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TextClassifier
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timo/rep/TextClassifier/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('..')\n",
    "\n",
    "from src.load_model import model\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>хорошо пошло!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>посмотрела, как будто дома побывала. как река ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>отдам котят 1,5 месяца в добрые руки.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0,5литровая баночка 200р стоит в таганроге. та...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>речь шла о радужных зонтиках над верандой.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24824</th>\n",
       "      <td>и ты будь здоров</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24825</th>\n",
       "      <td>не дорога а прям стекло но правда битое (h)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24826</th>\n",
       "      <td>спасибо большое. буду ждать хороших новостей. ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24827</th>\n",
       "      <td>активирую установку 🌈🌈🌈👍😎🔥🔥🔥</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24828</th>\n",
       "      <td>а вы курс российского рубля видели, кошмар!!!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24829 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0                                          хорошо пошло!      0\n",
       "1      посмотрела, как будто дома побывала. как река ...      0\n",
       "2                  отдам котят 1,5 месяца в добрые руки.      0\n",
       "3      0,5литровая баночка 200р стоит в таганроге. та...      0\n",
       "4             речь шла о радужных зонтиках над верандой.      0\n",
       "...                                                  ...    ...\n",
       "24824                                   и ты будь здоров      0\n",
       "24825        не дорога а прям стекло но правда битое (h)      0\n",
       "24826  спасибо большое. буду ждать хороших новостей. ...      0\n",
       "24827                       активирую установку 🌈🌈🌈👍😎🔥🔥🔥      0\n",
       "24828      а вы курс российского рубля видели, кошмар!!!      0\n",
       "\n",
       "[24829 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits = {'train': 'train.jsonl', 'test': 'test.jsonl'}\n",
    "test = pd.read_json(\"hf://datasets/AlexSham/Toxic_Russian_Comments/\" + splits[\"test\"], lines=True)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 97/97 [00:39<00:00,  2.44it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# Размер батча\n",
    "batch_size = 256\n",
    "\n",
    "test['pred'] = [\n",
    "    model(text).item()\n",
    "    for batch in tqdm(range(0, len(test), batch_size))\n",
    "    for text in test['text'][batch:batch + batch_size]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   non-toxic       0.99      0.98      0.98     20369\n",
      "       toxic       0.92      0.94      0.93      4460\n",
      "\n",
      "    accuracy                           0.98     24829\n",
      "   macro avg       0.96      0.96      0.96     24829\n",
      "weighted avg       0.98      0.98      0.98     24829\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "threshold = 0.5\n",
    "\n",
    "pred = test['pred'].apply(lambda x: 1 if x >= threshold else 0)\n",
    "\n",
    "# Вычисление основных метрик\n",
    "class_report = classification_report(test['label'], pred, target_names=['non-toxic', 'toxic'])\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# Предсказания модели: вероятности для положительного класса\n",
    "y_probs = test['pred']\n",
    "\n",
    "# Истинные метки\n",
    "y_true = test['label']\n",
    "\n",
    "# Вычисление Precision-Recall кривой для разных порогов\n",
    "precision, recall, thresholds = precision_recall_curve(y_true, y_probs)\n",
    "\n",
    "# Построение Precision-Recall кривой\n",
    "plt.plot(recall, precision, \n",
    "         color='blue',)\n",
    "plt.grid(\n",
    "    color='gray',\n",
    ")\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7482494115829468"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_thresholds(thresholds_precision):\n",
    "    return thresholds[np.where(precision>=thresholds_precision)[0][0]].item()\n",
    "\n",
    "get_thresholds(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   non-toxic       0.98      0.99      0.99     20369\n",
      "       toxic       0.95      0.91      0.93      4460\n",
      "\n",
      "    accuracy                           0.98     24829\n",
      "   macro avg       0.97      0.95      0.96     24829\n",
      "weighted avg       0.98      0.98      0.98     24829\n",
      "\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.75\n",
    "\n",
    "pred = test['pred'].apply(lambda x: 1 if x >= threshold else 0)\n",
    "\n",
    "# Вычисление основных метрик\n",
    "class_report = classification_report(test['label'], pred, target_names=['non-toxic', 'toxic'])\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}