Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "import string\n",
    "fields = ['title', 'selftext', 'flair']\n",
    "\n",
    "df = pd.read_csv('reddit_with_flairs.csv', skipinitialspace=True, usecols=fields)\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout, GlobalAveragePooling1D\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, Callback\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205363, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.fillna(\" \")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205142, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['title'].str.split().str.len().lt(55)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(203541, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['selftext'].str.split().str.len().lt(400)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_URL(text):\n",
    "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
    "    return url.sub(r'',text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_html(text):\n",
    "    html=re.compile(r'<.*?>')\n",
    "    return html.sub(r'',text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference : https://gist.github.com/slowkow/7a7f61f495e3dbb7e3d767f97bd7304b\n",
    "def remove_emoji(text):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punct(text):\n",
    "    table=str.maketrans('','',string.punctuation)\n",
    "    return text.translate(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You all, cannot'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CONTRACTION_MAP = {\n",
    "\"ain't\": \"is not\",\n",
    "\"aren't\": \"are not\",\n",
    "\"can't\": \"cannot\",\n",
    "\"can't've\": \"cannot have\",\n",
    "\"'cause\": \"because\",\n",
    "\"could've\": \"could have\",\n",
    "\"couldn't\": \"could not\",\n",
    "\"couldn't've\": \"could not have\",\n",
    "\"didn't\": \"did not\",\n",
    "\"doesn't\": \"does not\",\n",
    "\"don't\": \"do not\",\n",
    "\"hadn't\": \"had not\",\n",
    "\"hadn't've\": \"had not have\",\n",
    "\"hasn't\": \"has not\",\n",
    "\"haven't\": \"have not\",\n",
    "\"he'd\": \"he would\",\n",
    "\"he'd've\": \"he would have\",\n",
    "\"he'll\": \"he will\",\n",
    "\"he'll've\": \"he he will have\",\n",
    "\"he's\": \"he is\",\n",
    "\"how'd\": \"how did\",\n",
    "\"how'd'y\": \"how do you\",\n",
    "\"how'll\": \"how will\",\n",
    "\"how's\": \"how is\",\n",
    "\"I'd\": \"I would\",\n",
    "\"I'd've\": \"I would have\",\n",
    "\"I'll\": \"I will\",\n",
    "\"I'll've\": \"I will have\",\n",
    "\"I'm\": \"I am\",\n",
    "\"I've\": \"I have\",\n",
    "\"i'd\": \"i would\",\n",
    "\"i'd've\": \"i would have\",\n",
    "\"i'll\": \"i will\",\n",
    "\"i'll've\": \"i will have\",\n",
    "\"i'm\": \"i am\",\n",
    "\"i've\": \"i have\",\n",
    "\"isn't\": \"is not\",\n",
    "\"it'd\": \"it would\",\n",
    "\"it'd've\": \"it would have\",\n",
    "\"it'll\": \"it will\",\n",
    "\"it'll've\": \"it will have\",\n",
    "\"it's\": \"it is\",\n",
    "\"let's\": \"let us\",\n",
    "\"ma'am\": \"madam\",\n",
    "\"mayn't\": \"may not\",\n",
    "\"might've\": \"might have\",\n",
    "\"mightn't\": \"might not\",\n",
    "\"mightn't've\": \"might not have\",\n",
    "\"must've\": \"must have\",\n",
    "\"mustn't\": \"must not\",\n",
    "\"mustn't've\": \"must not have\",\n",
    "\"needn't\": \"need not\",\n",
    "\"needn't've\": \"need not have\",\n",
    "\"o'clock\": \"of the clock\",\n",
    "\"oughtn't\": \"ought not\",\n",
    "\"oughtn't've\": \"ought not have\",\n",
    "\"shan't\": \"shall not\",\n",
    "\"sha'n't\": \"shall not\",\n",
    "\"shan't've\": \"shall not have\",\n",
    "\"she'd\": \"she would\",\n",
    "\"she'd've\": \"she would have\",\n",
    "\"she'll\": \"she will\",\n",
    "\"she'll've\": \"she will have\",\n",
    "\"she's\": \"she is\",\n",
    "\"should've\": \"should have\",\n",
    "\"shouldn't\": \"should not\",\n",
    "\"shouldn't've\": \"should not have\",\n",
    "\"so've\": \"so have\",\n",
    "\"so's\": \"so as\",\n",
    "\"that'd\": \"that would\",\n",
    "\"that'd've\": \"that would have\",\n",
    "\"that's\": \"that is\",\n",
    "\"there'd\": \"there would\",\n",
    "\"there'd've\": \"there would have\",\n",
    "\"there's\": \"there is\",\n",
    "\"they'd\": \"they would\",\n",
    "\"they'd've\": \"they would have\",\n",
    "\"they'll\": \"they will\",\n",
    "\"they'll've\": \"they will have\",\n",
    "\"they're\": \"they are\",\n",
    "\"they've\": \"they have\",\n",
    "\"to've\": \"to have\",\n",
    "\"wasn't\": \"was not\",\n",
    "\"we'd\": \"we would\",\n",
    "\"we'd've\": \"we would have\",\n",
    "\"we'll\": \"we will\",\n",
    "\"we'll've\": \"we will have\",\n",
    "\"we're\": \"we are\",\n",
    "\"we've\": \"we have\",\n",
    "\"weren't\": \"were not\",\n",
    "\"what'll\": \"what will\",\n",
    "\"what'll've\": \"what will have\",\n",
    "\"what're\": \"what are\",\n",
    "\"what's\": \"what is\",\n",
    "\"what've\": \"what have\",\n",
    "\"when's\": \"when is\",\n",
    "\"when've\": \"when have\",\n",
    "\"where'd\": \"where did\",\n",
    "\"where's\": \"where is\",\n",
    "\"where've\": \"where have\",\n",
    "\"who'll\": \"who will\",\n",
    "\"who'll've\": \"who will have\",\n",
    "\"who's\": \"who is\",\n",
    "\"who've\": \"who have\",\n",
    "\"why's\": \"why is\",\n",
    "\"why've\": \"why have\",\n",
    "\"will've\": \"will have\",\n",
    "\"won't\": \"will not\",\n",
    "\"won't've\": \"will not have\",\n",
    "\"would've\": \"would have\",\n",
    "\"wouldn't\": \"would not\",\n",
    "\"wouldn't've\": \"would not have\",\n",
    "\"y'all\": \"you all\",\n",
    "\"y'all'd\": \"you all would\",\n",
    "\"y'all'd've\": \"you all would have\",\n",
    "\"y'all're\": \"you all are\",\n",
    "\"y'all've\": \"you all have\",\n",
    "\"you'd\": \"you would\",\n",
    "\"you'd've\": \"you would have\",\n",
    "\"you'll\": \"you will\",\n",
    "\"you'll've\": \"you will have\",\n",
    "\"you're\": \"you are\",\n",
    "\"you've\": \"you have\"\n",
    "}\n",
    "\n",
    "def expand_contractions(text, contraction_mapping=CONTRACTION_MAP):\n",
    "    \n",
    "    contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), \n",
    "                                      flags=re.IGNORECASE|re.DOTALL)\n",
    "    def expand_match(contraction):\n",
    "        match = contraction.group(0)\n",
    "        first_char = match[0]\n",
    "        expanded_contraction = contraction_mapping.get(match)\\\n",
    "                                if contraction_mapping.get(match)\\\n",
    "                                else contraction_mapping.get(match.lower())                       \n",
    "        expanded_contraction = first_char+expanded_contraction[1:]\n",
    "        return expanded_contraction\n",
    "        \n",
    "    expanded_text = contractions_pattern.sub(expand_match, text)\n",
    "    expanded_text = re.sub(\"'\", \"\", expanded_text)\n",
    "    return expanded_text\n",
    "\n",
    "expand_contractions(\"Y'all, can't\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining selftext and title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(203541, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"] = df[\"title\"] +\" \"+ df[\"selftext\"]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text']=df['text'].apply(lambda x : expand_contractions(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_URL(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_punct(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_html(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_emoji(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Crocodile in water tiger on land Humans Gays a...\n",
       "1         Samsung Galaxy S9 Will launch Next Month At MW...\n",
       "2         Exposed Kerala madrasas teaching Wahabism Saud...\n",
       "3         How can we start a small service provider firm...\n",
       "4         Fsociety Aadhaar  To generate the password the...\n",
       "5                            Should I drop out of college  \n",
       "6         Samsung Galaxy A8 2018 Launched in India for R...\n",
       "7         ASKRandia doctors Is there a metric or a sign ...\n",
       "8         Post Bhima Koregaon Violence Police Target Dal...\n",
       "9         The Marriage Algorithm Indias CasteConscious A...\n",
       "10        Cervical cancer vaccine that RSS wing wanted o...\n",
       "11        Saw an ad of escort which provides a tamil act...\n",
       "12        India I need your help When I was in Sweden I ...\n",
       "13        Whar are some Good magazines you subscribed to   \n",
       "14        Soon pay for each and every banking transactio...\n",
       "15        Are People Without Aadhaar NonExistent for Gov...\n",
       "16        India  should it focus on Service sector or mo...\n",
       "17                Home prices drop first time in 10 years  \n",
       "18        Heard a bit of information regarding Jawaharla...\n",
       "19        TIL that a whistleblower of the Vyapam scam ha...\n",
       "20        Two suns No it is a supernova drawn 6000 years...\n",
       "21        Saw an ad of escort which provides a tamil act...\n",
       "22        AMU PhD scholar from JKs Kupwara joins Hizbul ...\n",
       "23            Telangana Hostel warden thrashes minor boys  \n",
       "24        Yogi Adityanath a Nathuram Godse supporter do ...\n",
       "25        AgustaWestland scam Milan court acquits 2 Ital...\n",
       "26        Image of the day Defence Minister Nirmala Sith...\n",
       "27        My Take Karnataka turning into Congress BJPs H...\n",
       "28        Not the workers but Tamil Nadu MLAs reward the...\n",
       "29        India likely to register 73 per cent growth in...\n",
       "                                ...                        \n",
       "205333    Startups VCs in India request ‘relief package’...\n",
       "205334    Covid19 Karnataka ministerslegislators to take...\n",
       "205335    COVID19 Karnataka ministerslegislators to take...\n",
       "205336             Leopard Cat Rat Puzzle  Picture Puzzle  \n",
       "205337    COVID19 Karnataka ministerslegislators to take...\n",
       "205338    COVID19 Karnataka ministerslegislators to take...\n",
       "205339    COVID19 Karnataka ministerslegislators to take...\n",
       "205340    Madhya Pradesh  CM Shivraj Chouhan transfers R...\n",
       "205341    After India Exports Hydroxychloroquine To US S...\n",
       "205342    Can moderators please create a new sub reddit ...\n",
       "205343    COVID19 Karnataka ministers legislators to tak...\n",
       "205344          Is this thing true about Mahabharata Epic  \n",
       "205345    Opinion Governance and Administration particip...\n",
       "205346    Is this story of Mahabharata true or not There...\n",
       "205347    998 workforce in IT sector incapable of remote...\n",
       "205348    With liquor shops around the country shut have...\n",
       "205349    ADB Assures  22 Billion Support To India Durin...\n",
       "205350    Blood donations needed Please visit near by ho...\n",
       "205351    Siddis In It For The Long Run  Unique Stories ...\n",
       "205352    Drug sent to US unavailable for BMC doctors  M...\n",
       "205353    Will lockdown extend even after 14th April Wil...\n",
       "205354                                      Good luck Goa  \n",
       "205355    Indians least active second most sleepdeprived...\n",
       "205356    ‘Parallel governance’ Shiv Sena objects to Gov...\n",
       "205357    My little cousins made this Tik Tok and it mad...\n",
       "205358    How Modi Has Trampled the Founding Idea of Ind...\n",
       "205359    Jaipur Police takes a potshot at Masakali 20 ‘...\n",
       "205360    When do u think international flights will res...\n",
       "205361                        Kangna Vilayati Song Lyrics  \n",
       "205362    What do you guys know about girish nanavati an...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "df_n, df_test = model_selection.train_test_split(\n",
    "        df,\n",
    "        test_size=0.2,\n",
    "        random_state=42,\n",
    "        stratify=df.flair.values\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7b0366240>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_test.flair.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count            162832\n",
       "unique                7\n",
       "top       Non-Political\n",
       "freq              55420\n",
       "Name: flair, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n.flair.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_n.to_csv('reddit_model_train_valid.csv', index=False)\n",
    "df_test.to_csv('reddit_model_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code for BERT this will be implemented later in Kaggle because of lack of compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_FILE = \"reddit_model_train_valid.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import transformers\n",
    "\n",
    "\n",
    "MAX_LEN = 512\n",
    "TRAIN_BATCH_SIZE = 4\n",
    "VALID_BATCH_SIZE = 2\n",
    "EPOCHS = 10\n",
    "BERT_PATH = \"./bert_uncased/\"\n",
    "MODEL_PATH = \"app/model.bin\"\n",
    "TRAINING_FILE = \"reddit_model_train_valid.csv\"\n",
    "TOKENIZER = transformers.BertTokenizer.from_pretrained(\n",
    "    BERT_PATH,\n",
    "    do_lower_case=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Coronavirus', 'Politics', 'Non-Political', 'AskIndia',\n",
       "       'Policy/Economy', 'Science/Technology', 'Business/Finance'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dfx = pd.read_csv(TRAINING_FILE).fillna(\"none\")\n",
    "dfx.flair.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for LSTM without pretrained embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 180763 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "MAX_NB_WORDS = 50000\n",
    "\n",
    "MAX_SEQUENCE_LENGTH = 512\n",
    "# This is fixed.\n",
    "EMBEDDING_DIM = 100\n",
    "\n",
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n",
    "tokenizer.fit_on_texts(dfx['text'].values)\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (162832, 512)\n"
     ]
    }
   ],
   "source": [
    "X = tokenizer.texts_to_sequences(dfx['text'].values)\n",
    "X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "print('Shape of data tensor:', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of label tensor: (162832, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Y = pd.get_dummies(dfx['flair']).values\n",
    "print('Shape of label tensor:', Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146548, 512) (146548, 7)\n",
      "(16284, 512) (16284, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.10, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 512, 100)          5000000   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_1 (Spatial (None, 512, 100)          0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 7)                 707       \n",
      "=================================================================\n",
      "Total params: 5,081,107\n",
      "Trainable params: 5,081,107\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=X.shape[1]))\n",
    "model.add(SpatialDropout1D(0.2))\n",
    "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(7, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 117238 samples, validate on 29310 samples\n",
      "Epoch 1/10\n",
      "117238/117238 [==============================] - 714s 6ms/step - loss: 1.2518 - acc: 0.5493 - val_loss: 1.0478 - val_acc: 0.6309\n",
      "Epoch 2/10\n",
      "117238/117238 [==============================] - 650s 6ms/step - loss: 0.9191 - acc: 0.6767 - val_loss: 0.9731 - val_acc: 0.6531\n",
      "Epoch 3/10\n",
      "117238/117238 [==============================] - 656s 6ms/step - loss: 0.7932 - acc: 0.7212 - val_loss: 1.0033 - val_acc: 0.6442\n",
      "Epoch 4/10\n",
      "117238/117238 [==============================] - 659s 6ms/step - loss: 0.7070 - acc: 0.7493 - val_loss: 1.0261 - val_acc: 0.6478\n"
     ]
    }
   ],
   "source": [
    "\n",
    "epochs = 10\n",
    "batch_size = 256\n",
    "\n",
    "history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.0001)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AskIndia</th>\n",
       "      <th>Business/Finance</th>\n",
       "      <th>Coronavirus</th>\n",
       "      <th>Non-Political</th>\n",
       "      <th>Policy/Economy</th>\n",
       "      <th>Politics</th>\n",
       "      <th>Science/Technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <th>8</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AskIndia  Business/Finance  Coronavirus  Non-Political  Policy/Economy  \\\n",
       "0         0                 0            1              0               0   \n",
       "1         0                 0            0              0               0   \n",
       "2         0                 0            0              0               0   \n",
       "3         0                 0            0              1               0   \n",
       "4         1                 0            0              0               0   \n",
       "5         0                 0            0              0               0   \n",
       "6         0                 0            0              0               1   \n",
       "7         0                 0            0              0               0   \n",
       "8         0                 0            0              0               0   \n",
       "9         0                 0            0              0               0   \n",
       "\n",
       "   Politics  Science/Technology  \n",
       "0         0                   0  \n",
       "1         1                   0  \n",
       "2         1                   0  \n",
       "3         0                   0  \n",
       "4         0                   0  \n",
       "5         1                   0  \n",
       "6         0                   0  \n",
       "7         1                   0  \n",
       "8         1                   0  \n",
       "9         1                   0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.get_dummies(dfx['flair'])\n",
    "Y.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16284/16284 [==============================] - 45s 3ms/step\n",
      "Test set\n",
      "  Loss: 1.002\n",
      "  Accuracy: 0.656\n"
     ]
    }
   ],
   "source": [
    "accr = model.evaluate(X_test,Y_test)\n",
    "print('Test set\\n  Loss: {:0.3f}\\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.002420417215923, 0.6556742815325987]\n"
     ]
    }
   ],
   "source": [
    "print(accr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "jj=model.predict(X_test)\n",
    "jj=np.argmax(jj,axis=1)\n",
    "\n",
    "Y_true=np.argmax(Y_test,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16284,)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Report on Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "          AskIndia       0.60      0.63      0.61      2567\n",
      "  Business/Finance       0.64      0.57      0.61      1030\n",
      "       Coronavirus       0.66      0.58      0.62       544\n",
      "     Non-Political       0.65      0.66      0.66      5529\n",
      "    Policy/Economy       0.49      0.41      0.45       907\n",
      "          Politics       0.73      0.78      0.76      4912\n",
      "Science/Technology       0.56      0.32      0.41       795\n",
      "\n",
      "          accuracy                           0.66     16284\n",
      "         macro avg       0.62      0.57      0.59     16284\n",
      "      weighted avg       0.65      0.66      0.65     16284\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = ['AskIndia', 'Business/Finance', 'Coronavirus','Non-Political', 'Policy/Economy', 'Politics', 'Science/Technology']\n",
    "print(classification_report(Y_true, jj, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1OSIShhToISI6ynjwytEMzkjke88vJb+o1OuSRCTMKNBDSFJcLLOvyyU2OoobnlrC7rJKr0sSkTCiQA8xfVPjefzaMWzbU853n19KZbVmvohIYBToIWhM/1R+942T+GjDLv7z1RW6hJ2IBESzXELUxaN7s76wlIfmr2dwZiI3nT7Q65JEJMQp0EPYj6Ydz4biUn7z1moGpCdwVk6W1yWJSAhTyyWERUUZ//fNUQzvlcLtL3zK6u0635mINC/8An33Jnj/d7BlCdRE/pV/unWJZvZ1uSTFxXLj03kU7a/wuiQRCVHhF+hblsD838ATZ8HvBsDcq+CTWVC8DiL0x8Os5DhmX5fLrrJKZjybR3lVjdcliUgICs/T55aVwKaFkD8fNsyHPV/5Hk/uAwMn+29nQGJmcIoNEf9csZ1bnlvGhSN78cAVozAzr0sSkQ7W0ulzw/NH0YQ0OPES3w1g10ZfsG9YAGteh8+e8z2eeSIMmuIL+P6ToEuCRwUHx/ThPZl5zgnc968vGZSRyO1nDfG6JBEJIeEZ6I2lDvDdcr8DtTWw4wv/6H2Brx2z+CGIioW+42CgP+B7jYbo8Dv8700eRH5RKX94Zy2DMhP42km9vC5JREJEeLZcjkbVQfhqsS/cNyyA7V8ADrqmwIDT6ls0aYMhTFoYFdU1fGvWxyzfupcXb57IqL7dvS5JRDpISy2XyA/0xspKYOP7/oAP3/57SWkFFz/8AeVVtfz91lPo1b2b1yWJSAdQoDfHOdi9sX70vuF9KN/j25Y1vD7gQ7T/vnbnfi59+EP6psbz0i0TSegafi0kETk6CvRA1dbA9s/rR+9ffQQ1lf7++/j6gA+h/vuCLwv5zlNLmDosi8euHkNUVHi0jUQiWtVBKCuGA8VwoMTXGThQXP/YoDPrJ3UcJQX6sao8AFs+qv+BdccXvscP679PgbRBnvbfn/xgI7/8xypuPmMgd507zLM6RCKSc1Cxzx/GJf6ALm4Q0CUNthX7wruqrOnXsmiIT4MJ34XTfnRM5UTetMWO0iXe9y/poDN998uKYeNC3+g9f4FviiTU998HTYEBp3d4//36SdmsLyzlsfc3MDgjkW/m9u3Q9xcJK7U1cHB3oxAuPjyoG46qD5T4vqk3JSYO4tN9U6nj0yF9SIP7/scS0usfi+veroM/jdCPVV3/vW70vnFhE/33KdB/Yof036tqarn+yU/4ZOMunrthPOMHprX7e4qEhOrKw0P5iBF0MRzYVf/3wd3gmrnOQNdkXxA3DOH49KYfS0iH2PgO/3aulktHqK2B7Z/V/8DauP8+aLIv4HuOarf++94DVVzyyAfsLqvk1VtPoX9a6P2QK9Ii56CyrPnec8NRc12AVzR30jqD+NQGo+S0+v8e8Vi6b9+Yrh16uMdCge6FygOHz3/voP77puIyLn74A9ITu/LX700iOS42aK8tctRqa33fXAPtPR8ohurypl8rKraFkXPDEbT/frfjICq6Y4+3AyjQQ0FZcf389/wFsLfB/Pe60fuAMyAxo81vtTi/hGue+JiJg9J48vqxxESH3znYJETVVNeHc7O950b/dc2cTC424fD2Rd0o+bBgbtCP7pocNov/2lObAt3M5gBfAwqdc8Ob2G7AA8B5wAHgeufcstaK6nSB3pBzsGtD/eh94/tQvte3LWuEb2FTG/vvLy75ijteWc61E/vzq4uO+NhEfA5Nr2s0Sm6q91xWXP87UVPiujcK49QmgrnBaDpWi+GORVtnuTwFPAQ808z2c4Eh/tt44BH/f6U5Zr5WS9ogGHvD4f33/PnwyeO+889Ed/HPf/cHfK/RAX+FvHxsP9YXljJr0UYGZyZy7cTsdj0kCRHO+UJ333bYtw32b2/hB8MAptfVtS+yhh/Z0mjYj45PhWi197wWUMvFzLKB15sZoT8GLHDOzfXf/xKY7Jzb3tJrduoRemsO9d/r5r8v9z0elwLZ/v77oDMhdWCLX0Frah0znsljwdoinrx+LKcf3/Z2jniothbKimDfVl9Q79vW4NbgsaoDRz73sOl1zf0o2CCk47pDlFp1oai956H3BrY0uF/gf6zFQJcWdImHwVN9N6jvv9dNkayb/57St3703kT/PTrKeODK0XzjkQ+59fll/O3WSQzOTOrYY5HAVFdC6Y76cD40wm4Q2vu3Q22jq3RFxUBST98tazgMOQeSe0JyL0jqBUk9ICHD17pT/zniBWOE/jpwr3Pu3/777wJ3OOeOGH6b2QxgBkC/fv3GbN68uU3Fd0qH+u8N57836r8PmgL9Jvn+YQC27jnIRQ99QHyXaF699RRSE7p4V39nVFnW/Gi67lZWeOTzYrr5gjm5FyT39gd1b1941z2WkKGRdCfT5lkuarmEsNoa2PZZfcBv+dg3//1Q/30yDJzCsur+XDF7CaP6dOfZG8fRNSbypnN1OOd8i1QOjaC3HTnC3rcNKvYe+dy47g1CuleDoG7wWDuvKpTw1N6Bfj5wG75ZLuOBB51z41p7TQV6O6ksazT/vb7/vu24sTz8VV8Sh53FHVedh2lk17zaGigtPLLt0Ti8j5gzbb5TPxwW0k2MsP3fnkSOVlunLc4FJgPpwE7gHiAWwDn3qH/a4kPAdHzTFr/dVLulMQV6ByktanD+9wWw1/dzx/6uPUjKmeYbwQdp/nvYqK5o4kfFxv3qHUfOn46KbaLt0evw8E7qodke0q60sEh8nKO2OJ+/vPQs3bd/wNS4NcRW7fdtyxrhX+A0+bD+e9ip2N9yUO/b5puy11hsQhP9av8Pi3WPxaepXy2eU6DLYcqrarj88Y9Yv2Mv/7g0gYH7ltSff6a26oj+O71Geb+E2jnfIpd9W1tog2xv+rwe3VKb6Vc3GGFrFaKECQW6HKFwXzkX/ekDAP5+6ylkJsfV99/z5/uu3rSzwfz3AafXB3wr89+PWk01lO70t0G2NjPC3g41FYc/z6IgMas+lJOaGmH31IpEiSgKdGnSym17+eajixmSmciLN08kLrbRKPxQ/91//vd9Bb7HU/r5579P9t0S0pt/k6ryBqPp7U2PsEt3Hnk60+gujUK6iX51YlbIXDlKpKMo0KVZb6/cwc3PLeW8ET354xWjm7+EXd389/z3/PPfF9VPx+sxwhfsXZMbjLD94X1w15Gv1TW5idkfjfvVqWqBiDRBVyySZp19Yg/umD6Ue99aw6CMRH407fimd2x4/plxN/naJNvr5r+/Dx896uu/x6f7QjmlN/Qde+QoO6knxCV37EGKdBIKdOHm0weSX1jKg++uY1BGAheN6t36k6JjoE+u73b6TF9rxSwsLhAgEqk0B0swM359yQjGZacy8+UvWPbV7qN/kdg4hbmIxxToAkCXmCgevWYMPZLjmPFMHgW7mzhjn4iENAW6HJKa0IU51+dSUV3LjU/nUVpR3fqTRCRkKNDlMIMzk/jTVSezrrCU2+d+Sk2tN7OgROToKdDlCKcfn8E9F+Tw7ppC7n1rtdfliEiANMtFmnTtxOzDLmF3+dh+XpckIq3QCF2adffXcjhtSDo/+9sKFueXeF2OiLRCgS7NiomO4qGrTiY7PYHvPr+UjcXNXFBYREKCAl1alNItlieuy8WAG55awt4DVV6XJCLNUKBLq/qnJfDYNbls2X2A7/15KVU1ta0/SUQ6nAJdAjJuQCq/uWQEH6wv4RevrcSrk7qJSPM0y0UC9s3cvqwvKuWx9zcwODORb58ywOuSRKQBBboclTvOGcqGojL+6/VVZKcnMOWETK9LEhE/tVzkqERFGfdfPoqhPZL5/p8/5csd+70uSUT8FOhy1BK6xvDE9bl06xLNDU8vobi0ovUniUi7U6DLMemZ0o3Z1+ZStL+CW55dSkV1jdcliXR6CnQ5ZiP7duf/LhtJ3ubd3PXKcs18EfGYfhSVNvnaSb3ILyzjD++sBeDWMwczKCPR46pEOicFurTZD6YO5kBlNU9+uIm/fbaVqUOzuOm0AYwbkIrpQs8iHca8+pqcm5vr8vLyPHlvaR9F+yt49qPNPPfRZnaVVXJSnxRuPG0g5w3vQUy0unsiwWBmS51zuU1uU6BLsJVX1fDKsgKeWLSRDcVl9O7ejW+fks3lY/uSFBfrdXkiYU2BLp6orXW8u6aQWYs28MnGXSR1jeHK8f24flI2vbp387o8kbDU5kA3s+nAA0A0MNs5d2+j7f2BOUAGsAu42jlX0NJrKtA7ly8K9jBr0UbeXL4dA752Uk9uPG0gw3uneF2aSFhpU6CbWTSwFpgGFABLgCudc6sa7PMS8Lpz7mkzOxP4tnPumpZeV4HeORXsPsCTH2zihU++oqyyhokD07jp9AFMPj6TqCj9gCrSmrYG+kTgF865c/z37wJwzv22wT4rgenOuS3mm9aw1zmX3NLrKtA7t33lVbzwyVc8+cEmtu8tZ3BmIjeeOoCLR/cmLjba6/JEQlZLgR7I1IPewJYG9wv8jzX0OfB1/9+XAElmltZEITPMLM/M8oqKigJ4a4lUyXGxzDh9EAt/MoX7Lx9F15go7vzrck79n/d44J117Cqr9LpEkbATrLlkPwbOMLNPgTOArcARa8Gdc48753Kdc7kZGRlBemsJZ7HRUVw8ujevf/9U/nzTeE7q050/vLOWib99l5/+bTn5RaVelygSNgJZWLQV6Nvgfh//Y4c457bhH6GbWSJwqXNuT7CKlMhnZkwalM6kQemsL9zP7EUbeXlpAXM/+UoLlUQCFEgPPQbfj6JT8QX5EuAq59zKBvukA7ucc7Vm9mugxjl3d0uvqx66tKZuodKzizex+0AVJ/VJ4abTBnKuFipJJ9amHrpzrhq4DfgXsBr4i3NupZn9yswu9O82GfjSzNYCWcCvg1K5dGoZSV350bTj+fDOqfz3xcPZX17N9+d+yhn3LWD2og2UVlR7XaJISNHCIgkbWqgkopWiEoE+37KHWYs28NaKHVqoJJ2KAl0ilhYqSWejQJeIp4VK0lko0KXTqKqp5Y0vtjNr0QZWbttHemIXrpmQzTUT+5Oa0MXr8kTaTIEunY5zjsX5JcxatIH5XxYRFxvFpSf34YZTBzBQV1SSMNZSoOuKRRKRzIxJg9OZNDiddTv388S/N/LS0gL+rIVKEsE0QpdOo/FCpZH+KyppoZKEE7VcRBo4WOm/otK/N7KxwRWVrhjXj8Su+tIqoU2BLtKEQwuVFm7gk01aqCThQYEu0gotVJJwoUAXCdCWXb6FSi8u8S1UmjQojZtOG8gZx2dooZKEBAW6yFHae7B+odKOfVqoJKFDgS5yjLRQSUKNAl2kjbRQSUKFFhaJtFHjhUqzF23kpTwtVJLQohG6yDEq2l/Bs4s38exHm7VQSTqMWi4i7aiphUrfOXUAl4/tq4VKEnQKdJEOUFvreGf1TmYv2uhbqBQXw1Xj+nH9Kdn0TNFCJQkOBbpIB9NCJWkvCnQRj2ihkgSbAl3EY1qoJMGiQBcJEZXVtbyxfBuzFm5k1XbfQqVrJ2Zz9QQtVJLAKNBFQowWKsmx0sIikRDT0kKls4ZlcdNpAxmbfZwWKslR0QhdJERooZIEQi0XkTBysLKGl5cVMEcLlaQJCnSRMKSFStKUNge6mU0HHgCigdnOuXsbbe8HPA109+9zp3PuzZZeU4EuErjP6hYqLd9OlJkWKnVibQp0M4sG1gLTgAJgCXClc25Vg30eBz51zj1iZjnAm8657JZeV4EucvS0UElaCvRAfmkZB6x3zm1wzlUCLwAXNdrHAcn+v1OAbcdarIg0r29qPHdfkMOHd03lrnOHsqGojG8/tYSz71/Ii0u+oryqxusSxUOBjNC/AUx3zt3ov38NMN45d1uDfXoCbwPHAQnAWc65pU281gxgBkC/fv3GbN68OVjHIdIpaaFS59PWEXogrgSecs71Ac4DnjWzI17bOfe4cy7XOZebkZERpLcW6by6xERxyeg+vPGDU/nzjeMZ3juF389by6R73+Vnf1tOflGp1yVKBwpkDtRWoG+D+338jzV0AzAdwDm32MzigHSgMBhFikjLmluo9PzHXzG0RxJn52QxLacHw3sna7FSBAuk5RKD70fRqfiCfAlwlXNuZYN93gJedM49ZWbDgHeB3kproggAAAnjSURBVK6FF9ePoiLtq2h/Ba9+upV5q3aSt3kXtQ56psRx1rAspuVkMWFgGl1itGAp3ARj2uJ5wP34piTOcc792sx+BeQ5517zz2yZBSTi+4H0J865t1t6TQW6SMcpKa3gvTWFzFu1k4XriiivqiWpawxnnJDBtJwsJp+QSUq3WK/LlABoYZGIHFJeVcO/1xUzb9VO3l2zk+LSSmKijAkD05iWk8VZOVn07q6FS6FKgS4iTaqpdXy2ZTdvr9rJvFU72VBUBsCJvZKZluNrzeT0VN89lCjQRSQg+UWlzPOH+7KvduMc9O7e7VC4jxuQSqxOFOYpBbqIHLWi/RW8t8YX7ovWFVNRXUtyXAxThmYyLSeLM47PIClOffeOpkAXkTY5UFnNIn/f/b01hewqqyQ22pg4KN03eh+WRY+UOK/L7BQU6CISNDW1jqWbdzNv1Q7mrdrJppIDAJzUJ4Vpw7KYdmIWJ2Qlqe/eThToItIunHOsLyw99KPqZ1v2ANA3tRvThvVgWk4WY7OP0wU6gkiBLiIdonBfOe+sLmTeqh18kF9CZXUt3eNjOfMEX9/99OMzSNBFOtpEgS4iHa6sopqFa4v8890L2Xuwii4xUZwyKI1pOT04a1gmmcnqux8tBbqIeKq6ppYlm3b7pkSu3sGWXQcBGNW3O9Nysjg7J4vBmYnquwdAgS4iIcM5x5c79zNv5U7mrd7JFwV7AchOi/fPd+/BmP7HEa0LdjRJgS4iIWv73oP+vvtOFucXU1XjSE3owpn++e6nDUknvov67nUU6CISFvaXV/G+v+/+3ppC9pdX0zUmitOG+Oa7Tx2WRXpiV6/L9JQCXUTCTlVNLZ9s3HXoVARb9xzEDE7ud9yhUxEMykj0uswOp0AXkbDmnGPV9n2Hwn3ltn0ADMxIOPSj6qi+naPvrkAXkYiydc9B3vGH+0cbSqiudaQndmHqUN/I/dQh6cTFRntdZrtQoItIxNp7sIoFX/p+VH3/yyL2V1TTLTb6sL57JF0wu6VA10/HIhLWUrrFctGo3lw0qjeV1bV8tKGEeat28s7qnby9aidRBrn9Uw/13bPTE7wuud1ohC4iEck5x4qt+5i3agdvr9rJmh37ARiSmXgo3Ef26U5UmPXd1XIRkU5vy64Dh35U/WTTLmpqHZlJXZk6zPej6sRBaWHRd1egi4g0sOdAJfMb9N3LKmuI7xLNGcf7Lpp95tBMuseHZt9dgS4i0oyK6ho+zPf33VftpHB/BdFRxtjs45iW04Ozc7LomxrvdZmHKNBFRAJQW+v4YuveQxfvWLuzFIChPZIO9d1H9E7x9CRiCnQRkWOwuaSMeat8s2XyNu2i1kGP5DjOyslkWk4PJg5Mo0tMx168Q4EuItJGu8oqeW+N7+IdC9cWc7CqhsSuMZxxQgZn52Qx+YRMUrq1/0WzFegiIkFUXlXDB+uL/fPdCykurSAmyhg/MNV/XdUe9O7erV3eW4EuItJOamsdn27Z458SuYP8ojIAcnomH+q7n9grOWh9dwW6iEgH2VBUemi++9KvduMc9O7ejbOG+fru4wemEtuGi2a3OdDNbDrwABANzHbO3dto+x+AKf678UCmc657S6+pQBeRSFdcWsF7qwt5e9VO/r2+iPKqWpLiYrh96hBuPG3gMb1mm87lYmbRwJ+AaUABsMTMXnPOrarbxzn3Hw32/z4w+pgqFRGJIOmJXblsbF8uG9uXg5U1LFrnu3hHVjtdHDuQk3ONA9Y75zYAmNkLwEXAqmb2vxK4JzjliYhEhm5dojn7xB6cfWKPdnuPQBo5vYEtDe4X+B87gpn1BwYA7zWzfYaZ5ZlZXlFR0dHWKiIiLQj2jPgrgJedczVNbXTOPe6cy3XO5WZkZAT5rUVEOrdAAn0r0LfB/T7+x5pyBTC3rUWJiMjRCyTQlwBDzGyAmXXBF9qvNd7JzIYCxwGLg1uiiIgEotVAd85VA7cB/wJWA39xzq00s1+Z2YUNdr0CeMF5NbFdRKSTC+gSdM65N4E3Gz12d6P7vwheWSIicrQ69jRhIiLSbhToIiIRwrNzuZhZEbD5GJ+eDhQHsRwv6VhCU6QcS6QcB+hY6vR3zjU579uzQG8LM8tr7lwG4UbHEpoi5Vgi5ThAxxIItVxERCKEAl1EJEKEa6A/7nUBQaRjCU2RciyRchygY2lVWPbQRUTkSOE6QhcRkUYU6CIiESKkA93MppvZl2a23szubGJ7VzN70b/9YzPL7vgqAxPAsVxvZkVm9pn/dqMXdbbGzOaYWaGZrWhmu5nZg/7j/MLMTu7oGgMVwLFMNrO9DT6Tu5vaz2tm1tfM5pvZKjNbaWa3N7FPWHwuAR5LuHwucWb2iZl97j+WXzaxT3AzzDkXkjd81y/NBwYCXYDPgZxG+3wPeNT/9xXAi17X3YZjuR54yOtaAziW04GTgRXNbD8PeAswYALwsdc1t+FYJgOve11nAMfREzjZ/3cSsLaJ/32FxecS4LGEy+diQKL/71jgY2BCo32CmmGhPEI/dOk751wlUHfpu4YuAp72//0yMNXMrANrDFQgxxIWnHMLgV0t7HIR8Izz+QjobmY9O6a6oxPAsYQF59x259wy/9/78Z0VtfFVxcLicwnwWMKC///Wpf67sf5b41koQc2wUA70QC59d2gf5zvN714grUOqOzqBXsbvUv/X4ZfNrG8T28NBwJcsDBMT/V+Z3zKzE70upjX+r+yj8Y0GGwq7z6WFY4Ew+VzMLNrMPgMKgXnOuWY/l2BkWCgHemfzDyDbOXcSMI/6f7XFO8vwnTdjJPBH4FWP62mRmSUCrwA/dM7t87qetmjlWMLmc3HO1TjnRuG70ts4Mxvenu8XyoEeyKXvDu1jZjFAClDSIdUdnVaPxTlX4pyr8N+dDYzpoNqC7WguWRjSnHP76r4yO981AWLNLN3jsppkZrH4AvB559xfm9glbD6X1o4lnD6XOs65PcB8YHqjTUHNsFAO9EAuffcacJ3/728A7zn/rwshptVjadTPvBBf7zAcvQZc659VMQHY65zb7nVRx8LMetT1M81sHL7/fwm5AYO/xieA1c653zezW1h8LoEcSxh9Lhlm1t3/dzdgGrCm0W5BzbCArljkBedctZnVXfouGpjj/Je+A/Kcc6/h++CfNbP1+H7cusK7ipsX4LH8wHyX9KvGdyzXe1ZwC8xsLr5ZBulmVgDcg+/HHpxzj+K7stV5wHrgAPBtbyptXQDH8g3gu2ZWDRwErgjRAcMpwDXAcn+/FuCnQD8Iu88lkGMJl8+lJ/C0mUXj+0fnL86519szw7T0X0QkQoRyy0VERI6CAl1EJEIo0EVEIoQCXUQkQijQRUQihAJdRCRCKNBFRCLE/wdjtl9Oc07DkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Loss')\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model and weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# serialize model to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"lstm_no_glove.json\", \"w\") as json_file:\n",
    "\tjson_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"lstm_no_glove.h5\")\n",
    "print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7.7152285e-03 1.7205245e-04 1.0427540e-04 2.2130255e-02 1.1592682e-03\n",
      "  9.6858370e-01 1.3516496e-04]] Politics\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "new_complaint = ['BJP wants us to see Indian Sonia Gandhi as Italian but Canadian Akshay Kumar as Indian']\n",
    "seq = tokenizer.texts_to_sequences(new_complaint)\n",
    "padded = pad_sequences(seq, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "pred = model.predict(padded)\n",
    "labels = ['AskIndia', 'Business/Finance', 'Coronavirus','Non-Political', 'Policy/Economy', 'Politics', 'Science/Technology']\n",
    "print(pred, labels[np.argmax(pred)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for LSTM with pretrained embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 151548 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "MAX_NB_WORDS = 50000\n",
    "\n",
    "MAX_SEQUENCE_LENGTH = 500\n",
    "# This is fixed.\n",
    "EMBEDDING_DIM = 100\n",
    "\n",
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n",
    "tokenizer.fit_on_texts(dfx['text'].values)\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading word vectors...\n",
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "# load in pre-trained word vectors\n",
    "print('Loading word vectors...')\n",
    "word2vec = {}\n",
    "with open(os.path.join('glove.6B.%sd.txt' % EMBEDDING_DIM)) as f:\n",
    "    for line in f:\n",
    "        values = line.split()\n",
    "        word = values[0]\n",
    "        vec = np.asarray(values[1:], dtype='float32')\n",
    "        word2vec[word] = vec\n",
    "print('Found %s word vectors.' % len(word2vec))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filling pre-trained embeddings...\n"
     ]
    }
   ],
   "source": [
    "# prepare embedding matrix\n",
    "print('Filling pre-trained embeddings...')\n",
    "num_words = min(MAX_NB_WORDS, len(word_index) + 1)\n",
    "embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\n",
    "for word, i in word_index.items():\n",
    "  if i < MAX_NB_WORDS:\n",
    "    embedding_vector = word2vec.get(word)\n",
    "    if embedding_vector is not None:\n",
    "      # words not found in embedding index will be all zeros.\n",
    "      embedding_matrix[i] = embedding_vector\n",
    "\n",
    "\n",
    "\n",
    "# load pre-trained word embeddings into an Embedding layer\n",
    "# note that we set trainable = False so as to keep the embeddings fixed\n",
    "embedding_layer = Embedding(\n",
    "  num_words,\n",
    "  EMBEDDING_DIM,\n",
    "  weights=[embedding_matrix],\n",
    "  input_length=MAX_SEQUENCE_LENGTH,\n",
    "  trainable=False\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ = Input(shape=(MAX_SEQUENCE_LENGTH,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 500, 100)          5000000   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_1 (Spatial (None, 500, 100)          0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 7)                 707       \n",
      "=================================================================\n",
      "Total params: 5,081,107\n",
      "Trainable params: 81,107\n",
      "Non-trainable params: 5,000,000\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(embedding_layer)\n",
    "model.add(SpatialDropout1D(0.2))\n",
    "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(7, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (162832, 500)\n"
     ]
    }
   ],
   "source": [
    "X = tokenizer.texts_to_sequences(dfx['text'].values)\n",
    "X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "print('Shape of data tensor:', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of label tensor: (162832, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Y = pd.get_dummies(dfx['flair']).values\n",
    "print('Shape of label tensor:', Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146548, 500) (146548, 7)\n",
      "(16284, 500) (16284, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.10, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 117238 samples, validate on 29310 samples\n",
      "Epoch 1/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0380 - acc: 0.6157 - val_loss: 1.0097 - val_acc: 0.6263\n",
      "Epoch 2/20\n",
      "117238/117238 [==============================] - 497s 4ms/step - loss: 1.0314 - acc: 0.6187 - val_loss: 1.0049 - val_acc: 0.6299\n",
      "Epoch 3/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0245 - acc: 0.6208 - val_loss: 0.9997 - val_acc: 0.6308\n",
      "Epoch 4/20\n",
      "117238/117238 [==============================] - 494s 4ms/step - loss: 1.0207 - acc: 0.6225 - val_loss: 0.9998 - val_acc: 0.6311\n",
      "Epoch 5/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0166 - acc: 0.6256 - val_loss: 0.9936 - val_acc: 0.6342\n",
      "Epoch 6/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0119 - acc: 0.6253 - val_loss: 0.9940 - val_acc: 0.6334\n",
      "Epoch 7/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0073 - acc: 0.6281 - val_loss: 0.9902 - val_acc: 0.6353\n",
      "Epoch 8/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 1.0026 - acc: 0.6312 - val_loss: 0.9878 - val_acc: 0.6353\n",
      "Epoch 9/20\n",
      "117238/117238 [==============================] - 495s 4ms/step - loss: 0.9981 - acc: 0.6320 - val_loss: 0.9870 - val_acc: 0.6357\n",
      "Epoch 10/20\n",
      "117238/117238 [==============================] - 510s 4ms/step - loss: 0.9962 - acc: 0.6308 - val_loss: 0.9835 - val_acc: 0.6377\n",
      "Epoch 11/20\n",
      "117238/117238 [==============================] - 536s 5ms/step - loss: 0.9917 - acc: 0.6342 - val_loss: 0.9822 - val_acc: 0.6371\n",
      "Epoch 12/20\n",
      "117238/117238 [==============================] - 497s 4ms/step - loss: 0.9878 - acc: 0.6344 - val_loss: 0.9821 - val_acc: 0.6366\n",
      "Epoch 13/20\n",
      "117238/117238 [==============================] - 496s 4ms/step - loss: 0.9863 - acc: 0.6348 - val_loss: 0.9819 - val_acc: 0.6377\n",
      "Epoch 14/20\n",
      "117238/117238 [==============================] - 496s 4ms/step - loss: 0.9824 - acc: 0.6372 - val_loss: 0.9789 - val_acc: 0.6396\n",
      "Epoch 15/20\n",
      "117238/117238 [==============================] - 496s 4ms/step - loss: 0.9798 - acc: 0.6386 - val_loss: 0.9765 - val_acc: 0.6402\n",
      "Epoch 16/20\n",
      "117238/117238 [==============================] - 496s 4ms/step - loss: 0.9788 - acc: 0.6383 - val_loss: 0.9774 - val_acc: 0.6407\n",
      "Epoch 17/20\n",
      "117238/117238 [==============================] - 496s 4ms/step - loss: 0.9760 - acc: 0.6398 - val_loss: 0.9782 - val_acc: 0.6384\n"
     ]
    }
   ],
   "source": [
    "\n",
    "epochs = 20\n",
    "batch_size = 512\n",
    "\n",
    "history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.0001)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# serialize model to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"lstm_glove.json\", \"w\") as json_file:\n",
    "\tjson_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"lstm_glove.h5\")\n",
    "print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16284/16284 [==============================] - 29s 2ms/step\n",
      "Test set\n",
      "  Loss: 0.969\n",
      "  Accuracy: 0.646\n"
     ]
    }
   ],
   "source": [
    "accr = model.evaluate(X_test,Y_test)\n",
    "print('Test set\\n  Loss: {:0.3f}\\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "          AskIndia       0.61      0.63      0.62      2567\n",
      "  Business/Finance       0.62      0.58      0.60      1030\n",
      "       Coronavirus       0.63      0.49      0.55       544\n",
      "     Non-Political       0.62      0.69      0.65      5529\n",
      "    Policy/Economy       0.52      0.33      0.41       907\n",
      "          Politics       0.74      0.75      0.74      4912\n",
      "Science/Technology       0.50      0.34      0.40       795\n",
      "\n",
      "          accuracy                           0.65     16284\n",
      "         macro avg       0.61      0.54      0.57     16284\n",
      "      weighted avg       0.64      0.65      0.64     16284\n",
      "\n"
     ]
    }
   ],
   "source": [
    "jj=model.predict(X_test)\n",
    "jj=np.argmax(jj,axis=1)\n",
    "\n",
    "Y_true=np.argmax(Y_test,axis=1)\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = ['AskIndia', 'Business/Finance', 'Coronavirus','Non-Political', 'Policy/Economy', 'Politics', 'Science/Technology']\n",
    "print(classification_report(Y_true, jj, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.title('Loss')\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM on only titles (w/o pretrained word embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx['title']=dfx['title'].apply(lambda x : expand_contractions(x))\n",
    "dfx['title']=dfx['title'].apply(lambda x : remove_URL(x))\n",
    "dfx['title']=dfx['title'].apply(lambda x : remove_punct(x))\n",
    "dfx['title']=dfx['title'].apply(lambda x : remove_html(x))\n",
    "dfx['title']=dfx['title'].apply(lambda x : remove_emoji(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 88142 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "MAX_NB_WORDS = 50000\n",
    "# Max number of words in each complaint.\n",
    "MAX_SEQUENCE_LENGTH = 400\n",
    "# This is fixed.\n",
    "EMBEDDING_DIM = 50\n",
    "\n",
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~', lower=True)\n",
    "tokenizer.fit_on_texts(dfx['title'].values)\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (162832, 400)\n"
     ]
    }
   ],
   "source": [
    "X = tokenizer.texts_to_sequences(dfx['title'].values)\n",
    "X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "print('Shape of data tensor:', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of label tensor: (162832, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Y = pd.get_dummies(dfx['flair']).values\n",
    "print('Shape of label tensor:', Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(146548, 400) (146548, 7)\n",
      "(16284, 400) (16284, 7)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.10, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 400, 50)           2500000   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_2 (Spatial (None, 400, 50)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 100)               60400     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 7)                 707       \n",
      "=================================================================\n",
      "Total params: 2,561,107\n",
      "Trainable params: 2,561,107\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=X.shape[1]))\n",
    "model.add(SpatialDropout1D(0.2))\n",
    "model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(7, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 117238 samples, validate on 29310 samples\n",
      "Epoch 1/20\n",
      "117238/117238 [==============================] - 421s 4ms/step - loss: 1.3086 - acc: 0.5170 - val_loss: 1.1136 - val_acc: 0.6028\n",
      "Epoch 2/20\n",
      "117238/117238 [==============================] - 421s 4ms/step - loss: 0.9748 - acc: 0.6562 - val_loss: 1.0048 - val_acc: 0.6422\n",
      "Epoch 3/20\n",
      "117238/117238 [==============================] - 420s 4ms/step - loss: 0.8368 - acc: 0.7046 - val_loss: 0.9954 - val_acc: 0.6459\n",
      "Epoch 4/20\n",
      "117238/117238 [==============================] - 427s 4ms/step - loss: 0.7526 - acc: 0.7333 - val_loss: 1.0320 - val_acc: 0.6394\n",
      "Epoch 5/20\n",
      "117238/117238 [==============================] - 428s 4ms/step - loss: 0.6919 - acc: 0.7544 - val_loss: 1.0533 - val_acc: 0.6349\n"
     ]
    }
   ],
   "source": [
    "\n",
    "epochs = 20\n",
    "batch_size = 256\n",
    "\n",
    "history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.0001)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.title('Loss')\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16284/16284 [==============================] - 21s 1ms/step\n",
      "Test set\n",
      "  Loss: 1.036\n",
      "  Accuracy: 0.642\n"
     ]
    }
   ],
   "source": [
    "accr = model.evaluate(X_test,Y_test)\n",
    "print('Test set\\n  Loss: {:0.3f}\\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "          AskIndia       0.56      0.58      0.57      2567\n",
      "  Business/Finance       0.65      0.57      0.61      1030\n",
      "       Coronavirus       0.63      0.62      0.62       544\n",
      "     Non-Political       0.65      0.62      0.64      5529\n",
      "    Policy/Economy       0.51      0.41      0.45       907\n",
      "          Politics       0.71      0.80      0.75      4912\n",
      "Science/Technology       0.47      0.42      0.44       795\n",
      "\n",
      "          accuracy                           0.64     16284\n",
      "         macro avg       0.60      0.57      0.58     16284\n",
      "      weighted avg       0.64      0.64      0.64     16284\n",
      "\n"
     ]
    }
   ],
   "source": [
    "jj=model.predict(X_test)\n",
    "jj=np.argmax(jj,axis=1)\n",
    "\n",
    "Y_true=np.argmax(Y_test,axis=1)\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "target_names = ['AskIndia', 'Business/Finance', 'Coronavirus','Non-Political', 'Policy/Economy', 'Politics', 'Science/Technology']\n",
    "print(classification_report(Y_true, jj, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# serialize model to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"lstm_no_glove_title.json\", \"w\") as json_file:\n",
    "\tjson_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"lstm_no_glove_title.h5\")\n",
    "print(\"Saved model to disk\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code for BERT finetuning (code is complete just need to train the model, we can see in the output that my GPU was insufficient for training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import torch\n",
    "\n",
    "\n",
    "class BERTDataset:\n",
    "    def __init__(self, text, flair):\n",
    "        self.text = text\n",
    "        self.flair = flair\n",
    "        self.tokenizer = TOKENIZER\n",
    "        self.max_len = MAX_LEN\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.text)\n",
    "    \n",
    "    def __getitem__(self, item):\n",
    "        text = str(self.text[item])\n",
    "        text = \" \".join(text.split())\n",
    "\n",
    "        inputs = self.tokenizer.encode_plus(\n",
    "            text,\n",
    "            None,\n",
    "            add_special_tokens=True,\n",
    "            max_length=self.max_len,\n",
    "            pad_to_max_length=True\n",
    "        )\n",
    "\n",
    "        ids = inputs[\"input_ids\"]\n",
    "        mask = inputs[\"attention_mask\"]\n",
    "        token_type_ids = inputs[\"token_type_ids\"]\n",
    "\n",
    "        return {\n",
    "            'ids': torch.tensor(ids, dtype=torch.long),\n",
    "            'mask': torch.tensor(mask, dtype=torch.long),\n",
    "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
    "            'targets': torch.tensor(self.flair[item], dtype=torch.long)\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def loss_fn(outputs, targets):\n",
    "    return nn.CrossEntropyLoss()(outputs, targets.view(-1, 1))\n",
    "\n",
    "\n",
    "def train_fn(data_loader, model, optimizer, device, scheduler):\n",
    "    model.train()\n",
    "\n",
    "    for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
    "        ids = d[\"ids\"]\n",
    "        token_type_ids = d[\"token_type_ids\"]\n",
    "        mask = d[\"mask\"]\n",
    "        targets = d[\"targets\"]\n",
    "\n",
    "        ids = ids.to(device, dtype=torch.long)\n",
    "        token_type_ids = token_type_ids.to(device, dtype=torch.long)\n",
    "        mask = mask.to(device, dtype=torch.long)\n",
    "        targets = targets.to(device, dtype=torch.float)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(\n",
    "            ids=ids,\n",
    "            mask=mask,\n",
    "            token_type_ids=token_type_ids\n",
    "        )\n",
    "\n",
    "        loss = loss_fn(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "\n",
    "def eval_fn(data_loader, model, device):\n",
    "    model.eval()\n",
    "    fin_targets = []\n",
    "    fin_outputs = []\n",
    "    with torch.no_grad():\n",
    "        for bi, d in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
    "            ids = d[\"ids\"]\n",
    "            token_type_ids = d[\"token_type_ids\"]\n",
    "            mask = d[\"mask\"]\n",
    "            targets = d[\"targets\"]\n",
    "\n",
    "            ids = ids.to(device, dtype=torch.long)\n",
    "            token_type_ids = token_type_ids.to(device, dtype=torch.long)\n",
    "            mask = mask.to(device, dtype=torch.long)\n",
    "            targets = targets.to(device, dtype=torch.long)\n",
    "\n",
    "            outputs = model(\n",
    "                ids=ids,\n",
    "                mask=mask,\n",
    "                token_type_ids=token_type_ids\n",
    "            )\n",
    "            fin_targets.extend(targets.cpu().detach().numpy().tolist())\n",
    "            fin_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist())\n",
    "    return fin_outputs, fin_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import transformers\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class BERTBaseUncased(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(BERTBaseUncased, self).__init__()\n",
    "        self.bert = transformers.BertModel.from_pretrained(BERT_PATH)\n",
    "        self.bert_drop = nn.Dropout(0.3)\n",
    "        self.out = nn.Linear(768, 7)\n",
    "    \n",
    "    def forward(self, ids, mask, token_type_ids):\n",
    "        _, o2 = self.bert(\n",
    "            ids, \n",
    "            attention_mask=mask,\n",
    "            token_type_ids=token_type_ids\n",
    "        )\n",
    "        bo = self.bert_drop(o2)\n",
    "        output = self.out(bo)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Coronavirus', 'Politics', 'Non-Political', 'AskIndia',\n",
       "       'Policy/Economy', 'Science/Technology', 'Business/Finance'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dfx = pd.read_csv(TRAINING_FILE).fillna(\"none\")\n",
    "dfx.flair.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AskIndia' 'Business/Finance' 'Coronavirus' 'Non-Political'\n",
      " 'Policy/Economy' 'Politics' 'Science/Technology']\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "dfx['flair'] = le.fit_transform(dfx.flair.values)\n",
    "print(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 90.00 MiB (GPU 0; 3.81 GiB total capacity; 2.56 GiB already allocated; 65.75 MiB free; 2.60 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-a0ddcff3027a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m     \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-19-a0ddcff3027a>\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0mbest_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, module, device_ids, output_device, dim)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_ids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m    305\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \"\"\"\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m                     \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m                 \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    305\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \"\"\"\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 90.00 MiB (GPU 0; 3.81 GiB total capacity; 2.56 GiB already allocated; 65.75 MiB free; 2.60 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "\n",
    "import torch\n",
    "import pandas as pd\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "from transformers import AdamW\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "\n",
    "def run():\n",
    "    df_train, df_valid = model_selection.train_test_split(\n",
    "        dfx,\n",
    "        test_size=0.1,\n",
    "        random_state=42,\n",
    "        stratify=dfx.flair.values\n",
    "    )\n",
    "\n",
    "    df_train = df_train.reset_index(drop=True)\n",
    "    df_valid = df_valid.reset_index(drop=True)\n",
    "\n",
    "    train_dataset = BERTDataset(\n",
    "        text=df_train.text.values,\n",
    "        flair=df_train.flair.values\n",
    "    )\n",
    "\n",
    "    train_data_loader = torch.utils.data.DataLoader(\n",
    "        train_dataset,\n",
    "        batch_size=TRAIN_BATCH_SIZE,\n",
    "        num_workers=4\n",
    "    )\n",
    "\n",
    "    valid_dataset = BERTDataset(\n",
    "        text=df_valid.text.values,\n",
    "        flair=df_valid.flair.values\n",
    "    )\n",
    "\n",
    "    valid_data_loader = torch.utils.data.DataLoader(\n",
    "        valid_dataset,\n",
    "        batch_size=VALID_BATCH_SIZE,\n",
    "        num_workers=1\n",
    "    )\n",
    "\n",
    "    device = torch.device(\"cpu\")\n",
    "    model = BERTBaseUncased()\n",
    "    torch.cuda.empty_cache()  # entirely clear all allocated memory\n",
    "\n",
    "    model.to(device)\n",
    "    \n",
    "    param_optimizer = list(model.named_parameters())\n",
    "    no_decay = [\"bias\", \"LayerNorm.bias\", \"LayerNorm.weight\"]\n",
    "    optimizer_parameters = [\n",
    "        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.001},\n",
    "        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},\n",
    "    ]\n",
    "\n",
    "    num_train_steps = int(len(df_train) / TRAIN_BATCH_SIZE * EPOCHS)\n",
    "    optimizer = AdamW(optimizer_parameters, lr=3e-5)\n",
    "    scheduler = get_linear_schedule_with_warmup(\n",
    "        optimizer,\n",
    "        num_warmup_steps=0,\n",
    "        num_training_steps=num_train_steps\n",
    "    )\n",
    "\n",
    "    model = nn.DataParallel(model)\n",
    "\n",
    "    best_accuracy = 0\n",
    "    for epoch in range(EPOCHS):\n",
    "        train_fn(train_data_loader, model, optimizer, device, scheduler)\n",
    "        outputs, targets = eval_fn(valid_data_loader, model, device)\n",
    "        outputs = np.array(outputs) >= 0.5\n",
    "        accuracy = metrics.accuracy_score(targets, outputs)\n",
    "        print(f\"Accuracy Score = {accuracy}\")\n",
    "        if accuracy > best_accuracy:\n",
    "            torch.save(model.state_dict(), MODEL_PATH)\n",
    "            best_accuracy = accuracy\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    run()"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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