TP03_modeles_linaires_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1eYB-KBM_AxI6wa74I3b2lZHdlb3aTOCJ",
      "authorship_tag": "ABX9TyPeaVUFge7cb2m1Fp+GgSpl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gguex/ISH_ressources_cours_ML/blob/main/TP03_modeles_linaires_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TP 3:  modèles linéaires pour la classification\n",
        "\n",
        "Dans ce troisième TP, nous allons faire des régressions linéaires et logistiques pour de la classification. Il y aura deux parties :\n",
        "\n",
        "* La régression linéaire et logistique pour une variable binaire\n",
        "* La régression linéaire et logistique pour une variable multimodale\n",
        "\n",
        "Un exercice consistera à refaire ces modèles, afin d'optimiser cette fois la mesure F1, sur un nouveau jeu de données.\n",
        "\n",
        "Les librairies et modules nécessaires sont"
      ],
      "metadata": {
        "id": "qNpexjLWmjpE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "1ZGiKBDBh6R7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# Pour modifier la taille de sortie de graphiques dans le notebook\n",
        "plt.rcParams['figure.figsize'] = [20, 10]\n",
        "# Les modèles de régression\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "# Pour la CV\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "# Nos différentes mesures\n",
        "from sklearn.metrics import make_scorer, accuracy_score, \\\n",
        "  precision_score, recall_score\n",
        "# Pour encoder les sorties en vecteurs one-hot\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "AtpWtaA5oSr2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Variable binaire\n",
        "\n",
        "Nous allons utiliser ici le jeu de données \"BreastCancer.csv\" (https://www.rdocumentation.org/packages/mlbench/versions/2.1-3/topics/BreastCancer) possédant une variable \"Class\" indiquant si la tumeur prélevée est bénigne ou maligne. Les autres variables sont numériques.\n",
        "\n",
        "On commence par charger le jeu de données"
      ],
      "metadata": {
        "id": "ZazFet2X2VZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"drive/MyDrive/Colab Notebooks/ml_data/TP3/BreastCancer.csv\"\n",
        "data = pd.read_csv(file_path)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GD-0rMr9moRh",
        "outputId": "c7bd7cb8-c221-4f1c-e2e8-de66779ffb90"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Id  Cl.thickness  Cell.size  Cell.shape  Marg.adhesion  \\\n",
              "0    1000025             5          1           1              1   \n",
              "1    1002945             5          4           4              5   \n",
              "2    1015425             3          1           1              1   \n",
              "3    1016277             6          8           8              1   \n",
              "4    1017023             4          1           1              3   \n",
              "..       ...           ...        ...         ...            ...   \n",
              "678   776715             3          1           1              1   \n",
              "679   841769             2          1           1              1   \n",
              "680   888820             5         10          10              3   \n",
              "681   897471             4          8           6              4   \n",
              "682   897471             4          8           8              5   \n",
              "\n",
              "     Epith.c.size  Bare.nuclei  Bl.cromatin  Normal.nucleoli  Mitoses  \\\n",
              "0               2            1            3                1        1   \n",
              "1               7           10            3                2        1   \n",
              "2               2            2            3                1        1   \n",
              "3               3            4            3                7        1   \n",
              "4               2            1            3                1        1   \n",
              "..            ...          ...          ...              ...      ...   \n",
              "678             3            2            1                1        1   \n",
              "679             2            1            1                1        1   \n",
              "680             7            3            8               10        2   \n",
              "681             3            4           10                6        1   \n",
              "682             4            5           10                4        1   \n",
              "\n",
              "         Class  \n",
              "0       benign  \n",
              "1       benign  \n",
              "2       benign  \n",
              "3       benign  \n",
              "4       benign  \n",
              "..         ...  \n",
              "678     benign  \n",
              "679     benign  \n",
              "680  malignant  \n",
              "681  malignant  \n",
              "682  malignant  \n",
              "\n",
              "[683 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efe6faf5-13d6-4edf-b60c-721b5217438c\" class=\"colab-df-container\">\n",
              "    <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>Id</th>\n",
              "      <th>Cl.thickness</th>\n",
              "      <th>Cell.size</th>\n",
              "      <th>Cell.shape</th>\n",
              "      <th>Marg.adhesion</th>\n",
              "      <th>Epith.c.size</th>\n",
              "      <th>Bare.nuclei</th>\n",
              "      <th>Bl.cromatin</th>\n",
              "      <th>Normal.nucleoli</th>\n",
              "      <th>Mitoses</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000025</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002945</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1015425</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1016277</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1017023</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>678</th>\n",
              "      <td>776715</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>841769</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>888820</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>683 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efe6faf5-13d6-4edf-b60c-721b5217438c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-efe6faf5-13d6-4edf-b60c-721b5217438c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-efe6faf5-13d6-4edf-b60c-721b5217438c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-db0e89ce-f06f-4476-8a1f-bafd60f321d3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db0e89ce-f06f-4476-8a1f-bafd60f321d3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db0e89ce-f06f-4476-8a1f-bafd60f321d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2ce2a3be-a37b-42db-8716-f39957e34f3f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2ce2a3be-a37b-42db-8716-f39957e34f3f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 683,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 620644,\n        \"min\": 63375,\n        \"max\": 13454352,\n        \"num_unique_values\": 630,\n        \"samples\": [\n          1287971,\n          385103,\n          636375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cl.thickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cell.size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cell.shape\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marg.adhesion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Epith.c.size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          7,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bare.nuclei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          10,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bl.cromatin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          9,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normal.nucleoli\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mitoses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"malignant\",\n          \"benign\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On isole nos entrées et sorties, et on transforme ces dernières en un vecteur binaire avec \"benign\" = 0 et \"malignant\" = 1."
      ],
      "metadata": {
        "id": "tcW6CKnno3k3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = data.drop([\"Id\", \"Class\"], axis=1)\n",
        "outputs = (data[\"Class\"].to_numpy() == \"malignant\")*1\n",
        "outputs"
      ],
      "metadata": {
        "id": "_lwRDh1GnaPY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd0b05be-55a0-4ce0-c4de-909374f29228"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
              "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va maintenant séparer notre jeu de données en jeu d'entrainement/validation et test, avec l'option `stratify=outputs`, c'est-à-dire qu'on prend garde à ce que les classes contenues dans le vecteur `outputs` soient réparties de manière similaire dans les deux jeux de données."
      ],
      "metadata": {
        "id": "KStKIDgHpDLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs_train_val, inputs_test, outputs_train_val, outputs_test \\\n",
        "  = train_test_split(inputs, outputs, test_size=0.2, stratify=outputs)\n",
        "# On observe les proportions\n",
        "print(f\"Proportion de malignant dans train_val : \"\n",
        "      f\"{np.sum(outputs_train_val) / len(outputs_train_val):.2%}\")\n",
        "print(f\"Proportion de malignant dans test : \"\n",
        "      f\"{np.sum(outputs_test) / len(outputs_test):.2%}\")"
      ],
      "metadata": {
        "id": "dUA0yZiRswQT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3138de9c-add3-4f32-d1a9-76eb1d429b2c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Proportion de malignant dans train_val : 34.98%\n",
            "Proportion de malignant dans test : 35.04%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va maintenant entrainer un modèle linéaire sur le jeu d'entrainement/validation"
      ],
      "metadata": {
        "id": "FHkKmVS-pk4w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_reg = LinearRegression()\n",
        "linear_reg.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "RpxyUaCMtM9N",
        "outputId": "4a409f90-e78d-44dd-f2fa-d8707bf6bcb8"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-14 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-14 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-14 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-14 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-14 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-14 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-14 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-14 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-14 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-14 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-14 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Les prédictions $\\widehat{y}_i = 1$ s'obtiennent ssi $\\widehat{z}_i > 5$. On calcul ensuite l'exactitude, la précision et le rappel, pour les jeux d'entrainement et de test"
      ],
      "metadata": {
        "id": "s6XLC8x_pope"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Les prédictions\n",
        "outputs_lin_train = (linear_reg.predict(inputs_train_val) > 0.5)*1\n",
        "outputs_lin_test = (linear_reg.predict(inputs_test) > 0.5)*1\n",
        "# Les scores\n",
        "accuracy_lin_train = np.mean(outputs_train_val == outputs_lin_train)\n",
        "accuracy_lin_test = np.mean(outputs_test == outputs_lin_test)\n",
        "precision_lin_train = precision_score(outputs_train_val, outputs_lin_train)\n",
        "precision_lin_test = precision_score(outputs_test, outputs_lin_test)\n",
        "recall_lin_train = recall_score(outputs_train_val, outputs_lin_train)\n",
        "recall_lin_test = recall_score(outputs_test, outputs_lin_test)"
      ],
      "metadata": {
        "id": "fjCe_518ulep"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La régression logistique sera régularisée avec un hyperparamètre $\\lambda \\in [0.001, 0.5]$. On effectue donc une `GridSearchCV`, mais en observant nos trois mesures (`refit`, qui permet d'entrainer automatiquement le meilleur modèle, doit être mis selon une de ces mesures ou sur `False`)"
      ],
      "metadata": {
        "id": "VzKk5djmqI13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Paramètres pour la CV\n",
        "params = np.linspace(0.001, 0.5, 50)\n",
        "param_dict = {\"C\": params}\n",
        "k = 5\n",
        "# Recherche sur grille avec CV\n",
        "logistic_reg = LogisticRegression()\n",
        "gride_search = GridSearchCV(logistic_reg,\n",
        "                            param_dict,\n",
        "                            cv=k,\n",
        "                            scoring=[\"accuracy\", \"precision\", \"recall\"],\n",
        "                            refit=\"recall\")\n",
        "gride_search.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "8zq5T6KOp2cq",
        "outputId": "8f0afd94-a368-465c-a6c1-063e3272807e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={'C': array([0.001     , 0.01118367, 0.02136735, 0.03155102, 0.04173469,\n",
              "       0.05191837, 0.06210204, 0.07228571, 0.08246939, 0.09265306,\n",
              "       0.10283673, 0.11302041, 0.12320408, 0.13338776, 0.14357143,\n",
              "       0.1537551 , 0.16393878, 0.17412245, 0.18430612, 0.1944898 ,\n",
              "       0.20467347, 0.21485714, 0.22504082, 0.23522449, 0.24540816,\n",
              "       0.25559184, 0.26577551, 0.27595918, 0.28614286, 0.29632653,\n",
              "       0.3065102 , 0.31669388, 0.32687755, 0.33706122, 0.3472449 ,\n",
              "       0.35742857, 0.36761224, 0.37779592, 0.38797959, 0.39816327,\n",
              "       0.40834694, 0.41853061, 0.42871429, 0.43889796, 0.44908163,\n",
              "       0.45926531, 0.46944898, 0.47963265, 0.48981633, 0.5       ])},\n",
              "             refit='recall', scoring=['accuracy', 'precision', 'recall'])"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-15 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-15 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-15 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-15 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-15 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-15 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-15 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-15 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={&#x27;C&#x27;: array([0.001     , 0.01118367, 0.02136735, 0.03155102, 0.04173469,\n",
              "       0.05191837, 0.06210204, 0.07228571, 0.08246939, 0.09265306,\n",
              "       0.10283673, 0.11302041, 0.12320408, 0.13338776, 0.14357143,\n",
              "       0.1537551 , 0.16393878, 0.17412245, 0.18430612, 0.1944898 ,\n",
              "       0.20467347, 0.21485714, 0.22504082, 0.23522449, 0.24540816,\n",
              "       0.25559184, 0.26577551, 0.27595918, 0.28614286, 0.29632653,\n",
              "       0.3065102 , 0.31669388, 0.32687755, 0.33706122, 0.3472449 ,\n",
              "       0.35742857, 0.36761224, 0.37779592, 0.38797959, 0.39816327,\n",
              "       0.40834694, 0.41853061, 0.42871429, 0.43889796, 0.44908163,\n",
              "       0.45926531, 0.46944898, 0.47963265, 0.48981633, 0.5       ])},\n",
              "             refit=&#x27;recall&#x27;, scoring=[&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={&#x27;C&#x27;: array([0.001     , 0.01118367, 0.02136735, 0.03155102, 0.04173469,\n",
              "       0.05191837, 0.06210204, 0.07228571, 0.08246939, 0.09265306,\n",
              "       0.10283673, 0.11302041, 0.12320408, 0.13338776, 0.14357143,\n",
              "       0.1537551 , 0.16393878, 0.17412245, 0.18430612, 0.1944898 ,\n",
              "       0.20467347, 0.21485714, 0.22504082, 0.23522449, 0.24540816,\n",
              "       0.25559184, 0.26577551, 0.27595918, 0.28614286, 0.29632653,\n",
              "       0.3065102 , 0.31669388, 0.32687755, 0.33706122, 0.3472449 ,\n",
              "       0.35742857, 0.36761224, 0.37779592, 0.38797959, 0.39816327,\n",
              "       0.40834694, 0.41853061, 0.42871429, 0.43889796, 0.44908163,\n",
              "       0.45926531, 0.46944898, 0.47963265, 0.48981633, 0.5       ])},\n",
              "             refit=&#x27;recall&#x27;, scoring=[&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.04173469387755102)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.04173469387755102)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va faire le graphique de ces scores en fonction de $\\lambda$"
      ],
      "metadata": {
        "id": "PJPpngoIqzuQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot des scores\n",
        "accuracy_scores = gride_search.cv_results_[\"mean_test_accuracy\"]\n",
        "precision_scores = gride_search.cv_results_[\"mean_test_precision\"]\n",
        "recall_scores = gride_search.cv_results_[\"mean_test_recall\"]\n",
        "# Plot des scores\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(params, accuracy_scores, c=\"red\", label=\"Accuracy\")\n",
        "ax.plot(params, precision_scores, c=\"blue\", label=\"Precision\")\n",
        "ax.plot(params, recall_scores, c=\"green\", label=\"Recall\")\n",
        "ax.set_xlabel(\"$C$\")\n",
        "ax.set_ylabel(\"$Score$\")\n",
        "ax.legend()\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "trBmabzG6405",
        "outputId": "4041940f-2327-4550-be75-4998e4bf0ef0"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sélectionnons le meilleur hyparamètre selon le rappel, puis entrainons ce modèle sur tout notre jeu d'entrainement/validation"
      ],
      "metadata": {
        "id": "QhOet8tFq47J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Les meilleurs modèles selon le recall\n",
        "index_best_recall = np.where(recall_scores == max(recall_scores))[0][0]\n",
        "best_recall_par = params[index_best_recall]\n",
        "best_recall_log_reg = LogisticRegression(C=best_recall_par)\n",
        "best_recall_log_reg.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "KF-o9DIgqI9h",
        "outputId": "468c18c6-0c9b-4826-c424-0d289e2dcfe8"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.04173469387755102)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-16 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-16 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-16 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-16 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-16 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-16 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-16 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-16 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-16 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-16 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-16 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-16 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.04173469387755102)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.04173469387755102)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On calcule maintenant les différentes mesures selon ce modèle, sur le jeu d'entrainement/validation et test"
      ],
      "metadata": {
        "id": "AQX4r9r2rIMn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Les prédictions\n",
        "outputs_log_train = best_recall_log_reg.predict(inputs_train_val)\n",
        "outputs_log_test = best_recall_log_reg.predict(inputs_test)\n",
        "# Les scores\n",
        "accuracy_log_train = accuracy_score(outputs_train_val, outputs_log_train)\n",
        "accuracy_log_test = accuracy_score(outputs_test, outputs_log_test)\n",
        "precision_log_train = precision_score(outputs_train_val, outputs_log_train)\n",
        "precision_log_test = precision_score(outputs_test, outputs_log_test)\n",
        "recall_log_train = recall_score(outputs_train_val, outputs_log_train)\n",
        "recall_log_test = recall_score(outputs_test, outputs_log_test)"
      ],
      "metadata": {
        "id": "6tkT4kWNuclW"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparons les résultats entre notre modèle linéaire et le modèle logisitique retenu"
      ],
      "metadata": {
        "id": "kzcJqb17rPxM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"L'exactitude pour le modèle linéaire est de \"\n",
        "f\"{accuracy_lin_train:.2%} (train) {accuracy_lin_test:.2%} (test)\")\n",
        "print(f\"La précision pour le modèle linéaire est de \"\n",
        "f\"{precision_lin_train:.2%} (train) {precision_lin_test:.2%} (test)\")\n",
        "print(f\"Le rappel pour le modèle linéaire est de \"\n",
        "f\"{recall_lin_train:.2%} (train) {recall_lin_test:.2%} (test)\")\n",
        "print(f\"L'exactitude pour le modèle logistique (C={best_recall_par:.4f}) est \"\n",
        "f\"de {accuracy_log_train:.2%} (train) {accuracy_log_test:.2%} (test)\")\n",
        "print(f\"La précision pour le modèle logistique (C={best_recall_par:.4f}) est \"\n",
        "f\"de {precision_log_train:.2%} (train) {precision_log_test:.2%} (test)\")\n",
        "print(f\"Le rappel pour le modèle logistique (C={best_recall_par:.4f}) est \"\n",
        "f\"de {recall_log_train:.2%} (train) {recall_log_test:.2%} (test)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcR07z9tzzts",
        "outputId": "22963672-97a5-4a0f-dec2-2bcdfc411f83"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L'exactitude pour le modèle linéaire est de 96.15% (train) 96.35% (test)\n",
            "La précision pour le modèle linéaire est de 97.22% (train) 93.88% (test)\n",
            "Le rappel pour le modèle linéaire est de 91.62% (train) 95.83% (test)\n",
            "L'exactitude pour le modèle logistique (C=0.0417) est de 97.25% (train) 95.62% (test)\n",
            "La précision pour le modèle logistique (C=0.0417) est de 96.81% (train) 92.00% (test)\n",
            "Le rappel pour le modèle logistique (C=0.0417) est de 95.29% (train) 95.83% (test)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "HWv40bm61UXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Multiples classes\n",
        "\n",
        "La même procédure peut être effectuée sur un jeu de données contenant une variable avec de multiples classes, en l'occurence les Iris de Fisher, qui contient le type d'Iris dans \"variety\". On charge les données"
      ],
      "metadata": {
        "id": "Zkv5CgG62N8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"drive/MyDrive/Colab Notebooks/ml_data/TP3/iris.csv\"\n",
        "data = pd.read_csv(file_path)\n",
        "inputs = data.drop([\"variety\"], axis=1)\n",
        "outputs = data[\"variety\"]"
      ],
      "metadata": {
        "id": "PPNhJXwa1cZq"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va maintenant utiliser une instance de `OneHotEncoder()` pour recoder notre variable catégorielle en vecteurs one-hot. Notons que l'argument `categories_` continent l'ordre dans lequel le recodage a été effectué"
      ],
      "metadata": {
        "id": "KGzX2H-grotf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_1hot = OneHotEncoder()\n",
        "outputs_1hot = encoder_1hot.fit_transform(\n",
        "    outputs.to_numpy().reshape(-1, 1)\n",
        "    ).toarray()\n",
        "print(outputs_1hot[:10,:])\n",
        "# Une array contenant les classes dans le bon ordre\n",
        "classes = encoder_1hot.categories_[0]\n",
        "print(classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnYHY_kPiw2g",
        "outputId": "6923adf4-64f8-4ff5-9379-b08d82b46af5"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]]\n",
            "['Setosa' 'Versicolor' 'Virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Séparons en jeu d'entrainement/validation et test, tout en stratifiant selon `outputs`"
      ],
      "metadata": {
        "id": "W0VtvJp5sE9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs_train_val, inputs_test, outputs_train_val, outputs_test, \\\n",
        "outputs_1hot_train_val, outputs_1hot_test \\\n",
        "  = train_test_split(inputs, outputs, outputs_1hot, stratify=outputs)"
      ],
      "metadata": {
        "id": "yXKXldtijDO4"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La fonction ci-dessous nous permettra d'entrainer autant de modèles linaires qu'il y a de classe dans notre matrice de vecteur one-hot. Chaque modèle va essayer de prédire un des vecteurs one-hot"
      ],
      "metadata": {
        "id": "sszwhMaxsOvR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_lin_models(inputs, outputs_1hot):\n",
        "  # Pour contenir les modèles\n",
        "  lin_models = []\n",
        "  # Boucle sur le nombre de classes\n",
        "  for j in range(outputs_1hot.shape[1]):\n",
        "    # On fait la régression avec comme sortie la colonne j\n",
        "    lin_reg = LinearRegression()\n",
        "    lin_reg.fit(inputs, outputs_1hot[:, j])\n",
        "    lin_models.append(lin_reg)\n",
        "  # Retour du vecteur de modèles\n",
        "  return lin_models"
      ],
      "metadata": {
        "id": "2Js_vKGJrAos"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On utilise cette fonction pour entrainer tous nos modèles linaires"
      ],
      "metadata": {
        "id": "WlFMPvDMseyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trained_lin_models = train_lin_models(inputs_train_val, outputs_1hot_train_val)"
      ],
      "metadata": {
        "id": "aghBNjhsknpD"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La fonction ci-dessous nous permet de prédire les classes en fonction des entrées et des différents modèles. La classe retenue correspond au numéro de la régression linéaire qui donne la plus grande sortie"
      ],
      "metadata": {
        "id": "MFla9o1Usi0C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_class(lin_models, inputs):\n",
        "  # Nombre d'individus dans les entrées\n",
        "  n_indiv = inputs.shape[0]\n",
        "  # Nombre de modèles linéaires\n",
        "  n_model = len(trained_lin_models)\n",
        "  # Boucle pour remplir les prédictions linéaire dans\n",
        "  # une matrice de taille n_indiv x n_model\n",
        "  pred_mat = np.zeros((n_indiv, n_model))\n",
        "  for i in range(n_model):\n",
        "    pred = lin_models[i].predict(inputs)\n",
        "    pred_mat[:, i] = pred\n",
        "  # Transformation en vecteur de groupe\n",
        "  class_pred = np.argmax(pred_mat, axis=1)\n",
        "  # Retour du vecteur de class\n",
        "  return class_pred\n"
      ],
      "metadata": {
        "id": "SADpPgbasXB0"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On fait les prédictions sur les jeux d'entrainement/validation et test"
      ],
      "metadata": {
        "id": "iFDQ7gHktoo2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs_num_lin_train = predict_class(trained_lin_models, inputs_train_val)\n",
        "outputs_num_lin_test = predict_class(trained_lin_models, inputs_test)\n",
        "outputs_lin_train = classes[outputs_num_lin_train]\n",
        "outputs_lin_test = classes[outputs_num_lin_test]\n",
        "print(outputs_lin_test)"
      ],
      "metadata": {
        "id": "yo2xwGZep2zZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d86fd45-af85-4924-cb1c-85dc29850d65"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Versicolor' 'Versicolor' 'Setosa' 'Virginica' 'Virginica' 'Setosa'\n",
            " 'Virginica' 'Versicolor' 'Setosa' 'Setosa' 'Virginica' 'Setosa'\n",
            " 'Virginica' 'Setosa' 'Versicolor' 'Virginica' 'Setosa' 'Setosa'\n",
            " 'Virginica' 'Versicolor' 'Setosa' 'Versicolor' 'Setosa' 'Setosa'\n",
            " 'Versicolor' 'Virginica' 'Virginica' 'Virginica' 'Virginica' 'Versicolor'\n",
            " 'Virginica' 'Versicolor' 'Setosa' 'Versicolor' 'Versicolor' 'Setosa'\n",
            " 'Virginica' 'Virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On calcule maintenant l'exactitude et la précision"
      ],
      "metadata": {
        "id": "cGLpm4edtylD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_lin_train = np.mean(outputs_train_val == outputs_lin_train)\n",
        "accuracy_lin_test = np.mean(outputs_test == outputs_lin_test)\n",
        "precision_lin_train = precision_score(outputs_train_val, outputs_lin_train,\n",
        "                                      average=\"weighted\")\n",
        "precision_lin_test = precision_score(outputs_test, outputs_lin_test,\n",
        "                                     average=\"weighted\")"
      ],
      "metadata": {
        "id": "w7aeiL6ily4a"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On va rafaire tout cela avec une régression logistique multinomiale régularisée avec $\\lambda \\in [0.001, 2]$. **Attention**, l'option `multi_class` doit être sur `\"multinomial\"` lors de l'instanciation de la classe `LogisticRegression`, afin de bien faire une régression avec la fonction *softmax*. On remarque ici que l'on doit créer un score personnalisé (qui s'appuie sur `precision_score`) afin de dire comment doit être calculé la précision avec des classes multiples (ici, l'option `average=\"weighted\"` permet de faire la moyenne pondérée de la précision sur chaque classe)."
      ],
      "metadata": {
        "id": "JjWKBpmLw74L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Paramètres pour la CV\n",
        "params = np.linspace(0.001, 5, 50)\n",
        "param_dict = {\"C\": params}\n",
        "k = 5\n",
        "# Fonction particulière pour le scoring :\n",
        "# on fait la moyenne pondérée des précisions par classe, one-vs-rest\n",
        "def my_precision(y_true, y_pred):\n",
        "  return precision_score(y_true, y_pred, average=\"weighted\",\n",
        "                         zero_division=np.nan)\n",
        "# Pour ce modèle, on a du augmenter le nombre d'itérations\n",
        "# dans la descente du gradient\n",
        "logistic_reg = LogisticRegression(max_iter=10000)\n",
        "# Recherche sur grille avec CV\n",
        "grid_search = GridSearchCV(logistic_reg,\n",
        "                           param_dict,\n",
        "                           cv=k,\n",
        "                           scoring={\"accuracy\": \"accuracy\",\n",
        "                                    \"precision\": make_scorer(my_precision)},\n",
        "                           refit=False,\n",
        "                           error_score='raise')\n",
        "grid_search.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "nM4kxFsKl-MQ",
        "outputId": "844da7b6-6ed9-4267-b79d-419104c168b7"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise',\n",
              "             estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={'C': array([1.00000000e-03, 1.03020408e-01, 2.05040816e-01, 3.07061224e-01,\n",
              "       4.09081633e-01, 5.11102041e-01, 6.13122449e-01, 7.15142857e-01,\n",
              "       8.17163265e-01, 9.19183673e-01, 1.02120408e+00, 1.12322449e+00,\n",
              "       1.22524490e+00, 1.32726531e+00, 1.42928571e+00, 1.53130612e+00,\n",
              "       1.63...\n",
              "       3.26565306e+00, 3.36767347e+00, 3.46969388e+00, 3.57171429e+00,\n",
              "       3.67373469e+00, 3.77575510e+00, 3.87777551e+00, 3.97979592e+00,\n",
              "       4.08181633e+00, 4.18383673e+00, 4.28585714e+00, 4.38787755e+00,\n",
              "       4.48989796e+00, 4.59191837e+00, 4.69393878e+00, 4.79595918e+00,\n",
              "       4.89797959e+00, 5.00000000e+00])},\n",
              "             refit=False,\n",
              "             scoring={'accuracy': 'accuracy',\n",
              "                      'precision': make_scorer(my_precision, response_method='predict')})"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-17 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-17 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-17 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-17 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-17 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-17 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-17 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-17 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, error_score=&#x27;raise&#x27;,\n",
              "             estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 1.03020408e-01, 2.05040816e-01, 3.07061224e-01,\n",
              "       4.09081633e-01, 5.11102041e-01, 6.13122449e-01, 7.15142857e-01,\n",
              "       8.17163265e-01, 9.19183673e-01, 1.02120408e+00, 1.12322449e+00,\n",
              "       1.22524490e+00, 1.32726531e+00, 1.42928571e+00, 1.53130612e+00,\n",
              "       1.63...\n",
              "       3.26565306e+00, 3.36767347e+00, 3.46969388e+00, 3.57171429e+00,\n",
              "       3.67373469e+00, 3.77575510e+00, 3.87777551e+00, 3.97979592e+00,\n",
              "       4.08181633e+00, 4.18383673e+00, 4.28585714e+00, 4.38787755e+00,\n",
              "       4.48989796e+00, 4.59191837e+00, 4.69393878e+00, 4.79595918e+00,\n",
              "       4.89797959e+00, 5.00000000e+00])},\n",
              "             refit=False,\n",
              "             scoring={&#x27;accuracy&#x27;: &#x27;accuracy&#x27;,\n",
              "                      &#x27;precision&#x27;: make_scorer(my_precision, response_method=&#x27;predict&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, error_score=&#x27;raise&#x27;,\n",
              "             estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 1.03020408e-01, 2.05040816e-01, 3.07061224e-01,\n",
              "       4.09081633e-01, 5.11102041e-01, 6.13122449e-01, 7.15142857e-01,\n",
              "       8.17163265e-01, 9.19183673e-01, 1.02120408e+00, 1.12322449e+00,\n",
              "       1.22524490e+00, 1.32726531e+00, 1.42928571e+00, 1.53130612e+00,\n",
              "       1.63...\n",
              "       3.26565306e+00, 3.36767347e+00, 3.46969388e+00, 3.57171429e+00,\n",
              "       3.67373469e+00, 3.77575510e+00, 3.87777551e+00, 3.97979592e+00,\n",
              "       4.08181633e+00, 4.18383673e+00, 4.28585714e+00, 4.38787755e+00,\n",
              "       4.48989796e+00, 4.59191837e+00, 4.69393878e+00, 4.79595918e+00,\n",
              "       4.89797959e+00, 5.00000000e+00])},\n",
              "             refit=False,\n",
              "             scoring={&#x27;accuracy&#x27;: &#x27;accuracy&#x27;,\n",
              "                      &#x27;precision&#x27;: make_scorer(my_precision, response_method=&#x27;predict&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=10000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On peut maintenant faire le graphique de nos mesures en fonction de $\\lambda$"
      ],
      "metadata": {
        "id": "8Z67hlgjxTHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot des scores\n",
        "accuracy_scores = grid_search.cv_results_[\"mean_test_accuracy\"]\n",
        "precision_scores = grid_search.cv_results_[\"mean_test_precision\"]\n",
        "# Plot des scores\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(params, accuracy_scores, c=\"red\", label=\"Accuracy\")\n",
        "ax.plot(params, precision_scores, c=\"blue\", label=\"Precision\")\n",
        "ax.set_xlabel(\"$\\lambda$\")\n",
        "ax.set_ylabel(\"$Score$\")\n",
        "ax.legend()\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "1McXkke7o2zS",
        "outputId": "7eb0b427-4a94-448a-f793-1b95e0733086"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On retient le meilleur modèle selon la précision"
      ],
      "metadata": {
        "id": "TnBTd8X7xZ8R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Les meilleurs modèles selon la précision\n",
        "index_best_precision = np.where(precision_scores == max(precision_scores))[0][0]\n",
        "best_precision_par = params[index_best_precision]\n",
        "best_precision_log_reg = LogisticRegression(max_iter=10000, C=best_precision_par)\n",
        "best_precision_log_reg.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "vJmHnujxVIfa",
        "outputId": "1b690623-f69e-409d-c265-a4bba55065fb"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.5313061224489792, max_iter=10000)"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-18 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-18 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-18 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-18 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-18 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-18 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-18 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-18 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1.5313061224489792, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" checked><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1.5313061224489792, max_iter=10000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On calcule les mesures"
      ],
      "metadata": {
        "id": "aWpa2ExtxgGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Les prédictions\n",
        "outputs_log_train = best_precision_log_reg.predict(inputs_train_val)\n",
        "outputs_log_test = best_precision_log_reg.predict(inputs_test)\n",
        "# Les scores\n",
        "accuracy_log_train = accuracy_score(outputs_train_val, outputs_log_train)\n",
        "accuracy_log_test = accuracy_score(outputs_test, outputs_log_test)\n",
        "precision_log_train = my_precision(outputs_train_val, outputs_log_train)\n",
        "precision_log_test = my_precision(outputs_test, outputs_log_test)"
      ],
      "metadata": {
        "id": "C81-QRSTV_1f"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Et on compare à la régression linéaire"
      ],
      "metadata": {
        "id": "EBTQ5mNoxizv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"L'exactitude pour le modèle linéaire est de \"\n",
        "f\"{accuracy_lin_train:.2%} (train) {accuracy_lin_test:.2%} (test)\")\n",
        "print(f\"La précision pour le modèle linéaire est de \"\n",
        "f\"{precision_lin_train:.2%} (train) {precision_lin_test:.2%} (test)\")\n",
        "print(f\"L'exactitude pour le modèle logistique (C={best_recall_par:.4f}) est \"\n",
        "f\"de {accuracy_log_train:.2%} (train) {accuracy_log_test:.2%} (test)\")\n",
        "print(f\"La précision pour le modèle logistique (C={best_recall_par:.4f}) est \"\n",
        "f\"de {precision_log_train:.2%} (train) {precision_log_test:.2%} (test)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45b6EE0nnzC8",
        "outputId": "ae530967-d340-4062-9066-95de9e5cb992"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L'exactitude pour le modèle linéaire est de 84.82% (train) 86.84% (test)\n",
            "La précision pour le modèle linéaire est de 85.52% (train) 86.93% (test)\n",
            "L'exactitude pour le modèle logistique (C=0.0417) est de 98.21% (train) 97.37% (test)\n",
            "La précision pour le modèle logistique (C=0.0417) est de 98.31% (train) 97.56% (test)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finalement, on va afficher, à l'aide de `contourf`, les zones de décision de notre modèle. Il s'agit pour cela de créer des points d'une grille (définie sur les variables \"petal.length\" et \"petal.width\", les variables associées aux sépales seront maintenues constantes sur leur moyenne) puis d'estimer les prédictions de ces différents points. Remarquez que nous utilisons ici `predict_proba` qui premet d'obtenir les probabilités d'être dans chaque classe. Les `contourf` utilisent ensuite ces probabilités pour tracer les contours"
      ],
      "metadata": {
        "id": "qcV7tYL_xm6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# On fait une grille\n",
        "grid_size = 500\n",
        "p_length_min = data[\"petal.length\"].min()\n",
        "p_length_max =  data[\"petal.length\"].max()\n",
        "p_width_min = data[\"petal.width\"].min()\n",
        "p_width_max =  data[\"petal.width\"].max()\n",
        "grid_x, grid_y = np.meshgrid(np.linspace(p_length_min, p_length_max, grid_size),\n",
        "                             np.linspace(p_width_min, p_width_max, grid_size))\n",
        "\n",
        "# On recrée un jeu de données avec les points de la grille\n",
        "mean_sepal_length = data[\"sepal.length\"].mean()\n",
        "mean_sepal_width = data[\"sepal.width\"].mean()\n",
        "grid_data_mat = np.stack((np.repeat([mean_sepal_length], grid_size**2),\n",
        "                          np.repeat([mean_sepal_width], grid_size**2),\n",
        "                          grid_x.flatten(),\n",
        "                          grid_y.flatten())).T\n",
        "grid_data = pd.DataFrame(grid_data_mat, columns=inputs.columns)\n",
        "\n",
        "# Les prédicions probabiliste sur la grille\n",
        "grid_proba_pred = best_precision_log_reg.predict_proba(grid_data)\n",
        "\n",
        "# Création d'un dictionnaire pour les couleurs\n",
        "colors = {\"Setosa\": \"blue\", \"Versicolor\": \"green\", \"Virginica\": \"red\"}\n",
        "\n",
        "# Création des objets servant à l'affichage graphique\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# On ajoute notre contour\n",
        "pred_0 = grid_proba_pred[:, 0].reshape(grid_x.shape)\n",
        "pred_1 = grid_proba_pred[:, 1].reshape(grid_x.shape)\n",
        "pred_2 = grid_proba_pred[:, 2].reshape(grid_x.shape)\n",
        "ax.contourf(grid_x, grid_y, pred_0, cmap=\"Blues\", alpha=0.4)\n",
        "ax.contourf(grid_x, grid_y, pred_1, cmap=\"Greens\", alpha=0.4)\n",
        "ax.contourf(grid_x, grid_y, pred_2, cmap=\"Reds\", alpha=0.4)\n",
        "\n",
        "# On fait une boucle sur les espèces\n",
        "for species in data[\"variety\"].unique():\n",
        "\n",
        "  # Selection de l'espèce\n",
        "  species_df = data[data[\"variety\"] == species]\n",
        "\n",
        "  # Scatter plot de l'espèce\n",
        "  ax.scatter(x=species_df[\"petal.length\"],\n",
        "            y=species_df[\"petal.width\"],\n",
        "            c=colors[species],\n",
        "            label=species)\n",
        "\n",
        "# Ajout du nom des axes, d'un titre, d'une légende, et d'une grille\n",
        "ax.set_xlabel(\"Petal Length\")\n",
        "ax.set_ylabel(\"Petal Width\")\n",
        "ax.set_title(\"Iris\")\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "cJ--cNK4WlfY",
        "outputId": "1abe191d-a5ef-44ac-86b4-2ffb92cdf300"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercice\n",
        "\n",
        "Dans cet exercice, vous allez effectuer la même procédure que dans les exemples précédents, mais sur le jeu de données \"PimaIndiansDiabetes.csv\" (plus d'info https://www.rdocumentation.org/packages/mlbench/versions/2.1-3/topics/PimaIndiansDiabetes) et pour optimiser la mesure F1 (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html).\n",
        "\n",
        "* La variable dépendante est \"diabetes\", qui peut être \"pos\" ou \"neg\". Toutes les autres variables sont numériques.\n",
        "* Il s'agira de faire une régression linaire et une régression logistique régularisée. À vous de trouver l'intervalle des hyparamètres à tester.\n",
        "* La mesure à optimiser est la mesure F1, qui peut être calculée avec `sklearn.metrics.f1_score`.\n",
        "\n",
        "Bonne chance !"
      ],
      "metadata": {
        "id": "pZda0S9RyjmK"
      }
    }
  ]
}