TP03c_corrige.ipynb

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        "<a href=\"https://colab.research.google.com/github/gguex/ISH_ressources_cours_ML/blob/main/TP03c_corrige.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# TP 3 : Corrigé"
      ],
      "metadata": {
        "id": "kT1-ytum9B89"
      }
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      "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'] = [15, 8]\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 score F1\n",
        "from sklearn.metrics import f1_score\n",
        "# Pour encoder les sorties en vecteurs one-hot\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# On charge le fichier de données\n",
        "file_path = \"drive/MyDrive/Colab Notebooks/ml_data/TP3/PimaIndiansDiabetes.csv\"\n",
        "data = pd.read_csv(file_path)\n",
        "data"
      ],
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              "     pregnant  glucose  pressure  triceps  insulin  mass  pedigree  age  \\\n",
              "0           6      148        72       35        0  33.6     0.627   50   \n",
              "1           1       85        66       29        0  26.6     0.351   31   \n",
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              "4           0      137        40       35      168  43.1     2.288   33   \n",
              "..        ...      ...       ...      ...      ...   ...       ...  ...   \n",
              "763        10      101        76       48      180  32.9     0.171   63   \n",
              "764         2      122        70       27        0  36.8     0.340   27   \n",
              "765         5      121        72       23      112  26.2     0.245   30   \n",
              "766         1      126        60        0        0  30.1     0.349   47   \n",
              "767         1       93        70       31        0  30.4     0.315   23   \n",
              "\n",
              "    diabetes  \n",
              "0        pos  \n",
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              "2        pos  \n",
              "3        neg  \n",
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              "..       ...  \n",
              "763      neg  \n",
              "764      neg  \n",
              "765      neg  \n",
              "766      pos  \n",
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              "        document.querySelector('#id_037fd831-4952-4616-9bb8-93a3dd862a00 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\": 768,\n  \"fields\": [\n    {\n      \"column\": \"pregnant\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"triceps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.8841603203754405,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pedigree\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33132859501277484,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diabetes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"neg\",\n          \"pos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Les inputs sont toutes les données sauf \"diabetes\"\n",
        "inputs = data.drop(\"diabetes\", axis=1)\n",
        "# Les sorties sont la variable \"diabetes\", qu'on transforme en variable binaire\n",
        "outputs = (data[\"diabetes\"].to_numpy() == \"pos\")*1\n",
        "# On sépare en jeux d'entrainement/validation et test\n",
        "inputs_train_val, inputs_test, outputs_train_val, outputs_test \\\n",
        "  = train_test_split(inputs, outputs, test_size=0.2, stratify=outputs)"
      ],
      "metadata": {
        "id": "2KblI6gm2cTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# On fait le modèle linéaire\n",
        "linear_reg = LinearRegression()\n",
        "linear_reg.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "kW9UQxSj2mMH",
        "outputId": "0c32855c-48cb-4306-a63f-be95e71c9855"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\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-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  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-1 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-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# On calcule 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",
        "# On calcule le F1 train et test\n",
        "f1_lin_train = f1_score(outputs_train_val, outputs_lin_train)\n",
        "f1_lin_test = f1_score(outputs_test, outputs_lin_test)"
      ],
      "metadata": {
        "id": "6ZFJL2E23Xaz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Paramètres pour la CV\n",
        "params = np.linspace(0.001, 10, 100)\n",
        "param_dict = {\"C\": params}\n",
        "k = 10\n",
        "# Modèle avec un peu plus d'itérations\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=\"f1\")\n",
        "grid_search.fit(inputs_train_val, outputs_train_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "-EgRfgRa3lct",
        "outputId": "795666e0-5c3b-438f-8ea9-2be829aaffe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={'C': array([1.000e-03, 1.020e-01, 2.030e-01, 3.040e-01, 4.050e-01, 5.060e-01,\n",
              "       6.070e-01, 7.080e-01, 8.090e-01, 9.100e-01, 1.011e+00, 1.112e+00,\n",
              "       1.213e+00, 1.314e+00, 1.415e+00, 1.516e+00, 1.617e+00, 1.718e+00,\n",
              "       1.819e+00, 1.920e+00, 2.021e+00, 2.122e+00, 2.223e+00, 2.324e+00,\n",
              "       2.425e+00, 2.526e+00, 2.6...\n",
              "       6.667e+00, 6.768e+00, 6.869e+00, 6.970e+00, 7.071e+00, 7.172e+00,\n",
              "       7.273e+00, 7.374e+00, 7.475e+00, 7.576e+00, 7.677e+00, 7.778e+00,\n",
              "       7.879e+00, 7.980e+00, 8.081e+00, 8.182e+00, 8.283e+00, 8.384e+00,\n",
              "       8.485e+00, 8.586e+00, 8.687e+00, 8.788e+00, 8.889e+00, 8.990e+00,\n",
              "       9.091e+00, 9.192e+00, 9.293e+00, 9.394e+00, 9.495e+00, 9.596e+00,\n",
              "       9.697e+00, 9.798e+00, 9.899e+00, 1.000e+01])},\n",
              "             scoring='f1')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\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-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={&#x27;C&#x27;: array([1.000e-03, 1.020e-01, 2.030e-01, 3.040e-01, 4.050e-01, 5.060e-01,\n",
              "       6.070e-01, 7.080e-01, 8.090e-01, 9.100e-01, 1.011e+00, 1.112e+00,\n",
              "       1.213e+00, 1.314e+00, 1.415e+00, 1.516e+00, 1.617e+00, 1.718e+00,\n",
              "       1.819e+00, 1.920e+00, 2.021e+00, 2.122e+00, 2.223e+00, 2.324e+00,\n",
              "       2.425e+00, 2.526e+00, 2.6...\n",
              "       6.667e+00, 6.768e+00, 6.869e+00, 6.970e+00, 7.071e+00, 7.172e+00,\n",
              "       7.273e+00, 7.374e+00, 7.475e+00, 7.576e+00, 7.677e+00, 7.778e+00,\n",
              "       7.879e+00, 7.980e+00, 8.081e+00, 8.182e+00, 8.283e+00, 8.384e+00,\n",
              "       8.485e+00, 8.586e+00, 8.687e+00, 8.788e+00, 8.889e+00, 8.990e+00,\n",
              "       9.091e+00, 9.192e+00, 9.293e+00, 9.394e+00, 9.495e+00, 9.596e+00,\n",
              "       9.697e+00, 9.798e+00, 9.899e+00, 1.000e+01])},\n",
              "             scoring=&#x27;f1&#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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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=10, estimator=LogisticRegression(max_iter=10000),\n",
              "             param_grid={&#x27;C&#x27;: array([1.000e-03, 1.020e-01, 2.030e-01, 3.040e-01, 4.050e-01, 5.060e-01,\n",
              "       6.070e-01, 7.080e-01, 8.090e-01, 9.100e-01, 1.011e+00, 1.112e+00,\n",
              "       1.213e+00, 1.314e+00, 1.415e+00, 1.516e+00, 1.617e+00, 1.718e+00,\n",
              "       1.819e+00, 1.920e+00, 2.021e+00, 2.122e+00, 2.223e+00, 2.324e+00,\n",
              "       2.425e+00, 2.526e+00, 2.6...\n",
              "       6.667e+00, 6.768e+00, 6.869e+00, 6.970e+00, 7.071e+00, 7.172e+00,\n",
              "       7.273e+00, 7.374e+00, 7.475e+00, 7.576e+00, 7.677e+00, 7.778e+00,\n",
              "       7.879e+00, 7.980e+00, 8.081e+00, 8.182e+00, 8.283e+00, 8.384e+00,\n",
              "       8.485e+00, 8.586e+00, 8.687e+00, 8.788e+00, 8.889e+00, 8.990e+00,\n",
              "       9.091e+00, 9.192e+00, 9.293e+00, 9.394e+00, 9.495e+00, 9.596e+00,\n",
              "       9.697e+00, 9.798e+00, 9.899e+00, 1.000e+01])},\n",
              "             scoring=&#x27;f1&#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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.10200000000000001, 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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=0.10200000000000001, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot des scores\n",
        "f1_scores = grid_search.cv_results_[\"mean_test_score\"]\n",
        "# Plot des scores\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(params, f1_scores, c=\"red\")\n",
        "ax.set_xlabel(\"$C$\")\n",
        "ax.set_ylabel(\"$F1 score$\")\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "c1gXxGjB3zL-",
        "outputId": "ed6a3401-677c-4798-b9e7-c817ac30b17b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# On sauve le meilleur modèle\n",
        "best_C = grid_search.best_params_[\"C\"]\n",
        "best_log_reg = grid_search.best_estimator_\n",
        "# Les prédictions\n",
        "outputs_log_train = best_log_reg.predict(inputs_train_val)\n",
        "outputs_log_test = best_log_reg.predict(inputs_test)\n",
        "# Les scores f1\n",
        "f1_log_train = f1_score(outputs_train_val, outputs_log_train)\n",
        "f1_log_test = f1_score(outputs_test, outputs_log_test)"
      ],
      "metadata": {
        "id": "BcssWVgJ4Bsi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Le F1 pour le modèle linéaire est de \"\n",
        "f\"{f1_lin_train:.2%} (train) {f1_lin_test:.2%} (test)\")\n",
        "print(f\"Le F1 pour le modèle logistique (C={best_C:.4f}) est \"\n",
        "f\"de {f1_log_train:.2%} (train) {f1_log_test:.2%} (test)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2grm2qI5duv",
        "outputId": "a10b8ec8-a6b8-4746-e3ab-9d2cfdfca428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Le F1 pour le modèle linéaire est de 62.27% (train) 64.58% (test)\n",
            "Le F1 pour le modèle logistique (C=0.1020) est de 63.92% (train) 63.92% (test)\n"
          ]
        }
      ]
    }
  ]
}