part3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "pandas.set_option('max_colwidth', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LentaRecord(\n",
       "    url='https://lenta.ru/news/2018/12/14/cancer/',\n",
       "    title='Названы регионы России с\\xa0самой высокой смертностью от\\xa0рака',\n",
       "    text='Вице-премьер по социальным вопросам Татьяна Голикова рассказала, в каких регионах России зафиксирована наиболее высокая смертность от рака, сообщает РИА Новости. По словам Голиковой, чаще всего онкологические заболевания становились причиной смерти в Псковской, Тверской, Тульской и Орловской областях, а также в Севастополе. Вице-премьер напомнила, что главные факторы смертности в России — рак и болезни системы кровообращения. В начале года стало известно, что смертность от онкологических заболеваний среди россиян снизилась впервые за три года. По данным Росстата, в 2017 году от рака умерли 289 тысяч человек. Это на 3,5 процента меньше, чем годом ранее.',\n",
       "    topic='Россия',\n",
       "    tags='Общество'\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from corus import load_lenta\n",
    "path = 'data/lenta-ru-news.csv.gz'\n",
    "records = load_lenta(path)\n",
    "next(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing: lower-case, commas, lemmatization\n",
    "#PRACTICE: implement preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['выплата', 'на', 'второй', 'ребенок']\n",
      "['выплата', 'на', 'второй', 'ребенок']\n"
     ]
    }
   ],
   "source": [
    "import razdel\n",
    "from pymystem3 import Mystem\n",
    "#from pymorphy2 import MorphAnalyzer\n",
    "\n",
    "_LEMMATIZER = None\n",
    "def tag(word):\n",
    "    global _LEMMATIZER\n",
    "    if _LEMMATIZER is None:\n",
    "        _LEMMATIZER = Mystem()\n",
    "        #_LEMMATIZER = MorphAnalyzer('ru')\n",
    "    try:\n",
    "        processed = _LEMMATIZER.analyze(word)[0]\n",
    "    except:\n",
    "        return word\n",
    "    if not processed.get('analysis'):\n",
    "        return word.strip().lower()\n",
    "    lemma = processed[\"analysis\"][0][\"lex\"].lower().strip()\n",
    "    return lemma\n",
    "\n",
    "import re\n",
    "def tokenize_re(doc):\n",
    "    return [tag(x) for x in re.split('(\\W+)', doc) if x.strip()]\n",
    "\n",
    "def tokenize(doc):\n",
    "    # упомянуть регулярные выражения\n",
    "    return [tag(t.text) for t in razdel.tokenize(doc)]\n",
    "\n",
    "import sentencepiece\n",
    "def tokenize_bpe(doc):\n",
    "    #FIXME\n",
    "    pass\n",
    "\n",
    "print(tokenize_re('Выплаты на второго ребёнка'))\n",
    "print(tokenize('Выплаты на второго ребёнка'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bceb9a45a4540e4af4fd60c9f772cf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=25000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[называть, регион, россия, с, самый, высокий, смертность, от, рак]</td>\n",
       "      <td>Россия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[австрия, не, представлять, доказательство, вина, российский, биатлонист]</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[обнаруживать, самый, счастливый, место, на, планета]</td>\n",
       "      <td>Путешествия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[в, сша, раскрывать, сумма, расход, на, расследование, «, российский, дело, »]</td>\n",
       "      <td>Мир</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[хакер, рассказывать, о, план, великобритания, заминировать, севастополь]</td>\n",
       "      <td>Мир</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                            title  \\\n",
       "0              [называть, регион, россия, с, самый, высокий, смертность, от, рак]   \n",
       "1       [австрия, не, представлять, доказательство, вина, российский, биатлонист]   \n",
       "2                           [обнаруживать, самый, счастливый, место, на, планета]   \n",
       "3  [в, сша, раскрывать, сумма, расход, на, расследование, «, российский, дело, »]   \n",
       "4       [хакер, рассказывать, о, план, великобритания, заминировать, севастополь]   \n",
       "\n",
       "         class  \n",
       "0       Россия  \n",
       "1        Спорт  \n",
       "2  Путешествия  \n",
       "3          Мир  \n",
       "4          Мир  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "from tqdm import tqdm_notebook as tq\n",
    "\n",
    "records = load_lenta(path)\n",
    "# overfitting and underfitting\n",
    "# splitting the dataset. how many examples to take for a test?\n",
    "dataset = []\n",
    "for i, r in tq(enumerate(records), total=25000):\n",
    "    if not r.topic:\n",
    "        # есть примеры в датасете, где нет категории\n",
    "        continue\n",
    "    dataset.append((tokenize(r.title), r.topic))\n",
    "    if len(dataset) >= 25000:\n",
    "        break\n",
    "df = pandas.DataFrame(dataset, columns=['title', 'class'])\n",
    "print(len(df))\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'69-я параллель': 0,\n",
       " 'Бизнес': 1,\n",
       " 'Бывший СССР': 2,\n",
       " 'Дом': 3,\n",
       " 'Из жизни': 4,\n",
       " 'Интернет и СМИ': 5,\n",
       " 'Крым': 6,\n",
       " 'Культпросвет ': 7,\n",
       " 'Культура': 8,\n",
       " 'Мир': 9,\n",
       " 'Наука и техника': 10,\n",
       " 'Путешествия': 11,\n",
       " 'Россия': 12,\n",
       " 'Силовые структуры': 13,\n",
       " 'Спорт': 14,\n",
       " 'Ценности': 15,\n",
       " 'Экономика': 16}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = sorted(set([_[1] for _ in dataset]))\n",
    "y_size = len(classes)\n",
    "print(y_size)\n",
    "vec_y = {c: i for i, c in enumerate(classes)}\n",
    "vec_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['!',\n",
      " 'бомбить',\n",
      " 'гризли',\n",
      " 'игнашевич',\n",
      " 'линчевать',\n",
      " 'ничья',\n",
      " 'пломбир',\n",
      " 'пропадать',\n",
      " 'скульптура',\n",
      " 'ударный',\n",
      " 'ярошенко']\n",
      "Vocabulary size: 8499\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vec_x = CountVectorizer(ngram_range=(1, 1), tokenizer=lambda x: x, lowercase=False, min_df=2, binary=False)\n",
    "vec_x.fit_transform(X_train)\n",
    "x_size = len(vec_x.get_feature_names())\n",
    "import pprint; pprint.pprint(vec_x.get_feature_names()[::x_size//10])\n",
    "print(\"Vocabulary size:\", x_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('!', 0), ('&', 1), (\"'\", 2), ('+', 3), (',', 4)]\n"
     ]
    }
   ],
   "source": [
    "vec_x_ids = {v:k for k,v in enumerate(vec_x.get_feature_names())}\n",
    "print(list(vec_x_ids.items())[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000 20000 2500 2500 2500 2500\n"
     ]
    },
    {
     "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>x_train</th>\n",
       "      <th>y_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[в, россия, закрывать, чемпионат, мир, по, футбол]</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[назначать, орешкин, неизвестный, таджик, обретать, миссия]</td>\n",
       "      <td>Экономика</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[в, киев, массово, лопнуть, труба, после, подача, горячий, вода]</td>\n",
       "      <td>Бывший СССР</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[россиянин, избегать, тюрьма, за, убийство, оскорблять, он, девушка]</td>\n",
       "      <td>Россия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[умирать, режиссер, «, чегемский, детектив, », александр, светлов]</td>\n",
       "      <td>Культура</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                x_train  \\\n",
       "0                    [в, россия, закрывать, чемпионат, мир, по, футбол]   \n",
       "1           [назначать, орешкин, неизвестный, таджик, обретать, миссия]   \n",
       "2      [в, киев, массово, лопнуть, труба, после, подача, горячий, вода]   \n",
       "3  [россиянин, избегать, тюрьма, за, убийство, оскорблять, он, девушка]   \n",
       "4    [умирать, режиссер, «, чегемский, детектив, », александр, светлов]   \n",
       "\n",
       "       y_train  \n",
       "0        Спорт  \n",
       "1    Экономика  \n",
       "2  Бывший СССР  \n",
       "3       Россия  \n",
       "4     Культура  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>x_dev</th>\n",
       "      <th>y_dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[телеведущая, отказывать, в, работа, из-за, «, недостаточно, большой, грудь, »]</td>\n",
       "      <td>Интернет и СМИ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[шарапов, отвечать, на, отказ, серена, уильямс, играть, против, она]</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[bvlgari, представлять, новый, вариация, на, змеиный, тема]</td>\n",
       "      <td>Ценности</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[находить, новый, способ, побеждать, особо, опасный, рак]</td>\n",
       "      <td>Наука и техника</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[анестезиолог, изнасиловать, россиянка, под, наркоз, и, пойти, под, суд]</td>\n",
       "      <td>Силовые структуры</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                             x_dev  \\\n",
       "0  [телеведущая, отказывать, в, работа, из-за, «, недостаточно, большой, грудь, »]   \n",
       "1             [шарапов, отвечать, на, отказ, серена, уильямс, играть, против, она]   \n",
       "2                      [bvlgari, представлять, новый, вариация, на, змеиный, тема]   \n",
       "3                        [находить, новый, способ, побеждать, особо, опасный, рак]   \n",
       "4         [анестезиолог, изнасиловать, россиянка, под, наркоз, и, пойти, под, суд]   \n",
       "\n",
       "               y_dev  \n",
       "0     Интернет и СМИ  \n",
       "1              Спорт  \n",
       "2           Ценности  \n",
       "3    Наука и техника  \n",
       "4  Силовые структуры  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train, dev, test\n",
    "#20000, 2500, 2500 \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_dev, y_train, y_dev = train_test_split([_[0] for _ in dataset], \n",
    "                                                  [_[1] for _ in dataset], test_size=2500, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, \n",
    "                                                    y_train, test_size=2500, random_state=42)\n",
    "print(len(X_train), len(y_train), len(X_dev), len(y_dev), len(X_test), len(y_test))\n",
    "display(pandas.DataFrame(zip(X_train[:5], y_train[:5]), columns=['x_train', 'y_train']))\n",
    "display(pandas.DataFrame(zip(X_dev[:5], y_dev[:5]), columns=['x_dev', 'y_dev']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook as tq\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data.dataset import Dataset\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if use_cuda else \"cpu\")\n",
    "#device = torch.device('cpu')\n",
    "params = {\n",
    "    'batch_size': 2,\n",
    "    'shuffle': True,\n",
    "    'num_workers': 0}\n",
    "eval_params = {\n",
    "    'batch_size': 64,\n",
    "    'shuffle': False,\n",
    "    'num_workers': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([962, 6464, 2308, 8151, 3720, 5108, 7977], 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[8151, 7977, 6464, 5108, 3720, 2308, 962]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class WordsDataset(Dataset):\n",
    "    def __init__(self, x, y, vec_x_ids, vec_y):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.vec_x_ids = vec_x_ids \n",
    "        self.vec_y = vec_y\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x_list = [self.vec_x_ids[w] for w in self.x[index] if w in self.vec_x_ids]\n",
    "        y_id = self.vec_y[self.y[index]]\n",
    "        return x_list, y_id\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "\n",
    "train_dataset = WordsDataset(X_train, y_train, vec_x_ids, vec_y)\n",
    "example = train_dataset[0]\n",
    "display(example)\n",
    "sorted(example[0], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEBUG = 0\n",
    "def generate_batch(batch):\n",
    "    \"\"\"\n",
    "    Since the text entries have different lengths, a custom function\n",
    "    generate_batch() is used to generate data batches and offsets,\n",
    "    which are compatible with EmbeddingBag. The function is passed\n",
    "    to 'collate_fn' in torch.utils.data.DataLoader. The input to\n",
    "    'collate_fn' is a list of tensors with the size of batch_size,\n",
    "    and the 'collate_fn' function packs them into a mini-batch.\n",
    "    Pay attention here and make sure that 'collate_fn' is declared\n",
    "    as a top level def. This ensures that the function is available\n",
    "    in each worker.\n",
    "    Output:\n",
    "        text: the text entries in the data_batch are packed into a list and\n",
    "            concatenated as a single tensor for the input of nn.EmbeddingBag.\n",
    "        offsets: the offsets is a tensor of delimiters to represent the beginning\n",
    "            index of the individual sequence in the text tensor.\n",
    "        label: a tensor saving the labels of individual text entries.\n",
    "    \"\"\"\n",
    "    if DEBUG: print('batch:', batch)\n",
    "    label = torch.tensor([entry[1] for entry in batch])\n",
    "    if DEBUG: print('label:', label)\n",
    "    texts = [torch.LongTensor(entry[0]) for entry in batch]\n",
    "    if DEBUG: print('texts:', texts)\n",
    "    offsets = [0] + [len(entry) for entry in texts]\n",
    "    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
    "    text = torch.cat(texts)\n",
    "    return (text, offsets), label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: tensor([ 962, 5216, 2271, 6450, 2206, 7633, 6805, 5050, 7089, 2586, 5834, 6849,\n",
      "        4660, 5108,  336, 1820, 2953,  337, 5019])\n",
      "Offsets: tensor([0, 8])\n",
      "Labels: tensor([13, 12])\n"
     ]
    }
   ],
   "source": [
    "train_loader = DataLoader(train_dataset, collate_fn=generate_batch, **params)\n",
    "for (_text, _offsets), _label in train_loader:\n",
    "    print(\"Text:\", _text)\n",
    "    print(\"Offsets:\", _offsets)\n",
    "    print(\"Labels:\", _label)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0512\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    ">>> # an Embedding module containing 10 tensors of size 3\n",
    ">>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')\n",
    ">>> # a batch of 2 samples of 4 indices each\n",
    ">>> input = torch.LongTensor([1,2,4,5,4,3,2,9])\n",
    ">>> offsets = torch.LongTensor([0,4])\n",
    ">>> embedding_sum(input, offsets)\n",
    "tensor([[-0.8861, -5.4350, -0.0523],\n",
    "        [ 1.1306, -2.5798, -1.0044]])\n",
    "\"\"\"\n",
    "class TextClassifier(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_dim, hidden_size, num_classes):\n",
    "        super().__init__()\n",
    "        if hidden_size:\n",
    "            layers = [\n",
    "                nn.Linear(embed_dim, hidden_size),\n",
    "                nn.Linear(hidden_size, num_classes)\n",
    "            ]\n",
    "        else:\n",
    "            layers = [\n",
    "                nn.Linear(embed_dim, num_classes),\n",
    "            ]\n",
    "        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n",
    "        self.fc = nn.Sequential(*layers)            \n",
    "        initrange = 0.1\n",
    "        self.embedding.weight.data.uniform_(-initrange, initrange)\n",
    "        self.fc[0].weight.data.uniform_(-initrange, initrange)\n",
    "        self.fc[0].bias.data.zero_()\n",
    "        \n",
    "    def forward(self, text, offsets):\n",
    "        x = self.embedding(text, offsets)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "def evaluate_model(model, dataset, params=eval_params):\n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "    model.eval()\n",
    "    val_loader = DataLoader(dataset, collate_fn=generate_batch, **params)\n",
    "    predicts = []\n",
    "    trues = []\n",
    "    with torch.no_grad():\n",
    "        total_loss = 0.\n",
    "        total_count = 0\n",
    "        with tq(val_loader, leave=False) as tq_batches:\n",
    "            for (texts, offsets), batch_y in tq_batches:\n",
    "                texts = texts.to(device)\n",
    "                offsets = offsets.to(device)\n",
    "                batch_y = batch_y.to(device)\n",
    "                output = model(texts, offsets)\n",
    "                predicted = torch.argmax(output, dim=1)\n",
    "                predicts.extend([x.item() for x in predicted])\n",
    "                trues.extend([x.item() for x in batch_y])\n",
    "                loss = criterion(output, batch_y.long())\n",
    "                total_loss += loss.item() * len(batch_y)\n",
    "                total_count += len(batch_y)\n",
    "                tq_batches.set_postfix(loss=f\"{total_loss/total_count:.3f}\", \n",
    "                                       refresh=False)\n",
    "    return predicts, trues\n",
    "\n",
    "def train(model, dataset, epochs, lr_base, scale=1):\n",
    "    #print(\"Using params:\", params)\n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "    train_size = len(dataset)\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=lr_base, )\n",
    "    train_loader = DataLoader(dataset, collate_fn=generate_batch, **params)\n",
    "    losses = []\n",
    "    with tq(range(epochs)) as tq_epochs:\n",
    "        for e in tq_epochs:\n",
    "            model.train()\n",
    "            total_loss = 0.\n",
    "            total_count = 0\n",
    "            with tq(train_loader, leave=False) as tq_batches:\n",
    "                for (texts, offsets), batch_y in tq_batches:\n",
    "                    texts = texts.to(device)\n",
    "                    offsets = offsets.to(device)\n",
    "                    batch_y = batch_y.to(device)\n",
    "                    lr = lr_base * (train_size*(scale*epochs-e)-total_count) / (train_size * scale * epochs)\n",
    "                    for g in optimizer.param_groups:\n",
    "                        g['lr'] = lr\n",
    "                    optimizer.zero_grad()\n",
    "                    output = model(texts, offsets)\n",
    "                    loss = criterion(output, batch_y.long())\n",
    "                    loss = loss.to(device)\n",
    "                    loss.backward()\n",
    "                    loss_item = loss.item() \n",
    "                    total_loss += loss_item * len(batch_y)\n",
    "                    total_count += len(batch_y)\n",
    "                    losses.append(loss_item)\n",
    "                    optimizer.step()\n",
    "                    tq_batches.set_postfix(epoch=e, lr=f\"{lr:.3f}\",\n",
    "                                           loss=f\"{total_loss/total_count:.3f}\", \n",
    "                                           refresh=False)\n",
    "            predicted, expected = evaluate_model(model, val_dataset)\n",
    "            accuracy = accuracy_score(predicted, expected)\n",
    "            print(f'Epoch: {e:2d} Accuracy: {accuracy:.3f} Loss:{total_loss/total_count:.3f}')\n",
    "    return losses\n",
    "\n",
    "model = TextClassifier(x_size, 100, 0, y_size).to(device)\n",
    "val_dataset = WordsDataset(X_dev, y_dev, vec_x_ids, vec_y)\n",
    "predicted, expected = evaluate_model(model, val_dataset)\n",
    "accuracy = accuracy_score(predicted, expected)\n",
    "print(accuracy)"
   ]
  },
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      "Epoch:  0 Accuracy: 0.554 Loss:1.990\n",
      "\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "model = TextClassifier(x_size, 16, 0, y_size).to(device)\n",
    "params['batch_size'] = 1\n",
    "losses = train(model, train_dataset, 1, 0.1, 2)\n",
    "plt.plot(losses);\n",
    "params['batch_size'] = 2\n",
    "losses = train(model, train_dataset, 1, 0.1, 2)\n",
    "plt.plot(losses);\n",
    "params['batch_size'] = 4\n",
    "losses = train(model, train_dataset, 1, 0.1, 2)\n",
    "plt.plot(losses);\n",
    "params['batch_size'] = 8\n",
    "losses = train(model, train_dataset, 1, 0.1, 2)\n",
    "plt.plot(losses);\n",
    "params['batch_size'] = 16\n",
    "losses = train(model, train_dataset, 1, 0.2, 2)\n",
    "plt.plot(losses);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c99d8321b3104f87a172b7a86792e671",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 Accuracy: 0.694 Loss:0.742\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  1 Accuracy: 0.693 Loss:0.644\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  2 Accuracy: 0.702 Loss:0.563\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  3 Accuracy: 0.709 Loss:0.496\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=40), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  4 Accuracy: 0.703 Loss:0.439\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1250), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params['batch_size'] = 16\n",
    "losses = train(model, train_dataset, 100, 0.4, 1)\n",
    "plt.plot(losses);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}