{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b047c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'купить этот наушник пара неделя назад звук просто потрясать но через несколько день начать трещать на высокий громкость немного разочаровать хотя поначалу всё нравиться'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "from sklearn import set_config\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pymorphy3\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "\n",
    "morph = pymorphy3.MorphAnalyzer()\n",
    "\n",
    "def _preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text\n",
    "\n",
    "def _remove_punctuation(text):\n",
    "    return re.sub(r'[^\\w\\s]', ' ', text)\n",
    "\n",
    "def _remove_emojis(text):\n",
    "    emoji_pattern = re.compile(\n",
    "        \"[\\U0001F600-\\U0001F64F\"\n",
    "        \"\\U0001F300-\\U0001F5FF\"\n",
    "        \"\\U0001F680-\\U0001F6FF\"\n",
    "        \"\\U0001F700-\\U0001F77F\"\n",
    "        \"\\U0001F780-\\U0001F7FF\"\n",
    "        \"\\U0001F800-\\U0001F8FF\"\n",
    "        \"\\U0001F900-\\U0001F9FF\"\n",
    "        \"\\U0001FA70-\\U0001FAFF\"\n",
    "        \"\\U00002702-\\U000027B0\"\n",
    "        \"\\U000024C2-\\U0001F251\"\n",
    "        \"]+\", flags=re.UNICODE\n",
    "    )\n",
    "    return emoji_pattern.sub(r'', text)\n",
    "\n",
    "\n",
    "def _lemmatize(text):\n",
    "    words = text.split()\n",
    "    lemmas = [morph.parse(word)[0].normal_form for word in words]\n",
    "    return ' '.join(lemmas)\n",
    "\n",
    "def preprocess_for_prediction(text):\n",
    "    text = _remove_punctuation(text)\n",
    "    text = _remove_emojis(text)\n",
    "    text = _preprocess_text(text)\n",
    "    text = _lemmatize(text)\n",
    "    return text\n",
    "\n",
    "preprocess_for_prediction(\"Купили эти наушники пару недель назад — звук просто потрясающий! 🎧 Но через несколько дней начали трещать на высокой громкости. Немного разочарованы 😕, хотя поначалу всё нравилось.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "179d7abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "def load_and_prepare_data(file_path):\n",
    "    data = pd.read_csv(file_path, sep=',', encoding='utf-8')\n",
    "    \n",
    "    data['text'] = data['text'].astype(str).apply(preprocess_for_prediction)\n",
    "    data = data[data['text'].str.strip() != '']\n",
    "    \n",
    "    data['class'] = data['Полученный класс'].apply(lambda x: 0 if x == 1 else 1)\n",
    "    \n",
    "    data = shuffle(data, random_state=42).reset_index(drop=True)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e173254e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов до балансировки:\n",
      "class\n",
      "1    36847\n",
      "0    21621\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import (\n",
    "    classification_report, accuracy_score, precision_score,\n",
    "    recall_score, f1_score, confusion_matrix\n",
    ")\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "df = load_and_prepare_data(\"data/wb_reviews.csv\")\n",
    "print(\"Распределение классов до балансировки:\")\n",
    "print(df['class'].value_counts())\n",
    "\n",
    "X = df['text']\n",
    "y = df['class']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f3ae87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Распределение классов после балансировки (undersampling):\n",
      "class\n",
      "0    21621\n",
      "1    21621\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X.values.reshape(-1,1), y)\n",
    "X_resampled = pd.Series(X_resampled.flatten())\n",
    "\n",
    "print(\"\\nРаспределение классов после балансировки (undersampling):\")\n",
    "print(pd.Series(y_resampled).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb7e6ebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    name  \\\n",
      "1                                 Кольца набор бижутерия   \n",
      "3             Зеркало напольное в полный рост на колесах   \n",
      "5      Беспроводные наушники блютуз с шумоподавлением...   \n",
      "6                  Жакет Пиджак женский больших размеров   \n",
      "9             Комод деревянный узкий для вещей с ящиками   \n",
      "...                                                  ...   \n",
      "58455          Полка настенная для книг навесная из лдсп   \n",
      "58458                    Обогреватель для дома настенный   \n",
      "58464              Зимний комбинезон из мембранной ткани   \n",
      "58465  Вешалка напольная для одежды металлическая в п...   \n",
      "58467              Платье праздничное нарядное вечернее    \n",
      "\n",
      "                                                    text  mark  \\\n",
      "1      при расширение кольцо лопнуть тянуть не сильно...     3   \n",
      "3      очень хлипкий как детский низкий искажать отра...     2   \n",
      "5      быстро садиться один наушник тихий второй пере...     2   \n",
      "6      расцветка не очень маломерить даже по спинка б...     3   \n",
      "9      собирать по схема но один ящик войти кривой на...     1   \n",
      "...                                                  ...   ...   \n",
      "58455  неправильно сделать отверстие под винт нет жёс...     3   \n",
      "58458  прийти ворда бы всё хорошо упаковать но прийти...     3   \n",
      "58464  очень красивый но прийти с брак нет липучка на...     3   \n",
      "58465  ужасный вещалка мало тот что сделать плохо так...     1   \n",
      "58467                                       не по размер     4   \n",
      "\n",
      "       Полученный класс                             Описание классификации  \\\n",
      "1                     1  Потому что содержит негативные моменты (лопнул...   \n",
      "3                     1  Отзыв содержит только негативные моменты: \"Оче...   \n",
      "5                     1  Отзыв содержит только негативные моменты: быст...   \n",
      "6                     1  Отзыв содержит только негативные моменты: несо...   \n",
      "9                     1  Отзыв содержит только негативные моменты: несо...   \n",
      "...                 ...                                                ...   \n",
      "58455                 1  Потому что отзыв содержит только негативные мо...   \n",
      "58458                 1  Отзыв содержит жалобу на трещину и дырки на то...   \n",
      "58464                 1  Отзыв содержит как положительные (\"Очень краси...   \n",
      "58465                 1  Отзыв содержит только негативные моменты: жало...   \n",
      "58467                 1  Потому что содержит жалобу на несоответствие т...   \n",
      "\n",
      "       class  \n",
      "1          0  \n",
      "3          0  \n",
      "5          0  \n",
      "6          0  \n",
      "9          0  \n",
      "...      ...  \n",
      "58455      0  \n",
      "58458      0  \n",
      "58464      0  \n",
      "58465      0  \n",
      "58467      0  \n",
      "\n",
      "[21621 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "df_class_0 = df[df['class'] == 0]\n",
    "print(df_class_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5fbcebc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainval, X_test, y_trainval, y_test = train_test_split(\n",
    "    X_resampled, y_resampled, test_size=0.2, random_state=42, stratify=y_resampled)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_trainval, y_trainval, test_size=0.25, random_state=42, stratify=y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "163e7aac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class Weights: {0: 1.0, 1: 1.0}\n"
     ]
    }
   ],
   "source": [
    "classes = np.array(sorted(set(y_train)))\n",
    "class_weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train)\n",
    "\n",
    "class_weights_dict = dict(zip(classes, class_weights))\n",
    "print(\"\\nClass Weights:\", class_weights_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d440849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n[Валидация]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.94      0.97      0.95      4324\\n           1       0.97      0.93      0.95      4325\\n\\n    accuracy                           0.95      8649\\n   macro avg       0.95      0.95      0.95      8649\\nweighted avg       0.95      0.95      0.95      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4192,  132],\n",
       "       [ 288, 4037]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Тест]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.93      0.97      0.95      4325\\n           1       0.97      0.93      0.95      4324\\n\\n    accuracy                           0.95      8649\\n   macro avg       0.95      0.95      0.95      8649\\nweighted avg       0.95      0.95      0.95      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4202,  123],\n",
       "       [ 297, 4027]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Валидация]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.95      0.96      0.95      4324\\n           1       0.96      0.95      0.95      4325\\n\\n    accuracy                           0.95      8649\\n   macro avg       0.95      0.95      0.95      8649\\nweighted avg       0.95      0.95      0.95      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4147,  177],\n",
       "       [ 233, 4092]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Тест]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.95      0.96      0.96      4325\\n           1       0.96      0.94      0.95      4324\\n\\n    accuracy                           0.95      8649\\n   macro avg       0.95      0.95      0.95      8649\\nweighted avg       0.95      0.95      0.95      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4172,  153],\n",
       "       [ 240, 4084]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Валидация]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.93      0.94      0.94      4324\\n           1       0.94      0.93      0.94      4325\\n\\n    accuracy                           0.94      8649\\n   macro avg       0.94      0.94      0.94      8649\\nweighted avg       0.94      0.94      0.94      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4061,  263],\n",
       "       [ 295, 4030]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Тест]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.93      0.94      0.94      4325\\n           1       0.94      0.93      0.94      4324\\n\\n    accuracy                           0.94      8649\\n   macro avg       0.94      0.94      0.94      8649\\nweighted avg       0.94      0.94      0.94      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4058,  267],\n",
       "       [ 293, 4031]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Валидация]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.93      0.95      0.94      4324\\n           1       0.95      0.93      0.94      4325\\n\\n    accuracy                           0.94      8649\\n   macro avg       0.94      0.94      0.94      8649\\nweighted avg       0.94      0.94      0.94      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4119,  205],\n",
       "       [ 296, 4029]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n[Тест]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.93      0.95      0.94      4325\\n           1       0.95      0.93      0.94      4324\\n\\n    accuracy                           0.94      8649\\n   macro avg       0.94      0.94      0.94      8649\\nweighted avg       0.94      0.94      0.94      8649\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Confusion matrix (validation):\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[4127,  198],\n",
       "       [ 287, 4037]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=1000, class_weight=class_weights_dict),\n",
    "    \"SVM (Linear)\": LinearSVC(class_weight=class_weights_dict),\n",
    "    \"Naive Bayes\": MultinomialNB(),\n",
    "    \"Random Forest\": RandomForestClassifier(class_weight=class_weights_dict, random_state=42)\n",
    "}\n",
    "results = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    pipe = Pipeline([\n",
    "        ('tfidf', TfidfVectorizer()),\n",
    "        ('clf', model)\n",
    "    ])\n",
    "    pipe.fit(X_train, y_train)\n",
    "    y_pred = pipe.predict(X_val)\n",
    "\n",
    "    report = classification_report(y_val, y_pred, output_dict=True)\n",
    "    cm = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "\n",
    "    y_pred_test = pipe.predict(X_test)\n",
    "\n",
    "    report_test = classification_report(y_test, y_pred_test, output_dict=True)\n",
    "    cm_test = confusion_matrix(y_test, y_pred_test)\n",
    "\n",
    "    results[name] = {\n",
    "        'val_report': report,\n",
    "        'val_cm': cm,\n",
    "        'test_report':report_test,\n",
    "        'test_cm':cm\n",
    "    }\n",
    "\n",
    "    display(\"\\n[Валидация]\")\n",
    "    display(classification_report(y_val, y_pred))\n",
    "    display(\"Confusion matrix (validation):\\n\", cm)\n",
    "\n",
    "    display(\"\\n[Тест]\")\n",
    "    display(classification_report(y_test, y_pred_test))\n",
    "    display(\"Confusion matrix (validation):\\n\", cm_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "584e5dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, (name, data) in enumerate(results.items()):\n",
    "    cm = data['test_cm']\n",
    "    ax = axes[i]\n",
    "    im = ax.imshow(cm, cmap='Blues')\n",
    "\n",
    "    ax.set_title(name)\n",
    "    ax.set_xlabel('Predicted')\n",
    "    ax.set_ylabel('Actual')\n",
    "\n",
    "    for (j, k), val in np.ndenumerate(cm):\n",
    "        ax.text(k, j, str(val), ha='center', va='center')\n",
    "\n",
    "fig.suptitle(\"Матрицы ошибок моделей\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "metric_names = ['accuracy', 'precision', 'recall', 'f1-score']\n",
    "metrics_summary = {metric: [] for metric in metric_names}\n",
    "model_names = list(results.keys())\n",
    "\n",
    "for model in model_names:\n",
    "    report = results[model]['test_report']\n",
    "    metrics_summary['f1-score'].append(report['weighted avg']['f1-score'])\n",
    "    metrics_summary['precision'].append(report['weighted avg']['precision'])\n",
    "    metrics_summary['recall'].append(report['weighted avg']['recall'])\n",
    "\n",
    "    acc = accuracy_score(\n",
    "        y_test,\n",
    "        Pipeline([('tfidf', TfidfVectorizer()), ('clf', models[model])])\n",
    "        .fit(X_train, y_train).predict(X_test)\n",
    "    )\n",
    "    metrics_summary['accuracy'].append(acc)\n",
    "\n",
    "for metric in metric_names:\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(model_names, metrics_summary[metric], marker='o', linestyle='-')\n",
    "    plt.title(f'{metric.capitalize()} по моделям (валидация)')\n",
    "    plt.xlabel('Модель')\n",
    "    plt.ylabel(metric.capitalize())\n",
    "    plt.ylim(0, 1.05)\n",
    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
    "    plt.xticks(rotation=15)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mai-iCsk_EVY-py3.12",
   "language": "python",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}