Classification_commentaires_toxiques.ipynb

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        "<a href=\"https://colab.research.google.com/github/GatienVilain/Toxic-Comment-classification/blob/main/Classification_commentaires_toxiques.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlkF8etRlKlu"
      },
      "source": [
        "<h1> Classification de commentaires <h1>\n",
        "\n",
        "# Sommaire\n",
        "\n",
        "1. [Importation des packages](#scrollTo=_lyJzHuKRNWX)\n",
        "2. [Importation des données](#scrollTo=xZLpUMItRWc0)\n",
        "3. [Etude du jeu de données](#scrollTo=-kUWzBzISpsK)\n",
        "  1. [Les données d'entraînement](#scrollTo=rpYAjfU1a6b5)\n",
        "  2. [Les données de test](#scrollTo=K8j6JdPJbg39)\n",
        "4. [Préparation des données](#scrollTo=YIzpSrAjSsAM)\n",
        "  1. [Création des fonctions](#scrollTo=FNpY_oJ1KjWc)\n",
        "  2. [Nettoyage des données](#scrollTo=hp2PjMtJK3ht)\n",
        "  3. [Vectorisation des données](#scrollTo=rgzmbm-XYGQm)\n",
        "5. [Entraînement du modèle baseline](#scrollTo=ndSnViq8bPif)\n",
        "6. [Itération de la modélisation](#scrollTo=2a8IWbTFTHXh)\n",
        "  1. [Utilisation d'un modèle de réseaux de neuronnes avec LSTM](#scrollTo=kY6nFhoGLSSB)\n",
        "  2. [Modification d'un paramètre concernant la préparation des données : Max length = 30 (taille maximale du commentaire)](#scrollTo=90ozOXSjLYR2)\n",
        "  3. [Nouveau modèle en limitant la taille du bag of words à 20 000](#scrollTo=qCM-Qb8BNhVp)\n",
        "  4. [Utilisation d'une matrice d'embedding (Glove6B)](#srollTo=7Gs_PGqIQgtC)\n",
        "7. [Exemple d'utilisation de notre pipeline](#scrollTo=-k3pjv0Q1Lcm)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_lyJzHuKRNWX"
      },
      "source": [
        "# 1. Importation des packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "613a1cc3-e72d-4817-e70a-0c3c6088ed0b"
      },
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-addons\n",
            "  Downloading tensorflow_addons-0.19.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m32.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.8/dist-packages (from tensorflow-addons) (2.7.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tensorflow-addons) (23.0)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.19.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-addons"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MUj96QiIQsJ_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import nltk, string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import TweetTokenizer\n",
        "import re\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer \n",
        "from sklearn.model_selection import train_test_split  \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "import random\n",
        "\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n",
        "import tensorflow_addons as tfa\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.utils import class_weight\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZLpUMItRWc0"
      },
      "source": [
        "# 2. Importation des données"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbE3MzVKRaV2"
      },
      "source": [
        "Ajoutez un raccourci de ce dossier à votre google drive :\n",
        "\n",
        "https://drive.google.com/drive/folders/1mx-CAzT10YKrmxHfYDP_1Oef7PVGUr7s?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FktfCVaRVrs",
        "outputId": "837ec680-d86c-45c3-e87f-bdc370d30668"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVhdr9Lu1Fuu"
      },
      "source": [
        "Importe les données sources :\n",
        "\n",
        "(Si vous avez préparer les données en amonts, vous pouvez directement aller à la partie [Vectorisation des données](#scrollTo=rgzmbm-XYGQm).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qky64Lq8Rocy"
      },
      "outputs": [],
      "source": [
        "data_train = pd.read_csv('/content/drive/MyDrive/data_classification_commentaires_toxiques/train.csv')\n",
        "data_test = pd.read_csv('/content/drive/MyDrive/data_classification_commentaires_toxiques/test.csv')\n",
        "data_test_labels = pd.read_csv('/content/drive/MyDrive/data_classification_commentaires_toxiques/test_labels.csv')\n",
        "data_test = pd.merge(data_test, data_test_labels, on = ['id'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kUWzBzISpsK"
      },
      "source": [
        "# 3. Etude du jeu de données"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpYAjfU1a6b5"
      },
      "source": [
        "## 3.1 Les données d'entraînement"
      ]
    },
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              "                  id                                       comment_text  \\\n",
              "50  001d8e7be417776a  \"\\n\\nBI, you said you wanted to talk\\n\\nAt the...   \n",
              "51  001dc38a83d420cf  GET FUCKED UP. GET FUCKEEED UP.  GOT A DRINK T...   \n",
              "52  001e89eb3f0b0915  Are you threatening me for disputing neutralit...   \n",
              "53  001ee16c46a99262  Thanks! Undeletion was more than I'd hoped for...   \n",
              "54  001ffdcc3e7fb49c  Awesome! Then I'll simply disregard your notic...   \n",
              "55  0020e7119b96eeeb  Stupid peace of shit stop deleting my stuff as...   \n",
              "56  0020fd96ed3b8c8b  =Tony Sidaway is obviously a fistfuckee. He lo...   \n",
              "57  00218d74784ce50b  \"\\n\\n GA Review II \\n\\nI'm sorry to say this, ...   \n",
              "58  0021fe88bc4da3e6  My Band Page's deletion. You thought I was gon...   \n",
              "59  002264ea4d5f2887  Why can't you believe how fat Artie is? Did yo...   \n",
              "\n",
              "    toxic  severe_toxic  obscene  threat  insult  identity_hate  \n",
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    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "zK_GX0AD0Ml-"
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      "source": [
        "Commentaires type :"
      ]
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    {
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              "'\"\\n\\n GA Review II \\n\\nI\\'m sorry to say this, but I have to fail this article\\'s GAN. The several major problems that  brought up in this article\\'s previous GA assessment have not been issued. As s/he has said before me, \"\"he article\\'s WP:LEAD should be longer. The History, Media attention, Academics, Student Government, Demographics, Trivia, and Additional Information section are very deficient in citiations. Also, Triva and Additional information should be integrated into the rest of the article.\"\" Again, please look over WP:WIAGA and fix these problem, then renominate it for GA-class. However, I have re-assessed this article as B-class, as it does meet B-class criteria. My regards,   \"'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_train['comment_text'][57]"
      ]
    },
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              "50    \"\\n\\nBI, you said you wanted to talk\\n\\nAt the...\n",
              "51    GET FUCKED UP. GET FUCKEEED UP.  GOT A DRINK T...\n",
              "52    Are you threatening me for disputing neutralit...\n",
              "53    Thanks! Undeletion was more than I'd hoped for...\n",
              "54    Awesome! Then I'll simply disregard your notic...\n",
              "55    Stupid peace of shit stop deleting my stuff as...\n",
              "56    =Tony Sidaway is obviously a fistfuckee. He lo...\n",
              "57    \"\\n\\n GA Review II \\n\\nI'm sorry to say this, ...\n",
              "58    My Band Page's deletion. You thought I was gon...\n",
              "59    Why can't you believe how fat Artie is? Did yo...\n",
              "Name: comment_text, dtype: object"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_train['comment_text'][50:60]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1i-Vgj41J6k"
      },
      "source": [
        "Proportion de données toxique dans les données d'entrainement :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOwwlvGfdbIV",
        "outputId": "7903e0b6-7905-4e52-d0ec-21d51989693b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre de donnée totale :  159571\n"
          ]
        }
      ],
      "source": [
        "number_of_lines = data_train['toxic'].count()\n",
        "print('Nombre de donnée totale : ', number_of_lines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQfu0_Sy3P0s",
        "outputId": "18f353fe-5a38-4e68-ce6e-1f5cf663fea0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Proportion de données toxique dans les données d'entrainement (en nombre) :\n",
            " toxic            15294\n",
            "severe_toxic      1595\n",
            "obscene           8449\n",
            "threat             478\n",
            "insult            7877\n",
            "identity_hate     1405\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print( \"Proportion de données toxique dans les données d'entrainement (en nombre) :\\n\",\n",
        "      data_train.drop(columns=['id', 'comment_text']).sum()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFdTMNteSaD3",
        "outputId": "6abb3e72-ddd5-4e18-e112-e46e2aceff80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Proportion de données toxique dans les données d'entrainement (en pourcentage) :\n",
            " toxic            9.584\n",
            "severe_toxic     1.000\n",
            "obscene          5.295\n",
            "threat           0.300\n",
            "insult           4.936\n",
            "identity_hate    0.880\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print( \"Proportion de données toxique dans les données d'entrainement (en pourcentage) :\\n\",\n",
        "      (data_train.drop(columns=['id', 'comment_text']).sum() / number_of_lines * 100 ).round(3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8j6JdPJbg39"
      },
      "source": [
        "## 3.2 Les données de test\n",
        "\n",
        "On fait les mêmes analyses pour les données de test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PbZ2t-DZB6wE",
        "outputId": "43163348-0525-4902-f028-4d1ba8ae70e9"
      },
      "outputs": [
        {
          "data": {
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              "      <th>57</th>\n",
              "      <td>0016ec04bb0f869a</td>\n",
              "      <td>== Hebrean-Judeo-Christian == \\n\\n What is Heb...</td>\n",
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              "      <th>58</th>\n",
              "      <td>00177176f33f587e</td>\n",
              "      <td>== Can you work your magic? == \\n\\n Hi.  I was...</td>\n",
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              "      <td>:Fuck off, you anti-semitic cunt.  |</td>\n",
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            "text/plain": [
              "                  id                                       comment_text  \\\n",
              "50  001421530a1aa622  I have been perfectly civil in what quite clea...   \n",
              "51  001426f56de6a49b  *I agree with Billfruge. The author describes ...   \n",
              "52  00148cabab3f0abd  ==Category:Italian Jews== \\n\\n :Category:Itali...   \n",
              "53  0015d9af6dbc03c0  == Someday Never == \\n\\n Why was the image del...   \n",
              "54  00166b5f5bf2d758  \", 5 December 2012 (UTC) \\n\\n :::The above is,...   \n",
              "55  0016909ad784fe8d  \":I see that you moved  to Talat Hamdani. Perh...   \n",
              "56  0016b94c8b20ffa6  I WILL BURN YOU TO HELL IF YOU REVOKE MY TALK ...   \n",
              "57  0016ec04bb0f869a  == Hebrean-Judeo-Christian == \\n\\n What is Heb...   \n",
              "58  00177176f33f587e  == Can you work your magic? == \\n\\n Hi.  I was...   \n",
              "59  0017d4d47894af05               :Fuck off, you anti-semitic cunt.  |   \n",
              "\n",
              "    toxic  severe_toxic  obscene  threat  insult  identity_hate  \n",
              "50     -1            -1       -1      -1      -1             -1  \n",
              "51      0             0        0       0       0              0  \n",
              "52     -1            -1       -1      -1      -1             -1  \n",
              "53     -1            -1       -1      -1      -1             -1  \n",
              "54     -1            -1       -1      -1      -1             -1  \n",
              "55     -1            -1       -1      -1      -1             -1  \n",
              "56      0             0        0       0       0              0  \n",
              "57     -1            -1       -1      -1      -1             -1  \n",
              "58      0             0        0       0       0              0  \n",
              "59      1             0        1       0       1              0  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_test[50:60]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zs9SpomWB6wg"
      },
      "source": [
        "La valeur -1 que l’on peut retrouver sur certain ligne indique que le commentaire n’a pas été utilisé. On les supprimera lors de la préparation des données.\n",
        "\n",
        "Commentaires type :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "wNXpy3Xh-fLj",
        "outputId": "2b6d05ee-a316-4c0e-e3b5-8d6dcf228e0c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"== Can you work your magic? == \\n\\n Hi.  I was wondring if you had time to help out with Wikipedia:Featured article candidates/W. E. B. Du Bois/archive1.  Any input would be appreciated:  a quick scan or a full review.  I've noticed your work before and I respect your skills. Thanks.\""
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_test['comment_text'][58]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOX_5YxG-fL9",
        "outputId": "9ec9b1eb-0245-46fa-c856-f6b74b618b25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50    I have been perfectly civil in what quite clea...\n",
              "51    *I agree with Billfruge. The author describes ...\n",
              "52    ==Category:Italian Jews== \\n\\n :Category:Itali...\n",
              "53    == Someday Never == \\n\\n Why was the image del...\n",
              "54    \", 5 December 2012 (UTC) \\n\\n :::The above is,...\n",
              "55    \":I see that you moved  to Talat Hamdani. Perh...\n",
              "56    I WILL BURN YOU TO HELL IF YOU REVOKE MY TALK ...\n",
              "57    == Hebrean-Judeo-Christian == \\n\\n What is Heb...\n",
              "58    == Can you work your magic? == \\n\\n Hi.  I was...\n",
              "59                 :Fuck off, you anti-semitic cunt.  |\n",
              "Name: comment_text, dtype: object"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_test['comment_text'][50:60]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpVQO16l-fL_"
      },
      "source": [
        "Proportion de données toxique dans les données de test :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EryWCA3b-fL_",
        "outputId": "6de67258-0670-4cb1-8ebc-eeca3236c433"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre de donnée totale :  153164\n"
          ]
        }
      ],
      "source": [
        "number_of_lines = data_test['toxic'].count()\n",
        "print('Nombre de donnée totale : ', number_of_lines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-adi__NBvPkv",
        "outputId": "79553825-baa9-449a-c1b2-7a88ed3e7a32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nombre de données totales exploitables (sans les -1) :  63978\n"
          ]
        }
      ],
      "source": [
        "number_of_lines = data_test[data_test['toxic'] >= 0]['toxic'].count()\n",
        "print('Nombre de données totales exploitables (sans les -1) : ', number_of_lines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUJNgxoP-fMA",
        "outputId": "0a060866-08aa-4b4d-8b70-ad5bec1e90a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Proportion de données toxique dans les données de test (en nombre) :\n",
            " toxic            6090\n",
            "severe_toxic      367\n",
            "obscene          3691\n",
            "threat            211\n",
            "insult           3427\n",
            "identity_hate     712\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print( \"Proportion de données toxique dans les données de test (en nombre) :\\n\",\n",
        "      data_test.drop(columns=['id', 'comment_text'])[data_test['toxic'] >= 0].sum()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAkVZ1Ec-fMA",
        "outputId": "08b26b4d-e64d-4275-d40e-54e7a4ffe721"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Proportion de données toxique dans les données de test (en pourcentage) :\n",
            " toxic            9.519\n",
            "severe_toxic     0.574\n",
            "obscene          5.769\n",
            "threat           0.330\n",
            "insult           5.357\n",
            "identity_hate    1.113\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print( \"Proportion de données toxique dans les données de test (en pourcentage) :\\n\",\n",
        "      (data_test.drop(columns=['id', 'comment_text'])[data_test['toxic'] >= 0].sum() / number_of_lines * 100 ).round(3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIzpSrAjSsAM"
      },
      "source": [
        "# 4. Préparation des données"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNpY_oJ1KjWc"
      },
      "source": [
        "## 4.1 Création des fonctions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ed3EnuTEL0wd"
      },
      "source": [
        "Récupère les stopwords et le tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WMk85t8S_zX",
        "outputId": "fc441216-1778-424b-fccb-b17733827228"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = TweetTokenizer(strip_handles=True)\n",
        "nltk.download('stopwords')\n",
        "stop_words = nltk.corpus.stopwords.words('english')\n",
        "corpus = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4z2klncJCEQ"
      },
      "source": [
        "On crée une fonction prepare_string() pour nettoyer facilement les commentaires (retirer les abbréviations, homogénéiser les expressions, etc…) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "74MXPhDeSOyV"
      },
      "outputs": [],
      "source": [
        "def clean(tweet): \n",
        "            \n",
        "    # Contractions\n",
        "    tweet = re.sub(r\"he's\", \"he is\", tweet)\n",
        "    tweet = re.sub(r\"there's\", \"there is\", tweet)\n",
        "    tweet = re.sub(r\"We're\", \"We are\", tweet)\n",
        "    tweet = re.sub(r\"That's\", \"That is\", tweet)\n",
        "    tweet = re.sub(r\"won't\", \"will not\", tweet)\n",
        "    tweet = re.sub(r\"they're\", \"they are\", tweet)\n",
        "    tweet = re.sub(r\"Can't\", \"Cannot\", tweet)\n",
        "    tweet = re.sub(r\"wasn't\", \"was not\", tweet)\n",
        "    tweet = re.sub(r\"don\\x89Ûªt\", \"do not\", tweet)\n",
        "    tweet = re.sub(r\"aren't\", \"are not\", tweet)\n",
        "    tweet = re.sub(r\"isn't\", \"is not\", tweet)\n",
        "    tweet = re.sub(r\"What's\", \"What is\", tweet)\n",
        "    tweet = re.sub(r\"haven't\", \"have not\", tweet)\n",
        "    tweet = re.sub(r\"hasn't\", \"has not\", tweet)\n",
        "    tweet = re.sub(r\"There's\", \"There is\", tweet)\n",
        "    tweet = re.sub(r\"He's\", \"He is\", tweet)\n",
        "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
        "    tweet = re.sub(r\"You're\", \"You are\", tweet)\n",
        "    tweet = re.sub(r\"I'M\", \"I am\", tweet)\n",
        "    tweet = re.sub(r\"shouldn't\", \"should not\", tweet)\n",
        "    tweet = re.sub(r\"wouldn't\", \"would not\", tweet)\n",
        "    tweet = re.sub(r\"i'm\", \"I am\", tweet)\n",
        "    tweet = re.sub(r\"I\\x89Ûªm\", \"I am\", tweet)\n",
        "    tweet = re.sub(r\"I'm\", \"I am\", tweet)\n",
        "    tweet = re.sub(r\"Isn't\", \"is not\", tweet)\n",
        "    tweet = re.sub(r\"Here's\", \"Here is\", tweet)\n",
        "    tweet = re.sub(r\"you've\", \"you have\", tweet)\n",
        "    tweet = re.sub(r\"you\\x89Ûªve\", \"you have\", tweet)\n",
        "    tweet = re.sub(r\"we're\", \"we are\", tweet)\n",
        "    tweet = re.sub(r\"what's\", \"what is\", tweet)\n",
        "    tweet = re.sub(r\"couldn't\", \"could not\", tweet)\n",
        "    tweet = re.sub(r\"we've\", \"we have\", tweet)\n",
        "    tweet = re.sub(r\"it\\x89Ûªs\", \"it is\", tweet)\n",
        "    tweet = re.sub(r\"doesn\\x89Ûªt\", \"does not\", tweet)\n",
        "    tweet = re.sub(r\"It\\x89Ûªs\", \"It is\", tweet)\n",
        "    tweet = re.sub(r\"Here\\x89Ûªs\", \"Here is\", tweet)\n",
        "    tweet = re.sub(r\"who's\", \"who is\", tweet)\n",
        "    tweet = re.sub(r\"I\\x89Ûªve\", \"I have\", tweet)\n",
        "    tweet = re.sub(r\"y'all\", \"you all\", tweet)\n",
        "    tweet = re.sub(r\"can\\x89Ûªt\", \"cannot\", tweet)\n",
        "    tweet = re.sub(r\"would've\", \"would have\", tweet)\n",
        "    tweet = re.sub(r\"it'll\", \"it will\", tweet)\n",
        "    tweet = re.sub(r\"we'll\", \"we will\", tweet)\n",
        "    tweet = re.sub(r\"wouldn\\x89Ûªt\", \"would not\", tweet)\n",
        "    tweet = re.sub(r\"We've\", \"We have\", tweet)\n",
        "    tweet = re.sub(r\"he'll\", \"he will\", tweet)\n",
        "    tweet = re.sub(r\"Y'all\", \"You all\", tweet)\n",
        "    tweet = re.sub(r\"Weren't\", \"Were not\", tweet)\n",
        "    tweet = re.sub(r\"Didn't\", \"Did not\", tweet)\n",
        "    tweet = re.sub(r\"they'll\", \"they will\", tweet)\n",
        "    tweet = re.sub(r\"they'd\", \"they would\", tweet)\n",
        "    tweet = re.sub(r\"DON'T\", \"DO NOT\", tweet)\n",
        "    tweet = re.sub(r\"That\\x89Ûªs\", \"That is\", tweet)\n",
        "    tweet = re.sub(r\"they've\", \"they have\", tweet)\n",
        "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
        "    tweet = re.sub(r\"should've\", \"should have\", tweet)\n",
        "    tweet = re.sub(r\"You\\x89Ûªre\", \"You are\", tweet)\n",
        "    tweet = re.sub(r\"where's\", \"where is\", tweet)\n",
        "    tweet = re.sub(r\"Don\\x89Ûªt\", \"Do not\", tweet)\n",
        "    tweet = re.sub(r\"we'd\", \"we would\", tweet)\n",
        "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
        "    tweet = re.sub(r\"weren't\", \"were not\", tweet)\n",
        "    tweet = re.sub(r\"They're\", \"They are\", tweet)\n",
        "    tweet = re.sub(r\"Can\\x89Ûªt\", \"Cannot\", tweet)\n",
        "    tweet = re.sub(r\"you\\x89Ûªll\", \"you will\", tweet)\n",
        "    tweet = re.sub(r\"I\\x89Ûªd\", \"I would\", tweet)\n",
        "    tweet = re.sub(r\"let's\", \"let us\", tweet)\n",
        "    tweet = re.sub(r\"it's\", \"it is\", tweet)\n",
        "    tweet = re.sub(r\"can't\", \"cannot\", tweet)\n",
        "    tweet = re.sub(r\"don't\", \"do not\", tweet)\n",
        "    tweet = re.sub(r\"you're\", \"you are\", tweet)\n",
        "    tweet = re.sub(r\"i've\", \"I have\", tweet)\n",
        "    tweet = re.sub(r\"that's\", \"that is\", tweet)\n",
        "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
        "    tweet = re.sub(r\"doesn't\", \"does not\", tweet)\n",
        "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
        "    tweet = re.sub(r\"didn't\", \"did not\", tweet)\n",
        "    tweet = re.sub(r\"ain't\", \"am not\", tweet)\n",
        "    tweet = re.sub(r\"you'll\", \"you will\", tweet)\n",
        "    tweet = re.sub(r\"I've\", \"I have\", tweet)\n",
        "    tweet = re.sub(r\"Don't\", \"do not\", tweet)\n",
        "    tweet = re.sub(r\"I'll\", \"I will\", tweet)\n",
        "    tweet = re.sub(r\"I'd\", \"I would\", tweet)\n",
        "    tweet = re.sub(r\"Let's\", \"Let us\", tweet)\n",
        "    tweet = re.sub(r\"you'd\", \"You would\", tweet)\n",
        "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
        "    tweet = re.sub(r\"Ain't\", \"am not\", tweet)\n",
        "    tweet = re.sub(r\"Haven't\", \"Have not\", tweet)\n",
        "    tweet = re.sub(r\"Could've\", \"Could have\", tweet)\n",
        "    tweet = re.sub(r\"youve\", \"you have\", tweet)  \n",
        "    tweet = re.sub(r\"donå«t\", \"do not\", tweet)  \n",
        "    \n",
        "    tweet = re.sub(r\"some1\", \"someone\", tweet)\n",
        "    tweet = re.sub(r\"yrs\", \"years\", tweet)\n",
        "    tweet = re.sub(r\"hrs\", \"hours\", tweet)\n",
        "    tweet = re.sub(r\"2morow|2moro\", \"tomorrow\", tweet)\n",
        "    tweet = re.sub(r\"2day\", \"today\", tweet)\n",
        "    tweet = re.sub(r\"4got|4gotten\", \"forget\", tweet)\n",
        "    tweet = re.sub(r\"b-day|bday\", \"b-day\", tweet)\n",
        "    tweet = re.sub(r\"mother's\", \"mother\", tweet)\n",
        "    tweet = re.sub(r\"mom's\", \"mom\", tweet)\n",
        "    tweet = re.sub(r\"dad's\", \"dad\", tweet)\n",
        "    tweet = re.sub(r\"hahah|hahaha|hahahaha\", \"haha\", tweet)\n",
        "    tweet = re.sub(r\"lmao|lolz|rofl\", \"lol\", tweet)\n",
        "    tweet = re.sub(r\"thanx|thnx\", \"thanks\", tweet)\n",
        "    tweet = re.sub(r\"goood\", \"good\", tweet)\n",
        "    tweet = re.sub(r\"some1\", \"someone\", tweet)\n",
        "    tweet = re.sub(r\"some1\", \"someone\", tweet)\n",
        "    # Character entity references\n",
        "    tweet = re.sub(r\"&gt;\", \">\", tweet)\n",
        "    tweet = re.sub(r\"&lt;\", \"<\", tweet)\n",
        "    tweet = re.sub(r\"&amp;\", \"&\", tweet)\n",
        "    # Typos, slang and informal abbreviations\n",
        "    tweet = re.sub(r\"w/e\", \"whatever\", tweet)\n",
        "    tweet = re.sub(r\"w/\", \"with\", tweet)\n",
        "    tweet = re.sub(r\"<3\", \"love\", tweet)\n",
        "    # Urls\n",
        "    tweet = re.sub(r\"http\\S+\", \"\", tweet)\n",
        "    # Numbers\n",
        "    tweet = re.sub(r'[0-9]', '', tweet)\n",
        "    # Eliminating the mentions\n",
        "    tweet = re.sub(\"(@[A-Za-z0-9_]+)\",\"\", tweet)\n",
        "    # Remove punctuation and special chars (keep '!')\n",
        "    for p in string.punctuation.replace('!', ''):\n",
        "        tweet = tweet.replace(p, '')\n",
        "        \n",
        "    # ... and ..\n",
        "    tweet = tweet.replace('...', ' ... ')\n",
        "    if '...' not in tweet:\n",
        "        tweet = tweet.replace('..', ' ... ')\n",
        "        \n",
        "    # Tokenize\n",
        "    tweet_words = tokenizer.tokenize(tweet)\n",
        "    \n",
        "    # Eliminating the word if its length is less than 3\n",
        "    tweet = [w for w in tweet_words if len(w)>2]\n",
        "    \n",
        "    # remove stopwords\n",
        "    tweet = [w.lower() for w in tweet if not w in stop_words]  \n",
        "    \n",
        "    corpus.append(tweet)\n",
        "    \n",
        "    # join back\n",
        "    tweet = ' '.join(tweet)\n",
        "        \n",
        "        \n",
        "    return tweet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jYRcB1YDS4KB"
      },
      "outputs": [],
      "source": [
        "variable_name = \"\"\n",
        "abbreviations = {\n",
        "    \"$\" : \" dollar \",\n",
        "    \"€\" : \" euro \",\n",
        "    \"4ao\" : \"for adults only\",\n",
        "    \"a.m\" : \"before midday\",\n",
        "    \"a3\" : \"anytime anywhere anyplace\",\n",
        "    \"aamof\" : \"as a matter of fact\",\n",
        "    \"acct\" : \"account\",\n",
        "    \"adih\" : \"another day in hell\",\n",
        "    \"afaic\" : \"as far as i am concerned\",\n",
        "    \"afaict\" : \"as far as i can tell\",\n",
        "    \"afaik\" : \"as far as i know\",\n",
        "    \"afair\" : \"as far as i remember\",\n",
        "    \"afk\" : \"away from keyboard\",\n",
        "    \"app\" : \"application\",\n",
        "    \"approx\" : \"approximately\",\n",
        "    \"apps\" : \"applications\",\n",
        "    \"asap\" : \"as soon as possible\",\n",
        "    \"asl\" : \"age, sex, location\",\n",
        "    \"atk\" : \"at the keyboard\",\n",
        "    \"ave.\" : \"avenue\",\n",
        "    \"aymm\" : \"are you my mother\",\n",
        "    \"ayor\" : \"at your own risk\", \n",
        "    \"b&b\" : \"bed and breakfast\",\n",
        "    \"b+b\" : \"bed and breakfast\",\n",
        "    \"b.c\" : \"before christ\",\n",
        "    \"b2b\" : \"business to business\",\n",
        "    \"b2c\" : \"business to customer\",\n",
        "    \"b4\" : \"before\",\n",
        "    \"b4n\" : \"bye for now\",\n",
        "    \"b@u\" : \"back at you\",\n",
        "    \"bae\" : \"before anyone else\",\n",
        "    \"bak\" : \"back at keyboard\",\n",
        "    \"bbbg\" : \"bye bye be good\",\n",
        "    \"bbc\" : \"british broadcasting corporation\",\n",
        "    \"bbias\" : \"be back in a second\",\n",
        "    \"bbl\" : \"be back later\",\n",
        "    \"bbs\" : \"be back soon\",\n",
        "    \"be4\" : \"before\",\n",
        "    \"bfn\" : \"bye for now\",\n",
        "    \"blvd\" : \"boulevard\",\n",
        "    \"bout\" : \"about\",\n",
        "    \"brb\" : \"be right back\",\n",
        "    \"bros\" : \"brothers\",\n",
        "    \"brt\" : \"be right there\",\n",
        "    \"bsaaw\" : \"big smile and a wink\",\n",
        "    \"btw\" : \"by the way\",\n",
        "    \"bwl\" : \"bursting with laughter\",\n",
        "    \"c/o\" : \"care of\",\n",
        "    \"cet\" : \"central european time\",\n",
        "    \"cf\" : \"compare\",\n",
        "    \"cia\" : \"central intelligence agency\",\n",
        "    \"csl\" : \"can not stop laughing\",\n",
        "    \"cu\" : \"see you\",\n",
        "    \"cul8r\" : \"see you later\",\n",
        "    \"cv\" : \"curriculum vitae\",\n",
        "    \"cwot\" : \"complete waste of time\",\n",
        "    \"cya\" : \"see you\",\n",
        "    \"cyt\" : \"see you tomorrow\",\n",
        "    \"dae\" : \"does anyone else\",\n",
        "    \"dbmib\" : \"do not bother me i am busy\",\n",
        "    \"diy\" : \"do it yourself\",\n",
        "    \"dm\" : \"direct message\",\n",
        "    \"dwh\" : \"during work hours\",\n",
        "    \"e123\" : \"easy as one two three\",\n",
        "    \"eet\" : \"eastern european time\",\n",
        "    \"eg\" : \"example\",\n",
        "    \"embm\" : \"early morning business meeting\",\n",
        "    \"encl\" : \"enclosed\",\n",
        "    \"encl.\" : \"enclosed\",\n",
        "    \"etc\" : \"and so on\",\n",
        "    \"faq\" : \"frequently asked questions\",\n",
        "    \"fawc\" : \"for anyone who cares\",\n",
        "    \"fb\" : \"facebook\",\n",
        "    \"fc\" : \"fingers crossed\",\n",
        "    \"fig\" : \"figure\",\n",
        "    \"fimh\" : \"forever in my heart\", \n",
        "    \"ft.\" : \"feet\",\n",
        "    \"ft\" : \"featuring\",\n",
        "    \"ftl\" : \"for the loss\",\n",
        "    \"ftw\" : \"for the win\",\n",
        "    \"fwiw\" : \"for what it is worth\",\n",
        "    \"fyi\" : \"for your information\",\n",
        "    \"g9\" : \"genius\",\n",
        "    \"gahoy\" : \"get a hold of yourself\",\n",
        "    \"gal\" : \"get a life\",\n",
        "    \"gcse\" : \"general certificate of secondary education\",\n",
        "    \"gfn\" : \"gone for now\",\n",
        "    \"gg\" : \"good game\",\n",
        "    \"gl\" : \"good luck\",\n",
        "    \"glhf\" : \"good luck have fun\",\n",
        "    \"gmt\" : \"greenwich mean time\",\n",
        "    \"gmta\" : \"great minds think alike\",\n",
        "    \"gn\" : \"good night\",\n",
        "    \"g.o.a.t\" : \"greatest of all time\",\n",
        "    \"goat\" : \"greatest of all time\",\n",
        "    \"goi\" : \"get over it\",\n",
        "    \"gps\" : \"global positioning system\",\n",
        "    \"gr8\" : \"great\",\n",
        "    \"gratz\" : \"congratulations\",\n",
        "    \"gyal\" : \"girl\",\n",
        "    \"h&c\" : \"hot and cold\",\n",
        "    \"hp\" : \"horsepower\",\n",
        "    \"hr\" : \"hour\",\n",
        "    \"hrh\" : \"his royal highness\",\n",
        "    \"ht\" : \"height\",\n",
        "    \"ibrb\" : \"i will be right back\",\n",
        "    \"ic\" : \"i see\",\n",
        "    \"icq\" : \"i seek you\",\n",
        "    \"icymi\" : \"in case you missed it\",\n",
        "    \"idc\" : \"i do not care\",\n",
        "    \"idgadf\" : \"i do not give a damn fuck\",\n",
        "    \"idgaf\" : \"i do not give a fuck\",\n",
        "    \"idk\" : \"i do not know\",\n",
        "    \"ie\" : \"that is\",\n",
        "    \"i.e\" : \"that is\",\n",
        "    \"ifyp\" : \"i feel your pain\",\n",
        "    \"IG\" : \"instagram\",\n",
        "    \"iirc\" : \"if i remember correctly\",\n",
        "    \"ilu\" : \"i love you\",\n",
        "    \"ily\" : \"i love you\",\n",
        "    \"imho\" : \"in my humble opinion\",\n",
        "    \"imo\" : \"in my opinion\",\n",
        "    \"imu\" : \"i miss you\",\n",
        "    \"iow\" : \"in other words\",\n",
        "    \"irl\" : \"in real life\",\n",
        "    \"j4f\" : \"just for fun\",\n",
        "    \"jic\" : \"just in case\",\n",
        "    \"jk\" : \"just kidding\",\n",
        "    \"jsyk\" : \"just so you know\",\n",
        "    \"l8r\" : \"later\",\n",
        "    \"lb\" : \"pound\",\n",
        "    \"lbs\" : \"pounds\",\n",
        "    \"ldr\" : \"long distance relationship\",\n",
        "    \"lmao\" : \"laugh my ass off\",\n",
        "    \"lmfao\" : \"laugh my fucking ass off\",\n",
        "    \"lol\" : \"laughing out loud\",\n",
        "    \"ltd\" : \"limited\",\n",
        "    \"ltns\" : \"long time no see\",\n",
        "    \"m8\" : \"mate\",\n",
        "    \"mf\" : \"motherfucker\",\n",
        "    \"mfs\" : \"motherfuckers\",\n",
        "    \"mfw\" : \"my face when\",\n",
        "    \"mofo\" : \"motherfucker\",\n",
        "    \"mph\" : \"miles per hour\",\n",
        "    \"mr\" : \"mister\",\n",
        "    \"mrw\" : \"my reaction when\",\n",
        "    \"ms\" : \"miss\",\n",
        "    \"mte\" : \"my thoughts exactly\",\n",
        "    \"nagi\" : \"not a good idea\",\n",
        "    \"nbc\" : \"national broadcasting company\",\n",
        "    \"nbd\" : \"not big deal\",\n",
        "    \"nfs\" : \"not for sale\",\n",
        "    \"ngl\" : \"not going to lie\",\n",
        "    \"nhs\" : \"national health service\",\n",
        "    \"nrn\" : \"no reply necessary\",\n",
        "    \"nsfl\" : \"not safe for life\",\n",
        "    \"nsfw\" : \"not safe for work\",\n",
        "    \"nth\" : \"nice to have\",\n",
        "    \"nvr\" : \"never\",\n",
        "    \"nyc\" : \"new york city\",\n",
        "    \"oc\" : \"original content\",\n",
        "    \"og\" : \"original\",\n",
        "    \"ohp\" : \"overhead projector\",\n",
        "    \"oic\" : \"oh i see\",\n",
        "    \"omdb\" : \"over my dead body\",\n",
        "    \"omg\" : \"oh my god\",\n",
        "    \"omw\" : \"on my way\",\n",
        "    \"p.a\" : \"per annum\",\n",
        "    \"p.m\" : \"after midday\",\n",
        "    \"pm\" : \"prime minister\",\n",
        "    \"poc\" : \"people of color\",\n",
        "    \"pov\" : \"point of view\",\n",
        "    \"pp\" : \"pages\",\n",
        "    \"ppl\" : \"people\",\n",
        "    \"prw\" : \"parents are watching\",\n",
        "    \"ps\" : \"postscript\",\n",
        "    \"pt\" : \"point\",\n",
        "    \"ptb\" : \"please text back\",\n",
        "    \"pto\" : \"please turn over\",\n",
        "    \"qpsa\" : \"what happens\", \n",
        "    \"ratchet\" : \"rude\",\n",
        "    \"rbtl\" : \"read between the lines\",\n",
        "    \"rlrt\" : \"real life retweet\", \n",
        "    \"rofl\" : \"rolling on the floor laughing\",\n",
        "    \"roflol\" : \"rolling on the floor laughing out loud\",\n",
        "    \"rotflmao\" : \"rolling on the floor laughing my ass off\",\n",
        "    \"rt\" : \"retweet\",\n",
        "    \"ruok\" : \"are you ok\",\n",
        "    \"sfw\" : \"safe for work\",\n",
        "     \"sk8\" : \"skate\",\n",
        "    \"smh\" : \"shake my head\",\n",
        "    \"sq\" : \"square\",\n",
        "    \"srsly\" : \"seriously\", \n",
        "    \"ssdd\" : \"same stuff different day\",\n",
        "    \"tbh\" : \"to be honest\",\n",
        "    \"tbs\" : \"tablespooful\",\n",
        "    \"tbsp\" : \"tablespooful\",\n",
        "    \"tfw\" : \"that feeling when\",\n",
        "    \"thks\" : \"thank you\",\n",
        "    \"tho\" : \"though\",\n",
        "    \"thx\" : \"thank you\",\n",
        "    \"tia\" : \"thanks in advance\",\n",
        "    \"til\" : \"today i learned\",\n",
        "    \"tl;dr\" : \"too long i did not read\",\n",
        "    \"tldr\" : \"too long i did not read\",\n",
        "    \"tmb\" : \"tweet me back\",\n",
        "    \"tntl\" : \"trying not to laugh\",\n",
        "    \"ttyl\" : \"talk to you later\",\n",
        "    \"u\" : \"you\",\n",
        "    \"u2\" : \"you too\",\n",
        "    \"u4e\" : \"yours for ever\",\n",
        "    \"utc\" : \"coordinated universal time\",\n",
        "    \"w/\" : \"with\",\n",
        "    \"w/o\" : \"without\",\n",
        "    \"w8\" : \"wait\",\n",
        "    \"wassup\" : \"what is up\",\n",
        "    \"wb\" : \"welcome back\",\n",
        "    \"wtf\" : \"what the fuck\",\n",
        "    \"wtg\" : \"way to go\",\n",
        "    \"wtpa\" : \"where the party at\",\n",
        "    \"wuf\" : \"where are you from\",\n",
        "    \"wuzup\" : \"what is up\",\n",
        "    \"wywh\" : \"wish you were here\",\n",
        "    \"yd\" : \"yard\",\n",
        "    \"ygtr\" : \"you got that right\",\n",
        "    \"ynk\" : \"you never know\",\n",
        "    \"zzz\" : \"sleeping bored and tired\"\n",
        "}\n",
        "\n",
        "def convert_abbrev_in_text(tweet):\n",
        "    t=[]\n",
        "    words=tweet.split()\n",
        "    t = [abbreviations[w.lower()] if w.lower() in abbreviations.keys() else w for w in words]\n",
        "    return ' '.join(t) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh_4-VkuKCIG"
      },
      "outputs": [],
      "source": [
        "def prepare_string(tweet):\n",
        "  tweet = clean(tweet)\n",
        "  tweet = convert_abbrev_in_text(tweet)\n",
        "  return tweet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jVq-dR3q6QGF"
      },
      "outputs": [],
      "source": [
        "#Function taking in input, a comment to classify\n",
        "#Return a dictionnary, where keys are the differents classes \n",
        "#and values are either 1 or 0 depending of the toxicity of the comment\n",
        "def predictToxicity(comment):\n",
        "\n",
        "  labels_name = [\"Toxic\", \"Severe toxic\", \"Obscene\", \"Threat\", \"Insult\", \"Identity hate\"]\n",
        "  # Preparation of the comment in order to be able to give it to the model\n",
        "  modify_comment = [prepare_string(comment)]\n",
        "  modify_comment = tokenizerDL.texts_to_sequences(modify_comment)\n",
        "  modify_comment = np.array(pad_sequences(modify_comment, maxlen=max_length, padding=padding_type, truncating=trunc_type))\n",
        "  \n",
        "  # We use the model to classify the comment\n",
        "  prediction = (model_matrice_embedding.predict(modify_comment))[0]\n",
        "  prediction = (prediction > 0.5).astype(int)\n",
        "\n",
        "  # Convert the prediction in dictionnary to be more understandable for the user\n",
        "  prediction = dictionary = dict(zip(labels_name, prediction))\n",
        "  return prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hp2PjMtJK3ht"
      },
      "source": [
        "## 4.2 Nettoyage des données"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yRT7ZCfSEW8"
      },
      "source": [
        "La colonne id n’étant pas utile pour notre entraînement, on ne la prend pas dans ces variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t0uGR1nhR1Zr"
      },
      "outputs": [],
      "source": [
        "data_train.drop(columns=[\"id\"], inplace=True) #We drop \"id\" column from dataframe\n",
        "data_test.drop(columns=[\"id\"], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjknDktsSbnv"
      },
      "source": [
        "Comme on l’a vue durant l’étude des données, la valeur -1 que l’on peut retrouver sur certain ligne des données de tests indique que le commentaire n’a pas été utilisé et on ne les utilisera donc pas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hteBYdtZScID",
        "outputId": "e5ace02f-4ae4-4aa2-caf1-c356f39f9d95"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                         comment_text  toxic  severe_toxic  \\\n",
              "5   Thank you for understanding. I think very high...      0             0   \n",
              "7                    :Dear god this site is horrible.      0             0   \n",
              "11  \"::: Somebody will invariably try to add Relig...      0             0   \n",
              "13  \" \\n\\n It says it right there that it IS a typ...      0             0   \n",
              "14  \" \\n\\n == Before adding a new product to the l...      0             0   \n",
              "\n",
              "    obscene  threat  insult  identity_hate  \n",
              "5         0       0       0              0  \n",
              "7         0       0       0              0  \n",
              "11        0       0       0              0  \n",
              "13        0       0       0              0  \n",
              "14        0       0       0              0  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_test.drop( \n",
        "    data_test[ data_test['toxic'] == -1 ].index,\n",
        "    inplace = True\n",
        ")\n",
        "data_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pr0QHOfiNWna"
      },
      "source": [
        "On utilise la fonction prepare_strings sur les données d’entrainement pour enlever les abbréviations, etc..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoGgXZEtTKff",
        "outputId": "e73d6a5a-cbb5-4611-c806-da7627228c16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Exemple:  review sorry say fail articles gan the several major problems brought articles previous assessment issued said articles wplead longer the history media attention academics student government demographics trivia additional information section deficient citiations also triva additional information integrated rest article again please look wpwiaga fix problem renominate gaclass however reassessed article bclass meet bclass criteria regards\n",
            "CPU times: user 1min 36s, sys: 438 ms, total: 1min 36s\n",
            "Wall time: 1min 39s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# Apply prepare_string to all rows in 'tweets' column\n",
        "data_train['comment_text'] = data_train['comment_text'].apply(lambda s : prepare_string(s))\n",
        "\n",
        "# Drop empty values from dataframe\n",
        "data_train['comment_text'].replace('', np.nan, inplace=True)\n",
        "data_train.dropna(subset=['comment_text'], inplace=True)\n",
        "print(\"\\nExemple: \",data_train['comment_text'][57])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EU91v4sEUrGH"
      },
      "source": [
        "De même pour les données de tests."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68ZV51oztZq6",
        "outputId": "ce86c466-f9b4-4ab2-b388-4d576dab0f9b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Exemple:  can work magic wondring time help wikipediafeatured article candidatesw boisarchive any input would appreciated quick scan full review noticed work respect skills thanks\n",
            "CPU times: user 37.8 s, sys: 140 ms, total: 37.9 s\n",
            "Wall time: 38.3 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# Apply prepare_string to all rows in 'tweets' column\n",
        "data_test[\"comment_text\"] = data_test[\"comment_text\"].apply(lambda s : prepare_string(s))\n",
        "\n",
        "# Drop empty values from dataframe\n",
        "data_test.replace('', np.nan, inplace=True)\n",
        "data_test.dropna(subset=['comment_text'], inplace=True)\n",
        "print(\"\\nExemple: \",data_test['comment_text'][58])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXZ3Kwd-XXsZ"
      },
      "source": [
        "On enregistre les données pour ne pas avoir à les préparer de nouveau."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8L1RIhmSXtym"
      },
      "outputs": [],
      "source": [
        "data_train.to_csv('/content/drive/MyDrive/toxic_comment_classification/cleaned_comments_train.csv', index=False) #We store our datas in a csv file in our drive\n",
        "data_test.to_csv('/content/drive/MyDrive/toxic_comment_classification/cleaned_comments_test.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgzmbm-XYGQm"
      },
      "source": [
        "## 4.3 Vectorisation des données"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7YiJy9EZUqv"
      },
      "source": [
        "Si on ne vient pas de préparer les données et que l’on souhaite récupérer les données précédemment préparées."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XrRLRldcUkTL",
        "outputId": "1353a569-7760-4e93-b901-9243474ee955"
      },
      "outputs": [
        {
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              "      <td>awesome then simply disregard notice thanks</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <th>56</th>\n",
              "      <td>tony sidaway obviously fistfuckee loves arm ass</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <th>57</th>\n",
              "      <td>review sorry say fail articles gan the several...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>band pages deletion you thought gone deleting ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>why cannot believe fat artie did see recent ap...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcdbc8aa-5d66-40b5-ae74-585f77f5bf25')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-bcdbc8aa-5d66-40b5-ae74-585f77f5bf25 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-bcdbc8aa-5d66-40b5-ae74-585f77f5bf25');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                         comment_text  toxic  severe_toxic  \\\n",
              "50  said wanted talk bottom lead section written i...      0             0   \n",
              "51  get fucked get fuckeeed got drink that you can...      1             0   \n",
              "52  are threatening disputing neutrality know coun...      0             0   \n",
              "53  thanks undeletion would hoped researching stat...      0             0   \n",
              "54        awesome then simply disregard notice thanks      0             0   \n",
              "55  stupid peace shit stop deleting stuff asshole ...      1             1   \n",
              "56    tony sidaway obviously fistfuckee loves arm ass      1             0   \n",
              "57  review sorry say fail articles gan the several...      0             0   \n",
              "58  band pages deletion you thought gone deleting ...      1             0   \n",
              "59  why cannot believe fat artie did see recent ap...      1             0   \n",
              "\n",
              "    obscene  threat  insult  identity_hate  \n",
              "50        0       0       0              0  \n",
              "51        1       0       0              0  \n",
              "52        0       0       0              0  \n",
              "53        0       0       0              0  \n",
              "54        0       0       0              0  \n",
              "55        1       0       1              0  \n",
              "56        1       0       1              0  \n",
              "57        0       0       0              0  \n",
              "58        1       0       0              0  \n",
              "59        0       0       0              0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_train = pd.read_csv('/content/drive/MyDrive/toxic_comment_classification/cleaned_comments_train.csv')\n",
        "data_test = pd.read_csv('/content/drive/MyDrive/toxic_comment_classification/cleaned_comments_test.csv')\n",
        "data_train[50:60]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdEJTNzQHmCn"
      },
      "source": [
        "On sépare d’abord les commentaires et les catégories dans deux variables différentes (X_train et y_train pour data_train ; X_test et y_test pour data_test). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BLIFJvouF0SF",
        "vscode": {
          "languageId": "plaintext"
        }
      },
      "outputs": [],
      "source": [
        "X_train = data_train['comment_text']\n",
        "y_train = data_train.drop(columns=['comment_text'])\n",
        "X_test = data_test['comment_text']\n",
        "y_test = data_test.drop(columns=['comment_text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C66EGISQagjB"
      },
      "source": [
        "Puis on transforme les commentaires en vecteur de mots."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kN0lhv5WVIf"
      },
      "outputs": [],
      "source": [
        "tfIdfVectorizer = TfidfVectorizer()\n",
        "X_train_vect = tfIdfVectorizer.fit_transform(X_train)\n",
        "X_test_vect = tfIdfVectorizer.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndSnViq8bPif"
      },
      "source": [
        "# 5. Entraînement du modèle baseline\n",
        "\n",
        "On commence par un modèle simple de random forest."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txdUCjSnbow9"
      },
      "source": [
        "Préparation du modèle :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pH9UGkusc67x"
      },
      "outputs": [],
      "source": [
        "# Use only 15 estimator to save your ram\n",
        "text_classifier = RandomForestClassifier(\n",
        "    n_estimators = 15,\n",
        "    max_depth = 100,\n",
        "    random_state = 0\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgtwfhRScDUh"
      },
      "source": [
        "Entrainement du modèle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQ-pJNEtdJiN",
        "outputId": "59da31a6-abc5-42a7-d339-78cf7acb629b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=100, n_estimators=15, random_state=0)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "text_classifier.fit(X_train_vect, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbdjrlnQcIAW"
      },
      "source": [
        "Teste du modèle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQ054edccLYY"
      },
      "outputs": [],
      "source": [
        "predictions = text_classifier.predict(X_test_vect)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phQOFOBsc9O8"
      },
      "source": [
        "On affiche les résultats des mesures de performances :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oReAW7ACdWws",
        "outputId": "6050814a-9af2-405d-eec3-e31fe73322cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.06      0.11      6087\n",
            "           1       1.00      0.01      0.01       367\n",
            "           2       0.94      0.05      0.09      3688\n",
            "           3       0.00      0.00      0.00       211\n",
            "           4       0.92      0.03      0.05      3424\n",
            "           5       1.00      0.00      0.00       712\n",
            "\n",
            "   micro avg       0.93      0.04      0.08     14489\n",
            "   macro avg       0.80      0.02      0.04     14489\n",
            "weighted avg       0.92      0.04      0.08     14489\n",
            " samples avg       0.01      0.00      0.00     14489\n",
            "\n",
            "0.9024940115541779\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, predictions))\n",
        "\n",
        "print(accuracy_score(y_test, predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a8IWbTFTHXh"
      },
      "source": [
        "# 6. Itération de la modélisation \n",
        "\n",
        "Utilisation de modèle de deep learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY6nFhoGLSSB"
      },
      "source": [
        "## 6.1 Utilisation d'un modèle de réseaux de neuronnes avec LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RZYt-mYp5Ps"
      },
      "source": [
        "Création et initialisation du bag of words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kzvGNx6Dpx-1"
      },
      "outputs": [],
      "source": [
        "tokenizerDL = Tokenizer()\n",
        "tokenizerDL.fit_on_texts(X_train)\n",
        "\n",
        "word_index = tokenizerDL.word_index\n",
        "vocab_size=len(word_index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9tayfdEu2XC"
      },
      "source": [
        "Création et initialisation des paramètres pour la préparation des données"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rlcNGHB1u1wg"
      },
      "outputs": [],
      "source": [
        "max_length = 20        # Maximum size of a comment\n",
        "trunc_type='post'      # Truncates the comment if it is longer than max_length\n",
        "padding_type='post'    # Adds padding to the end of the comment if it is shorter than max_length\n",
        "oov_tok = \"<OOV>\"      # Token \"<OOV>\" replaces words that are not part of the vocabulary (Out Of Vocabulary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6O-s1Bo6qC8X"
      },
      "source": [
        "Préparation des données pour l'entraînement et pour la validation:\n",
        "- Vectorisation des commentaires\n",
        "- Ajout de padding si le commentaire est trop court, ou on le tronque s'il est trop long"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpeYaiMvqCeY"
      },
      "outputs": [],
      "source": [
        "train_sequences = tokenizerDL.texts_to_sequences(X_train)\n",
        "training_padded = np.array(pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))\n",
        "\n",
        "test_sequences = tokenizerDL.texts_to_sequences(X_test) # We vectorize our comments to be used in our model\n",
        "testing_padded = np.array(pad_sequences(test_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)) # We add padding or we truncate comment if necessary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYo0FVB6pEEA"
      },
      "source": [
        "Initialisation des paramètres de notre modèle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xGRsCZ_fpAI9"
      },
      "outputs": [],
      "source": [
        "embedding_dim = 100    # Dimensions used for glove6b100\n",
        "num_epochs = 10        # Number of epoch for the model training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0GmCWGDpZR6"
      },
      "source": [
        "Création du modèle de réseaux de neurones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Ts4bYfnpXvw"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length),\n",
        "    LSTM(units=64),\n",
        "    tf.keras.layers.Dense(6, activation='sigmoid') # Output layer with 6 neurons because we have 6 categories\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=[tfa.metrics.F1Score(num_classes=6, threshold=0.5)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPvAT50uqcqD"
      },
      "source": [
        "Entraînement du modèle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwBeeKddtZ5D",
        "outputId": "d13defa2-2702-4899-fd83-9dd832ff3aa8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "624/624 [==============================] - 36s 55ms/step - loss: 0.1140 - f1_score: 0.2988 - val_loss: 0.0814 - val_f1_score: 0.3630\n",
            "Epoch 2/10\n",
            "624/624 [==============================] - 13s 21ms/step - loss: 0.0497 - f1_score: 0.4176 - val_loss: 0.0730 - val_f1_score: 0.3706\n",
            "Epoch 3/10\n",
            "624/624 [==============================] - 14s 22ms/step - loss: 0.0398 - f1_score: 0.4761 - val_loss: 0.0848 - val_f1_score: 0.3977\n",
            "Epoch 4/10\n",
            "624/624 [==============================] - 12s 20ms/step - loss: 0.0333 - f1_score: 0.5339 - val_loss: 0.0831 - val_f1_score: 0.4204\n",
            "Epoch 5/10\n",
            "624/624 [==============================] - 11s 18ms/step - loss: 0.0274 - f1_score: 0.6242 - val_loss: 0.0974 - val_f1_score: 0.4622\n",
            "Epoch 6/10\n",
            "624/624 [==============================] - 11s 18ms/step - loss: 0.0223 - f1_score: 0.6875 - val_loss: 0.0974 - val_f1_score: 0.4501\n",
            "Epoch 7/10\n",
            "624/624 [==============================] - 11s 17ms/step - loss: 0.0176 - f1_score: 0.7636 - val_loss: 0.1128 - val_f1_score: 0.4981\n",
            "Epoch 8/10\n",
            "624/624 [==============================] - 9s 15ms/step - loss: 0.0141 - f1_score: 0.8221 - val_loss: 0.1252 - val_f1_score: 0.5135\n",
            "Epoch 9/10\n",
            "624/624 [==============================] - 12s 19ms/step - loss: 0.0115 - f1_score: 0.8652 - val_loss: 0.1418 - val_f1_score: 0.4886\n",
            "Epoch 10/10\n",
            "624/624 [==============================] - 12s 19ms/step - loss: 0.0099 - f1_score: 0.8879 - val_loss: 0.1367 - val_f1_score: 0.4853\n",
            "Training Complete\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(training_padded, \n",
        "                      y_train, \n",
        "                      epochs=num_epochs, \n",
        "                      validation_data=(testing_padded, y_test),\n",
        "                      batch_size = 256,\n",
        "                      verbose=1)\n",
        "  \n",
        "print(\"Training Complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9ySX_vaqos9"
      },
      "source": [
        "Affichage de graphiques représentant l'efficacité de notre modèle suivant la métric du F1score pour le premier, et le loss pour le second\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "KQ7dz0st6wH6",
        "outputId": "445d30a8-c7f0-4398-f8df-f4ac03abf4ce"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history.history['f1_score']\n",
        "val_acc=history.history['val_f1_score']\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation f1-score')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"F1-score\")\n",
        "plt.legend([\"Accuracy\",\"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90ozOXSjLYR2"
      },
      "source": [
        "## 6.2 Modification d'un paramètre concernant la préparation des données : Max length = 30 (taille maximale du commentaire)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PydG4AOavlSM"
      },
      "source": [
        "Modification du paramètre max_length pour la préparation des données"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A6qUWVXSvsNo"
      },
      "outputs": [],
      "source": [
        "max_length = 30"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iumB1zDRv0iC"
      },
      "source": [
        "Préparation des données pour l'entraînement et pour la validation:\n",
        "- Vectorisation des commentaires\n",
        "- Ajout de padding si le commentaire est trop court, ou on le tronque s'il est trop long"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKsCxPNDLje5"
      },
      "outputs": [],
      "source": [
        "train_sequences = tokenizerDL.texts_to_sequences(X_train)\n",
        "training_padded = np.array(pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))\n",
        "\n",
        "test_sequences = tokenizerDL.texts_to_sequences(X_test)\n",
        "testing_padded = np.array(pad_sequences(test_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ND8dloS3v_Cu"
      },
      "source": [
        "Création du modèle de réseaux de neurones 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lSeU6S4NL5ql"
      },
      "outputs": [],
      "source": [
        "model2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length),\n",
        "    LSTM(units=64),\n",
        "    tf.keras.layers.Dense(6, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model2.compile(loss='binary_crossentropy',optimizer='adam',metrics=[tfa.metrics.F1Score(num_classes=6, threshold=0.5)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cNw5I5bwCxn"
      },
      "source": [
        "Entraînement du modèle 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdtZYmehL99A",
        "outputId": "5c4edc7c-c599-43c5-9b24-68f6ba828771"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "624/624 [==============================] - 54s 81ms/step - loss: 0.1440 - f1_score: 0.1258 - val_loss: 0.0872 - val_f1_score: 0.3444\n",
            "Epoch 2/10\n",
            "624/624 [==============================] - 26s 41ms/step - loss: 0.0552 - f1_score: 0.3887 - val_loss: 0.0746 - val_f1_score: 0.3756\n",
            "Epoch 3/10\n",
            "624/624 [==============================] - 18s 29ms/step - loss: 0.0412 - f1_score: 0.4409 - val_loss: 0.0764 - val_f1_score: 0.3430\n",
            "Epoch 4/10\n",
            "624/624 [==============================] - 14s 22ms/step - loss: 0.0349 - f1_score: 0.4864 - val_loss: 0.0863 - val_f1_score: 0.3843\n",
            "Epoch 5/10\n",
            "624/624 [==============================] - 14s 22ms/step - loss: 0.0307 - f1_score: 0.5161 - val_loss: 0.0863 - val_f1_score: 0.3793\n",
            "Epoch 6/10\n",
            "624/624 [==============================] - 15s 23ms/step - loss: 0.0268 - f1_score: 0.5474 - val_loss: 0.0889 - val_f1_score: 0.3841\n",
            "Epoch 7/10\n",
            "624/624 [==============================] - 13s 20ms/step - loss: 0.0226 - f1_score: 0.6294 - val_loss: 0.0887 - val_f1_score: 0.4136\n",
            "Epoch 8/10\n",
            "624/624 [==============================] - 12s 20ms/step - loss: 0.0186 - f1_score: 0.6858 - val_loss: 0.1081 - val_f1_score: 0.4428\n",
            "Epoch 9/10\n",
            "624/624 [==============================] - 11s 18ms/step - loss: 0.0150 - f1_score: 0.7410 - val_loss: 0.1154 - val_f1_score: 0.4563\n",
            "Epoch 10/10\n",
            "624/624 [==============================] - 12s 19ms/step - loss: 0.0123 - f1_score: 0.7894 - val_loss: 0.1234 - val_f1_score: 0.4737\n",
            "Training Complete\n"
          ]
        }
      ],
      "source": [
        "history2 = model2.fit(training_padded, \n",
        "                      y_train, \n",
        "                      epochs=num_epochs, \n",
        "                      validation_data=(testing_padded, y_test),\n",
        "                      batch_size = 256,\n",
        "                      verbose=1)\n",
        "  \n",
        "print(\"Training Complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAV5AUY4wK0j"
      },
      "source": [
        "Affichage de graphiques représentant l'efficacité de notre second modèle suivant la métric du F1score pour le premier, et le loss pour le second"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "cWv-_-6vL_Vk",
        "outputId": "48efa8c9-7094-426c-998a-7dc06e48b402"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history2.history['f1_score']\n",
        "val_acc=history2.history['val_f1_score']\n",
        "loss=history2.history['loss']\n",
        "val_loss=history2.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation f1-score')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"F1-score\")\n",
        "plt.legend([\"F1-score\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCM-Qb8BNhVp"
      },
      "source": [
        "## 6.3 Nouveau modèle en limitant la taille du bag of words à 20 000\n",
        "\n",
        "Le fait de mettre une taille maximale de commentaire (max_length) n'ayant pas augmenter le F1-Score, on retourne à la valeur d'origine (20) pour tester un nouveau paramètre."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QAx27EixuVi"
      },
      "source": [
        "Nouveaux paramètres pour la préparation des données"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x78DAGa4w2c9"
      },
      "outputs": [],
      "source": [
        "max_length = 20               # Maximum size of a tweet\n",
        "size_bag_of_words = 20000     # Size for the bag of words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtXGFLMAxpdh"
      },
      "source": [
        "Création et initialisation du nouveau bag of words (avec 20 000 mots)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hOt22KuUw9VU"
      },
      "outputs": [],
      "source": [
        "tokenizerDL = Tokenizer(size_bag_of_words)\n",
        "tokenizerDL.fit_on_texts(X_train)\n",
        "\n",
        "word_index = tokenizerDL.word_index\n",
        "vocab_size=len(word_index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gm21D-1oyF_u"
      },
      "source": [
        "Préparation des données pour l'entraînement et pour la validation:\n",
        "- Vectorisation des commentaires\n",
        "- Ajout de padding si le commentaire est trop court, ou on le tronque s'il est trop long"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PSzIfETkxFn3"
      },
      "outputs": [],
      "source": [
        "train_sequences = tokenizerDL.texts_to_sequences(X_train)\n",
        "training_padded = np.array(pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))\n",
        "\n",
        "test_sequences = tokenizerDL.texts_to_sequences(X_test)\n",
        "testing_padded = np.array(pad_sequences(test_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UsMDC5SyI6_"
      },
      "source": [
        "Création du modèle de réseaux de neurones 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qQrHf-tmNpDw"
      },
      "outputs": [],
      "source": [
        "model3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(size_bag_of_words+1, embedding_dim, input_length=max_length),\n",
        "    LSTM(units=64),\n",
        "    tf.keras.layers.Dense(6, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model3.compile(loss='binary_crossentropy',optimizer='adam',metrics=[tfa.metrics.F1Score(num_classes=6, threshold=0.5)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAzePg-0yO5a"
      },
      "source": [
        "Entraînement du modèle 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxedsKE2xIiU",
        "outputId": "6c70c225-d7bc-4136-a69b-33eb2594e125"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "624/624 [==============================] - 46s 70ms/step - loss: 0.1090 - f1_score: 0.3339 - val_loss: 0.1010 - val_f1_score: 0.3459\n",
            "Epoch 2/10\n",
            "624/624 [==============================] - 13s 20ms/step - loss: 0.0528 - f1_score: 0.3942 - val_loss: 0.0832 - val_f1_score: 0.3537\n",
            "Epoch 3/10\n",
            "624/624 [==============================] - 8s 12ms/step - loss: 0.0473 - f1_score: 0.4470 - val_loss: 0.0873 - val_f1_score: 0.3888\n",
            "Epoch 4/10\n",
            "624/624 [==============================] - 8s 12ms/step - loss: 0.0426 - f1_score: 0.4716 - val_loss: 0.0829 - val_f1_score: 0.3941\n",
            "Epoch 5/10\n",
            "624/624 [==============================] - 6s 10ms/step - loss: 0.0387 - f1_score: 0.5024 - val_loss: 0.0846 - val_f1_score: 0.4005\n",
            "Epoch 6/10\n",
            "624/624 [==============================] - 7s 11ms/step - loss: 0.0348 - f1_score: 0.5621 - val_loss: 0.0911 - val_f1_score: 0.4189\n",
            "Epoch 7/10\n",
            "624/624 [==============================] - 8s 12ms/step - loss: 0.0312 - f1_score: 0.6055 - val_loss: 0.1030 - val_f1_score: 0.4604\n",
            "Epoch 8/10\n",
            "624/624 [==============================] - 6s 10ms/step - loss: 0.0281 - f1_score: 0.6448 - val_loss: 0.0985 - val_f1_score: 0.4521\n",
            "Epoch 9/10\n",
            "624/624 [==============================] - 8s 13ms/step - loss: 0.0250 - f1_score: 0.6989 - val_loss: 0.1055 - val_f1_score: 0.4725\n",
            "Epoch 10/10\n",
            "624/624 [==============================] - 8s 13ms/step - loss: 0.0220 - f1_score: 0.7546 - val_loss: 0.1105 - val_f1_score: 0.4910\n",
            "Training Complete\n"
          ]
        }
      ],
      "source": [
        "history3 = model3.fit(training_padded, \n",
        "                      y_train, \n",
        "                      epochs=num_epochs, \n",
        "                      validation_data=(testing_padded, y_test),\n",
        "                      batch_size = 256,\n",
        "                      verbose=1)\n",
        "  \n",
        "print(\"Training Complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0I3VyEfyYIQ"
      },
      "source": [
        "Affichage de graphiques représentant l'efficacité de notre troisième modèle suivant la métric du F1score pour le premier, et le loss pour le second"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "andxxlQNW-iT",
        "outputId": "83ba43a8-1ad6-40ec-b074-dfae79a11bad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history3.history['f1_score']\n",
        "val_acc=history3.history['val_f1_score']\n",
        "loss=history3.history['loss']\n",
        "val_loss=history3.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation f1-score')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"F1-score\")\n",
        "plt.legend([\"F1-score\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjKaSYHcycQ4"
      },
      "source": [
        "On peut voir qu'en limitant la taile du bag of words, on a diminué le nombre de paramètres de notre modèle passant de 20millions à 2millions. C'est ce qu'on cherchait à faire pour pouvoir éviter de faire de l'overfitting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApqGCYoPb9iQ",
        "outputId": "d5e9b828-88da-428f-9e52-819773ecf291"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_3 (Embedding)     (None, 20, 100)           2000100   \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 64)                42240     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,042,730\n",
            "Trainable params: 2,042,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Gs_PGqIQgtC"
      },
      "source": [
        "## 6.4 Utilisation d'une matrice d'embedding (Glove6B)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSy61GkPzdR2"
      },
      "source": [
        "Création de la matrice d'embedding à partir de Glove6B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OS7spYbUNrgX"
      },
      "outputs": [],
      "source": [
        "embeddings_index = {};\n",
        "\n",
        "with open('/content/drive/MyDrive/RNN_sentiment_dataset/glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = values[0];\n",
        "        coefs = np.asarray(values[1:], dtype='float32');\n",
        "        embeddings_index[word] = coefs;\n",
        "\n",
        "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kp3VtGBJziBn"
      },
      "source": [
        "Création de notre quatrième modèle, utilisant cette fois une matrice d'embedding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "daqYS28MQnCl"
      },
      "outputs": [],
      "source": [
        "model_matrice_embedding = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights=[embeddings_matrix], trainable=False),\n",
        "    LSTM(units=64),\n",
        "    tf.keras.layers.Dense(6, activation='sigmoid')\n",
        "])\n",
        "model_matrice_embedding.compile(loss='binary_crossentropy',optimizer='adam',metrics=[tfa.metrics.F1Score(num_classes=6, threshold=0.5)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twpR90xxzvDB"
      },
      "source": [
        "Entraînement de notre quatrième modèle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujlmzQbVREPZ",
        "outputId": "af2cd45e-05eb-4f23-9e10-2bd2a950be1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
            "Instructions for updating:\n",
            "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "624/624 [==============================] - 62s 84ms/step - loss: 0.0997 - f1_score: 0.2791 - val_loss: 0.0821 - val_f1_score: 0.3100\n",
            "Epoch 2/10\n",
            "624/624 [==============================] - 46s 74ms/step - loss: 0.0620 - f1_score: 0.3770 - val_loss: 0.0802 - val_f1_score: 0.3681\n",
            "Epoch 3/10\n",
            "624/624 [==============================] - 53s 85ms/step - loss: 0.0580 - f1_score: 0.4077 - val_loss: 0.0783 - val_f1_score: 0.3874\n",
            "Epoch 4/10\n",
            "624/624 [==============================] - 61s 97ms/step - loss: 0.0552 - f1_score: 0.4414 - val_loss: 0.0835 - val_f1_score: 0.4131\n",
            "Epoch 5/10\n",
            "624/624 [==============================] - 59s 95ms/step - loss: 0.0534 - f1_score: 0.4746 - val_loss: 0.0781 - val_f1_score: 0.4463\n",
            "Epoch 6/10\n",
            "624/624 [==============================] - 59s 95ms/step - loss: 0.0518 - f1_score: 0.5063 - val_loss: 0.0716 - val_f1_score: 0.4789\n",
            "Epoch 7/10\n",
            "624/624 [==============================] - 47s 75ms/step - loss: 0.0502 - f1_score: 0.5450 - val_loss: 0.0881 - val_f1_score: 0.5327\n",
            "Epoch 8/10\n",
            "624/624 [==============================] - 70s 112ms/step - loss: 0.0492 - f1_score: 0.5628 - val_loss: 0.0775 - val_f1_score: 0.5130\n",
            "Epoch 9/10\n",
            "624/624 [==============================] - 76s 122ms/step - loss: 0.0480 - f1_score: 0.5806 - val_loss: 0.0744 - val_f1_score: 0.5188\n",
            "Epoch 10/10\n",
            "624/624 [==============================] - 57s 91ms/step - loss: 0.0469 - f1_score: 0.5921 - val_loss: 0.0791 - val_f1_score: 0.5269\n",
            "Training Complete\n"
          ]
        }
      ],
      "source": [
        "history_matrice_embedding = model_matrice_embedding.fit(training_padded, \n",
        "                      y_train, \n",
        "                      epochs=num_epochs, \n",
        "                      validation_data=(testing_padded, y_test),\n",
        "                      batch_size = 256,\n",
        "                      verbose=1)\n",
        "  \n",
        "print(\"Training Complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Affichage de graphiques représentant l'efficacité de notre dernier modèle suivant la métric du F1score pour le premier, et le loss pour le second\n",
        "\n"
      ],
      "metadata": {
        "id": "9DO7wGsWKmgl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "0-Fb97MKRIqH",
        "outputId": "daf338b1-9642-47ba-c706-f381c5e90e12"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history_matrice_embedding.history['f1_score']\n",
        "val_acc=history_matrice_embedding.history['val_f1_score']\n",
        "loss=history_matrice_embedding.history['loss']\n",
        "val_loss=history_matrice_embedding.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation f1-score')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"F1-score\")\n",
        "plt.legend([\"F1-score\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grJ5Hxcm0vjo"
      },
      "source": [
        "Finalement, on se rend compte que le meilleure modèle est celui utilisant une matrice d'embedding, avec un F1-score sur le dataset de validation, de 52%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-k3pjv0Q1Lcm"
      },
      "source": [
        "# 7. Exemple d'utilisation de notre pipeline :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Yzgxf1R2Cpc"
      },
      "source": [
        "Exemples de commentaires récupérés sur twitter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ACF7VIN50h_t"
      },
      "outputs": [],
      "source": [
        "comment1 = \"You are a moron !\"\n",
        "comment2 = \"I was ban for nothing, go fuck yourself admin. You mother fucker, I'll kill you\"\n",
        "comment3 = \"A cybertruck is the ultimate truck that we all need for any situation, even an Alien Apocalypse.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wcMTWqih2CKc",
        "outputId": "e6c63c0f-e27e-4564-aa1b-7e7b1dcf8052"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 33ms/step\n",
            "You are a moron ! :\n",
            " {'Toxic': 1, 'Severe toxic': 0, 'Obscene': 1, 'Threat': 0, 'Insult': 1, 'Identity hate': 0}\n"
          ]
        }
      ],
      "source": [
        "print(f\"{comment1} :\\n {predictToxicity(comment1)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwINlfGS5iTU",
        "outputId": "43da4c84-76ab-4782-d7cb-930d3ab944e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 102ms/step\n",
            "I was ban for nothing, go fuck yourself admin. You mother fucker, I'll kill you :\n",
            " {'Toxic': 1, 'Severe toxic': 1, 'Obscene': 1, 'Threat': 1, 'Insult': 1, 'Identity hate': 0}\n"
          ]
        }
      ],
      "source": [
        "print(f\"{comment2} :\\n {predictToxicity(comment2)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cH8cDAo5lzu",
        "outputId": "b9bcf5ee-468f-4a49-dddc-cb7eb6c4b168"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 42ms/step\n",
            "A cybertruck is the ultimate truck that we all need for any situation, even an Alien Apocalypse. :\n",
            " {'Toxic': 0, 'Severe toxic': 0, 'Obscene': 0, 'Threat': 0, 'Insult': 0, 'Identity hate': 0}\n"
          ]
        }
      ],
      "source": [
        "print(f\"{comment3} :\\n {predictToxicity(comment3)}\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}