articles_embeddings = pd.read_pickle(data_path + \"articles_embeddings.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "43a660b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364047, 250)\n"
     ]
    }
   ],
   "source": [
    "print(articles_embeddings.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dd05d9ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.16118301, -0.95723313, -0.13794445,  0.05085534,  0.83005524,\n",
       "        0.90136534, -0.33514765, -0.55956066, -0.50060284,  0.16518293,\n",
       "        0.4284342 ,  0.3550556 ,  0.87443674, -0.52888286,  0.6254872 ,\n",
       "        0.2689198 , -0.8228351 , -0.703853  , -0.62584543, -0.15285493,\n",
       "       -0.6662412 ,  0.04329487,  0.1786375 ,  0.04689008,  0.5945311 ,\n",
       "       -0.18334764,  0.19510683, -0.46763963, -0.30480695,  0.35317516,\n",
       "        0.27818817,  0.5386231 , -0.37120935,  0.48989806, -0.10383289,\n",
       "        0.11917368,  0.13243659, -0.62108386, -0.45331132,  0.34662652,\n",
       "       -0.06174106, -0.7305939 , -0.38411567, -0.94075835,  0.06134219,\n",
       "        0.4825816 ,  0.28968322, -0.62269634, -0.05000444,  0.42151213,\n",
       "       -0.24257636,  0.6687105 , -0.509004  , -0.46179956,  0.04390178,\n",
       "        0.28848746,  0.4498246 , -0.28486234,  0.916729  ,  0.70312876,\n",
       "        0.85167396, -0.6272441 ,  0.35773164,  0.3901894 ,  0.65293527,\n",
       "        0.1036527 ,  0.79829276, -0.10254639, -0.2045143 ,  0.37861153,\n",
       "       -0.09026147, -0.2511573 ,  0.3259828 ,  0.08588007, -0.18056485,\n",
       "        0.04752322, -0.15973987, -0.6201472 ,  0.3003347 , -0.7965467 ,\n",
       "        0.47771874,  0.40991876,  0.32778588,  0.07257079, -0.84338844,\n",
       "        0.5743214 ,  0.33120632,  0.73519886, -0.05114948, -0.42130408,\n",
       "       -0.9015315 , -0.30527022, -0.13556914, -0.46817523, -0.256514  ,\n",
       "       -0.02803535,  0.29472083, -0.06734743, -0.46152946,  0.07213261,\n",
       "       -0.5707245 , -0.05998378, -0.5503721 , -0.6039815 ,  0.40252253,\n",
       "       -0.3064805 ,  0.3307103 ,  0.14233193,  0.33290517,  0.23447387,\n",
       "        0.6906535 ,  0.48634282,  0.42737266, -0.1159763 ,  0.3854025 ,\n",
       "        0.3957128 , -0.65858316, -0.55600464,  0.7825072 ,  0.12878653,\n",
       "        0.6511364 , -0.20835122, -0.5025915 ,  0.42170998, -0.31492347,\n",
       "       -0.02744605, -0.24444908,  0.80920833,  0.18782361,  0.16195548,\n",
       "       -0.14996661, -0.7144809 , -0.00697921, -0.31291643,  0.94464755,\n",
       "        0.24019346, -0.17193855, -0.71315813,  0.01460485,  0.29979667,\n",
       "       -0.7239213 , -0.37642562,  0.68271834, -0.603598  , -0.5735651 ,\n",
       "       -0.4823226 , -0.6750509 ,  0.12579046, -0.08218385, -0.46767712,\n",
       "        0.87283933,  0.35769752,  0.79911405, -0.7901175 ,  0.49681464,\n",
       "        0.5684371 , -0.34781384,  0.6503434 , -0.5989994 ,  0.42142177,\n",
       "        0.10738157, -0.46030676,  0.30180693, -0.3994389 , -0.03487772,\n",
       "        0.12320779, -0.00179706, -0.21508627, -0.40875113,  0.20472752,\n",
       "       -0.60365415, -0.22466736, -0.42120966,  0.6471822 , -0.33045855,\n",
       "       -0.29070365, -0.05361752,  0.5163881 ,  0.33017814,  0.37607408,\n",
       "       -0.17617662, -0.56767094, -0.00996806, -0.4003482 , -0.49794638,\n",
       "       -0.22630718, -0.57593477, -0.365481  ,  0.29230362,  0.68888617,\n",
       "        0.87204033, -0.05710741, -0.3094548 , -0.42777112, -0.6890967 ,\n",
       "        0.65392166, -0.4179235 , -0.13035183, -0.9389396 , -0.61100435,\n",
       "        0.56593966, -0.00279831, -0.04636448,  0.59413934,  0.16963427,\n",
       "        0.2505927 , -0.06282282, -0.40642437,  0.17572297, -0.25898445,\n",
       "        0.7643812 ,  0.27564424, -0.69213825, -0.3932401 , -0.32569295,\n",
       "        0.16337723, -0.15495214, -0.7016393 ,  0.711825  , -0.8390629 ,\n",
       "       -0.4591448 ,  0.9199131 ,  0.62183356, -0.64047134,  0.38993424,\n",
       "        0.76417774,  0.16485116, -0.7470226 ,  0.41103414,  0.7509008 ,\n",
       "        0.8606434 ,  0.61475503,  0.54965967,  0.33443713, -0.38805893,\n",
       "       -0.70372987, -0.5674205 ,  0.00647845, -0.20659018, -0.3852717 ,\n",
       "        0.3212482 ,  0.31399873,  0.6364123 ,  0.16917853,  0.540524  ,\n",
       "       -0.8131821 ,  0.28687033, -0.231686  ,  0.5974159 ,  0.40962312],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles_embeddings[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bfc62f",
   "metadata": {},
   "source": [
    "## Fusion de clicks et articles_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e39a086a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"user_article\"] = df[\"clicks\"].merge(\n",
    "    df[\"articles_metadata\"],\n",
    "    left_on=\"click_article_id\",\n",
    "    right_on=\"article_id\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "13cc461b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2988181 entries, 0 to 2988180\n",
      "Data columns (total 17 columns):\n",
      " #   Column               Dtype \n",
      "---  ------               ----- \n",
      " 0   user_id              object\n",
      " 1   session_id           object\n",
      " 2   session_start        object\n",
      " 3   session_size         object\n",
      " 4   click_article_id     object\n",
      " 5   click_timestamp      object\n",
      " 6   click_environment    object\n",
      " 7   click_deviceGroup    object\n",
      " 8   click_os             object\n",
      " 9   click_country        object\n",
      " 10  click_region         object\n",
      " 11  click_referrer_type  object\n",
      " 12  article_id           int64 \n",
      " 13  category_id          int64 \n",
      " 14  created_at_ts        int64 \n",
      " 15  publisher_id         int64 \n",
      " 16  words_count          int64 \n",
      "dtypes: int64(5), object(12)\n",
      "memory usage: 410.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df[\"user_article\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "62aab030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2988181, 17)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user_article\"].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0db1f9",
   "metadata": {},
   "source": [
    "Explorons maintenant ce dataset :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc005705",
   "metadata": {},
   "source": [
    "#### Nombre d'utilisateurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b714cc81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre d'utilisateurs :  322897\n"
     ]
    }
   ],
   "source": [
    "nb_users = df[\"user_article\"][\"user_id\"].nunique()\n",
    "print(\"Nombre d'utilisateurs : \", nb_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb50231e",
   "metadata": {},
   "source": [
    "#### Nombre d'articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0783eb04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre d'articles :  46033\n"
     ]
    }
   ],
   "source": [
    "nb_articles = df[\"user_article\"][\"article_id\"].nunique()\n",
    "print(\"Nombre d'articles : \", nb_articles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a401245",
   "metadata": {},
   "source": [
    "#### Nombre de catégories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5e79e4b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre d'articles :  316\n"
     ]
    }
   ],
   "source": [
    "nb_cats = df[\"user_article\"][\"category_id\"].nunique()\n",
    "print(\"Nombre d'articles : \", nb_cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f048f0",
   "metadata": {},
   "source": [
    "### Répartition du nombre de clics par session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2104ac2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(df['user_article']['session_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5fa2de61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['user_article']['session_size'].hist(figsize=(18,9), bins=100)\n",
    "plt.xlabel(\"Nombre de clics par session\")\n",
    "plt.ylabel(\"Nombre de sessions\")\n",
    "plt.title(\"Nombre de clics\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b003afd",
   "metadata": {},
   "source": [
    "Nous allons zoomer un peu pour voir un peu mieux le nombre de clics par session le plus courrant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "71ed9735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['user_article']['session_size'].hist(figsize=(18,9), bins=124)\n",
    "plt.xlim([0,20])\n",
    "plt.xlabel(\"Nombre de clics\")\n",
    "plt.ylabel(\"Nombre de sessions\")\n",
    "plt.title(\"Nombre de clics par session\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97f34e2",
   "metadata": {},
   "source": [
    "### Répartition du nombre d'articles lus par utilisateur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "023e906f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101518"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(df['user_article'].groupby(by=\"user_id\")['click_article_id'].nunique().value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "99ffa159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['user_article'].groupby(by=\"user_id\")['click_article_id'].nunique().value_counts().plot(\n",
    "    figsize=(18,9),\n",
    "    kind = 'bar', \n",
    "    title=\"Nombre d'articles lus par utilisateur\", \n",
    "    xticks=range(0, 100, 10), \n",
    "    xlabel = \"Nombre d'articles lus\", \n",
    "    ylabel=\"Nombre d'utilisateurs\"\n",
    ")\n",
    "plt.xlim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "298a895d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(df, open(\"df.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf99ae2",
   "metadata": {},
   "source": [
    "### Nettoyage des données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255096c1",
   "metadata": {},
   "source": [
    "Commençons par vérifier s'il existe des doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "649c6511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_article'].duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3927b78a",
   "metadata": {},
   "source": [
    "Regardons également s'il y a des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2c36e8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                0\n",
       "session_id             0\n",
       "session_start          0\n",
       "session_size           0\n",
       "click_article_id       0\n",
       "click_timestamp        0\n",
       "click_environment      0\n",
       "click_deviceGroup      0\n",
       "click_os               0\n",
       "click_country          0\n",
       "click_region           0\n",
       "click_referrer_type    0\n",
       "article_id             0\n",
       "category_id            0\n",
       "created_at_ts          0\n",
       "publisher_id           0\n",
       "words_count            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user_article'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4fb5d0",
   "metadata": {},
   "source": [
    "Il n'y a ni doublon ni valeur nulle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9b520b",
   "metadata": {},
   "source": [
    "#### ACP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cc6a704b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_art_emb = pd.DataFrame(\n",
    "    articles_embeddings,\n",
    "    columns=[\"emb_\" + str(i) for i in range(articles_embeddings.shape[1])]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "aace7e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emb_0</th>\n",
       "      <th>emb_1</th>\n",
       "      <th>emb_2</th>\n",
       "      <th>emb_3</th>\n",
       "      <th>emb_4</th>\n",
       "      <th>emb_5</th>\n",
       "      <th>emb_6</th>\n",
       "      <th>emb_7</th>\n",
       "      <th>emb_8</th>\n",
       "      <th>emb_9</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_240</th>\n",
       "      <th>emb_241</th>\n",
       "      <th>emb_242</th>\n",
       "      <th>emb_243</th>\n",
       "      <th>emb_244</th>\n",
       "      <th>emb_245</th>\n",
       "      <th>emb_246</th>\n",
       "      <th>emb_247</th>\n",
       "      <th>emb_248</th>\n",
       "      <th>emb_249</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.161183</td>\n",
       "      <td>-0.957233</td>\n",
       "      <td>-0.137944</td>\n",
       "      <td>0.050855</td>\n",
       "      <td>0.830055</td>\n",
       "      <td>0.901365</td>\n",
       "      <td>-0.335148</td>\n",
       "      <td>-0.559561</td>\n",
       "      <td>-0.500603</td>\n",
       "      <td>0.165183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321248</td>\n",
       "      <td>0.313999</td>\n",
       "      <td>0.636412</td>\n",
       "      <td>0.169179</td>\n",
       "      <td>0.540524</td>\n",
       "      <td>-0.813182</td>\n",
       "      <td>0.286870</td>\n",
       "      <td>-0.231686</td>\n",
       "      <td>0.597416</td>\n",
       "      <td>0.409623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.523216</td>\n",
       "      <td>-0.974058</td>\n",
       "      <td>0.738608</td>\n",
       "      <td>0.155234</td>\n",
       "      <td>0.626294</td>\n",
       "      <td>0.485297</td>\n",
       "      <td>-0.715657</td>\n",
       "      <td>-0.897996</td>\n",
       "      <td>-0.359747</td>\n",
       "      <td>0.398246</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487843</td>\n",
       "      <td>0.823124</td>\n",
       "      <td>0.412688</td>\n",
       "      <td>-0.338654</td>\n",
       "      <td>0.320787</td>\n",
       "      <td>0.588643</td>\n",
       "      <td>-0.594137</td>\n",
       "      <td>0.182828</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>-0.834364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.619619</td>\n",
       "      <td>-0.972960</td>\n",
       "      <td>-0.207360</td>\n",
       "      <td>-0.128861</td>\n",
       "      <td>0.044748</td>\n",
       "      <td>-0.387535</td>\n",
       "      <td>-0.730477</td>\n",
       "      <td>-0.066126</td>\n",
       "      <td>-0.754899</td>\n",
       "      <td>-0.242004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454756</td>\n",
       "      <td>0.473184</td>\n",
       "      <td>0.377866</td>\n",
       "      <td>-0.863887</td>\n",
       "      <td>-0.383365</td>\n",
       "      <td>0.137721</td>\n",
       "      <td>-0.810877</td>\n",
       "      <td>-0.447580</td>\n",
       "      <td>0.805932</td>\n",
       "      <td>-0.285284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.740843</td>\n",
       "      <td>-0.975749</td>\n",
       "      <td>0.391698</td>\n",
       "      <td>0.641738</td>\n",
       "      <td>-0.268645</td>\n",
       "      <td>0.191745</td>\n",
       "      <td>-0.825593</td>\n",
       "      <td>-0.710591</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.110514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271535</td>\n",
       "      <td>0.036040</td>\n",
       "      <td>0.480029</td>\n",
       "      <td>-0.763173</td>\n",
       "      <td>0.022627</td>\n",
       "      <td>0.565165</td>\n",
       "      <td>-0.910286</td>\n",
       "      <td>-0.537838</td>\n",
       "      <td>0.243541</td>\n",
       "      <td>-0.885329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.279052</td>\n",
       "      <td>-0.972315</td>\n",
       "      <td>0.685374</td>\n",
       "      <td>0.113056</td>\n",
       "      <td>0.238315</td>\n",
       "      <td>0.271913</td>\n",
       "      <td>-0.568816</td>\n",
       "      <td>0.341194</td>\n",
       "      <td>-0.600554</td>\n",
       "      <td>-0.125644</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238286</td>\n",
       "      <td>0.809268</td>\n",
       "      <td>0.427521</td>\n",
       "      <td>-0.615932</td>\n",
       "      <td>-0.503697</td>\n",
       "      <td>0.614450</td>\n",
       "      <td>-0.917760</td>\n",
       "      <td>-0.424061</td>\n",
       "      <td>0.185484</td>\n",
       "      <td>-0.580292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      emb_0     emb_1     emb_2     emb_3     emb_4     emb_5     emb_6  \\\n",
       "0 -0.161183 -0.957233 -0.137944  0.050855  0.830055  0.901365 -0.335148   \n",
       "1 -0.523216 -0.974058  0.738608  0.155234  0.626294  0.485297 -0.715657   \n",
       "2 -0.619619 -0.972960 -0.207360 -0.128861  0.044748 -0.387535 -0.730477   \n",
       "3 -0.740843 -0.975749  0.391698  0.641738 -0.268645  0.191745 -0.825593   \n",
       "4 -0.279052 -0.972315  0.685374  0.113056  0.238315  0.271913 -0.568816   \n",
       "\n",
       "      emb_7     emb_8     emb_9  ...   emb_240   emb_241   emb_242   emb_243  \\\n",
       "0 -0.559561 -0.500603  0.165183  ...  0.321248  0.313999  0.636412  0.169179   \n",
       "1 -0.897996 -0.359747  0.398246  ... -0.487843  0.823124  0.412688 -0.338654   \n",
       "2 -0.066126 -0.754899 -0.242004  ...  0.454756  0.473184  0.377866 -0.863887   \n",
       "3 -0.710591 -0.040099 -0.110514  ...  0.271535  0.036040  0.480029 -0.763173   \n",
       "4  0.341194 -0.600554 -0.125644  ...  0.238286  0.809268  0.427521 -0.615932   \n",
       "\n",
       "    emb_244   emb_245   emb_246   emb_247   emb_248   emb_249  \n",
       "0  0.540524 -0.813182  0.286870 -0.231686  0.597416  0.409623  \n",
       "1  0.320787  0.588643 -0.594137  0.182828  0.397090 -0.834364  \n",
       "2 -0.383365  0.137721 -0.810877 -0.447580  0.805932 -0.285284  \n",
       "3  0.022627  0.565165 -0.910286 -0.537838  0.243541 -0.885329  \n",
       "4 -0.503697  0.614450 -0.917760 -0.424061  0.185484 -0.580292  \n",
       "\n",
       "[5 rows x 250 columns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_art_emb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81aee60",
   "metadata": {},
   "source": [
    "Nous allons réaliser une ACP afin de réduire le nombre de features de cet embeddings, et donc la taille du fichier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "418ec9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=250)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=250)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=250)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covar_matrix = PCA(\n",
    "    n_components=articles_embeddings.shape[1]\n",
    ")\n",
    "covar_matrix.fit(articles_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "e484a64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scree_plot(pca):\n",
    "    scree = pca.explained_variance_ratio_*100\n",
    "    plt.bar(np.arange(len(scree))+1, scree)\n",
    "    plt.plot(np.arange(len(scree))+1, scree.cumsum(),c=\"red\",marker='o')\n",
    "    plt.xlabel(\"rang de l'axe d'inertie\")\n",
    "    plt.ylabel(\"pourcentage d'inertie\")\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(15,15)\n",
    "    plt.title(\"Eboulis des valeurs propres\")\n",
    "    plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "228edde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_scree_plot(covar_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "199a12eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Variance expliquée et cumulée')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10)) #\n",
    "\n",
    "plt.plot(np.cumsum(covar_matrix.explained_variance_ratio_))\n",
    "plt.axvline(x=70, color=\"r\", linestyle=\"--\")\n",
    "plt.axhline(y=0.9773075504808983, color=\"r\", linestyle=\"--\")\n",
    "plt.xlabel('Nombre de composants')\n",
    "plt.ylabel('Variance expliquée et cumulée')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226b456a",
   "metadata": {},
   "source": [
    "Nous allons conserver 70 composants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "77d83197",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=70)\n",
    "pca.fit(df_art_emb)\n",
    "art_emb_pca = pca.transform(df_art_emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "581bc8e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance expliquée :  0.9773075504808983\n"
     ]
    }
   ],
   "source": [
    "variance = sum(pca.explained_variance_ratio_)\n",
    "print(\"variance expliquée : \", variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b7a2c9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_art_emb_pca = pd.DataFrame(\n",
    "    art_emb_pca,\n",
    "    columns = [\"emb_\" + str(i) for i in range(art_emb_pca.shape[1])]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f4562b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emb_0</th>\n",
       "      <th>emb_1</th>\n",
       "      <th>emb_2</th>\n",
       "      <th>emb_3</th>\n",
       "      <th>emb_4</th>\n",
       "      <th>emb_5</th>\n",
       "      <th>emb_6</th>\n",
       "      <th>emb_7</th>\n",
       "      <th>emb_8</th>\n",
       "      <th>emb_9</th>\n",
       "      <th>...</th>\n",
       "      <th>emb_60</th>\n",
       "      <th>emb_61</th>\n",
       "      <th>emb_62</th>\n",
       "      <th>emb_63</th>\n",
       "      <th>emb_64</th>\n",
       "      <th>emb_65</th>\n",
       "      <th>emb_66</th>\n",
       "      <th>emb_67</th>\n",
       "      <th>emb_68</th>\n",
       "      <th>emb_69</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.176778</td>\n",
       "      <td>-1.316917</td>\n",
       "      <td>-1.029055</td>\n",
       "      <td>0.901900</td>\n",
       "      <td>-1.809533</td>\n",
       "      <td>2.064726</td>\n",
       "      <td>1.221895</td>\n",
       "      <td>0.024460</td>\n",
       "      <td>0.927227</td>\n",
       "      <td>0.669742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.230794</td>\n",
       "      <td>-0.100359</td>\n",
       "      <td>0.049112</td>\n",
       "      <td>0.058206</td>\n",
       "      <td>-0.319569</td>\n",
       "      <td>0.528240</td>\n",
       "      <td>0.113952</td>\n",
       "      <td>-0.417471</td>\n",
       "      <td>-0.002487</td>\n",
       "      <td>0.071560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.735181</td>\n",
       "      <td>0.489886</td>\n",
       "      <td>3.268564</td>\n",
       "      <td>0.087844</td>\n",
       "      <td>1.473064</td>\n",
       "      <td>0.932691</td>\n",
       "      <td>-1.841634</td>\n",
       "      <td>0.881792</td>\n",
       "      <td>-0.207228</td>\n",
       "      <td>-0.816806</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103000</td>\n",
       "      <td>0.083815</td>\n",
       "      <td>-0.036991</td>\n",
       "      <td>0.419636</td>\n",
       "      <td>0.298784</td>\n",
       "      <td>-0.111680</td>\n",
       "      <td>-0.146413</td>\n",
       "      <td>0.195302</td>\n",
       "      <td>-0.158367</td>\n",
       "      <td>0.085617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.912690</td>\n",
       "      <td>-2.089345</td>\n",
       "      <td>1.865867</td>\n",
       "      <td>-1.202533</td>\n",
       "      <td>2.530592</td>\n",
       "      <td>0.521945</td>\n",
       "      <td>-0.224343</td>\n",
       "      <td>-1.479939</td>\n",
       "      <td>-0.191826</td>\n",
       "      <td>-1.356808</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105127</td>\n",
       "      <td>0.102635</td>\n",
       "      <td>0.192903</td>\n",
       "      <td>0.220059</td>\n",
       "      <td>0.142572</td>\n",
       "      <td>-0.136309</td>\n",
       "      <td>0.146346</td>\n",
       "      <td>-0.140555</td>\n",
       "      <td>0.068443</td>\n",
       "      <td>0.269176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.096563</td>\n",
       "      <td>0.212953</td>\n",
       "      <td>4.183521</td>\n",
       "      <td>-0.649570</td>\n",
       "      <td>-0.130879</td>\n",
       "      <td>-1.126562</td>\n",
       "      <td>-1.063989</td>\n",
       "      <td>0.662888</td>\n",
       "      <td>0.348124</td>\n",
       "      <td>-1.463915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179905</td>\n",
       "      <td>-0.028757</td>\n",
       "      <td>-0.371627</td>\n",
       "      <td>-0.105237</td>\n",
       "      <td>0.365489</td>\n",
       "      <td>0.295829</td>\n",
       "      <td>0.351317</td>\n",
       "      <td>-0.044445</td>\n",
       "      <td>-0.050126</td>\n",
       "      <td>-0.298399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.193779</td>\n",
       "      <td>-0.263952</td>\n",
       "      <td>1.896584</td>\n",
       "      <td>-1.834365</td>\n",
       "      <td>1.270380</td>\n",
       "      <td>1.723270</td>\n",
       "      <td>-0.329014</td>\n",
       "      <td>-0.283813</td>\n",
       "      <td>-0.659787</td>\n",
       "      <td>-1.223723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448678</td>\n",
       "      <td>-0.348934</td>\n",
       "      <td>0.222146</td>\n",
       "      <td>-0.081992</td>\n",
       "      <td>0.406375</td>\n",
       "      <td>-0.120155</td>\n",
       "      <td>0.379303</td>\n",
       "      <td>0.108424</td>\n",
       "      <td>0.159025</td>\n",
       "      <td>-0.140482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      emb_0     emb_1     emb_2     emb_3     emb_4     emb_5     emb_6  \\\n",
       "0 -2.176778 -1.316917 -1.029055  0.901900 -1.809533  2.064726  1.221895   \n",
       "1 -1.735181  0.489886  3.268564  0.087844  1.473064  0.932691 -1.841634   \n",
       "2 -0.912690 -2.089345  1.865867 -1.202533  2.530592  0.521945 -0.224343   \n",
       "3  1.096563  0.212953  4.183521 -0.649570 -0.130879 -1.126562 -1.063989   \n",
       "4  0.193779 -0.263952  1.896584 -1.834365  1.270380  1.723270 -0.329014   \n",
       "\n",
       "      emb_7     emb_8     emb_9  ...    emb_60    emb_61    emb_62    emb_63  \\\n",
       "0  0.024460  0.927227  0.669742  ...  0.230794 -0.100359  0.049112  0.058206   \n",
       "1  0.881792 -0.207228 -0.816806  ... -0.103000  0.083815 -0.036991  0.419636   \n",
       "2 -1.479939 -0.191826 -1.356808  ...  0.105127  0.102635  0.192903  0.220059   \n",
       "3  0.662888  0.348124 -1.463915  ...  0.179905 -0.028757 -0.371627 -0.105237   \n",
       "4 -0.283813 -0.659787 -1.223723  ...  0.448678 -0.348934  0.222146 -0.081992   \n",
       "\n",
       "     emb_64    emb_65    emb_66    emb_67    emb_68    emb_69  \n",
       "0 -0.319569  0.528240  0.113952 -0.417471 -0.002487  0.071560  \n",
       "1  0.298784 -0.111680 -0.146413  0.195302 -0.158367  0.085617  \n",
       "2  0.142572 -0.136309  0.146346 -0.140555  0.068443  0.269176  \n",
       "3  0.365489  0.295829  0.351317 -0.044445 -0.050126 -0.298399  \n",
       "4  0.406375 -0.120155  0.379303  0.108424  0.159025 -0.140482  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_art_emb_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5f07118",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(df_art_emb_pca, open(\"df_art_env_pca.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d512b1e8",
   "metadata": {},
   "source": [
    "# Systèmes de recommandation de contenus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f129e5",
   "metadata": {},
   "source": [
    "Il existe différentes approches pour les systèmes de recommandation. Ils peuvent être basés sur :\n",
    "- le contenu (content based RS)\n",
    "- les métadonnées (content based RS)\n",
    "- l'historique de chaque utilisateur (collaborative filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cd4ce7",
   "metadata": {},
   "source": [
    "### Filtrage collaboratif (Collaborative filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75ed371",
   "metadata": {},
   "source": [
    "Il s'agit de l’ensemble des techniques qui utilisent le lien utilisateur-produit. Ce lien est représenté par des avis ou des notes, il est utilisé pour construire un système de recommandation personnalisé.  \n",
    "\n",
    "On peut par exemple retrouver ce genre de systèmes chez Amazon lorsque des articles vous sont recommandés avec la mention \"les utilisateurs ayant aimé cet article ont aussi aimé xxx\".\n",
    "\n",
    "Pour faire du filtrage coollaboratif, on peut faire du filtrage basé sur l'utilisateur, ou du filtrage basé sur l'article. Ces notes peuvent être attribuées de façon active (on demande à l'utilisateur de noter l'article), ou passive (par exemple, le nombre d'écoutes d'une musique sur *Spotify*, ou dans notre cas, le nombre de clics sur un article).\n",
    "\n",
    "On créée une matrice utilisateur-article creuse, c'est-à-dire avec beaucoup de champs vides. Chaque utilisateur dans la base de données peut alors être considéré comme un vecteur incomplet de notes, où la composante **a** est la note de l'utilisateur correspondant à l'article **a**. De la même manière, un article peut être considéré comme un vecteur où chaque composante représente un utilisateur et sa note pour l'article."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1a8286",
   "metadata": {},
   "source": [
    "#### Préparation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1799c921",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id        session_id  session_start session_size click_article_id  \\\n",
       "0       0  1506825423271737  1506825423000            2           157541   \n",
       "1       0  1506825423271737  1506825423000            2            68866   \n",
       "2       1  1506825426267738  1506825426000            2           235840   \n",
       "3       1  1506825426267738  1506825426000            2            96663   \n",
       "4       2  1506825435299739  1506825435000            2           119592   \n",
       "\n",
       "  click_timestamp click_environment click_deviceGroup click_os click_country  \\\n",
       "0   1506826828020                 4                 3       20             1   \n",
       "1   1506826858020                 4                 3       20             1   \n",
       "2   1506827017951                 4                 1       17             1   \n",
       "3   1506827047951                 4                 1       17             1   \n",
       "4   1506827090575                 4                 1       17             1   \n",
       "\n",
       "  click_region click_referrer_type  \n",
       "0           20                   2  \n",
       "1           20                   2  \n",
       "2           16                   2  \n",
       "3           16                   2  \n",
       "4           24                   2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59f37827",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   article_id  category_id  created_at_ts  publisher_id  words_count\n",
       "0           0            0  1513144419000             0          168\n",
       "1           1            1  1405341936000             0          189\n",
       "2           2            1  1408667706000             0          250\n",
       "3           3            1  1408468313000             0          230\n",
       "4           4            1  1407071171000             0          162"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"articles_metadata\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9de79599",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>51099</td>\n",
       "      <td>98243</td>\n",
       "      <td>220</td>\n",
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       "<p>2988181 rows × 3 columns</p>\n",
       "</div>"
      ],
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       "        user_id  article_id  category_id\n",
       "0             0      157541          281\n",
       "1            20      157541          281\n",
       "2            44      157541          281\n",
       "3            45      157541          281\n",
       "4            76      157541          281\n",
       "...         ...         ...          ...\n",
       "2988176  195186        2221            1\n",
       "2988177   75658      271117          399\n",
       "2988178  217129       20204            9\n",
       "2988179  217129       70196          136\n",
       "2988180   51099       98243          220\n",
       "\n",
       "[2988181 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user_article\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6929f34f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1882302 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  category_id  0\n",
       "0              0          136  1\n",
       "1              0          186  2\n",
       "2              0          209  1\n",
       "3              0          281  2\n",
       "4              0          375  1\n",
       "...          ...          ... ..\n",
       "1882297   322894          297  1\n",
       "1882298   322895          133  1\n",
       "1882299   322895          418  1\n",
       "1882300   322896           26  1\n",
       "1882301   322896          281  1\n",
       "\n",
       "[1882302 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df[\"user_article\"].groupby( # type(tmp) == Series\n",
    "    [\n",
    "        \"user_id\",\n",
    "        \"category_id\"\n",
    "    ]\n",
    ").size()\n",
    "df[\"user_rating\"] = tmp.to_frame()\n",
    "df[\"user_rating\"] = df[\"user_rating\"].reset_index()\n",
    "\n",
    "df[\"user_rating\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e751e5ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"user_rating\"].rename(\n",
    "    columns = {0 : \"rating\"},\n",
    "    inplace = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "815e7078",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>1882302 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  category_id  rating\n",
       "0              0          136       1\n",
       "1              0          186       2\n",
       "2              0          209       1\n",
       "3              0          281       2\n",
       "4              0          375       1\n",
       "...          ...          ...     ...\n",
       "1882297   322894          297       1\n",
       "1882298   322895          133       1\n",
       "1882299   322895          418       1\n",
       "1882300   322896           26       1\n",
       "1882301   322896          281       1\n",
       "\n",
       "[1882302 rows x 3 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user_rating\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5d775c3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "le nombre maximum de clics pour un article est de  535\n"
     ]
    }
   ],
   "source": [
    "print(\"le nombre maximum de clics pour un article est de \", np.max(df[\"user_rating\"][\"rating\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf00874c",
   "metadata": {},
   "source": [
    "Il pourrait être judicieux de normaliser la colonne rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e55115a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df[\"user_rating\"][\"rating_norm\"] = MinMaxScaler(\n",
    "    feature_range=(0,1)\n",
    ").fit_transform(\n",
    "    np.array(df[\"user_rating\"][\"rating\"]).reshape(-1,1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "81b5f916",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(df, open(\"df.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "04d3fbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>rating</th>\n",
       "      <th>rating_norm</th>\n",
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       "      <td>322895</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882300</th>\n",
       "      <td>322896</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882301</th>\n",
       "      <td>322896</td>\n",
       "      <td>281</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1882302 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  category_id  rating  rating_norm\n",
       "0              0          136       1     0.000000\n",
       "1              0          186       2     0.001873\n",
       "2              0          209       1     0.000000\n",
       "3              0          281       2     0.001873\n",
       "4              0          375       1     0.000000\n",
       "...          ...          ...     ...          ...\n",
       "1882297   322894          297       1     0.000000\n",
       "1882298   322895          133       1     0.000000\n",
       "1882299   322895          418       1     0.000000\n",
       "1882300   322896           26       1     0.000000\n",
       "1882301   322896          281       1     0.000000\n",
       "\n",
       "[1882302 rows x 4 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"user_rating\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8613459f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_cf = df[\"user_rating\"][\n",
    "    [\n",
    "        \"user_id\",\n",
    "        \"category_id\",\n",
    "        \"rating_norm\"\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d86fd28",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cf = X_cf[X_cf[\"rating_norm\"]!=0.]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7491efb4",
   "metadata": {},
   "source": [
    "Nous allons maintenant utiliser un *reader* de la librairie *Surprise*. La classe *Reader* est utilisée pour parser un dataframe contenant des notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba7cba4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "503616"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_cf[\"rating_norm\"][X_cf[\"rating_norm\"]!=0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26a9a6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "503616"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_cf) - len(X_cf[\"rating_norm\"][X_cf[\"rating_norm\"]==0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3e847c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(0,1))\n",
    "data = Dataset.load_from_df(\n",
    "    X_cf[\n",
    "        [\n",
    "            \"user_id\",\n",
    "            \"category_id\",\n",
    "            \"rating_norm\"\n",
    "        ]\n",
    "    ],\n",
    "    reader\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "3b83e660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre d'interactions :  1882302\n"
     ]
    }
   ],
   "source": [
    "print(\"Nombre d'interactions : \", len(X_cf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce38924",
   "metadata": {},
   "source": [
    "#### Préparation des données pour l'entraînement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24640101",
   "metadata": {},
   "source": [
    "Séparons maintenant notre jeu de données en jeu d'entraînement et jeu de test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5ed0fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(\n",
    "    data,\n",
    "    test_size=0.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7fae4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(X_cf, open(\"X_cf.pkl\", \"wb\"))\n",
    "pickle.dump(data, open(\"data.pkl\", \"wb\"))\n",
    "pickle.dump(train, open(\"train.pkl\", \"wb\"))\n",
    "pickle.dump(test, open(\"test.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1138cb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cf = pickle.load((open(\"X_cf.pkl\", \"rb\")))\n",
    "data = pickle.load((open(\"data.pkl\", \"rb\")))\n",
    "train = pickle.load((open(\"train.pkl\", \"rb\")))\n",
    "test = pickle.load((open(\"test.pkl\", \"rb\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f8cbf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surprise.trainset.Trainset"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "19614a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "00a11c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "le jeu de test comprend 376461 lignes\n",
      "le jeu d'entraînement' comprend 1505841 lignes\n"
     ]
    }
   ],
   "source": [
    "print(\"le jeu de test comprend {} lignes\".format(len(test)))\n",
    "print(\"le jeu d'entraînement' comprend {} lignes\".format(len(X_cf)-len(test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efb6d93",
   "metadata": {},
   "source": [
    "### Benchmark des algorithmes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcbf7bb",
   "metadata": {},
   "source": [
    "Et si nous testions et comparions plusieurs algorithmes ?\n",
    "\n",
    "Voici la liste des algorithmes que nous allons tester :\n",
    "\n",
    "- **NormalPredictor** : L'algorithme NormalPredictor prédit une note aléatoire basée sur la distribution de l'ensemble d'apprentissage, qui est supposé être normal. C'est l'un des algorithmes les plus basiques qui ne fonctionne bien évidemment pas beaucoup\n",
    "\n",
    "- **BaselineOnly** : Cet algorithme prédit une estimation de base pour un utilisateur et un élément donnés\n",
    "\n",
    "- **SVD** : L'algorithme SVD est équivalent à la factorisation matricielle probabiliste [https://proceedings.neurips.cc/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf](https://proceedings.neurips.cc/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf)\n",
    "\n",
    "- **SVDpp** : L'algorithme SVDpp est une extension de SVD qui prend en compte les notations implicites.\n",
    "\n",
    "- **NMF** : NMF est un algorithme de filtrage collaboratif basé sur la factorisation matricielle non négative. C'est très similaire à SVD.\n",
    "\n",
    "- **Slope One** : Slope One est une implémentation simple de l'algorithme SlopeOne [https://arxiv.org/abs/cs/0702144](https://arxiv.org/abs/cs/0702144)\n",
    "\n",
    "- **Co-clustering** : un algorithme de filtrage collaboratif basé sur le co-clustering (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.6458&rep=rep1&type=pdf)[https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.6458&rep=rep1&type=pdf]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b427c1",
   "metadata": {},
   "source": [
    "#### Métriques"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1482ed3d",
   "metadata": {},
   "source": [
    "Les métriques que nous utiliserons sont :\n",
    "- la racine de l'erreur quadratique moyenne (**RMSE**)\n",
    "- l'erreur absolur moyenne (**MAE**)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1be64b",
   "metadata": {},
   "source": [
    "##### Définitions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6007d2cc",
   "metadata": {},
   "source": [
    "La **MSE**, ou erreur quadratique moyenne, est la moyenne des carrés des erreurs, définie par la formule : \n",
    "${{MSE = \\frac{1}{n} \\sum_{i=1}^n e_i^2 = \\frac{1}{n} \\sum_{i=1}^n (y_i – \\hat{y}_i)^2 }}$\n",
    "\n",
    "La **RMSE**, ou racine de l’erreur quadratique moyenne, est – comme son nom l’indique – la racine carrée de la **MSE**. Mathématiquement, elle est définie par : \n",
    "\n",
    "${{RMSE = \\sqrt{MSE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (y_i – \\hat{y}_i)^2}}}$ \n",
    "\n",
    "Contrairement à la **MSE**, la **RMSE** s’exprime dans la même unité que la variable à prédire et est par conséquent plus facile à interpréter. Ces métriques quantifient les erreurs réalisées par le modèle. Plus elles sont élevées, moins le modèle est performant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef3338e",
   "metadata": {},
   "source": [
    "La **MAE**, ou erreur absolue moyenne, est la moyenne des valeurs absolues des erreurs, définie par la formule :\n",
    "\n",
    "${{MAE = \\frac{1}{n} \\sum_{i=1}^n |e_i| = \\frac{1}{n} \\sum_{i=1}^n |y_i – \\hat{y}_i|}}$\n",
    "\n",
    "La **MAE** est dans la même unité que la variable à prédire. Par conséquent, elle est facile à interpréter.\n",
    "\n",
    "Cette métrique quantifie l’erreur réalisée par le modèle. Plus elle est élevée, moins le modèle est performant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "abf5758b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Début:  <surprise.prediction_algorithms.matrix_factorization.SVD object at 0x000002A741918C70>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.matrix_factorization.SVD object at 0x000002A741918C70> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.matrix_factorization.SVDpp object at 0x000002A741919810>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.matrix_factorization.SVDpp object at 0x000002A741919810> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.slope_one.SlopeOne object at 0x000002A7419196C0>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.slope_one.SlopeOne object at 0x000002A7419196C0> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.matrix_factorization.NMF object at 0x000002A7419184C0>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.matrix_factorization.NMF object at 0x000002A7419184C0> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.random_pred.NormalPredictor object at 0x000002A74191B340>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.random_pred.NormalPredictor object at 0x000002A74191B340> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.baseline_only.BaselineOnly object at 0x000002A74191ABF0>\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fin:  <surprise.prediction_algorithms.baseline_only.BaselineOnly object at 0x000002A74191ABF0> \n",
      "\n",
      "\n",
      "Début:  <surprise.prediction_algorithms.co_clustering.CoClustering object at 0x000002A741919A20>\n",
      "Fin:  <surprise.prediction_algorithms.co_clustering.CoClustering object at 0x000002A741919A20> \n",
      "\n",
      "\n",
      "\n",
      "\tTERMINÉ\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_780\\2037993766.py:46: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  tmp = tmp.append(\n"
     ]
    }
   ],
   "source": [
    "benchmark = []\n",
    "\n",
    "algorithms = [\n",
    "    SVD(), \n",
    "    SVDpp(), \n",
    "    SlopeOne(), \n",
    "    NMF(), \n",
    "    NormalPredictor(), \n",
    "#     KNNBaseline(), # demande trop de mémoire\n",
    "#     KNNBasic(), # idem\n",
    "#     KNNWithMeans(), \n",
    "#     KNNWithZScore(), \n",
    "    BaselineOnly(), \n",
    "    CoClustering()\n",
    "]\n",
    "\n",
    "for algorithm in algorithms:\n",
    "    print(\"Début: \" ,str(algorithm))\n",
    "    # Effectue la validation croisée\n",
    "    results = cross_validate(\n",
    "        algorithm, \n",
    "        data, \n",
    "        measures=[\"RMSE\", \"MAE\"], \n",
    "        cv=3, \n",
    "        verbose=False\n",
    "    )\n",
    "\n",
    "    \n",
    "    # Associe les résultats et les noms des algorithmes\n",
    "    tmp = pd.DataFrame.from_dict(results).mean(axis=0)\n",
    "    tmp = tmp.append(\n",
    "        pd.Series(\n",
    "            [str(algorithm).split(' ')[0].split('.')[-1]], \n",
    "            index=['Algorithm']\n",
    "        )\n",
    "    )\n",
    "    benchmark.append(tmp)\n",
    "    print(\"Fin: \" ,str(algorithm), \"\\n\\n\")\n",
    "\n",
    "print ('\\n\\tTERMINÉ\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "de94db21",
   "metadata": {},
   "outputs": [],
   "source": [
    "surprise_results = pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "052dde0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>test_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BaselineOnly</th>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>3.512999</td>\n",
       "      <td>1.803000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SlopeOne</th>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>2.584334</td>\n",
       "      <td>4.942999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NMF</th>\n",
       "      <td>0.003752</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>28.937999</td>\n",
       "      <td>4.921168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoClustering</th>\n",
       "      <td>0.003765</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>45.746334</td>\n",
       "      <td>2.191500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVDpp</th>\n",
       "      <td>0.003804</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>16.660834</td>\n",
       "      <td>13.544000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormalPredictor</th>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>1.590500</td>\n",
       "      <td>1.788333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVD</th>\n",
       "      <td>0.005112</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>12.457150</td>\n",
       "      <td>5.390833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 test_rmse  test_mae   fit_time  test_time\n",
       "Algorithm                                                 \n",
       "BaselineOnly      0.003435  0.001391   3.512999   1.803000\n",
       "SlopeOne          0.003673  0.001573   2.584334   4.942999\n",
       "NMF               0.003752  0.001119  28.937999   4.921168\n",
       "CoClustering      0.003765  0.001128  45.746334   2.191500\n",
       "SVDpp             0.003804  0.001489  16.660834  13.544000\n",
       "NormalPredictor   0.004463  0.002452   1.590500   1.788333\n",
       "SVD               0.005112  0.001670  12.457150   5.390833"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "0bf97f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "surprise_results = pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_mae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "44eaa3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>test_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NMF</th>\n",
       "      <td>0.003752</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>28.937999</td>\n",
       "      <td>4.921168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoClustering</th>\n",
       "      <td>0.003765</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>45.746334</td>\n",
       "      <td>2.191500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaselineOnly</th>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>3.512999</td>\n",
       "      <td>1.803000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVDpp</th>\n",
       "      <td>0.003804</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>16.660834</td>\n",
       "      <td>13.544000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SlopeOne</th>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>2.584334</td>\n",
       "      <td>4.942999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVD</th>\n",
       "      <td>0.005112</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>12.457150</td>\n",
       "      <td>5.390833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormalPredictor</th>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.002452</td>\n",
       "      <td>1.590500</td>\n",
       "      <td>1.788333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 test_rmse  test_mae   fit_time  test_time\n",
       "Algorithm                                                 \n",
       "NMF               0.003752  0.001119  28.937999   4.921168\n",
       "CoClustering      0.003765  0.001128  45.746334   2.191500\n",
       "BaselineOnly      0.003435  0.001391   3.512999   1.803000\n",
       "SVDpp             0.003804  0.001489  16.660834  13.544000\n",
       "SlopeOne          0.003673  0.001573   2.584334   4.942999\n",
       "SVD               0.005112  0.001670  12.457150   5.390833\n",
       "NormalPredictor   0.004463  0.002452   1.590500   1.788333"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f299c815",
   "metadata": {},
   "source": [
    "### Fine Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527f95ca",
   "metadata": {},
   "source": [
    "#### NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "06e739f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST RMSE: \t 0.00375291586030639\n",
      "BEST MAE: \t 0.0011030216710017368\n",
      "BEST params: \t {'n_factors': 5, 'n_epochs': 25, 'reg_pu': 0.04, 'reg_qi': 0.04}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"n_factors\": [5,10,15],\n",
    "    \"n_epochs\": [25, 50],\n",
    "#     \"biased\": [True, False],\n",
    "    \"reg_pu\": [0.04,0.06,0.08],\n",
    "    \"reg_qi\": [0.04,0.06,0.08],\n",
    "}\n",
    "gs = GridSearchCV(\n",
    "    NMF, \n",
    "    param_grid, \n",
    "    measures=[\"rmse\", \"mae\"], \n",
    "    refit=True, \n",
    "    cv=5\n",
    ")\n",
    "\n",
    "gs.fit(data)\n",
    "\n",
    "training_parameters = gs.best_params[\"rmse\"]\n",
    "\n",
    "print(\"BEST RMSE: \\t\", gs.best_score[\"rmse\"])\n",
    "print(\"BEST MAE: \\t\", gs.best_score[\"mae\"])\n",
    "print(\"BEST params: \\t\", gs.best_params[\"mae\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "e9ad2687",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(training_parameters, open(\"NMF_training_parameters\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3091edd",
   "metadata": {},
   "source": [
    "#### Entraînement avec les meilleurs paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4fa5e01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.NMF at 0x18cecc8d480>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# algo = gs.best_estimator[\"mae\"]\n",
    "algo = NMF(\n",
    "    n_factors=5,\n",
    "    n_epochs=25,\n",
    "    reg_pu=0.04,\n",
    "    reg_qi=0.04\n",
    ")\n",
    "algo.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d5d14959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de prédictions :  100724\n"
     ]
    }
   ],
   "source": [
    "predictions = algo.test(test)\n",
    "print(\"Nombre de prédictions : \", len(predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d329d22f",
   "metadata": {},
   "source": [
    "#### Évaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "c7f034dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  0.0011\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0010987106592538131"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy.mae(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "5a6534c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.003645206926031605"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b3e883",
   "metadata": {},
   "source": [
    "#### Sauvegarde du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eff432a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Starting dump\n",
      ">> Dump done\n",
      "./_NMF_model\n"
     ]
    }
   ],
   "source": [
    "# certains modèles ne peuvent être sauvegardés avec Surprise v1.1.2\n",
    "# Dans mon cas, la meilleure solution était de downgrade vers v1.1.1\n",
    "# Un ticket a été ouvert sur github\n",
    "model_filename = \"./_NMF_model\"\n",
    "print (\">> Starting dump\")\n",
    "file_name = os.path.expanduser(model_filename)\n",
    "# dump.dump(file_name, algo=algo, predictions=predictions)\n",
    "dump.dump(file_name, algo=algo)\n",
    "print (\">> Dump done\")\n",
    "print(model_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3f966891",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sauvegardons également les fichiers qui seront nécessaires à notre application\n",
    "df_clicks_azure = df_tmp[\"clicks\"][[\"user_id\", \"click_article_id\"]]\n",
    "pickle.dump(df_clicks_azure, open(\"df_clicks_azure.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d36bda",
   "metadata": {},
   "source": [
    "#### Création du système de recommandation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1137e9e7",
   "metadata": {},
   "source": [
    "Nous allons d'abord créer une fonction qui retournera les top-N recommandations pour chaque utilisateur depuis un ensemble de prédictions. Cette fonction va d'abord mapper les prédicitons pour chaque utilisateur, puis va trier les prédictions pour chaque utilisateur pour récupérer les K prédictions avec le score le plus élevé"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2026e351",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommandation_cf(user_id, articles, clicks, algo, n=10):\n",
    "    idx=list(articles.index)\n",
    "    articles_read = clicks[\n",
    "        clicks[\"user_id\"] == user_id\n",
    "    ][\"click_article_id\"].tolist()\n",
    "    for item in articles_read:\n",
    "        if item in idx:\n",
    "            idx.remove(item)\n",
    "    res = {}\n",
    "    for i in idx:\n",
    "        pred = algo.predict(user_id, i)\n",
    "        res[pred.iid]=pred.est\n",
    "    return nlargest(n, res, key=res.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "8b6bdfbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[140, 216, 232, 268, 293, 295, 303, 304, 311, 316]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reco_cf = get_recommandation_cf(42, df_art_emb, df[\"clicks\"], algo)\n",
    "reco_cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "f8dbf4dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[140, 216, 232, 268, 293, 295, 303, 304, 311, 316]"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reco_cf_pca = get_recommandation_cf(42, df_art_emb_pca, df[\"clicks\"], algo)\n",
    "reco_cf_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f55b38",
   "metadata": {},
   "source": [
    "## Content-based : En utilisant l'embedding des articles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659ba7d8",
   "metadata": {},
   "source": [
    "Nous allons ici utiliser le calcul du cosinus entre les articles lus par l'utilisateur et les autres articles projetés dans un espace vectoriel via la matrice d'embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "46eeecc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_size</th>\n",
       "      <th>click_article_id</th>\n",
       "      <th>click_timestamp</th>\n",
       "      <th>click_environment</th>\n",
       "      <th>click_deviceGroup</th>\n",
       "      <th>click_os</th>\n",
       "      <th>click_country</th>\n",
       "      <th>click_region</th>\n",
       "      <th>click_referrer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1506825423271737</td>\n",
       "      <td>1506825423000</td>\n",
       "      <td>2</td>\n",
       "      <td>157541</td>\n",
       "      <td>1506826828020</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1506825423271737</td>\n",
       "      <td>1506825423000</td>\n",
       "      <td>2</td>\n",
       "      <td>68866</td>\n",
       "      <td>1506826858020</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>235840</td>\n",
       "      <td>1506827017951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1506825426267738</td>\n",
       "      <td>1506825426000</td>\n",
       "      <td>2</td>\n",
       "      <td>96663</td>\n",
       "      <td>1506827047951</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1506825435299739</td>\n",
       "      <td>1506825435000</td>\n",
       "      <td>2</td>\n",
       "      <td>119592</td>\n",
       "      <td>1506827090575</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_id        session_id  session_start session_size click_article_id  \\\n",
       "0       0  1506825423271737  1506825423000            2           157541   \n",
       "1       0  1506825423271737  1506825423000            2            68866   \n",
       "2       1  1506825426267738  1506825426000            2           235840   \n",
       "3       1  1506825426267738  1506825426000            2            96663   \n",
       "4       2  1506825435299739  1506825435000            2           119592   \n",
       "\n",
       "  click_timestamp click_environment click_deviceGroup click_os click_country  \\\n",
       "0   1506826828020                 4                 3       20             1   \n",
       "1   1506826858020                 4                 3       20             1   \n",
       "2   1506827017951                 4                 1       17             1   \n",
       "3   1506827047951                 4                 1       17             1   \n",
       "4   1506827090575                 4                 1       17             1   \n",
       "\n",
       "  click_region click_referrer_type  \n",
       "0           20                   2  \n",
       "1           20                   2  \n",
       "2           16                   2  \n",
       "3           16                   2  \n",
       "4           24                   2  "
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clicks\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "52b7d434",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommandation_cb(user_id, articles, clicks, n=10):\n",
    "    articles_read = clicks[clicks['user_id'] == user_id]['click_article_id'].tolist()\n",
    "    if len(articles_read) == 0:\n",
    "        return \"L'utilisateur n'a lu aucun article\"\n",
    "    articles_read_embedding = articles.loc[articles_read]\n",
    "    articles = articles.drop(articles_read)\n",
    "    matrix = cosine_similarity(articles_read_embedding, articles)\n",
    "    recommandations = []\n",
    "    for i in range(n):\n",
    "        coord_x = floor(np.argmax(matrix)/matrix.shape[1])\n",
    "        coord_y = np.argmax(matrix)%matrix.shape[1]\n",
    "        recommandations.append(int(articles.index[coord_y]))\n",
    "        matrix[coord_x][coord_y] = 0\n",
    "    return recommandations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fa1b22",
   "metadata": {},
   "source": [
    "Nous allons comparer les résultats entre l'utilisation du fichier d'embeddings d'origine et l'embeddings réduit par l'ACP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "284647ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[228299, 235079, 233829, 228944, 345924]\n",
      "[228299, 345924, 235079, 233829, 228944]\n",
      "[228299, 228944, 233829, 235079, 345924]\n",
      "[228299, 228944, 233829, 235079, 345924]\n"
     ]
    }
   ],
   "source": [
    "reco_emb = get_recommandation_cb(1, df_art_emb, df[\"clicks\"], 5)\n",
    "reco_emb_pca = get_recommandation_cb(1, df_art_emb_pca, df[\"clicks\"], 5)\n",
    "print(reco_emb)\n",
    "print(reco_emb_pca)\n",
    "print(sorted(reco_emb))\n",
    "print(sorted(reco_emb_pca))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0952a865",
   "metadata": {},
   "source": [
    "On peut constater que les résultats sont similaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f18268b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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