8) Modélisation_1_(CamemBERT).ipynb

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      "source": [
        "**1. IMPORTER LIBRAIRIES**"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#!pip install -q transformers==4.31.0 tensorflow==2.12.0 sentencepiece\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "# Train_Test_Split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Camembert\n",
        "import tensorflow as tf\n",
        "import torch\n",
        "import torchvision\n",
        "import transformers\n",
        "from transformers import CamembertTokenizer, TFCamembertForSequenceClassification\n",
        "# from transformers import CamembertTokenizer, TFCamembertModel, CamembertForSequenceClassification\n",
        "\n",
        "# CNN\n",
        "import keras\n",
        "from keras.utils import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, GlobalAveragePooling2D, Activation\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Callbacks\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
        "\n",
        "# Performances\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Enregistrer le modèle\n",
        "from sklearn import model_selection\n",
        "import joblib\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
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      "source": [
        "**2. IMPORTER LE FICHIER**"
      ]
    },
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      "source": [
        "df = pd.read_csv(\"2. Pre-processing & Feature engineering_1.csv\")\n",
        "df = df[['commentaires', 'notes']]\n",
        "df = df[:1000] # Tester sur un échantillon de données\n",
        "# Remplace les notes 1,2,3,4,5 par 0,1,2,3,4\n",
        "df['notes'] = df['notes'].replace((1, 2, 3, 4, 5), (0, 1, 2, 3, 4))\n",
        "\n",
        "# Target / Features\n",
        "X = df['commentaires']\n",
        "y = df['notes']"
      ]
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      "source": [
        "**3. PRE-PROCESSING**"
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
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      "source": [
        "### TOKENIZATION ###\n",
        "# Initialisation du tokenizer de CamemBERT\n",
        "tokenizer = CamembertTokenizer.from_pretrained('camembert-base', do_lower_case=True)\n",
        "\n",
        "commentaires = df['commentaires'].to_list()\n",
        "# notes = df['notes'].to_list()\n",
        "\n",
        "max_len = 100\n",
        "# Tokenization : Convertir les phrases en tokens\n",
        "input_ids  = [tokenizer.encode(phrases, add_special_tokens=True, max_length=max_len) for phrases in commentaires]"
      ]
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      "execution_count": 7,
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      "metadata": {
        "id": "667138ee-c5a9-4e37-b0d9-d44d4b887fd8"
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      "outputs": [],
      "source": [
        "### PADDING ###\n",
        "input_ids = pad_sequences(input_ids, maxlen = max_len, dtype=\"long\", truncating=\"post\", padding=\"post\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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      "metadata": {
        "id": "bdd69c98-472e-4bef-8c05-a2e938800c5a"
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      "outputs": [],
      "source": [
        "### ATTENTION MASKS ###\n",
        "attention_masks = []\n",
        "for seq in input_ids:\n",
        "    seq_mask = [float(i>0) for i in seq]\n",
        "    attention_masks.append(seq_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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      "source": [
        "### DICHOTOMISATION ###\n",
        "y = df['notes']\n",
        "y = pd.get_dummies(y)\n",
        "# Remplacer les valeur booléennes par des entiers\n",
        "rep = {False: 0, True: 1}\n",
        "y[[0, 1, 2, 3, 4]] = y[[0, 1, 2, 3, 4]].replace(to_replace = rep)\n",
        "# Remplacement des colonnes avec le nom des classes\n",
        "colnames = {0:'Cl1',1:'Cl2', 2:'Cl3',3:'Cl4',4:'Cl5'}\n",
        "y = y.rename(columns=colnames)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae091d5c-133b-4a07-8420-4f3abf2ce8ea",
      "metadata": {
        "id": "ae091d5c-133b-4a07-8420-4f3abf2ce8ea"
      },
      "source": [
        "**4. TRAIN_TEST_SPLIT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "db445365-e21a-40f5-a46e-d43e61b07521",
      "metadata": {
        "colab": {
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        "outputId": "6de427fa-13bc-4a0d-e91b-cec197be5652"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train = (800, 100)\n",
            "y_train = (800, 5)\n",
            "X_test = (200, 100)\n",
            "y_test = (200, 5)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(input_ids,\n",
        "                                                                    y,\n",
        "                                                                    random_state=42,\n",
        "                                                                    test_size=0.2)\n",
        "### Vérification des shape ###\n",
        "print (\"X_train =\", X_train.shape)\n",
        "print (\"y_train =\", y_train.shape)\n",
        "print (\"X_test =\", X_test.shape)\n",
        "print (\"y_test =\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "098305a6-2454-48c2-958b-a11534d12024",
      "metadata": {
        "id": "098305a6-2454-48c2-958b-a11534d12024"
      },
      "source": [
        "**5. MODELISATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "2ec8ce99-9425-4a34-9992-90460fcf63fb",
      "metadata": {
        "id": "2ec8ce99-9425-4a34-9992-90460fcf63fb"
      },
      "outputs": [],
      "source": [
        "### CONSTANTES ###\n",
        "nb_class = y_train.shape[1]\n",
        "batch_size = 64\n",
        "epochs = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "5029de52-d27c-4e71-b806-3b493ccfcee7",
      "metadata": {
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      "outputs": [],
      "source": [
        "### CALLBACKS ###\n",
        "reduce_learning_rate = ReduceLROnPlateau(monitor = 'val_loss',\n",
        "                                         min_delta = 1e-1,\n",
        "                                         patience = 5,\n",
        "                                         factor = 5e-2,\n",
        "                                         cooldown = 2,\n",
        "                                         verbose = 1)\n",
        "\n",
        "early_stopping = EarlyStopping(monitor = 'val_loss',\n",
        "                               min_delta = 1e-3,\n",
        "                               patience = 3,\n",
        "                               verbose = 1,\n",
        "                               restore_best_weights = True)"
      ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFCamembertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFCamembertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "### IMPORTER LE MODELE CAMEMBERT ###\n",
        "model = TFCamembertForSequenceClassification.from_pretrained(\"camembert-base\", num_labels=nb_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "006781ee-7bea-4074-b251-e8bf52d7288c",
      "metadata": {
        "id": "006781ee-7bea-4074-b251-e8bf52d7288c"
      },
      "outputs": [],
      "source": [
        "### COMPILATION ###\n",
        "model.compile(optimizer = \"adam\",\n",
        "              loss = \"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "50401614-0e01-4607-8ff1-0fe2ae4b41fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "7003aba8-3a5a-4601-95c9-1a771f345cdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:AutoGraph could not transform <function infer_framework at 0x7cb96421ce50> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING: AutoGraph could not transform <function infer_framework at 0x7cb96421ce50> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "13/13 [==============================] - 1099s 76s/step - loss: 2.5548 - accuracy: 0.5138 - val_loss: 0.8865 - val_accuracy: 0.4450 - lr: 0.0010\n",
            "Epoch 2/3\n",
            "13/13 [==============================] - 954s 73s/step - loss: 2.7849 - accuracy: 0.4525 - val_loss: 0.7253 - val_accuracy: 0.4450 - lr: 0.0010\n",
            "Epoch 3/3\n",
            "13/13 [==============================] - 931s 72s/step - loss: 6.3264 - accuracy: 0.4550 - val_loss: 15.3928 - val_accuracy: 0.4450 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "### ENTRAÎNEMENT DU MODELE ###\n",
        "history = model.fit(X_train, y_train,\n",
        "                    validation_data = (X_test, y_test),\n",
        "                    batch_size = batch_size,\n",
        "                    epochs = epochs,\n",
        "                    callbacks = [reduce_learning_rate, early_stopping])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77be6875-6bdd-454e-99ce-e31af39a8a91",
      "metadata": {
        "id": "77be6875-6bdd-454e-99ce-e31af39a8a91"
      },
      "source": [
        "**6. PERFORMANCES DU MODELE**"
      ]
    },
    {
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          "text": [
            "7/7 [==============================] - 64s 9s/step\n",
            "Score modèle : 0.445\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        90\n",
            "           1       0.00      0.00      0.00         7\n",
            "           2       0.00      0.00      0.00         2\n",
            "           3       0.00      0.00      0.00        12\n",
            "           4       0.45      1.00      0.62        89\n",
            "\n",
            "    accuracy                           0.45       200\n",
            "   macro avg       0.09      0.20      0.12       200\n",
            "weighted avg       0.20      0.45      0.27       200\n",
            "\n"
          ]
        },
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              "Classe prédite   4\n",
              "Classe réelle     \n",
              "0               90\n",
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              "2                2\n",
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              "4               89"
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Classe prédite</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Classe réelle</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d4df47a-35e6-4813-83dc-b88df9451e5c')\"\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",
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              "  </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-0d4df47a-35e6-4813-83dc-b88df9451e5c 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-0d4df47a-35e6-4813-83dc-b88df9451e5c');\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-2b47688a-f0c4-4d41-ab6a-9b909d0bcfbf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2b47688a-f0c4-4d41-ab6a-9b909d0bcfbf')\"\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-2b47688a-f0c4-4d41-ab6a-9b909d0bcfbf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "confusion_matrix",
              "summary": "{\n  \"name\": \"confusion_matrix\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Classe r\\u00e9elle\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 2,\n        \"max\": 90,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7,\n          89,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_pred = model.predict(X_test)\n",
        "\n",
        "y_pred_labels = np.argmax(y_pred.logits, axis=1)\n",
        "y_test_labels = np.argmax(y_test, axis=1)\n",
        "print('Score modèle :', accuracy_score(y_test_labels, pred_labels))\n",
        "\n",
        "print(classification_report(y_test_labels, y_pred_labels))\n",
        "\n",
        "confusion_matrix = pd.crosstab(y_test_labels, y_pred_labels, rownames=['Classe réelle'], colnames=['Classe prédite'])\n",
        "display(confusion_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "5d5b7083-ed80-4d6a-adc2-ea593b9f9648",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "5d5b7083-ed80-4d6a-adc2-ea593b9f9648",
        "outputId": "2793bb34-48b9-46ae-ab08-e7a313b1922e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(121)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model acc by epoch')\n",
        "plt.ylabel('acc')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='right')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss by epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='right')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e05e7c48-28e8-4374-823e-8953e4cf567e",
      "metadata": {
        "id": "e05e7c48-28e8-4374-823e-8953e4cf567e"
      },
      "source": [
        "**7. ENREGISTREMENT DU MODELE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
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