RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install torchtext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "from nltk.tokenize import word_tokenize, sent_tokenize, wordpunct_tokenize, TreebankWordTokenizer, TweetTokenizer, MWETokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import ngrams\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import PorterStemmer\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from os import path\n",
    "sns.set()\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_score, f1_score, average_precision_score\n",
    "\n",
    "import calendar\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import time\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import string\n",
    "import os\n",
    "import numpy as np\n",
    "#from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "from PIL import Image\n",
    "import urllib\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc, classification_report\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import time\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import operator\n",
    "#import folium\n",
    "from itertools import cycle, islice\n",
    "from pandas import options\n",
    "import warnings\n",
    "import pickle\n",
    "import re\n",
    "import nltk\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch\n",
    "from matplotlib.pyplot import figure\n",
    "from nltk.corpus import stopwords \n",
    "import nltk\n",
    "#from torchtext import data\n",
    "#from torchtext.vocab import Vectors, GloVe\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "#import torchtext\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_16396\\3219277780.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  train_df=pd.read_csv(\"C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_training_data_with_class_labels.csv\" , error_bad_lines=False)\n"
     ]
    }
   ],
   "source": [
    "train_df=pd.read_csv(\"C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_training_data_with_class_labels.csv\" , error_bad_lines=False) \n",
    "#test_df = pd.read_csv(\"C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_test_data.csv\", error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Parent Comments</th>\n",
       "      <th>Class Labels</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ocxtitan</td>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>I should've put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "0          ocxtitan                                   Central Illinois   \n",
       "1         LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
       "2     throwitskrub8  But then again; you have to consider that all ...   \n",
       "3  fresherthanyouuu                                            ughhhhh   \n",
       "4         _kushagra                                I should've put the   \n",
       "\n",
       "                                     Parent Comments  Class Labels  Unnamed: 4  \n",
       "0                          Jesus; where do you live?  non-sarcastic        NaN  \n",
       "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic        NaN  \n",
       "2  agree to that part.It can also mean that gujra...  non-sarcastic        NaN  \n",
       "3  If a guy told you he doesn't use social media ...  non-sarcastic        NaN  \n",
       "4  No; it's just a programming bug. After all; th...      sarcastic        NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      <td>ocxtitan</td>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>I should've put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "0          ocxtitan                                   Central Illinois   \n",
       "1         LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
       "2     throwitskrub8  But then again; you have to consider that all ...   \n",
       "3  fresherthanyouuu                                            ughhhhh   \n",
       "4         _kushagra                                I should've put the   \n",
       "\n",
       "                                     Parent Comments  Class Labels   \n",
       "0                          Jesus; where do you live?  non-sarcastic  \n",
       "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
       "2  agree to that part.It can also mean that gujra...  non-sarcastic  \n",
       "3  If a guy told you he doesn't use social media ...  non-sarcastic  \n",
       "4  No; it's just a programming bug. After all; th...      sarcastic  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop(train_df.columns[[4]], axis=1, inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>Class Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I should've put the</td>\n",
       "      <td>sarcastic</td>\n",
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      "text/plain": [
       "                                            Comments  Class Labels \n",
       "0                                   Central Illinois  non-sarcastic\n",
       "1  To think - CNN used to be the acronym synonymo...  non-sarcastic\n",
       "2  But then again; you have to consider that all ...  non-sarcastic\n",
       "3                                            ughhhhh  non-sarcastic\n",
       "4                                I should've put the      sarcastic"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=train_df.iloc[:, [1,3]]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_16396\\3756746671.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2.rename(columns = {' Class Labels ':'label'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df2.rename(columns = {' Class Labels ':'label'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": 25,
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       "      <th>label</th>\n",
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       "      <th>0</th>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I should've put the</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                            Comments          label\n",
       "0                                   Central Illinois  non-sarcastic\n",
       "1  To think - CNN used to be the acronym synonymo...  non-sarcastic\n",
       "2  But then again; you have to consider that all ...  non-sarcastic\n",
       "3                                            ughhhhh  non-sarcastic\n",
       "4                                I should've put the      sarcastic"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Labels=df2.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dict(zip(df2.label, df2.Comments))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_16396\\3310686905.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"label\"] = np.where(df2[\"label\"] == \"sarcastic\", 1, 0)\n"
     ]
    }
   ],
   "source": [
    "df2[\"label\"] = np.where(df2[\"label\"] == \"sarcastic\", 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>Central Illinois</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>I should've put the</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                                            Comments  label\n",
       "0                                   Central Illinois      0\n",
       "1  To think - CNN used to be the acronym synonymo...      0\n",
       "2  But then again; you have to consider that all ...      0\n",
       "3                                            ughhhhh      0\n",
       "4                                I should've put the      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                          central illinois\n",
       "1         to think  cnn used to be the acronym synonymou...\n",
       "2         but then again you have to consider that all h...\n",
       "3                                                   ughhhhh\n",
       "4                                        i shouldve put the\n",
       "                                ...                        \n",
       "808098    50 hpdef elgif impiety orb bourn jewel checkin...\n",
       "808099                                        rubicon mango\n",
       "808100                      essentially gap insurance right\n",
       "808101    yeah but sometimes that weird kid keeps trying...\n",
       "808102    petroleum doesnt really require a terroir like...\n",
       "Name: Comments, Length: 808062, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "# Removing the null comments\n",
    "train_df.dropna(subset=['Comments'], inplace=True)\n",
    "train_df['Comments'] = train_df['Comments'].str.lower()\n",
    "train_df['Comments'] = train_df['Comments'].str.replace('[^\\w\\s]','', regex=True)\n",
    "\n",
    "corpus = train_df['Comments'].astype(str)\n",
    "\n",
    "\n",
    "\n",
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Parent Comments</th>\n",
       "      <th>Class Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ocxtitan</td>\n",
       "      <td>central illinois</td>\n",
       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>to think  cnn used to be the acronym synonymou...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>but then again you have to consider that all h...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>i shouldve put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808098</th>\n",
       "      <td>Arectarius</td>\n",
       "      <td>50 hpdef elgif impiety orb bourn jewel checkin...</td>\n",
       "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808099</th>\n",
       "      <td>dale1v</td>\n",
       "      <td>rubicon mango</td>\n",
       "      <td>Hey Reddit; if you could only drink one flavor...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808100</th>\n",
       "      <td>Semyonov</td>\n",
       "      <td>essentially gap insurance right</td>\n",
       "      <td>I'm sure you can pay for it. Most insurance pl...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808101</th>\n",
       "      <td>futalover99</td>\n",
       "      <td>yeah but sometimes that weird kid keeps trying...</td>\n",
       "      <td>Ive been subscribed to that for a while; and a...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808102</th>\n",
       "      <td>SuperSpartacus</td>\n",
       "      <td>petroleum doesnt really require a terroir like...</td>\n",
       "      <td>It must be very different for oil then. I know...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808060 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID                                           Comments  \\\n",
       "0               ocxtitan                                   central illinois   \n",
       "1              LeChuckly  to think  cnn used to be the acronym synonymou...   \n",
       "2          throwitskrub8  but then again you have to consider that all h...   \n",
       "3       fresherthanyouuu                                            ughhhhh   \n",
       "4              _kushagra                                 i shouldve put the   \n",
       "...                  ...                                                ...   \n",
       "808098        Arectarius  50 hpdef elgif impiety orb bourn jewel checkin...   \n",
       "808099            dale1v                                      rubicon mango   \n",
       "808100          Semyonov                    essentially gap insurance right   \n",
       "808101       futalover99  yeah but sometimes that weird kid keeps trying...   \n",
       "808102    SuperSpartacus  petroleum doesnt really require a terroir like...   \n",
       "\n",
       "                                          Parent Comments  Class Labels   \n",
       "0                               Jesus; where do you live?  non-sarcastic  \n",
       "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
       "2       agree to that part.It can also mean that gujra...  non-sarcastic  \n",
       "3       If a guy told you he doesn't use social media ...  non-sarcastic  \n",
       "4       No; it's just a programming bug. After all; th...      sarcastic  \n",
       "...                                                   ...            ...  \n",
       "808098  Hate to break it to you; but Gloomy can't do a...      sarcastic  \n",
       "808099  Hey Reddit; if you could only drink one flavor...  non-sarcastic  \n",
       "808100  I'm sure you can pay for it. Most insurance pl...  non-sarcastic  \n",
       "808101  Ive been subscribed to that for a while; and a...  non-sarcastic  \n",
       "808102  It must be very different for oil then. I know...  non-sarcastic  \n",
       "\n",
       "[808060 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dropna(subset=['Comments', 'Parent Comments'], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [str (item) for item in corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I should've put the</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Comments  label\n",
       "0                                   Central Illinois      0\n",
       "1  To think - CNN used to be the acronym synonymo...      0\n",
       "2  But then again; you have to consider that all ...      0\n",
       "3                                            ughhhhh      0\n",
       "4                                I should've put the      1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Comments'].str.lower()\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the null comments\n",
    "train_df.dropna(subset=['Comments'], inplace=True)\n",
    "train_df['Comments'] = train_df['Comments'].str.lower()\n",
    "train_df['Comments'] = train_df['Comments'].str.replace('[^\\w\\s]','', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Class Labels</th>\n",
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       "      <td>ocxtitan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>to think  cnn used to be the acronym synonymou...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>but then again you have to consider that all h...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>i shouldve put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "0          ocxtitan                                   central illinois   \n",
       "1         LeChuckly  to think  cnn used to be the acronym synonymou...   \n",
       "2     throwitskrub8  but then again you have to consider that all h...   \n",
       "3  fresherthanyouuu                                            ughhhhh   \n",
       "4         _kushagra                                 i shouldve put the   \n",
       "\n",
       "                                     Parent Comments  Class Labels   \n",
       "0                          Jesus; where do you live?  non-sarcastic  \n",
       "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
       "2  agree to that part.It can also mean that gujra...  non-sarcastic  \n",
       "3  If a guy told you he doesn't use social media ...  non-sarcastic  \n",
       "4  No; it's just a programming bug. After all; th...      sarcastic  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>Comments</th>\n",
       "      <th>Parent Comments</th>\n",
       "      <th>label</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ocxtitan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>to think  cnn used to be the acronym synonymou...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>but then again you have to consider that all h...</td>\n",
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       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>i shouldve put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "0          ocxtitan                                   central illinois   \n",
       "1         LeChuckly  to think  cnn used to be the acronym synonymou...   \n",
       "2     throwitskrub8  but then again you have to consider that all h...   \n",
       "3  fresherthanyouuu                                            ughhhhh   \n",
       "4         _kushagra                                 i shouldve put the   \n",
       "\n",
       "                                     Parent Comments          label  \n",
       "0                          Jesus; where do you live?  non-sarcastic  \n",
       "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
       "2  agree to that part.It can also mean that gujra...  non-sarcastic  \n",
       "3  If a guy told you he doesn't use social media ...  non-sarcastic  \n",
       "4  No; it's just a programming bug. After all; th...      sarcastic  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=train_df\n",
    "new_df.rename(columns = {' Class Labels ':'label'}, inplace = True)\n",
    "new_df.rename(columns = {'clean_Comments':'Comments'}, inplace = True)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Parent Comments</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ocxtitan</td>\n",
       "      <td>central illinois</td>\n",
       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>to think cnn use to be the acronym synonymous ...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>but then again you have to consider that all h...</td>\n",
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       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>i shouldve put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "0          ocxtitan                                   central illinois   \n",
       "1         LeChuckly  to think cnn use to be the acronym synonymous ...   \n",
       "2     throwitskrub8  but then again you have to consider that all h...   \n",
       "3  fresherthanyouuu                                            ughhhhh   \n",
       "4         _kushagra                                 i shouldve put the   \n",
       "\n",
       "                                     Parent Comments          label  \n",
       "0                          Jesus; where do you live?  non-sarcastic  \n",
       "1  Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic  \n",
       "2  agree to that part.It can also mean that gujra...  non-sarcastic  \n",
       "3  If a guy told you he doesn't use social media ...  non-sarcastic  \n",
       "4  No; it's just a programming bug. After all; th...      sarcastic  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "w_tokenizer = nltk.tokenize.WhitespaceTokenizer()\n",
    "lemmatizer = nltk.stem.WordNetLemmatizer()\n",
    "\n",
    "def lemmatize_text(text):\n",
    "    return \" \".join([lemmatizer.lemmatize(w, pos=\"v\") for w in w_tokenizer.tokenize(text)])\n",
    "\n",
    "new_df['Comments'] = new_df.Comments.apply(lemmatize_text).copy()\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808062, 385796)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "method=\"Binary\"\n",
    "\n",
    "if method==\"Tfidf\":\n",
    "    vectorizer= TfidfVectorizer(stop_words='english', ngram_range=(1,1))\n",
    "elif method==\"Binary\":\n",
    "    vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,2), min_df=2, lowercase='false')\n",
    "#elif method==\"Bigram\":\n",
    "    #vectorizer = CountVectorizer(ngram_range=(1, 2),token_pattern=r'\\b\\w+\\b', min_df=1).build_analyzer()\n",
    "\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=new_df['label'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "x_train_tfidf = tfidf_transformer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device available for running: \n",
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# Use cuda if present\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Device available for running: \")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train:  (800041, 1)\n",
      "Shape of y_train:  (800041, 1)\n",
      "Shape of x_test:   (4041, 1)\n",
      "Shape of y_test:   (4041, 1)\n",
      "Shape of x_val:   (4021, 1)\n",
      "Shape of y_val:   (4021, 1)\n"
     ]
    }
   ],
   "source": [
    "x = pd.DataFrame(df2, columns = ['Comments']) \n",
    "y = pd.DataFrame(df2, columns = ['label']) \n",
    "\n",
    "# Split dataset to train and test set.\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.005, random_state=42)\n",
    "\n",
    "# Split train dataset to train and validation set.\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.005, random_state=42)\n",
    "\n",
    "print(\"Shape of x_train: \", X_train.shape)\n",
    "print(\"Shape of y_train: \", Y_train.shape)\n",
    "print(\"Shape of x_test:  \", X_test.shape)\n",
    "print(\"Shape of y_test:  \", Y_test.shape)\n",
    "print(\"Shape of x_val:  \", X_val.shape)\n",
    "print(\"Shape of y_val:  \", Y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800041, 2)\n",
      "(4021, 2)\n",
      "(4041, 2)\n"
     ]
    }
   ],
   "source": [
    "train = pd.concat([X_train, Y_train], axis=1)\n",
    "print(train.shape)\n",
    "\n",
    "valid = pd.concat([X_val, Y_val], axis=1)\n",
    "print(valid.shape)\n",
    "\n",
    "test = pd.concat([X_test, Y_test], axis=1)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset preparation - Tokenization and Lemmatization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = [ \"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \n",
    "             \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\",\n",
    "             \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \n",
    "             \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\",\n",
    "             \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\",\n",
    "             \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \n",
    "             \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\",\n",
    "             \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\",\n",
    "             \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\",\n",
    "             \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\",\n",
    "             \"your\", \"yours\", \"yourself\", \"yourselves\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_16396\\4036370795.py:11: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  data_without_stopwords['clean_Comments'] = data_without_stopwords['clean_Comments'].str.replace('[{}]'.format(string.punctuation), ' ')\n"
     ]
    }
   ],
   "source": [
    "def remove_stopwords(data):\n",
    "  data['Comments without stopwords'] = data['Comments'].apply(lambda x : ' '.join([word for word in x.split() if word not in (stopwords)]))\n",
    "  return data\n",
    "\n",
    "def remove_tags(string):\n",
    "    result = re.sub('<.*?>','',string)\n",
    "    return result\n",
    "    \n",
    "data_without_stopwords = remove_stopwords(new_df)\n",
    "data_without_stopwords['clean_Comments']= data_without_stopwords['Comments without stopwords'].apply(lambda cw : remove_tags(cw))\n",
    "data_without_stopwords['clean_Comments'] = data_without_stopwords['clean_Comments'].str.replace('[{}]'.format(string.punctuation), ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_without_stopwords[['clean_Comments', 'label']]\n",
    "data.to_csv('data1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'BucketIterator'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\VATSALA NEMA\\Documents\\SEM 7\\NLP 2022\\Project- Sarcasm detection in Soc med\\RNN.ipynb Cell 33\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/VATSALA%20NEMA/Documents/SEM%207/NLP%202022/Project-%20Sarcasm%20detection%20in%20Soc%20med/RNN.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m BATCH_SIZE\u001b[39m=\u001b[39m\u001b[39m2048\u001b[39m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/VATSALA%20NEMA/Documents/SEM%207/NLP%202022/Project-%20Sarcasm%20detection%20in%20Soc%20med/RNN.ipynb#X34sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m train_iter, valid_iter, test_iter \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39;49mBucketIterator\u001b[39m.\u001b[39msplits(\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/VATSALA%20NEMA/Documents/SEM%207/NLP%202022/Project-%20Sarcasm%20detection%20in%20Soc%20med/RNN.ipynb#X34sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     (train, valid, test), sort_key\u001b[39m=\u001b[39m\u001b[39mlambda\u001b[39;00m x: \u001b[39mlen\u001b[39m(x\u001b[39m.\u001b[39mText),\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/VATSALA%20NEMA/Documents/SEM%207/NLP%202022/Project-%20Sarcasm%20detection%20in%20Soc%20med/RNN.ipynb#X34sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     batch_size\u001b[39m=\u001b[39mBATCH_SIZE, repeat\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/VATSALA%20NEMA/Documents/SEM%207/NLP%202022/Project-%20Sarcasm%20detection%20in%20Soc%20med/RNN.ipynb#X34sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     device\u001b[39m=\u001b[39mdevice)\n",
      "File \u001b[1;32mc:\\Apps\\Conda\\envs\\myenv\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_info_axis\u001b[39m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'BucketIterator'"
     ]
    }
   ],
   "source": [
    "\n",
    "BATCH_SIZE=2048\n",
    "\n",
    "train_iter, valid_iter, test_iter = data.BucketIterator.splits(\n",
    "    (train, valid, test), sort_key=lambda x: len(x.Text),\n",
    "    batch_size=BATCH_SIZE, repeat=False, shuffle=True,\n",
    "    device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RNN Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNClassifier_(torch.nn.Module):\n",
    "    def __init__(self, cell_type, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, weights):\n",
    "        super(RNNClassifier_, self).__init__()\n",
    "        cells={\"LSTM\":nn.LSTM, \"GRU\": nn.GRU}\n",
    "\n",
    "        self.output_size=output_size\n",
    "        self.embedding_dim=embedding_dim\n",
    "        self.n_layers=n_layers\n",
    "        self.cell_type=cell_type\n",
    "        self.word_embeddings=torch.nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.word_embeddings.weight=torch.nn.Parameter(weights, requires_grad=False)\n",
    "\n",
    "        self.nn=cells[cell_type](\n",
    "            input_size=self.embedding_dim, \n",
    "            hidden_size=self.hidden_dim,\n",
    "            num_layers=self.n_layers,\n",
    "            bidirectional=True,\n",
    "            batch_first=True,\n",
    "            dropout=0.2\n",
    "        )\n",
    "\n",
    "        self.label_layer=torch.nn.Linear(hidden_dim*2, output_size)\n",
    "\n",
    "        self.act=torch.nn.Sigmoid()\n",
    "\n",
    "        def forward(self, x, hidden):\n",
    "            batch_size=x.size(0)\n",
    "            x=self.word_embeddings(x)\n",
    "            lstm_out, hidden=self.nn(x, hidden)\n",
    "            out=lstm_out.contiguous().view(-1, self.hidden_dim*2)\n",
    "            out=self.label_layer(out)\n",
    "            #Keep only the hidden representation of the last item of the sequence as the representative of the sample.\n",
    "            out = out.view(batch_size, -1, self.output_size)\n",
    "            out = out[:, -1, :]\n",
    "\n",
    "            out = self.act(out)\n",
    "            \n",
    "            return out, hidden\n",
    "    \n",
    "\n"
   ]
  }
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