Rapport.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rapport : Résolution de Problèmes / Machine Learning\n",
    "\n",
    "#### IMT Mines Alès 2022\n",
    "##### INFRES 13 \n",
    "- **Quentin BIALOTA**\n",
    "- **Tom L'HERMENIER**\n",
    "\n",
    "![Logo IMT](https://www.fondation-mines-telecom.org/wp-content/uploads/2018/02/imt_mines_ales-300x158.jpg)\n",
    "\n",
    "---\n",
    "\n",
    "[//]: <> (Generally define your task)\n",
    "[//]: <> (Analyse the corresponding dataset.)\n",
    "[//]: <> (Define the problem as a Machine Learning problem.)\n",
    "[//]: <> (Define the methodology and protocol you will use to tackle the problem.)\n",
    "[//]: <> (Implement various approaches that are suited to this problem.)\n",
    "[//]: <> (Discuss the results you have obtained.)\n",
    "[//]: <> (Eventually recommend your final solution motivating it.)\n",
    "\n",
    "Dans le cadre de du cours de **Résolution de Problèmes - Machine Learning** à l'IMT Mines Alès, nous avons réalisé un projet de notre choix, visant à résoudre un problème de machine learning, le tout en évaluant et comparant différents modèles pour la résolution du problème.\n",
    "\n",
    "Dans ce projet, nous avons choisi de traiter une problématique visant à prédire un nombre de FPS (Frame Per Second / Image par Seconde), en fonction du jeu auquel on joue, et des différentes caractéristiques de l'ordinateur sur lequel on fait tourner le programme (Nom du processeur, nombre de coeurs, nombre de threads, nom de la carte graphique, ...)\n",
    "\n",
    "---\n",
    "\n",
    "Afin de traiter ce problème, nous avons décidé d'utiliser un dataset déjà existant et disponible sur le site OpenML. Ce dataset nommé `fps-in-video-game` est accessible au lien suivant : https://www.openml.org/d/42737.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Imports globaux\n",
    "Avant toutes choses, veuillez lancer la cellule suivante pour importer les librairies et outils utilisés dans ce rapport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from math import trunc\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### Import du dataset\n",
    "\n",
    "Via la cellule suivante, vous pouvez importer le dataset depuis le site d'OpenML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Informations sur le dataset : \n",
      " **Author**: Sven Peeters  \n",
      "**Source**: userbenchmark.com, fpsbenchmark.com, techpowerup.com - June 2020  \n",
      "**Please cite**: TBA\n",
      "\n",
      "# Achieved Frames per Second (FPS) in video games\n",
      "\n",
      "This dataset contains FPS measurement of video games executed on computers. Each row of the dataset describes the outcome of FPS measurement (outcome is attribute FPS) for a video game executed on a computer. A computer is characterized by the CPU and the GPU. For both the name is resolved to technical specifications (features starting with Cpu and Gpu). The technical specification of CPU and GPU are technical specification that describe the factory state of the respective component. The game is characterized by the name, the displayed resolution, and the quality setting that was adjusted during the measurement (features starting with Game). In the following there is a short descriptions of the data sources and a description for each feature in the dataset.\n",
      "\n",
      "## Source of Data\n",
      "\n",
      "The measurements are gathered from the webpages userbenchmark.com and fpsbenchmark.com. The source of each measurement is indicated by the attribute dataset wich takes the value userbenchmark or fpsbenchmark. For userbenchmark the FPS values are extracted from the published histograms. Hence, the FPS values are binned to bins with width 10 starting at 10 and there are multiple measurements for the same computer and software configuration (CpuName, GpuName, GameName, GameSetting, GameResolution, Dataset). The technical specifications are gathered from the webpage techpowerup.com. All FPS measurements and technical specifications were crawled in June 2020.\n",
      "\n",
      "## Description of the technical features\n",
      "\n",
      "### CPU (Central Processing Unit)\n",
      "\n",
      "* CpuNumberOfCores: number of physical cores \n",
      "* CpuNumberOfThreads: number of threads\n",
      "* CpuBaseClock: base clock in Mhz\n",
      "* CpuCacheL1: total size of level 1 cache in kB \n",
      "* CpuCacheL2: total size of level 2 cache in kB \n",
      "* CpuCacheL3: total size of level 3 cache in MB\n",
      "* CpuDieSize: physical size of the die in square meter\n",
      "* CpuFrequency: frequency in Mhz\n",
      "* CpuMultiplier: multiplier of Cpu\n",
      "* CpuMultiplierUnlocked: 0=multiplier locked, 1=multiplier unlocked \n",
      "* CpuProcessSize: used process size in nanometer \n",
      "* CpuSMPNumberOfCpus: number of symmetric multiprocessors\n",
      "* CpuTDP: thermal design power in watt\n",
      "* CpuTransistors: count of transistors in million\n",
      "* CpuTurbo Clock: turbo clock in Mhz\n",
      "\n",
      "### GPU (Graphics Processing Unit)\n",
      "\n",
      "* GpuBandwidth: bandwidth in MB/s \n",
      "* GpuBaseClock: base clock in MHz\n",
      "* GpuBoostClock: boost clock in MHz \n",
      "* GpuComputeUnits: number of computing units\n",
      "* GpuDieSize: physical size of die in square meter \n",
      "* GpuNumberOfExecutionUnits: number of execution units \n",
      "* GpuFP32Performance: theoretical Float 32 performance in MFLOP/s \n",
      "* GpuMemoryBus: width of memory bus in bits\n",
      "* GpuMemorySize: size of memory in MB \n",
      "* GpuPixelRate: theoretical pixel rate in MPixel/s \n",
      "* GpuProcessSize:  used process size in nanometer \n",
      "* GpuNumberOfROPs: number of render output units\n",
      "* GpuNumberOfShadingUnits: number of shading units\n",
      "* GpuNumberOfTMUs: number of texture mapping units \n",
      "* GpuTextureRate: theoretical texture rate in KTexel/s\n",
      "* GpuTransistors: number of transistors in million\n",
      "* GpuArchitecture: architecture code\n",
      "* GpuMemoryType: memory type\n",
      "* GpuBusInterface: bus interface\n",
      "* GpuOpenCL: version of OpenCL\n",
      "* GpuShaderModel: version of shader model\n",
      "* GpuVulkan: version of Vulkan \n",
      "* GpuOpenGL: version of OpenGL\n",
      "\n",
      "### Game\n",
      "\n",
      "* GameName: name \n",
      "* GameResolution: resolution \n",
      "* GameSetting: quality setting \n",
      "\n",
      "### Dataset indicator and Target\n",
      "\n",
      "* Dataset: indicator for the source of measurement \n",
      "* FPS: FPS value\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "### Import du dataset depuis OpenML (lien du dataset : https://www.openml.org/d/42737)\n",
    "\n",
    "dataset = sklearn.datasets.fetch_openml(name=\"fps-in-video-games\")\n",
    "\n",
    "### Stockage du dataset dans une dataframe\n",
    "\n",
    "dataset_df = pd.DataFrame(data=np.c_[dataset.data, dataset.target], columns=dataset.feature_names+['target'])\n",
    "\n",
    "### Informations sur le dataset\n",
    "\n",
    "print(\"Informations sur le dataset : \\n\",dataset[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### Structure du dataset :\n",
    "\n",
    "Le dataset utilisé contient au total 44 caractéristiques (features) labélisées avec le nombre de FPS observé dans chaque cas, et au total ce dataset contient 425 833 observations.\n",
    "\n",
    "Le problème principal de ces données est qu'il y a beaucoup de valeurs non renseignées, voir même des colonnes entièrement vides (remplies de NaN). C'est pas exemple le cas `GpuNumberOfExecutionUnits` qui n'est pas renseignée pour quasiment toutes les observations.\n",
    "\n",
    "Afin de voir au mieux ces données non renseignées, voici un récapitulatif (dans la cellule suivante) du nombre total de \"trous\" dans le dataset (valeur à NaN), ainsi que le nombre de \"trous\" par caractéristique.\n",
    "\n",
    "Pour finir au niveau de la structure, nous avons pu remarquer beaucoup de duplicatas sur les inputs (valeurs de caractéristiques), avec des targets (valeurs cibles) différentes. De ce fait, on peut considérer d'ores et déjà que ce dataset ne contient pas toutes les informations nécéssaires pour une meilleure prédiction du nombre de FPS. On peut par exemple penser à la RAM (mémoire vive) qui peut influer sur les FPS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre total de Nan dans le dataset :  1299988\n",
      "Nombre de Nan pour chaque caracteristique : \n",
      " [['CpuName' 0]\n",
      " ['CpuNumberOfCores' 0]\n",
      " ['CpuNumberOfThreads' 0]\n",
      " ['CpuBaseClock' 0]\n",
      " ['CpuCacheL1' 0]\n",
      " ['CpuCacheL2' 0]\n",
      " ['CpuCacheL3' 5055]\n",
      " ['CpuDieSize' 202534]\n",
      " ['CpuFrequency' 0]\n",
      " ['CpuMultiplier' 0]\n",
      " ['CpuMultiplierUnlocked' 0]\n",
      " ['CpuProcessSize' 0]\n",
      " ['CpuTDP' 0]\n",
      " ['CpuNumberOfTransistors' 226039]\n",
      " ['CpuTurboClock' 0]\n",
      " ['GpuName' 0]\n",
      " ['GpuArchitecture' 0]\n",
      " ['GpuBandwidth' 15441]\n",
      " ['GpuBaseClock' 0]\n",
      " ['GpuBoostClock' 0]\n",
      " ['GpuBus nterface' 0]\n",
      " ['GpuNumberOfComputeUnits' 357107]\n",
      " ['GpuDieSize' 11196]\n",
      " ['GpuDirectX' 0]\n",
      " ['GpuNumberOfExecutionUnits' 412935]\n",
      " ['GpuFP32Performance' 56]\n",
      " ['GpuMemoryBus' 15441]\n",
      " ['GpuMemorySize' 15441]\n",
      " ['GpuMemoryType' 15441]\n",
      " ['GpuOpenCL' 177]\n",
      " ['GpuOpenGL' 0]\n",
      " ['GpuPixelRate' 0]\n",
      " ['GpuProcessSize' 0]\n",
      " ['GpuNumberOfROPs' 0]\n",
      " ['GpuShaderModel' 5]\n",
      " ['GpuNumberOfShadingUnits' 56]\n",
      " ['GpuNumberOfTMUs' 0]\n",
      " ['GpuTextureRate' 0]\n",
      " ['GpuNumberOfTransistors' 11539]\n",
      " ['GpuVulkan' 11525]\n",
      " ['GameName' 0]\n",
      " ['GameResolution' 0]\n",
      " ['GameSetting' 0]\n",
      " ['Dataset' 0]]\n",
      "Sous forme de graphe :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Nombre de valeurs nulles par caractéristique'}, xlabel='Caractéristique (numero)', ylabel='Nombre de valeurs nulles'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Compte le nombre de NaN dans le dataset\n",
    "\n",
    "bc_df = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
    "\n",
    "tab = []\n",
    "j = 0\n",
    "\n",
    "for feature in bc_df.columns:\n",
    "    tab.append(bc_df[feature].isna().sum())\n",
    "\n",
    "print(\"Nombre total de Nan dans le dataset : \",bc_df.isna().sum().sum())\n",
    "print(\"Nombre de Nan pour chaque caracteristique : \\n\",np.c_[bc_df.columns,tab])\n",
    "\n",
    "print (\"Sous forme de graphe :\")\n",
    "affiche = pd.DataFrame(np.c_[bc_df.columns,tab])\n",
    "affiche.plot(kind='bar', title='Nombre de valeurs nulles par caractéristique', xlabel='Caractéristique (numero)', ylabel='Nombre de valeurs nulles')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tri du dataset\n",
    "\n",
    "Afin de pouvoir utiliser notre dataset, il nous faut le trier. D'une part car les valeurs NaN vont poser problème, et d'autre part car il y a un trop grand nombre d'observations pour traiter le problème dans des temps raisonnables sur nos ordinateurs (environ un demi million d'observations).\n",
    "\n",
    "Pour trier donc ce dataset, se proposait à nous deux méthodologies principales (avec plusieurs variantes minimes à chaque fois, mais nous ne le détaillerons pas ici) :\n",
    "\n",
    "- Supprimer toutes les colonnes du dataset contenant des valeurs nulles (NaN), puis choisir dans les colonnes restantes, celles qui sont pertinentes. Mais cela nous enlève beaucoup de caractéristiques, et ça pourrait rendre les prédictions moins bonnes. De plus ça garde l'ensemble des observations, il faudrait donc trier par dessus lesquelles garder.\n",
    "- Supprimer la colonne `GpuNumberOfExecutionUnits` (car c'est une colonne contenant que des valeurs nulles) ainsi que les colonnes `GpuNumberOfComputeUnits , CpuNumberOfTransistors , CpuDieSize , Dataset` (car ce sont des colonnes avec beaucoup de lignes nulles), puis supprimer toutes les lignes contenant des valeurs nulles (NaN) (sur le reste des caractéristiques non supprimées).\n",
    "\n",
    "Une fois fait, dans les deux méthodes nous avons décidé de supprimer les doublons afin de réduire le nombre d'observations. Ce que l'on appelle doublon ou duplicatas, sont les observations où il y a les mêmes valeurs de caractéristiques, sans prendre en compte la target (le fps). Ce qui veut dire que l'on supprime à chaque fois dans le dataset des observations ayant les mêmes valeurs de caractéristiques, mais des targets différents, le tout en gardant à chaque fois la première observation dans la liste. \n",
    "\n",
    "Comme dit plus haut, on peut comprendre via ces duplicatas que les caractéristiques ne sont pas complète, ce qui jouera beaucoup sur les performances de prédiction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Méthode 1 (non retenue) : Suppression des colonnes contenants des valeurs nulles (NaN)\n",
    "\n",
    "Dans le cas de la méthode 1, voici la liste des caractéristiques / colonnes que nous devons supprimer :\n",
    "- `CpuCacheL3`\n",
    "- `CpuDieSize`\n",
    "- `CpuNumberOfTransistors`\n",
    "- `GpuBandwidth`\n",
    "- `GpuNumberOfComputeUnits`\n",
    "- `GpuDieSize`\n",
    "- `GpuNumberOfExecutionUnits`\n",
    "- `GpuFP32Performance`\n",
    "- `GpuMemoryBus`\n",
    "- `GpuMemorySize`\n",
    "- `GpuMemoryType`\n",
    "- `GpuOpenCL`\n",
    "- `GpuShaderModel`\n",
    "- `GpuNumberOfShadingUnits`\n",
    "- `GpuNumberOfTransistors`\n",
    "- `GpuVulkan`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liste des caractéristiques : Index(['CpuName', 'CpuNumberOfCores', 'CpuNumberOfThreads', 'CpuBaseClock',\n",
      "       'CpuCacheL1', 'CpuCacheL2', 'CpuFrequency', 'CpuMultiplier',\n",
      "       'CpuMultiplierUnlocked', 'CpuProcessSize', 'CpuTDP', 'CpuTurboClock',\n",
      "       'GpuName', 'GpuArchitecture', 'GpuBaseClock', 'GpuBoostClock',\n",
      "       'GpuBus nterface', 'GpuDirectX', 'GpuOpenGL', 'GpuPixelRate',\n",
      "       'GpuProcessSize', 'GpuNumberOfROPs', 'GpuNumberOfTMUs',\n",
      "       'GpuTextureRate', 'GameName', 'GameResolution', 'GameSetting',\n",
      "       'Dataset', 'target'],\n",
      "      dtype='object')\n",
      "Nombre de colonnes x Nombre de lignes (Avec duplication):  425833 x 29\n",
      "Nombre de colonnes x Nombre de lignes (Sans duplication):  107078 x 29\n"
     ]
    }
   ],
   "source": [
    "### Avec les observations \"dupliquées\" (mêmes valeurs de caractéristiques, mais fps différents)\n",
    "\n",
    "dataset_df_method1 = dataset_df.dropna(axis='columns')\n",
    "\n",
    "print('Liste des caractéristiques :',dataset_df_method1.columns)\n",
    "print('Nombre de colonnes x Nombre de lignes (Avec duplication): ',\n",
    "    str(len(dataset_df_method1.index)) + ' x '+ str(len(dataset_df_method1.columns)))\n",
    "\n",
    "### Sans les observations \"dupliquées\" (mêmes valeurs de caractéristiques, mais fps différents)\n",
    "\n",
    "dataset_df_method1_noduplicates = dataset_df_method1.drop_duplicates(subset=dataset_df_method1.columns.difference(['target']))\n",
    "\n",
    "print('Nombre de colonnes x Nombre de lignes (Sans duplication): ',\n",
    "    str(len(dataset_df_method1_noduplicates.index)) + ' x '+ str(len(dataset_df_method1_noduplicates.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Après avoir fait quelques tests sur un modèle linéaire via (LinearRegression) ces données là (que nous ne détaillerons pas ici car il s'agit des mêmes tests que nous ferons avec l'autre méthode), nous obtenons globalement les mêmes résultats (un peu moins bon) qu'avec l'autre méthode. Cependant, cette première méthode créé beaucoup plus de problèmes quant au choix du dataset (quelles observations enlever ?)\n",
    "\n",
    "De plus, même en ayant supprimer les doublons / duplicatas, on garde un très grand nombre d'observations.\n",
    "\n",
    "---\n",
    "\n",
    "##### Méthode 2 : Supression de certaines colonnes, et suppression des observations avec une valeur nulle (minimum)\n",
    "\n",
    "Comme précisé plus tôt, cette méthode consiste à supprimer les colonnes suivantes :\n",
    "- `GpuNumberOfExecutionUnits` - 412935 valeurs nulles\n",
    "- `GpuNumberOfComputeUnits` - 357107 valeurs nulles\n",
    "- `CpuNumberOfTransistors`- 226039 valeurs nulles\n",
    "- `CpuDieSize` - 202534 valeurs nulles\n",
    "- `Dataset` - Caractéristique inutile qui spécifie simplement de quel benchmark (logiciel de test) vient l'observation\n",
    "\n",
    "Via le code suivant, on enlève donc ces caractéristiques du dataset, puis on supprime toutes les observations contenant des valeurs nulles. Afin de réduire encore le nombre d'observations, et d'éviter les problèmes lors des prédictions, nous supprimons en plus les observations dupliquées (mêmes valeurs de caractéristiques, mais targets possiblement différentes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liste des caractéristiques : Index(['CpuName', 'CpuNumberOfCores', 'CpuNumberOfThreads', 'CpuBaseClock',\n",
      "       'CpuCacheL1', 'CpuCacheL2', 'CpuCacheL3', 'CpuFrequency',\n",
      "       'CpuMultiplier', 'CpuMultiplierUnlocked', 'CpuProcessSize', 'CpuTDP',\n",
      "       'CpuTurboClock', 'GpuName', 'GpuArchitecture', 'GpuBandwidth',\n",
      "       'GpuBaseClock', 'GpuBoostClock', 'GpuBus nterface', 'GpuDieSize',\n",
      "       'GpuDirectX', 'GpuFP32Performance', 'GpuMemoryBus', 'GpuMemorySize',\n",
      "       'GpuMemoryType', 'GpuOpenCL', 'GpuOpenGL', 'GpuPixelRate',\n",
      "       'GpuProcessSize', 'GpuNumberOfROPs', 'GpuShaderModel',\n",
      "       'GpuNumberOfShadingUnits', 'GpuNumberOfTMUs', 'GpuTextureRate',\n",
      "       'GpuNumberOfTransistors', 'GpuVulkan', 'GameName', 'GameResolution',\n",
      "       'GameSetting', 'target'],\n",
      "      dtype='object')\n",
      "Nombre de colonnes x Nombre de lignes (Avec duplication):  396028 x 40\n",
      "Nombre de colonnes x Nombre de lignes (Sans duplication):  93903 x 40\n"
     ]
    }
   ],
   "source": [
    "### Supression des colonnes, et supression des valeurs nulles (les lignes avec des valeurs nulles)\n",
    "\n",
    "dataset_df_reduced_rows = dataset_df.drop(\n",
    "    columns=['GpuNumberOfExecutionUnits', 'GpuNumberOfComputeUnits','CpuNumberOfTransistors', 'CpuDieSize','Dataset']).dropna()\n",
    "\n",
    "print('Liste des caractéristiques :',dataset_df_reduced_rows.columns)\n",
    "print('Nombre de colonnes x Nombre de lignes (Avec duplication): ',\n",
    "    str(len(dataset_df_reduced_rows.index)) + ' x '+ str(len(dataset_df_reduced_rows.columns)))\n",
    "\n",
    "### Supression des observations dupliquées (voir plus haut pour explication)\n",
    "### Et retri des index (car les index ne changent pas si on supprime des lignes)\n",
    "\n",
    "dataset_df_reduced_rows = dataset_df_reduced_rows.drop_duplicates(subset=dataset_df_reduced_rows.columns.difference(['target'])\n",
    "    ).reset_index().drop(columns=['index'])\n",
    "\n",
    "print('Nombre de colonnes x Nombre de lignes (Sans duplication): ',\n",
    "    str(len(dataset_df_reduced_rows.index)) + ' x '+ str(len(dataset_df_reduced_rows.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans la suite du projet, nous utiliserons cette méthode, sans la duplication des observations.\n",
    "\n",
    "---\n",
    "\n",
    "Avant d'effectuer des tests de modèles sur ce dataset, voici quelques graphiques pour mieux cerner les données contennue dans ce dataset.\n",
    "\n",
    "1. Premièrement, il faut savoir que ce dataset est fait pour plusieurs jeux. Et le nombre d'observations par jeu est assez inégal (voir premier graphique). Cette hétérogénéité des données pourrait causer des baisses de performances au niveau des modèles.\n",
    "2. Ensuite nous verrons une Correlation Heatmap (carte de chaleur de corrélation) afin de voir rapidement les possibles corrélations entre les caractéristiques du dataset.\n",
    "\n",
    "##### Graphique relatif aux jeux du dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de jeux :  30\n",
      "Nombre d'observation par jeux : \n",
      " GameName\n",
      "aWayOut                         1026\n",
      "airMechStrike                   1026\n",
      "apexLegends                     4760\n",
      "arkSurvivalEvolved              1938\n",
      "battlefield1                    4845\n",
      "battlefield4                    3570\n",
      "battletech                      1026\n",
      "callOfDutyBlackOps4             2331\n",
      "callOfDutyWW2                   1026\n",
      "counterStrikeGlobalOffensive    9451\n",
      "destiny2                        1026\n",
      "dota2                           3690\n",
      "farCry5                         1026\n",
      "fortnite                        8223\n",
      "frostpunk                       1026\n",
      "grandTheftAuto5                 7715\n",
      "leagueOfLegends                 6166\n",
      "minecraft                       2894\n",
      "overwatch                       7753\n",
      "pathOfExile                     1026\n",
      "playerUnknownsBattlegrounds     8736\n",
      "radicalHeights                  1026\n",
      "rainbowSixSiege                 1026\n",
      "rocketLeague                    1588\n",
      "rust                            2130\n",
      "seaOfThieves                    1026\n",
      "starcraft2                      1026\n",
      "totalWar3Kingdoms               1026\n",
      "warframe                        1026\n",
      "worldOfTanks                    3749\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':\"Nombre d'observations par jeu\"}, xlabel='Jeu', ylabel=\"Nombre d'observations\">"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_df_reduced_rows_pivot = dataset_df_reduced_rows.pivot_table(columns=['GameName'], aggfunc='size')\n",
    "\n",
    "print(\"Nombre de jeux : \",len(dataset_df_reduced_rows_pivot.index))\n",
    "print(\"Nombre d'observation par jeux : \\n\",dataset_df_reduced_rows_pivot)\n",
    "\n",
    "dataset_df_reduced_rows_pivot.plot(kind='bar', title='Nombre d\\'observations par jeu', xlabel='Jeu', ylabel='Nombre d\\'observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Correlation Heatmap\n",
    "\n",
    "Informations pour la lecture du graphique :\n",
    "\n",
    "- **Si la valeur est 1, c'est une corrélation positive entre les deux variables.** Quand une variable augmente, l'autre variable augmente aussi.\n",
    "- **Si la valeur est -1, c'est une corrélation négative entre les deux variables.** Quand une variable augmente, l'autre variable diminue.\n",
    "- **Si la valeur est 0, il n'y a pas de correlations entre les deux variables.** Quand une variable change d'une manière quelconque, il n'y a aucun impact sur l'autre variable.\n",
    "\n",
    "*(explications issues du site : https://vitalflux.com/correlation-heatmap-with-seaborn-pandas/)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Création d'un dataset temporaire pour l'affichage (dataset sans le target)\n",
    "\n",
    "dataset_temp = pd.DataFrame(data=dataset.data, columns=dataset.feature_names)\n",
    "\n",
    "### Supression des colonnes, et supression des valeurs nulles (les lignes avec des valeurs nulles)\n",
    "\n",
    "dataset_temp = dataset_temp.drop(\n",
    "    columns=['GpuNumberOfExecutionUnits', 'GpuNumberOfComputeUnits','CpuNumberOfTransistors', 'CpuDieSize','Dataset']).dropna()\n",
    "\n",
    "dataset_temp = dataset_temp.drop_duplicates(subset=dataset_temp.columns.difference(['target'])).reset_index().drop(columns=['index'])\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "corr_matrix=dataset_temp.corr()\n",
    "fig,ax=plt.subplots(figsize=(20,10))\n",
    "ax=sns.heatmap(corr_matrix, annot=True, linewidths=0.5, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme on peut le voir sur le graphe ci-dessus, plus on se rapproche de la couleur bleu foncée, plus on a une corrélation positive, et plus on se rapproche de la couleur blanche, plus on a une corrélation négative. Et on peut remarquer plusieurs caractéristiques assez corrélées comme le nombre de coeurs d'un CPU (`CpuNumberOfCore`) et le Cache L1 (`CPUCacheL1`).\n",
    "\n",
    "---\n",
    "\n",
    "##### Présence de valeurs de caractéristiques de type \"string\" (chaine de caractères)\n",
    "\n",
    "Autre point concernant le dataset, certaines caractéristiques contiennent des valeurs de type String. Par exemple la caractéristique `CpuName` (qui contient le nom du CPU utilisé dans l'observation) est une chaine de caractères.\n",
    "\n",
    "Afin de pouvoir traiter ces données de types string, nous allons transformer chaque chaine de caractère en un chiffre. Et afin de retrouver quelle chaine de caractère correspond un chiffre (int), nous stockons les correspondances dans un dictionnaire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Algorithme parcourant le dataset et remplaçant les valeurs de type string, par un int \n",
    "### (le tout en stockant les correspondances dans un dictionnaire)\n",
    "\n",
    "dataset_reduced_without_string = dataset_df_reduced_rows.copy()\n",
    "dict_global = {}\n",
    "for name, values in dataset_df_reduced_rows.iteritems():\n",
    "    compt = 0\n",
    "    tab = {}\n",
    "    for i in range(values.size):\n",
    "        if isinstance(dataset_reduced_without_string[name][i], str):\n",
    "            if dataset_reduced_without_string[name][i] not in tab:\n",
    "                tab[dataset_reduced_without_string[name][i]] = compt\n",
    "                dataset_reduced_without_string[name][i] = compt\n",
    "                compt += 1\n",
    "            else:\n",
    "                dataset_reduced_without_string[name][i] = tab[dataset_reduced_without_string[name][i]]\n",
    "        else:\n",
    "            break\n",
    "    dict_global[name] = tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'counterStrikeGlobalOffensive': 0, 'grandTheftAuto5': 1, 'worldOfTanks': 2, 'rust': 3, 'leagueOfLegends': 4, 'arkSurvivalEvolved': 5, 'apexLegends': 6, 'minecraft': 7, 'playerUnknownsBattlegrounds': 8, 'battlefield1': 9, 'fortnite': 10, 'rocketLeague': 11, 'callOfDutyBlackOps4': 12, 'dota2': 13, 'overwatch': 14, 'battlefield4': 15, 'callOfDutyWW2': 16, 'pathOfExile': 17, 'destiny2': 18, 'radicalHeights': 19, 'seaOfThieves': 20, 'frostpunk': 21, 'totalWar3Kingdoms': 22, 'warframe': 23, 'airMechStrike': 24, 'battletech': 25, 'farCry5': 26, 'starcraft2': 27, 'rainbowSixSiege': 28, 'aWayOut': 29}\n"
     ]
    }
   ],
   "source": [
    "### Affichage du dictionnaire (Très gros)\n",
    "\n",
    "# UNCOMMENT\n",
    "# print(dict_global)\n",
    "\n",
    "### Affichage d'une des caractéristiques du dictionnaire (pour tester)\n",
    "\n",
    "print(dict_global['GameName'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---\n",
    "\n",
    "### Partie Machine Learning\n",
    "\n",
    "Maintenant que nous avons vu le dataset que nous allons utiliser, nous pouvons expliquer avec plus de précision quel problème (en terme de machine learning) nous allons traiter.\n",
    "\n",
    "##### Identification du problème technique et Méthodologie\n",
    "\n",
    "Comme précisé à plusieurs reprises, notre but est de pouvoir prédire un nombre d'images par seconde (fps) sur un jeu, donc une valeur dans un domaine continue, via une liste de caractéristiques sur les composants de l'ordinateur faisant fonctionner le jeu en question. Etant donné que notre target (valeur cible), ici le fps, est dans un domaine continue, nous allons traiter d'une problématique de **régression**.\n",
    "\n",
    "Afin de répondre à cette problématique de régression, nous allons tester plusieurs modèles : \n",
    "- Linear Regression\n",
    "- Ridge\n",
    "- MLPRegressor (réseaux de neuronnes)\n",
    "\n",
    "Pour chaque modèle, nous calculerons plusieurs scores (r² score*) avec la méthode de la k-fold cross validation (k étapes, avec à chaque fois un jeu d'entrainement et de test différents et disjoints).\n",
    "Puis nous comparerons les scores en eux mêmes et leurs moyenne (pour départager les différents modèles)\n",
    "\n",
    "**(r² score : score basé sur la variation de la donnée par rapport à la moyenne et par rapport à notre prédicteur.*  \n",
    "*Globalement correspond au pourcentage de variation autour de notre prédicteur, par rapport à la moyenne.*  \n",
    "*Si j'ai un score de 0.8, alors mon prédicteur à 80% moins de variations autour de lui, qu'autour de la moyenne (mieux que la moyenne)*  \n",
    "*Si j'ai un score de -0.8, alors mon prédicteur à 80% plus de variations autour de lui, qu'autour de la moyenne (pire que la moyenne)*  \n",
    "*(Plus d'explications et formule avec cette vidéo : https://www.youtube.com/watch?v=2AQKmw14mHM))*  \n",
    "\n",
    "---\n",
    "\n",
    "*Note : Dans la suite du projet, nous traiterons aussi d'une problématique de classification en découpant la target (fps) en intervalles (ex : intervalle entre 10 et 20 fps).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Copie du dataset afin de ne pas le biaiser (opérations sur le dataset)\n",
    "\n",
    "dataset_copy = dataset_reduced_without_string.copy()\n",
    "\n",
    "### Mélange du dataset, car de base toutes les observations sur un même jeu sont à la suite\n",
    "### (ex : 500 observation sur le jeu A, puis 500 sur le jeu B, ...) \n",
    "### Il faut donc mélanger pour éviter de biaiser la k-fold cross validation\n",
    "\n",
    "dataset_copy = dataset_copy.sample(random_state=0, n=dataset_copy.shape[0])\n",
    "\n",
    "### Stockage des valeurs de caractéristiques (X) et des targets associées à chaque observation (Y)\n",
    "\n",
    "X = dataset_copy.drop(columns=['target'])\n",
    "y = dataset_copy['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### 1er modèle : Linear Regression\n",
    "\n",
    "Le premier modèle que nous allons tester pour répondre à notre problématique de régression n'est autre que la régression linéaire (Linear Regression dans sklearn : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)\n",
    "\n",
    "Pour ce modèle-ci, nous montrerons d'abord comment entrainer un modèle sans k-fold cross validation. En découpant \"à la main\" le jeu de test et le jeu d'entrainement, et en affichant les scores correspondants.  \n",
    "Pour tout les autres modèles, nous ne feront pas cette étape, et utiliserons directement la fonction `cross_val_score` qui permet de faire de la k-fold cross validation automatiquement (découpage des jeux, entrainement)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.20\n",
      "Test set score: 0.20\n"
     ]
    }
   ],
   "source": [
    "### Découpage du dataset en train et test (25% de test par défaut)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "### Utilisation d'un StandardScaler car après de multiples tests il a tendance à améliorer les scores\n",
    "### (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
    "\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Application du StandardScaler sur le jeu de test et le jeu d'entrainement\n",
    "\n",
    "X_train = scalar.fit_transform(X_train)\n",
    "X_test = scalar.fit_transform(X_test)\n",
    "\n",
    "### Entrainement du modèle avec le jeu d'entrainement\n",
    "\n",
    "lr = LinearRegression().fit(X_train,y_train)\n",
    "\n",
    "### Affichage du score\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(lr.score(X_train,y_train)))\n",
    "print(\"Test set score: {:.2f}\".format(lr.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score pour chaque étape (20 scores) : \n",
      " [0.18475617 0.20800448 0.18802065 0.19449724 0.19993379 0.20080621\n",
      " 0.20085064 0.17379266 0.20009085 0.1950523  0.22705892 0.18947588\n",
      " 0.20709001 0.18666864 0.20631745 0.21428214 0.20760923 0.20038641\n",
      " 0.23694345 0.2027996 ]\n",
      "0.20 de moyenne avec un écart-type de 0.01\n"
     ]
    }
   ],
   "source": [
    "X = dataset_copy.drop(columns=['target'])\n",
    "y = dataset_copy['target']\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "### Ici on souhaite utiliser le modèle : LinearRegression.\n",
    "### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "pipeline = Pipeline([('transformer', scalar), ('estimator', LinearRegression())])\n",
    "\n",
    "### k-fold cross validation (k=20)\n",
    "\n",
    "scores = cross_val_score(pipeline, X, y, cv=20)\n",
    "\n",
    "### Affichage de scores\n",
    "\n",
    "print(\"Score pour chaque étape (20 scores) : \\n\",scores)\n",
    "print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### 2e modèle : Ridge\n",
    "\n",
    "Le deuxième modèle que nous allons tester pour répondre à notre problématique de régression se nomme Ridge (Ridge regressor dans sklearn : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score pour chaque étape (20 scores) : \n",
      " [0.18477911 0.20795826 0.18804803 0.19440623 0.19982814 0.20081282\n",
      " 0.20084956 0.17379741 0.20009827 0.19508225 0.22712316 0.18971305\n",
      " 0.20694902 0.18671219 0.20629464 0.21431476 0.20764999 0.20032414\n",
      " 0.23686801 0.20287011]\n",
      "0.20 de moyenne avec un écart-type de 0.01\n"
     ]
    }
   ],
   "source": [
    "X = dataset_copy.drop(columns=['target'])\n",
    "y = dataset_copy['target']\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "### Ici on souhaite utiliser le modèle : Ridge.\n",
    "### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "pipeline = Pipeline([('transformer', scalar), ('estimator', Ridge(alpha=1.0))])\n",
    "\n",
    "### k-fold cross validation (k=20)\n",
    "\n",
    "scores = cross_val_score(pipeline, X, y, cv=20)\n",
    "\n",
    "### Affichage de scores\n",
    "\n",
    "print(\"Score pour chaque étape (20 scores) : \\n\",scores)\n",
    "print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### 3e modèle : MLPRegressor (Réseau de neuronnes)\n",
    "\n",
    "Le troisième modèle que nous allons tester pour répondre à notre problématique de régression est un réseau de neuronnes (MLPRegressor dans sklearn : https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset_copy.drop(columns=['target'])\n",
    "y = dataset_copy['target']\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "### Ici on souhaite utiliser le modèle : MLPRegressor.\n",
    "### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "pipeline = Pipeline([('transformer', scalar), ('estimator', MLPRegressor(random_state=1, max_iter=1000))])\n",
    "\n",
    "### k-fold cross validation (k=5)\n",
    "\n",
    "scores = cross_val_score(pipeline, X, y, cv=5)\n",
    "\n",
    "### Affichage de scores\n",
    "\n",
    "print(\"Score pour chaque étape (20 scores) : \\n\",scores)\n",
    "print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Résultats MLPRegressor :\n",
    "\n",
    "Score pour chaque étape (5 scores) :  \n",
    " [0.34184137 0.38539981 0.40500464 0.38363435 0.3813366 ]  \n",
    "0.38 de moyenne avec un écart-type de 0.02  \n",
    "\n",
    "*(Affichage en markdown, car beaucoup de warning sur la non convergences)*  \n",
    "*(K-fold cross validation avec k=5 car prend beaucoup de temps)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Via ces différents modèles, on peut voir que les résultats ne sont pas excellents. On obtient sur le modèle de régression linéaire un score r² moyen de 0.2, et un score similaire avec le modèle Ridge.\n",
    "Seul le modèle se basant sur le réseau neuronal a un score meilleur à 0.38 (meilleur résultats après plusieurs tests sur les hypers paramètres du MLPRegressor). On pourrait peut être améliorer ce score en augmentant le nombre maximum d'itérations, car avec 1000 le logiciel renvoit un avertissement sur la non convergence. (Il faudrait mettre 2000 ou plus, mais beaucoup trop long sur nos ordinateurs)\n",
    "\n",
    "Ces résultats peuvent s'expliquer en partie par le fait que nos caractéristiques ne sont pas complètes (comme expliqué plus tôt).\n",
    "Ils peuvent aussi s'expliquer par le fait qu'on entraine le modèle avec l'ensemble du dataset (avec tout les jeux).\n",
    "\n",
    "---\n",
    "\n",
    "##### Régression par jeu\n",
    "\n",
    "Dans cette seconde partie sur la régression, nous allons essayer d'entrainer le modèle de régression linéaire pour chaque jeu. Il y aura certainement des différences entre les scores au vu des observations, mais aussi au vu de leur nombre qui n'est pas homogène selon les jeux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeu : counterStrikeGlobalOffensive\n",
      "0.35 de moyenne avec un écart-type de 0.01\n",
      "Jeu : grandTheftAuto5\n",
      "0.35 de moyenne avec un écart-type de 0.07\n",
      "Jeu : worldOfTanks\n",
      "0.20 de moyenne avec un écart-type de 0.03\n",
      "Jeu : rust\n",
      "0.24 de moyenne avec un écart-type de 0.04\n",
      "Jeu : leagueOfLegends\n",
      "0.35 de moyenne avec un écart-type de 0.05\n",
      "Jeu : arkSurvivalEvolved\n",
      "0.09 de moyenne avec un écart-type de 0.10\n",
      "Jeu : apexLegends\n",
      "0.44 de moyenne avec un écart-type de 0.05\n",
      "Jeu : minecraft\n",
      "0.07 de moyenne avec un écart-type de 0.02\n",
      "Jeu : playerUnknownsBattlegrounds\n",
      "0.39 de moyenne avec un écart-type de 0.05\n",
      "Jeu : battlefield1\n",
      "0.34 de moyenne avec un écart-type de 0.07\n",
      "Jeu : fortnite\n",
      "0.38 de moyenne avec un écart-type de 0.03\n",
      "Jeu : rocketLeague\n",
      "0.38 de moyenne avec un écart-type de 0.06\n",
      "Jeu : callOfDutyBlackOps4\n",
      "0.22 de moyenne avec un écart-type de 0.06\n",
      "Jeu : dota2\n",
      "0.57 de moyenne avec un écart-type de 0.06\n",
      "Jeu : overwatch\n",
      "0.49 de moyenne avec un écart-type de 0.01\n",
      "Jeu : battlefield4\n",
      "0.24 de moyenne avec un écart-type de 0.06\n",
      "Jeu : callOfDutyWW2\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : pathOfExile\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : destiny2\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : radicalHeights\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : seaOfThieves\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : frostpunk\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : totalWar3Kingdoms\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : warframe\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : airMechStrike\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : battletech\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : farCry5\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : starcraft2\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : rainbowSixSiege\n",
      "0.96 de moyenne avec un écart-type de 0.01\n",
      "Jeu : aWayOut\n",
      "0.96 de moyenne avec un écart-type de 0.01\n"
     ]
    }
   ],
   "source": [
    "scalar = StandardScaler()\n",
    "\n",
    "for gameName in dict_global['GameName']:\n",
    "    \n",
    "    dataset_copy = dataset_reduced_without_string.copy()\n",
    "\n",
    "    print(\"Jeu : \" + gameName)\n",
    "\n",
    "    dataset_copy = dataset_copy[dataset_copy['GameName'] == dict_global['GameName'][gameName]]\n",
    "    dataset_copy = dataset_copy.sample(random_state=0, n=dataset_copy.shape[0])\n",
    "\n",
    "    X = dataset_copy.drop(columns=['target'])\n",
    "    y = dataset_copy['target']\n",
    "\n",
    "    ### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "    ### Ici on souhaite utiliser le modèle : LinearRegression.\n",
    "    ### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "    pipeline = Pipeline([('transformer', scalar), ('estimator', LinearRegression())])\n",
    "\n",
    "    ### k-fold cross validation (k=10)\n",
    "\n",
    "    scores = cross_val_score(pipeline, X, y, cv=10)\n",
    "\n",
    "    # Uncomment to see the score\n",
    "    # print(scores)\n",
    "\n",
    "    print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En voyant la moyenne des scores pour chaque jeu, on retrouve globalement de meilleurs résultats, même si sur certains jeu les scores sont moins bons.\n",
    "\n",
    "Pour être plus précis, on constate que tout les jeux avec un score à 0.96 ont \"peu\" d'observations liées dans le dataset (1026 observations à chaque fois).  \n",
    "Ce manque d'observations, et le fait que les observations se \"ressemblent\" (les données viennent d'ordinateur ayant des configurations à peu près similaires, donc avec des fps similaires) pourraient expliquer ce score.\n",
    "\n",
    "Sur les autres jeux avec plus de données, comme counterStrikeGlobalOffensive (score = 0.35, 9451 observations) ou playerUnknownsBattlegrounds (score = 0.07, 8736 observations) ont obtient des résultats beaucoup plus hétérogènes (qui sont peut être due aux valeurs de caractéristiques dans le dataset, aux observations ou encore aux benchmark faits sur ces jeux qui varient beaucoup) .\n",
    "\n",
    "Globalement il est plus intéressant de découper le dataset par jeu, car sur dans la grande partie des jeux, on gagne en performance sur les prédictions.\n",
    "\n",
    "---\n",
    "\n",
    "##### Passage à un problème de classification\n",
    "\n",
    "Comme précisé, nous avons vu qu'entrainer un modèle sur un problème de régression sur l'ensemble du dataset (pas de distinction par jeu) n'apporte pas des résultats très bons. Nous voulons donc voir quels seraient les résultats si nous transformons notre problème de régression en un problème de classification en passant d'une target en valeur continue de fps, à une target prenant un dictionnaire d'intervalles de fps. (ex : entre 20 et 30 fps)\n",
    "\n",
    "Pour traiter ce problème de classification, nous testerons deux modèles :\n",
    "- Decision Tree Classifier \n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Pas de l'intervale des fps. Ex : si pas = 10, alors de 0 à 10 fps, y deviendra 5, de 11 à 20, y deviendra 15\n",
    "### On prend à chaque fois le milieu de l'intervalle pour identifier l'intervalle\n",
    "\n",
    "pas = 30\n",
    "\n",
    "dataset_for_clasification = dataset_reduced_without_string.copy()\n",
    "for j in range(len(dataset_for_clasification['target'])):\n",
    "    dataset_for_clasification['target'][j] = trunc(dataset_for_clasification['target'][j] - dataset_for_clasification['target'][j] % pas) + pas/2\n",
    "\n",
    "dataset_for_clasification = dataset_for_clasification.astype({'target': 'int32'}).reset_index().drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "##### 1er modèle : Arbre de décision\n",
    "\n",
    "Le premier modèle que nous allons tester pour répondre à notre problématique de classification est un arbre de décision (Decision Tree Classifier dans sklearn : https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.51 \n",
      "Test set score: 0.46 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\quent\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score pour chaque étape (20 scores) : \n",
      " [0.23909779 0.22155842 0.12264784]\n",
      "0.19 de moyenne avec un écart-type de 0.05\n"
     ]
    }
   ],
   "source": [
    "X = dataset_for_clasification.drop(columns=['target'])\n",
    "y = dataset_for_clasification['target']\n",
    "\n",
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "scalar = StandardScaler()\n",
    "X_train_c = scalar.fit_transform(X_train_c)\n",
    "X_test_c = scalar.fit_transform(X_test_c)\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(random_state=0, max_depth=11)\n",
    "\n",
    "clf.fit(X_train_c, y_train_c)\n",
    "\n",
    "train_score_c = clf.score(X_train_c, y_train_c)\n",
    "test_score_c = clf.score(X_test_c, y_test_c)\n",
    "\n",
    "print(\"Training set score: {:.2f} \".format(train_score_c))\n",
    "print(\"Test set score: {:.2f} \".format(test_score_c))\n",
    "\n",
    "###########\n",
    "\n",
    "X = dataset_for_clasification.drop(columns=['target'])\n",
    "y = dataset_for_clasification['target']\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "### Ici on souhaite utiliser le modèle : Ridge.\n",
    "### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "pipeline = Pipeline([('transformer', scalar), ('estimator', tree.DecisionTreeClassifier(random_state=0, max_depth=5))])\n",
    "\n",
    "### k-fold cross validation (k=3)\n",
    "\n",
    "scores = cross_val_score(pipeline, X, y, cv=3)\n",
    "\n",
    "### Affichage de scores\n",
    "\n",
    "print(\"Score pour chaque étape (20 scores) : \\n\",scores)\n",
    "print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec le modèle de l'arbre de décision, on obtient des résultats assez variés.  \n",
    "Sans k-fold cross validation, on obtient un score de 0.46 sur le jeu de test (46% d'observations bien classifiées).  \n",
    "Avec k-fold cross validation (k=3) on obtient des résultats moins bons (0.19 en moyenne avec une profondeur maximale de 5).  \n",
    "\n",
    "Ces résultats différents viennent de plusieurs facteurs : la profondeur maximale allouée, le nombre d'élément dans le dataset de test (et de train), le pas utilisé pour créer les classes (ici test avec un pas de 30, mais les résultats sont meilleurs avec un pas plus grand, et moins bons avec un pas plus petit).\n",
    "\n",
    "*Note : `UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.` vient du fait que certaines classes (de notre target) n'ont qu'une seule valeur au niveau des observations (une observation a pour target cette classe).*\n",
    "\n",
    "---\n",
    "\n",
    "##### 2e modèle : Régression logistique\n",
    "\n",
    "Le second modèle que nous allons tester pour répondre à notre problématique de classification est le modèle de la régression logistique (Logistic Regression dans sklearn : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.30 \n",
      "Test set score: 0.30 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\quent\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score pour chaque étape (20 scores) : \n",
      " [0.27208349 0.29519195 0.32431713 0.31794462 0.21304579]\n",
      "0.28 de moyenne avec un écart-type de 0.04\n"
     ]
    }
   ],
   "source": [
    "X = dataset_for_clasification.drop(columns=['target'])\n",
    "y = dataset_for_clasification['target']\n",
    "\n",
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "clf = LogisticRegression(solver='lbfgs', max_iter=2000, n_jobs=6)\n",
    "\n",
    "clf.fit(X_train_c, y_train_c)\n",
    "\n",
    "train_score_c = clf.score(X_train_c, y_train_c)\n",
    "test_score_c = clf.score(X_test_c, y_test_c)\n",
    "\n",
    "print(\"Training set score: {:.2f} \".format(train_score_c))\n",
    "print(\"Test set score: {:.2f} \".format(test_score_c))\n",
    "\n",
    "########### Avec k-fold cross validation (à décommenter pour tester)\n",
    "\n",
    "X = dataset_for_clasification.drop(columns=['target'])\n",
    "y = dataset_for_clasification['target']\n",
    "scalar = StandardScaler()\n",
    "\n",
    "### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "### Ici on souhaite utiliser le modèle : Ridge.\n",
    "### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "pipeline = Pipeline([('transformer', scalar), ('estimator', LogisticRegression(solver='lbfgs', max_iter=2000, n_jobs=6))])\n",
    "\n",
    "### k-fold cross validation (k=5)\n",
    "\n",
    "scores = cross_val_score(pipeline, X, y, cv=5)\n",
    "\n",
    "### Affichage de scores\n",
    "\n",
    "print(\"Score pour chaque étape (20 scores) : \\n\",scores)\n",
    "print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec le modèle de la régression logistique, on obtient des résultats un peu mieux (bien que globalement similaire) au modèle précédent.  \n",
    "Sans k-fold cross validation, on obtient un score de 0.30 sur le jeu de test et sur le jeu de train (30% d'observations bien classifiées).  \n",
    "Avec k-fold cross validation (k=5) on obtient des résultats similaires (0.30 en moyenne avec 0.04 d'écart-type).  \n",
    "\n",
    "Ces résultats sont assez mauvais globalement sur les deux modèles, on est toujours en dessous de 50% de prédictions correctes. Ces résultats peuvent venir de multiples facteurs : le dataset utilisé qui a plusieurs défauts, la façon de créer les intervalles de targets (plus l'intervalle est grande, meilleurs sont les scores et inversement), les hyper paramètres des modèles, etc.\n",
    "\n",
    "*Note : `UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.` vient du fait que certaines classes (de notre target) n'ont qu'une seule valeur au niveau des observations (une observation a pour target cette classe).*\n",
    "\n",
    "---\n",
    "\n",
    "##### Classification par jeu\n",
    "\n",
    "Dans cette seconde partie sur la classification, nous allons essayer d'entrainer le modèle d'arbre de décision (modèle pour de la classification) pour chaque jeu. Il y aura certainement des différences entre les scores au vu des observations, mais aussi au vu de leur nombre qui n'est pas homogène selon les jeux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeu : counterStrikeGlobalOffensive\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.20279276 0.21777778 0.22095238]\n",
      "0.21 de moyenne avec un écart-type de 0.01\n",
      "Jeu : grandTheftAuto5\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.62674961 0.61469673 0.63788409]\n",
      "0.63 de moyenne avec un écart-type de 0.01\n",
      "Jeu : worldOfTanks\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.4744     0.4752     0.48839071]\n",
      "0.48 de moyenne avec un écart-type de 0.01\n",
      "Jeu : rust\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.45915493 0.4915493  0.48028169]\n",
      "0.48 de moyenne avec un écart-type de 0.01\n",
      "Jeu : leagueOfLegends\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.30836576 0.28856448 0.3026764 ]\n",
      "0.30 de moyenne avec un écart-type de 0.01\n",
      "Jeu : arkSurvivalEvolved\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.60216718 0.60990712 0.59752322]\n",
      "0.60 de moyenne avec un écart-type de 0.01\n",
      "Jeu : apexLegends\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.46754883 0.47132955 0.45901639]\n",
      "0.47 de moyenne avec un écart-type de 0.01\n",
      "Jeu : minecraft\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.1761658  0.16994819 0.14834025]\n",
      "0.16 de moyenne avec un écart-type de 0.01\n",
      "Jeu : playerUnknownsBattlegrounds\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.52472527 0.53777473 0.52815934]\n",
      "0.53 de moyenne avec un écart-type de 0.01\n",
      "Jeu : battlefield1\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.54860681 0.54427245 0.54303406]\n",
      "0.55 de moyenne avec un écart-type de 0.00\n",
      "Jeu : fortnite\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.36045239 0.37723459 0.37686976]\n",
      "0.37 de moyenne avec un écart-type de 0.01\n",
      "Jeu : rocketLeague\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.3245283  0.32703214 0.29300567]\n",
      "0.31 de moyenne avec un écart-type de 0.02\n",
      "Jeu : callOfDutyBlackOps4\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.38867439 0.3989704  0.35907336]\n",
      "0.38 de moyenne avec un écart-type de 0.02\n",
      "Jeu : dota2\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.36666667 0.4300813  0.41219512]\n",
      "0.40 de moyenne avec un écart-type de 0.03\n",
      "Jeu : overwatch\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.41547389 0.40712074 0.40750774]\n",
      "0.41 de moyenne avec un écart-type de 0.00\n",
      "Jeu : battlefield4\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.50588235 0.51764706 0.48235294]\n",
      "0.50 de moyenne avec un écart-type de 0.01\n",
      "Jeu : callOfDutyWW2\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.79824561 0.73976608 0.71052632]\n",
      "0.75 de moyenne avec un écart-type de 0.04\n",
      "Jeu : pathOfExile\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.75730994 0.69883041 0.76023392]\n",
      "0.74 de moyenne avec un écart-type de 0.03\n",
      "Jeu : destiny2\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.73976608 0.79239766 0.79824561]\n",
      "0.78 de moyenne avec un écart-type de 0.03\n",
      "Jeu : radicalHeights\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.84795322 0.80701754 0.76900585]\n",
      "0.81 de moyenne avec un écart-type de 0.03\n",
      "Jeu : seaOfThieves\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.75730994 0.7748538  0.72807018]\n",
      "0.75 de moyenne avec un écart-type de 0.02\n",
      "Jeu : frostpunk\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.79824561 0.76900585 0.76900585]\n",
      "0.78 de moyenne avec un écart-type de 0.01\n",
      "Jeu : totalWar3Kingdoms\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.88596491 0.88888889 0.88888889]\n",
      "0.89 de moyenne avec un écart-type de 0.00\n",
      "Jeu : warframe\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.73976608 0.7251462  0.69298246]\n",
      "0.72 de moyenne avec un écart-type de 0.02\n",
      "Jeu : airMechStrike\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.7748538  0.78362573 0.7748538 ]\n",
      "0.78 de moyenne avec un écart-type de 0.00\n",
      "Jeu : battletech\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.89181287 0.86549708 0.83333333]\n",
      "0.86 de moyenne avec un écart-type de 0.02\n",
      "Jeu : farCry5\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.81871345 0.78362573 0.84502924]\n",
      "0.82 de moyenne avec un écart-type de 0.03\n",
      "Jeu : starcraft2\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.76315789 0.78362573 0.76023392]\n",
      "0.77 de moyenne avec un écart-type de 0.01\n",
      "Jeu : rainbowSixSiege\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.86842105 0.88011696 0.8245614 ]\n",
      "0.86 de moyenne avec un écart-type de 0.02\n",
      "Jeu : aWayOut\n",
      "Score pour chaque étape (20 scores) : \n",
      " [0.7748538  0.7251462  0.76900585]\n",
      "0.76 de moyenne avec un écart-type de 0.02\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "## Supprime les warnings\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "##########################\n",
    "\n",
    "scalar = StandardScaler()\n",
    "\n",
    "for gameName in dict_global['GameName']:\n",
    "\n",
    "    dataset_copy = dataset_for_clasification.copy()\n",
    "\n",
    "    print(\"Jeu : \" + gameName)\n",
    "\n",
    "    dataset_copy = dataset_copy[dataset_copy['GameName'] == dict_global['GameName'][gameName]]\n",
    "    dataset_copy = dataset_copy.sample(random_state=0, n=dataset_copy.shape[0])\n",
    "\n",
    "    X = dataset_copy.drop(columns=['target'])\n",
    "    y = dataset_copy['target']\n",
    "\n",
    "    ### Le pipeline sert à appliquer des règles au cross_val_score.\n",
    "    ### Ici on souhaite utiliser le modèle : DecisionTreeClassifier.\n",
    "    ### Et on souhaite appliquer un StandardScaler sur les jeux de test et de train (une fois découpés)\n",
    "\n",
    "    pipeline = Pipeline([('transformer', scalar), ('estimator', tree.DecisionTreeClassifier(random_state=0, max_depth=5))])\n",
    "\n",
    "    ### k-fold cross validation (k=3)\n",
    "\n",
    "    scores = cross_val_score(pipeline, X, y, cv=3)\n",
    "\n",
    "    ### Affichage de scores\n",
    "\n",
    "    print(\"Score pour chaque étape (3 scores) : \\n\",scores)\n",
    "    print(\"%0.2f de moyenne avec un écart-type de %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En voyant la moyenne des scores pour chaque jeu, on retrouve globalement, à la manière de la régression par jeu, de meilleurs résultats, même si sur certains jeu les scores sont moins bons.  \n",
    "Comme pour la régression, on remarque que la majorité des jeux ayant peu d'observations, et ayant des observations assez similaires (caractéristiques du PC faisant fonctionner le jeu assez similaires) ont de meilleurs scores que les autres (ex : starcraft2 qui a un score de 0.77 (77%))  \n",
    "Sur les autres jeux avec plus de données, comme counterStrikeGlobalOffensive (score = 0.21, 9451 observations) ou playerUnknownsBattlegrounds (score = 0.53, 8736 observations) ont obtient des résultats beaucoup plus hétérogènes (qui sont peut être due aux valeurs de caractéristiques dans le dataset, aux observations ou encore aux benchmark faits sur ces jeux qui varient beaucoup) .\n",
    "\n",
    "Globalement il est plus intéressant de découper le dataset par jeu, car dans la grande partie des jeux, on gagne en performance sur les prédictions.\n",
    "\n",
    "Nous avons aussi testé avec le modèle de regression logistique, mais nous n'avons jamais pu le faire \"passer\" sur l'ensemble des jeux. D'une part car il y a beaucoup d'erreurs sur la non convergence (que nous ne sommes pas parvenu à résoudre), et d'autre part car même avec le résolveur le plus rapide (lbfgs) l'entrainement du modèle prend beaucoup trop de temps sur nos ordinateurs.\n",
    "\n",
    "---\n",
    "\n",
    "##### Analyse et Conclusion\n",
    "\n",
    "Comme nous venons de le voir, les résultats obtenues varient beaucoup en fonction du problème (classification ou régression, même si il est difficile de comparer les résultats entre ces deux problématiques car les scores ne signifient pas la même chose), en fonction de la méthode utilisée (entrainement sur le dataset global, ou pour chaque jeu), et probablement en fonction de la préparation du dataset.\n",
    "\n",
    "Comme dit, il est difficile de comparer les résultats entre la régression et la classifications, mais dans les deux cas, certains modèles fonctionnent mieux que d'autres, et certaines méthodes fonctionnent mieux aussi (le découpage par jeu).\n",
    "\n",
    "Avec ce dataset plutôt complexe avec plusieurs caractéristiques assez variées que ce soit en signification ou en type (string et int), nous avons du faire des choix au niveau de la méthodologie qui ne sont peut être pas les bons, et qui jouent dans les résultats que nous obtenons (bons résultats sur certains jeux, très mauvais sur d'autres). Ces choix portent sur tellement d'aspects (Tri du dataset, comment gérer les valeurs nulles, comment gérer les chaines de caractères, quel modèle choisir, quel hyper-paramètre choisir pour ces modèles, ...) qu'il est difficile de tous les traiter, et peut être que ce dataset admet des résultats bien meilleurs pour cette problématique avec une autre approche.\n",
    "\n",
    "Pour terminer, même si les résultats sont plutôt mitigés pour nous, nous avons appris beaucoup de choses avec ce projet, que ce soit sur la gestion d'une grande quantitié de donnée, ou sur l'utilisation et la compréhension de modèles de machines learning. "
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