RF_Modele1_Constructeur_65.ipynb

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  {
   "cell_type": "markdown",
   "id": "50104c1a",
   "metadata": {},
   "source": [
    "<div id = \"monlogo\"><center> <img src=\"https://datascientest.fr/train/assets/logo_datascientest.png\", style=\"height:150px\"></center></div>\n",
    "\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\"><br>\n",
    "<center> <h1>Challenge Sorbonne</h1> </center> <br>\n",
    "<center> <h3>Exemple Modélisation</h3> </center> <br>\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\"> \n",
    "\n",
    "### Modélisation Machine Learning\n",
    "\n",
    ">  L'objectif est de créer un modèle permettant d'**identifier pour chaque course de la saison 2021, le vainqueur ainsi que le podium**. Il faut donc créer un modèle de classification binaire, ainsi qu'un modèle de classification multiclasse. Pour rappel, chacun de ces 2 modèles servira pour la moitié de la note de modélisation.\n",
    ">\n",
    "> Il est nécessaire de garder ces éléments en tête :\n",
    "> - **Données futures** : Les Grand Prix ont lieu les uns après les autres, et vous avez dans les différentes tables accès à des données qui n'existent pas encore au moment du départ d'un Grand Prix. Créer un modèle reposant sur des informations futures serait un non-sens mathématiques et une grosse erreur de méthodologie.\n",
    "> - **Preprocessing** : Il y a bon nombre de données manquantes, incomplètes ou inutiles dans le jeu de données, faites le tri. \n",
    "> - **Feature Engineering** : Vous avez un grand nombre de tables, certaines contiennent des features directement exploitables pour le machine learning, d'autres sont intéressantes mais nécessitent des modifications afin d'être utilisées à bon escient. Vous avez également la possibilité d'augmenter vos données à l'aide de librairies comme [FastF1](https://theoehrly.github.io/Fast-F1/), ou en scrapant sur le web.\n",
    "> - **Métriques et interprétabilités** : Vos modèles seront évaluées selon la métrique bien connue du [f1-score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), prenez cela en compte lors de l'optimisation de vos modèles.\n",
    "\n",
    "### Exemple\n",
    "\n",
    "> Vous trouverez ci-dessous un exemple de modèle naïf de classification binaire réalisé à partir des tables `results.csv`, `races.csv`, `circuits.csv` et `constructor_standings.csv`, se basant uniquement sur **la position de départ de chaque pilote sur la grille**, **le pays du circuit**, **le classement et le nombre de points constructeur au moment du départ**.\n",
    ">\n",
    "> Attention, ce travail de modélisation a uniquement vocation à vous donner une piste pour démarrer, celui-ci n'est pas parfaitement optimisé et ne constitue pas un bon exemple à suivre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94b42565",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#Chargement des trois tables\n",
    "results = pd.read_csv(r\"data/results.csv\", sep=',')\n",
    "races = pd.read_csv(r\"data/races.csv\", sep=',')\n",
    "circuits = pd.read_csv(r\"data/circuits.csv\", sep=',')\n",
    "constructors = pd.read_csv(r\"data/constructor_standings.csv\", sep=',')\n",
    "\n",
    "#Jointure entre les différentes tables afin de conserver les 2 features, la variable cible 'positionOrder' et l'année pour effectuer le train_test_split\n",
    "df1 = pd.merge(results[['driverId', 'constructorId', 'raceId', 'grid', 'positionOrder']], races[['raceId', 'year', 'circuitId']], on = 'raceId')\n",
    "df2 = pd.merge(df1, constructors[['raceId', 'constructorId', 'position', 'points']], on = ['raceId', 'constructorId'])\n",
    "df3 = pd.merge(df2, circuits[['circuitId','country']], on = 'circuitId')\n",
    "df3['winner'] = df3['positionOrder'].apply(lambda x: 1 if x==1 else 0)\n",
    "df4 = df3.drop(['circuitId', 'constructorId', 'positionOrder'], axis = 1).set_index(['raceId', 'driverId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d840376e-0805-48a7-9934-f47a563d48c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#On encode numériquement les pays\n",
    "label_encoder = LabelEncoder()\n",
    "df4.loc[:, 'country'] = label_encoder.fit_transform(df3['country'])\n",
    "df4.loc[:,\"position**2\"] = df4[\"position\"] ** 2\n",
    "df4.loc[:,\"points**2\"] = df4[\"points\"] ** 2\n",
    "\n",
    "\n",
    "#On effectue notre train_test_split selon les dates puis on sépare X et y\n",
    "df_train = df4[df4['year']<=2020]\n",
    "df_test = df4[df4['year']==2021]\n",
    "\n",
    "X_train = df_train[['grid', 'country',\"points\",\"position\", 'points**2',\"position**2\"]]\n",
    "X_test = df_test[['grid', 'country', \"points\",\"position\",'points**2',\"position**2\"]]\n",
    "\n",
    "y_train = df_train[['winner']]\n",
    "#y_test ne contient pas d'informations, puisque c'est ce qu'il faut prédire et soumettre sur la plateforme :)\n",
    "y_pred = df_test[['winner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1d7923f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train.to_csv(\"MetaModel/y_train_RF.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "443869f7-dee4-4256-b4b6-62eb35745e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 25}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Définition du modèle\n",
    "random_forest = RandomForestClassifier(random_state = 42)\n",
    "\n",
    "# Définition de la grille de paramètres et du GridSearchCV\n",
    "param_grid = {\n",
    "    'n_estimators': [25, 30],\n",
    "    'max_depth': [25,30,35],\n",
    "    'min_samples_split' : [2]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(random_forest, param_grid, scoring='recall')\n",
    "\n",
    "# Entraînement du modèle\n",
    "grid_search.fit(X_train, y_train.values.ravel())\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d4dae0",
   "metadata": {},
   "source": [
    "best parametres : {'max_depth': 30, 'n_estimators': 25}\n",
    "\n",
    "best parametres : {'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 25}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70b2cc35",
   "metadata": {},
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
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       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
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       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
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       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
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       "\n",
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       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
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       "#sk-container-id-1 div.sk-toggleable {\n",
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       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
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       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=30, n_estimators=25, random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=30, n_estimators=25, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=30, n_estimators=25, random_state=42)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = RandomForestClassifier(random_state= 42, max_depth=30, min_samples_split=2, n_estimators=25)\n",
    "grid_search.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54eb18e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9957995929502447"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0a5e6bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pour le bien du meta model\n",
    "#y_pred = y_train\n",
    "#y_pred[\"winner\"] = y_pred['winner'].apply(lambda x: 0 if x!=10000 else 0)\n",
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14aaad84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_19436\\3802020013.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y_pred.loc[:,'PREpositionOrder'] = probabilite\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>winner</th>\n",
       "      <th>PREpositionOrder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1052</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1073</th>\n",
       "      <th>849</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 winner  PREpositionOrder\n",
       "raceId driverId                          \n",
       "1052   1              0              0.10\n",
       "       830            0              0.04\n",
       "       822            0              0.52\n",
       "       846            0              0.00\n",
       "       815            0              0.04\n",
       "...                 ...               ...\n",
       "1073   849            0              0.00\n",
       "       841            0              0.00\n",
       "       847            0              0.00\n",
       "       8              0              0.00\n",
       "       853            0              0.00\n",
       "\n",
       "[440 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#On souhaite ajouter un podium fait à partir des probabilités retournées par le modèle random forest\n",
    "\n",
    "probabilite = grid_search.predict_proba(X_test)\n",
    "probabilite = pd.DataFrame(probabilite)\n",
    "probabilite = probabilite.drop(columns=(0))\n",
    "probabilite = np.array(probabilite)\n",
    "probabilite\n",
    "\n",
    "y_pred.loc[:,'PREpositionOrder'] = probabilite\n",
    "y_pred\n",
    "# on a donc un classement fait à partir des probabilité qui faut transformer en un classement ordonné du premier au dernier arrivé (1 à 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1828482",
   "metadata": {},
   "source": [
    "l'objectif est de réaliser un classement pour chaque course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99b5be79",
   "metadata": {},
   "outputs": [],
   "source": [
    "Taille = y_pred.index\n",
    "raceID = Taille.get_level_values('raceId').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3ef8828",
   "metadata": {},
   "outputs": [],
   "source": [
    "#driverID = y_pred.index.get_level_values('driverId')\n",
    "PodiumOr0 = []\n",
    "Top1 = []\n",
    "for RACE in raceID:\n",
    "    driverID = y_pred[\"PREpositionOrder\"][RACE].index.get_level_values('driverId')\n",
    "    array = np.array(y_pred[\"PREpositionOrder\"][RACE])\n",
    "    ranks = array.argsort()\n",
    "    number = len(driverID)\n",
    "    ranks = number - ranks.argsort()\n",
    "    count = 0\n",
    "    for DRIVER in driverID:\n",
    "        rang = ranks[count]\n",
    "        if rang <= 3:\n",
    "            PodiumOr0.append(ranks[count])\n",
    "            if rang == 1:\n",
    "                Top1.append(1)\n",
    "            else:\n",
    "                Top1.append(0)\n",
    "        else:\n",
    "            PodiumOr0.append(0)\n",
    "            Top1.append(0)\n",
    "        count += 1 \n",
    "    count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05ba7044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440 440\n"
     ]
    }
   ],
   "source": [
    "print(len(PodiumOr0),len(Top1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c96ecd6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_19436\\1890261867.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y_pred.loc[:,'positionOrder'] = PodiumOr0\n"
     ]
    }
   ],
   "source": [
    "y_pred.loc[:,'winner'] = Top1\n",
    "y_pred.loc[:,'positionOrder'] = PodiumOr0\n",
    "y_pred = y_pred.drop(columns=(\"PREpositionOrder\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e672543",
   "metadata": {},
   "outputs": [],
   "source": [
    "#On exporte notre y_pred au format CSV \n",
    "y_pred.to_csv('model1OnlyConstructeur_version2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74bcc19c",
   "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></th>\n",
       "      <th>winner</th>\n",
       "      <th>positionOrder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1052</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1073</th>\n",
       "      <th>849</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 winner  positionOrder\n",
       "raceId driverId                       \n",
       "1052   1              0              2\n",
       "       830            0              3\n",
       "       822            1              1\n",
       "       846            0              0\n",
       "       815            0              0\n",
       "...                 ...            ...\n",
       "1073   849            0              0\n",
       "       841            0              0\n",
       "       847            0              0\n",
       "       8              0              0\n",
       "       853            0              0\n",
       "\n",
       "[440 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08342a57",
   "metadata": {},
   "source": [
    "score vers les 65,\n",
    "\n",
    "score version 2 : ***65.79***"
   ]
  }
 ],
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