Odds_Football_leagues.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Projet 1 : Classification : Odds Football leagues.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQmgmPc1LzL2"
      },
      "source": [
        "Projet de Machine Learning\n",
        "\n",
        "Problème de classification multivariée en apprentissage supervisé.\n",
        "\n",
        "---\n",
        "\n",
        "Données : http://www.football-data.co.uk/germanym.php\n",
        "\n",
        "Ou déjà téléchargé dans mes dossiers : Data\\Projets de Machine Learning\\Odds football\\Datasets\n",
        "\n",
        "---\n",
        "\n",
        "L'objectif de ce projet est de **prédire l'issue d'un match de foot**, c'est-à-dire prédire quelle équipe va gagner ou s'il y aura un match nul.\n",
        "\n",
        "Je n'ai pas eu besoin de beaucoup nettoyer le jeu de données car il n'y avait pas beaucoup de donnée manquante. Par contre, j'ai fait beaucoup de feature engineering. J'ai par exemple créé les variables 'Saison' et 'Numéro de la journée'. Cela m'a permis ensuite de créer des variables relatives à l'état de forme de chaque équipe comme le nombre de buts marqués et pris ou nombre de points obtenus et cela pour les 10 derniers matchs.\n",
        "\n",
        "\n",
        "J'ai commencer par entrainer mes modèles sur les données de 2 saisons consécutives et à les tester sur la saison suivante. La performance des modèles tournaient autour des 50% de bonnes prédictions. Vu qu'il y avait dans les datasets les côtes avant-match pour les victoires et match nul, j'ai pu calculer si mes modèles étaient rentables. Très peu l'étaient. En plus de ça, ils n'étaient pas robustes. C'est-à-dire que lors qu'on décaler tout d'une saison (les 2 saisons pour l'apprentissage et la saison pour le test), la rentabilité n'était pas la même. \n",
        "\n",
        "Problème : beaucoup d'over-fitting car le résultat d'un match de foot reste imprévisible\n",
        "\n",
        "J'ai donc essayer d'entrainer mes modèles non pas sur les deux saisons précédents la saison à prédire mais sur les 10 saisons précédentes. Cela afin de réduite l'over-fitting. Les performances tournaient alors entre 40 à 60% selon les modèles et la saison à prédire. Certains modèles étaient rentables pour une saison mais pas pour la saison d'après. En résumé, je n'ai pas trouvé de modèle vraiment robuste.\n",
        "\n",
        "Les deux modèles qui n'étaient certes pas totalement robustes mais qui étaient souvent rentables étaient le **DecisionTreeClassifier** et le **KNeighborsClassifier**.\n",
        "\n",
        "Je les ai testé sur les saisons de Bundesliga, Premier League ainsi que de Ligue 1.  \n",
        "\n",
        "Bilan du projet : **DecisionTreeClassifier** et **KNeighborsClassifier** étaient parfois rentables mais jamais robustes. C'est-à-dire que leurs hyper-paramètres avec lesquels ils étaient rentables variés selon la saison et la ligue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t_bV3rGw6ZZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "53cd3da3-ac2d-4e0d-e282-7c54b04c21bc"
      },
      "source": [
        "# Import de bibliothèques\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cF4e9Uq6qD5w"
      },
      "source": [
        "Connexion avec le Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDJCpfHOjR7B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "8652ee46-6f8b-4246-f728-9f860b0aadb7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYCxQVMJqIH1"
      },
      "source": [
        "Gestion de l'affichage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vebiu1RX854e"
      },
      "source": [
        "pd.set_option('display.max_row', 100)\n",
        "pd.set_option('display.max_column', 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9Xg0ZaS9E68"
      },
      "source": [
        "**Importation des datasets**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1t4K_CZSxEmX"
      },
      "source": [
        "liste_annees = ['2008_2009','2009_2010','2010_2011','2011_2012',\n",
        "                '2012_2013','2013_2014','2014_2015','2015_2016','2016_2017','2017_2018','2018_2019','2019_2020']\n",
        "annee_debut = 2009 # 2008-2009\n",
        "annee_fin = 2020 # 2019-2020"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5voFHQBmh_Qi"
      },
      "source": [
        "def importation_donnees(liste_annees):\n",
        "  liste_data = []\n",
        "  for annee in liste_annees:\n",
        "    data = 'data' + annee\n",
        "    # data = pd.read_csv(f'/content/Bundesliga{annee}.csv')\n",
        "    data = pd.read_csv(f'/content/PL{annee}.csv')\n",
        "    liste_data.append(data)\n",
        "  return liste_data\n",
        "\n",
        "liste_data = importation_donnees(liste_annees)\n",
        "\n",
        "nombre_equipe = 20\n",
        "nombre_journee = liste_data[0].shape[0] / (nombre_equipe/2)\n",
        "liste_data[-1].tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-78SDk2iH6H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d93dc46-4890-44a2-ac0c-d5577f0626c5"
      },
      "source": [
        "def rapport_data(liste_data, liste_annees):\n",
        "  for data, annee in zip(liste_data, liste_annees):\n",
        "    print(f'Années {annee} :')\n",
        "    print(f'Dimension : {data.shape[0]} lignes et {data.shape[1]} colonnes')\n",
        "    print('Répartition target (en %) :')\n",
        "    print(round(data['FTR'].value_counts(normalize=True)*100,2))\n",
        "    print('\\n_______________________\\n')\n",
        "\n",
        "rapport_data(liste_data, liste_annees)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Années 2008_2009 :\n",
            "Dimension : 380 lignes et 71 colonnes\n",
            "Répartition target (en %) :\n",
            "H    45.53\n",
            "A    28.95\n",
            "D    25.53\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2009_2010 :\n",
            "Dimension : 380 lignes et 71 colonnes\n",
            "Répartition target (en %) :\n",
            "H    50.79\n",
            "D    25.26\n",
            "A    23.95\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2010_2011 :\n",
            "Dimension : 380 lignes et 71 colonnes\n",
            "Répartition target (en %) :\n",
            "H    47.11\n",
            "D    29.21\n",
            "A    23.68\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2011_2012 :\n",
            "Dimension : 380 lignes et 71 colonnes\n",
            "Répartition target (en %) :\n",
            "H    45.00\n",
            "A    30.53\n",
            "D    24.47\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2012_2013 :\n",
            "Dimension : 380 lignes et 74 colonnes\n",
            "Répartition target (en %) :\n",
            "H    43.68\n",
            "D    28.42\n",
            "A    27.89\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2013_2014 :\n",
            "Dimension : 380 lignes et 68 colonnes\n",
            "Répartition target (en %) :\n",
            "H    47.11\n",
            "A    32.37\n",
            "D    20.53\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2014_2015 :\n",
            "Dimension : 381 lignes et 68 colonnes\n",
            "Répartition target (en %) :\n",
            "H    45.26\n",
            "A    30.26\n",
            "D    24.47\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2015_2016 :\n",
            "Dimension : 380 lignes et 65 colonnes\n",
            "Répartition target (en %) :\n",
            "H    41.32\n",
            "A    30.53\n",
            "D    28.16\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2016_2017 :\n",
            "Dimension : 380 lignes et 65 colonnes\n",
            "Répartition target (en %) :\n",
            "H    49.21\n",
            "A    28.68\n",
            "D    22.11\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2017_2018 :\n",
            "Dimension : 380 lignes et 65 colonnes\n",
            "Répartition target (en %) :\n",
            "H    45.53\n",
            "A    28.42\n",
            "D    26.05\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2018_2019 :\n",
            "Dimension : 380 lignes et 62 colonnes\n",
            "Répartition target (en %) :\n",
            "H    47.63\n",
            "A    33.68\n",
            "D    18.68\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n",
            "Années 2019_2020 :\n",
            "Dimension : 288 lignes et 106 colonnes\n",
            "Répartition target (en %) :\n",
            "H    44.79\n",
            "A    30.21\n",
            "D    25.00\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0E4usDt0k28O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "23f390a7-8744-4cd3-a65d-38f8ed271f37"
      },
      "source": [
        "def nettoyage(liste_data):\n",
        "  new_liste = []\n",
        "  for data in liste_data:\n",
        "    data = data[['Date','HomeTeam','AwayTeam','FTHG','FTAG','FTR','B365H','B365D','B365A']]\n",
        "    new_liste.append(data)\n",
        "  return new_liste\n",
        "\n",
        "liste_data = nettoyage(liste_data)\n",
        "\n",
        "print(liste_data[0].dtypes)\n",
        "liste_data[0].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date         object\n",
            "HomeTeam     object\n",
            "AwayTeam     object\n",
            "FTHG          int64\n",
            "FTAG          int64\n",
            "FTR          object\n",
            "B365H       float64\n",
            "B365D       float64\n",
            "B365A       float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>HomeTeam</th>\n",
              "      <th>AwayTeam</th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>FTR</th>\n",
              "      <th>B365H</th>\n",
              "      <th>B365D</th>\n",
              "      <th>B365A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16/08/08</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>West Brom</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>H</td>\n",
              "      <td>1.20</td>\n",
              "      <td>6.5</td>\n",
              "      <td>15.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16/08/08</td>\n",
              "      <td>Bolton</td>\n",
              "      <td>Stoke</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>1.83</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16/08/08</td>\n",
              "      <td>Everton</td>\n",
              "      <td>Blackburn</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16/08/08</td>\n",
              "      <td>Hull</td>\n",
              "      <td>Fulham</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>2.60</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16/08/08</td>\n",
              "      <td>Middlesbrough</td>\n",
              "      <td>Tottenham</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>3.20</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Date       HomeTeam   AwayTeam  FTHG  FTAG FTR  B365H  B365D  B365A\n",
              "0  16/08/08        Arsenal  West Brom     1     0   H   1.20    6.5  15.00\n",
              "1  16/08/08         Bolton      Stoke     3     1   H   1.83    3.5   4.50\n",
              "2  16/08/08        Everton  Blackburn     2     3   A   2.00    3.3   4.00\n",
              "3  16/08/08           Hull     Fulham     2     1   H   2.60    3.2   2.80\n",
              "4  16/08/08  Middlesbrough  Tottenham     2     1   H   3.20    3.4   2.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HRu_szRljSd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "02f6c704-5fbf-4bb7-f109-6d546cdd774b"
      },
      "source": [
        "def recodage_date(liste_data, liste_annees):\n",
        "  new_liste = []\n",
        "  for data, annee in zip(liste_data, liste_annees):\n",
        "    if annee in ['2017_2018','2018_2019','2019_2020']:\n",
        "      data['Date'] = pd.to_datetime(data['Date'], format=\"%d/%m/%Y\")\n",
        "      new_liste.append(data)\n",
        "    else:\n",
        "      data['Date'] = pd.to_datetime(data['Date'], format=\"%d/%m/%y\")\n",
        "      new_liste.append(data)\n",
        "  return new_liste\n",
        "\n",
        "liste_data = recodage_date(liste_data, liste_annees)\n",
        "\n",
        "print(liste_data[0].dtypes)\n",
        "liste_data[0].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Date        datetime64[ns]\n",
            "HomeTeam            object\n",
            "AwayTeam            object\n",
            "FTHG                 int64\n",
            "FTAG                 int64\n",
            "FTR                 object\n",
            "B365H              float64\n",
            "B365D              float64\n",
            "B365A              float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>HomeTeam</th>\n",
              "      <th>AwayTeam</th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>FTR</th>\n",
              "      <th>B365H</th>\n",
              "      <th>B365D</th>\n",
              "      <th>B365A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-08-16</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>West Brom</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>H</td>\n",
              "      <td>1.20</td>\n",
              "      <td>6.5</td>\n",
              "      <td>15.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-08-16</td>\n",
              "      <td>Bolton</td>\n",
              "      <td>Stoke</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>1.83</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-08-16</td>\n",
              "      <td>Everton</td>\n",
              "      <td>Blackburn</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-08-16</td>\n",
              "      <td>Hull</td>\n",
              "      <td>Fulham</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>2.60</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-08-16</td>\n",
              "      <td>Middlesbrough</td>\n",
              "      <td>Tottenham</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>3.20</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date       HomeTeam   AwayTeam  FTHG  FTAG FTR  B365H  B365D  B365A\n",
              "0 2008-08-16        Arsenal  West Brom     1     0   H   1.20    6.5  15.00\n",
              "1 2008-08-16         Bolton      Stoke     3     1   H   1.83    3.5   4.50\n",
              "2 2008-08-16        Everton  Blackburn     2     3   A   2.00    3.3   4.00\n",
              "3 2008-08-16           Hull     Fulham     2     1   H   2.60    3.2   2.80\n",
              "4 2008-08-16  Middlesbrough  Tottenham     2     1   H   3.20    3.4   2.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2N12cu-m4-B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "6dd6c9b2-177b-408d-9929-8b6e610a2647"
      },
      "source": [
        "def fusion(liste_data):\n",
        "  return pd.concat(liste_data)\n",
        "\n",
        "data = fusion(liste_data)\n",
        "rapport_data([data], [f'de {liste_annees[0]} à {liste_annees[-1]}'])\n",
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Années de 2008_2009 à 2019_2020 :\n",
            "Dimension : 4469 lignes et 9 colonnes\n",
            "Répartition target (en %) :\n",
            "H    46.11\n",
            "A    29.07\n",
            "D    24.82\n",
            "Name: FTR, dtype: float64\n",
            "\n",
            "_______________________\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date        datetime64[ns]\n",
              "HomeTeam            object\n",
              "AwayTeam            object\n",
              "FTHG               float64\n",
              "FTAG               float64\n",
              "FTR                 object\n",
              "B365H              float64\n",
              "B365D              float64\n",
              "B365A              float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsJFXEpomfcy"
      },
      "source": [
        "**Ajout d'une colonne** 'Saison'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1S1cZvqulOh2"
      },
      "source": [
        "def transforme_saison(date):\n",
        "  if pd.Timestamp(2003,8,1) < date < pd.Timestamp(2004,7,1):\n",
        "    return 2004\n",
        "  elif pd.Timestamp(2004,8,1) < date < pd.Timestamp(2005,7,1):\n",
        "    return 2005\n",
        "  elif pd.Timestamp(2005,8,1) < date < pd.Timestamp(2006,7,1):\n",
        "    return 2006\n",
        "  elif pd.Timestamp(2006,8,1) < date < pd.Timestamp(2007,7,1):\n",
        "    return 2007\n",
        "  elif pd.Timestamp(2007,8,1) < date < pd.Timestamp(2008,7,1):\n",
        "    return 2008\n",
        "  elif pd.Timestamp(2008,8,1) < date < pd.Timestamp(2009,7,1):\n",
        "    return 2009\n",
        "  elif pd.Timestamp(2009,8,1) < date < pd.Timestamp(2010,7,1):\n",
        "    return 2010\n",
        "  elif pd.Timestamp(2010,8,1) < date < pd.Timestamp(2011,7,1):\n",
        "    return 2011\n",
        "  elif pd.Timestamp(2011,8,1) < date < pd.Timestamp(2012,7,1):\n",
        "    return 2012\n",
        "  elif pd.Timestamp(2012,8,1) < date < pd.Timestamp(2013,7,1):\n",
        "    return 2013\n",
        "  elif pd.Timestamp(2013,8,1) < date < pd.Timestamp(2014,7,1):\n",
        "    return 2014\n",
        "  elif pd.Timestamp(2014,8,1) < date < pd.Timestamp(2015,7,1):\n",
        "    return 2015\n",
        "  elif pd.Timestamp(2015,8,1) < date < pd.Timestamp(2016,7,1):\n",
        "    return 2016\n",
        "  elif pd.Timestamp(2016,8,1) < date < pd.Timestamp(2017,7,1):\n",
        "    return 2017\n",
        "  elif pd.Timestamp(2017,8,1) < date < pd.Timestamp(2018,7,1):\n",
        "    return 2018\n",
        "  elif pd.Timestamp(2018,8,1) < date < pd.Timestamp(2019,7,1):\n",
        "    return 2019\n",
        "  elif pd.Timestamp(2019,8,1) < date < pd.Timestamp(2020,7,31):\n",
        "    return 2020"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LP5lnHepT8r3"
      },
      "source": [
        "**Ajout** des colonnes 'Historique_FTHG' et 'Historique_FTAG' contenant le score de la dernière confrontation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwmz_NhDT6Eb"
      },
      "source": [
        "def historique_FTHG(saison,ht,at):\n",
        "  result = (data[(data['Saison'] == saison - 1) & \n",
        "                 (data['HomeTeam']==ht) & \n",
        "                 (data['AwayTeam']==at)]['FTHG']).to_string()[-1] \n",
        "  if (saison != annee_debut) and (result in ['0','1','2','3','4','5','6','7','8','9','10']):\n",
        "    return int(result)\n",
        "  else:\n",
        "    return -1\n",
        "\n",
        "def historique_FTAG(saison,ht,at):\n",
        "  result = (data[(data['Saison'] == saison - 1) & (data['HomeTeam']==ht) & (data['AwayTeam']==at)]['FTAG']).to_string()[-1] \n",
        "  if (saison != annee_debut) and (result in ['0','1','2','3','4','5','6','7','8','9','10']):\n",
        "    return int(result)\n",
        "  else:\n",
        "    return -1\n",
        "\n",
        "def historique_FTHG_inverse(saison,ht,at):\n",
        "  result = (data[(data['Saison'] == saison - 1) & \n",
        "                 (data['HomeTeam']==at) & \n",
        "                 (data['AwayTeam']==ht)]['FTAG']).to_string()[-1] \n",
        "  if (saison != annee_debut) and (result in ['0','1','2','3','4','5','6','7','8','9','10']):\n",
        "    return int(result)\n",
        "  else:\n",
        "    return -1\n",
        "\n",
        "def historique_FTAG_inverse(saison,ht,at):\n",
        "  result = (data[(data['Saison'] == saison - 1) &\n",
        "                 (data['HomeTeam']==at) &\n",
        "                 (data['AwayTeam']==ht)]['FTHG']).to_string()[-1] \n",
        "  if (saison != annee_debut) and (result in ['0','1','2','3','4','5','6','7','8','9','10']):\n",
        "    return int(result)\n",
        "  else:\n",
        "    return -1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQKCipssUOVS"
      },
      "source": [
        "**Ajout** d'une colonne 'Derniere_confrontation' contenant le résultat (H,D,A) de la dernière confrontation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsWTvNnPm3gg"
      },
      "source": [
        "def historique_result(saison,ht,at):\n",
        "  result = (data[(data['Saison'] == saison - 1) & (data['HomeTeam']==ht) & (data['AwayTeam']==at)]['FTR']).to_string()[-1] \n",
        "  if (saison != annee_debut) and (result in ['D','H','A']):\n",
        "    return result\n",
        "  else:\n",
        "    return np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xB_fMdRK1Byw"
      },
      "source": [
        "**Ajout** des colonnes 'H_pst_an_dernier' et 'A_pst_an_dernier' contenant le résultat (H,D,A) de la dernière confrontation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8ptejm8wrMo"
      },
      "source": [
        "def ancien_2H(saison,ht,dc):\n",
        "  if (saison != annee_debut) and (ht in data[data['Saison']==saison-1]['HomeTeam'].unique()):\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "def ancien_2A(saison,at,dc):\n",
        "  if (saison != annee_debut) and (at in data[data['Saison']==saison-1]['AwayTeam'].unique()):\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "#print(data[data['Saison']==2019]['H_pst_an_dernier'].value_counts()) # 2 équipes x 17 matchs = 34 modalité 0\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9-8RDpqIzUx"
      },
      "source": [
        "**Ajout** d'une colonne 'Numero_journee'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awJj4bPx187j"
      },
      "source": [
        "def num_journee(data):\n",
        "  data['Numero_journee'] = 0\n",
        "  for saison in [2016,2017,2018,2019]:\n",
        "    for team in data[data['Saison']==saison]['HomeTeam'].unique():\n",
        "      cpt = 1\n",
        "      for indice, value in data[(data['Saison']==saison) & ((data['HomeTeam']==team) | (data['AwayTeam']==team))].iterrows():\n",
        "        data.loc[indice,'Numero_journee'] = cpt\n",
        "        cpt += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iocty1zamI5O"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_buts_marques_10' et 'A_Nb_buts_marques_10' avec les nombre de buts marqués sur les 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_IigkVQTdfu"
      },
      "source": [
        "def nb_buts_marque_H10(saison,nj,ht):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTHG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTAG'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj)]['FTHG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj)]['FTAG'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['FTHG'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['FTAG'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4 \n",
        "\n",
        "def nb_buts_marque_A10(saison,nj,at):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTHG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTAG'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj)]['FTHG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj)]['FTAG'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['FTHG'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['FTAG'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "#data[((data['Saison']==2018) | (data['Saison']==2019)) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))][['Numero_journee','HomeTeam','AwayTeam','FTHG','FTAG','Saison','H_Nb_buts_marques_10','A_Nb_buts_marques_10','H_Nb_buts_pris_10','A_Nb_buts_pris_10']]\n",
        "#data.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZbWCUsMmiec"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_buts_pris_10' et 'A_Nb_buts_pris_10' avec les nombre de buts encaissés sur les 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rb9jLAE7e2ja"
      },
      "source": [
        "def nb_buts_encaisse_H10(saison,nj,ht):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTAG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTHG'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj)]['FTAG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj)]['FTHG'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['FTAG'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['FTHG'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "def nb_buts_encaisse_A10(saison,nj,at):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTAG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['FTHG'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj)]['FTAG'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj)]['FTHG'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['FTAG'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['FTHG'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "#data[(data['Saison']==2019) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))]\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWK_Xb03JAND"
      },
      "source": [
        "**Ajout** des colonnes 'Point_H' et 'Point_A' pour pouvoir ensuite calculer les nb de points et de victoire, défaite, nul sur les 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQBr74q8Gpgh"
      },
      "source": [
        "def point_H(ftr):\n",
        "  if ftr == 'D':\n",
        "    return 1\n",
        "  elif ftr == 'H':\n",
        "    return 3\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "def point_A(ftr):\n",
        "  if ftr == 'D':\n",
        "    return 1\n",
        "  elif ftr == 'A':\n",
        "    return 3\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "#data.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxEO5t95NCJ2"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_points_10' et 'A_Nb_points_10' contenant le nombre de points obtenus lors des 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsTZdPcrJWVs"
      },
      "source": [
        "def H_Nb_points_10(saison,nj,ht):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['Point_H'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['Point_A'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj)]['Point_H'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj)]['Point_A'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['Point_H'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==ht) & (data['Numero_journee']>=nombre_journee-n)]['Point_A'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "def A_Nb_points_10(saison,nj,at):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['Point_H'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10)]['Point_A'].sum()\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj)]['Point_H'].sum()\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj)]['Point_A'].sum()\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['Point_H'].sum() \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==at) & (data['Numero_journee']>=nombre_journee-n)]['Point_A'].sum() \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "#data[((data['Saison']==2018) | (data['Saison']==2019)) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))][['HomeTeam','AwayTeam','FTR','Numero_journee','Point_H','Point_A','H_Nb_points_10','A_Nb_points_10']]\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L58xPsrfPJwa"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_victoire_10' et 'A_Nb_victoire_10' contenant le nombre de victoires obtenues lors des 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnuwKj9XNO9S"
      },
      "source": [
        "def H_Nb_victoire_10(saison,nj,ht):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='H')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='A')].shape[0]\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['FTR']=='H')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['FTR']=='A')].shape[0]\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==ht) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='H')].shape[0]\n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==ht) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='A')].shape[0] \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "def A_Nb_victoire_10(saison,nj,at):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='H')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='A')].shape[0]\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['FTR']=='H')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['FTR']=='A')].shape[0]\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==at) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='H')].shape[0] \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==at) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='A')].shape[0] \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "#data[((data['Saison']==2018) | (data['Saison']==2019)) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))][['HomeTeam','AwayTeam','FTR','Numero_journee','H_Nb_victoire_10','A_Nb_victoire_10']]\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLTq8Rx5QkSj"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_defaite_10' et 'A_Nb_defaite_10' contenant le nombre de défaites obtenues lors des 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwHXSsvVQvaF"
      },
      "source": [
        "def H_Nb_defaite_10(saison,nj,ht):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='A')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='H')].shape[0]\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==ht) & (data['Numero_journee']<nj) & (data['FTR']=='A')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==ht) & (data['Numero_journee']<nj) & (data['FTR']=='H')].shape[0]\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==ht) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='A')].shape[0] \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==ht) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='H')].shape[0] \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "def A_Nb_defaite_10(saison,nj,at):\n",
        "  if nj > 10:\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='A')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='H')].shape[0]\n",
        "    return somme1 + somme2\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & (data['HomeTeam']==at) & (data['Numero_journee']<nj) & (data['FTR']=='A')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison) & (data['AwayTeam']==at) & (data['Numero_journee']<nj) & (data['FTR']=='H')].shape[0]\n",
        "    somme3 = data[(data['Saison']==saison-1) & (data['HomeTeam']==at) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='A')].shape[0] \n",
        "    somme4 = data[(data['Saison']==saison-1) & (data['AwayTeam']==at) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='H')].shape[0] \n",
        "    return somme1 + somme2 + somme3 + somme4\n",
        "\n",
        "#data[((data['Saison']==2018) | (data['Saison']==2019)) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))][['HomeTeam','AwayTeam','FTR','Numero_journee','H_Nb_defaite_10','A_Nb_defaite_10']]\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJ1biWVQRoxi"
      },
      "source": [
        "**Ajout** des colonnes 'H_Nb_nul_10' et 'A_Nb_nul_10' contenant le nombre de matchs nuls obtenus lors des 10 derniers matchs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnnhWcbyR0Uw"
      },
      "source": [
        "def Nb_nul_10(saison,nj,t):\n",
        "  if nj > 10:\n",
        "    return data[(data['Saison']==saison) & ((data['HomeTeam']==t) | (data['AwayTeam']==t)) & (data['Numero_journee']<nj) & (data['Numero_journee']>=nj-10) & (data['FTR']=='D')].shape[0]\n",
        "  else:\n",
        "    n = 10 - nj\n",
        "    somme1 = data[(data['Saison']==saison) & ((data['HomeTeam']==t) | (data['AwayTeam']==t)) & (data['Numero_journee']<nj) & (data['FTR']=='D')].shape[0]\n",
        "    somme2 = data[(data['Saison']==saison-1) & ((data['HomeTeam']==t) | (data['AwayTeam']==t)) & (data['Numero_journee']>=nombre_journee-n) & (data['FTR']=='D')].shape[0] \n",
        "    return somme1 + somme2\n",
        "\n",
        "#data[((data['Saison']==2018) | (data['Saison']==2019)) & ((data['HomeTeam']=='Bayern Munich') | (data['AwayTeam']=='Bayern Munich'))][['HomeTeam','AwayTeam','FTR','Numero_journee','H_Nb_nul_10','A_Nb_nul_10']]\n",
        "#data.tail(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1OODzAW4GI4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "4ea705e1-2163-4146-dd43-8fb44c38c0a3"
      },
      "source": [
        "def feature_engineering(data):\n",
        "  data.sort_values(by=['Date'], inplace=True)\n",
        "  data['Saison'] = data['Date'].map(transforme_saison) \n",
        "  data['Historique_FTHG'] = data.apply(lambda x : historique_FTHG(x['Saison'],x['HomeTeam'],x['AwayTeam']), axis=1)\n",
        "  data['Historique_FTAG'] = data.apply(lambda x : historique_FTAG(x['Saison'],x['HomeTeam'],x['AwayTeam']), axis=1)\n",
        "\n",
        "  data['Historique_FTHG_inverse'] = data.apply(lambda x : historique_FTHG_inverse(x['Saison'],x['HomeTeam'],x['AwayTeam']), axis=1)\n",
        "  data['Historique_FTAG_inverse'] = data.apply(lambda x : historique_FTAG_inverse(x['Saison'],x['HomeTeam'],x['AwayTeam']), axis=1)\n",
        "\n",
        "  data['Derniere_confrontation'] = data.apply(lambda x : historique_result(x['Saison'],x['HomeTeam'],x['AwayTeam']), axis=1)\n",
        "  data['H_pst_an_dernier'] = data.apply(lambda x : ancien_2H(x['Saison'],x['HomeTeam'],x['Derniere_confrontation']), axis=1)\n",
        "  data['A_pst_an_dernier'] = data.apply(lambda x : ancien_2A(x['Saison'],x['AwayTeam'],x['Derniere_confrontation']), axis=1)\n",
        "  num_journee(data)\n",
        "  data['H_Nb_buts_marques_10'] = data.apply(lambda x : nb_buts_marque_H10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_buts_marques_10'] = data.apply(lambda x : nb_buts_marque_A10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "  data['H_Nb_buts_pris_10'] = data.apply(lambda x : nb_buts_encaisse_H10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_buts_pris_10'] = data.apply(lambda x : nb_buts_encaisse_A10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "  data['Point_H'] = data['FTR'].map(point_H) \n",
        "  data['Point_A'] = data['FTR'].map(point_A) \n",
        "  data['H_Nb_points_10'] = data.apply(lambda x : H_Nb_points_10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_points_10'] = data.apply(lambda x : A_Nb_points_10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "  data['H_Nb_victoire_10'] = data.apply(lambda x : H_Nb_victoire_10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_victoire_10'] = data.apply(lambda x : A_Nb_victoire_10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "  data['H_Nb_defaite_10'] = data.apply(lambda x : H_Nb_defaite_10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_defaite_10'] = data.apply(lambda x : A_Nb_defaite_10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "  data['H_Nb_nul_10'] = data.apply(lambda x : Nb_nul_10(x['Saison'],x['Numero_journee'],x['HomeTeam']), axis=1)\n",
        "  data['A_Nb_nul_10'] = data.apply(lambda x : Nb_nul_10(x['Saison'],x['Numero_journee'],x['AwayTeam']), axis=1)\n",
        "\n",
        "\n",
        "  data = data[(data['Saison'] != annee_debut)]\n",
        "  data = data[['FTR','Saison','HomeTeam','AwayTeam','B365H','B365D','B365A','Historique_FTHG', 'Historique_FTAG',\n",
        "             'Historique_FTHG_inverse','Historique_FTAG_inverse','Derniere_confrontation','Numero_journee','H_Nb_buts_marques_10',\n",
        "             'A_Nb_buts_marques_10','H_Nb_buts_pris_10','A_Nb_buts_pris_10','H_pst_an_dernier','A_pst_an_dernier',\n",
        "             'H_Nb_points_10','A_Nb_points_10', 'H_Nb_victoire_10','A_Nb_victoire_10','H_Nb_defaite_10','A_Nb_defaite_10',\n",
        "             'H_Nb_nul_10','A_Nb_nul_10']]\n",
        "  return data\n",
        "\n",
        "data = feature_engineering(data)\n",
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>FTR</th>\n",
              "      <th>Saison</th>\n",
              "      <th>HomeTeam</th>\n",
              "      <th>AwayTeam</th>\n",
              "      <th>B365H</th>\n",
              "      <th>B365D</th>\n",
              "      <th>B365A</th>\n",
              "      <th>Historique_FTHG</th>\n",
              "      <th>Historique_FTAG</th>\n",
              "      <th>Historique_FTHG_inverse</th>\n",
              "      <th>Historique_FTAG_inverse</th>\n",
              "      <th>Derniere_confrontation</th>\n",
              "      <th>Numero_journee</th>\n",
              "      <th>H_Nb_buts_marques_10</th>\n",
              "      <th>A_Nb_buts_marques_10</th>\n",
              "      <th>H_Nb_buts_pris_10</th>\n",
              "      <th>A_Nb_buts_pris_10</th>\n",
              "      <th>H_pst_an_dernier</th>\n",
              "      <th>A_pst_an_dernier</th>\n",
              "      <th>H_Nb_points_10</th>\n",
              "      <th>A_Nb_points_10</th>\n",
              "      <th>H_Nb_victoire_10</th>\n",
              "      <th>A_Nb_victoire_10</th>\n",
              "      <th>H_Nb_defaite_10</th>\n",
              "      <th>A_Nb_defaite_10</th>\n",
              "      <th>H_Nb_nul_10</th>\n",
              "      <th>A_Nb_nul_10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>D</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>Burnley</td>\n",
              "      <td>Tottenham</td>\n",
              "      <td>2.70</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>H</td>\n",
              "      <td>29</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>H</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>Man United</td>\n",
              "      <td>Man City</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.33</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>29</td>\n",
              "      <td>16.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>H</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>Chelsea</td>\n",
              "      <td>Everton</td>\n",
              "      <td>1.80</td>\n",
              "      <td>3.90</td>\n",
              "      <td>4.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>D</td>\n",
              "      <td>29</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>H</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>Leicester</td>\n",
              "      <td>Aston Villa</td>\n",
              "      <td>1.36</td>\n",
              "      <td>5.25</td>\n",
              "      <td>8.00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     FTR  Saison    HomeTeam     AwayTeam  B365H  B365D  B365A  \\\n",
              "284    D  2020.0     Burnley    Tottenham   2.70   3.30   2.62   \n",
              "286    H  2020.0  Man United     Man City   5.00   4.33   1.60   \n",
              "285    H  2020.0     Chelsea      Everton   1.80   3.90   4.20   \n",
              "287    H  2020.0   Leicester  Aston Villa   1.36   5.25   8.00   \n",
              "380  NaN     NaN         NaN          NaN    NaN    NaN    NaN   \n",
              "\n",
              "     Historique_FTHG  Historique_FTAG  Historique_FTHG_inverse  \\\n",
              "284                0                0                        0   \n",
              "286                0                0                        0   \n",
              "285                0                0                        0   \n",
              "287               -1               -1                       -1   \n",
              "380               -1               -1                       -1   \n",
              "\n",
              "     Historique_FTAG_inverse Derniere_confrontation  Numero_journee  \\\n",
              "284                        0                      H              29   \n",
              "286                        0                      A              29   \n",
              "285                        0                      D              29   \n",
              "287                       -1                    NaN              28   \n",
              "380                       -1                    NaN               0   \n",
              "\n",
              "     H_Nb_buts_marques_10  A_Nb_buts_marques_10  H_Nb_buts_pris_10  \\\n",
              "284                  10.0                  12.0               10.0   \n",
              "286                  16.0                  18.0                8.0   \n",
              "285                  14.0                  17.0               14.0   \n",
              "287                  14.0                  11.0               16.0   \n",
              "380                   0.0                   0.0                0.0   \n",
              "\n",
              "     A_Nb_buts_pris_10  H_pst_an_dernier  A_pst_an_dernier  H_Nb_points_10  \\\n",
              "284               12.0                 1                 1              14   \n",
              "286                9.0                 1                 1              17   \n",
              "285               13.0                 1                 1              13   \n",
              "287               22.0                 1                 0              11   \n",
              "380                0.0                 0                 0               0   \n",
              "\n",
              "     A_Nb_points_10  H_Nb_victoire_10  A_Nb_victoire_10  H_Nb_defaite_10  \\\n",
              "284              11                 4                 3                4   \n",
              "286              19                 5                 6                3   \n",
              "285              18                 3                 5                3   \n",
              "287              10                 3                 3                5   \n",
              "380               0                 0                 0                0   \n",
              "\n",
              "     A_Nb_defaite_10  H_Nb_nul_10  A_Nb_nul_10  \n",
              "284                4            2            2  \n",
              "286                2            2            1  \n",
              "285                2            4            3  \n",
              "287                6            2            1  \n",
              "380                0            0            0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH3WiBYqM2Vh"
      },
      "source": [
        "data.drop(data.tail(1).index,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70pN50aFPuFh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "e0b9fbf0-3692-4099-bfa0-03a9078b945e"
      },
      "source": [
        "print(f'Dimension : {data.shape[0]} lignes et {data.shape[1]} colonnes \\n')\n",
        "print(f'Répartition du nombre de match par saison :')\n",
        "print(data['Saison'].value_counts(),'\\n')\n",
        "print(f'Types des variables :\\n{data.dtypes}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension : 4088 lignes et 27 colonnes \n",
            "\n",
            "Répartition du nombre de match par saison :\n",
            "2019.0    380\n",
            "2015.0    380\n",
            "2018.0    380\n",
            "2014.0    380\n",
            "2013.0    380\n",
            "2012.0    380\n",
            "2011.0    380\n",
            "2010.0    380\n",
            "2017.0    380\n",
            "2016.0    380\n",
            "2020.0    288\n",
            "Name: Saison, dtype: int64 \n",
            "\n",
            "Types des variables :\n",
            "FTR                         object\n",
            "Saison                     float64\n",
            "HomeTeam                    object\n",
            "AwayTeam                    object\n",
            "B365H                      float64\n",
            "B365D                      float64\n",
            "B365A                      float64\n",
            "Historique_FTHG              int64\n",
            "Historique_FTAG              int64\n",
            "Historique_FTHG_inverse      int64\n",
            "Historique_FTAG_inverse      int64\n",
            "Derniere_confrontation      object\n",
            "Numero_journee               int64\n",
            "H_Nb_buts_marques_10       float64\n",
            "A_Nb_buts_marques_10       float64\n",
            "H_Nb_buts_pris_10          float64\n",
            "A_Nb_buts_pris_10          float64\n",
            "H_pst_an_dernier             int64\n",
            "A_pst_an_dernier             int64\n",
            "H_Nb_points_10               int64\n",
            "A_Nb_points_10               int64\n",
            "H_Nb_victoire_10             int64\n",
            "A_Nb_victoire_10             int64\n",
            "H_Nb_defaite_10              int64\n",
            "A_Nb_defaite_10              int64\n",
            "H_Nb_nul_10                  int64\n",
            "A_Nb_nul_10                  int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMqHzI52_75d"
      },
      "source": [
        "y = data[['FTR','Saison']]\n",
        "X = data.drop(['FTR'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awOZsfwjITm4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "db5052d7-d825-42fa-dbed-dbea92f25f32"
      },
      "source": [
        "print(y.select_dtypes('object').columns)\n",
        "print(X.select_dtypes('object').columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['FTR'], dtype='object')\n",
            "Index(['HomeTeam', 'AwayTeam', 'Derniere_confrontation'], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9P5-n5ID_-lG"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjIfxz0TKyxp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "8e8a35f9-9854-4fca-e3a7-cb77f902d52c"
      },
      "source": [
        "label_encode = LabelEncoder()\n",
        "y['FTR'] = label_encode.fit_transform(y['FTR'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.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",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ne2bx78I8qtv"
      },
      "source": [
        "X = pd.get_dummies(X, prefix_sep=\"_\", columns=['Derniere_confrontation','HomeTeam','AwayTeam'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF6ismwe7vmx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "6a35ee86-b2a1-43de-92f6-ecc6b8a65615"
      },
      "source": [
        "print(X.shape)\n",
        "X.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4088, 100)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>Saison</th>\n",
              "      <th>B365H</th>\n",
              "      <th>B365D</th>\n",
              "      <th>B365A</th>\n",
              "      <th>Historique_FTHG</th>\n",
              "      <th>Historique_FTAG</th>\n",
              "      <th>Historique_FTHG_inverse</th>\n",
              "      <th>Historique_FTAG_inverse</th>\n",
              "      <th>Numero_journee</th>\n",
              "      <th>H_Nb_buts_marques_10</th>\n",
              "      <th>A_Nb_buts_marques_10</th>\n",
              "      <th>H_Nb_buts_pris_10</th>\n",
              "      <th>A_Nb_buts_pris_10</th>\n",
              "      <th>H_pst_an_dernier</th>\n",
              "      <th>A_pst_an_dernier</th>\n",
              "      <th>H_Nb_points_10</th>\n",
              "      <th>A_Nb_points_10</th>\n",
              "      <th>H_Nb_victoire_10</th>\n",
              "      <th>A_Nb_victoire_10</th>\n",
              "      <th>H_Nb_defaite_10</th>\n",
              "      <th>A_Nb_defaite_10</th>\n",
              "      <th>H_Nb_nul_10</th>\n",
              "      <th>A_Nb_nul_10</th>\n",
              "      <th>Derniere_confrontation_A</th>\n",
              "      <th>Derniere_confrontation_D</th>\n",
              "      <th>Derniere_confrontation_H</th>\n",
              "      <th>HomeTeam_Arsenal</th>\n",
              "      <th>HomeTeam_Aston Villa</th>\n",
              "      <th>HomeTeam_Birmingham</th>\n",
              "      <th>HomeTeam_Blackburn</th>\n",
              "      <th>HomeTeam_Blackpool</th>\n",
              "      <th>HomeTeam_Bolton</th>\n",
              "      <th>HomeTeam_Bournemouth</th>\n",
              "      <th>HomeTeam_Brighton</th>\n",
              "      <th>HomeTeam_Burnley</th>\n",
              "      <th>HomeTeam_Cardiff</th>\n",
              "      <th>HomeTeam_Chelsea</th>\n",
              "      <th>HomeTeam_Crystal Palace</th>\n",
              "      <th>HomeTeam_Everton</th>\n",
              "      <th>HomeTeam_Fulham</th>\n",
              "      <th>HomeTeam_Huddersfield</th>\n",
              "      <th>HomeTeam_Hull</th>\n",
              "      <th>HomeTeam_Leicester</th>\n",
              "      <th>HomeTeam_Liverpool</th>\n",
              "      <th>HomeTeam_Man City</th>\n",
              "      <th>HomeTeam_Man United</th>\n",
              "      <th>HomeTeam_Middlesbrough</th>\n",
              "      <th>HomeTeam_Newcastle</th>\n",
              "      <th>HomeTeam_Norwich</th>\n",
              "      <th>HomeTeam_Portsmouth</th>\n",
              "      <th>HomeTeam_QPR</th>\n",
              "      <th>HomeTeam_Reading</th>\n",
              "      <th>HomeTeam_Sheffield United</th>\n",
              "      <th>HomeTeam_Southampton</th>\n",
              "      <th>HomeTeam_Stoke</th>\n",
              "      <th>HomeTeam_Sunderland</th>\n",
              "      <th>HomeTeam_Swansea</th>\n",
              "      <th>HomeTeam_Tottenham</th>\n",
              "      <th>HomeTeam_Watford</th>\n",
              "      <th>HomeTeam_West Brom</th>\n",
              "      <th>HomeTeam_West Ham</th>\n",
              "      <th>HomeTeam_Wigan</th>\n",
              "      <th>HomeTeam_Wolves</th>\n",
              "      <th>AwayTeam_Arsenal</th>\n",
              "      <th>AwayTeam_Aston Villa</th>\n",
              "      <th>AwayTeam_Birmingham</th>\n",
              "      <th>AwayTeam_Blackburn</th>\n",
              "      <th>AwayTeam_Blackpool</th>\n",
              "      <th>AwayTeam_Bolton</th>\n",
              "      <th>AwayTeam_Bournemouth</th>\n",
              "      <th>AwayTeam_Brighton</th>\n",
              "      <th>AwayTeam_Burnley</th>\n",
              "      <th>AwayTeam_Cardiff</th>\n",
              "      <th>AwayTeam_Chelsea</th>\n",
              "      <th>AwayTeam_Crystal Palace</th>\n",
              "      <th>AwayTeam_Everton</th>\n",
              "      <th>AwayTeam_Fulham</th>\n",
              "      <th>AwayTeam_Huddersfield</th>\n",
              "      <th>AwayTeam_Hull</th>\n",
              "      <th>AwayTeam_Leicester</th>\n",
              "      <th>AwayTeam_Liverpool</th>\n",
              "      <th>AwayTeam_Man City</th>\n",
              "      <th>AwayTeam_Man United</th>\n",
              "      <th>AwayTeam_Middlesbrough</th>\n",
              "      <th>AwayTeam_Newcastle</th>\n",
              "      <th>AwayTeam_Norwich</th>\n",
              "      <th>AwayTeam_Portsmouth</th>\n",
              "      <th>AwayTeam_QPR</th>\n",
              "      <th>AwayTeam_Reading</th>\n",
              "      <th>AwayTeam_Sheffield United</th>\n",
              "      <th>AwayTeam_Southampton</th>\n",
              "      <th>AwayTeam_Stoke</th>\n",
              "      <th>AwayTeam_Sunderland</th>\n",
              "      <th>AwayTeam_Swansea</th>\n",
              "      <th>AwayTeam_Tottenham</th>\n",
              "      <th>AwayTeam_Watford</th>\n",
              "      <th>AwayTeam_West Brom</th>\n",
              "      <th>AwayTeam_West Ham</th>\n",
              "      <th>AwayTeam_Wigan</th>\n",
              "      <th>AwayTeam_Wolves</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>2.55</td>\n",
              "      <td>3.20</td>\n",
              "      <td>2.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>2.70</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.33</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>16.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>3.90</td>\n",
              "      <td>4.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>1.36</td>\n",
              "      <td>5.25</td>\n",
              "      <td>8.00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>28</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Saison  B365H  B365D  B365A  Historique_FTHG  Historique_FTAG  \\\n",
              "280  2020.0   2.55   3.20   2.90                0                0   \n",
              "284  2020.0   2.70   3.30   2.62                0                0   \n",
              "286  2020.0   5.00   4.33   1.60                0                0   \n",
              "285  2020.0   1.80   3.90   4.20                0                0   \n",
              "287  2020.0   1.36   5.25   8.00               -1               -1   \n",
              "\n",
              "     Historique_FTHG_inverse  Historique_FTAG_inverse  Numero_journee  \\\n",
              "280                        0                        0              28   \n",
              "284                        0                        0              29   \n",
              "286                        0                        0              29   \n",
              "285                        0                        0              29   \n",
              "287                       -1                       -1              28   \n",
              "\n",
              "     H_Nb_buts_marques_10  A_Nb_buts_marques_10  H_Nb_buts_pris_10  \\\n",
              "280                  10.0                  15.0               13.0   \n",
              "284                  10.0                  12.0               10.0   \n",
              "286                  16.0                  18.0                8.0   \n",
              "285                  14.0                  17.0               14.0   \n",
              "287                  14.0                  11.0               16.0   \n",
              "\n",
              "     A_Nb_buts_pris_10  H_pst_an_dernier  A_pst_an_dernier  H_Nb_points_10  \\\n",
              "280               11.0                 1                 1              13   \n",
              "284               12.0                 1                 1              14   \n",
              "286                9.0                 1                 1              17   \n",
              "285               13.0                 1                 1              13   \n",
              "287               22.0                 1                 0              11   \n",
              "\n",
              "     A_Nb_points_10  H_Nb_victoire_10  A_Nb_victoire_10  H_Nb_defaite_10  \\\n",
              "280              15                 3                 4                4   \n",
              "284              11                 4                 3                4   \n",
              "286              19                 5                 6                3   \n",
              "285              18                 3                 5                3   \n",
              "287              10                 3                 3                5   \n",
              "\n",
              "     A_Nb_defaite_10  H_Nb_nul_10  A_Nb_nul_10  Derniere_confrontation_A  \\\n",
              "280                3            4            3                         1   \n",
              "284                4            2            2                         0   \n",
              "286                2            2            1                         1   \n",
              "285                2            4            3                         0   \n",
              "287                6            2            1                         0   \n",
              "\n",
              "     Derniere_confrontation_D  Derniere_confrontation_H  HomeTeam_Arsenal  \\\n",
              "280                         0                         0                 0   \n",
              "284                         0                         1                 0   \n",
              "286                         0                         0                 0   \n",
              "285                         1                         0                 0   \n",
              "287                         0                         0                 0   \n",
              "\n",
              "     HomeTeam_Aston Villa  HomeTeam_Birmingham  HomeTeam_Blackburn  \\\n",
              "280                     0                    0                   0   \n",
              "284                     0                    0                   0   \n",
              "286                     0                    0                   0   \n",
              "285                     0                    0                   0   \n",
              "287                     0                    0                   0   \n",
              "\n",
              "     HomeTeam_Blackpool  HomeTeam_Bolton  HomeTeam_Bournemouth  \\\n",
              "280                   0                0                     0   \n",
              "284                   0                0                     0   \n",
              "286                   0                0                     0   \n",
              "285                   0                0                     0   \n",
              "287                   0                0                     0   \n",
              "\n",
              "     HomeTeam_Brighton  HomeTeam_Burnley  HomeTeam_Cardiff  HomeTeam_Chelsea  \\\n",
              "280                  0                 0                 0                 0   \n",
              "284                  0                 1                 0                 0   \n",
              "286                  0                 0                 0                 0   \n",
              "285                  0                 0                 0                 1   \n",
              "287                  0                 0                 0                 0   \n",
              "\n",
              "     HomeTeam_Crystal Palace  HomeTeam_Everton  HomeTeam_Fulham  \\\n",
              "280                        1                 0                0   \n",
              "284                        0                 0                0   \n",
              "286                        0                 0                0   \n",
              "285                        0                 0                0   \n",
              "287                        0                 0                0   \n",
              "\n",
              "     HomeTeam_Huddersfield  HomeTeam_Hull  HomeTeam_Leicester  \\\n",
              "280                      0              0                   0   \n",
              "284                      0              0                   0   \n",
              "286                      0              0                   0   \n",
              "285                      0              0                   0   \n",
              "287                      0              0                   1   \n",
              "\n",
              "     HomeTeam_Liverpool  HomeTeam_Man City  HomeTeam_Man United  \\\n",
              "280                   0                  0                    0   \n",
              "284                   0                  0                    0   \n",
              "286                   0                  0                    1   \n",
              "285                   0                  0                    0   \n",
              "287                   0                  0                    0   \n",
              "\n",
              "     HomeTeam_Middlesbrough  HomeTeam_Newcastle  HomeTeam_Norwich  \\\n",
              "280                       0                   0                 0   \n",
              "284                       0                   0                 0   \n",
              "286                       0                   0                 0   \n",
              "285                       0                   0                 0   \n",
              "287                       0                   0                 0   \n",
              "\n",
              "     HomeTeam_Portsmouth  HomeTeam_QPR  HomeTeam_Reading  \\\n",
              "280                    0             0                 0   \n",
              "284                    0             0                 0   \n",
              "286                    0             0                 0   \n",
              "285                    0             0                 0   \n",
              "287                    0             0                 0   \n",
              "\n",
              "     HomeTeam_Sheffield United  HomeTeam_Southampton  HomeTeam_Stoke  \\\n",
              "280                          0                     0               0   \n",
              "284                          0                     0               0   \n",
              "286                          0                     0               0   \n",
              "285                          0                     0               0   \n",
              "287                          0                     0               0   \n",
              "\n",
              "     HomeTeam_Sunderland  HomeTeam_Swansea  HomeTeam_Tottenham  \\\n",
              "280                    0                 0                   0   \n",
              "284                    0                 0                   0   \n",
              "286                    0                 0                   0   \n",
              "285                    0                 0                   0   \n",
              "287                    0                 0                   0   \n",
              "\n",
              "     HomeTeam_Watford  HomeTeam_West Brom  HomeTeam_West Ham  HomeTeam_Wigan  \\\n",
              "280                 0                   0                  0               0   \n",
              "284                 0                   0                  0               0   \n",
              "286                 0                   0                  0               0   \n",
              "285                 0                   0                  0               0   \n",
              "287                 0                   0                  0               0   \n",
              "\n",
              "     HomeTeam_Wolves  AwayTeam_Arsenal  AwayTeam_Aston Villa  \\\n",
              "280                0                 0                     0   \n",
              "284                0                 0                     0   \n",
              "286                0                 0                     0   \n",
              "285                0                 0                     0   \n",
              "287                0                 0                     1   \n",
              "\n",
              "     AwayTeam_Birmingham  AwayTeam_Blackburn  AwayTeam_Blackpool  \\\n",
              "280                    0                   0                   0   \n",
              "284                    0                   0                   0   \n",
              "286                    0                   0                   0   \n",
              "285                    0                   0                   0   \n",
              "287                    0                   0                   0   \n",
              "\n",
              "     AwayTeam_Bolton  AwayTeam_Bournemouth  AwayTeam_Brighton  \\\n",
              "280                0                     0                  0   \n",
              "284                0                     0                  0   \n",
              "286                0                     0                  0   \n",
              "285                0                     0                  0   \n",
              "287                0                     0                  0   \n",
              "\n",
              "     AwayTeam_Burnley  AwayTeam_Cardiff  AwayTeam_Chelsea  \\\n",
              "280                 0                 0                 0   \n",
              "284                 0                 0                 0   \n",
              "286                 0                 0                 0   \n",
              "285                 0                 0                 0   \n",
              "287                 0                 0                 0   \n",
              "\n",
              "     AwayTeam_Crystal Palace  AwayTeam_Everton  AwayTeam_Fulham  \\\n",
              "280                        0                 0                0   \n",
              "284                        0                 0                0   \n",
              "286                        0                 0                0   \n",
              "285                        0                 1                0   \n",
              "287                        0                 0                0   \n",
              "\n",
              "     AwayTeam_Huddersfield  AwayTeam_Hull  AwayTeam_Leicester  \\\n",
              "280                      0              0                   0   \n",
              "284                      0              0                   0   \n",
              "286                      0              0                   0   \n",
              "285                      0              0                   0   \n",
              "287                      0              0                   0   \n",
              "\n",
              "     AwayTeam_Liverpool  AwayTeam_Man City  AwayTeam_Man United  \\\n",
              "280                   0                  0                    0   \n",
              "284                   0                  0                    0   \n",
              "286                   0                  1                    0   \n",
              "285                   0                  0                    0   \n",
              "287                   0                  0                    0   \n",
              "\n",
              "     AwayTeam_Middlesbrough  AwayTeam_Newcastle  AwayTeam_Norwich  \\\n",
              "280                       0                   0                 0   \n",
              "284                       0                   0                 0   \n",
              "286                       0                   0                 0   \n",
              "285                       0                   0                 0   \n",
              "287                       0                   0                 0   \n",
              "\n",
              "     AwayTeam_Portsmouth  AwayTeam_QPR  AwayTeam_Reading  \\\n",
              "280                    0             0                 0   \n",
              "284                    0             0                 0   \n",
              "286                    0             0                 0   \n",
              "285                    0             0                 0   \n",
              "287                    0             0                 0   \n",
              "\n",
              "     AwayTeam_Sheffield United  AwayTeam_Southampton  AwayTeam_Stoke  \\\n",
              "280                          0                     0               0   \n",
              "284                          0                     0               0   \n",
              "286                          0                     0               0   \n",
              "285                          0                     0               0   \n",
              "287                          0                     0               0   \n",
              "\n",
              "     AwayTeam_Sunderland  AwayTeam_Swansea  AwayTeam_Tottenham  \\\n",
              "280                    0                 0                   0   \n",
              "284                    0                 0                   1   \n",
              "286                    0                 0                   0   \n",
              "285                    0                 0                   0   \n",
              "287                    0                 0                   0   \n",
              "\n",
              "     AwayTeam_Watford  AwayTeam_West Brom  AwayTeam_West Ham  AwayTeam_Wigan  \\\n",
              "280                 1                   0                  0               0   \n",
              "284                 0                   0                  0               0   \n",
              "286                 0                   0                  0               0   \n",
              "285                 0                   0                  0               0   \n",
              "287                 0                   0                  0               0   \n",
              "\n",
              "     AwayTeam_Wolves  \n",
              "280                0  \n",
              "284                0  \n",
              "286                0  \n",
              "285                0  \n",
              "287                0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zi9J-r56uN5"
      },
      "source": [
        "from sklearn.model_selection import train_test_split      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-TBwSSYN-1W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "93e2a0ef-aa16-4bbb-b542-da45dfda37d1"
      },
      "source": [
        "# X_train, X_test = train_test_split(X, test_size=0.2, random_state=0)\n",
        "# y_train, y_test = train_test_split(y, test_size=0.2, random_state=0)\n",
        "\n",
        "X_train = X[X['Saison']!=annee_fin]\n",
        "y_train = y[y['Saison']!=annee_fin]\n",
        "\n",
        "X_test = X[X['Saison']==annee_fin]\n",
        "y_test = y[y['Saison']==annee_fin]\n",
        "\n",
        "# X_train = X_train.drop(['Saison','Historique_FTAG_inverse','Numero_journee','H_pst_an_dernier','A_pst_an_dernier','H_Nb_defaite_10','A_Nb_defaite_10','H_Nb_nul_10','Derniere_confrontation_D','Derniere_confrontation_H'],axis=1)\n",
        "# X_test = X_test.drop(['Saison','Historique_FTAG_inverse','Numero_journee','H_pst_an_dernier','A_pst_an_dernier','H_Nb_defaite_10','A_Nb_defaite_10','H_Nb_nul_10','Derniere_confrontation_D','Derniere_confrontation_H'],axis=1)\n",
        "y_train = y_train.drop('Saison',axis=1)\n",
        "y_test = y_test.drop('Saison',axis=1)\n",
        "\n",
        "print(f'Dimension X_train : {X_train.shape}')\n",
        "print(f'Dimension X_test : {X_test.shape}')\n",
        "print(f'Dimension y_train : {y_train.shape}')\n",
        "print(f'Dimension y_test : {y_test.shape}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension X_train : (3800, 100)\n",
            "Dimension X_test : (288, 100)\n",
            "Dimension y_train : (3800, 1)\n",
            "Dimension y_test : (288, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvx7dJ5yU3SE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bb5e3fb-6d4c-48db-fedf-29292bf88351"
      },
      "source": [
        "print(y_train.dtypes, '\\n')\n",
        "print(X_train.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FTR    int64\n",
            "dtype: object \n",
            "\n",
            "Saison                       float64\n",
            "B365H                        float64\n",
            "B365D                        float64\n",
            "B365A                        float64\n",
            "Historique_FTHG                int64\n",
            "Historique_FTAG                int64\n",
            "Historique_FTHG_inverse        int64\n",
            "Historique_FTAG_inverse        int64\n",
            "Numero_journee                 int64\n",
            "H_Nb_buts_marques_10         float64\n",
            "A_Nb_buts_marques_10         float64\n",
            "H_Nb_buts_pris_10            float64\n",
            "A_Nb_buts_pris_10            float64\n",
            "H_pst_an_dernier               int64\n",
            "A_pst_an_dernier               int64\n",
            "H_Nb_points_10                 int64\n",
            "A_Nb_points_10                 int64\n",
            "H_Nb_victoire_10               int64\n",
            "A_Nb_victoire_10               int64\n",
            "H_Nb_defaite_10                int64\n",
            "A_Nb_defaite_10                int64\n",
            "H_Nb_nul_10                    int64\n",
            "A_Nb_nul_10                    int64\n",
            "Derniere_confrontation_A       uint8\n",
            "Derniere_confrontation_D       uint8\n",
            "Derniere_confrontation_H       uint8\n",
            "HomeTeam_Arsenal               uint8\n",
            "HomeTeam_Aston Villa           uint8\n",
            "HomeTeam_Birmingham            uint8\n",
            "HomeTeam_Blackburn             uint8\n",
            "HomeTeam_Blackpool             uint8\n",
            "HomeTeam_Bolton                uint8\n",
            "HomeTeam_Bournemouth           uint8\n",
            "HomeTeam_Brighton              uint8\n",
            "HomeTeam_Burnley               uint8\n",
            "HomeTeam_Cardiff               uint8\n",
            "HomeTeam_Chelsea               uint8\n",
            "HomeTeam_Crystal Palace        uint8\n",
            "HomeTeam_Everton               uint8\n",
            "HomeTeam_Fulham                uint8\n",
            "HomeTeam_Huddersfield          uint8\n",
            "HomeTeam_Hull                  uint8\n",
            "HomeTeam_Leicester             uint8\n",
            "HomeTeam_Liverpool             uint8\n",
            "HomeTeam_Man City              uint8\n",
            "HomeTeam_Man United            uint8\n",
            "HomeTeam_Middlesbrough         uint8\n",
            "HomeTeam_Newcastle             uint8\n",
            "HomeTeam_Norwich               uint8\n",
            "HomeTeam_Portsmouth            uint8\n",
            "HomeTeam_QPR                   uint8\n",
            "HomeTeam_Reading               uint8\n",
            "HomeTeam_Sheffield United      uint8\n",
            "HomeTeam_Southampton           uint8\n",
            "HomeTeam_Stoke                 uint8\n",
            "HomeTeam_Sunderland            uint8\n",
            "HomeTeam_Swansea               uint8\n",
            "HomeTeam_Tottenham             uint8\n",
            "HomeTeam_Watford               uint8\n",
            "HomeTeam_West Brom             uint8\n",
            "HomeTeam_West Ham              uint8\n",
            "HomeTeam_Wigan                 uint8\n",
            "HomeTeam_Wolves                uint8\n",
            "AwayTeam_Arsenal               uint8\n",
            "AwayTeam_Aston Villa           uint8\n",
            "AwayTeam_Birmingham            uint8\n",
            "AwayTeam_Blackburn             uint8\n",
            "AwayTeam_Blackpool             uint8\n",
            "AwayTeam_Bolton                uint8\n",
            "AwayTeam_Bournemouth           uint8\n",
            "AwayTeam_Brighton              uint8\n",
            "AwayTeam_Burnley               uint8\n",
            "AwayTeam_Cardiff               uint8\n",
            "AwayTeam_Chelsea               uint8\n",
            "AwayTeam_Crystal Palace        uint8\n",
            "AwayTeam_Everton               uint8\n",
            "AwayTeam_Fulham                uint8\n",
            "AwayTeam_Huddersfield          uint8\n",
            "AwayTeam_Hull                  uint8\n",
            "AwayTeam_Leicester             uint8\n",
            "AwayTeam_Liverpool             uint8\n",
            "AwayTeam_Man City              uint8\n",
            "AwayTeam_Man United            uint8\n",
            "AwayTeam_Middlesbrough         uint8\n",
            "AwayTeam_Newcastle             uint8\n",
            "AwayTeam_Norwich               uint8\n",
            "AwayTeam_Portsmouth            uint8\n",
            "AwayTeam_QPR                   uint8\n",
            "AwayTeam_Reading               uint8\n",
            "AwayTeam_Sheffield United      uint8\n",
            "AwayTeam_Southampton           uint8\n",
            "AwayTeam_Stoke                 uint8\n",
            "AwayTeam_Sunderland            uint8\n",
            "AwayTeam_Swansea               uint8\n",
            "AwayTeam_Tottenham             uint8\n",
            "AwayTeam_Watford               uint8\n",
            "AwayTeam_West Brom             uint8\n",
            "AwayTeam_West Ham              uint8\n",
            "AwayTeam_Wigan                 uint8\n",
            "AwayTeam_Wolves                uint8\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Oi5b8MdeS4Q"
      },
      "source": [
        "Importation des models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2cyuB9cLUBd"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import xgboost as xgb\n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import precision_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isRVnv1HeVC-"
      },
      "source": [
        "Création et test du model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xu9pbq-ALV7A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcb5d040-4e56-4fe9-db78-c1e4265f5968"
      },
      "source": [
        "# Création et test de plusieurs modèles\n",
        "\n",
        "model_1 = make_pipeline(StandardScaler(), AdaBoostClassifier(random_state=0))\n",
        "model_2_1 = DecisionTreeClassifier(random_state=0, criterion='gini', max_depth=3)\n",
        "model_2_2 = DecisionTreeClassifier(random_state=0)\n",
        "model_3 = RandomForestClassifier(random_state=0,criterion='entropy',max_depth=7,max_features='log2',n_estimators=600)\n",
        "model_4 = make_pipeline(StandardScaler(), SVC(random_state=0))\n",
        "model_5_1 = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=20))\n",
        "model_5_2 = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
        "model_6 = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))\n",
        "model_7 = make_pipeline(StandardScaler(), GradientBoostingClassifier(random_state=0))\n",
        "model_8 = BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=0),n_estimators=100)\n",
        "model_9 = make_pipeline(StandardScaler(), xgb.XGBClassifier(random_state=0))\n",
        "\n",
        "dict_models = {'AdaBoostClassifier':model_1,\n",
        "               'DecisionTreeClassifier_hp':model_2_1,\n",
        "               'DecisionTreeClassifier':model_2_2,\n",
        "               'RandomForestClassifier':model_3,\n",
        "               'SVC':model_4,\n",
        "               'KNeighborsClassifier_hp':model_5_1,\n",
        "               'KNeighborsClassifier':model_5_2,\n",
        "               'LogisticRegression':model_6,\n",
        "               'GradientBoostingClassifier':model_7,\n",
        "               'BaggingClassifier':model_8,\n",
        "               'XGBClassifier':model_9}\n",
        "\n",
        "liste = []\n",
        "for name, model in dict_models.items():\n",
        "  precision = cross_val_score(model,X_train, y_train, cv=4, scoring='precision_macro').mean()\n",
        "  liste.append([name, precision])\n",
        "\n",
        "for elt in liste:\n",
        "  print(f'{elt[0]} -> {elt[1]}\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_bagging.py:645: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AdaBoostClassifier -> 0.39198350711387536\n",
            "\n",
            "DecisionTreeClassifier_hp -> 0.35584117202257143\n",
            "\n",
            "DecisionTreeClassifier -> 0.39241183971060173\n",
            "\n",
            "RandomForestClassifier -> 0.3332006998661157\n",
            "\n",
            "SVC -> 0.4090146825255472\n",
            "\n",
            "KNeighborsClassifier_hp -> 0.408123074311196\n",
            "\n",
            "KNeighborsClassifier -> 0.40121613760547115\n",
            "\n",
            "LogisticRegression -> 0.4228756381335866\n",
            "\n",
            "GradientBoostingClassifier -> 0.4103533424099999\n",
            "\n",
            "BaggingClassifier -> 0.42332174088780994\n",
            "\n",
            "XGBClassifier -> 0.41704053176268574\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfZt9dW4Wt_x"
      },
      "source": [
        "# Le modèle de prédiction est-il rentable ? Faisons un bilan !\n",
        "\n",
        "def bilan(model):\n",
        "  model.fit(X_train,y_train)\n",
        "  prediction = pd.Series(model.predict(X_test))\n",
        "\n",
        "  cpt_total, cpt_home, cpt_draw, cpt_away, bonne_reponse,somme_cote_domicile,somme_cote_nul, somme_cote_away = 0,0,0,0,0,0,0,0\n",
        "\n",
        "  '''\n",
        "  H -> 2\n",
        "  D -> 1\n",
        "  A -> 0\n",
        "  '''\n",
        "\n",
        "  for pred, elt_test, B365H, B365D, B365A in zip(prediction,y_test.iterrows(),X_test['B365H'], X_test['B365D'], X_test['B365A']):\n",
        "    cpt_total += 1\n",
        "    if cpt_total < 11:\n",
        "      indice = 11\n",
        "    elif cpt_total < 101:\n",
        "      indice = 12\n",
        "    else :\n",
        "      indice = 13\n",
        "    if (pred == 2) and (str(elt_test)[indice] == '2'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_home += 1\n",
        "      somme_cote_domicile += B365H\n",
        "    elif (pred == 1) and (str(elt_test)[indice] == '1'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_draw += 1\n",
        "      somme_cote_nul += B365D\n",
        "    elif (pred == 0) and (str(elt_test)[indice] == '0'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_away += 1\n",
        "      somme_cote_away += B365A\n",
        "\n",
        "  print(f'Nombre de prédictions : {cpt_total}')\n",
        "  if cpt_home != 0:\n",
        "    print(f'Nombre de bonne prédiction sur la victoire de l\\'équipe à DOMICILE : {cpt_home} (cote moyenne de {somme_cote_domicile/cpt_home})')\n",
        "  if cpt_draw != 0:\n",
        "    print(f'Nombre de bonne prédiction sur un match NUL : {cpt_draw} (cote moyenne de  {somme_cote_nul/cpt_draw})')\n",
        "  if cpt_away != 0:\n",
        "    print(f'Nombre de bonne prédiction sur la victoire de l\\'équipe à l\\'EXTÉRIEUR  : {cpt_away} (cote moyenne de {somme_cote_away/cpt_away})')\n",
        "\n",
        "  y_pred = model.predict_proba(X_test)\n",
        "  precision = precision_score(y_test, y_pred, average='macro')\n",
        "  print(f'Nombre de bonne prédictions : {bonne_reponse} (soit {(bonne_reponse/cpt_total)*100} %)\\n\\n\\\n",
        "  Le score de précision du model est : {precision} %\\n\\n\\\n",
        "  Gain total des cotes gagnantes : {somme_cote_domicile+somme_cote_nul+somme_cote_away} (auquel on enlève {cpt_total})\\n\\\n",
        "  Rentable ? {somme_cote_domicile+somme_cote_nul+somme_cote_away - cpt_total}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHTVqXPyIr0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "105a16ce-7c84-4ca4-a84c-ee0cfdc68f35"
      },
      "source": [
        "for name, model in dict_models.items():\n",
        "  print(name)\n",
        "  print(bilan(model),'\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AdaBoostClassifier\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Nombre de prédictions : 269\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à DOMICILE : 84 (cote moyenne de 1.7569047619047626)\n",
            "Nombre de bonne prédiction sur un match NUL : 5 (cote moyenne de  3.6999999999999993)\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à l'EXTÉRIEUR  : 41 (cote moyenne de 1.7319512195121949)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-151-5129cf2f70fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdict_models\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbilan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-143-c06a171260d9>\u001b[0m in \u001b[0;36mbilan\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m   \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m   \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'macro'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Nombre de bonne prédictions : {bonne_reponse} (soit {(bonne_reponse/cpt_total)*100} %)\\n\\n  Le score de précision du model est : {precision} %\\n\\n  Gain total des cotes gagnantes : {somme_cote_domicile+somme_cote_nul+somme_cote_away} (auquel on enlève {cpt_total})\\n  Rentable ? {somme_cote_domicile+somme_cote_nul+somme_cote_away - cpt_total}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1670\u001b[0m                                                  \u001b[0mwarn_for\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'precision'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m                                                  \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1672\u001b[0;31m                                                  zero_division=zero_division)\n\u001b[0m\u001b[1;32m   1673\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1482\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >=0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m     labels = _check_set_wise_labels(y_true, y_pred, average, labels,\n\u001b[0;32m-> 1484\u001b[0;31m                                     pos_label)\n\u001b[0m\u001b[1;32m   1485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m     \u001b[0;31m# Calculate tp_sum, pred_sum, true_sum ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m   1299\u001b[0m                          str(average_options))\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1301\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1302\u001b[0m     \u001b[0mpresent_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maverage\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'binary'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 90\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wavx3lUoR5kK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e97a2ece-ef14-40bd-9609-dec642fd0a80"
      },
      "source": [
        "model_3.fit(X_train,y_train)\n",
        "y_pred = model.predict_proba(X_test)\n",
        "\n",
        "somme, cpt, nb_draw = 0,0,0\n",
        "for pred, arg, real, B365H, B365D, B365A in zip(y_pred, np.argmax(y_pred,axis=1), y_test.values.flatten(), X_test['B365H'], X_test['B365D'], X_test['B365A']):\n",
        "  print(pred, pred[arg], arg, real, B365H, B365D, B365A)\n",
        "  if pred[arg] > 0.5:\n",
        "    cpt += 1\n",
        "    if arg == real:      \n",
        "      if arg == 0:\n",
        "        somme += B365A\n",
        "      elif arg == 1:\n",
        "        somme += B365D\n",
        "      else:\n",
        "        somme += B365H\n",
        "\n",
        "print(f'Nombre de paris : {cpt}\\nGain : {somme-cpt} €')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0.30906863 0.3296447  0.36128667] 0.36128666737341775 2 1 1.14 8.0 15.0\n",
            "[0.32810022 0.32829034 0.34360944] 0.3436094393325936 2 2 1.2 7.0 13.0\n",
            "[0.33392632 0.33337245 0.33270123] 0.3339263215885395 0 2 2.25 3.25 3.4\n",
            "[0.32863175 0.33181131 0.33955694] 0.3395569350339163 2 2 1.25 6.0 12.0\n",
            "[0.33017745 0.3301637  0.33965885] 0.3396588522597451 2 0 1.75 3.75 4.75\n",
            "[0.33540653 0.33291999 0.33167348] 0.3354065306203185 0 2 1.95 3.5 4.0\n",
            "[0.33161359 0.33261766 0.33576875] 0.3357687464052196 2 1 2.0 3.5 3.75\n",
            "[0.33247347 0.33306513 0.3344614 ] 0.3344614042873603 2 2 2.25 3.6 3.0\n",
            "[0.3319941  0.33662239 0.33138351] 0.3366223892287403 1 0 5.0 3.8 1.7\n",
            "[0.33547    0.32978352 0.33474648] 0.3354699951674047 0 0 6.5 4.75 1.44\n",
            "[0.33467249 0.33019853 0.33512898] 0.33512897976753186 2 0 2.37 3.5 2.9\n",
            "[0.33375407 0.33170572 0.33454021] 0.33454020912862464 2 0 2.9 3.4 2.4\n",
            "[0.33517399 0.33221423 0.33261179] 0.33517398832610557 0 0 9.0 4.75 1.36\n",
            "[0.32873917 0.33170396 0.33955687] 0.3395568704419747 2 2 1.85 3.6 4.2\n",
            "[0.41038105 0.20007391 0.38954505] 0.41038104643026996 0 1 2.2 3.4 3.3\n",
            "[0.33774882 0.33256917 0.32968201] 0.3377488167088004 0 0 4.75 4.0 1.7\n",
            "[0.33010226 0.33233266 0.33756508] 0.3375650822486275 2 2 1.5 4.5 6.0\n",
            "[0.33324396 0.33472011 0.33203592] 0.3347201143976152 1 0 2.4 3.4 2.9\n",
            "[0.32805635 0.33328732 0.33865633] 0.3386563253248113 2 0 3.2 4.0 2.05\n",
            "[0.20614059 0.33347523 0.46038418] 0.4603841810932115 2 2 1.08 11.0 26.0\n",
            "[0.33568478 0.33527025 0.32904497] 0.3356847790449756 0 0 2.37 3.5 3.0\n",
            "[0.33555041 0.32958902 0.33486057] 0.33555041302073585 0 1 1.53 4.75 5.25\n",
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            "[0.32919073 0.33261626 0.33819302] 0.33819301613503194 2 2 1.72 3.8 4.75\n",
            "[0.33290887 0.33271118 0.33437995] 0.33437994603509386 2 0 1.9 3.6 4.0\n",
            "[0.33282984 0.33193938 0.33523078] 0.33523077979732824 2 1 2.37 3.3 3.0\n",
            "[0.33179159 0.33150507 0.33670334] 0.3367033431539302 2 2 2.2 3.6 3.1\n",
            "[0.33290887 0.33271118 0.33437995] 0.33437994603509386 2 2 1.83 4.0 4.0\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 0 3.4 3.8 2.05\n",
            "[0.33621876 0.31247124 0.35131   ] 0.351310002286905 2 2 1.2 7.0 13.0\n",
            "[0.33844455 0.33194575 0.3296097 ] 0.33844455281596786 0 0 7.0 5.75 1.36\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 1 2.62 3.2 2.7\n",
            "[0.33089092 0.3315125  0.33759659] 0.33759658524649966 2 1 1.44 5.0 6.5\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 0 3.0 4.0 2.1\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 2 2.3 3.4 3.1\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 1 2.1 3.5 3.4\n",
            "[0.33125224 0.32929668 0.33945107] 0.3394510744467403 2 2 1.65 4.2 5.0\n",
            "[0.33196745 0.33317542 0.33485713] 0.33485712954747326 2 1 2.2 3.6 3.2\n",
            "[0.33675987 0.33197534 0.33126479] 0.3367598701233262 0 1 3.8 3.8 1.9\n",
            "[0.33129884 0.33305957 0.33564159] 0.33564159298498536 2 0 3.1 3.9 2.15\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 1 1.9 3.75 3.8\n",
            "[0.2025153  0.33844777 0.45903693] 0.4590369319765541 2 2 1.11 10.0 21.0\n",
            "[0.32806653 0.33225394 0.33967952] 0.33967952356050296 2 2 1.72 4.0 4.2\n",
            "[0.33392632 0.33337245 0.33270123] 0.3339263215885395 0 0 3.4 3.5 2.1\n",
            "[0.32896657 0.33303212 0.33800131] 0.3380013061604232 2 2 1.5 4.33 6.0\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 1 2.1 3.6 3.25\n",
            "[0.33274007 0.33297049 0.33428944] 0.33428943638965447 2 0 2.1 3.6 3.2\n",
            "[0.32811403 0.33248427 0.3394017 ] 0.3394017033849036 2 1 1.3 5.5 9.0\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 0 2.9 3.6 2.25\n",
            "[0.33179159 0.33150507 0.33670334] 0.3367033431539302 2 1 1.85 3.9 3.9\n",
            "[0.40891658 0.39506726 0.19601616] 0.4089165832711913 0 0 15.0 7.0 1.18\n",
            "[0.40940659 0.19648886 0.39410454] 0.4094065943126878 0 0 4.75 4.0 1.66\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 2 2.3 3.4 3.0\n",
            "[0.33675987 0.33197534 0.33126479] 0.3367598701233262 0 0 4.2 4.0 1.72\n",
            "[0.32511709 0.33038921 0.3444937 ] 0.34449369734273905 2 2 1.16 7.5 13.0\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 1 3.1 3.8 2.1\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 0 2.15 3.75 3.0\n",
            "[0.33277911 0.33062986 0.33659102] 0.3365910239995895 2 0 2.3 3.75 2.75\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 0 2.0 3.5 3.6\n",
            "[0.33991226 0.3303917  0.32969605] 0.33991225624552124 0 0 6.5 4.5 1.44\n",
            "[0.32873917 0.33170396 0.33955687] 0.3395568704419747 2 1 1.72 4.0 4.2\n",
            "[0.33755362 0.3303711  0.33207528] 0.33755362387084586 0 0 3.6 4.33 1.85\n",
            "[0.33274007 0.33297049 0.33428944] 0.33428943638965447 2 1 1.95 3.75 3.5\n",
            "[0.33032645 0.33365914 0.33601441] 0.33601441394491177 2 0 1.72 4.2 4.33\n",
            "[0.33648085 0.33273757 0.33078158] 0.336480852939934 0 1 4.33 4.0 1.72\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 1 2.2 3.5 3.1\n",
            "[0.33292039 0.33397137 0.33310824] 0.33397136655506815 1 2 2.6 3.4 2.7\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 2 2.3 3.6 2.8\n",
            "[0.33220948 0.3307626  0.33702792] 0.33702791747041744 2 1 1.3 5.5 6.0\n",
            "[0.33224232 0.3324118  0.33534588] 0.3353458841067509 2 1 1.9 3.75 3.8\n",
            "[0.3384309  0.33120142 0.33036768] 0.3384309010433087 0 0 4.75 4.2 1.66\n",
            "[0.33246398 0.3337346  0.33380142] 0.3338014166399406 2 2 1.9 3.75 3.75\n",
            "[0.33242426 0.33341288 0.33416286] 0.3341628566209615 2 0 2.75 3.75 2.37\n",
            "[0.33405823 0.33266266 0.33327912] 0.33405822507713473 0 0 2.5 3.4 2.8\n",
            "[0.33229082 0.33206439 0.33564479] 0.3356447925280237 2 0 1.95 3.75 3.75\n",
            "[0.17337868 0.40959711 0.41702421] 0.4170242118404934 2 2 1.08 13.0 19.0\n",
            "[0.32761372 0.33133981 0.34104646] 0.341046463286464 2 2 1.5 4.5 6.0\n",
            "[0.33872461 0.33118477 0.33009062] 0.338724612241091 0 0 8.5 5.5 1.33\n",
            "[0.33872461 0.33118477 0.33009062] 0.338724612241091 0 0 5.5 4.6 1.53\n",
            "[0.33255495 0.33270239 0.33474266] 0.33474266283309106 2 0 2.75 3.4 2.55\n",
            "[0.33675987 0.33197534 0.33126479] 0.3367598701233262 0 2 4.5 4.0 1.72\n",
            "[0.33499953 0.33055175 0.33444872] 0.3349995333043536 0 0 6.0 4.75 1.44\n",
            "[0.29960202 0.34675534 0.35364264] 0.3536426403607425 2 1 1.14 8.0 15.0\n",
            "[0.33246398 0.3337346  0.33380142] 0.3338014166399406 2 0 2.0 3.75 3.5\n",
            "[0.3326999  0.33264911 0.33465099] 0.33465099250034486 2 1 2.75 3.6 2.4\n",
            "[0.32924996 0.33353071 0.33721933] 0.3372193328392634 2 2 1.33 5.5 7.5\n",
            "[0.33527946 0.33245955 0.33226099] 0.33527946395971914 0 1 2.37 3.25 3.0\n",
            "[0.33675987 0.33197534 0.33126479] 0.3367598701233262 0 0 3.6 3.5 2.05\n",
            "Nombre de paris : 1\n",
            "Gain : -1 €\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmpxscXETjvz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "b6a38bf1-c86c-43bc-abf1-e98c1536e5e1"
      },
      "source": [
        "# Méthode d'ensemble\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "model_ensemble = VotingClassifier([#('AdaBoostClassifier',model_1), \n",
        "                                  ('DecisionTreeClassifier',model_2_2),\n",
        "                                  #('RandomForestClassifier',model_3), \n",
        "                                  #('SVC',model_4), \n",
        "                                  ('KNeighborsClassifier',model_5_2), \n",
        "                                  #('LogisticRegression',model_6), #\n",
        "                                  #('GradientBoostingClassifier',model_7),\n",
        "                                  #('BaggingClassifier', model_8)\n",
        "                                  ], voting='hard')\n",
        "\n",
        "precision = cross_val_score(model_ensemble,X_train, y_train, cv=5, scoring='precision_macro').mean()\n",
        "print(precision)\n",
        "\n",
        "# bilan(model_ensemble)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.4194205912254002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXveyISvOhkz"
      },
      "source": [
        "def gain_model(model):\n",
        "  model.fit(X_train,y_train)\n",
        "  prediction = pd.Series(model.predict(X_test))\n",
        "\n",
        "  cpt_total, somme_cote_domicile,somme_cote_nul, somme_cote_away = 0,0,0,0\n",
        "\n",
        "  for pred, elt_test, B365H, B365D, B365A in zip(prediction,y_test.iterrows(),X_test['B365H'], X_test['B365D'], X_test['B365A']):\n",
        "    cpt_total += 1\n",
        "    if cpt_total < 11:\n",
        "      indice = 11\n",
        "    elif cpt_total < 101:\n",
        "      indice = 12\n",
        "    else :\n",
        "      indice = 13\n",
        "    if (pred == 2) and (str(elt_test)[indice] == '2'):\n",
        "      somme_cote_domicile += B365H\n",
        "    elif (pred == 1) and (str(elt_test)[indice] == '1'):\n",
        "      somme_cote_nul += B365D\n",
        "    elif (pred == 0) and (str(elt_test)[indice] == '0'):\n",
        "      somme_cote_away += B365A\n",
        "\n",
        "  return somme_cote_domicile+somme_cote_nul+somme_cote_away - cpt_total"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gapg76CLC15X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94798071-6d85-431f-f853-c2f4416450bb"
      },
      "source": [
        "liste = []\n",
        "for k in range(1,200):\n",
        "  model = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=k))\n",
        "  if gain_model(model) > 0:\n",
        "    liste.append((k,gain_model(model)))\n",
        "\n",
        "for elt in liste:\n",
        "  print(elt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(6, 8.529999999999973)\n",
            "(8, 4.009999999999991)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PFBXQ1RUGnv"
      },
      "source": [
        "**Performances** de GradientBoostingClassifier (model_7) avec encodage de 'HomeTeam','AwayTeam','Derniere_confrontation' :\n",
        "- Train 2017-2019 et Test 2019-2020 : 49.63 % pour 14.44€ de gain\n",
        "\n",
        "- Dernière version : Train 2017-2019 et Test 2019-2020 : 50.37 % pour 26.22 € de gain\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_YaKcSgOGwM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "0da85bfb-b3cc-468a-a096-40954591f06e"
      },
      "source": [
        "liste_gain = [gain_model(model_5) for _ in range(10)] \n",
        "print(f'{liste_gain}\\n\\\n",
        "Gain minimum : {min(liste_gain)} €\\n\\\n",
        "Gain maximum : {max(liste_gain)} €\\n\\\n",
        "Gain moyen : {sum(liste_gain)/10} €')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-453-20de3b79b1f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mliste_gain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{liste_gain}\\nGain minimum : {min(liste_gain)} €\\nGain maximum : {max(liste_gain)} €\\nGain moyen : {sum(liste_gain)/10} €'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-453-20de3b79b1f4>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mliste_gain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{liste_gain}\\nGain minimum : {min(liste_gain)} €\\nGain maximum : {max(liste_gain)} €\\nGain moyen : {sum(liste_gain)/10} €'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-452-bdf4265576d3>\u001b[0m in \u001b[0;36mgain_model\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mcpt_total\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msomme_cote_domicile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msomme_cote_nul\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msomme_cote_away\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    352\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n\u001b[1;32m    353\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'passthrough'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1124\u001b[0m         \"\"\"\n\u001b[1;32m   1125\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKDTree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBallTree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1126\u001b[0;31m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    753\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    756\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFzyo3ZEuC4w"
      },
      "source": [
        "Les deux gagnants : VotingClassifier et DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDTtN4259WvE"
      },
      "source": [
        "**Performances** de DecisionTreeClassifier (model_2) avec encodage de 'HomeTeam','AwayTeam','Derniere_confrontation' :\n",
        "\n",
        "- Dernière version : Train 2017-2019 et Test 2019-2020 : 41.48 % pour -12.25 € de perte\n",
        "\n",
        "- Train 2017-2019 et Test 2019-2020 : 39.63 % pour -33.38€ de perte\n",
        "- Train 2016-2018 et Test 2018-2019 : 44.1 % pour 15.5€ de gain\n",
        "- Train 2015-2017 et Test 2017-2018 : 39.54 % pour -2.34€ de perte\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACYsXRVquCAh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "557bc881-6099-4ced-c86a-3ec9f1952c3c"
      },
      "source": [
        "liste_gain = [gain_model(model_2) for _ in range(20)] \n",
        "print(f'{liste_gain}\\n\\\n",
        "Gain minimum : {min(liste_gain)} €\\n\\\n",
        "Gain maximum : {max(liste_gain)} €\\n\\\n",
        "Gain moyen : {sum(liste_gain)/20} €')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018, -23.730000000000018]\n",
            "Gain minimum : -23.730000000000018 €\n",
            "Gain maximum : -23.730000000000018 €\n",
            "Gain moyen : -23.730000000000018 €\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTkFsIgj89K4"
      },
      "source": [
        "**Performances** de VotingClassifier (sans le model_8) avec encodage de 'HomeTeam','AwayTeam','Derniere_confrontation' :\n",
        "\n",
        "- Dernière version : Train 2017-2019 et Test 2019-2020 : 49.63 % pour -3.37 € de perte\n",
        "\n",
        "- Train 2017-2019 et Test 2019-2020 : 50 % pour -0.86€ de perte\n",
        "- Train 2016-2018 et Test 2018-2019 : 51.63 % pour 15.5€ de gain\n",
        "- Train 2015-2017 et Test 2017-2018 : 49 % pour 8.92€ de gain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHsVvvZAQdWD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "9a0ef814-a266-416a-e47d-57c4f3b3dd22"
      },
      "source": [
        "# model_1 : {'learning_rate': 0.2, 'n_estimators': 30} ou {'learning_rate': 0.1, 'n_estimators': 10}\n",
        "# model_4 : {'svc__C': 10, 'svc__gamma': 0.001}\n",
        "# model_2 : {'criterion': 'entropy', 'max_depth': 3}\n",
        "\n",
        "model_ensemble = VotingClassifier([('AdaBoostClassifier',model_1), \n",
        "                                  ('DecisionTreeClassifier',model_2),\n",
        "                                  ('RandomForestClassifier',model_3), \n",
        "                                  ('SVC',model_4), \n",
        "                                  ('KNeighborsClassifier',model_5), \n",
        "                                  ('LogisticRegression',model_6), #\n",
        "                                  ('GradientBoostingClassifier',model_7),\n",
        "                                  ('BaggingClassifier', model_8)\n",
        "                                  ], voting='hard')\n",
        "\n",
        "liste_gain = [gain_model(model_ensemble) for _ in range(10)] \n",
        "print(f'{liste_gain}\\n\\\n",
        "Gain minimum : {min(liste_gain)} €\\n\\\n",
        "Gain maximum : {max(liste_gain)} €\\n\\\n",
        "Gain moyen : {sum(liste_gain)/10} €')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[-40.02000000000004, -45.97000000000003, -42.41999999999999, -42.05000000000001, -39.670000000000016, -32.31999999999999, -48.52000000000004, -33.370000000000005, -39.170000000000044, -39.420000000000016]\n",
            "Gain minimum : -48.52000000000004 €\n",
            "Gain maximum : -32.31999999999999 €\n",
            "Gain moyen : -40.29300000000002 €\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY48LAgAGQHQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "78b30ef9-39ee-4f36-f043-ae91b3eea6b4"
      },
      "source": [
        "def courbe_apprentissage(model):    \n",
        "    N, train_score, val_score = learning_curve(model, X_train, y_train,\n",
        "                                              cv=4, train_sizes=np.linspace(0.1, 1, 10))\n",
        "    \n",
        "    \n",
        "    plt.figure(figsize=(12, 8))\n",
        "    plt.plot(N, train_score.mean(axis=1), label='train score')\n",
        "    plt.plot(N, val_score.mean(axis=1), label='validation score')\n",
        "    plt.legend()\n",
        "    \n",
        "courbe_apprentissage(model_5) # model_1, model_4, model_5, model_6, model_7 sont les modèles pour lesquels on a le moins d'overfitting"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py:354: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self._final_estimator.fit(Xt, y, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gP4PvYDJKa53"
      },
      "source": [
        "**Optimisation** des hyper-paramètres"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SOiu_mDJozA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2196f085-b01c-40ef-cf32-f59dbc8fa7d8"
      },
      "source": [
        "# Optimisation du modèle\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# hyper_params = {'AdaBoostClassifier__adaboostclassifier__n_estimators': range(10,60,10),\n",
        "#                 'AdaBoostClassifier__adaboostclassifier__learning_rate': np.linspace(0.1,0.5,6),\n",
        "#                 'SVC__svc__gamma':[1e-3,1e-4],\n",
        "#                 'SVC__svc__C': [1,10,100]}\n",
        "\n",
        "# hyper_params = {'svc__gamma':[1e-3,1e-4],\n",
        "#                 'svc__C': [1,10,100]}\n",
        "\n",
        "# hyper_params = {'criterion':['gini','entropy'],\n",
        "#                 'max_depth': np.arange(3, 15)}\n",
        "\n",
        "hyper_params = {'n_estimators': [600,700,800],\n",
        "                'max_features': ['log2'],\n",
        "                'max_depth' : [6,7,8],\n",
        "                'criterion' :['entropy']}\n",
        "# {'criterion': 'entropy', 'max_depth': 7, 'max_features': 'log2', 'n_estimators': 600}\n",
        "\n",
        "grid = GridSearchCV(model_3, hyper_params, cv=4, scoring='precision_macro')\n",
        "\n",
        "grid.fit(X_train,y_train)\n",
        "print(grid.best_params_)\n",
        "print(grid.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:739: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'criterion': 'entropy', 'max_depth': 7, 'max_features': 'log2', 'n_estimators': 600}\n",
            "0.5883557562978898\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qb5d3oLALtmt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "144205f8-5486-458f-f874-9282f4d8a400"
      },
      "source": [
        "# model_1 : {'learning_rate': 0.2, 'n_estimators': 30} ou {'learning_rate': 0.1, 'n_estimators': 10}\n",
        "# model_4 : {'svc__C': 10, 'svc__gamma': 0.001}\n",
        "# model_2 : {'criterion': 'entropy', 'max_depth': 3}\n",
        "\n",
        "grid.best_estimator_.fit(X_train,y_train)\n",
        "pd.Series(grid.best_estimator_.predict(X_test)).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    300\n",
              "0     80\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 430
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jSaMPDZKSh5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "e113801a-b3c9-440c-acf1-0168e7ba49d4"
      },
      "source": [
        "print('AVANT OPTIMISATION')\n",
        "bilan(model_4)\n",
        "\n",
        "print('\\nAPRES OPTIMISATION')\n",
        "bilan(grid.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AVANT OPTIMISATION\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Nombre de prédictions : 380\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à DOMICILE : 164 (cote moyenne de 1.8730487804878049)\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à l'EXTÉRIEUR  : 54 (cote moyenne de 1.5711111111111107)\n",
            "Nombre de bonne prédictions : 218 (soit 57.36842105263158 %)\n",
            "\n",
            "  Le score avec model.score est : 57.36842105263158 %\n",
            "\n",
            "  Gain total des cotes gagnantes : 392.02 (auquel on enlève 380)\n",
            "  Rentable ? 12.019999999999982\n",
            "\n",
            "APRES OPTIMISATION\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Nombre de prédictions : 380\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à DOMICILE : 165 (cote moyenne de 1.8914545454545457)\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à l'EXTÉRIEUR  : 53 (cote moyenne de 1.5728301886792448)\n",
            "Nombre de bonne prédictions : 218 (soit 57.36842105263158 %)\n",
            "\n",
            "  Le score avec model.score est : 57.36842105263158 %\n",
            "\n",
            "  Gain total des cotes gagnantes : 395.45 (auquel on enlève 380)\n",
            "  Rentable ? 15.449999999999989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAkheBPMOI1V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "fc47580d-058f-4d08-c719-7cc0048cc20d"
      },
      "source": [
        "courbe_apprentissage(grid.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02DA_LeSEJTt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "20f849a1-7f12-48c0-e400-ae9caab62506"
      },
      "source": [
        "# Création des jeux de train, test et validation\n",
        "X_train_full = X[X['Saison']!=annee_fin]\n",
        "y_train_full = y[y['Saison']!=annee_fin]\n",
        "\n",
        "X_test = X[X['Saison']==annee_fin]\n",
        "y_test = y[y['Saison']==annee_fin]\n",
        "\n",
        "y_train_full = y_train_full.drop('Saison',axis=1)\n",
        "y_test = y_test.drop('Saison',axis=1)\n",
        "\n",
        "X_train, X_validation = train_test_split(X_train_full, test_size=0.1, random_state=0)\n",
        "y_train, y_validation = train_test_split(y_train_full, test_size=0.1, random_state=0)\n",
        "\n",
        "print(X_train.shape, y_train.shape)\n",
        "print(X_validation.shape, y_validation.shape)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3420, 100) (3420, 1)\n",
            "(380, 100) (380, 1)\n",
            "(288, 100) (288, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prALq0deEZNV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6c1d96d2-1382-4b79-f93f-2947a4470496"
      },
      "source": [
        "# Encodage des données\n",
        "encode = StandardScaler()\n",
        "\n",
        "X_train = pd.DataFrame(encode.fit_transform(X_train))\n",
        "X_validation = pd.DataFrame(encode.transform(X_validation))\n",
        "X_test = pd.DataFrame(encode.transform(X_test))\n",
        "\n",
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3420, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GWcbwpEFCFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "02d6a278-6753-4574-fb8a-d8c2664ca742"
      },
      "source": [
        "# Création du modèle de réseau de neurones\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential([\n",
        "                          keras.layers.Dense(200, activation='relu', input_shape=X_train.shape[1:]),\n",
        "                          keras.layers.Dense(100, activation='relu'),\n",
        "                          keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "early_stopping = keras.callbacks.EarlyStopping(monitor='val_accuracy',\n",
        "                                               patience=10, \n",
        "                                               restore_best_weights=True)\n",
        "# Entraînement\n",
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    epochs=100,\n",
        "                    validation_split=0.2,\n",
        "                    callbacks=[early_stopping])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "95/95 [==============================] - 0s 3ms/step - loss: 3185.2915 - accuracy: 0.4556 - val_loss: 1.0640 - val_accuracy: 0.4658\n",
            "Epoch 2/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0693 - accuracy: 0.4618 - val_loss: 1.0582 - val_accuracy: 0.4658\n",
            "Epoch 3/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0658 - accuracy: 0.4618 - val_loss: 1.0565 - val_accuracy: 0.4658\n",
            "Epoch 4/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0641 - accuracy: 0.4618 - val_loss: 1.0554 - val_accuracy: 0.4658\n",
            "Epoch 5/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0634 - accuracy: 0.4618 - val_loss: 1.0555 - val_accuracy: 0.4658\n",
            "Epoch 6/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0629 - accuracy: 0.4618 - val_loss: 1.0567 - val_accuracy: 0.4658\n",
            "Epoch 7/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0628 - accuracy: 0.4618 - val_loss: 1.0567 - val_accuracy: 0.4658\n",
            "Epoch 8/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0627 - accuracy: 0.4618 - val_loss: 1.0570 - val_accuracy: 0.4658\n",
            "Epoch 9/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0626 - accuracy: 0.4618 - val_loss: 1.0560 - val_accuracy: 0.4658\n",
            "Epoch 10/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0627 - accuracy: 0.4618 - val_loss: 1.0563 - val_accuracy: 0.4658\n",
            "Epoch 11/100\n",
            "95/95 [==============================] - 0s 2ms/step - loss: 1.0627 - accuracy: 0.4618 - val_loss: 1.0564 - val_accuracy: 0.4658\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN-MEbmDNu-Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "435cc537-d0e1-4732-fd28-056778104d11"
      },
      "source": [
        "pd.DataFrame(history.history).plot(figsize=(20,10))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1VDHeDAOIiK"
      },
      "source": [
        "# Le modèle de prédiction est-il rentable ? Faisons un bilan !\n",
        "\n",
        "def bilan(model):\n",
        "  prediction = pd.Series(model.predict_classes(X_test))\n",
        "\n",
        "  cpt_total, cpt_home, cpt_draw, cpt_away, bonne_reponse,somme_cote_domicile,somme_cote_nul, somme_cote_away = 0,0,0,0,0,0,0,0\n",
        "\n",
        "  '''\n",
        "  H -> 2\n",
        "  D -> 1\n",
        "  A -> 0\n",
        "  '''\n",
        "\n",
        "  for pred, elt_test, B365H, B365D, B365A in zip(prediction,y_test.iterrows(),X_test.iloc[:,1], X_test.iloc[:,2], X_test.iloc[:,3]):\n",
        "    cpt_total += 1\n",
        "    if cpt_total < 11:\n",
        "      indice = 11\n",
        "    elif cpt_total < 101:\n",
        "      indice = 12\n",
        "    else :\n",
        "      indice = 13\n",
        "    if (pred == 2) and (str(elt_test)[indice] == '2'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_home += 1\n",
        "      somme_cote_domicile += B365H\n",
        "    elif (pred == 1) and (str(elt_test)[indice] == '1'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_draw += 1\n",
        "      somme_cote_nul += B365D\n",
        "    elif (pred == 0) and (str(elt_test)[indice] == '0'):\n",
        "      bonne_reponse += 1\n",
        "      cpt_away += 1\n",
        "      somme_cote_away += B365A\n",
        "\n",
        "  print(f'Nombre de prédictions : {cpt_total}')\n",
        "  if cpt_home != 0:\n",
        "    print(f'Nombre de bonne prédiction sur la victoire de l\\'équipe à DOMICILE : {cpt_home} (cote moyenne de {somme_cote_domicile/cpt_home})')\n",
        "  if cpt_draw != 0:\n",
        "    print(f'Nombre de bonne prédiction sur un match NUL : {cpt_draw} (cote moyenne de  {somme_cote_nul/cpt_draw})')\n",
        "  if cpt_away != 0:\n",
        "    print(f'Nombre de bonne prédiction sur la victoire de l\\'équipe à l\\'EXTÉRIEUR  : {cpt_away} (cote moyenne de {somme_cote_away/cpt_away})')\n",
        "\n",
        "  y_pred = prediction\n",
        "  precision = precision_score(y_test, y_pred, average='macro')\n",
        "  print(f'Nombre de bonne prédictions : {bonne_reponse} (soit {(bonne_reponse/cpt_total)*100} %)\\n\\n\\\n",
        "  Le score de précision du model est : {precision} %\\n\\n\\\n",
        "  Gain total des cotes gagnantes : {somme_cote_domicile+somme_cote_nul+somme_cote_away} (auquel on enlève {cpt_total})\\n\\\n",
        "  Rentable ? {somme_cote_domicile+somme_cote_nul+somme_cote_away - cpt_total}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPFDxby9Ou_y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e11f3ca9-9760-479e-9686-0bf70c8bb497"
      },
      "source": [
        "bilan(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nombre de prédictions : 288\n",
            "Nombre de bonne prédiction sur la victoire de l'équipe à DOMICILE : 129 (cote moyenne de 2.2428682170542644)\n",
            "Nombre de bonne prédictions : 129 (soit 44.79166666666667 %)\n",
            "\n",
            "  Le score de précision du model est : 0.14930555555555555 %\n",
            "\n",
            "  Gain total des cotes gagnantes : 289.3300000000001 (auquel on enlève 288)\n",
            "  Rentable ? 1.3300000000000978\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}