Project_merged.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74041720",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618d0086",
   "metadata": {},
   "source": [
    "**Importing CSV files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "709b9e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_24044\\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)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "360ffd38",
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   "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",
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       "  <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",
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       "    <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",
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       "      <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",
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5eef4213",
   "metadata": {},
   "outputs": [
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       "      <td>ocxtitan</td>\n",
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       "      <td>non-sarcastic</td>\n",
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       "      <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",
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       "      <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",
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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",
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       "    <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": 4,
     "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": 5,
   "id": "ddeaa2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.rename(columns = {' Class Labels ':'label'}, inplace = True)\n",
    "\n",
    "train_df['label'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68e0fe5d",
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   "outputs": [
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       "      <th>ID</th>\n",
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       "      <td>ocxtitan</td>\n",
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       "      <td>Jesus; where do you live?</td>\n",
       "      <td>1</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>1</td>\n",
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       "      <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",
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       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>808098</th>\n",
       "      <td>Arectarius</td>\n",
       "      <td>50% hp/def elgif; impiety orb; bourn jewel che...</td>\n",
       "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
       "      <td>0</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808101</th>\n",
       "      <td>futalover99</td>\n",
       "      <td>Yeah; but sometimes that weird kid keeps tryin...</td>\n",
       "      <td>Ive been subscribed to that for a while; and a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808102</th>\n",
       "      <td>SuperSpartacus</td>\n",
       "      <td>Petroleum doesn't really require a terroir lik...</td>\n",
       "      <td>It must be very different for oil then. I know...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "<p>808062 rows × 4 columns</p>\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",
       "808098        Arectarius  50% hp/def elgif; impiety orb; bourn jewel che...   \n",
       "808099            dale1v                                     Rubicon Mango.   \n",
       "808100          Semyonov                   Essentially GAP insurance right?   \n",
       "808101       futalover99  Yeah; but sometimes that weird kid keeps tryin...   \n",
       "808102    SuperSpartacus  Petroleum doesn't really require a terroir lik...   \n",
       "\n",
       "                                          Parent Comments label  \n",
       "0                               Jesus; where do you live?     1  \n",
       "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...     1  \n",
       "2       agree to that part.It can also mean that gujra...     1  \n",
       "3       If a guy told you he doesn't use social media ...     1  \n",
       "4       No; it's just a programming bug. After all; th...     0  \n",
       "...                                                   ...   ...  \n",
       "808098  Hate to break it to you; but Gloomy can't do a...     0  \n",
       "808099  Hey Reddit; if you could only drink one flavor...     1  \n",
       "808100  I'm sure you can pay for it. Most insurance pl...     1  \n",
       "808101  Ive been subscribed to that for a while; and a...     1  \n",
       "808102  It must be very different for oil then. I know...     1  \n",
       "\n",
       "[808062 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1 = train_df[train_df.isna().any(axis=1)]\n",
    "\n",
    "df1.head()\n",
    "\n",
    "train_df.dropna(subset=['Comments', 'Parent Comments'], inplace=False)\n",
    "\n",
    "#df5=train_df.dropna(inplace=True)\n",
    "\n",
    "#df2 = test_df[test_df.isna().any(axis=1)]\n",
    "\n",
    "#df2\n",
    "\n",
    "#test_df=test_df.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bea85c7b",
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 808103 entries, 0 to 808102\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count   Dtype \n",
      "---  ------           --------------   ----- \n",
      " 0   ID               808101 non-null  object\n",
      " 1   Comments         808062 non-null  object\n",
      " 2   Parent Comments  808099 non-null  object\n",
      " 3   label            808080 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 24.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "\n",
    "#test_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28452deb",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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       "      <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>1</td>\n",
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       "      <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>1</td>\n",
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       "    <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>1</td>\n",
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       "    <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>0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>808098</th>\n",
       "      <td>Arectarius</td>\n",
       "      <td>50% hp/def elgif; impiety orb; bourn jewel che...</td>\n",
       "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
       "      <td>0</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808101</th>\n",
       "      <td>futalover99</td>\n",
       "      <td>Yeah; but sometimes that weird kid keeps tryin...</td>\n",
       "      <td>Ive been subscribed to that for a while; and a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808102</th>\n",
       "      <td>SuperSpartacus</td>\n",
       "      <td>Petroleum doesn't really require a terroir lik...</td>\n",
       "      <td>It must be very different for oil then. I know...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808103 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 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",
       "808098        Arectarius  50% hp/def elgif; impiety orb; bourn jewel che...   \n",
       "808099            dale1v                                     Rubicon Mango.   \n",
       "808100          Semyonov                   Essentially GAP insurance right?   \n",
       "808101       futalover99  Yeah; but sometimes that weird kid keeps tryin...   \n",
       "808102    SuperSpartacus  Petroleum doesn't really require a terroir lik...   \n",
       "\n",
       "                                          Parent Comments label  \n",
       "0                               Jesus; where do you live?     1  \n",
       "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...     1  \n",
       "2       agree to that part.It can also mean that gujra...     1  \n",
       "3       If a guy told you he doesn't use social media ...     1  \n",
       "4       No; it's just a programming bug. After all; th...     0  \n",
       "...                                                   ...   ...  \n",
       "808098  Hate to break it to you; but Gloomy can't do a...     0  \n",
       "808099  Hey Reddit; if you could only drink one flavor...     1  \n",
       "808100  I'm sure you can pay for it. Most insurance pl...     1  \n",
       "808101  Ive been subscribed to that for a while; and a...     1  \n",
       "808102  It must be very different for oil then. I know...     1  \n",
       "\n",
       "[808103 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73f5c3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = train_df['Comments'].values\n",
    "#.astype('U')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1404ec",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c75998b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                                                                                                                                                                                                                                                                                                                                                                                                                         0.500001\n",
      "1                                                                                                                                                                                                                                                                                                                                                                                                                                         0.499959\n",
      "Chaining help So i was dexnav chaining for a ralts on r102 and then i got the thing \"cannot be found in the area or whatever\" so then i start walking around to reset it and thrn a wild lillipup shows up so i sneak up to it assuming that if i dont battle it it will brwka my chain. Now im in battle with it right now but its level 17 (it was my 20th chain) does incountering a different wild pokemon reset my chain? thanks.    0.000001\n",
      "And I assume that's a big f you to all us people of color; gays; and women who voted for her instead of Bernie because we don't believe in his phoney promises; he has no history with us; and just joined the Democratic Party to us it to run; trash it and its leader; and leave again.                                                                                                                                                0.000001\n",
      "When a young moderately attractive gentleman appears and says with a quivering voice \"Hey how are you? :}\" (x-post from r/justneckbeardthings)                                                                                                                                                                                                                                                                                            0.000001\n",
      "Why is this so funny?                                                                                                                                                                                                                                                                                                                                                                                                                     0.000001\n",
      "What about some of the other races of the covenant?                                                                                                                                                                                                                                                                                                                                                                                       0.000001\n",
      "Played Skyrim for the first time...pretty sure this wasn't supposed to happen.                                                                                                                                                                                                                                                                                                                                                            0.000001\n",
      "I want that PR manager                                                                                                                                                                                                                                                                                                                                                                                                                    0.000001\n",
      "Israeli naval ship 'bombs Palestinian children on Gaza beach'; killing four... A journalist who saw the incident said some of the children who survived were running away when another shell seemed to be aimed at them.                                                                                                                                                                                                                  0.000001\n",
      "If I made money per line of code; I would use the enter key more.                                                                                                                                                                                                                                                                                                                                                                         0.000001\n",
      "Both Hitler and Stalin had mustaches. Did they fight in the name of mustaches? (Not original)                                                                                                                                                                                                                                                                                                                                             0.000001\n",
      "Name: label, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Sarcastic comments are normally distributed in length. Non-sarcastic comments has a right-skew.\\n\\n# Most common words in the sarcastic comments \\nwordcloud = WordCloud(background_color='black', stopwords = STOPWORDS,\\n                max_words = 200, max_font_size = 100, \\n                random_state = 17, width=800, height=400)\\n\\nplt.figure(figsize=(12, 12))\\nwordcloud.generate(str(train_df.loc[train_df['label'] == 1, 'Parent Comments']))\\nplt.grid(b= False)\\nplt.imshow(wordcloud);\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check number of sarcastic and non-sarcastic comments\n",
    "train_df['label'].value_counts()\n",
    "\n",
    "#Almost equal number of sarcastic and non-sarcastic comments => Dataset Balanced.\n",
    "\n",
    "print(train_df['label'].value_counts()/len(train_df))\n",
    "\n",
    "\n",
    "\"\"\"Sarcastic comments are normally distributed in length. Non-sarcastic comments has a right-skew.\n",
    "\n",
    "# Most common words in the sarcastic comments \n",
    "wordcloud = WordCloud(background_color='black', stopwords = STOPWORDS,\n",
    "                max_words = 200, max_font_size = 100, \n",
    "                random_state = 17, width=800, height=400)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "wordcloud.generate(str(train_df.loc[train_df['label'] == 1, 'Parent Comments']))\n",
    "plt.grid(b= False)\n",
    "plt.imshow(wordcloud);\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb34f72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare the cross-validation procedure\n",
    "#cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
    "\n",
    "# evaluate model\n",
    "#scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06502f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'plt.figure(figsize=(20,10))\\nax = sns.countplot(x=\\'label\\',  data= train_df)\\nax.set(title = \"Distribution of Classes\", xlabel=\"Sarcasm Status\", ylabel = \"Total Count\")\\ntotal = float(len(train_df))\\n\\nfor p in ax.patches:\\n    height = p.get_height()\\n    ax.text(p.get_x()+p.get_width()/2.,\\n            height + 3,\\n            \\'{:1.1f}%\\'.format((height/to\\n            tal)*100),\\n            ha=\"center\") \\n    \\nplt.show()\\n\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"plt.figure(figsize=(20,10))\n",
    "ax = sns.countplot(x='label',  data= train_df)\n",
    "ax.set(title = \"Distribution of Classes\", xlabel=\"Sarcasm Status\", ylabel = \"Total Count\")\n",
    "total = float(len(train_df))\n",
    "\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.1f}%'.format((height/to\n",
    "            tal)*100),\n",
    "            ha=\"center\") \n",
    "    \n",
    "plt.show()\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf520ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To check if there is any relation between the length of the comment and its label\n",
    "\n",
    "train_df.loc[train_df['label']==1,'Comments'].str.len().apply(np.log1p).hist(label='sarcastic', alpha=.5)\n",
    "train_df.loc[train_df['label']==0,'Comments'].str.len().apply(np.log1p).hist(label='non-sarcastic', alpha=.5)\n",
    "plt.legend()\n",
    "plt.title('Natural Log Length of Comments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee252272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing sarcastic comments with Parent comments\n",
    "sarcasm_comm_len = np.array(train_df.loc[train_df['label'] == 1]['Comments'].str.len())\n",
    "parent_comm_len = np.array(train_df.loc[train_df['label'] == 1]['Parent Comments'].str.len())\n",
    "ratio_len = np.array((train_df.loc[train_df['label'] == 1]['Comments'].str.len())/(train_df.loc[train_df['label'] == 1]['Parent Comments'].str.len()))\n",
    "\n",
    "d = pd.DataFrame({'Comment Length': sarcasm_comm_len, 'Parent Comment Length': parent_comm_len, 'Ratio Length': ratio_len}, columns=['Comment Length', 'Parent Comment Length', 'Ratio Length'])\n",
    "\n",
    "ax = plt.axes()\n",
    "sns.scatterplot(data=d, x=\"Comment Length\", y=\"Parent Comment Length\",  size=ratio_len)\n",
    "ax.set_title(\"Comparing Sarcastic Comment Length with Parent Comment\")\n",
    "\n",
    "# control x and y limits\n",
    "plt.ylim(0, 12000)\n",
    "plt.xlim(0, 800)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0309592f",
   "metadata": {},
   "source": [
    "**Data Pre-Processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83e9ea7c",
   "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": 15,
     "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": 16,
   "id": "5e19fa4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [str (item) for item in corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5b7fb6",
   "metadata": {},
   "source": [
    "**Feature Engineering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e62ad7e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808062, 385796)"
      ]
     },
     "execution_count": 17,
     "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6776edf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=train_df['label'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d4c3ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "x_train_tfidf = tfidf_transformer.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1311b54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Apps\\Conda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 28.7 TiB for an array with shape (808062, 4883496) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[1;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m tfidf \u001b[38;5;241m=\u001b[39m vectorizer\u001b[38;5;241m.\u001b[39mfit_transform(corpus)\n\u001b[0;32m      3\u001b[0m terms\u001b[38;5;241m=\u001b[39mvectorizer\u001b[38;5;241m.\u001b[39mget_feature_names()\n\u001b[1;32m----> 4\u001b[0m tfidf \u001b[38;5;241m=\u001b[39m \u001b[43mtfidf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m#Feature Engineering and Extraction \u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CountVectorizer\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\scipy\\sparse\\compressed.py:1039\u001b[0m, in \u001b[0;36m_cs_matrix.toarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m order \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1038\u001b[0m     order \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_swap(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcf\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 1039\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_toarray_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1040\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (out\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mc_contiguous \u001b[38;5;129;01mor\u001b[39;00m out\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mf_contiguous):\n\u001b[0;32m   1041\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOutput array must be C or F contiguous\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\scipy\\sparse\\base.py:1202\u001b[0m, in \u001b[0;36mspmatrix._process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1200\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n\u001b[0;32m   1201\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1202\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mzeros\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 28.7 TiB for an array with shape (808062, 4883496) and data type float64"
     ]
    }
   ],
   "source": [
    "vectorizer=TfidfVectorizer(stop_words='english',ngram_range=(1,3),token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "terms=vectorizer.get_feature_names()\n",
    "tfidf = tfidf.toarray()\n",
    "\n",
    "#Feature Engineering and Extraction \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer_1 = CountVectorizer(ngram_range=(1, 1)) #unigram\n",
    "vectorizer_2 = CountVectorizer (ngram_range=(2, 2)) #bigram\n",
    "\n",
    "# Function to calculate frequency of words in comments\n",
    "def freq_words(vectorizer, data):\n",
    "    X = vectorizer.fit_transform(data)\n",
    "    freqs = zip(vectorizer.get_feature_names(), np.asarray(X.sum(axis=0)).ravel())\n",
    "    return sorted(freqs, key = lambda x: x[1], reverse=True)[:10]\n",
    "\n",
    "# For unigrams from comments\n",
    "l = [freq_words(vectorizer_1, df2[df2['label']==1]['Comments']),\n",
    "     freq_words(vectorizer_1, df2[df2['label']==0]['Comments'])]\n",
    "list(map(list, zip(*l)))\n",
    "\n",
    "\"\"\"\n",
    "Here, the first column shows sarcastic comments and second column shows non-sarcastic comments. \n",
    "Since we have not removed stopwords, we are getting a lot of stopwords as unigrams here.\n",
    "Also, Unigrams from the sarcastic comments are mostly same as from the non-sarcastic comments,\n",
    "hence these unigramms do not help in seprating sarcastic comments from non-sarcastic comments.   \n",
    "\"\"\"\n",
    "\n",
    "#bigrams\n",
    "l = [freq_words(vectorizer_2, df2[df2['label']==1]['Comments']),\n",
    "     freq_words(vectorizer_2, df2[df2['label']==0]['Comments'])]\n",
    "list(map(list, zip(*l)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9cba2108",
   "metadata": {},
   "outputs": [
    {
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       "...                                                   ...\n",
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       "808061    (0, 275309)\\t0.30457013143872097\\n  (0, 2696...\n",
       "\n",
       "[808062 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(vectorizer.get_feature_names())\n",
    "#X=np.asmatrix(X)\n",
    "x_train_df=pd.DataFrame(x_train_tfidf)\n",
    "x_train_df.shape\n",
    "\n",
    "x_train_df.astype('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15191e66",
   "metadata": {},
   "outputs": [
    {
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       "808061    (0, 275309)\\t0.30457013143872097\\n  (0, 2696...\n",
       "\n",
       "[808062 rows x 1 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c009d53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 808062 entries, 0 to 808061\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   0       808062 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 6.2+ MB\n"
     ]
    },
    {
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   ],
   "source": [
    "x_train_df.info()\n",
    "x_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d91847bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_train_df, labels, test_size= 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de97e759",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"parameters = {'solver': [ 'lbfgs', 'liblinear', 'saga'], 'verbose': [0, 1, 2]}\n",
    "\n",
    "# multinomial logistic regression a.k.a softmax classifier\n",
    "logit = LogisticRegression(random_state= 42)\n",
    "\n",
    "logit_classifier = GridSearchCV(logit, parameters, cv = 10, n_jobs= 4)\n",
    "\n",
    "# sklearn's pipeline\n",
    "Binary_logit_pipeline = Pipeline([('vectorizer', vectorizer),  ('logit_classifier', logit_classifier)])\n",
    "\n",
    "\n",
    "# Evaluation\n",
    "print ('\\n Confusion Matrix \\n')  \n",
    "print (confusion_matrix(test_y,valid_pred))\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b63eae47",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [28]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Training and Test Split           \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m train_x, test_x, train_y, y_test \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcorpus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mstratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#Classifier pipeline\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m#Multinomial Naive bayes\u001b[39;00m\n\u001b[0;32m      7\u001b[0m clf_mnb\u001b[38;5;241m=\u001b[39mMultinomialNB(alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,fit_prior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, class_prior\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)  \n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2454\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[1;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[0;32m   2450\u001b[0m         CVClass \u001b[38;5;241m=\u001b[39m ShuffleSplit\n\u001b[0;32m   2452\u001b[0m     cv \u001b[38;5;241m=\u001b[39m CVClass(test_size\u001b[38;5;241m=\u001b[39mn_test, train_size\u001b[38;5;241m=\u001b[39mn_train, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[1;32m-> 2454\u001b[0m     train, test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marrays\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstratify\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\n\u001b[0;32m   2457\u001b[0m     chain\u001b[38;5;241m.\u001b[39mfrom_iterable(\n\u001b[0;32m   2458\u001b[0m         (_safe_indexing(a, train), _safe_indexing(a, test)) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m arrays\n\u001b[0;32m   2459\u001b[0m     )\n\u001b[0;32m   2460\u001b[0m )\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1613\u001b[0m, in \u001b[0;36mBaseShuffleSplit.split\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m   1583\u001b[0m \u001b[38;5;124;03m\"\"\"Generate indices to split data into training and test set.\u001b[39;00m\n\u001b[0;32m   1584\u001b[0m \n\u001b[0;32m   1585\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1610\u001b[0m \u001b[38;5;124;03mto an integer.\u001b[39;00m\n\u001b[0;32m   1611\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1612\u001b[0m X, y, groups \u001b[38;5;241m=\u001b[39m indexable(X, y, groups)\n\u001b[1;32m-> 1613\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train, test \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_indices(X, y, groups):\n\u001b[0;32m   1614\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m train, test\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1953\u001b[0m, in \u001b[0;36mStratifiedShuffleSplit._iter_indices\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m   1951\u001b[0m class_counts \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mbincount(y_indices)\n\u001b[0;32m   1952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmin(class_counts) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m-> 1953\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1954\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe least populated class in y has only 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1955\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m member, which is too few. The minimum\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1956\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m number of groups for any class cannot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1957\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be less than 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1958\u001b[0m     )\n\u001b[0;32m   1960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m<\u001b[39m n_classes:\n\u001b[0;32m   1961\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1962\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe train_size = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m should be greater or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1963\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mequal to the number of classes = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (n_train, n_classes)\n\u001b[0;32m   1964\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2."
     ]
    }
   ],
   "source": [
    "# Training and Test Split           \n",
    "train_x, test_x, train_y, y_test = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#Classifier pipeline\n",
    "\n",
    "#Multinomial Naive bayes\n",
    "clf_mnb=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
    "\n",
    "#Decision tree classifier\n",
    "clf_dt = DecisionTreeClassifier(random_state=40)\n",
    "clf_dt_parameters = {'clf__criterion':('gini', 'entropy'),'clf__max_features':('auto', 'sqrt', 'log2'),'clf__ccp_alpha':(0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1)}\n",
    "\n",
    "#Feature Extraction\n",
    "pipeline_lr = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_lr)]) \n",
    "\n",
    "pipeline_dt = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_dt)])\n",
    "\n",
    "pipeline_dt = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_mnb)])\n",
    "\n",
    "\n",
    "pipelines=[pipeline_lr, pipeline_dt, pipeline_mnb]\n",
    "\n",
    "best_accuracy=0.0\n",
    "best_classifier=0\n",
    "best_pipeline=\"\"\n",
    "\n",
    "# Dictionary of pipelines and classifier types for ease of reference\n",
    "pipe_dict = {0: 'Multinomial Naive bayes', 1: 'Decision Tree'}\n",
    "\n",
    "# Fit the pipelines\n",
    "for pipe in pipelines:\n",
    "\tpipe.fit(train_x, train_y)\n",
    "\n",
    "for i,model in enumerate(pipelines):\n",
    "    print(\"{} Test Accuracy: {}\".format(pipe_dict[i],model.score(test_x, y_test)))\n",
    "\n",
    "for i,model in enumerate(pipelines):\n",
    "    if model.score(test_x, y_test)>best_accuracy:\n",
    "        best_accuracy=model.score(X_test,y_test)\n",
    "        best_pipeline=model\n",
    "        best_classifier=i\n",
    "print('Classifier with best accuracy:{}'.format(pipe_dict[best_classifier]))\n",
    "\n",
    "#Classification\n",
    "parameters={**clf_lr_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)\n",
    "       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf7abaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "    ('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "    ('feature_selector', SelectKBest(chi2, k=1000)),         \n",
    "    ('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "    ('clf', clf),]) \n",
    "        \n",
    "feature_parameters = {\n",
    "    'vect__min_df': (2,3),\n",
    "    'vect__ngram_range': ((1,1),(1, 2),(1,3),(2,3)),  # Unigrams, Bigrams or Trigrams\n",
    "    }\n",
    "    \n",
    "# Classificaion\n",
    "parameters={**feature_parameters,**clf_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x, train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0169650",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = SVC(kernel='linear').fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "119f71b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred = clf.predict(test_x)\n",
    "valid_pred =list(predicted)\n",
    "\n",
    "#GridSearch - and find best parameters\n",
    "\n",
    "def model_score(y_test, valid_pred):\n",
    "    mp=average_precision_score(y_test, valid_pred, average='micro')\n",
    "\n",
    "    pre = precision_score(y_test, valid_pred, average='binary') \n",
    "    print ('\\n Precision:'+str(pr)) \n",
    "\n",
    "    rec = recall_score(y_test,valid_pred, average='binary') \n",
    "    print ('\\n Recall:'+str(rec))\n",
    "\n",
    "    f1 = f1_score(y_test, valid_pred, average='binary') \n",
    "    print ('\\n Micro Averaged F1-Score:'+str(fm))\n",
    "    \n",
    "    return \n",
    "\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "np.shape(X)\n",
    "labels=train_df['label'].to_list()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ccb8936",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(logit_classifier.best_params_)\n",
    "print(logit_classifier.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e722b213",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred = tfidf_logit_pipeline.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6810ce53",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy_score(y_test, valid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c870e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(y_test, valid_pred)\n",
    "pre = precision_score(y_test, valid_pred)\n",
    "rec = recall_score(y_test,valid_pred)\n",
    "f1 = f1_score(y_test, valid_pred)\n",
    "mp=average_precision_score(y_test, valid_pred, average='micro')\n",
    "\n",
    "print ('Model Performance: ')\n",
    "print ('Micro-averaged precision', mp )\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "\n",
    "cm = confusion_matrix(y_test,valid_pred)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "649fabe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Natural','Sarcastic'],columns=['Natural','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Ground Truth',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  }
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