docs/2. models.ipynb

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    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "kCwbVbNHHYmJ"
      },
      "source": [
        "# فاز ۲ پروژه"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "حامد سعادتی 401212884، بهنام ساعدی 401202942"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NRFf8ZjpCJBx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import nltk\n",
        "from nltk import word_tokenize, pos_tag\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords, wordnet\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.metrics import classification_report, f1_score, balanced_accuracy_score\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.linear_model import RidgeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "sns.set_theme(style='whitegrid')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "xqJZuyArHYmW"
      },
      "source": [
        "## آماده‌سازی و پیش‌پردازش داده‌ها"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5syieHxNHYmX"
      },
      "source": [
        "### خواندن دیتاست"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "ZMD5IvLfHx6h",
        "outputId": "c48967a5-6f77-424e-c30d-a3b104df19a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13XlJ4uhxxGprn6mnXwXNvV9PxSNyZCsY\n",
            "To: /content/VideoCommentsThreatCorpus.txt\n",
            "\r  0% 0.00/2.87M [00:00<?, ?B/s]\r100% 2.87M/2.87M [00:00<00:00, 181MB/s]\n"
          ]
        }
      ],
      "source": [
        "# doenload datset if needed\n",
        "!gdown \"13XlJ4uhxxGprn6mnXwXNvV9PxSNyZCsY\"\n",
        "!mkdir -p datasets\n",
        "!mv ./VideoCommentsThreatCorpus.txt ./datasets/"
      ]
    },
    {
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              "      <td>yeah we are all monsters..I'm gonna kill u rig...</td>\n",
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              "    <tr>\n",
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              "      <td>stupid brainwashed idiot..\\n</td>\n",
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              "    <tr>\n",
              "      <th>28640</th>\n",
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              "    <tr>\n",
              "      <th>28641</th>\n",
              "      <td>probably u mean to this monsters, fucker /watc...</td>\n",
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              "                                                    text  label\n",
              "0      It's because Europeans do not want to change t...      0\n",
              "1      The Muslims there do not want to assimilate pr...      0\n",
              "2      But it's ok....because Europe will soon rebel ...      1\n",
              "3      I forsee a big civil war in Europe in the futu...      0\n",
              "4      ISLAM – A Simple, Humanitarian and Attractive ...      0\n",
              "...                                                  ...    ...\n",
              "28638  yeah we are all monsters..I'm gonna kill u rig...      1\n",
              "28639                       stupid brainwashed idiot..\\n      0\n",
              "28640  have you EVER been to Serbia or kosovo...fucki...      0\n",
              "28641  probably u mean to this monsters, fucker /watc...      0\n",
              "28642  the fucking funniest thing is that fucking ame...      0\n",
              "\n",
              "[28643 rows x 2 columns]"
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      "source": [
        "def read_dataset(path):\n",
        "    def read_lines(lines):\n",
        "        is_header = True\n",
        "        for line in lines:\n",
        "            if is_header:\n",
        "                is_header = False\n",
        "                continue\n",
        "\n",
        "            if not line or line.isspace():\n",
        "                is_header = True\n",
        "                continue\n",
        "\n",
        "            label, comment = line.split(maxsplit=1)\n",
        "            yield comment, int(label)\n",
        "\n",
        "    with open(path, 'r') as file:\n",
        "        data = read_lines(file)\n",
        "        df = pd.DataFrame.from_records(data, columns=['text', 'label'])\n",
        "\n",
        "    return df\n",
        "\n",
        "df = read_dataset('datasets/VideoCommentsThreatCorpus.txt')\n",
        "df"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "9suaWW4cHYma"
      },
      "source": [
        "### حذف رکوردهای تکراری"
      ]
    },
    {
      "cell_type": "code",
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              "\n",
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              "            '<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",
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              "                                                    text  label\n",
              "0      It's because Europeans do not want to change t...      0\n",
              "1      The Muslims there do not want to assimilate pr...      0\n",
              "2      But it's ok....because Europe will soon rebel ...      1\n",
              "3      I forsee a big civil war in Europe in the futu...      0\n",
              "4      ISLAM – A Simple, Humanitarian and Attractive ...      0\n",
              "...                                                  ...    ...\n",
              "28286  yeah we are all monsters..I'm gonna kill u rig...      1\n",
              "28287                       stupid brainwashed idiot..\\n      0\n",
              "28288  have you EVER been to Serbia or kosovo...fucki...      0\n",
              "28289  probably u mean to this monsters, fucker /watc...      0\n",
              "28290  the fucking funniest thing is that fucking ame...      0\n",
              "\n",
              "[28291 rows x 2 columns]"
            ]
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          "metadata": {},
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      "source": [
        "df.drop_duplicates(inplace=True, ignore_index=True)\n",
        "df"
      ]
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      "metadata": {
        "id": "g6Hh3a-hHYmc"
      },
      "source": [
        "### نمایش توزیع برچسب‌ها"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "f_hkyVA9LJG4",
        "outputId": "3aba7d2c-9740-4608-a97c-722229203674"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.countplot(x=df['label'])\n",
        "ax.bar_label(ax.containers[0])\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ZS7XhLXaHYmd"
      },
      "source": [
        "<div dir=\"rtl\">\n",
        "می‌بینیم که برچسب‌ها بسیار نامتوازن هستند.\n",
        "</div>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BDdxe61KHYme"
      },
      "source": [
        "### جداسازی داده آموزشی و تست"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "24f7KZgvskR0"
      },
      "outputs": [],
      "source": [
        "test_size = 0.1\n",
        "\n",
        "X_raw, y = df['text'], df['label']\n",
        "X_train_raw, X_test_raw, y_train, y_test = train_test_split(\n",
        "    X_raw.values, y.values, test_size=test_size, stratify=y, random_state=0)\n",
        "X_train_raw, X_test_raw = X_train_raw.flatten(), X_test_raw.flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0ec8MOayslXc",
        "outputId": "44830848-9ac7-4a72-a064-8948b276086d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3a975859-9738-4e8a-84ea-0a629e58984c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label 0 count</th>\n",
              "      <th>label 1 count</th>\n",
              "      <th>label 0 ratio</th>\n",
              "      <th>label 1 ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>24238</td>\n",
              "      <td>1223</td>\n",
              "      <td>0.951966</td>\n",
              "      <td>0.048034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>2694</td>\n",
              "      <td>136</td>\n",
              "      <td>0.951943</td>\n",
              "      <td>0.048057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a975859-9738-4e8a-84ea-0a629e58984c')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-3a975859-9738-4e8a-84ea-0a629e58984c 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-3a975859-9738-4e8a-84ea-0a629e58984c');\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": [
              "       label 0 count  label 1 count  label 0 ratio  label 1 ratio\n",
              "train          24238           1223       0.951966       0.048034\n",
              "test            2694            136       0.951943       0.048057"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame({\n",
        "    'label 0 count': [(1 - y_train).sum(), (1 - y_test).sum()],\n",
        "    'label 1 count': [y_train.sum(), y_test.sum()],\n",
        "    'label 0 ratio': [(1 - y_train).mean(), (1 - y_test).mean()],\n",
        "    'label 1 ratio': [y_train.mean(), y_test.mean()],\n",
        "}, index=['train', 'test'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2_JYohlHMZW3"
      },
      "outputs": [],
      "source": [
        "# Helper function for reporting and submitting results\n",
        "clf_macro_f1_scores = dict()\n",
        "clf_bal_acc_scores = dict()\n",
        "\n",
        "def report_classifier(clf, name: str, X_train, X_test, batch_size=1024):\n",
        "    if hasattr(clf, 'predict'):\n",
        "        y_pred_train = clf.predict(X_train)\n",
        "        y_pred_test = clf.predict(X_test)\n",
        "    else:\n",
        "        def predict_batch(X, i):\n",
        "            return clf(input_ids=X.input_ids[i:i+batch_size].cuda(),\n",
        "                       token_type_ids=X.token_type_ids[i:i+batch_size].cuda(),\n",
        "                       attention_mask=X.attention_mask[i:i+batch_size].cuda()\n",
        "                       )[0].argmax(axis=1).cpu()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            y_pred_train = torch.cat([predict_batch(X_train, i) for i in range(0, len(X_train.input_ids), batch_size)])\n",
        "            y_pred_test = torch.cat([predict_batch(X_test, i) for i in range(0, len(X_test.input_ids), batch_size)])\n",
        "\n",
        "    f1_train = f1_score(y_train, y_pred_train, average='macro')\n",
        "    f1_test = f1_score(y_test, y_pred_test, average='macro')\n",
        "\n",
        "    acc_train = balanced_accuracy_score(y_train, y_pred_train)\n",
        "    acc_test = balanced_accuracy_score(y_test, y_pred_test)\n",
        "\n",
        "    print(f'Train F1 score: {f1_train:0.3f}')\n",
        "    print(f'Test F1 score: {f1_test:0.3f}')\n",
        "    print(f'Train balanced accuracy: {acc_train * 100:0.1f}%')\n",
        "    print(f'Test balanced accuracy: {acc_test * 100:0.1f}%')\n",
        "    print()\n",
        "\n",
        "    clf_macro_f1_scores[name] = f1_test\n",
        "    clf_bal_acc_scores[name] = acc_test\n",
        "\n",
        "    print(classification_report(y_test, y_pred_test, zero_division=1))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "_nJKqrHAHYmg"
      },
      "source": [
        "### ‫تبدیل متن به توکن با استفاده از nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZSBtycW2fmp",
        "outputId": "ba9f34a9-a485-48e2-f42b-a4440e7b26db"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('averaged_perceptron_tagger')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCZn5Y5QPVq6",
        "outputId": "d7315c7b-e2dc-4edb-fc51-b270f4b70eaa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of features without preprocesing and feature selection 2444\n"
          ]
        }
      ],
      "source": [
        "vectorizer = CountVectorizer(\n",
        "    strip_accents='unicode',\n",
        "    stop_words=stopwords.words('english'),\n",
        "    ngram_range=(1, 2),\n",
        "    min_df=0.0005,\n",
        "    max_df=0.8)\n",
        "\n",
        "num_features_basic = vectorizer.fit_transform(X_train_raw).shape[1]\n",
        "print('Number of features without preprocesing and feature selection', num_features_basic)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vI89-8bS7Wpb",
        "outputId": "0eb9b20a-0438-433d-cc14-621e51db4098"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ],
      "source": [
        "print(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "sA0u9BJfYq6_"
      },
      "outputs": [],
      "source": [
        "class LemmaTokenizer:\n",
        "    def __init__(self):\n",
        "        self.wnl = WordNetLemmatizer()\n",
        "        self.stopwords = set(stopwords.words('english'))\n",
        "\n",
        "    def __call__(self, doc):\n",
        "        nltk_tagged = pos_tag(word_tokenize(doc))\n",
        "        wordnet_tagged = ((x[0], self.nltk_pos_tagger(x[1])) for x in nltk_tagged)\n",
        "\n",
        "        return [self.wnl.lemmatize(token, tag)\n",
        "                for token, tag in wordnet_tagged\n",
        "                if token not in self.stopwords\n",
        "                    and any(c.isalpha() for c in token)]\n",
        "\n",
        "    def nltk_pos_tagger(self, nltk_tag):\n",
        "        if nltk_tag.startswith('J'):\n",
        "            return wordnet.ADJ\n",
        "        elif nltk_tag.startswith('V'):\n",
        "            return wordnet.VERB\n",
        "        elif nltk_tag.startswith('N'):\n",
        "            return wordnet.NOUN\n",
        "        elif nltk_tag.startswith('R'):\n",
        "            return wordnet.ADV\n",
        "        else:\n",
        "            return wordnet.NOUN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJpf_FJC4kSE",
        "outputId": "f26496b5-1275-47c3-8e82-f6eb5b544f77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The solutiin is simple.....free Breivik and give him a permit to shoot islamists and leftists on sight\n",
            "\n",
            "label is True\n"
          ]
        }
      ],
      "source": [
        "print(X_train_raw[0])\n",
        "print('label is', bool(y_train[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "e4Go0Hf64RMb",
        "outputId": "a562e4af-f84f-4f02-cceb-0e430c5858c8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'solutiin simple free breivik give permit shoot islamist leftist sight'"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# test the correctness of the LemmaTokenizer\n",
        "' '.join(LemmaTokenizer()(X_train_raw[0].lower()))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "IJsAjsB3HYmr"
      },
      "source": [
        "## مدل‌سازی با استفاده از ویژگی تعداد کلمات"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "b2XdxgwGHYms"
      },
      "source": [
        "### پیش‌پردازش شمارش توکن‌ها"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdzumutW09BT",
        "outputId": "c180f805-701e-4626-9857-bfd84538848e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<25461x2304 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 155667 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vectorizer = CountVectorizer(\n",
        "    tokenizer=LemmaTokenizer(),\n",
        "    strip_accents='unicode',\n",
        "    ngram_range=(1, 2),\n",
        "    min_df=0.0005,\n",
        "    max_df=0.8)\n",
        "\n",
        "X_train = vectorizer.fit_transform(X_train_raw)\n",
        "X_test = vectorizer.transform(X_test_raw)\n",
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8opbRSS4tOJ",
        "outputId": "5fb66306-6daa-4a97-ee89-bf2ddbc310ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[array(['simple', 'free', 'breivik', 'give', 'shoot', 'islamist',\n",
              "        'leftist'], dtype='<U20')]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vectorizer.inverse_transform(X_train[0])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BTVWSUHRHYm1"
      },
      "source": [
        "### ‫به‌دست آوردن دقت مدل baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "VDvj3zB3SH9w",
        "outputId": "74dfb9de-7ef2-462e-c058-9fa7a7328b16"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "DummyClassifier(strategy='most_frequent')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = DummyClassifier(strategy='most_frequent')\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KWJGV9ZMoxX",
        "outputId": "9f30dd82-b859-4811-c9a9-c33740c6215a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.488\n",
            "Test F1 score: 0.488\n",
            "Train balanced accuracy: 50.0%\n",
            "Test balanced accuracy: 50.0%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.98      2694\n",
            "           1       1.00      0.00      0.00       136\n",
            "\n",
            "    accuracy                           0.95      2830\n",
            "   macro avg       0.98      0.50      0.49      2830\n",
            "weighted avg       0.95      0.95      0.93      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'DummyClassifier (most frequent)', X_train, X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "idIlb8llHYm4",
        "outputId": "b6c7ff28-81c6-4fac-eaa7-527f0958c643"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(strategy=&#x27;uniform&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier(strategy=&#x27;uniform&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "DummyClassifier(strategy='uniform')"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = DummyClassifier(strategy='uniform')\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwNgDTPqHYm5",
        "outputId": "c9db41e0-6ede-4f09-f5cb-cb1c9267b12a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.373\n",
            "Test F1 score: 0.375\n",
            "Train balanced accuracy: 50.8%\n",
            "Test balanced accuracy: 49.4%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.51      0.66      2694\n",
            "           1       0.05      0.48      0.09       136\n",
            "\n",
            "    accuracy                           0.51      2830\n",
            "   macro avg       0.50      0.49      0.37      2830\n",
            "weighted avg       0.91      0.51      0.64      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'DummyClassifier (uniform)', X_train, X_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "KpW6Nqu2HYm6"
      },
      "source": [
        "### ‫مدل‌های Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "XrBIURP-LXhS",
        "outputId": "bc57e459-8ce3-4330-9d87-79c8c6876dcb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = MultinomialNB()\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onNI-YWCV0D8",
        "outputId": "3033198f-69bc-4921-9b0b-8c2135372859"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.783\n",
            "Test F1 score: 0.724\n",
            "Train balanced accuracy: 77.3%\n",
            "Test balanced accuracy: 70.4%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      2694\n",
            "           1       0.53      0.43      0.47       136\n",
            "\n",
            "    accuracy                           0.95      2830\n",
            "   macro avg       0.75      0.70      0.72      2830\n",
            "weighted avg       0.95      0.95      0.95      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'MultinomialNB (count vectorization)', X_train, X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "59ieYXqkU_yP",
        "outputId": "32447d25-2a59-4b0e-8856-00a5da988ee2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ComplementNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ComplementNB</label><div class=\"sk-toggleable__content\"><pre>ComplementNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "ComplementNB()"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = ComplementNB()\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yyppv81gVBOj",
        "outputId": "f6f822f6-a63d-4046-85f2-815da6584749"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.623\n",
            "Test F1 score: 0.606\n",
            "Train balanced accuracy: 87.2%\n",
            "Test balanced accuracy: 82.7%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.82      0.90      2694\n",
            "           1       0.19      0.83      0.31       136\n",
            "\n",
            "    accuracy                           0.82      2830\n",
            "   macro avg       0.59      0.83      0.61      2830\n",
            "weighted avg       0.95      0.82      0.87      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'ComplementNB (count vectorization)', X_train, X_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "hEu8kRDjHYm9"
      },
      "source": [
        "### ‫کاهش تعداد ویژگی با استفاده از SVD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7HM1PAZoBTz",
        "outputId": "7863b165-1762-44cf-8b02-ca8962f790ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((25461, 200), (2830, 200))"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "truncated_svd = TruncatedSVD(n_components=200, random_state=0)\n",
        "X_train_reduced = truncated_svd.fit_transform(X_train)\n",
        "X_test_reduced = truncated_svd.transform(X_test)\n",
        "X_train_reduced.shape, X_test_reduced.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWUoQHHxzDCF",
        "outputId": "8af79027-0e56-4bbb-f0e2-5e886d73bec5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5505850512482723"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "truncated_svd.explained_variance_ratio_.sum()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "SF0az-v6HYnA"
      },
      "source": [
        "### استانداردسازی ویژگی‌ها"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "sKHeKWOSHYni"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_reduced = scaler.fit_transform(X_train_reduced)\n",
        "X_test_reduced = scaler.transform(X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wtBvJdEWHYnj"
      },
      "source": [
        "### ‫دسته‌بند Ridge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7ZTuWqNYS3mP",
        "outputId": "10dad822-7575-40e8-bf80-0e83d6d3d875"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifier(alpha=10, class_weight=&#x27;balanced&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=10, class_weight=&#x27;balanced&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RidgeClassifier(alpha=10, class_weight='balanced', random_state=0)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {'alpha': (0, 0.1, 0.3, 1, 3, 10)}\n",
        "grid_search = GridSearchCV(RidgeClassifier(class_weight='balanced',\n",
        "                                           random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvZ8JKVkJmQO",
        "outputId": "593a1768-63ed-43b6-a3e4-dca3ef37b97d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.614\n",
            "Test F1 score: 0.611\n",
            "Train balanced accuracy: 78.2%\n",
            "Test balanced accuracy: 75.1%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.87      0.92      2694\n",
            "           1       0.20      0.63      0.30       136\n",
            "\n",
            "    accuracy                           0.86      2830\n",
            "   macro avg       0.59      0.75      0.61      2830\n",
            "weighted avg       0.94      0.86      0.89      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'RidgeClassifier (count vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "eMLgjsU5HYnp"
      },
      "source": [
        "### ‫دسته‌بند SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "7gOD8kDGHYnq",
        "outputId": "4b51a1c7-310d-4d5a-feff-32b861e3bb8e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:299: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=30, class_weight=&#x27;balanced&#x27;, kernel=&#x27;sigmoid&#x27;, max_iter=1000,\n",
              "    random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=30, class_weight=&#x27;balanced&#x27;, kernel=&#x27;sigmoid&#x27;, max_iter=1000,\n",
              "    random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVC(C=30, class_weight='balanced', kernel='sigmoid', max_iter=1000,\n",
              "    random_state=0)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'C': (1, 3, 10, 30, 100),\n",
        "    'kernel': ('linear', 'poly', 'rbf', 'sigmoid')\n",
        "}\n",
        "grid_search = RandomizedSearchCV(SVC(class_weight='balanced',\n",
        "                                     random_state=0,\n",
        "                                     max_iter=1000),\n",
        "                                 param_distributions=parameters,\n",
        "                                 scoring='f1_macro',\n",
        "                                 cv=3,\n",
        "                                 n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrGQGhbTHYnr",
        "outputId": "ffcb0298-2baf-4489-e632-56f927e6231c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.417\n",
            "Test F1 score: 0.431\n",
            "Train balanced accuracy: 62.1%\n",
            "Test balanced accuracy: 65.5%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.57      0.72      2694\n",
            "           1       0.08      0.74      0.14       136\n",
            "\n",
            "    accuracy                           0.57      2830\n",
            "   macro avg       0.53      0.65      0.43      2830\n",
            "weighted avg       0.93      0.57      0.69      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'SVC (count vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "TBhhfxj5HYns"
      },
      "source": [
        "### ‫دسته‌بند Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SusxTpIVCyYs",
        "outputId": "bdd499ca-9b7b-4d38-9246-56897d4c9621"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced', max_depth=8, random_state=0)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'criterion': ('gini', 'entropy'),\n",
        "    'max_depth': range(4, 13, 4),\n",
        "}\n",
        "grid_search = GridSearchCV(RandomForestClassifier(class_weight='balanced', \n",
        "                                                  random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPk-YLlCDKeI",
        "outputId": "207c3497-7566-444d-84a0-053950a18b00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.848\n",
            "Test F1 score: 0.698\n",
            "Train balanced accuracy: 89.0%\n",
            "Test balanced accuracy: 69.6%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      2694\n",
            "           1       0.43      0.42      0.43       136\n",
            "\n",
            "    accuracy                           0.95      2830\n",
            "   macro avg       0.70      0.70      0.70      2830\n",
            "weighted avg       0.94      0.95      0.95      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'RandomForestClassifier (count vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "q4e_72AUHYnv"
      },
      "source": [
        "### ‫دسته‌بند MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "A_D4vVzlHYn8",
        "outputId": "76006d68-ee9d-4e49-c1c8-050c8968ee46"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(400,), random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(400,), random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MLPClassifier(hidden_layer_sizes=(400,), random_state=0)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'hidden_layer_sizes': [(100, ), (200, ), (400, ), (100, 100), (200, 100)],\n",
        "}\n",
        "grid_search = GridSearchCV(MLPClassifier(activation='relu',\n",
        "                                         random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xcesnMoHYn9",
        "outputId": "b8053ee5-518b-49df-c107-e24357368387"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.978\n",
            "Test F1 score: 0.662\n",
            "Train balanced accuracy: 96.4%\n",
            "Test balanced accuracy: 62.9%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97      2694\n",
            "           1       0.49      0.27      0.35       136\n",
            "\n",
            "    accuracy                           0.95      2830\n",
            "   macro avg       0.73      0.63      0.66      2830\n",
            "weighted avg       0.94      0.95      0.94      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'MLPClassifier (count vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "CNuWr3vJHYoE"
      },
      "source": [
        "## ‫مدل‌سازی با استفاده از ویژگی‌های TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxtN9bpygJAo",
        "outputId": "219954d8-9410-4902-efcf-784bdb8593de"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<25461x2304 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 155667 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vectorizer = TfidfVectorizer(\n",
        "    tokenizer=LemmaTokenizer(),\n",
        "    strip_accents='unicode',\n",
        "    ngram_range=(1, 2),\n",
        "    min_df=0.0005,\n",
        "    max_df=0.8)\n",
        "\n",
        "X_train = vectorizer.fit_transform(X_train_raw)\n",
        "X_test = vectorizer.transform(X_test_raw)\n",
        "X_train"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "B1AGtLZyHYoG"
      },
      "source": [
        "### ‫مدل‌های Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3Ut8vAJoZTw-",
        "outputId": "0d98b258-b8ba-4081-bb25-a70834f6e2ef"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = MultinomialNB()\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwPmXr_oZTw-",
        "outputId": "e4e9cced-0398-4882-bf43-4df3a1b41931"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.582\n",
            "Test F1 score: 0.550\n",
            "Train balanced accuracy: 55.1%\n",
            "Test balanced accuracy: 53.3%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.98      2694\n",
            "           1       0.82      0.07      0.12       136\n",
            "\n",
            "    accuracy                           0.95      2830\n",
            "   macro avg       0.89      0.53      0.55      2830\n",
            "weighted avg       0.95      0.95      0.94      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'MultinomialNB (TF-IDF vectorization)', X_train, X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "55uDuP4yZTw-",
        "outputId": "fb5421bc-2312-4c8d-bf4c-cebe94826c1d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ComplementNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ComplementNB</label><div class=\"sk-toggleable__content\"><pre>ComplementNB()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "ComplementNB()"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = ComplementNB()\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2LmyUDIZTw-",
        "outputId": "90e5e5df-4518-4705-c717-63840b537825"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.621\n",
            "Test F1 score: 0.610\n",
            "Train balanced accuracy: 85.8%\n",
            "Test balanced accuracy: 82.7%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.83      0.90      2694\n",
            "           1       0.20      0.82      0.32       136\n",
            "\n",
            "    accuracy                           0.83      2830\n",
            "   macro avg       0.59      0.83      0.61      2830\n",
            "weighted avg       0.95      0.83      0.87      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'ComplementNB (TF-IDF vectorization)', X_train, X_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6YBm9fLOHYoP"
      },
      "source": [
        "### ‫کاهش تعداد ویژگی با استفاده از SVD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uM7bu0plKOQ5",
        "outputId": "6f1f5889-e4d1-429a-ed75-579c9970f6ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((25461, 200), (2830, 200))"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "truncated_svd = TruncatedSVD(n_components=200, random_state=0)\n",
        "X_train_reduced = truncated_svd.fit_transform(X_train)\n",
        "X_test_reduced = truncated_svd.transform(X_test)\n",
        "X_train_reduced.shape, X_test_reduced.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZDAo9aaKORC",
        "outputId": "2aa8e3af-6744-4f8f-9e29-b25585e844b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.3952687689167944"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "truncated_svd.explained_variance_ratio_.sum()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "aNbmEq_6HYoR"
      },
      "source": [
        "### استانداردسازی ویژگی‌ها"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "zDptKDzKHYoS"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_reduced = scaler.fit_transform(X_train_reduced)\n",
        "X_test_reduced = scaler.transform(X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "feZtyCU7HYoS"
      },
      "source": [
        "### ‫دسته‌بند Ridge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-_OfAyOUZTxA",
        "outputId": "d523372d-833d-48c7-e871-882a7a8d91ad"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeClassifier(alpha=3, class_weight=&#x27;balanced&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=3, class_weight=&#x27;balanced&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RidgeClassifier(alpha=3, class_weight='balanced', random_state=0)"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {'alpha': (0, 0.1, 0.3, 1, 3, 10)}\n",
        "grid_search = GridSearchCV(RidgeClassifier(class_weight='balanced',\n",
        "                                           random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6S0JRLULZTxA",
        "outputId": "e0470447-6178-41d9-fd30-db4c551a9570"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.654\n",
            "Test F1 score: 0.656\n",
            "Train balanced accuracy: 83.3%\n",
            "Test balanced accuracy: 82.2%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.89      0.93      2694\n",
            "           1       0.25      0.76      0.38       136\n",
            "\n",
            "    accuracy                           0.88      2830\n",
            "   macro avg       0.62      0.82      0.66      2830\n",
            "weighted avg       0.95      0.88      0.91      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'RidgeClassifier (TF-IDF vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "LPRiIybMHYoV"
      },
      "source": [
        "### ‫دسته‌بند SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "1ETmWJHPHYoV",
        "outputId": "af8becdc-ed7d-49a7-d1d0-502c6c4de381"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:299: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, class_weight=&#x27;balanced&#x27;, max_iter=1000, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, class_weight=&#x27;balanced&#x27;, max_iter=1000, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVC(C=10, class_weight='balanced', max_iter=1000, random_state=0)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'C': (1, 10, 30, 100),\n",
        "    'kernel': ('linear', 'poly', 'rbf', 'sigmoid')\n",
        "}\n",
        "grid_search = RandomizedSearchCV(SVC(class_weight='balanced',\n",
        "                                     random_state=0,\n",
        "                                     max_iter=1000),\n",
        "                                 param_distributions=parameters,\n",
        "                                 scoring='f1_macro',\n",
        "                                 cv=3,\n",
        "                                 n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I58dOcVLHYoZ",
        "outputId": "97ab321d-1c6a-4606-d23f-ecc6527cb724"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.612\n",
            "Test F1 score: 0.550\n",
            "Train balanced accuracy: 75.5%\n",
            "Test balanced accuracy: 62.9%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.86      0.91      2694\n",
            "           1       0.13      0.40      0.19       136\n",
            "\n",
            "    accuracy                           0.84      2830\n",
            "   macro avg       0.55      0.63      0.55      2830\n",
            "weighted avg       0.93      0.84      0.88      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'SVC (TF-IDF vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "UkHLVI5cHYoa"
      },
      "source": [
        "### ‫دسته‌بند Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "l3-g21c0NwOT",
        "outputId": "b48bd94b-8a94-497a-c467-3497d14a7232"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, criterion=&#x27;entropy&#x27;,\n",
              "                       max_depth=8, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, criterion=&#x27;entropy&#x27;,\n",
              "                       max_depth=8, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
              "                       max_depth=8, random_state=0)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'criterion': ('gini', 'entropy'),\n",
        "    'max_depth': range(4, 13, 4),\n",
        "}\n",
        "grid_search = GridSearchCV(RandomForestClassifier(class_weight='balanced', \n",
        "                                                  random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lg-DNWi7NxGs",
        "outputId": "c743cd10-d846-46ec-d3d5-dba510ab5018"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.852\n",
            "Test F1 score: 0.696\n",
            "Train balanced accuracy: 88.2%\n",
            "Test balanced accuracy: 69.8%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      2694\n",
            "           1       0.42      0.43      0.42       136\n",
            "\n",
            "    accuracy                           0.94      2830\n",
            "   macro avg       0.69      0.70      0.70      2830\n",
            "weighted avg       0.94      0.94      0.94      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'RandomForestClassifier (TF-IDF vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "CyG91bK0HYod"
      },
      "source": [
        "### ‫دسته‌بند MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "hisCuPqKHYoe",
        "outputId": "9b385b04-129d-4199-ae7c-cd67fc4fcbe3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(200, 100), random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(200, 100), random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MLPClassifier(hidden_layer_sizes=(200, 100), random_state=0)"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters = {\n",
        "    'hidden_layer_sizes': [(100, ), (200, ), (400, ), (100, 100), (200, 100)],\n",
        "}\n",
        "grid_search = GridSearchCV(MLPClassifier(activation='relu',\n",
        "                                         random_state=0),\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1_macro',\n",
        "                           cv=3,\n",
        "                           n_jobs=-1)\n",
        "grid_search.fit(X_train_reduced, y_train)\n",
        "\n",
        "clf = grid_search.best_estimator_\n",
        "clf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olZfy3Z5HYoe",
        "outputId": "27661ed1-4614-4817-bf61-b0057c340b00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.988\n",
            "Test F1 score: 0.726\n",
            "Train balanced accuracy: 99.0%\n",
            "Test balanced accuracy: 69.5%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      2694\n",
            "           1       0.57      0.40      0.47       136\n",
            "\n",
            "    accuracy                           0.96      2830\n",
            "   macro avg       0.77      0.69      0.73      2830\n",
            "weighted avg       0.95      0.96      0.95      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(clf, 'MLPClassifier (TF-IDF vectorization)', X_train_reduced, X_test_reduced)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "UrUeTgSPHYof"
      },
      "source": [
        "## ‫مدل مبتنی بر BERT transformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "pFIsg2S7JluS"
      },
      "outputs": [],
      "source": [
        "# install necessary packages if needed\n",
        "!pip install transformers[torch] -q\n",
        "!pip install accelerate -U -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "cHutX6uRHYog"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "from transformers import BertTokenizer, BertModel, BertForSequenceClassification, EarlyStoppingCallback, IntervalStrategy, Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "FEoFwc8YHYoh"
      },
      "outputs": [],
      "source": [
        "# Define the tokenizer\n",
        "bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "5GxIeLX8HYoi",
        "outputId": "71728a1f-c109-4373-c7ea-141493905657"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length covering 90% of text: 56\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Finding an optimum max token length for text truncation\n",
        "X_tokenized = df['text'].apply(bert_tokenizer).apply(lambda tokenized: len(tokenized.input_ids))\n",
        "sns.histplot(X_tokenized)\n",
        "print('Length covering 90% of text:', X_tokenized.sort_values()[len(df) // 10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "qb90LQzUHYoi"
      },
      "outputs": [],
      "source": [
        "# using the above graph we choose 64\n",
        "truncation_length = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqN2ahiEHYoj",
        "outputId": "9dd5dbf5-b649-4ece-c1b3-010457e065e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  101,  1996, 14017,  ...,     0,     0,     0],\n",
              "        [  101,  2057,  2064,  ...,     0,     0,     0],\n",
              "        [  101,  2000,  2070,  ...,     0,     0,     0],\n",
              "        ...,\n",
              "        [  101,  9413,  7842,  ...,  2094,   102,     0],\n",
              "        [  101,  2017,  2323,  ...,     0,     0,     0],\n",
              "        [  101,  2049,  2995,  ...,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [0, 0, 0,  ..., 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [1, 1, 1,  ..., 1, 1, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0],\n",
              "        [1, 1, 1,  ..., 0, 0, 0]])}"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Tokenize text\n",
        "X_train = bert_tokenizer(X_train_raw.tolist(), max_length=truncation_length,\n",
        "    padding=\"max_length\", truncation=True, return_tensors='pt')\n",
        "\n",
        "X_test = bert_tokenizer(X_test_raw.tolist(), max_length=truncation_length,\n",
        "    padding=\"max_length\", truncation=True, return_tensors='pt')\n",
        "\n",
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "LoFteUDKHYoj"
      },
      "outputs": [],
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, X, y):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.X.input_ids)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return {\n",
        "            'input_ids': self.X.input_ids[index],\n",
        "            'token_type_ids': self.X.token_type_ids[index],\n",
        "            'attention_mask': self.X.attention_mask[index],\n",
        "            'labels': torch.LongTensor([self.y[index]]),\n",
        "        }\n",
        "\n",
        "train_dataset = CustomDataset(X_train, y_train)\n",
        "val_dataset = CustomDataset(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tM7B4SQjHYok",
        "outputId": "9f320565-a17f-437d-c624-1bad93831385"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.04803424845842661, 0.9519657515415734]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculate class weights for training because of data imbalance\n",
        "class_weights = [(y_train == 1).sum() / len(y_train), (y_train == 0).sum() / len(y_train)]\n",
        "class_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "5JcbHeGMUe_r"
      },
      "outputs": [],
      "source": [
        "# release GPU memory if needed\n",
        "import gc\n",
        "\n",
        "if 'model' in globals():\n",
        "    model.cpu()\n",
        "    del model\n",
        "if 'trainer' in globals():\n",
        "    del trainer\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "1pppDb9XdIqR"
      },
      "outputs": [],
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self, embedding_tokenizer, embedding_model):\n",
        "        super().__init__()\n",
        "        self.embedding_size = 768\n",
        "\n",
        "        self.embedding = embedding_model\n",
        "        self.classifier = nn.Linear(self.embedding_size, 1)\n",
        "        self.sigmoid = torch.sigmoid\n",
        "\n",
        "    def forward(self, x):\n",
        "        tokenized = self.tokenizer(x, max_length=128, padding=\"max_length\", truncation=True, return_tensors='pt')\n",
        "        outputs = self.embedding(input_ids=tokenized.input_ids.cuda(),\n",
        "                                 token_type_ids=tokenized.token_type_ids.cuda(),\n",
        "                                 attention_mask=tokenized.attention_mask.cuda())\n",
        "        pooler_output = outputs.pooler_output\n",
        "        y = self.classifier(pooler_output)\n",
        "        return self.sigmoid(y)\n",
        "\n",
        "    def predict(self, x):\n",
        "        y = self.forward(x)\n",
        "        return (y > 0.5).float()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "id": "_aan-h0OHYol",
        "outputId": "28b338db-0d21-43ff-b73d-c238928436b0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2100' max='2388' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2100/2388 15:41 < 02:09, 2.23 it/s, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.666100</td>\n",
              "      <td>0.588990</td>\n",
              "      <td>0.690407</td>\n",
              "      <td>0.664156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.497000</td>\n",
              "      <td>0.329926</td>\n",
              "      <td>0.856255</td>\n",
              "      <td>0.788199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.320700</td>\n",
              "      <td>0.475878</td>\n",
              "      <td>0.844032</td>\n",
              "      <td>0.834679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.332900</td>\n",
              "      <td>0.611362</td>\n",
              "      <td>0.846595</td>\n",
              "      <td>0.829325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.481400</td>\n",
              "      <td>0.485688</td>\n",
              "      <td>0.863864</td>\n",
              "      <td>0.833516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.459500</td>\n",
              "      <td>0.464587</td>\n",
              "      <td>0.817776</td>\n",
              "      <td>0.837790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.375700</td>\n",
              "      <td>0.319648</td>\n",
              "      <td>0.881018</td>\n",
              "      <td>0.886413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.355400</td>\n",
              "      <td>0.350093</td>\n",
              "      <td>0.861522</td>\n",
              "      <td>0.865348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.208500</td>\n",
              "      <td>0.269916</td>\n",
              "      <td>0.928335</td>\n",
              "      <td>0.839926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.252700</td>\n",
              "      <td>0.547881</td>\n",
              "      <td>0.863193</td>\n",
              "      <td>0.879060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.243500</td>\n",
              "      <td>0.469466</td>\n",
              "      <td>0.877156</td>\n",
              "      <td>0.882496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.226200</td>\n",
              "      <td>0.527100</td>\n",
              "      <td>0.867426</td>\n",
              "      <td>0.886290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.222900</td>\n",
              "      <td>0.517241</td>\n",
              "      <td>0.848672</td>\n",
              "      <td>0.870643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.258500</td>\n",
              "      <td>0.379824</td>\n",
              "      <td>0.873294</td>\n",
              "      <td>0.878579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.222200</td>\n",
              "      <td>0.351719</td>\n",
              "      <td>0.890749</td>\n",
              "      <td>0.882729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.213400</td>\n",
              "      <td>0.587914</td>\n",
              "      <td>0.824608</td>\n",
              "      <td>0.866759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.104700</td>\n",
              "      <td>0.456273</td>\n",
              "      <td>0.877342</td>\n",
              "      <td>0.884043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.077100</td>\n",
              "      <td>0.515083</td>\n",
              "      <td>0.877899</td>\n",
              "      <td>0.888750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.154100</td>\n",
              "      <td>0.375670</td>\n",
              "      <td>0.883396</td>\n",
              "      <td>0.878114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.067500</td>\n",
              "      <td>0.442214</td>\n",
              "      <td>0.876414</td>\n",
              "      <td>0.876414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.047400</td>\n",
              "      <td>0.538145</td>\n",
              "      <td>0.858403</td>\n",
              "      <td>0.867375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2100, training_loss=0.2756029608136132, metrics={'train_runtime': 942.9045, 'train_samples_per_second': 81.008, 'train_steps_per_second': 2.533, 'total_flos': 2209409309621760.0, 'train_loss': 0.2756029608136132, 'epoch': 2.64})"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "class CustomTrainer(Trainer):\n",
        "    def __init__(self, class_weights, *args, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.class_weights = torch.FloatTensor(class_weights).cuda()\n",
        "\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.get('labels').view(-1)\n",
        "\n",
        "        # forward pass\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get('logits')\n",
        "\n",
        "        # compute custom loss\n",
        "        loss = F.cross_entropy(logits, labels, weight=self.class_weights)\n",
        "\n",
        "        return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    logging_steps=100,\n",
        "    output_dir='./results',          # output directory\n",
        "    num_train_epochs=3,              # total # of training epochs\n",
        "    per_device_train_batch_size=32,  # batch size per device during training\n",
        "    per_device_eval_batch_size=64,   # batch size for evaluation\n",
        "    warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        "    evaluation_strategy=IntervalStrategy.STEPS, \n",
        "    metric_for_best_model='f1',\n",
        "    load_best_model_at_end=True,\n",
        ")\n",
        "\n",
        "def compute_metrics(p):    \n",
        "    pred, labels = p\n",
        "    pred = np.argmax(pred, axis=1)\n",
        "    return {\n",
        "        \"balanced_accuracy\": balanced_accuracy_score(y_true=labels, y_pred=pred),\n",
        "        \"f1\": f1_score(y_true=labels, y_pred=pred, average='macro')\n",
        "    }\n",
        "\n",
        "trainer = CustomTrainer(\n",
        "    model=model,                         # the instantiated 🤗 Transformers model to be trained\n",
        "    args=training_args,                  # training arguments, defined above\n",
        "    train_dataset=train_dataset,         # training dataset\n",
        "    eval_dataset=val_dataset,            # evaluation dataset\n",
        "    class_weights=class_weights,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "jIH_26TeHYom",
        "outputId": "926c05aa-bf28-4a78-e7c7-ec0e34efcb64"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='45' max='45' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [45/45 00:09]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3517189919948578,\n",
              " 'eval_balanced_accuracy': 0.8907485043014979,\n",
              " 'eval_f1': 0.8827292680905443,\n",
              " 'eval_runtime': 9.6438,\n",
              " 'eval_samples_per_second': 293.453,\n",
              " 'eval_steps_per_second': 4.666,\n",
              " 'epoch': 2.64}"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTAOajL1NSMb",
        "outputId": "f6cda4f9-a510-4bb1-dfaa-cd1dcae937e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train F1 score: 0.957\n",
            "Test F1 score: 0.883\n",
            "Train balanced accuracy: 97.9%\n",
            "Test balanced accuracy: 89.1%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      2694\n",
            "           1       0.76      0.79      0.78       136\n",
            "\n",
            "    accuracy                           0.98      2830\n",
            "   macro avg       0.88      0.89      0.88      2830\n",
            "weighted avg       0.98      0.98      0.98      2830\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report_classifier(model, 'BERT classifier', X_train, X_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "eAkKjEmLtpPP"
      },
      "source": [
        "## ارزیابی مدل"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1PEMH3FHYon",
        "outputId": "87c002aa-1c1a-42b1-f1b5-8347bc452db2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'DummyClassifier (most frequent)': 0.5,\n",
              " 'DummyClassifier (uniform)': 0.49379612646840476,\n",
              " 'MultinomialNB (count vectorization)': 0.7035842176514259,\n",
              " 'ComplementNB (count vectorization)': 0.8272823049041442,\n",
              " 'RidgeClassifier (count vectorization)': 0.7514028996899428,\n",
              " 'SVC (count vectorization)': 0.6545455041704877,\n",
              " 'RandomForestClassifier (count vectorization)': 0.6956390017031312,\n",
              " 'MLPClassifier (count vectorization)': 0.6287911044150399,\n",
              " 'MultinomialNB (TF-IDF vectorization)': 0.5327170400454169,\n",
              " 'ComplementNB (TF-IDF vectorization)': 0.8267609939298659,\n",
              " 'RidgeClassifier (TF-IDF vectorization)': 0.8220691951613608,\n",
              " 'SVC (TF-IDF vectorization)': 0.6287447050089523,\n",
              " 'RandomForestClassifier (TF-IDF vectorization)': 0.6982018865452639,\n",
              " 'MLPClassifier (TF-IDF vectorization)': 0.6945963797545744,\n",
              " 'BERT classifier': 0.8907485043014979}"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_bal_acc_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJ4dl2ykHYon",
        "outputId": "ba68acbb-6bed-4c14-80c6-1da5c318032a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'DummyClassifier (most frequent)': 0.4876900796524258,\n",
              " 'DummyClassifier (uniform)': 0.3745097678069798,\n",
              " 'MultinomialNB (count vectorization)': 0.7237664471214347,\n",
              " 'ComplementNB (count vectorization)': 0.6056316430419871,\n",
              " 'RidgeClassifier (count vectorization)': 0.6114107588944804,\n",
              " 'SVC (count vectorization)': 0.43052301876377386,\n",
              " 'RandomForestClassifier (count vectorization)': 0.6984061517339121,\n",
              " 'MLPClassifier (count vectorization)': 0.6618631036489071,\n",
              " 'MultinomialNB (TF-IDF vectorization)': 0.549524870713749,\n",
              " 'ComplementNB (TF-IDF vectorization)': 0.6099926690996259,\n",
              " 'RidgeClassifier (TF-IDF vectorization)': 0.6563046152784804,\n",
              " 'SVC (TF-IDF vectorization)': 0.5504515115822537,\n",
              " 'RandomForestClassifier (TF-IDF vectorization)': 0.6961458597113194,\n",
              " 'MLPClassifier (TF-IDF vectorization)': 0.7258309404619725,\n",
              " 'BERT classifier': 0.8827292680905443}"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_macro_f1_scores"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "GK6E7Kw3HYom"
      },
      "source": [
        "### مقایسه نتایج"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "FH3t5zAuHYop",
        "outputId": "ad9292be-795d-4578-f6b3-fc1ef999ae7d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(dpi=100)\n",
        "df_macro_f1_score = pd.DataFrame({'Classifier': clf_macro_f1_scores.keys(), 'Macro F1 score': clf_macro_f1_scores.values()})\n",
        "sns.barplot(data=df_macro_f1_score, x='Macro F1 score', y='Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "XNLU_2gzHYoq",
        "outputId": "2bcb0ae9-435f-48df-a985-bfbe190dfcf7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(dpi=100)\n",
        "df_bal_acc_score = pd.DataFrame({'Classifier': clf_bal_acc_scores.keys(), 'Balanced accuracy': clf_bal_acc_scores.values()})\n",
        "sns.barplot(data=df_bal_acc_score, x='Balanced accuracy', y='Classifier')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "RAhWXrX26TPY"
      },
      "source": [
        "### ارزیابی نمودار آموزش مدل انتخاب شده با متریک‌های موردنظر"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "nx9e2fbj5VBI"
      },
      "outputs": [],
      "source": [
        "history = [i for i in trainer.state.log_history if 'eval_f1' in i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "E4-e7lnCnw9-",
        "outputId": "175b041f-9416-4d84-dac1-aa44afaf411e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot([i['step'] for i in history], [i['eval_f1'] for i in history])\n",
        "plt.title('eval macro F1 score')\n",
        "plt.xlabel('Step')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "6SHgfdz_4x9C",
        "outputId": "d4f32dc1-2c13-4179-c084-99e860eda6ab"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot([i['step'] for i in history], [i['eval_balanced_accuracy'] for i in history])\n",
        "plt.title('eval balanced accuracy score')\n",
        "plt.xlabel('Step')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "toc_visible": true
    },
    "interpreter": {
      "hash": "20e597611b894dd1498e4c0c74af388fb5316630fb95be6a94340375ebd28305"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}