detailed-fake-news-classificate-LSTM.ipynb

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   "source": [
    "## Классификация заголовоков новостей на фейковые и настоящие "
   ]
  },
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   "source": [
    "## 0. Установка и подгрузука библиотек\n",
    "Установка библиотек, под которым запускается данный бейзлайн."
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('max_colwidth', 200)\n",
    "pd.set_option('display.width', 100)\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "from random import sample\n",
    "import random\n",
    "\n",
    "import tqdm\n",
    "from copy import deepcopy\n",
    "import re\n",
    "\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logging.disable(logging.ERROR)\n",
    "\n",
    "from simple_elmo import ElmoModel\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Загрузим датасет"
   ]
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       "<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>title</th>\n",
       "      <th>is_fake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Москвичу Владимиру Клутину пришёл счёт за вмешательство в американские выборы</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Агент Кокорина назвал езду по встречке житейской историей</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Госдума рассмотрит возможность введения секретных статей Уголовного кодекса</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ФАС заблокировала поставку скоростных трамваев для Москвы</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Против Навального завели дело о недоносительстве на Волкова</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Российским студентам запретят учиться за рубежом</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Путин пишет книгу об истории Украины</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Россияне обхитрили рост цен</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Звезда «Ворониных» раскрыл подробности о своем состоянии</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Microsoft объявила дату выхода очков дополненной реальности Hololens</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           title  is_fake\n",
       "0  Москвичу Владимиру Клутину пришёл счёт за вмешательство в американские выборы        1\n",
       "1                      Агент Кокорина назвал езду по встречке житейской историей        0\n",
       "2    Госдума рассмотрит возможность введения секретных статей Уголовного кодекса        1\n",
       "3                      ФАС заблокировала поставку скоростных трамваев для Москвы        0\n",
       "4                    Против Навального завели дело о недоносительстве на Волкова        1\n",
       "5                               Российским студентам запретят учиться за рубежом        1\n",
       "6                                           Путин пишет книгу об истории Украины        1\n",
       "7                                                    Россияне обхитрили рост цен        0\n",
       "8                       Звезда «Ворониных» раскрыл подробности о своем состоянии        0\n",
       "9           Microsoft объявила дату выхода очков дополненной реальности Hololens        0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"dataset/train.tsv\", sep=\"\\t\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "* Датасет загружен, проанализируем его содержимое"
   ]
  },
  {
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   "source": [
    "## 2. Препроцессинг заголовков новостей\n",
    "1. Токенизация — разобьём каждый заголовок статьи на слова.\n",
    "2. Нормализация — приведём слова к «рафинированному» виду (единый регистр слов, отсутствие знаков пунктуации, расшифрованные сокращения, словесное написание чисел и т.д.).\n",
    "3. Стеммизация — приведём слова к его корню путем устранения придатков (суффикса, приставки, окончания).\n",
    "4. Лемматизация — приведём слова к начальной форме.\n",
    "5. Чистка — удаленим стоп-слова, которые не несут смысловой нагрузки (артикли, междометья).\n",
    "\n",
    "* Для этого будем использовать библиотеку pymorphy2"
   ]
  },
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       "  0%|          | 0/5758 [00:00<?, ?it/s]"
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     "text": [
      "капитан арсенал заказать результат жеребьёвка лига чемпион\n",
      "ответ приостановка поставка тухлый яйцо грузия закрыть завод боржоми саранск\n",
      "украина решить продать российский актив крым\n",
      "французский коммунист требовать вынести тело наполеон дом инвалид\n",
      "гость ялта просить нападать крокодил сбежать затопление крокодиляриум\n",
      "airbus получить февраль один заказ\n",
      "автор хороший комментарий защита бузов получить дом подмосковье\n",
      "глава фрс отчитаться конгресс покупка актив bear stearns\n",
      "германия согласовать митинг антипрививочник памятник доктор менгель\n",
      "честь летие иа панорама князь монако присвоить дворянство сотрудник издательство\n",
      "DELETED 8337 words\n"
     ]
    }
   ],
   "source": [
    "import pymorphy2\n",
    "count = 0\n",
    "all_words = []\n",
    "all_pre_words = []\n",
    "\n",
    "\n",
    "def sentence_editor(title):\n",
    "    global count\n",
    "    txt = re.sub(pattern, \"\", title)\n",
    "    txt = txt.strip(\" \").lower()\n",
    "    txt = txt.split() # Tokenizing\n",
    "    all_pre_words.extend(txt)\n",
    "    new_txt = []\n",
    "    for word in txt:\n",
    "        word = morph.parse(word)[0]\n",
    "        if word.tag.POS not in [\"PRCL\", \"INTJ\", \"PREP\", \"CONJ\"]:\n",
    "            new_txt.append(word.normal_form) # Lemmatizing\n",
    "        else:\n",
    "            count += 1\n",
    "    all_words.extend(new_txt)\n",
    "    txt = \" \".join(new_txt)\n",
    "    return txt\n",
    "\n",
    "\n",
    "morph = pymorphy2.MorphAnalyzer()\n",
    "new_title = []\n",
    "pattern = \"[^а-яА-Яa-zA-Z ё]\"\n",
    "\n",
    "for txt in tqdm.notebook.tqdm(data.title):\n",
    "    new_title.append(sentence_editor(txt))\n",
    "\n",
    "start = random.randint(0, len(new_title))\n",
    "print(*new_title[start:start + 10], sep=\"\\n\")\n",
    "print(\"DELETED\", count, \"words\")"
   ]
  },
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   "source": [
    "## 3. Визуализация данных\n",
    "\n",
    "\n",
    "### Есть ли дисбаланс классов?"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='is_fake', ylabel='count'>"
      ]
     },
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=data[\"is_fake\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.036091,
     "end_time": "2020-10-22T13:18:59.759379",
     "exception": false,
     "start_time": "2020-10-22T13:18:59.723288",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Колличество фейковых и реальных новостей полностью совпадает, что не может не радовать"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Топ самых частых слов до удаления стоп-слов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMBER = 30\n",
    "count_words = {}\n",
    "for word in all_pre_words:\n",
    "    if word in count_words:\n",
    "        count_words[word] += 1\n",
    "    else:\n",
    "        count_words[word] = 1\n",
    "\n",
    "most_frequency_words = {\"word\": sorted(count_words, key=lambda x: -count_words[x])[:NUMBER], \n",
    "                        \"number\": []}\n",
    "for word in most_frequency_words[\"word\"]:\n",
    "    most_frequency_words[\"number\"].append(count_words[word])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.barh(most_frequency_words[\"word\"][::-1], most_frequency_words[\"number\"][::-1])\n",
    "plt.ylabel(\"Слова\")\n",
    "plt.xlabel(\"Колличество\")\n",
    "plt.title(\"Топ %d слов в заголовках перед удалением стоп слов\" % (NUMBER))\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMBER = 30\n",
    "count_words = {}\n",
    "for word in all_words:\n",
    "    if word in count_words:\n",
    "        count_words[word] += 1\n",
    "    else:\n",
    "        count_words[word] = 1\n",
    "\n",
    "most_frequency_words = {\"word\": sorted(count_words, key=lambda x: -count_words[x])[:NUMBER], \n",
    "                        \"number\": []}\n",
    "for word in most_frequency_words[\"word\"]:\n",
    "    most_frequency_words[\"number\"].append(count_words[word])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.barh(most_frequency_words[\"word\"][::-1], most_frequency_words[\"number\"][::-1])\n",
    "plt.ylabel(\"Слова\")\n",
    "plt.xlabel(\"Колличество\")\n",
    "plt.title(\"Топ %d слов в заголовках после удаления стоп слов\" % (NUMBER))\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Переведём каждый обработанный заголовок в векторное представление\n",
    "* с помощью контекстуализированной модели от RusVectōrēs натренерованной на около 10 миллиардов слов\n",
    "* будем создавать вектор для каждого слова, а в качестве вектора предложения будем брать среднее значение между всеми словами в нём"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\vush6\\pycharmprojects\\fake or real title of news\\venv\\lib\\site-packages\\simple_elmo\\model.py:529: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
      "  lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(\n",
      "c:\\users\\vush6\\pycharmprojects\\fake or real title of news\\venv\\lib\\site-packages\\keras\\layers\\legacy_rnn\\rnn_cell_impl.py:984: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
      "  self._kernel = self.add_variable(\n",
      "c:\\users\\vush6\\pycharmprojects\\fake or real title of news\\venv\\lib\\site-packages\\keras\\layers\\legacy_rnn\\rnn_cell_impl.py:993: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
      "  self._bias = self.add_variable(\n",
      "c:\\users\\vush6\\pycharmprojects\\fake or real title of news\\venv\\lib\\site-packages\\keras\\layers\\legacy_rnn\\rnn_cell_impl.py:1009: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
      "  self._proj_kernel = self.add_variable(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The model is now loaded.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elmo_model = ElmoModel()\n",
    "elmo_model.load(\"sentence_embender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'москвич владимир клутина прийти счёт вмешательство американский выборы'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_title[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "386f68f062304682a032d4cd21c1a6f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(5758, 1024)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_matrix = np.array([[0] * 1024], dtype=float)\n",
    "batch_size = 32\n",
    "for i in tqdm.notebook.tqdm(range(0, len(new_title), batch_size)):\n",
    "    title_matrix = np.append(title_matrix, \n",
    "                             elmo_model.get_elmo_vector_average([x.split() for x in new_title[i:i + batch_size]]),\n",
    "                             axis=0)\n",
    "\n",
    "title_matrix = np.delete(title_matrix, 0, axis=0)\n",
    "title_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:07.162902Z",
     "iopub.status.busy": "2020-10-22T13:25:07.161827Z",
     "iopub.status.idle": "2020-10-22T13:25:07.168882Z",
     "shell.execute_reply": "2020-10-22T13:25:07.169617Z"
    },
    "papermill": {
     "duration": 0.087527,
     "end_time": "2020-10-22T13:25:07.169863",
     "exception": false,
     "start_time": "2020-10-22T13:25:07.082336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5758, 2)\n",
      "(5758, 1024)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(title_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Разделим трейн датасет на обучающую и валидационную подвыборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:07.293223Z",
     "iopub.status.busy": "2020-10-22T13:25:07.292330Z",
     "iopub.status.idle": "2020-10-22T13:25:09.288942Z",
     "shell.execute_reply": "2020-10-22T13:25:09.288112Z"
    },
    "papermill": {
     "duration": 2.062523,
     "end_time": "2020-10-22T13:25:09.289101",
     "exception": false,
     "start_time": "2020-10-22T13:25:07.226578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = np.concatenate((np.array(data.drop(\"is_fake\", axis=1)), title_matrix), axis=1)\n",
    "y = np.array([np.array(data[data.title == title[0]].is_fake)[0] for title in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Москвичу Владимиру Клутину пришёл счёт за вмешательство в американские выборы',\n",
       "        -0.026137593794597208, -0.03709144557447718, ...,\n",
       "        0.019916739216694315, -0.007809336559148129,\n",
       "        -0.07590965777116687],\n",
       "       ['Агент Кокорина назвал езду по встречке житейской историей',\n",
       "        0.0045856463767328754, 0.012149634418496334, ...,\n",
       "        0.04233165955172, 0.1038698998738525, -0.059589282419974195],\n",
       "       ['Госдума рассмотрит возможность введения секретных статей Уголовного кодекса',\n",
       "        0.016818846771092983, -0.08118769593812755, ...,\n",
       "        -0.0031721681378487033, 0.000560379545336687,\n",
       "        -0.018380930701067475],\n",
       "       ...,\n",
       "       ['«Мне стыдно перед дедом»: новый канцлер ФРГ обратился с первой речью к Бундестагу',\n",
       "        -0.029480071644749972, -0.04626696189487515, ...,\n",
       "        0.008875949963536522, 0.015297313644998036,\n",
       "        -0.026672368018459965],\n",
       "       ['Туркмения в декабре начнет поставки газа в Китай',\n",
       "        -0.020441723371993712, -0.00859203528694557, ...,\n",
       "        0.007692824775513004, -0.03756802822077244, -0.05194766314884742],\n",
       "       ['Бывший тренер Локомотива возглавил нальчикский Спартак',\n",
       "        -0.023272885309655704, 0.008665957894524304, ...,\n",
       "        0.017197930884526685, 0.025318200061893077, -0.08065423996282528]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:09.485680Z",
     "iopub.status.busy": "2020-10-22T13:25:09.484491Z",
     "iopub.status.idle": "2020-10-22T13:25:09.489504Z",
     "shell.execute_reply": "2020-10-22T13:25:09.490606Z"
    },
    "papermill": {
     "duration": 0.059416,
     "end_time": "2020-10-22T13:25:09.490885",
     "exception": false,
     "start_time": "2020-10-22T13:25:09.431469",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5758, 1025)\n",
      "(5758,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:09.596710Z",
     "iopub.status.busy": "2020-10-22T13:25:09.595872Z",
     "iopub.status.idle": "2020-10-22T13:25:11.184121Z",
     "shell.execute_reply": "2020-10-22T13:25:11.182844Z"
    },
    "papermill": {
     "duration": 1.647102,
     "end_time": "2020-10-22T13:25:11.184369",
     "exception": false,
     "start_time": "2020-10-22T13:25:09.537267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Train Test Split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, np.array(y), test_size=0.15, random_state=1, shuffle=True)\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.15, random_state=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_title = [x[0] for x in X_train]\n",
    "test_title = [x[0] for x in X_test]\n",
    "val_title = [x[0] for x in X_val]\n",
    "\n",
    "X_train = np.array([x[1:] for x in X_train], dtype=np.float64)\n",
    "X_test = np.array([x[1:] for x in X_test], dtype=np.float64)\n",
    "X_val = np.array([x[1:] for x in X_val], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4159, 1024)\n",
      "(864, 1024)\n",
      "(735, 1024)\n",
      "(4159,)\n",
      "(864,)\n",
      "(735,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(X_val.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)\n",
    "print(Y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045765,
     "end_time": "2020-10-22T13:25:11.277268",
     "exception": false,
     "start_time": "2020-10-22T13:25:11.231503",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 6. Здесь определяем саму линейную модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:11.384792Z",
     "iopub.status.busy": "2020-10-22T13:25:11.381658Z",
     "iopub.status.idle": "2020-10-22T13:25:11.537188Z",
     "shell.execute_reply": "2020-10-22T13:25:11.536506Z"
    },
    "papermill": {
     "duration": 0.215291,
     "end_time": "2020-10-22T13:25:11.537349",
     "exception": false,
     "start_time": "2020-10-22T13:25:11.322058",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mLSTM\u001b[39;00m(\u001b[43mnn\u001b[49m\u001b[38;5;241m.\u001b[39mModule):\n\u001b[0;32m      3\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m      4\u001b[0m         \u001b[38;5;28msuper\u001b[39m(LSTM, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'nn' is not defined"
     ]
    }
   ],
   "source": [
    "class LSTM(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.lstm = nn.LSTM(1024, 128,\n",
    "                            num_layers=1,\n",
    "                            batch_first=True)\n",
    "        self.fc = nn.Linear(2 * 128, 2)\n",
    "\n",
    "    def forward(self, text_emb):\n",
    "        packed_output, _ = self.lstm(text_emb)\n",
    "        text_fea = self.fc(packed_output)\n",
    "        return text_fea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045839,
     "end_time": "2020-10-22T13:25:11.722970",
     "exception": false,
     "start_time": "2020-10-22T13:25:11.677131",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 7. Переходим к самому скрипту обучения - циклы трейна и валидации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:25:11.823570Z",
     "iopub.status.busy": "2020-10-22T13:25:11.822752Z",
     "iopub.status.idle": "2020-10-22T13:29:38.802313Z",
     "shell.execute_reply": "2020-10-22T13:29:38.803213Z"
    },
    "papermill": {
     "duration": 267.03556,
     "end_time": "2020-10-22T13:29:38.803483",
     "exception": false,
     "start_time": "2020-10-22T13:25:11.767923",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_loop(model, optimizer, criterion, X_train, Y_train):\n",
    "    model.zero_grad()\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    outs = model(X_train)\n",
    "    loss = criterion(torch.max(outs, 1)[1], Y_train)\n",
    "\n",
    "    loss.backward()\n",
    "    torch.nn.utils.clip_grad_norm_(model.parameters(), 2)\n",
    "    optimizer.step()\n",
    "    return round(float(loss), 4)\n",
    "\n",
    "\n",
    "def val_loop(model, X_val, Y_val):\n",
    "    test_pred = torch.max(model(X_val), 1)[1]\n",
    "    return round(float(f1_score(test_pred, Y_val)), 4)\n",
    "    \n",
    "\n",
    "def train(X_train, Y_train, X_val, Y_val):\n",
    "    model = LSTM()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "    criterion = nn.NLLLoss() #  nn.CrossEntropyLoss()\n",
    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "            optimizer=optimizer, mode='max', factor=0.75, patience=15)\n",
    "\n",
    "    val_f1_score_history = [0]\n",
    "    best_model = None\n",
    "\n",
    "    X_train = torch.Tensor(X_train)\n",
    "    Y_train = torch.Tensor(np.array(Y_train, dtype=np.float64)).type(torch.LongTensor)\n",
    "    X_val = torch.Tensor(X_val)\n",
    "    Y_val = torch.Tensor(Y_val)\n",
    "\n",
    "    EPOCHS = 200\n",
    "    for epoch in range(EPOCHS):\n",
    "        loss = train_loop(model, optimizer, criterion, X_train, Y_train)\n",
    "        f1_score = val_loop(model, X_val, Y_val)\n",
    "        scheduler.step(f1_score)\n",
    "\n",
    "        for param_group in optimizer.param_groups:\n",
    "            lr = param_group['lr']\n",
    "        print(f\"Loss after iteration {epoch} is {loss}\",\n",
    "              f\"val f1-score: {f1_score}\", \n",
    "              f\"lr: {lr}\", sep=\"    \")\n",
    "\n",
    "        if f1_score > max(val_f1_score_history) and f1_score > 0.86:\n",
    "            model_save_path = os.path.join(\n",
    "                    \"save_dir\", f'model-{val_f1_score_history[-1]:.4f}.ckpt')\n",
    "            torch.save(model.state_dict(), model_save_path)\n",
    "            best_model = deepcopy(model)\n",
    "            print(\"MODEL SAVED\")\n",
    "        \n",
    "        val_f1_score_history.append(f1_score)\n",
    "    return EPOCHS, val_f1_score_history, best_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Запускаем обучение!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m EPOCHS, val_f1_score_history, best_model \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m(X_train, Y_train, X_val, Y_val)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": [
    "EPOCHS, val_f1_score_history, best_model = train(X_train, Y_train, X_val, Y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* посмотрим на изменение f1-score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(data={\"f1-score\": val_f1_score_history,\n",
    "                   \"epochs\": list(range(1, EPOCHS + 2))}, x=\"epochs\", y=\"f1-score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.052762,
     "end_time": "2020-10-22T13:29:38.914634",
     "exception": false,
     "start_time": "2020-10-22T13:29:38.861872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 9. Проверим точность нашей модели на тест части датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-22T13:29:39.032029Z",
     "iopub.status.busy": "2020-10-22T13:29:39.030838Z",
     "iopub.status.idle": "2020-10-22T13:29:41.545944Z",
     "shell.execute_reply": "2020-10-22T13:29:41.544260Z"
    },
    "papermill": {
     "duration": 2.57732,
     "end_time": "2020-10-22T13:29:41.546100",
     "exception": false,
     "start_time": "2020-10-22T13:29:38.968780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test = torch.Tensor(X_test)\n",
    "\n",
    "Y_test = torch.Tensor(np.array(Y_test, dtype=np.float64)).type(torch.LongTensor)\n",
    "\n",
    "y_head = best_model(X_test)\n",
    "y_pred = torch.max(y_head, 1)[1]\n",
    "\n",
    "for i in sample(list(range(500)), 10):\n",
    "    print(test_title[i], \"pred\", y_pred[i].item(), \"is_fake\", int(Y_test[i].item()))\n",
    "\n",
    "print(\"\\nModel accuracy is \", f1_score(y_pred, Y_test))\n",
    "model_plot = input()\n",
    "if model_plot != \"\":\n",
    "    with open(\"used_models.txt\", mode=\"a\") as f:\n",
    "        f.write(model_plot + \";\" + str(EPOCHS) + \";\" + str(f1_score(y_pred, Y_test)) + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Создаём предсказания для тестовых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e81e1bd2be0b49b3875fb069e038df49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5758 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5a6dbde7a204b7380dd6f36373f0d64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"dataset/test.tsv\", sep=\"\\t\", header=0)\n",
    "test_data = data.sample(frac=1)\n",
    "\n",
    "new_title = []\n",
    "for txt in tqdm.notebook.tqdm(data.title):\n",
    "    new_title.append(sentence_editor(txt))\n",
    "\n",
    "title_matrix = np.array([[0] * 1024], dtype=float)\n",
    "batch_size = 32\n",
    "for i in tqdm.notebook.tqdm(range(0, len(new_title), batch_size)):\n",
    "    title_matrix = np.append(title_matrix, \n",
    "                             elmo_model.get_elmo_vector_average([x.split() for x in new_title[i:i + batch_size]]), \n",
    "                             axis=0)\n",
    "\n",
    "title_matrix = np.delete(title_matrix, 0, axis=0)\n",
    "title_matrix.shape\n",
    "\n",
    "y_head = best_model(torch.Tensor(title_matrix))\n",
    "y_pred = torch.max(y_head, 1)[1]\n",
    "\n",
    "test_data.is_fake = y_pred\n",
    "test_data.to_csv(\"solution.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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