MetaModel2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#les données\n",
    "\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",
    "df1 = pd.merge(results[['driverId', 'constructorId', 'raceId', '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'])\n",
    "\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",
    "df4 = df4.rename(columns={\"points\" : 'pointsequipe'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   driverId  raceId  year  position  pointsequipe country  winner  \\\n",
      "0         1      18  2008         1          14.0       1       1   \n",
      "1         2      18  2008         3           8.0       1       0   \n",
      "2         3      18  2008         2           9.0       1       0   \n",
      "3         4      18  2008         4           5.0       1       0   \n",
      "4         5      18  2008         1          14.0       1       0   \n",
      "\n",
      "   position**2  points**2  \n",
      "0            1      196.0  \n",
      "1            9       64.0  \n",
      "2            4       81.0  \n",
      "3           16       25.0  \n",
      "4            1      196.0  \n"
     ]
    }
   ],
   "source": [
    "print(df4.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_4224\\1924429326.py:20: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  races['hour'] = pd.to_datetime(races['time']).dt.hour\n"
     ]
    }
   ],
   "source": [
    "drivers = pd.read_csv(r\"data\\driver_standings.csv\", sep=\",\") #on peut prendre le raceID, driverID, Points, Position, wins\n",
    "driversINFO = pd.read_csv(r\"data\\drivers.csv\",sep=\",\") #on prend la date de naisse dob[:4]\n",
    "races = pd.read_csv(r\"data\\races.csv\",sep=\",\") #raceID, year, circuitID, date, time\n",
    "results = pd.read_csv(r\"data\\results.csv\", sep=\",\") #driverID, raceID, positionORder, grid\n",
    "\n",
    "results = results[['raceId','driverId','grid','positionOrder']] \n",
    "driversINFO = driversINFO[['driverId','dob']]\n",
    "drivers = drivers[['raceId','driverId','points','wins']]\n",
    "races = races[['raceId','year','circuitId','date','time']]\n",
    "\n",
    "#transformation pour avoir la date de naissance des conducteurs\n",
    "driversINFO.loc[:,'dob'] = pd.to_datetime(driversINFO['dob']).dt.year\n",
    "\n",
    "#transformation de la date des courses pour avoir le mois \n",
    "races.loc[:,'date'] = pd.to_datetime(races['date']).dt.month\n",
    "races = races.rename(columns={'date' : 'month'})\n",
    "\n",
    "#retirer une erreur dans les dates et garder uniquement l'heure de la course\n",
    "races[\"time\"] = races['time'].replace({'\\\\N' : '12:00:00'})\n",
    "races['hour'] = pd.to_datetime(races['time']).dt.hour\n",
    "races = races.drop(columns = [\"time\"])\n",
    "\n",
    "# on joint resultat et races sur la base de raceID\n",
    "df12 = pd.merge(results[['raceId','driverId','grid','positionOrder']], races[['raceId','year','circuitId','month','hour']], on = 'raceId')\n",
    "\n",
    "# on joint le précédent avec drivers avec drivers info sur driverID\n",
    "df22 = pd.merge(drivers, driversINFO, on = 'driverId')\n",
    "\n",
    "df32 = pd.merge(df12,df22, on = ['raceId', 'driverId'])\n",
    "df32\n",
    "\n",
    "#on utilise positionOrder pour faire le winner\n",
    "df32['winner'] = df32['positionOrder'].apply(lambda x: 1 if x==1 else 0)\n",
    "df32[\"AgeAtRace\"] = df32['year'] - df32['dob']\n",
    "df42 = df32.drop(['positionOrder','circuitId', 'dob'], axis = 1)#.set_index(['raceId', 'driverId'])\n",
    "\n",
    "#on retire qui a gagné la course à chaque course\n",
    "df42[\"wins\"] = df42[\"wins\"] - df42[\"winner\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   raceId  driverId  grid  year month  hour  points  wins  winner AgeAtRace\n",
      "0      18         1     1  2008     3     4    10.0     0       1        23\n",
      "1      18         2     5  2008     3     4     8.0     0       0        31\n",
      "2      18         3     7  2008     3     4     6.0     0       0        23\n",
      "3      18         4    11  2008     3     4     5.0     0       0        27\n",
      "4      18         5     3  2008     3     4     4.0     0       0        27\n",
      "Index(['raceId', 'driverId', 'grid', 'year', 'month', 'hour', 'points', 'wins',\n",
      "       'winner', 'AgeAtRace'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df42.head())\n",
    "print(df42.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "\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>points**2</th>\n",
       "      <th>position**2</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18</th>\n",
       "      <th>1</th>\n",
       "      <td>196.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>196.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1096</th>\n",
       "      <th>854</th>\n",
       "      <td>1369.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>1369.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>265225.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>64.0</td>\n",
       "      <td>100</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29929.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23716 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 points**2  position**2  year month  hour  winner\n",
       "raceId driverId                                                  \n",
       "18     1             196.0            1  2008     3     4       1\n",
       "       2              64.0            9  2008     3     4       0\n",
       "       3              81.0            4  2008     3     4       0\n",
       "       4              25.0           16  2008     3     4       0\n",
       "       5             196.0            1  2008     3     4       0\n",
       "...                    ...          ...   ...   ...   ...     ...\n",
       "1096   854          1369.0           64  2022    11    13       0\n",
       "       825          1369.0           64  2022    11    13       0\n",
       "       1          265225.0            9  2022    11    13       0\n",
       "       849            64.0          100  2022    11    13       0\n",
       "       4           29929.0           16  2022    11    13       0\n",
       "\n",
       "[23716 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF = pd.merge(df4[['raceId','driverId', 'points**2',\"position**2\"]],df42[['raceId','driverId','year','month','hour','winner']], on=['raceId', 'driverId'])\n",
    "DF = DF.set_index(['raceId','driverId'])\n",
    "DF = DF.dropna(axis=1)\n",
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['points**2', 'position**2', 'year', 'month', 'hour', 'winner'], dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_train = DF[DF['year']<=2020]\n",
    "DF_test = DF[DF['year']==2021]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_1 = DF_train.drop(columns=\"winner\")\n",
    "Y_train_1 = DF_train[['winner']]\n",
    "\n",
    "X_test_1 = DF_test.drop(columns=\"winner\")\n",
    "Y_test_1 = DF_test[['winner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 30, 'min_samples_leaf': 1, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "RFClassifier = RandomForestClassifier(random_state = 48)\n",
    "\n",
    "param_grid = {\n",
    "    'criterion' : [\"gini\"],\n",
    "    'max_depth' : [30], \n",
    "    'min_samples_leaf' : [1],\n",
    "    'n_estimators' : [50]\n",
    "\n",
    "}\n",
    "\n",
    "GRID = GridSearchCV(RFClassifier, param_grid,scoring=\"recall\")\n",
    "GRID.fit(X_train_1,Y_train_1.values.ravel())\n",
    "\n",
    "print(GRID.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'criterion': 'gini', 'max_depth': 30, 'min_samples_leaf': 1, 'n_estimators': 50} pour  environ 3,1secondes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2603305785123967"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GRID.score(X_train_1,Y_train_1.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fromPROBA_to_WinnerAndPodium(X_dateSet, Y_dataSet, Model):\n",
    "    y_pred = Y_dataSet\n",
    "    probabilite = Model.predict_proba(X_dateSet)\n",
    "    probabilite = pd.DataFrame(probabilite)\n",
    "    probabilite = probabilite.drop(columns=(0))\n",
    "    probabilite = np.array(probabilite)\n",
    "    y_pred.loc[:,'PREpositionOrder'] = probabilite\n",
    "    \n",
    "    Taille = y_pred.index\n",
    "    raceID = Taille.get_level_values('raceId').unique()\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",
    "        nb = len(driverID)\n",
    "        ranks = nb - 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\n",
    "\n",
    "    y_pred.loc[:,'winner'] = Top1\n",
    "    y_pred.loc[:,'positionOrder'] = PodiumOr0\n",
    "    fin = y_pred\n",
    "    return fin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values (22836) does not match length of index (440)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[35], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m probabilite \u001b[38;5;241m=\u001b[39m probabilite\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m))\n\u001b[0;32m      5\u001b[0m probabilite \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(probabilite)\n\u001b[1;32m----> 6\u001b[0m \u001b[43my_pred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPREpositionOrder\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m probabilite\n\u001b[0;32m      8\u001b[0m Taille \u001b[38;5;241m=\u001b[39m y_pred\u001b[38;5;241m.\u001b[39mindex\n\u001b[0;32m      9\u001b[0m raceID \u001b[38;5;241m=\u001b[39m Taille\u001b[38;5;241m.\u001b[39mget_level_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraceId\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39munique()\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:911\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    908\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[0;32m    910\u001b[0m iloc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc\n\u001b[1;32m--> 911\u001b[0m \u001b[43miloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexing.py:1857\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m   1854\u001b[0m \u001b[38;5;66;03m# add a new item with the dtype setup\u001b[39;00m\n\u001b[0;32m   1855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(indexer[\u001b[38;5;241m0\u001b[39m]):\n\u001b[0;32m   1856\u001b[0m     \u001b[38;5;66;03m# We are setting an entire column\u001b[39;00m\n\u001b[1;32m-> 1857\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m value\n\u001b[0;32m   1858\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m   1859\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_array_like(value):\n\u001b[0;32m   1860\u001b[0m     \u001b[38;5;66;03m# GH#42099\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:4299\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   4296\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m   4297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   4298\u001b[0m     \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[1;32m-> 4299\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:4512\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   4502\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_set_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   4503\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4504\u001b[0m \u001b[38;5;124;03m    Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[0;32m   4505\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4510\u001b[0m \u001b[38;5;124;03m    ensure homogeneity.\u001b[39;00m\n\u001b[0;32m   4511\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4512\u001b[0m     value, refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sanitize_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4514\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   4515\u001b[0m         key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m   4516\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   4517\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype)\n\u001b[0;32m   4518\u001b[0m     ):\n\u001b[0;32m   4519\u001b[0m         \u001b[38;5;66;03m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[0;32m   4520\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:5253\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m   5250\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _reindex_for_setitem(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n\u001b[0;32m   5252\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_list_like(value):\n\u001b[1;32m-> 5253\u001b[0m     \u001b[43mcom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequire_length_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5254\u001b[0m arr \u001b[38;5;241m=\u001b[39m sanitize_array(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   5255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5256\u001b[0m     \u001b[38;5;28misinstance\u001b[39m(value, Index)\n\u001b[0;32m   5257\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5260\u001b[0m     \u001b[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001b[39;00m\n\u001b[0;32m   5261\u001b[0m     \u001b[38;5;66;03m# this deprecation\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\luang\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\common.py:573\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m    569\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \u001b[38;5;124;03mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m    571\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    572\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index):\n\u001b[1;32m--> 573\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    574\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    575\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    576\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoes not match length of index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    577\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(index)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    578\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length of values (22836) does not match length of index (440)"
     ]
    }
   ],
   "source": [
    "y_pred = Y_test_1\n",
    "probabilite = GRID.predict_proba(X_train_1)\n",
    "probabilite = pd.DataFrame(probabilite)\n",
    "probabilite = probabilite.drop(columns=(0))\n",
    "probabilite = np.array(probabilite)\n",
    "y_pred.loc[:,'PREpositionOrder'] = probabilite\n",
    "\n",
    "Taille = y_pred.index\n",
    "raceID = Taille.get_level_values('raceId').unique()\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",
    "    nb = len(driverID)\n",
    "    ranks = nb - 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": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_4224\\1054258265.py:7: 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",
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_4224\\1054258265.py:35: 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_preddata1 = fromPROBA_to_WinnerAndPodium(DF.drop(columns=\"winner\"),DF[[\"winner\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>winner</th>\n",
       "      <th>PREpositionOrder</th>\n",
       "      <th>positionOrder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.547000</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1096</th>\n",
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      "text/plain": [
       "                 winner  PREpositionOrder  positionOrder\n",
       "raceId driverId                                         \n",
       "18     1              0          0.547000              2\n",
       "       2              0          0.020000              3\n",
       "       3              0          0.000000              0\n",
       "       4              0          0.000000              0\n",
       "       5              1          0.547000              1\n",
       "...                 ...               ...            ...\n",
       "1096   854            0          0.064333              0\n",
       "       825            0          0.064333              0\n",
       "       1              0          0.076667              0\n",
       "       849            0          0.000000              0\n",
       "       4              0          0.000000              0\n",
       "\n",
       "[23716 rows x 3 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(len(PodiumOr0),len(Top1))\n",
    "y_preddata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred.loc[:,'winner'] = Top1\n",
    "#y_pred.loc[:,'positionOrder'] = PodiumOr0\n",
    "#y_pred = y_pred.drop(columns=(\"PREpositionOrder\"))\n",
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred.to_csv('MetaModel2/METAMODEL_RF_model_Constu.csv')\n",
    "#y_preddata1.to_csv('MetaModel2/YALL_METAMODEL_RF_model_Constu.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "resultat de : 65.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#l'objectif est maintenant de prendre les probabilités, le winner et le classement de modèle en RF pour l'ajouter à un modèle Logistic\n",
    "#Le logistic est sur les caractéristiques driver et les résultats du premier model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>positionOrder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 winner  PREpositionOrder  positionOrder\n",
       "raceId driverId                                         \n",
       "18     1              0          0.547000              2\n",
       "       2              0          0.020000              3\n",
       "       3              0          0.000000              0\n",
       "       4              0          0.000000              0\n",
       "       5              1          0.547000              1\n",
       "...                 ...               ...            ...\n",
       "1096   854            0          0.064333              0\n",
       "       825            0          0.064333              0\n",
       "       1              0          0.076667              0\n",
       "       849            0          0.000000              0\n",
       "       4              0          0.000000              0\n",
       "\n",
       "[23716 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preddata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>23716 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "       raceId  driverId  EwinnerRF  EprobaRF  EpositionOrderRF\n",
       "0          18         1          0  0.547000                 2\n",
       "1          18         2          0  0.020000                 3\n",
       "2          18         3          0  0.000000                 0\n",
       "3          18         4          0  0.000000                 0\n",
       "4          18         5          1  0.547000                 1\n",
       "...       ...       ...        ...       ...               ...\n",
       "23711    1096       854          0  0.064333                 0\n",
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       "23713    1096         1          0  0.076667                 0\n",
       "23714    1096       849          0  0.000000                 0\n",
       "23715    1096         4          0  0.000000                 0\n",
       "\n",
       "[23716 rows x 5 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ey_predRF = pd.read_csv(r'MetaModel2/YALL_METAMODEL_RF_model_Constu.csv', sep=',').rename(columns={'winner' : 'EwinnerRF', 'positionOrder' : 'EpositionOrderRF', 'PREpositionOrder' : 'EprobaRF'})\n",
    "Ey_predRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['raceId', 'driverId', 'EwinnerRF', 'EprobaRF', 'EpositionOrderRF'], dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ey_predRF.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2022</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2022</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23750 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EwinnerRF  EpositionOrderRF  EprobaRF  grid  year month  \\\n",
       "raceId driverId                                                            \n",
       "18     1                 0                 2  0.547000     1  2008     3   \n",
       "       2                 0                 3  0.020000     5  2008     3   \n",
       "       3                 0                 0  0.000000     7  2008     3   \n",
       "       4                 0                 0  0.000000    11  2008     3   \n",
       "       5                 1                 1  0.547000     3  2008     3   \n",
       "...                    ...               ...       ...   ...   ...   ...   \n",
       "1096   854               0                 0  0.064333    12  2022    11   \n",
       "       825               0                 0  0.064333    16  2022    11   \n",
       "       1                 0                 0  0.076667     5  2022    11   \n",
       "       849               0                 0  0.000000    20  2022    11   \n",
       "       4                 0                 0  0.000000    10  2022    11   \n",
       "\n",
       "                 hour  points  wins  winner AgeAtRace  \n",
       "raceId driverId                                        \n",
       "18     1            4    10.0     0       1        23  \n",
       "       2            4     8.0     0       0        31  \n",
       "       3            4     6.0     0       0        23  \n",
       "       4            4     5.0     0       0        27  \n",
       "       5            4     4.0     0       0        27  \n",
       "...               ...     ...   ...     ...       ...  \n",
       "1096   854         13    12.0     0       0        23  \n",
       "       825         13    25.0     0       0        30  \n",
       "       1           13   240.0     0       0        37  \n",
       "       849         13     2.0     0       0        27  \n",
       "       4           13    81.0     0       0        41  \n",
       "\n",
       "[23750 rows x 11 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF2 = pd.merge(Ey_predRF[['raceId', 'driverId', 'EwinnerRF', 'EpositionOrderRF','EprobaRF']],\n",
    "               df42[['raceId', 'driverId', 'grid', 'year', 'month', 'hour', 'points', 'wins','winner', 'AgeAtRace']],\n",
    "               on = ['raceId', 'driverId'])\n",
    "\n",
    "DF2 = DF2.set_index(['raceId','driverId'])\n",
    "DF2 = DF2.dropna(axis=1)\n",
    "DF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF2_train = DF2[DF2['year']<=2020]\n",
    "DF2_test = DF2[DF2['year'] ==2021]\n",
    "\n",
    "X_train_2 = DF2_train[['EwinnerRF', 'EpositionOrderRF','EprobaRF','grid','year','month','hour','points','wins','AgeAtRace']]\n",
    "X_test_2 = DF2_test[['EwinnerRF', 'EpositionOrderRF','EprobaRF','grid','year','month','hour','points','wins','AgeAtRace']]\n",
    "\n",
    "Y_train_2 = DF2_train[['winner']]\n",
    "Y_test_2 = DF2_test[['winner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_iter': 4000, 'penalty': None, 'solver': 'newton-cholesky'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\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",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 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-8 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",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 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-8 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-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 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",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 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-8 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-8 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-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 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-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=4000, penalty=None, random_state=42,\n",
       "                   solver=&#x27;newton-cholesky&#x27;)</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=4000, penalty=None, random_state=42,\n",
       "                   solver=&#x27;newton-cholesky&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=4000, penalty=None, random_state=42,\n",
       "                   solver='newton-cholesky')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Logistic = LogisticRegression(random_state = 42)\n",
    "\n",
    "# Définition de la grille de paramètres et du GridSearchCV\n",
    "param_grid = {\n",
    "    'penalty': [None],\n",
    "    'max_iter': [4000],\n",
    "    'solver' :[\"newton-cholesky\"]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(Logistic, param_grid, scoring='recall')\n",
    "\n",
    "# Entraînement du modèle\n",
    "grid_search.fit(X_train_2, Y_train_2.values.ravel())\n",
    "print(grid_search.best_params_)\n",
    "\n",
    "grid_search = LogisticRegression(random_state = 42, max_iter= 4000, penalty = None, solver = 'newton-cholesky')\n",
    "grid_search.fit(X_train_2, Y_train_2.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "best : {'max_iter': 4000, 'penalty': None, 'solver': 'newton-cholesky'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833843463052033"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_train_2,Y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_4224\\4210917276.py:7: 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",
      "C:\\Users\\luang\\AppData\\Local\\Temp\\ipykernel_4224\\4210917276.py:35: 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": [
    "X_train_2\n",
    "Y_pred_METAMODEL2 = fromPROBA_to_WinnerAndPodium(X_test_2,Y_test_2, grid_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred_METAMODEL2\n",
    "Y_pred_METAMODEL2.to_csv('MetaModel2\\META_logistic_onRF_fromDriverConstruc.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#résultats : 65.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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