naive_bayes_text_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "00009cca",
      "metadata": {
        "id": "00009cca"
      },
      "source": [
        "# Классификация текстов с использованием Наивного Байесовского Классификатора"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a52414b8",
      "metadata": {
        "id": "a52414b8"
      },
      "source": [
        "## Задание 1 (1 балл)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5f27b43",
      "metadata": {
        "id": "f5f27b43"
      },
      "source": [
        "Откройте данные. Узнайте, сколько в них спам- и не спам-писем. Визуализируйте полученные соотношение подходящим образом."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "ac369274",
      "metadata": {
        "id": "ac369274"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>email</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>martin a posted tassos papadopoulos the greek ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               email  label\n",
              "0   date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...      0\n",
              "1  martin a posted tassos papadopoulos the greek ...      0"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.naive_bayes import ComplementNB\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import \\\n",
        "    classification_report, roc_auc_score, roc_curve, auc\n",
        "\n",
        "data = pd.read_csv('spam_or_not_spam.zip')\n",
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "02ebaf70",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    2500\n",
              "1     500\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Расчет частоты для классов\n",
        "data['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "9b871636",
      "metadata": {
        "id": "9b871636"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Визуализация\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "sns.countplot(data=data, x='label')\n",
        "ax.set_title('КЛАССЫ', fontsize = 18)\n",
        "ax.set_ylabel('Частоты классов', fontsize = 18)\n",
        "ax.set_xlabel(' ')\n",
        "ax.set_xticks(ticks=[0,1], labels=['ham', 'spam'], fontsize = 18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b161614b",
      "metadata": {
        "id": "b161614b"
      },
      "source": [
        "## Задание 2 (2 балла)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd29763b",
      "metadata": {
        "id": "dd29763b"
      },
      "source": [
        "Вам необходимо предобработать ваши данные и перевести их в векторный вид. Подгрузим необходимый модуль:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "382eac9c",
      "metadata": {
        "id": "382eac9c"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89546b95",
      "metadata": {
        "id": "89546b95"
      },
      "source": [
        "Замените все пустоты (пустые строки) в данных на пропуски (NaN). После этого удалите из данных все строки, в которых наблюдаются пропущенные значения."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "051bb9dd",
      "metadata": {},
      "outputs": [],
      "source": [
        "data = data.replace ( r'^\\s\\*$' , np.nan , regex= True )\n",
        "data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ca2fe88",
      "metadata": {
        "id": "2ca2fe88"
      },
      "source": [
        "Переводим данные в векторный вид:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "29e9a37b",
      "metadata": {
        "id": "29e9a37b"
      },
      "outputs": [],
      "source": [
        "vectorizer = CountVectorizer()\n",
        "X = vectorizer.fit_transform(data[\"email\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88c8b726",
      "metadata": {
        "id": "88c8b726"
      },
      "source": [
        "Определите, сколько теперь признаков в нашем наборе данных:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "00a38278",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "34116"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.shape[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1803f282",
      "metadata": {
        "id": "1803f282"
      },
      "source": [
        "## Задание 3 (2 балла)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39f400b7",
      "metadata": {
        "id": "39f400b7"
      },
      "source": [
        "Определите целевую переменную и признаки:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "7865ddc0",
      "metadata": {},
      "outputs": [],
      "source": [
        "X = X.toarray()\n",
        "y = data['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b20f273e",
      "metadata": {
        "id": "b20f273e"
      },
      "source": [
        "Разделите выборку на обучающую и тестовую, размер тестовой выборки возьмите как 0.25, параметр random_state определите со значением 42:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "96538fb6",
      "metadata": {
        "id": "96538fb6"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "139dac3d",
      "metadata": {
        "id": "139dac3d"
      },
      "source": [
        "Рассчитайте среднее значение целевой переменной по тестовой выборке:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "e9baf72d",
      "metadata": {
        "id": "e9baf72d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.164"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "710e0e94",
      "metadata": {
        "id": "710e0e94"
      },
      "source": [
        "## Задание 4 (3 балла)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26cf6885",
      "metadata": {
        "id": "26cf6885"
      },
      "source": [
        "Определите и обучите подходящий алгоритм с параметром alpha = 0.01"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9118de1f",
      "metadata": {},
      "source": [
        "Классы несбалансированы, поэтому выбираем ComplementNB. Это адаптация стандартного полиномиального наивного байесовского алгоритма, которая особенно подходит для несбалансированных наборов данных."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "4865dd78",
      "metadata": {
        "id": "4865dd78"
      },
      "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>ComplementNB(alpha=0.01)</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\">ComplementNB</label><div class=\"sk-toggleable__content\"><pre>ComplementNB(alpha=0.01)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "ComplementNB(alpha=0.01)"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nbc = ComplementNB(alpha=0.01)\n",
        "nbc.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d803fbe3",
      "metadata": {
        "id": "d803fbe3"
      },
      "source": [
        "Оцените результат с точки зрения всех известных вам метрик (не менее трёх):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "b0bbc4d2",
      "metadata": {
        "id": "b0bbc4d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0      0.997     0.997     0.997       627\n",
            "           1      0.984     0.984     0.984       123\n",
            "\n",
            "    accuracy                          0.995       750\n",
            "   macro avg      0.990     0.990     0.990       750\n",
            "weighted avg      0.995     0.995     0.995       750\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_test_pred = nbc.predict(X_test)\n",
        "print(classification_report(y_test, y_test_pred, digits=3))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c85976d1",
      "metadata": {
        "id": "c85976d1"
      },
      "source": [
        "Нарисуйте ROC-кривую:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "919aa210",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y_pred_proba = nbc.predict_proba(X_test)[:,1]\n",
        "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
        "auc = roc_auc_score(y_test, y_pred_proba)\n",
        "fig, ax = plt.subplots(figsize=(8, 5))\n",
        "ax.plot(fpr,tpr,label=\"auc=\"+str(round(auc,4)))\n",
        "ax.plot([0, 1], [0, 1],'r--', lw=0.5)\n",
        "plt.title('ROC curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf0bb49b",
      "metadata": {
        "id": "cf0bb49b"
      },
      "source": [
        "## Задание 5 (3 балла)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30b7a0ae",
      "metadata": {
        "id": "30b7a0ae"
      },
      "source": [
        "Переберите несколько значений alpha с помощью кросс-валидации. Оцените, зависит ли от этого параметра качество классификации."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "3513d907",
      "metadata": {
        "id": "3513d907"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Кросс-валидация, среднее значение метрики по 5 фолдам\n",
            "alpha = 0.001; f1-score = 0.955\n",
            "alpha = 0.010; f1-score = 0.956\n",
            "alpha = 0.100; f1-score = 0.970\n",
            "alpha = 1.000; f1-score = 0.962\n",
            "alpha = 3.000; f1-score = 0.891\n",
            "alpha = 5.000; f1-score = 0.759\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print('Кросс-валидация, среднее значение метрики по 5 фолдам')\n",
        "a = [0.001, 0.01, 0.1, 1, 3, 5]\n",
        "for alpha in a:\n",
        "    nbc = ComplementNB(alpha=alpha)\n",
        "    f1_score = cross_val_score(nbc, X_train, y_train, cv=5, scoring='f1').mean()\n",
        "    \n",
        "    print('alpha = {:.3f}; f1-score = {:.3f}'.format(\n",
        "        alpha, f1_score ))\n",
        "    \n",
        "    #визуализация влияния параметра alfa    \n",
        "for alpha in [1e-8, 0.01, 1, 3, 5]:\n",
        "    nbc = ComplementNB(alpha=alpha)    \n",
        "    nbc.fit(X_train, y_train)\n",
        "    y_pred_proba = nbc.predict_proba(X_test)[:,1]\n",
        "    fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
        "    auc = roc_auc_score(y_test, y_pred_proba)\n",
        "    \n",
        "    plt.plot(fpr,tpr,label=\"alpha=\"+str(alpha))\n",
        "    plt.plot([0, 1], [0, 1],'r--', lw=0.1)\n",
        "    plt.title('ROC curve, обучение на всем X_train')\n",
        "    plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8fff57fe",
      "metadata": {},
      "source": [
        "## Вывод  \n",
        "Качество классификации зависит от значения параметра alpha. Можно найти оптимальное значение."
      ]
    }
  ],
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