Final Capstone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Introduction\n",
    "\n",
    "This notebook is about Natural Language Processing. Here, we will be using data from kaggle which contains comments from Reddit(https://www.kaggle.com/sherinclaudia/sarcastic-comments-on-reddit). Model needs to classify whether the comment is sarcastic or not. Data contains following features:\n",
    "    \n",
    "    Label:- sarcastic or not\n",
    "    comment:- Reply to a Parent Reddit comment\n",
    "    Author:- Person who commented\n",
    "    Subreddit:- Commented under which subreddit\n",
    "    Score:- Number of upvotes -(minus) Number of downvotes.\n",
    "    Ups:- Number of upvotes\n",
    "    Downs:- Number of downvotes\n",
    "    Date:- Commented date\n",
    "    Created_utc:- Commented time in the UTC Timezone\n",
    "    Parent_comment:- The Parent Reddit comment to which sarcastic replies are made\n",
    "\n",
    "Different models will be used to check which is best fit for this particular data. Deep learning will also be used for comparing LSTM model scores with other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import nltk\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Dense, Embedding, LSTM, Dropout, Flatten\n",
    "from keras.utils import to_categorical\n",
    "from collections import Counter\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/vivek/Downloads/train-balanced-sarcasm.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data contains around 1.3 million rows so with limited computation power we will be deviding this dataset by half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>303248.000000</td>\n",
       "      <td>303248.000000</td>\n",
       "      <td>303248.000000</td>\n",
       "      <td>303248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.500326</td>\n",
       "      <td>6.978335</td>\n",
       "      <td>5.574467</td>\n",
       "      <td>-0.145765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500001</td>\n",
       "      <td>53.339449</td>\n",
       "      <td>44.689326</td>\n",
       "      <td>0.352871</td>\n",
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       "      <th>min</th>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>9070.000000</td>\n",
       "      <td>5163.000000</td>\n",
       "      <td>0.000000</td>\n",
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      "text/plain": [
       "               label          score            ups          downs\n",
       "count  303248.000000  303248.000000  303248.000000  303248.000000\n",
       "mean        0.500326       6.978335       5.574467      -0.145765\n",
       "std         0.500001      53.339449      44.689326       0.352871\n",
       "min         0.000000    -507.000000    -507.000000      -1.000000\n",
       "25%         0.000000       1.000000       0.000000       0.000000\n",
       "50%         1.000000       2.000000       1.000000       0.000000\n",
       "75%         1.000000       4.000000       3.000000       0.000000\n",
       "max         1.000000    9070.000000    5163.000000       0.000000"
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     "execution_count": 4,
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     "output_type": "execute_result"
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   "source": [
    "df.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>comment</th>\n",
       "      <th>author</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>score</th>\n",
       "      <th>ups</th>\n",
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       "    <tr>\n",
       "      <th>608627</th>\n",
       "      <td>1</td>\n",
       "      <td>And he sure as hell is successful!</td>\n",
       "      <td>armiechedon</td>\n",
       "      <td>quityourbullshit</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-12</td>\n",
       "      <td>2015-12-09 23:41:48</td>\n",
       "      <td>Obama's smoked before? Or at least admitted it?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456977</th>\n",
       "      <td>1</td>\n",
       "      <td>wait, you have to win the candidates to challe...</td>\n",
       "      <td>killingfeels</td>\n",
       "      <td>chess</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-02</td>\n",
       "      <td>2016-02-13 00:04:43</td>\n",
       "      <td>You can say the same thing about any chess pla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803801</th>\n",
       "      <td>1</td>\n",
       "      <td>I mean how could anyone not see that it is ant...</td>\n",
       "      <td>Gnofar</td>\n",
       "      <td>katawashoujo</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-12-25 21:59:49</td>\n",
       "      <td>That isn't Hanako?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926708</th>\n",
       "      <td>0</td>\n",
       "      <td>Funny thing, most of the girl pants I've bough...</td>\n",
       "      <td>smischmal</td>\n",
       "      <td>transgender</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-03</td>\n",
       "      <td>2012-03-05 02:03:43</td>\n",
       "      <td>Girl Pants (X/post from TwoXChromosomes)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129279</th>\n",
       "      <td>0</td>\n",
       "      <td>Also Ranch dressing, because LBJ was Texan, an...</td>\n",
       "      <td>ElScreecho</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>2016-09-19 19:20:53</td>\n",
       "      <td>Lyndon Johnson salad: A carrot and 2 beetroots...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        label                                            comment  \\\n",
       "608627      1                 And he sure as hell is successful!   \n",
       "456977      1  wait, you have to win the candidates to challe...   \n",
       "803801      1  I mean how could anyone not see that it is ant...   \n",
       "926708      0  Funny thing, most of the girl pants I've bough...   \n",
       "129279      0  Also Ranch dressing, because LBJ was Texan, an...   \n",
       "\n",
       "              author         subreddit  score  ups  downs     date  \\\n",
       "608627   armiechedon  quityourbullshit      1    1      0  2015-12   \n",
       "456977  killingfeels             chess      1    1      0  2016-02   \n",
       "803801        Gnofar      katawashoujo      5    5      0  2014-12   \n",
       "926708     smischmal       transgender      2    2      0  2012-03   \n",
       "129279    ElScreecho         AskReddit     28   28      0  2016-09   \n",
       "\n",
       "                created_utc                                     parent_comment  \n",
       "608627  2015-12-09 23:41:48    Obama's smoked before? Or at least admitted it?  \n",
       "456977  2016-02-13 00:04:43  You can say the same thing about any chess pla...  \n",
       "803801  2014-12-25 21:59:49                                 That isn't Hanako?  \n",
       "926708  2012-03-05 02:03:43           Girl Pants (X/post from TwoXChromosomes)  \n",
       "129279  2016-09-19 19:20:53  Lyndon Johnson salad: A carrot and 2 beetroots...  "
      ]
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    "df.head()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 303248 entries, 608627 to 809346\n",
      "Data columns (total 10 columns):\n",
      "label             303248 non-null int64\n",
      "comment           303234 non-null object\n",
      "author            303248 non-null object\n",
      "subreddit         303248 non-null object\n",
      "score             303248 non-null int64\n",
      "ups               303248 non-null int64\n",
      "downs             303248 non-null int64\n",
      "date              303248 non-null object\n",
      "created_utc       303248 non-null object\n",
      "parent_comment    303248 non-null object\n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 25.4+ MB\n"
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    "df.info()"
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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['score', 'ups', 'downs']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303248, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing all the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303234, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Part Of Speech dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking Part Of Speech from every sentence and storing it into different dataframe pos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rw=0\n",
    "for sent in df['comment']:\n",
    "    tokens=nltk.word_tokenize(sent)\n",
    "    text = nltk.Text(tokens)\n",
    "    tagged = nltk.pos_tag(text)\n",
    "    counts = Counter(tag for word,tag in tagged)\n",
    "    for i in counts.most_common():\n",
    "        pos_df.loc[rw, i[0]] = i[1]\n",
    "    rw=rw+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove columns with punctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CC', 'PRP', 'RB', 'IN', 'NN', 'VBZ', 'JJ', '.', 'TO', 'VB', 'DT', ',',\n",
       "       'VBP', 'NNS', 'WRB', 'MD', 'VBN', 'VBD', 'JJS', '(', 'PRP$', ')', 'JJR',\n",
       "       'VBG', 'NNP', 'RBR', ':', 'POS', 'CD', 'UH', 'WP', 'WDT', 'PDT', '``',\n",
       "       '''', 'NNPS', 'EX', 'RP', '$', '#', 'FW', 'RBS', 'WP$', 'SYM', 'LS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_df=pos_df.drop(['.', ',', '``', \"''\", '(', ')', ':', '$', '#'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CC', 'PRP', 'RB', 'IN', 'NN', 'VBZ', 'JJ', 'TO', 'VB', 'DT', 'VBP',\n",
       "       'NNS', 'WRB', 'MD', 'VBN', 'VBD', 'JJS', 'PRP$', 'JJR', 'VBG', 'NNP',\n",
       "       'RBR', 'POS', 'CD', 'UH', 'WP', 'WDT', 'PDT', 'NNPS', 'EX', 'RP', 'FW',\n",
       "       'RBS', 'WP$', 'SYM', 'LS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pos_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-3065cbc20dcb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpos_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'C:/Users/vivek/Downloads/pos.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pos_df' is not defined"
     ]
    }
   ],
   "source": [
    "pos_df.to_csv('C:/Users/vivek/Downloads/pos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_df.to_csv('C:/Users/vivek/Downloads/pos3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Part Of Speech dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_loaded_df = pd.read_csv('C:/Users/vivek/Downloads/pos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_loaded_df = pd.read_csv('C:/Users/vivek/Downloads/pos3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_loaded_df = pos_loaded_df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running PCA on part of speech dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51616500e+05 -7.61128641e-01 -6.30917925e-01 ... -2.83732034e-01\n",
      "  -1.45990041e-01 -8.53002954e-01]\n",
      " [ 1.51615500e+05 -7.54616647e-01  1.39582071e+00 ...  4.03184876e-01\n",
      "   5.61467943e-01  2.08437743e+00]\n",
      " [ 1.51614500e+05 -7.61457838e-01  3.73037102e-01 ... -4.67619288e-01\n",
      "   3.40279686e-01 -2.36514805e-01]\n",
      " ...\n",
      " [-1.51614500e+05  2.13975086e-01 -1.72083725e+00 ...  6.02556576e-01\n",
      "  -2.64600732e-01  3.47420567e-01]\n",
      " [-1.51615500e+05  2.09673211e-01 -1.73476540e+00 ... -7.66126166e-01\n",
      "   8.56609859e-01 -5.84605846e-01]\n",
      " [-1.51616500e+05  1.21511792e+00 -1.54435909e+00 ...  7.34983520e-02\n",
      "  -1.11502513e+00 -6.68513544e-01]]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(10)\n",
    "pos_pca = pca.fit_transform(pos_loaded_df)\n",
    "print(pos_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_pca_df = pd.DataFrame(pos_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_pca_df.index = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.965991</td>\n",
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       "      <td>0.370145</td>\n",
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       "      <td>0.256960</td>\n",
       "      <td>-0.097978</td>\n",
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       "      <th>655372</th>\n",
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       "      <td>0.182254</td>\n",
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       "      <td>-0.053799</td>\n",
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       "      <td>1.719710</td>\n",
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       "      <td>0.115662</td>\n",
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       "      <td>0.259326</td>\n",
       "      <td>0.373597</td>\n",
       "      <td>-0.172967</td>\n",
       "      <td>0.487595</td>\n",
       "      <td>-1.138813</td>\n",
       "      <td>-1.488644</td>\n",
       "      <td>0.435542</td>\n",
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       "      <td>-0.731718</td>\n",
       "      <td>0.562843</td>\n",
       "      <td>1.156863</td>\n",
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       "      <td>-0.383199</td>\n",
       "      <td>-0.385382</td>\n",
       "      <td>-0.262470</td>\n",
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       "      <td>-0.171997</td>\n",
       "      <td>1.310957</td>\n",
       "      <td>-0.597400</td>\n",
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       "      <td>1.231263</td>\n",
       "      <td>-1.743601</td>\n",
       "      <td>0.883458</td>\n",
       "      <td>1.468917</td>\n",
       "      <td>-0.551552</td>\n",
       "      <td>-0.322847</td>\n",
       "      <td>0.498604</td>\n",
       "      <td>0.213913</td>\n",
       "      <td>0.086114</td>\n",
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       "    <tr>\n",
       "      <th>223589</th>\n",
       "      <td>151599.500000</td>\n",
       "      <td>0.251957</td>\n",
       "      <td>1.372716</td>\n",
       "      <td>-0.597216</td>\n",
       "      <td>0.045065</td>\n",
       "      <td>-0.448655</td>\n",
       "      <td>-0.589830</td>\n",
       "      <td>-0.463385</td>\n",
       "      <td>-1.005745</td>\n",
       "      <td>1.530985</td>\n",
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       "    <tr>\n",
       "      <th>274120</th>\n",
       "      <td>151598.500000</td>\n",
       "      <td>-0.770480</td>\n",
       "      <td>-1.247992</td>\n",
       "      <td>-0.755466</td>\n",
       "      <td>-2.812329</td>\n",
       "      <td>0.624101</td>\n",
       "      <td>1.881669</td>\n",
       "      <td>1.765711</td>\n",
       "      <td>-1.418794</td>\n",
       "      <td>-2.107772</td>\n",
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       "    <tr>\n",
       "      <th>537152</th>\n",
       "      <td>151597.500000</td>\n",
       "      <td>2.273106</td>\n",
       "      <td>-1.318857</td>\n",
       "      <td>1.281615</td>\n",
       "      <td>0.077540</td>\n",
       "      <td>-1.126231</td>\n",
       "      <td>-1.182056</td>\n",
       "      <td>0.218755</td>\n",
       "      <td>-0.771344</td>\n",
       "      <td>-0.327161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255424</th>\n",
       "      <td>151596.500000</td>\n",
       "      <td>0.231687</td>\n",
       "      <td>-1.789585</td>\n",
       "      <td>1.091684</td>\n",
       "      <td>1.805935</td>\n",
       "      <td>-0.094399</td>\n",
       "      <td>-0.155950</td>\n",
       "      <td>-0.028947</td>\n",
       "      <td>-0.160539</td>\n",
       "      <td>0.055740</td>\n",
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       "    <tr>\n",
       "      <th>407177</th>\n",
       "      <td>151595.500000</td>\n",
       "      <td>0.257929</td>\n",
       "      <td>-0.245059</td>\n",
       "      <td>0.469980</td>\n",
       "      <td>-1.791548</td>\n",
       "      <td>3.660880</td>\n",
       "      <td>-0.464416</td>\n",
       "      <td>-0.747243</td>\n",
       "      <td>0.468929</td>\n",
       "      <td>2.631134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635566</th>\n",
       "      <td>151594.500000</td>\n",
       "      <td>1.240688</td>\n",
       "      <td>-0.629810</td>\n",
       "      <td>0.176580</td>\n",
       "      <td>0.648646</td>\n",
       "      <td>-1.313935</td>\n",
       "      <td>-0.890042</td>\n",
       "      <td>1.022585</td>\n",
       "      <td>0.219058</td>\n",
       "      <td>-0.435851</td>\n",
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       "    <tr>\n",
       "      <th>175276</th>\n",
       "      <td>151593.500000</td>\n",
       "      <td>-0.762925</td>\n",
       "      <td>-0.677710</td>\n",
       "      <td>0.403730</td>\n",
       "      <td>0.767311</td>\n",
       "      <td>-0.497075</td>\n",
       "      <td>-0.164701</td>\n",
       "      <td>-1.548541</td>\n",
       "      <td>-1.176548</td>\n",
       "      <td>0.683975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972149</th>\n",
       "      <td>151592.500000</td>\n",
       "      <td>0.227774</td>\n",
       "      <td>-1.442876</td>\n",
       "      <td>-0.122363</td>\n",
       "      <td>-1.137036</td>\n",
       "      <td>-0.277443</td>\n",
       "      <td>1.896782</td>\n",
       "      <td>-0.936009</td>\n",
       "      <td>0.479519</td>\n",
       "      <td>-0.862899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902815</th>\n",
       "      <td>151591.500000</td>\n",
       "      <td>7.226628</td>\n",
       "      <td>-1.891970</td>\n",
       "      <td>0.866898</td>\n",
       "      <td>1.871903</td>\n",
       "      <td>-0.073134</td>\n",
       "      <td>-0.151812</td>\n",
       "      <td>-0.033762</td>\n",
       "      <td>-0.146013</td>\n",
       "      <td>0.060659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159302</th>\n",
       "      <td>151590.499999</td>\n",
       "      <td>6.261066</td>\n",
       "      <td>4.371595</td>\n",
       "      <td>-2.245032</td>\n",
       "      <td>-1.029118</td>\n",
       "      <td>-0.532937</td>\n",
       "      <td>0.335751</td>\n",
       "      <td>1.415143</td>\n",
       "      <td>0.401199</td>\n",
       "      <td>-0.652184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884232</th>\n",
       "      <td>151589.500000</td>\n",
       "      <td>-0.764378</td>\n",
       "      <td>-0.594592</td>\n",
       "      <td>0.055654</td>\n",
       "      <td>-0.085798</td>\n",
       "      <td>0.787442</td>\n",
       "      <td>-0.499167</td>\n",
       "      <td>0.106009</td>\n",
       "      <td>-0.092759</td>\n",
       "      <td>0.110042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590961</th>\n",
       "      <td>151588.499999</td>\n",
       "      <td>1.323475</td>\n",
       "      <td>2.689883</td>\n",
       "      <td>1.581539</td>\n",
       "      <td>1.144264</td>\n",
       "      <td>-0.562395</td>\n",
       "      <td>0.016899</td>\n",
       "      <td>-0.652494</td>\n",
       "      <td>-0.267697</td>\n",
       "      <td>-0.192337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419507</th>\n",
       "      <td>151587.500000</td>\n",
       "      <td>1.276677</td>\n",
       "      <td>0.625502</td>\n",
       "      <td>0.659385</td>\n",
       "      <td>-0.136859</td>\n",
       "      <td>0.263197</td>\n",
       "      <td>0.294991</td>\n",
       "      <td>1.186133</td>\n",
       "      <td>-0.580337</td>\n",
       "      <td>-1.241158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461807</th>\n",
       "      <td>-151587.500000</td>\n",
       "      <td>-0.785799</td>\n",
       "      <td>-0.787622</td>\n",
       "      <td>0.576151</td>\n",
       "      <td>1.181732</td>\n",
       "      <td>0.448420</td>\n",
       "      <td>0.670180</td>\n",
       "      <td>0.193230</td>\n",
       "      <td>-0.735151</td>\n",
       "      <td>1.070928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91981</th>\n",
       "      <td>-151588.500000</td>\n",
       "      <td>-0.782933</td>\n",
       "      <td>-0.862242</td>\n",
       "      <td>0.877456</td>\n",
       "      <td>1.997242</td>\n",
       "      <td>-0.070231</td>\n",
       "      <td>-0.146314</td>\n",
       "      <td>-0.044249</td>\n",
       "      <td>-0.177469</td>\n",
       "      <td>0.058787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136611</th>\n",
       "      <td>-151589.500000</td>\n",
       "      <td>-0.766660</td>\n",
       "      <td>1.956188</td>\n",
       "      <td>-2.790299</td>\n",
       "      <td>-5.496217</td>\n",
       "      <td>0.989011</td>\n",
       "      <td>-0.249413</td>\n",
       "      <td>-0.665092</td>\n",
       "      <td>-2.662997</td>\n",
       "      <td>-1.831177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106304</th>\n",
       "      <td>-151590.500000</td>\n",
       "      <td>-0.790238</td>\n",
       "      <td>-1.744246</td>\n",
       "      <td>0.805144</td>\n",
       "      <td>0.983304</td>\n",
       "      <td>1.150951</td>\n",
       "      <td>-0.992760</td>\n",
       "      <td>-0.283126</td>\n",
       "      <td>0.458336</td>\n",
       "      <td>-0.576821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117505</th>\n",
       "      <td>-151591.500000</td>\n",
       "      <td>-0.784105</td>\n",
       "      <td>0.302882</td>\n",
       "      <td>-0.152170</td>\n",
       "      <td>0.177196</td>\n",
       "      <td>0.587231</td>\n",
       "      <td>1.184112</td>\n",
       "      <td>-0.023560</td>\n",
       "      <td>0.009892</td>\n",
       "      <td>-0.483560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9929</th>\n",
       "      <td>-151592.500000</td>\n",
       "      <td>-0.771148</td>\n",
       "      <td>1.725285</td>\n",
       "      <td>-1.927398</td>\n",
       "      <td>-3.209993</td>\n",
       "      <td>-0.035516</td>\n",
       "      <td>-0.780554</td>\n",
       "      <td>-1.209815</td>\n",
       "      <td>0.887021</td>\n",
       "      <td>-1.002740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632255</th>\n",
       "      <td>-151593.500000</td>\n",
       "      <td>-0.782064</td>\n",
       "      <td>0.206495</td>\n",
       "      <td>0.206344</td>\n",
       "      <td>1.052590</td>\n",
       "      <td>0.198482</td>\n",
       "      <td>1.751801</td>\n",
       "      <td>-0.114529</td>\n",
       "      <td>-0.829637</td>\n",
       "      <td>-0.264295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33405</th>\n",
       "      <td>-151594.500000</td>\n",
       "      <td>-0.703878</td>\n",
       "      <td>0.899470</td>\n",
       "      <td>1.601197</td>\n",
       "      <td>-0.544302</td>\n",
       "      <td>0.496256</td>\n",
       "      <td>-0.539562</td>\n",
       "      <td>0.243753</td>\n",
       "      <td>-1.086211</td>\n",
       "      <td>0.053544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776609</th>\n",
       "      <td>-151595.500000</td>\n",
       "      <td>3.239240</td>\n",
       "      <td>3.473755</td>\n",
       "      <td>-2.114798</td>\n",
       "      <td>-1.754274</td>\n",
       "      <td>-0.491055</td>\n",
       "      <td>-0.152575</td>\n",
       "      <td>0.423897</td>\n",
       "      <td>-0.586571</td>\n",
       "      <td>-1.768543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713866</th>\n",
       "      <td>-151596.500000</td>\n",
       "      <td>1.211741</td>\n",
       "      <td>-1.741780</td>\n",
       "      <td>0.699565</td>\n",
       "      <td>1.007343</td>\n",
       "      <td>-0.092240</td>\n",
       "      <td>1.141927</td>\n",
       "      <td>0.067420</td>\n",
       "      <td>0.073424</td>\n",
       "      <td>-0.532558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463475</th>\n",
       "      <td>-151597.500000</td>\n",
       "      <td>-0.792210</td>\n",
       "      <td>-0.636680</td>\n",
       "      <td>-0.037479</td>\n",
       "      <td>-0.433705</td>\n",
       "      <td>1.762697</td>\n",
       "      <td>0.792856</td>\n",
       "      <td>-0.555218</td>\n",
       "      <td>0.262771</td>\n",
       "      <td>2.225354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386062</th>\n",
       "      <td>-151598.500000</td>\n",
       "      <td>-0.782933</td>\n",
       "      <td>-0.862243</td>\n",
       "      <td>0.877456</td>\n",
       "      <td>1.997242</td>\n",
       "      <td>-0.070232</td>\n",
       "      <td>-0.146314</td>\n",
       "      <td>-0.044249</td>\n",
       "      <td>-0.177469</td>\n",
       "      <td>0.058787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347723</th>\n",
       "      <td>-151599.500000</td>\n",
       "      <td>-0.773665</td>\n",
       "      <td>1.038912</td>\n",
       "      <td>0.429060</td>\n",
       "      <td>2.416957</td>\n",
       "      <td>0.002480</td>\n",
       "      <td>-0.136246</td>\n",
       "      <td>-0.071030</td>\n",
       "      <td>-0.196574</td>\n",
       "      <td>0.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194778</th>\n",
       "      <td>-151600.500000</td>\n",
       "      <td>-0.784332</td>\n",
       "      <td>-0.649339</td>\n",
       "      <td>0.080283</td>\n",
       "      <td>-0.009882</td>\n",
       "      <td>1.243063</td>\n",
       "      <td>-0.418415</td>\n",
       "      <td>0.238385</td>\n",
       "      <td>-0.218683</td>\n",
       "      <td>-0.499519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609579</th>\n",
       "      <td>-151601.500000</td>\n",
       "      <td>-0.778631</td>\n",
       "      <td>0.567986</td>\n",
       "      <td>-1.059528</td>\n",
       "      <td>-2.002742</td>\n",
       "      <td>1.256689</td>\n",
       "      <td>0.088508</td>\n",
       "      <td>-0.830288</td>\n",
       "      <td>0.624973</td>\n",
       "      <td>-2.047887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911738</th>\n",
       "      <td>-151602.500000</td>\n",
       "      <td>0.223475</td>\n",
       "      <td>0.200134</td>\n",
       "      <td>0.194489</td>\n",
       "      <td>1.194594</td>\n",
       "      <td>-0.107541</td>\n",
       "      <td>0.140392</td>\n",
       "      <td>0.252366</td>\n",
       "      <td>-1.041436</td>\n",
       "      <td>-0.539692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934770</th>\n",
       "      <td>-151603.500000</td>\n",
       "      <td>-0.711446</td>\n",
       "      <td>-0.925958</td>\n",
       "      <td>1.872879</td>\n",
       "      <td>-1.299852</td>\n",
       "      <td>-1.476241</td>\n",
       "      <td>0.529717</td>\n",
       "      <td>-0.721160</td>\n",
       "      <td>-1.153233</td>\n",
       "      <td>0.241627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306800</th>\n",
       "      <td>-151604.500000</td>\n",
       "      <td>-0.782762</td>\n",
       "      <td>-0.679379</td>\n",
       "      <td>0.238002</td>\n",
       "      <td>0.433216</td>\n",
       "      <td>-0.204320</td>\n",
       "      <td>-0.889248</td>\n",
       "      <td>-0.605940</td>\n",
       "      <td>-0.127525</td>\n",
       "      <td>0.080661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528245</th>\n",
       "      <td>-151605.500000</td>\n",
       "      <td>1.221815</td>\n",
       "      <td>0.571166</td>\n",
       "      <td>-1.110884</td>\n",
       "      <td>-1.677211</td>\n",
       "      <td>-0.507280</td>\n",
       "      <td>-1.107920</td>\n",
       "      <td>1.008567</td>\n",
       "      <td>0.992479</td>\n",
       "      <td>2.114606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895092</th>\n",
       "      <td>-151606.500000</td>\n",
       "      <td>2.227219</td>\n",
       "      <td>1.420789</td>\n",
       "      <td>-1.106141</td>\n",
       "      <td>-0.978890</td>\n",
       "      <td>-0.234172</td>\n",
       "      <td>-1.311574</td>\n",
       "      <td>-1.130412</td>\n",
       "      <td>0.729966</td>\n",
       "      <td>-0.537126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772584</th>\n",
       "      <td>-151607.500000</td>\n",
       "      <td>-0.770044</td>\n",
       "      <td>1.283467</td>\n",
       "      <td>-0.306681</td>\n",
       "      <td>0.862796</td>\n",
       "      <td>-1.553218</td>\n",
       "      <td>-0.216515</td>\n",
       "      <td>0.549979</td>\n",
       "      <td>0.635126</td>\n",
       "      <td>-0.357355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618410</th>\n",
       "      <td>-151608.500000</td>\n",
       "      <td>-0.791118</td>\n",
       "      <td>-1.352236</td>\n",
       "      <td>-0.437018</td>\n",
       "      <td>-1.888479</td>\n",
       "      <td>-2.388690</td>\n",
       "      <td>1.538571</td>\n",
       "      <td>-1.784268</td>\n",
       "      <td>0.050766</td>\n",
       "      <td>1.194826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703155</th>\n",
       "      <td>-151609.500000</td>\n",
       "      <td>-0.784336</td>\n",
       "      <td>-1.697148</td>\n",
       "      <td>0.735592</td>\n",
       "      <td>0.909698</td>\n",
       "      <td>-0.568224</td>\n",
       "      <td>-0.242952</td>\n",
       "      <td>-0.738367</td>\n",
       "      <td>-0.901789</td>\n",
       "      <td>0.401521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506761</th>\n",
       "      <td>-151610.500000</td>\n",
       "      <td>0.222422</td>\n",
       "      <td>0.298844</td>\n",
       "      <td>-0.122274</td>\n",
       "      <td>0.460425</td>\n",
       "      <td>-0.239951</td>\n",
       "      <td>0.057390</td>\n",
       "      <td>0.572920</td>\n",
       "      <td>-0.781575</td>\n",
       "      <td>0.855876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336440</th>\n",
       "      <td>-151611.500000</td>\n",
       "      <td>-0.782884</td>\n",
       "      <td>-0.799613</td>\n",
       "      <td>0.693169</td>\n",
       "      <td>1.632717</td>\n",
       "      <td>-0.532928</td>\n",
       "      <td>-0.322528</td>\n",
       "      <td>0.483244</td>\n",
       "      <td>0.203714</td>\n",
       "      <td>0.088904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308261</th>\n",
       "      <td>-151612.500000</td>\n",
       "      <td>0.211154</td>\n",
       "      <td>-1.790529</td>\n",
       "      <td>0.954432</td>\n",
       "      <td>1.546197</td>\n",
       "      <td>-0.340675</td>\n",
       "      <td>0.392111</td>\n",
       "      <td>-0.411316</td>\n",
       "      <td>0.305183</td>\n",
       "      <td>-0.080834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129263</th>\n",
       "      <td>-151613.500000</td>\n",
       "      <td>-0.779831</td>\n",
       "      <td>0.329673</td>\n",
       "      <td>-0.117612</td>\n",
       "      <td>0.531989</td>\n",
       "      <td>-0.732087</td>\n",
       "      <td>-0.018458</td>\n",
       "      <td>2.194095</td>\n",
       "      <td>0.608765</td>\n",
       "      <td>0.807932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361111</th>\n",
       "      <td>-151614.500000</td>\n",
       "      <td>0.213975</td>\n",
       "      <td>-1.720837</td>\n",
       "      <td>0.697943</td>\n",
       "      <td>0.888751</td>\n",
       "      <td>0.416150</td>\n",
       "      <td>0.725286</td>\n",
       "      <td>0.602557</td>\n",
       "      <td>-0.264601</td>\n",
       "      <td>0.347421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369605</th>\n",
       "      <td>-151615.500000</td>\n",
       "      <td>0.209673</td>\n",
       "      <td>-1.734765</td>\n",
       "      <td>0.714444</td>\n",
       "      <td>0.905581</td>\n",
       "      <td>0.371573</td>\n",
       "      <td>0.076243</td>\n",
       "      <td>-0.766126</td>\n",
       "      <td>0.856610</td>\n",
       "      <td>-0.584606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809346</th>\n",
       "      <td>-151616.500000</td>\n",
       "      <td>1.215118</td>\n",
       "      <td>-1.544359</td>\n",
       "      <td>0.053696</td>\n",
       "      <td>-0.543328</td>\n",
       "      <td>-0.308653</td>\n",
       "      <td>-0.866569</td>\n",
       "      <td>0.073498</td>\n",
       "      <td>-1.115025</td>\n",
       "      <td>-0.668514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303234 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0         1         2         3          4         5  \\\n",
       "608627  151616.500000 -0.761129 -0.630918  0.222937   0.353453  0.235507   \n",
       "456977  151615.500000 -0.754617  1.395821 -0.642808  -0.163310  0.093168   \n",
       "803801  151614.500000 -0.761458  0.373037 -0.265671  -0.178419  1.454629   \n",
       "926708  151613.500000 -0.745755  3.584052 -5.154210 -10.490454  0.197868   \n",
       "129279  151612.500000  2.239032  0.402433 -0.554391  -0.533962  1.170083   \n",
       "951335  151611.500000 -0.768255 -1.725251  0.965991   1.450976 -0.356154   \n",
       "655372  151610.500000 -0.759314 -0.618679  0.230855   0.381738 -1.254514   \n",
       "632636  151609.500000  4.246648  1.405318 -1.053863  -0.671150  0.104613   \n",
       "397220  151608.500000 -0.768397 -1.544883  0.318015  -0.141907  0.035001   \n",
       "412260  151607.500000 -0.765090 -0.558702 -0.053799  -0.344552  0.624231   \n",
       "913023  151606.500000 -0.772793 -1.669649  0.659140   0.543560  1.719710   \n",
       "193954  151605.500000 -0.765158 -0.745903  0.606154   1.269889  0.898352   \n",
       "770346  151604.500000 -0.762011 -0.642187  0.259326   0.373597 -0.172967   \n",
       "249317  151603.500000 -0.761822 -0.731718  0.562843   1.156863  0.227862   \n",
       "831748  151602.500000  1.237262 -0.603106  0.018118   0.118185 -0.577819   \n",
       "20132   151601.500000 -0.713410  2.836417 -0.698091  -2.056818  0.612326   \n",
       "161090  151600.500000  1.231263 -1.743601  0.883458   1.468917 -0.551552   \n",
       "223589  151599.500000  0.251957  1.372716 -0.597216   0.045065 -0.448655   \n",
       "274120  151598.500000 -0.770480 -1.247992 -0.755466  -2.812329  0.624101   \n",
       "537152  151597.500000  2.273106 -1.318857  1.281615   0.077540 -1.126231   \n",
       "255424  151596.500000  0.231687 -1.789585  1.091684   1.805935 -0.094399   \n",
       "407177  151595.500000  0.257929 -0.245059  0.469980  -1.791548  3.660880   \n",
       "635566  151594.500000  1.240688 -0.629810  0.176580   0.648646 -1.313935   \n",
       "175276  151593.500000 -0.762925 -0.677710  0.403730   0.767311 -0.497075   \n",
       "972149  151592.500000  0.227774 -1.442876 -0.122363  -1.137036 -0.277443   \n",
       "902815  151591.500000  7.226628 -1.891970  0.866898   1.871903 -0.073134   \n",
       "159302  151590.499999  6.261066  4.371595 -2.245032  -1.029118 -0.532937   \n",
       "884232  151589.500000 -0.764378 -0.594592  0.055654  -0.085798  0.787442   \n",
       "590961  151588.499999  1.323475  2.689883  1.581539   1.144264 -0.562395   \n",
       "419507  151587.500000  1.276677  0.625502  0.659385  -0.136859  0.263197   \n",
       "...               ...       ...       ...       ...        ...       ...   \n",
       "461807 -151587.500000 -0.785799 -0.787622  0.576151   1.181732  0.448420   \n",
       "91981  -151588.500000 -0.782933 -0.862242  0.877456   1.997242 -0.070231   \n",
       "136611 -151589.500000 -0.766660  1.956188 -2.790299  -5.496217  0.989011   \n",
       "106304 -151590.500000 -0.790238 -1.744246  0.805144   0.983304  1.150951   \n",
       "117505 -151591.500000 -0.784105  0.302882 -0.152170   0.177196  0.587231   \n",
       "9929   -151592.500000 -0.771148  1.725285 -1.927398  -3.209993 -0.035516   \n",
       "632255 -151593.500000 -0.782064  0.206495  0.206344   1.052590  0.198482   \n",
       "33405  -151594.500000 -0.703878  0.899470  1.601197  -0.544302  0.496256   \n",
       "776609 -151595.500000  3.239240  3.473755 -2.114798  -1.754274 -0.491055   \n",
       "713866 -151596.500000  1.211741 -1.741780  0.699565   1.007343 -0.092240   \n",
       "463475 -151597.500000 -0.792210 -0.636680 -0.037479  -0.433705  1.762697   \n",
       "386062 -151598.500000 -0.782933 -0.862243  0.877456   1.997242 -0.070232   \n",
       "347723 -151599.500000 -0.773665  1.038912  0.429060   2.416957  0.002480   \n",
       "194778 -151600.500000 -0.784332 -0.649339  0.080283  -0.009882  1.243063   \n",
       "609579 -151601.500000 -0.778631  0.567986 -1.059528  -2.002742  1.256689   \n",
       "911738 -151602.500000  0.223475  0.200134  0.194489   1.194594 -0.107541   \n",
       "934770 -151603.500000 -0.711446 -0.925958  1.872879  -1.299852 -1.476241   \n",
       "306800 -151604.500000 -0.782762 -0.679379  0.238002   0.433216 -0.204320   \n",
       "528245 -151605.500000  1.221815  0.571166 -1.110884  -1.677211 -0.507280   \n",
       "895092 -151606.500000  2.227219  1.420789 -1.106141  -0.978890 -0.234172   \n",
       "772584 -151607.500000 -0.770044  1.283467 -0.306681   0.862796 -1.553218   \n",
       "618410 -151608.500000 -0.791118 -1.352236 -0.437018  -1.888479 -2.388690   \n",
       "703155 -151609.500000 -0.784336 -1.697148  0.735592   0.909698 -0.568224   \n",
       "506761 -151610.500000  0.222422  0.298844 -0.122274   0.460425 -0.239951   \n",
       "336440 -151611.500000 -0.782884 -0.799613  0.693169   1.632717 -0.532928   \n",
       "308261 -151612.500000  0.211154 -1.790529  0.954432   1.546197 -0.340675   \n",
       "129263 -151613.500000 -0.779831  0.329673 -0.117612   0.531989 -0.732087   \n",
       "361111 -151614.500000  0.213975 -1.720837  0.697943   0.888751  0.416150   \n",
       "369605 -151615.500000  0.209673 -1.734765  0.714444   0.905581  0.371573   \n",
       "809346 -151616.500000  1.215118 -1.544359  0.053696  -0.543328 -0.308653   \n",
       "\n",
       "               6         7         8         9  \n",
       "608627  0.409963 -0.283732 -0.145990 -0.853003  \n",
       "456977 -0.907316  0.403185  0.561468  2.084377  \n",
       "803801 -0.034730 -0.467619  0.340280 -0.236515  \n",
       "926708  3.029185 -4.867664 -0.784345 -0.433363  \n",
       "129279  1.715365  2.286157 -1.691611  0.165336  \n",
       "951335  0.370145 -0.447158  0.256960 -0.097978  \n",
       "655372  0.040769 -1.644357 -1.196110  0.427220  \n",
       "632636 -0.757219  1.325200 -0.197307  1.465430  \n",
       "397220  0.031413 -0.140495  0.182254 -0.678122  \n",
       "412260 -0.158668  0.635052 -0.405478  1.765080  \n",
       "913023 -0.789495  0.115662  0.608697  1.226635  \n",
       "193954 -0.577103 -0.003558  0.225530  0.360639  \n",
       "770346  0.487595 -1.138813 -1.488644  0.435542  \n",
       "249317 -0.879947 -0.383199 -0.385382 -0.262470  \n",
       "831748  0.566940  0.149342 -0.380287  0.424117  \n",
       "20132  -0.470951 -0.171997  1.310957 -0.597400  \n",
       "161090 -0.322847  0.498604  0.213913  0.086114  \n",
       "223589 -0.589830 -0.463385 -1.005745  1.530985  \n",
       "274120  1.881669  1.765711 -1.418794 -2.107772  \n",
       "537152 -1.182056  0.218755 -0.771344 -0.327161  \n",
       "255424 -0.155950 -0.028947 -0.160539  0.055740  \n",
       "407177 -0.464416 -0.747243  0.468929  2.631134  \n",
       "635566 -0.890042  1.022585  0.219058 -0.435851  \n",
       "175276 -0.164701 -1.548541 -1.176548  0.683975  \n",
       "972149  1.896782 -0.936009  0.479519 -0.862899  \n",
       "902815 -0.151812 -0.033762 -0.146013  0.060659  \n",
       "159302  0.335751  1.415143  0.401199 -0.652184  \n",
       "884232 -0.499167  0.106009 -0.092759  0.110042  \n",
       "590961  0.016899 -0.652494 -0.267697 -0.192337  \n",
       "419507  0.294991  1.186133 -0.580337 -1.241158  \n",
       "...          ...       ...       ...       ...  \n",
       "461807  0.670180  0.193230 -0.735151  1.070928  \n",
       "91981  -0.146314 -0.044249 -0.177469  0.058787  \n",
       "136611 -0.249413 -0.665092 -2.662997 -1.831177  \n",
       "106304 -0.992760 -0.283126  0.458336 -0.576821  \n",
       "117505  1.184112 -0.023560  0.009892 -0.483560  \n",
       "9929   -0.780554 -1.209815  0.887021 -1.002740  \n",
       "632255  1.751801 -0.114529 -0.829637 -0.264295  \n",
       "33405  -0.539562  0.243753 -1.086211  0.053544  \n",
       "776609 -0.152575  0.423897 -0.586571 -1.768543  \n",
       "713866  1.141927  0.067420  0.073424 -0.532558  \n",
       "463475  0.792856 -0.555218  0.262771  2.225354  \n",
       "386062 -0.146314 -0.044249 -0.177469  0.058787  \n",
       "347723 -0.136246 -0.071030 -0.196574  0.063000  \n",
       "194778 -0.418415  0.238385 -0.218683 -0.499519  \n",
       "609579  0.088508 -0.830288  0.624973 -2.047887  \n",
       "911738  0.140392  0.252366 -1.041436 -0.539692  \n",
       "934770  0.529717 -0.721160 -1.153233  0.241627  \n",
       "306800 -0.889248 -0.605940 -0.127525  0.080661  \n",
       "528245 -1.107920  1.008567  0.992479  2.114606  \n",
       "895092 -1.311574 -1.130412  0.729966 -0.537126  \n",
       "772584 -0.216515  0.549979  0.635126 -0.357355  \n",
       "618410  1.538571 -1.784268  0.050766  1.194826  \n",
       "703155 -0.242952 -0.738367 -0.901789  0.401521  \n",
       "506761  0.057390  0.572920 -0.781575  0.855876  \n",
       "336440 -0.322528  0.483244  0.203714  0.088904  \n",
       "308261  0.392111 -0.411316  0.305183 -0.080834  \n",
       "129263 -0.018458  2.194095  0.608765  0.807932  \n",
       "361111  0.725286  0.602557 -0.264601  0.347421  \n",
       "369605  0.076243 -0.766126  0.856610 -0.584606  \n",
       "809346 -0.866569  0.073498 -1.115025 -0.668514  \n",
       "\n",
       "[303234 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_pca_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding other features to POS dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_pca_df['score'] = df['score']\n",
    "pos_pca_df['ups'] = df['ups']\n",
    "pos_pca_df['downs'] = df['downs']\n",
    "pos_pca_df['count'] = df['comment'].apply(lambda x: len(x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_added_df = pd.concat([df, pos_pca_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(pca_added_df['count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA with text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_counts = [pos_loaded_df[x].sum() for x in pos_loaded_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[45975277761, 87837.0, 267430.0, 244766.0, 307931.0, 480769.0, 128384.0, 221833.0, 72377.0, 172089.0, 290342.0, 141495.0, 145744.0, 26876.0, 62526.0, 48582.0, 79531.0, 7810.0, 49035.0, 12470.0, 71617.0, 231968.0, 5979.0, 11375.0, 46558.0, 15968.0, 20896.0, 12535.0, 4357.0, 2966.0, 6927.0, 16781.0, 1247.0, 1394.0, 96.0, 134.0, 54.0]\n",
      "Index(['Unnamed: 0', 'CC', 'PRP', 'RB', 'IN', 'NN', 'VBZ', 'JJ', 'TO', 'VB',\n",
      "       'DT', 'VBP', 'NNS', 'WRB', 'MD', 'VBN', 'VBD', 'JJS', 'PRP$', 'JJR',\n",
      "       'VBG', 'NNP', 'RBR', 'POS', 'CD', 'UH', 'WP', 'WDT', 'PDT', 'NNPS',\n",
      "       'EX', 'RP', 'FW', 'RBS', 'WP$', 'SYM', 'LS'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(pos_counts)\n",
    "print(pos_loaded_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff41f0cc18>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(pca_added_df['count'], pca_added_df['count'])\n",
    "# So we have around 8 outliers in counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing those outliers\n",
    "pca_added_df = pca_added_df[pca_added_df['count']<3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff3937bef0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(pca_added_df['count'], pca_added_df['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.01761e+05, 1.38500e+03, 5.10000e+01, 1.00000e+01, 5.00000e+00,\n",
       "        4.00000e+00, 1.00000e+00, 1.00000e+00, 0.00000e+00, 4.00000e+00]),\n",
       " array([1.000e+00, 2.480e+02, 4.950e+02, 7.420e+02, 9.890e+02, 1.236e+03,\n",
       "        1.483e+03, 1.730e+03, 1.977e+03, 2.224e+03, 2.471e+03]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pca_added_df['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    160473.000000\n",
       "mean          1.889552\n",
       "std           2.317549\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max         281.000000\n",
       "Name: author, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_sentences = pca_added_df.groupby('author')['author'].count()\n",
    "author_sentences.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(20,10))\n",
    "fig=sns.countplot(author_sentences, ax=ax)\n",
    "plt.title(\"Count of number of comments for authors\")\n",
    "for item in fig.get_xticklabels():\n",
    "    item.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most authors have 1 comment in the dataset so we can expect to have difference in the way of sarcasm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting text data into Tfidf Vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(lowercase=True, stop_words='english', min_df=0.0, max_df=1.0, ngram_range=(1,4))\n",
    "tfidf_text_vector = tfidf_vectorizer.fit_transform(df['comment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Latent Dirichlet Allocation to reduce dimensionality of tfidf vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:796: RuntimeWarning: overflow encountered in exp\n",
      "  return np.exp(-1.0 * perword_bound)\n"
     ]
    }
   ],
   "source": [
    "lda = LatentDirichletAllocation(n_components=500)\n",
    "tfidf_lda = lda.fit_transform(tfidf_text_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing train-test split for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(tfidf_lda, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pos, X_test_pos, Y_train_pos, Y_test_pos = train_test_split(pos_pca_df, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestClassifier with tfidf vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.5008508299439376\n",
      "Test data score:  0.5009959239668114\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=30)\n",
    "rfc.fit(X_train, Y_train)\n",
    "print(\"Train data score: \", rfc.score(X_train, Y_train))\n",
    "print(\"Test data score: \", rfc.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestClassifier with POS dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.5774607013301088\n",
      "Test data score:  0.5757891543220462\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=30)\n",
    "rfc.fit(X_train_pos, Y_train_pos)\n",
    "print(\"Train data score: \", rfc.score(X_train_pos, Y_train_pos))\n",
    "print(\"Test data score: \", rfc.score(X_test_pos, Y_test_pos))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingClassifier with tfidf vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.5017742112784435\n",
      "Test data score:  0.500850822461713\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(learning_rate=0.3)\n",
    "gbc.fit(X_train, Y_train)\n",
    "print(\"Train data score: \", gbc.score(X_train, Y_train))\n",
    "print(\"Test data score: \", gbc.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingClassifier with POS dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.6020490271518083\n",
      "Test data score:  0.593267290163437\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(learning_rate=0.3)\n",
    "gbc.fit(X_train_pos, Y_train_pos)\n",
    "print(\"Train data score: \", gbc.score(X_train_pos, Y_train_pos))\n",
    "print(\"Test data score: \", gbc.score(X_test_pos, Y_test_pos))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression with tfidf vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.5004419039243707\n",
      "Test data score:  0.5009827329208933\n"
     ]
    }
   ],
   "source": [
    "lrc = LogisticRegression(penalty='l1')\n",
    "lrc.fit(X_train, Y_train)\n",
    "print(\"Train data score: \", lrc.score(X_train, Y_train))\n",
    "print(\"Test data score: \", lrc.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression with POS dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data score:  0.5322457953171376\n",
      "Test data score:  0.5330765476394623\n"
     ]
    }
   ],
   "source": [
    "lrc = LogisticRegression(penalty='l2')\n",
    "lrc.fit(X_train_pos, Y_train_pos)\n",
    "print(\"Train data score: \", lrc.score(X_train_pos, Y_train_pos))\n",
    "print(\"Test data score: \", lrc.score(X_test_pos, Y_test_pos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [50, 100, 500, 1000, 5000, 10000, 50000]\n",
    "rfc_train_score = []\n",
    "rfc_test_score = []\n",
    "rfc_train_error = []\n",
    "rfc_test_error = []\n",
    "gbc_train_score = []\n",
    "gbc_test_score = []\n",
    "gbc_train_error = []\n",
    "gbc_test_error = []\n",
    "lrc_train_score = []\n",
    "lrc_test_score = []\n",
    "lrc_train_error = []\n",
    "lrc_test_error = []\n",
    "\n",
    "for x in features:\n",
    "    \n",
    "    tfidf_vectorizer = TfidfVectorizer(lowercase=True, stop_words='english', min_df=0.0, max_df=1.0, ngram_range=(1,4), max_features=x)\n",
    "    tfidf_text_vector = tfidf_vectorizer.fit_transform(df['comment'])\n",
    "    \n",
    "    X = tfidf_text_vector\n",
    "    Y = df['label']\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y)\n",
    "    \n",
    "    \n",
    "    rfc = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=30)\n",
    "    rfc.fit(X_train, Y_train)\n",
    "    rfc_train_score.append(rfc.score(X_train, Y_train))\n",
    "    rfc_test_score.append(rfc.score(X_test, Y_test))\n",
    "    y_pred_train = rfc.predict(X_train)\n",
    "    y_pred_test = rfc.predict(X_test)\n",
    "    rfc_train_error.append(mean_squared_error(Y_train, y_pred_train))\n",
    "    rfc_test_error.append(mean_squared_error(Y_test, y_pred_test))\n",
    "    \n",
    "    \n",
    "    gbc = GradientBoostingClassifier(learning_rate=0.3)\n",
    "    gbc.fit(X_train, Y_train)\n",
    "    gbc_train_score.append(gbc.score(X_train, Y_train))\n",
    "    gbc_test_score.append(gbc.score(X_test, Y_test))\n",
    "    y_pred_train = gbc.predict(X_train)\n",
    "    y_pred_test = gbc.predict(X_test)\n",
    "    gbc_train_error.append(mean_squared_error(Y_train, y_pred_train))\n",
    "    gbc_test_error.append(mean_squared_error(Y_test, y_pred_test))\n",
    "    \n",
    "    \n",
    "    lrc = LogisticRegression(penalty='l1', solver='liblinear')\n",
    "    lrc.fit(X_train, Y_train)\n",
    "    lrc_train_score.append(lrc.score(X_train, Y_train))\n",
    "    lrc_test_score.append(lrc.score(X_test, Y_test))\n",
    "    y_pred_train = lrc.predict(X_train)\n",
    "    y_pred_test = lrc.predict(X_test)\n",
    "    lrc_train_error.append(mean_squared_error(Y_train, y_pred_train))\n",
    "    lrc_test_error.append(mean_squared_error(Y_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Accuracy Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature size')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2, figsize=(13, 12))\n",
    "axes[0,0].plot(rfc_train_score, features, alpha=0.5)\n",
    "axes[0,0].scatter(rfc_train_score, features)\n",
    "axes[0,0].set_title(\"Random Forest Accuracy To Feature Size\")\n",
    "axes[0,0].set_xlabel(\"Accuracy\")\n",
    "axes[0,0].set_ylabel(\"Feature size\")\n",
    "axes[0,1].plot(rfc_train_error, features, alpha=0.5)\n",
    "axes[0,1].scatter(rfc_train_error, features)\n",
    "axes[0,1].set_title(\"Random Forest Error To Feature Size\")\n",
    "axes[0,1].set_xlabel(\"Error\")\n",
    "axes[0,1].set_ylabel(\"Feature size\")\n",
    "axes[1,0].plot(rfc_test_score, features, alpha=0.5)\n",
    "axes[1,0].scatter(rfc_test_score, features)\n",
    "axes[1,0].set_title(\"Random Forest Accuracy To Feature Size For Test Data\")\n",
    "axes[1,0].set_xlabel(\"Error\")\n",
    "axes[1,0].set_ylabel(\"Feature size\")\n",
    "axes[1,1].plot(rfc_test_error, features, alpha=0.5)\n",
    "axes[1,1].scatter(rfc_test_error, features)\n",
    "axes[1,1].set_title(\"Random Forest Error To Feature Size For Test Data\")\n",
    "axes[1,1].set_xlabel(\"Error\")\n",
    "axes[1,1].set_ylabel(\"Feature size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Accuracy Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature size')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2, figsize=(14, 12))\n",
    "axes[0,0].plot(gbc_train_score, features, alpha=0.5)\n",
    "axes[0,0].scatter(gbc_train_score, features)\n",
    "axes[0,0].set_title(\"Gradient Boosting Accuracy To Feature Size\")\n",
    "axes[0,0].set_xlabel(\"Accuracy\")\n",
    "axes[0,0].set_ylabel(\"Feature size\")\n",
    "axes[0,1].plot(gbc_train_error, features, alpha=0.5)\n",
    "axes[0,1].scatter(gbc_train_error, features)\n",
    "axes[0,1].set_title(\"Gradient Boosting Error To Feature Size\")\n",
    "axes[0,1].set_xlabel(\"Error\")\n",
    "axes[0,1].set_ylabel(\"Feature size\")\n",
    "axes[1,0].plot(gbc_test_score, features, alpha=0.5)\n",
    "axes[1,0].scatter(gbc_test_score, features)\n",
    "axes[1,0].set_title(\"Gradient Boosting Accuracy To Feature Size For Test Data\")\n",
    "axes[1,0].set_xlabel(\"Error\")\n",
    "axes[1,0].set_ylabel(\"Feature size\")\n",
    "axes[1,1].plot(gbc_test_error, features, alpha=0.5)\n",
    "axes[1,1].scatter(gbc_test_error, features)\n",
    "axes[1,1].set_title(\"Gradient Boosting Error To Feature Size For Test Data\")\n",
    "axes[1,1].set_xlabel(\"Error\")\n",
    "axes[1,1].set_ylabel(\"Feature size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Accuracy Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature size')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2, figsize=(14, 12))\n",
    "axes[0,0].plot(lrc_train_score, features, alpha=0.5)\n",
    "axes[0,0].scatter(lrc_train_score, features)\n",
    "axes[0,0].set_title(\"Logistic Regression Accuracy To Feature Size\")\n",
    "axes[0,0].set_xlabel(\"Accuracy\")\n",
    "axes[0,0].set_ylabel(\"Feature size\")\n",
    "axes[0,1].plot(lrc_train_error, features, alpha=0.5)\n",
    "axes[0,1].scatter(lrc_train_error, features)\n",
    "axes[0,1].set_title(\"Logistic Regression Error To Feature Size\")\n",
    "axes[0,1].set_xlabel(\"Error\")\n",
    "axes[0,1].set_ylabel(\"Feature size\")\n",
    "axes[1,0].plot(lrc_test_score, features, alpha=0.5)\n",
    "axes[1,0].scatter(lrc_test_score, features)\n",
    "axes[1,0].set_title(\"Logistic Regression Accuracy To Feature Size For Test Data\")\n",
    "axes[1,0].set_xlabel(\"Error\")\n",
    "axes[1,0].set_ylabel(\"Feature size\")\n",
    "axes[1,1].plot(lrc_test_error, features, alpha=0.5)\n",
    "axes[1,1].scatter(lrc_test_error, features)\n",
    "axes[1,1].set_title(\"Logistic Regression Error To Feature Size For Test Data\")\n",
    "axes[1,1].set_xlabel(\"Error\")\n",
    "axes[1,1].set_ylabel(\"Feature size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_df = pca_added_df.sample(frac=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM with comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = deep_df['comment']\n",
    "text = text.apply(lambda x: x.lower() if type(x)=='str' else str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145147     I'm sorry but at this point if you're being ca...\n",
      "796203              Sure I'm so worried about them right now\n",
      "44502      Just wait until your hormones kick in and you'...\n",
      "349806     Dry cigars are acceptable in a survival situat...\n",
      "832598                  she *IS* the younger one, after all.\n",
      "960035                     Yes, that is totally what I said.\n",
      "912863     Yes it was the Palestinians who crossed contin...\n",
      "700344                        OH yeah stigmata is sooo sexy!\n",
      "622606     Wow.. Americans really take care of their vete...\n",
      "288830        Saving this to repost on valentine's day 2017.\n",
      "598778     its really too bad Bryant screwed up, the way ...\n",
      "77109      If they get amnesty for running over pedestria...\n",
      "403977                                     We did it Reddit!\n",
      "740713                               Kettle Corn Master Race\n",
      "746133           You mustn't ever forget the Muller Corners!\n",
      "937517     I gave an easy one to remember not long in the...\n",
      "40664               Well except that guy who hit freddy rip.\n",
      "884069     Not easy to do what he's about to *cough*Tendu...\n",
      "412367                          Ron Swanson was never a baby\n",
      "776093     Perhaps education could be considered a natura...\n",
      "636519     That's 104 straight days, without time to eat,...\n",
      "150118     YOU SJWS DONT UNDERSTAND, IT'S NECESSARY BECAU...\n",
      "789882                          STEM Master race, represent!\n",
      "293271                                     **JohnsonRising**\n",
      "748412                                  The spice must flow.\n",
      "721921     Detonate, so if you throw it down and die, you...\n",
      "750248     I would definitely like to see them playing so...\n",
      "251091     She tried getting his patreon shut down, that'...\n",
      "943820                                         \"swag\" \"yolo\"\n",
      "312866          NPR was the absolute fucking worst for this.\n",
      "                                 ...                        \n",
      "401506     Now that the primaries are getting less white,...\n",
      "30319                        Fuck yeah love a good civil war\n",
      "828701     I don't think that might be a good idea, since...\n",
      "618246     I'm your peer, and I just reviewed your scienc...\n",
      "616442     Don't ya know, competitve match making is gonn...\n",
      "980652     Except the part where Presto doesn't use NFC, ...\n",
      "153529                                                 Knox.\n",
      "846700                                     That's Portuguese\n",
      "2684                                           Awesome photo\n",
      "1009035    Unless she's had any alcohol to drink and then...\n",
      "139210     i saw a guy jump off a bridge and found $100 t...\n",
      "277224             If only huma would've blown something....\n",
      "521540     You can get them for almost 100$ cheaper after...\n",
      "368774     If you can't hit the phones and you're interes...\n",
      "168395     Does this really need to be posted every time ...\n",
      "225599                           Niente vidi, niente sacciu.\n",
      "142836     Since there's only about 2 days a month when t...\n",
      "540478                             God is a Pakistani agent.\n",
      "281055                                         Cheezwozzers.\n",
      "361353           True fans would have made this bet in June!\n",
      "675315             No, that'd give the infil too much power.\n",
      "352786     It's because, like food, military tactics are ...\n",
      "248939        minigolf has more depth than league of legends\n",
      "634790                       hahahahahahahahah It's so funny\n",
      "679676                                          Nasa quenya.\n",
      "170087     I have a masters degree in physics, but I have...\n",
      "579249     I can see this working great for pizza vans on...\n",
      "310504                 Isn't it fun to pull useless R units?\n",
      "260580                             I need to watch more porn\n",
      "580266                               Me getting on the fp :(\n",
      "Name: comment, Length: 60647, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenizing the words with 40000 most common words. It's basically the vocabulary of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 40000\n",
    "tokenizer = Tokenizer(num_words=batch_size, split='(?u)\\b\\w\\w+\\b')\n",
    "tokenizer.fit_on_texts(text.values)\n",
    "X = tokenizer.texts_to_sequences(text.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
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       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
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       "          0,    0,    0,    0,    0,    0, 2752])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'to_numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-63-6e68cddca3ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# converting pos dataframe to numpy array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mX_train_pos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_train_pos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'to_numpy'"
     ]
    }
   ],
   "source": [
    "# converting pos dataframe to numpy array\n",
    "X_train_pos=X_train_pos.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = deep_df['label']\n",
    "Y = to_categorical(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=87)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0723 21:58:33.918729 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n",
      "  after removing the cwd from sys.path.\n",
      "W0723 21:58:34.040745 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0723 21:58:34.065050 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0723 21:58:34.322837 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0723 21:58:34.328788 27252 deprecation.py:506] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "embed_dim=128\n",
    "lstm_out=256\n",
    "model = Sequential()\n",
    "model.add(Embedding(batch_size, embed_dim, input_length=X.shape[1], dropout=0.2))\n",
    "model.add(LSTM(lstm_out, dropout=0.2))\n",
    "model.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 293, 128)          5120000   \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 256)               394240    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 514       \n",
      "=================================================================\n",
      "Total params: 5,514,754\n",
      "Trainable params: 5,514,754\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0723 21:58:34.488517 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0723 21:58:34.512325 27252 deprecation_wrapper.py:119] From C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0723 21:58:34.621148 27252 deprecation.py:323] From C:\\Users\\vivek\\AppData\\Roaming\\Python\\Python37\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 586s 13ms/step - loss: 0.6911 - acc: 0.5186 - val_loss: 0.6862 - val_acc: 0.5354\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 695s 15ms/step - loss: 0.6592 - acc: 0.6775 - val_loss: 0.6835 - val_acc: 0.5505\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 1040s 23ms/step - loss: 0.5126 - acc: 0.7389 - val_loss: 0.6927 - val_acc: 0.5365\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 1232s 27ms/step - loss: 0.4182 - acc: 0.7533 - val_loss: 0.7124 - val_acc: 0.5483\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 1318s 29ms/step - loss: 0.3796 - acc: 0.7579 - val_loss: 0.7303 - val_acc: 0.5481\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 1376s 30ms/step - loss: 0.3688 - acc: 0.7579 - val_loss: 0.7444 - val_acc: 0.5464\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 1413s 31ms/step - loss: 0.3641 - acc: 0.7583 - val_loss: 0.7523 - val_acc: 0.5468\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 1437s 32ms/step - loss: 0.3625 - acc: 0.7557 - val_loss: 0.7589 - val_acc: 0.5485\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 1579s 35ms/step - loss: 0.3612 - acc: 0.7585 - val_loss: 0.7635 - val_acc: 0.5468\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 1591s 35ms/step - loss: 0.3597 - acc: 0.7612 - val_loss: 0.7695 - val_acc: 0.5469\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x27a28b90710>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train, epochs=10, batch_size=1000, validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural network with POS dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train_pos' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-8d175a7197c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mlstm_out\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_input_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m412\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train_pos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'relu'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_train_pos' is not defined"
     ]
    }
   ],
   "source": [
    "embed_dim=128\n",
    "lstm_out=256\n",
    "model2 = Sequential()\n",
    "model2.add(Dense(200, batch_input_shape=(412, X_train_pos.shape[1], )))\n",
    "model2.add(Dropout(rate=0.2))\n",
    "model2.add(Dense(100, activation='relu'))\n",
    "model2.add(Dropout(rate=0.2))\n",
    "model2.add(Dense(50, activation='relu'))\n",
    "model2.add(Dense(1, activation='softmax'))\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 303232 samples, validate on 75808 samples\n",
      "Epoch 1/5\n",
      "303232/303232 [==============================] - 3s 10us/step - loss: 7.9597 - acc: 0.5007 - val_loss: 7.9792 - val_acc: 0.4995\n",
      "Epoch 2/5\n",
      "303232/303232 [==============================] - 3s 9us/step - loss: 7.9597 - acc: 0.5007 - val_loss: 7.9792 - val_acc: 0.4995\n",
      "Epoch 3/5\n",
      "303232/303232 [==============================] - 3s 9us/step - loss: 7.9597 - acc: 0.5007 - val_loss: 7.9792 - val_acc: 0.4995\n",
      "Epoch 4/5\n",
      "303232/303232 [==============================] - 3s 9us/step - loss: 7.9597 - acc: 0.5007 - val_loss: 7.9792 - val_acc: 0.4995\n",
      "Epoch 5/5\n",
      "303232/303232 [==============================] - 3s 9us/step - loss: 7.9597 - acc: 0.5007 - val_loss: 7.9792 - val_acc: 0.4995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26898221e80>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X_train_pos, Y_train_pos, epochs=5, batch_size=412, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM with parent_comment and comment combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we only used the column comment to get our features but we should also consider parent_comment as it has the context of next sentence. So new data will contain both the sentences seperated by a space. It will go through the same process but with bigger sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_text = deep_df['parent_comment'] + ' ' + deep_df['comment']\n",
    "all_text = all_text.apply(lambda x: x.lower() if type(x)=='str' else str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50000\n",
    "tokenizer = Tokenizer(num_words=batch_size, split='(?u)\\b\\w\\w+\\b')\n",
    "tokenizer.fit_on_texts(all_text.values)\n",
    "X = tokenizer.texts_to_sequences(all_text.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=87)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "embed_dim=128\n",
    "lstm_out=256\n",
    "model = Sequential()\n",
    "model.add(Embedding(batch_size, embed_dim, input_length=X_train.shape[1], dropout=0.2))\n",
    "model.add(LSTM(lstm_out, dropout=0.2))\n",
    "model.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 4494s 99ms/step - loss: 0.6869 - acc: 0.5330 - val_loss: 0.6808 - val_acc: 0.5469\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 5595s 123ms/step - loss: 1.0866 - acc: 0.5311 - val_loss: 0.6894 - val_acc: 0.5069\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 5597s 123ms/step - loss: 0.6623 - acc: 0.6453 - val_loss: 0.6825 - val_acc: 0.5354\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 5628s 124ms/step - loss: 0.6183 - acc: 0.6620 - val_loss: 0.6982 - val_acc: 0.5382\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 5644s 124ms/step - loss: 0.5614 - acc: 0.6706 - val_loss: 0.7323 - val_acc: 0.5386\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 5639s 124ms/step - loss: 0.5249 - acc: 0.6748 - val_loss: 0.7792 - val_acc: 0.5313\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 5690s 125ms/step - loss: 0.5117 - acc: 0.6753 - val_loss: 0.7779 - val_acc: 0.5394\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 12009s 264ms/step - loss: 0.5006 - acc: 0.6787 - val_loss: 0.7995 - val_acc: 0.5262\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 6769s 149ms/step - loss: 0.4939 - acc: 0.6790 - val_loss: 0.8035 - val_acc: 0.5379\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 5689s 125ms/step - loss: 0.4903 - acc: 0.6816 - val_loss: 0.8230 - val_acc: 0.5305\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x27a1f76ccf8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train, epochs=10, batch_size=500, validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 231s 5ms/step - loss: 0.6907 - acc: 0.5235 - val_loss: 0.6842 - val_acc: 0.5373\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 229s 5ms/step - loss: 0.6793 - acc: 0.5391 - val_loss: 0.6784 - val_acc: 0.5377\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 230s 5ms/step - loss: 0.6765 - acc: 0.5394 - val_loss: 0.6780 - val_acc: 0.5379\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 229s 5ms/step - loss: 0.6759 - acc: 0.5392 - val_loss: 0.6777 - val_acc: 0.5380\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 227s 5ms/step - loss: 0.6755 - acc: 0.5398 - val_loss: 0.6787 - val_acc: 0.5379\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 225s 5ms/step - loss: 0.6757 - acc: 0.5417 - val_loss: 0.6776 - val_acc: 0.5380\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 223s 5ms/step - loss: 0.6754 - acc: 0.5414 - val_loss: 0.6778 - val_acc: 0.5377\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 169s 4ms/step - loss: 0.6755 - acc: 0.5418 - val_loss: 0.6778 - val_acc: 0.5379\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 127s 3ms/step - loss: 0.6755 - acc: 0.5415 - val_loss: 0.6777 - val_acc: 0.5378\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 127s 3ms/step - loss: 0.6753 - acc: 0.5420 - val_loss: 0.6777 - val_acc: 0.5381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 651s 14ms/step - loss: 0.6885 - acc: 0.5249 - val_loss: 0.6829 - val_acc: 0.5115\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 775s 17ms/step - loss: 0.6735 - acc: 0.5498 - val_loss: 0.6730 - val_acc: 0.5497\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 890s 20ms/step - loss: 0.6661 - acc: 0.5563 - val_loss: 0.6724 - val_acc: 0.5489\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 922s 20ms/step - loss: 0.6645 - acc: 0.5561 - val_loss: 0.6721 - val_acc: 0.5503\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 937s 21ms/step - loss: 0.6637 - acc: 0.5568 - val_loss: 0.6728 - val_acc: 0.5500\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 1051s 23ms/step - loss: 0.6635 - acc: 0.5557 - val_loss: 0.6728 - val_acc: 0.5491\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 1031s 23ms/step - loss: 0.6653 - acc: 0.5531 - val_loss: 0.6743 - val_acc: 0.5479\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 1062s 23ms/step - loss: 0.6638 - acc: 0.5561 - val_loss: 0.6738 - val_acc: 0.5483\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 1049s 23ms/step - loss: 0.6633 - acc: 0.5562 - val_loss: 0.6737 - val_acc: 0.5486\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 1007s 22ms/step - loss: 0.6629 - acc: 0.5573 - val_loss: 0.6737 - val_acc: 0.5489\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 1038s 23ms/step - loss: 0.6898 - acc: 0.5268 - val_loss: 0.6820 - val_acc: 0.5440\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 962s 21ms/step - loss: 0.6656 - acc: 0.5736 - val_loss: 0.6715 - val_acc: 0.5555\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 957s 21ms/step - loss: 0.6560 - acc: 0.5795 - val_loss: 0.6811 - val_acc: 0.5288\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 956s 21ms/step - loss: 0.6325 - acc: 0.5824 - val_loss: 0.6805 - val_acc: 0.5551\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 958s 21ms/step - loss: 0.6233 - acc: 0.5886 - val_loss: 0.6881 - val_acc: 0.5563\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 953s 21ms/step - loss: 0.6197 - acc: 0.5872 - val_loss: 0.6949 - val_acc: 0.5566\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 951s 21ms/step - loss: 0.6175 - acc: 0.5875 - val_loss: 0.6999 - val_acc: 0.5554\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 954s 21ms/step - loss: 0.6160 - acc: 0.5868 - val_loss: 0.7046 - val_acc: 0.5557\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 953s 21ms/step - loss: 0.6147 - acc: 0.5871 - val_loss: 0.7101 - val_acc: 0.5560\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 953s 21ms/step - loss: 0.6140 - acc: 0.5884 - val_loss: 0.7121 - val_acc: 0.5559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `dropout` argument is no longer support in `Embedding`. You can apply a `keras.layers.SpatialDropout1D` layer right after the `Embedding` layer to get the same behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45485 samples, validate on 15162 samples\n",
      "Epoch 1/10\n",
      "45485/45485 [==============================] - 956s 21ms/step - loss: 0.6914 - acc: 0.5173 - val_loss: 0.6860 - val_acc: 0.5400\n",
      "Epoch 2/10\n",
      "45485/45485 [==============================] - 941s 21ms/step - loss: 0.6382 - acc: 0.6866 - val_loss: 0.6860 - val_acc: 0.5379\n",
      "Epoch 3/10\n",
      "45485/45485 [==============================] - 936s 21ms/step - loss: 0.4673 - acc: 0.7861 - val_loss: 0.7147 - val_acc: 0.5400\n",
      "Epoch 4/10\n",
      "45485/45485 [==============================] - 1062s 23ms/step - loss: 0.3200 - acc: 0.8046 - val_loss: 0.7662 - val_acc: 0.5419\n",
      "Epoch 5/10\n",
      "45485/45485 [==============================] - 1049s 23ms/step - loss: 0.2966 - acc: 0.8068 - val_loss: 0.7731 - val_acc: 0.5433\n",
      "Epoch 6/10\n",
      "45485/45485 [==============================] - 1045s 23ms/step - loss: 0.2920 - acc: 0.8087 - val_loss: 0.7821 - val_acc: 0.5461\n",
      "Epoch 7/10\n",
      "45485/45485 [==============================] - 1067s 23ms/step - loss: 0.2886 - acc: 0.8093 - val_loss: 0.7905 - val_acc: 0.5433\n",
      "Epoch 8/10\n",
      "45485/45485 [==============================] - 1063s 23ms/step - loss: 0.2869 - acc: 0.8102 - val_loss: 0.7898 - val_acc: 0.5433\n",
      "Epoch 9/10\n",
      "45485/45485 [==============================] - 1040s 23ms/step - loss: 0.2870 - acc: 0.8075 - val_loss: 0.7850 - val_acc: 0.5444\n",
      "Epoch 10/10\n",
      "45485/45485 [==============================] - 1079s 24ms/step - loss: 0.2853 - acc: 0.8063 - val_loss: 0.7898 - val_acc: 0.5455\n"
     ]
    }
   ],
   "source": [
    "batch_size = [100, 500, 5000, 50000]\n",
    "lstm_train_acc = []\n",
    "lstm_test_acc = []\n",
    "lstm_train_err = []\n",
    "lstm_test_err = []\n",
    "\n",
    "for x in batch_size:\n",
    "    \n",
    "    tokenizer = Tokenizer(num_words=x, split='(?u)\\b\\w\\w+\\b')\n",
    "    tokenizer.fit_on_texts(text.values)\n",
    "    \n",
    "    X = tokenizer.texts_to_sequences(text.values)\n",
    "    X = pad_sequences(X)\n",
    "    Y = deep_df['label']\n",
    "    Y = to_categorical(Y)\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=87)\n",
    "    \n",
    "    embed_dim=128\n",
    "    lstm_out=256\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(x, embed_dim, input_length=X.shape[1], dropout=0.2))\n",
    "    model.add(LSTM(lstm_out, dropout=0.2))\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    history = model.fit(X_train, Y_train, epochs=10, batch_size=1000, validation_data=(X_test, Y_test))\n",
    "    \n",
    "    lstm_train_acc.append(history.history['acc'])\n",
    "    lstm_test_acc.append(history.history['val_acc'])\n",
    "    lstm_train_err.append(history.history['loss'])\n",
    "    lstm_test_err.append(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "b=[]\n",
    "c=[]\n",
    "d=[]\n",
    "for i in lstm_train_acc:\n",
    "    a.append(i[-1])\n",
    "for i in lstm_train_err:\n",
    "    b.append(i[-1])\n",
    "for i in lstm_test_acc:\n",
    "    c.append(i[-1])\n",
    "for i in lstm_test_err:\n",
    "    d.append(i[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Vocabulary')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14, 5))\n",
    "ax1.plot(a, batch_size)\n",
    "ax1.scatter(a, batch_size)\n",
    "ax1.set_title(\"Accuracy To Vocabulary\")\n",
    "ax1.set_xlabel(\"Accuracy\")\n",
    "ax1.set_ylabel(\"Vocabulary\")\n",
    "ax2.plot(b, batch_size)\n",
    "ax2.scatter(b, batch_size)\n",
    "ax2.set_title(\"Error To Vocabulary\")\n",
    "ax2.set_xlabel(\"Error\")\n",
    "ax2.set_ylabel(\"Vocabulary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Vocabulary')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14, 5))\n",
    "ax1.plot(c, batch_size)\n",
    "ax1.scatter(c, batch_size)\n",
    "ax1.set_title(\"Accuracy To Vocabulary\")\n",
    "ax1.set_xlabel(\"Accuracy\")\n",
    "ax1.set_ylabel(\"Vocabulary\")\n",
    "ax2.plot(d, batch_size)\n",
    "ax2.scatter(d, batch_size)\n",
    "ax2.set_title(\"Error To Vocabulary\")\n",
    "ax2.set_xlabel(\"Error\")\n",
    "ax2.set_ylabel(\"Vocabulary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion \n",
    "\n",
    "In conclusion, deep learning is best performer with comments data which has accuracy of around 75% in training data and 56% in testing data. We can increase accuracy by adding more vocabulary to the tokenizer but it takes more computational power to create and run such data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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