MetaModel_RF1_Logistic2.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Logistic2 = pd.read_csv(r'MetaModel\\Logistic_model2OnlyDriverMETAMODEL.csv',sep=\",\")\n",
    "RF1 = pd.read_csv(r'MetaModel\\model1OnlyConstructeurMETAMODEL.csv',sep=\",\")\n",
    "Logistic2_y = pd.read_csv(r'MetaModel\\y_train_Logistic.csv',sep=\",\")\n",
    "RF1_y = pd.read_csv(r'MetaModel\\y_train_RF.csv',sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   raceId  driverId  winner  positionOrder\n",
      "0      18         1       1              1\n",
      "1      18         2       0              3\n",
      "2      18         3       0              0\n",
      "3      18         4       0              0\n",
      "4      18         5       0              2\n",
      "   raceId  driverId  winner\n",
      "0      18         1       1\n",
      "1      18         2       0\n",
      "2      18         3       0\n",
      "3      18         4       0\n",
      "4      18         5       0\n",
      "   raceId  driverId  winner  positionOrder\n",
      "0      18         1       1              1\n",
      "1      18         2       0              0\n",
      "2      18         3       0              3\n",
      "3      18         4       0              0\n",
      "4      18         5       0              2\n",
      "   raceId  driverId  winner\n",
      "0      18         1       1\n",
      "1      18         2       0\n",
      "2      18         3       0\n",
      "3      18         4       0\n",
      "4      18         5       0\n"
     ]
    }
   ],
   "source": [
    "print(Logistic2.head())\n",
    "print(Logistic2_y.head())\n",
    "print(RF1.head())\n",
    "print(RF1_y.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_y = pd.merge(Logistic2_y,RF1_y, on = ['raceId','driverId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
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      "text/plain": [
       "                 winner_x\n",
       "raceId driverId          \n",
       "18     1                1\n",
       "       2                0\n",
       "       3                0\n",
       "       4                0\n",
       "       5                0\n",
       "...                   ...\n",
       "1047   841              0\n",
       "       849              0\n",
       "       825              0\n",
       "       850              0\n",
       "       815              0\n",
       "\n",
       "[22836 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_y = Data_y.drop(columns=('winner_y')).set_index(['raceId','driverId'])\n",
    "Data_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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      ],
      "text/plain": [
       "                 winner_x  positionOrder_x  winner_y  positionOrder_y\n",
       "raceId driverId                                                      \n",
       "18     1                1                1         1                1\n",
       "       2                0                3         0                0\n",
       "       3                0                0         0                3\n",
       "       4                0                0         0                0\n",
       "       5                0                2         0                2\n",
       "...                   ...              ...       ...              ...\n",
       "1047   841              0                0         0                0\n",
       "       849              0                0         0                0\n",
       "       825              0                0         0                0\n",
       "       850              0                0         0                0\n",
       "       815              0                0         0                0\n",
       "\n",
       "[22836 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_x = pd.merge(Logistic2, RF1, on=['raceId','driverId']).set_index(['raceId','driverId'])\n",
    "Data_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "LogisticMetaModel = LogisticRegression(random_state=48)\n",
    "param_grid = {\n",
    "    'penalty': [ \"l2\"],\n",
    "    'max_iter': [5000],\n",
    "    'solver' :[\"liblinear\"]\n",
    "}\n",
    "\n",
    "Grid = GridSearchCV(LogisticMetaModel,param_grid, scoring='recall')\n",
    "Grid.fit(Data_x, Data_y.values.ravel())\n",
    "print(Grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6549586776859504"
      ]
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "Grid.score(Data_x,Data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "<p>440 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                 winner_x  positionOrder_x  winner_y  positionOrder_y\n",
       "raceId driverId                                                      \n",
       "1052   1                1                1         0                2\n",
       "       830              0                3         0                3\n",
       "       822              0                0         1                1\n",
       "       846              0                0         0                0\n",
       "       815              0                2         0                0\n",
       "...                   ...              ...       ...              ...\n",
       "1073   849              0                0         0                0\n",
       "       841              0                0         0                0\n",
       "       847              0                0         0                0\n",
       "       8                0                0         0                0\n",
       "       853              0                0         0                0\n",
       "\n",
       "[440 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Logistic_test = pd.read_csv(r'DOC_csv\\Logistic_model2OnlyDriver.csv',sep=\",\")\n",
    "RF1_test = pd.read_csv(r'DOC_csv\\RF_model1OnlyConstructeur.csv',sep=\",\")\n",
    "Data_test = pd.merge(Logistic_test, RF1_test, on=['raceId','driverId']).set_index(['raceId','driverId'])\n",
    "Data_test"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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      "text/plain": [
       "                 winner_x\n",
       "raceId driverId          \n",
       "1052   1                1\n",
       "       830              0\n",
       "       822              0\n",
       "       846              0\n",
       "       815              0\n",
       "...                   ...\n",
       "1073   849              0\n",
       "       841              0\n",
       "       847              0\n",
       "       8                0\n",
       "       853              0\n",
       "\n",
       "[440 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = Data_test.drop(columns=['positionOrder_x','winner_y', 'positionOrder_y'])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>winner_x</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>1</td>\n",
       "      <td>0.018332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>0</td>\n",
       "      <td>0.016662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>0</td>\n",
       "      <td>0.624272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0</td>\n",
       "      <td>0.016960</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>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>0</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>0</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 winner_x  PREpositionOrder\n",
       "raceId driverId                            \n",
       "1052   1                1          0.018332\n",
       "       830              0          0.016662\n",
       "       822              0          0.624272\n",
       "       846              0          0.015515\n",
       "       815              0          0.016960\n",
       "...                   ...               ...\n",
       "1073   849              0          0.015515\n",
       "       841              0          0.015515\n",
       "       847              0          0.015515\n",
       "       8                0          0.015515\n",
       "       853              0          0.015515\n",
       "\n",
       "[440 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probabilite = Grid.predict_proba(Data_test)\n",
    "probabilite = pd.DataFrame(probabilite)\n",
    "probabilite = probabilite.drop(columns=(0))\n",
    "probabilite = np.array(probabilite)\n",
    "y_pred.loc[:,'PREpositionOrder'] = probabilite\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440 440\n"
     ]
    }
   ],
   "source": [
    "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",
    "print(len(PodiumOr0),len(Top1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "  <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",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1052</th>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>830</th>\n",
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       "      <th>822</th>\n",
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       "    </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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <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              0\n",
       "       822            1              1\n",
       "       846            0              0\n",
       "       815            0              3\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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = y_pred.drop(columns=['winner_x'])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred.to_csv(\"MetaModel/MetaModelResultat.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "resultat : 67.272"
   ]
  }
 ],
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