SUPPORT VECTOR MACHINES.ipynb

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   "source": [
    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n",
    "\n",
    "EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n",
    "\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# [1]. Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it is easier to query the data and visualise the data efficiently.\n",
    "<br> \n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score is above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "a9f9d4181b33e7fc54041d2198216af38ceea6ab",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (50000, 10)\n"
     ]
    },
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "# using SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite') \n",
    "\n",
    "# filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "# SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000, will give top 500000 data points\n",
    "# you can change the number to any other number based on your computing power\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 50000\"\"\", con) \n",
    "\n",
    "# Give reviews with Score>3 a positive rating(1), and reviews with a score<3 a negative rating(0).\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "#changing reviews with score less than 3 to be positive and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative\n",
    "print(\"Number of data points in our data\", filtered_data.shape)\n",
    "filtered_data.head(3)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f4c90304e96e42ad2c45950431780f346d746f1b"
   },
   "source": [
    "### FEATURES OF DATA "
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
    "_uuid": "2717c52e75af79605b0971ae164f7faee13f3eaf"
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    {
     "data": {
      "text/plain": [
       "Index(['Id', 'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator',\n",
       "       'HelpfulnessDenominator', 'Score', 'Time', 'Summary', 'Text'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "682ebe24c8f1bc744b68d83857c386bd560fa53d"
   },
   "outputs": [],
   "source": [
    "display = pd.read_sql_query(\"\"\"\n",
    "SELECT UserId, ProductId, ProfileName, Time, Score, Text, COUNT(*)\n",
    "FROM Reviews\n",
    "GROUP BY UserId\n",
    "HAVING COUNT(*)>1\n",
    "\"\"\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "_uuid": "f421cc133cbc852ec5476e9b8692143a975ca581",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80668, 7)\n"
     ]
    },
    {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#oc-R115TNMSPFT9I7</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Breyton</td>\n",
       "      <td>1331510400</td>\n",
       "      <td>2</td>\n",
       "      <td>Overall its just OK when considering the price...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#oc-R11D9D7SHXIJB9</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Louis E. Emory \"hoppy\"</td>\n",
       "      <td>1342396800</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife has recurring extreme muscle spasms, u...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#oc-R11DNU2NBKQ23Z</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Kim Cieszykowski</td>\n",
       "      <td>1348531200</td>\n",
       "      <td>1</td>\n",
       "      <td>This coffee is horrible and unfortunately not ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#oc-R11O5J5ZVQE25C</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Penguin Chick</td>\n",
       "      <td>1346889600</td>\n",
       "      <td>5</td>\n",
       "      <td>This will be the bottle that you grab from the...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#oc-R12KPBODL2B5ZD</td>\n",
       "      <td>B007OSBEV0</td>\n",
       "      <td>Christopher P. Presta</td>\n",
       "      <td>1348617600</td>\n",
       "      <td>1</td>\n",
       "      <td>I didnt like this coffee. Instead of telling y...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               UserId   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B005ZBZLT4                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ESG  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B005ZBZLT4        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ESG           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBEV0   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "8904aae6a6e7c3def02a00a07aa0424b0d750db4",
    "scrolled": true
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    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80638</th>\n",
       "      <td>AZY10LLTJ71NX</td>\n",
       "      <td>B001ATMQK2</td>\n",
       "      <td>undertheshrine \"undertheshrine\"</td>\n",
       "      <td>1296691200</td>\n",
       "      <td>5</td>\n",
       "      <td>I bought this 6 pack because for the price tha...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              UserId   ProductId                      ProfileName        Time  \\\n",
       "80638  AZY10LLTJ71NX  B001ATMQK2  undertheshrine \"undertheshrine\"  1296691200   \n",
       "\n",
       "       Score                                               Text  COUNT(*)  \n",
       "80638      5  I bought this 6 pack because for the price tha...         5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "af566a69aaac0a9c20332ccff048d3d83be0a9bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393063"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display['COUNT(*)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "395692b42f9e8e7593770307c9676b90e2934ba6"
   },
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1517e04c7680a6aa5e0b45389300e00a08954701"
   },
   "source": [
    "## [2.1] Data Cleaning: Deduplication\n",
    "\n",
    "It is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "a5df497f64d06e78eb9f389127e86cd6ad936f6b",
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78445</td>\n",
       "      <td>B000HDL1RQ</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138317</td>\n",
       "      <td>B000HDOPYC</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138277</td>\n",
       "      <td>B000HDOPYM</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73791</td>\n",
       "      <td>B000HDOPZG</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155049</td>\n",
       "      <td>B000PAQ75C</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
       "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "\n",
       "   HelpfulnessDenominator  Score        Time  \\\n",
       "0                       2      5  1199577600   \n",
       "1                       2      5  1199577600   \n",
       "2                       2      5  1199577600   \n",
       "3                       2      5  1199577600   \n",
       "4                       2      5  1199577600   \n",
       "\n",
       "                             Summary  \\\n",
       "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "\n",
       "                                                Text  \n",
       "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND UserId=\"AR5J8UI46CURR\"\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "33310cb688a43bf179116ccf404fc6ebf0148441"
   },
   "source": [
    "As it can be seen above that same user has multiple reviews with same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text and on doing analysis it was found that <br>\n",
    "<br> \n",
    "ProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n",
    "<br> \n",
    "ProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n",
    "\n",
    "It was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n",
    "\n",
    "The method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "2410ee728ea452f96b98e5b9321363f02827d921"
   },
   "outputs": [],
   "source": [
    "#Sorting data according to SCORE in ascending order\n",
    "# sorted_data=filtered_data.sort_values('Score', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "4913ce55ad43244d123291725fd148ae2c8f8010"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    41788\n",
       "0     8212\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "e8b6aafa9112d12b4b90f1458889763f48298b5d"
   },
   "outputs": [],
   "source": [
    "#Sorting data according to TIME in ascending order\n",
    "sorted_data=filtered_data.sort_values('Time', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "3f2b18364973531f0d208427975c5e08d0b72ea5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>1245</td>\n",
       "      <td>B00002Z754</td>\n",
       "      <td>A29Z5PI9BW2PU3</td>\n",
       "      <td>Robbie</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>961718400</td>\n",
       "      <td>Great Product</td>\n",
       "      <td>This was a really good idea and the final prod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>1244</td>\n",
       "      <td>B00002Z754</td>\n",
       "      <td>A3B8RCEI0FXFI6</td>\n",
       "      <td>B G Chase</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>962236800</td>\n",
       "      <td>WOW Make your own 'slickers' !</td>\n",
       "      <td>I just received my shipment and could hardly w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28087</th>\n",
       "      <td>30630</td>\n",
       "      <td>B00008RCMI</td>\n",
       "      <td>A284C7M23F0APC</td>\n",
       "      <td>A. Mendoza</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1067040000</td>\n",
       "      <td>Best sugarless gum ever!</td>\n",
       "      <td>I love this stuff. It is sugar-free so it does...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28086</th>\n",
       "      <td>30629</td>\n",
       "      <td>B00008RCMI</td>\n",
       "      <td>A19E94CF5O1LY7</td>\n",
       "      <td>Andrew Arnold</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1067040000</td>\n",
       "      <td>I've chewed this gum many times, but used?</td>\n",
       "      <td>Nothing against the product, but it does bothe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38740</th>\n",
       "      <td>42069</td>\n",
       "      <td>B0000EIEQU</td>\n",
       "      <td>A1YMJX4YWCE6P4</td>\n",
       "      <td>Jim Carson \"http://www.jimcarson.com\"</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1067558400</td>\n",
       "      <td>These limes rule!</td>\n",
       "      <td>Fresh limes are underappreciated, but a joy to...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id   ProductId          UserId  \\\n",
       "1146    1245  B00002Z754  A29Z5PI9BW2PU3   \n",
       "1145    1244  B00002Z754  A3B8RCEI0FXFI6   \n",
       "28087  30630  B00008RCMI  A284C7M23F0APC   \n",
       "28086  30629  B00008RCMI  A19E94CF5O1LY7   \n",
       "38740  42069  B0000EIEQU  A1YMJX4YWCE6P4   \n",
       "\n",
       "                                 ProfileName  HelpfulnessNumerator  \\\n",
       "1146                                  Robbie                     7   \n",
       "1145                               B G Chase                    10   \n",
       "28087                             A. Mendoza                     0   \n",
       "28086                          Andrew Arnold                     0   \n",
       "38740  Jim Carson \"http://www.jimcarson.com\"                    12   \n",
       "\n",
       "       HelpfulnessDenominator  Score        Time  \\\n",
       "1146                        7      1   961718400   \n",
       "1145                       10      1   962236800   \n",
       "28087                       0      1  1067040000   \n",
       "28086                       0      1  1067040000   \n",
       "38740                      12      1  1067558400   \n",
       "\n",
       "                                          Summary  \\\n",
       "1146                                Great Product   \n",
       "1145               WOW Make your own 'slickers' !   \n",
       "28087                    Best sugarless gum ever!   \n",
       "28086  I've chewed this gum many times, but used?   \n",
       "38740                           These limes rule!   \n",
       "\n",
       "                                                    Text  \n",
       "1146   This was a really good idea and the final prod...  \n",
       "1145   I just received my shipment and could hardly w...  \n",
       "28087  I love this stuff. It is sugar-free so it does...  \n",
       "28086  Nothing against the product, but it does bothe...  \n",
       "38740  Fresh limes are underappreciated, but a joy to...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "eacbf9a0d146431b823e9f63e71a068a34cf59b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    41788\n",
       "0     8212\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "30443f0b8903f562782da64f907d0ed5d16b9e29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46072, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deduplication of entries\n",
    "final=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "c86178175c0f159fa4c408ab9542dc4b1e6d4bd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    38480\n",
       "0     7592\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "502ffa6bebd36b9f156b71c49cad259de35ea43b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.144"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f427e035c3f008beda8676b8731aeed18eab465f"
   },
   "source": [
    "<b>Observation:-</b> It was also seen that in two rows given below the value of HelpfulnessNumerator is greater than HelpfulnessDenominator which is not practically possible hence these two rows too are removed from calcualtions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "bbd3ba13f6ce41d4bbba2c497677a76c8a3e1f6d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   ProductId          UserId              ProfileName  \\\n",
       "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     3                       1      5  1224892800   \n",
       "1                     3                       2      4  1212883200   \n",
       "\n",
       "                                        Summary  \\\n",
       "0             Bought This for My Son at College   \n",
       "1  Pure cocoa taste with crunchy almonds inside   \n",
       "\n",
       "                                                Text  \n",
       "0  My son loves spaghetti so I didn't hesitate or...  \n",
       "1  It was almost a 'love at first bite' - the per...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND Id=44737 OR Id=64422\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "3e2f32a506631b563323b704cf3958a864b83ef0"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "25c8d59ae4476a7969ab7a2e2e92cc83a1bf43c8",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46071, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    38479\n",
       "0     7592\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Before starting the next phase of preprocessing lets see the number of entries left\n",
    "print(final.shape)\n",
    "\n",
    "#How many positive and negative reviews are present in our dataset?\n",
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "7a58edb5ae948097cd1f90efc0797fe863093e18"
   },
   "outputs": [],
   "source": [
    "x=final.drop('Score',axis=1)\n",
    "y=final.filter(['Score'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "35cdf0ec9b2a38cfc7400f7bc382ee7c3c8115ea",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((46071, 9), (46071, 1))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "c6bcc9dfa710d14ba9c7cfbcd5789f80bf8c0083",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46071"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "16694732c0f757fa2ced0c96a842c901f13c3a56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32249.699999999997"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.7*46071"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "19344da79047c7b883a97ee6dc5269fc3e24409b"
   },
   "source": [
    "### TIME BASE SPLITTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "449fbacbb18a001eff2836525a4240159551fda4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22574.789999999997"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32249.699999999997*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "7ac2c81249b91d33cd6cd82122c13a0ff05ae7bb"
   },
   "outputs": [],
   "source": [
    "x_tr=x[:22574]\n",
    "y_tr=y[:22574]\n",
    "x_cv=x[22574:32249]\n",
    "y_cv=y[22574:32249]\n",
    "x_test=x[32249:]\n",
    "y_test=y[32249:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "95dbda74797ec1ab2b808a174a776178a9165f2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22574, 9), (22574, 1))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_tr.shape,y_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "592bb2a6fb774c6bf83cfb2461083b4046a49520"
   },
   "outputs": [],
   "source": [
    "x_ker=x[:40000]\n",
    "y_ker=y[:40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "2b04bf9747d8b8e499815a9df8dfd3ab74a605ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28000.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "40000*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "f35cbe282381ef7298bfdd7c14cd902dfec75743"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19600.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28000*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "6599c7f64a7ac1d7a457daf1e9a032f4e32462c8"
   },
   "outputs": [],
   "source": [
    "x_ker_tr=x_ker[:19600]\n",
    "y_ker_tr=y_ker[:19600]\n",
    "x_ker_cv=x_ker[19600:28000]\n",
    "y_ker_cv=y_ker[19600:28000]\n",
    "x_ker_test=x_ker[28000:]\n",
    "y_ker_test=y_ker[28000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "0b6f9af14795222689055a740161a716dc5f56ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    19232\n",
       "0     3342\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_tr['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ef334e7137ba5b557f612e7e7c2eedb6c207ca04"
   },
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "02673331c2a15d63d3089528c488c2741eabf3af"
   },
   "source": [
    "## [3.1].  Preprocessing Review Text\n",
    "\n",
    "Now that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n",
    "\n",
    "Hence in the Preprocessing phase we do the following in the order below:-\n",
    "\n",
    "1. Begin by removing the html tags\n",
    "2. Remove any punctuations or limited set of special characters like , or . or # etc.\n",
    "3. Check if the word is made up of english letters and is not alpha-numeric\n",
    "4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n",
    "5. Convert the word to lowercase\n",
    "6. Remove Stopwords\n",
    "7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n",
    "\n",
    "After which we collect the words used to describe positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "1525756c13828079b78884fb5ee9a93b6b26e90b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was a really good idea and the final product is outstanding. I use the decals on my car window and everybody asks where i bought the decals i made.  Two thumbs up!\n",
      "==================================================\n",
      "If your trying to do a low carb product.  This works out great.  There are receipies on tova's website on how to make poundcake, pancakes, and a variety of other items.  They turn out pretty great.<br /><br />Try it.  It's a great product.<br /><br /> ~Donna\n",
      "==================================================\n",
      "The orange and lemon peels make this tea very hippy.  Despite  the initial oohing and ahing over the pretty blue flowers, this is a regrettable purchase.  I was hoping for a stronger bergamot component than Twinings' Earl Grey but instead I got something that seems very herbal.  Blech.  I disagree with the positive reviews.\n",
      "==================================================\n",
      "I tried to change my review to 4 stars, but I don't seem able to do that.  These are 3 oz cans of 4 different flavors of fish based catfood.  Chicken and rabbit are often more recommended for cats.  The first ingredient is fish broth, making it somewhat high in moisture.  I've heard that cats often need more water than they drink, so I'd assume its a good thing to get more moisture down them, but this is an expensive way to do it (you could just add a little water to their food).  The good news is that there appears to be no grains in the ingredient list, looks like almost all meat based (except for the usual additives).  The grains in most catfoods are thought to not be good for the cats.  This catfood is not cheap, but it appears to be a better than usual cat food.\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# printing some random reviews\n",
    "sent_0 = final['Text'].values[0]\n",
    "print(sent_0)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_1000 = final['Text'].values[1000]\n",
    "print(sent_1000)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_1500 = final['Text'].values[1500]\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)\n",
    "\n",
    "sent_4900 = final['Text'].values[4900]\n",
    "print(sent_4900)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "d878cb3769859f1e4f2ed74544b0e1b796c928ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was a really good idea and the final product is outstanding. I use the decals on my car window and everybody asks where i bought the decals i made.  Two thumbs up!\n"
     ]
    }
   ],
   "source": [
    "# remove urls from text python: https://stackoverflow.com/a/40823105/4084039\n",
    "sent_0 = re.sub(r\"http\\S+\", \"\", sent_0)\n",
    "sent_1000 = re.sub(r\"http\\S+\", \"\", sent_1000)\n",
    "sent_150 = re.sub(r\"http\\S+\", \"\", sent_1500)\n",
    "sent_4900 = re.sub(r\"http\\S+\", \"\", sent_4900)\n",
    "\n",
    "print(sent_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "cd71451a6ed21de51cbb458bf4ab3d0dd8b0698b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was a really good idea and the final product is outstanding. I use the decals on my car window and everybody asks where i bought the decals i made.  Two thumbs up!\n",
      "==================================================\n",
      "If your trying to do a low carb product.  This works out great.  There are receipies on tova's website on how to make poundcake, pancakes, and a variety of other items.  They turn out pretty great.Try it.  It's a great product. ~Donna\n",
      "==================================================\n",
      "The orange and lemon peels make this tea very hippy.  Despite  the initial oohing and ahing over the pretty blue flowers, this is a regrettable purchase.  I was hoping for a stronger bergamot component than Twinings' Earl Grey but instead I got something that seems very herbal.  Blech.  I disagree with the positive reviews.\n",
      "==================================================\n",
      "I tried to change my review to 4 stars, but I don't seem able to do that.  These are 3 oz cans of 4 different flavors of fish based catfood.  Chicken and rabbit are often more recommended for cats.  The first ingredient is fish broth, making it somewhat high in moisture.  I've heard that cats often need more water than they drink, so I'd assume its a good thing to get more moisture down them, but this is an expensive way to do it (you could just add a little water to their food).  The good news is that there appears to be no grains in the ingredient list, looks like almost all meat based (except for the usual additives).  The grains in most catfoods are thought to not be good for the cats.  This catfood is not cheap, but it appears to be a better than usual cat food.\n"
     ]
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/16206380/python-beautifulsoup-how-to-remove-all-tags-from-an-element\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "soup = BeautifulSoup(sent_0, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_1000, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_1500, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)\n",
    "print(\"=\"*50)\n",
    "\n",
    "soup = BeautifulSoup(sent_4900, 'lxml')\n",
    "text = soup.get_text()\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "a016a511b6230916ac661443e7fb536acb664eae"
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/47091490/4084039\n",
    "import re\n",
    "\n",
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "1a1068e3176ddd0bb8455b2b4a7bf5fa750e72f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The orange and lemon peels make this tea very hippy.  Despite  the initial oohing and ahing over the pretty blue flowers, this is a regrettable purchase.  I was hoping for a stronger bergamot component than Twinings' Earl Grey but instead I got something that seems very herbal.  Blech.  I disagree with the positive reviews.\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "sent_1500 = decontracted(sent_1500)\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "1e649ef6115c8da6c5c5031d4ab61e68ad14e790"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was a really good idea and the final product is outstanding. I use the decals on my car window and everybody asks where i bought the decals i made.  Two thumbs up!\n"
     ]
    }
   ],
   "source": [
    "#remove words with numbers python: https://stackoverflow.com/a/18082370/4084039\n",
    "sent_0 = re.sub(\"\\S*\\d\\S*\", \"\", sent_0).strip()\n",
    "print(sent_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "c35f1a2fb7c89d3cf4251e71881027be34f21abd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The orange and lemon peels make this tea very hippy Despite the initial oohing and ahing over the pretty blue flowers this is a regrettable purchase I was hoping for a stronger bergamot component than Twinings Earl Grey but instead I got something that seems very herbal Blech I disagree with the positive reviews \n"
     ]
    }
   ],
   "source": [
    "#remove spacial character: https://stackoverflow.com/a/5843547/4084039\n",
    "sent_1500 = re.sub('[^A-Za-z0-9]+', ' ', sent_1500)\n",
    "print(sent_1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "bf2b2db350f7b12ef7663505a88c52960d7f5d1d"
   },
   "outputs": [],
   "source": [
    "# https://gist.github.com/sebleier/554280\n",
    "# we are removing the words from the stop words list: 'no', 'nor', 'not'\n",
    "# <br /><br /> ==> after the above steps, we are getting \"br br\"\n",
    "# we are including them into stop words list\n",
    "# instead of <br /> if we have <br/> these tags would have revmoved in the 1st step\n",
    "\n",
    "stopwords= set(['br', 'the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
    "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d8e0bdfd2aeab6ebd88f87fa5d1fd50bdb84a3f8"
   },
   "source": [
    "#### PREPROCESSING OF TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "41e183e5747f5d3a68742fdd83186a8d1a39571d"
   },
   "outputs": [],
   "source": [
    "def prepro(x):\n",
    "    # Combining all the above stundents \n",
    "    from tqdm import tqdm\n",
    "    preprocessed_reviews = []\n",
    "    # tqdm is for printing the status bar\n",
    "    for sentance in tqdm(x['Text'].values):\n",
    "        sentance = re.sub(r\"http\\S+\", \"\", sentance)\n",
    "        sentance = BeautifulSoup(sentance, 'lxml').get_text()\n",
    "        sentance = decontracted(sentance)\n",
    "        sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n",
    "        sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n",
    "        # https://gist.github.com/sebleier/554280\n",
    "        sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n",
    "        preprocessed_reviews.append(sentance.strip())\n",
    "    return preprocessed_reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "795f087e8c33452b529e45af73c29acbf944ce4a"
   },
   "source": [
    "### PREPROCESSING TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "2e9be943818a95ba123d318ec9285890b242365f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 22574/22574 [00:12<00:00, 1862.61it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews=prepro(x_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fac198f50d7432c5945c3e9d8f7124a5618eb7c2"
   },
   "source": [
    "### PREPROCESSING CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "4280f3dc18f27d5ffd3cab490b3d055a567caad7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 9675/9675 [00:05<00:00, 1843.57it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews_cv=prepro(x_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "94390ad295e56f3bf4d1006c5ac10c7d5de7210e"
   },
   "source": [
    "### PREPROCESSING TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "0220e6dffc05038400c50ccdd3366d47a578f5c9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 13822/13822 [00:07<00:00, 1858.50it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews_test=prepro(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6fa293bf39c2bd7894a69cda9cc1ce6ae34c765f"
   },
   "source": [
    "### PREPROCESSING KERNEL TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "ade5b906a717e363fa2de7e081fbd6e2f78d8408",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 19600/19600 [00:10<00:00, 1854.53it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews_ker_tr=prepro(x_ker_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "26329e05d50ddc35c66e61b20b132485433a2fd4"
   },
   "source": [
    "### PREPROCESSING KERNEL CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "adefd57989b31187b2c19c29555fce9db388b632",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 8400/8400 [00:04<00:00, 1903.81it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews_ker_cv=prepro(x_ker_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "afc2bb870cc9c7b5867b9f9938684f122798c9d5"
   },
   "source": [
    "### PREPROCESSING KERNEL TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "2fcb406b4107d8391c3cb18cf27cc77be0b0da9a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 12000/12000 [00:06<00:00, 1979.25it/s]\n"
     ]
    }
   ],
   "source": [
    "preprocessed_reviews_ker_test=prepro(x_ker_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6c767940bce6e335fb6e37f91db5f2a85816ae96"
   },
   "source": [
    "<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "520c563cd1a0989e6d4f71c63e967df8e8acc5a9",
    "collapsed": true
   },
   "source": [
    "## Similartly you can do preprocessing for review summary also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9cbe6b17ce497e13e9cbec1631a054122c63a63c"
   },
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "601e7c3f97ddd8c064001d3e827710950dd41c70"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "402991df943d95fa95aa0a9ac7f6897defedad79"
   },
   "source": [
    "# FOR LINEAR SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5ab6ab1f127a4dd370085e438000747aadd7b44a"
   },
   "source": [
    "#### VECTORIZING FOR  TRAIN DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "8f3ae38dd5a3a3b9bc2b1fd10400ea129d257034",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaaa', 'aaaaaaaaaaaaaaa', 'aaaaah', 'aaaand', 'aachen', 'aadp', 'aaf', 'aafco', 'aahing']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (22574, 18420)\n",
      "the number of unique words  18420\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer(max_features=18420) #in scikit-learn\n",
    "count_vect.fit(preprocessed_reviews)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts = count_vect.fit_transform(preprocessed_reviews)\n",
    "print(\"the type of count vectorizer \",type(final_counts))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts.get_shape())\n",
    "print(\"the number of unique words \", final_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "253287d11f38a90e4716be7d82db021884b8e508"
   },
   "source": [
    "#### VECTORIZING FOR CV DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "8feb9ed7cedd44742e980bc6637f097cb8c8196b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaaa', 'aaaaaaaaaaaa', 'aaaaaaahhhhhh', 'aaaaaawwwwwwwwww', 'aaah', 'aahs', 'aarthur', 'aback']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (9675, 18420)\n",
      "the number of unique words  18420\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect.fit(preprocessed_reviews_cv)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts_cv = count_vect.transform(preprocessed_reviews_cv)\n",
    "print(\"the type of count vectorizer \",type(final_counts_cv))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts_cv.get_shape())\n",
    "print(\"the number of unique words \", final_counts_cv.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7bf34c560867d4add29d60031c081eb0b65a8d52"
   },
   "source": [
    "#### VECTORIZING FOR TEST DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "6c28d253b7642caa790710c69e04bd6a68333c4e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaa', 'aafco', 'aah', 'aahhhs', 'aback', 'abandoned', 'abates', 'abb']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (13822, 18420)\n",
      "the number of unique words  18420\n"
     ]
    }
   ],
   "source": [
    " #in scikit-learn\n",
    "count_vect.fit(preprocessed_reviews_test)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts_test= count_vect.transform(preprocessed_reviews_test)\n",
    "print(\"the type of count vectorizer \",type(final_counts_test))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts_test.get_shape())\n",
    "print(\"the number of unique words \", final_counts_test.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "70f13f1442ff3668576d66528d3d5886a07335ef"
   },
   "source": [
    "## FOR KERNEL SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3ba9cf76105b3954e7272a358ae99aa184b0ba7f"
   },
   "source": [
    "#### VECTORIZING FOR  TRAIN DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "736c9d29213b486afb5324914256b049dfb8c7e6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['able', 'absolutely', 'actually', 'add', 'added', 'aftertaste', 'ago', 'almonds', 'almost', 'also']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (19600, 500)\n",
      "the number of unique words  500\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect_ker= CountVectorizer(min_df=10,max_features=500) #in scikit-learn\n",
    "count_vect_ker.fit(preprocessed_reviews_ker_tr)\n",
    "print(\"some feature names \", count_vect_ker.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts_ker = count_vect_ker.fit_transform(preprocessed_reviews_ker_tr)\n",
    "print(\"the type of count vectorizer \",type(final_counts_ker))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts_ker.get_shape())\n",
    "print(\"the number of unique words \", final_counts_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2e43013c6aa52ee8342a734355ddf9333dd550e8"
   },
   "source": [
    "#### VECTORIZING FOR CV DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "075c6db8d6a70d62e16f48cf807a7becbd9e9fef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['able', 'absolutely', 'acid', 'actually', 'add', 'added', 'adding', 'aftertaste', 'ago', 'almost']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (8400, 500)\n",
      "the number of unique words  500\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect_ker.fit(preprocessed_reviews_ker_cv)\n",
    "print(\"some feature names \", count_vect_ker.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts_cv_ker = count_vect_ker.transform(preprocessed_reviews_ker_cv)\n",
    "print(\"the type of count vectorizer \",type(final_counts_cv_ker))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts_cv_ker.get_shape())\n",
    "print(\"the number of unique words \", final_counts_cv_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "59da17dd749906e1cd31a8b9e4afd1e8cb349726"
   },
   "source": [
    "#### VECTORIZING FOR TEST DATA IN BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "17e423029e8125d8e7f20548c0f5495841c7bb15",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['able', 'absolutely', 'actually', 'add', 'added', 'adding', 'aftertaste', 'ago', 'almonds', 'almost']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (12000, 500)\n",
      "the number of unique words  500\n"
     ]
    }
   ],
   "source": [
    " #in scikit-learn\n",
    "count_vect_ker.fit(preprocessed_reviews_ker_test)\n",
    "print(\"some feature names \", count_vect_ker.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "final_counts_test_ker= count_vect_ker.transform(preprocessed_reviews_ker_test)\n",
    "print(\"the type of count vectorizer \",type(final_counts_test_ker))\n",
    "print(\"the shape of out text BOW vectorizer \",final_counts_test_ker.get_shape())\n",
    "print(\"the number of unique words \", final_counts_test_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "65b4459945c9d0f545b1b2fc93f6b9ba6f13b41a"
   },
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "f0925ecbdf017eb216638185a065ac57f7372021"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (22574, 5000)\n",
      "the number of unique words including both unigrams and bigrams  5000\n"
     ]
    }
   ],
   "source": [
    "#bi-gram, tri-gram and n-gram\n",
    "\n",
    "#removing stop words like \"not\" should be avoided before building n-grams\n",
    "# count_vect = CountVectorizer(ngram_range=(1,2))\n",
    "# please do read the CountVectorizer documentation http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "\n",
    "# you can choose these numebrs min_df=10, max_features=5000, of your choice\n",
    "count_vect = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=5000)\n",
    "final_bigram_counts = count_vect.fit_transform(preprocessed_reviews)\n",
    "print(\"the type of count vectorizer \",type(final_bigram_counts))\n",
    "print(\"the shape of out text BOW vectorizer \",final_bigram_counts.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_bigram_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "012d18901c936b8cf9287bed4fc0639b6bf5f00b"
   },
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1b89b890a999e14bb111594a2e0a0b98b4ac2a19"
   },
   "source": [
    "## LINEAR SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "44fcf1a565644f01990104925a6bd669db0ac5b8"
   },
   "source": [
    "#### TF-IDF FOR TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "1781cdad44f3cd1fd617f6d8a73446016e82994c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['ability', 'able', 'able buy', 'able find', 'able get', 'able order', 'absolute', 'absolute favorite', 'absolutely', 'absolutely best']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (22574, 5990)\n",
      "the number of unique words including both unigrams and bigrams  5990\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10,max_features=5990)\n",
    "tf_idf_vect.fit_transform(preprocessed_reviews)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf = tf_idf_vect.fit_transform(preprocessed_reviews)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e60cddb811a1dcca68371b32326ba219d1abfc18"
   },
   "source": [
    "#### TF-IDF FOR CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "f90271f7c48d7761f3c4fe6087dd828ec37e3491"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['ability', 'able', 'able buy', 'able drink', 'able eat', 'able find', 'able get', 'able make', 'able order', 'able use']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (9675, 5990)\n",
      "the number of unique words including both unigrams and bigrams  5990\n"
     ]
    }
   ],
   "source": [
    "# tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "tf_idf_vect.fit_transform(preprocessed_reviews_cv)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf_cv = tf_idf_vect.transform(preprocessed_reviews_cv)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_cv))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_cv.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_cv.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "891d263595cadc2570021446f7a99d68724395ad"
   },
   "source": [
    "#### TF-IDF FOR TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "1e1db38dcac31480926c76ef54a9a56f5156114e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['able', 'able buy', 'able eat', 'able find', 'able get', 'absolute', 'absolute best', 'absolute favorite', 'absolutely', 'absolutely best']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (13822, 5990)\n",
      "the number of unique words including both unigrams and bigrams  5990\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect.fit_transform(preprocessed_reviews_test)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf_test = tf_idf_vect.transform(preprocessed_reviews_test)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_test))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_test.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_test.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "84c8dfc724766e2c81279002a2bc2558e5e181b5"
   },
   "source": [
    "## KERNEL SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "8e5716174be988bc0f90acfcbda74899af2c24bd",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['able', 'absolutely', 'actually', 'add', 'added', 'aftertaste', 'ago', 'almonds', 'almost', 'also']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (19600, 500)\n",
      "the number of unique words including both unigrams and bigrams  500\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect_ker = TfidfVectorizer(ngram_range=(1,2), min_df=10,max_features=500)\n",
    "tf_idf_vect_ker.fit_transform(preprocessed_reviews_ker_tr)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect_ker.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf_ker = tf_idf_vect_ker.fit_transform(preprocessed_reviews_ker_tr)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_ker))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_ker.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c6e649673705340d06492d633377b4cb8bbe02e4"
   },
   "source": [
    "#### TF-IDF FOR CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "d33264eb60ee767b34dcb5c2a92015f5f2df4795"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['able', 'absolutely', 'acid', 'actually', 'add', 'added', 'adding', 'aftertaste', 'ago', 'almost']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (8400, 500)\n",
      "the number of unique words including both unigrams and bigrams  500\n"
     ]
    }
   ],
   "source": [
    "# tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "tf_idf_vect_ker.fit_transform(preprocessed_reviews_ker_cv)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect_ker.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf_cv_ker = tf_idf_vect_ker.transform(preprocessed_reviews_ker_cv)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_cv_ker))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_cv_ker.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_cv_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4cf2f7a00be7146f108e66db5279ae0d5a479ee5"
   },
   "source": [
    "#### TF-IDF FOR TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "86d07da08f65cd7006c12f05156efe4eb2c63314"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['able', 'absolutely', 'actually', 'add', 'added', 'aftertaste', 'ago', 'almonds', 'almost', 'also']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (12000, 500)\n",
      "the number of unique words including both unigrams and bigrams  500\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect_ker.fit_transform(preprocessed_reviews_ker_test)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect_ker.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "final_tf_idf_test_ker = tf_idf_vect_ker.transform(preprocessed_reviews_ker_test)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_test_ker))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_test_ker.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_test_ker.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a72b9ccfd485fe8157b60d4ea77edb961941add6"
   },
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3f5aa10d3d03348d9197e0a4674246994dd9a7bf"
   },
   "source": [
    "#### w2v for TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "c19b9eb84f8acf51e30039509fcbf2cb6a268f4e"
   },
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "i=0\n",
    "list_of_sentance=[]\n",
    "for sentance in preprocessed_reviews:\n",
    "    list_of_sentance.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "cd6d319e6109c3feee03cf0bd1b99bc930ecc248",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('awesome', 0.8097169399261475), ('fantastic', 0.7998897433280945), ('good', 0.7975150346755981), ('excellent', 0.7899484634399414), ('terrific', 0.7849385738372803), ('wonderful', 0.7690883278846741), ('perfect', 0.7659608125686646), ('amazing', 0.7048388719558716), ('decent', 0.6845704317092896), ('satisfied', 0.6685174107551575)]\n",
      "==================================================\n",
      "[('ive', 0.8017846345901489), ('best', 0.7953061461448669), ('tastiest', 0.7877910137176514), ('sampled', 0.7383346557617188), ('nastiest', 0.73429274559021), ('eaten', 0.7111603021621704), ('closest', 0.7109853029251099), ('hooked', 0.7103910446166992), ('encountered', 0.6979016065597534), ('beer', 0.6953872442245483)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model=Word2Vec(list_of_sentance,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "e6320698f1fd1771a1d41c5cab1d83f70da522ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  9130\n",
      "sample words  ['really', 'good', 'idea', 'final', 'product', 'outstanding', 'use', 'car', 'window', 'everybody', 'asks', 'bought', 'made', 'two', 'thumbs', 'received', 'shipment', 'could', 'hardly', 'wait', 'try', 'love', 'call', 'instead', 'stickers', 'removed', 'easily', 'daughter', 'designed', 'signs', 'printed', 'reverse', 'windows', 'beautifully', 'print', 'shop', 'program', 'going', 'lot', 'fun', 'everywhere', 'like', 'tv', 'computer', 'stuff', 'sugar', 'free', 'not', 'rot', 'gums']\n"
     ]
    }
   ],
   "source": [
    "w2v_words = list(w2v_model.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words))\n",
    "print(\"sample words \", w2v_words[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4221fa5fd8e22038ff0a1f250346bcefbde4fa04"
   },
   "source": [
    "#### w2v FOR CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "121616babeac685047e42613f4d162be119a2907"
   },
   "outputs": [],
   "source": [
    "\n",
    "i=0\n",
    "list_of_sentance_cv=[]\n",
    "for sentance in preprocessed_reviews_cv:\n",
    "    list_of_sentance_cv.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "bd3a44832397535fe429507dbc0e36f418547b79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('excellent', 0.8669454455375671), ('good', 0.8494023084640503), ('makes', 0.8051506280899048), ('well', 0.7669053077697754), ('overall', 0.7641651034355164), ('fantastic', 0.761696457862854), ('awesome', 0.7582005858421326), ('right', 0.7565374970436096), ('works', 0.7521684765815735), ('decent', 0.7504843473434448)]\n",
      "==================================================\n",
      "[('favorites', 0.9824845790863037), ('personal', 0.9819837808609009), ('vita', 0.9815971851348877), ('hands', 0.9809101223945618), ('cherry', 0.9806168079376221), ('mate', 0.9805591106414795), ('blueberry', 0.9804791808128357), ('eggnog', 0.9797982573509216), ('beverages', 0.9772326350212097), ('finest', 0.9770099520683289)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model_cv=Word2Vec(list_of_sentance_cv,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model_cv.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model_cv.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "c889b96f5d506b795be572fd166f2e0b1abb5bdc",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  5730\n",
      "sample words  ['put', 'efficient', 'use', 'researching', 'best', 'organic', 'cat', 'food', 'price', 'found', 'delighted', 'one', 'thing', 'always', 'signed', 'auto', 'ship', 'service', 'stated', 'would', 'able', 'change', 'frequency', 'order', 'time', 'love', 'saves', 'receive', 'free', 'shipping', 'errands', 'especially', 'seemingly', 'endless', 'fabulous', 'idea', 'running', 'scary', 'midnight', 'realize', 'master', 'learned', 'people', 'dogs', 'cats', 'true', 'though', 'remain', 'charge', 'also']\n"
     ]
    }
   ],
   "source": [
    "w2v_words_cv = list(w2v_model_cv.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words_cv))\n",
    "print(\"sample words \", w2v_words_cv[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d892e6666210cc7d34b95823bef16db65603b6d0"
   },
   "source": [
    "#### w2v FOR TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_uuid": "5e64ac781220ac1f689b3e84ba92fff06f2c1260"
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sentance_test=[]\n",
    "for sentance in preprocessed_reviews_test:\n",
    "    list_of_sentance_test.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_uuid": "5c1a2fed7ec29b49ea1e9d5f48fa19be0f00faa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('excellent', 0.846183180809021), ('good', 0.8266117572784424), ('amazing', 0.7669278979301453), ('awesome', 0.7319532632827759), ('fantastic', 0.7222875952720642), ('wonderful', 0.7191231846809387), ('delicious', 0.7073187828063965), ('super', 0.6862500905990601), ('love', 0.6776154041290283), ('well', 0.673266589641571)]\n",
      "==================================================\n",
      "[('smoothest', 0.9292718768119812), ('compare', 0.9289912581443787), ('dave', 0.9238349199295044), ('name', 0.9229879975318909), ('remember', 0.9221051931381226), ('concentrates', 0.9214602708816528), ('duplicate', 0.9208076596260071), ('admit', 0.9203765392303467), ('fillings', 0.9193471074104309), ('maria', 0.9185217618942261)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model_test=Word2Vec(list_of_sentance_test,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model_test.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model_test.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "293b91898efd05eb793e705d85379b09b6f4da9b",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  6980\n",
      "sample words  ['orange', 'peels', 'great', 'tasting', 'love', 'honey', 'added', 'used', 'baking', 'snacking', 'avoderm', 'best', 'dog', 'food', 'found', 'tried', 'many', 'brands', 'last', 'years', 'large', 'dogs', 'keep', 'fit', 'help', 'senior', 'blend', 'bought', 'local', 'grocery', 'store', 'wish', 'would', 'read', 'review', 'first', 'positive', 'hold', 'together', 'well', 'look', 'like', 'muffin', 'negative', 'bitter', 'could', 'barely', 'finish', 'half', 'recently']\n"
     ]
    }
   ],
   "source": [
    "w2v_words_test = list(w2v_model_test.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words_test))\n",
    "print(\"sample words \", w2v_words_test[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f8dcbac64b1f19ea6e6cbef618817a976476b457"
   },
   "source": [
    "### FOR KERNEL SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "c19b9eb84f8acf51e30039509fcbf2cb6a268f4e"
   },
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "i=0\n",
    "list_of_sentance_ker=[]\n",
    "for sentance in preprocessed_reviews_ker_tr:\n",
    "    list_of_sentance_ker.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_uuid": "cd6d319e6109c3feee03cf0bd1b99bc930ecc248",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('good', 0.8170279860496521), ('awesome', 0.8031275272369385), ('fantastic', 0.8004112839698792), ('excellent', 0.800104022026062), ('wonderful', 0.7980159521102905), ('perfect', 0.7598353624343872), ('decent', 0.7313312292098999), ('delicious', 0.7253332138061523), ('terrific', 0.7174073457717896), ('amazing', 0.7136910557746887)]\n",
      "==================================================\n",
      "[('ive', 0.823634922504425), ('tastiest', 0.8183972835540771), ('sampled', 0.8073025941848755), ('best', 0.8070061802864075), ('encountered', 0.790328860282898), ('greatest', 0.7897909879684448), ('hottest', 0.789413571357727), ('hooked', 0.7886714339256287), ('closest', 0.7757998108863831), ('eaten', 0.7539447546005249)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model_ker=Word2Vec(list_of_sentance_ker,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model_ker.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model_ker.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "e6320698f1fd1771a1d41c5cab1d83f70da522ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  8501\n",
      "sample words  ['really', 'good', 'idea', 'final', 'product', 'outstanding', 'use', 'car', 'window', 'everybody', 'asks', 'bought', 'made', 'two', 'thumbs', 'received', 'shipment', 'could', 'hardly', 'wait', 'try', 'love', 'call', 'instead', 'stickers', 'removed', 'easily', 'daughter', 'designed', 'signs', 'printed', 'reverse', 'windows', 'beautifully', 'print', 'shop', 'program', 'going', 'lot', 'fun', 'everywhere', 'like', 'tv', 'computer', 'stuff', 'sugar', 'free', 'not', 'rot', 'gums']\n"
     ]
    }
   ],
   "source": [
    "w2v_words_ker = list(w2v_model_ker.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words_ker))\n",
    "print(\"sample words \", w2v_words_ker[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4221fa5fd8e22038ff0a1f250346bcefbde4fa04"
   },
   "source": [
    "#### w2v FOR CV DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "121616babeac685047e42613f4d162be119a2907"
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sentance_cv_ker=[]\n",
    "for sentance in preprocessed_reviews_ker_cv:\n",
    "    list_of_sentance_cv_ker.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "bd3a44832397535fe429507dbc0e36f418547b79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('excellent', 0.8719509840011597), ('good', 0.8643816709518433), ('makes', 0.8382137417793274), ('fantastic', 0.8201830387115479), ('overall', 0.816028892993927), ('value', 0.8155813217163086), ('tasteful', 0.8047893047332764), ('right', 0.7998217344284058), ('amazing', 0.7774211168289185), ('outstanding', 0.7760794162750244)]\n",
      "==================================================\n",
      "[('popular', 0.9938234090805054), ('pu', 0.9925667643547058), ('cappuccino', 0.9909967184066772), ('er', 0.9909411668777466), ('cola', 0.9901368618011475), ('absolute', 0.9901279807090759), ('midnight', 0.9896441102027893), ('served', 0.9895060062408447), ('blends', 0.9889463186264038), ('emerald', 0.9886804819107056)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model_cv_ker=Word2Vec(list_of_sentance_cv_ker,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model_cv_ker.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model_cv_ker.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "c889b96f5d506b795be572fd166f2e0b1abb5bdc",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  5495\n",
      "sample words  ['ordered', 'tea', 'excited', 'thought', 'finally', 'found', 'equal', 'another', 'brand', 'rose', 'individual', 'bags', 'loose', 'quality', 'along', 'anticipated', 'light', 'delicate', 'taste', 'would', 'hoped', 'sadly', 'mistaken', 'opened', 'hit', 'sweet', 'syrupy', 'cotton', 'candy', 'like', 'smell', 'coming', 'bag', 'not', 'good', 'scent', 'determine', 'steeped', 'recommended', 'tablespoons', 'amount', 'time', 'indicated', 'tried', 'overpowering', 'gave', 'bitter', 'reminiscent', 'previous', 'experience']\n"
     ]
    }
   ],
   "source": [
    "w2v_words_cv_ker= list(w2v_model_cv_ker.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words_cv_ker))\n",
    "print(\"sample words \", w2v_words_cv_ker[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d892e6666210cc7d34b95823bef16db65603b6d0"
   },
   "source": [
    "#### w2v FOR TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "5e64ac781220ac1f689b3e84ba92fff06f2c1260"
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sentance_test_ker=[]\n",
    "for sentance in preprocessed_reviews_ker_test:\n",
    "    list_of_sentance_test_ker.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "5c1a2fed7ec29b49ea1e9d5f48fa19be0f00faa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('good', 0.8551160097122192), ('excellent', 0.8533438444137573), ('amazing', 0.7495375871658325), ('awesome', 0.7477109432220459), ('well', 0.7118093967437744), ('wonderful', 0.7038278579711914), ('decent', 0.702526330947876), ('makes', 0.6874515414237976), ('unbeatable', 0.6658475995063782), ('quick', 0.6609251499176025)]\n",
      "==================================================\n",
      "[('ive', 0.9385446310043335), ('textures', 0.9372638463973999), ('kinds', 0.9347732663154602), ('compares', 0.9341492652893066), ('world', 0.9318863153457642), ('claimed', 0.9314832091331482), ('nip', 0.9284759759902954), ('particular', 0.9275521039962769), ('mate', 0.9264660477638245), ('altoids', 0.9253639578819275)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model_test_ker=Word2Vec(list_of_sentance_test_ker,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model_test_ker.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model_test_ker.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_uuid": "293b91898efd05eb793e705d85379b09b6f4da9b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  6517\n",
      "sample words  ['honest', 'drinking', 'tea', 'life', 'not', 'started', 'pg', 'tips', 'rate', 'worlds', 'best', 'ever', 'go', 'always', 'box', 'case', 'tried', 'lipton', 'omg', 'like', 'dish', 'water', 'want', 'cuppa', 'satisfy', 'thirst', 'worst', 'sure', 'hit', 'everywhere', 'first', 'sip', 'last', 'product', 'suggests', 'european', 'heritage', 'deep', 'southern', 'france', 'seem', 'making', 'calories', 'keurig', 'chai', 'latte', 'actually', 'tastes', 'pretty', 'good']\n"
     ]
    }
   ],
   "source": [
    "w2v_words_test_ker = list(w2v_model_test_ker.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words_test_ker))\n",
    "print(\"sample words \", w2v_words_test_ker[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0d403d72029e2a01075957050f66eda3703291b6"
   },
   "source": [
    "## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4ee2ffc4374e0cbccae2a6873f271efae68f6f98"
   },
   "source": [
    "### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_uuid": "e5dce54516f43318729c6580231b7a26d9272b85"
   },
   "outputs": [],
   "source": [
    "def avgw2v(list_of_sentance,w2v_words,w2v_model):\n",
    "    # average Word2Vec\n",
    "    # compute average word2vec for each review.\n",
    "    sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "    for sent in tqdm(list_of_sentance): # for each review/sentence\n",
    "        sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n",
    "        cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "        for word in sent: # for each word in a review/sentence\n",
    "            if word in w2v_words:\n",
    "                vec = w2v_model.wv[word]\n",
    "                sent_vec += vec\n",
    "                cnt_words += 1\n",
    "        if cnt_words != 0:\n",
    "            sent_vec /= cnt_words\n",
    "        sent_vectors.append(sent_vec)\n",
    "    print(len(sent_vectors))\n",
    "    print(len(sent_vectors[0]))\n",
    "    return sent_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_uuid": "b4ff0515f7633aa7d3b69326c5943210ce5fb05c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 22574/22574 [01:04<00:00, 351.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22574\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors=avgw2v(list_of_sentance,w2v_words,w2v_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_uuid": "9a634d860991336ced25c471f7c499fb8f127346"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 9675/9675 [00:16<00:00, 574.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9675\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_cv=avgw2v(list_of_sentance_cv,w2v_words_cv,w2v_model_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_uuid": "36d60e39f98491a468581d4929585375d0e39141",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 13822/13822 [00:23<00:00, 584.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13822\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_test=avgw2v(list_of_sentance_test,w2v_words_test,w2v_model_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_uuid": "e2daa28be0a397afc42af6123acbf1586fbf8c0e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 19600/19600 [00:40<00:00, 482.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19600\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_ker=avgw2v(list_of_sentance_ker,w2v_words_ker,w2v_model_ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "1b7c5a8f00174d11a55d3c840daa7d5235adf2b6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 8400/8400 [00:11<00:00, 740.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8400\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_cv_ker=avgw2v(list_of_sentance_cv_ker,w2v_words_cv_ker,w2v_model_cv_ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "f576ee8b934c4007cc67664e811f5aaf64e8a0bb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 12000/12000 [00:20<00:00, 587.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12000\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_test_ker=avgw2v(list_of_sentance_test_ker,w2v_words_test_ker,w2v_model_test_ker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bee4439ca42de5c4e7d2c34df2de575b379401d2"
   },
   "source": [
    "### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e477577d6a7d36181f1fb9d1bd78040a9d0f86e9"
   },
   "source": [
    "#### TFIDF weighted w2v for TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "bb93209fc8e834d67c2cd014e606357998a7fa7c",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def tfidfwei_w2v(preprocessed_reviews,list_of_sentance,w2v_words,w2v_model):\n",
    "    # S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "    model = TfidfVectorizer()\n",
    "    tf_idf_matrix = model.fit_transform(preprocessed_reviews)\n",
    "    # we are converting a dictionary with word as a key, and the idf as a value\n",
    "    dictionary = dict(zip(model.get_feature_names(), list(model.idf_)))\n",
    "    # TF-IDF weighted Word2Vec\n",
    "    tfidf_feat = model.get_feature_names() # tfidf words/col-names\n",
    "    # final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "\n",
    "\n",
    "\n",
    "    tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "    row=0;\n",
    "    for sent in tqdm(list_of_sentance): # for each review/sentence \n",
    "        sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "        weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "        for word in sent: # for each word in a review/sentence\n",
    "            if word in w2v_words and word in tfidf_feat:\n",
    "                vec = w2v_model.wv[word]\n",
    "                #tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n",
    "                # to reduce the computation we are \n",
    "                # dictionary[word] = idf value of word in whole courpus\n",
    "                # sent.count(word) = tf valeus of word in this review\n",
    "                tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "                sent_vec += (vec * tf_idf)\n",
    "                weight_sum += tf_idf\n",
    "        if weight_sum != 0:\n",
    "            sent_vec /= weight_sum\n",
    "        tfidf_sent_vectors.append(sent_vec)\n",
    "        row += 1\n",
    "    return tfidf_sent_vectors,model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfidfwei_w2v1(model,preprocessed_reviews,list_of_sentance,w2v_words,w2v_model):\n",
    "    # S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "    tf_idf_matrix = model.transform(preprocessed_reviews)\n",
    "    # we are converting a dictionary with word as a key, and the idf as a value\n",
    "    dictionary = dict(zip(model.get_feature_names(), list(model.idf_)))\n",
    "    # TF-IDF weighted Word2Vec\n",
    "    tfidf_feat = model.get_feature_names() # tfidf words/col-names\n",
    "    # final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "\n",
    "\n",
    "\n",
    "    tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "    row=0;\n",
    "    for sent in tqdm(list_of_sentance): # for each review/sentence \n",
    "        sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "        weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "        for word in sent: # for each word in a review/sentence\n",
    "            if word in w2v_words and word in tfidf_feat:\n",
    "                vec = w2v_model.wv[word]\n",
    "                #tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n",
    "                # to reduce the computation we are \n",
    "                # dictionary[word] = idf value of word in whole courpus\n",
    "                # sent.count(word) = tf valeus of word in this review\n",
    "                tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "                sent_vec += (vec * tf_idf)\n",
    "                weight_sum += tf_idf\n",
    "        if weight_sum != 0:\n",
    "            sent_vec /= weight_sum\n",
    "        tfidf_sent_vectors.append(sent_vec)\n",
    "        row += 1\n",
    "    return tfidf_sent_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "67e766f1382ca0055039d74de19f04c3a99b3b48",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 22574/22574 [09:08<00:00, 33.79it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors,model=tfidfwei_w2v(preprocessed_reviews,list_of_sentance,w2v_words,w2v_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_uuid": "1e73b72b7fc8237d22892e8cd3662a01a694887a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 9675/9675 [03:27<00:00, 46.55it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors_cv=tfidfwei_w2v1(model,preprocessed_reviews_cv,list_of_sentance_cv,w2v_words_cv,w2v_model_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_uuid": "29ff507b81664e3405326528d62924af70b40564",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 13822/13822 [05:10<00:00, 44.51it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors_test=tfidfwei_w2v1(model,preprocessed_reviews_test,list_of_sentance_test,w2v_words_test,w2v_model_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_uuid": "d9af9f8f860d98858ab8591f586ee3747df63fa0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 19600/19600 [07:19<00:00, 36.40it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors_ker,model=tfidfwei_w2v(preprocessed_reviews_ker_tr,list_of_sentance_ker,w2v_words_ker,w2v_model_ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_uuid": "8da507f265e0250f3ea648bea8055d78d03eddd8",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 8400/8400 [03:06<00:00, 44.99it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors_ker_cv=tfidfwei_w2v1(model,preprocessed_reviews_ker_cv,list_of_sentance_cv_ker,w2v_words_cv_ker,w2v_model_cv_ker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_uuid": "6f497d144ac49c58243e990badfcebca48e9c56a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 12000/12000 [04:13<00:00, 47.26it/s]\n"
     ]
    }
   ],
   "source": [
    "tfidf_sent_vectors_ker_test=tfidfwei_w2v1(model,preprocessed_reviews_ker_test,list_of_sentance_test_ker,w2v_words_test_ker,w2v_model_test_ker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "051a37abd337e2d4974f08e12d372f8ceee9c4cd"
   },
   "source": [
    "# [5] Assignment 7: SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3fc1a1dbe5c81897fc42b6445fd945436559cf25"
   },
   "source": [
    "<ol>\n",
    "    <li><strong>Apply SVM on these feature sets</strong>\n",
    "        <ul>\n",
    "            <li><font color='red'>SET 1:</font>Review text, preprocessed one converted into vectors using (BOW)</li>\n",
    "            <li><font color='red'>SET 2:</font>Review text, preprocessed one converted into vectors using (TFIDF)</li>\n",
    "            <li><font color='red'>SET 3:</font>Review text, preprocessed one converted into vectors using (AVG W2v)</li>\n",
    "            <li><font color='red'>SET 4:</font>Review text, preprocessed one converted into vectors using (TFIDF W2v)</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Procedure</strong>\n",
    "        <ul>\n",
    "    <li>You need to work with 2 versions of SVM\n",
    "        <ul><li>Linear kernel</li>\n",
    "            <li>RBF kernel</li></ul>\n",
    "    <li>When you are working with linear kernel, use SGDClassifier’ with hinge loss because it is computationally less expensive.</li>\n",
    "    <li>When you are working with ‘SGDClassifier’ with hinge loss and trying to find the AUC\n",
    "        score, you would have to use <a href='https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html'>CalibratedClassifierCV</a></li>\n",
    "    <li>Similarly, like kdtree of knn, when you are working with RBF kernel it's better to reduce\n",
    "the number of dimensions. You can put min_df = 10, max_features = 500 and consider a sample size of 40k points.</li>                \n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Hyper paramter tuning (find best alpha in range [10^-4 to 10^4], and the best penalty among 'l1', 'l2')</strong>\n",
    "        <ul>\n",
    "    <li>Find the best hyper parameter which will give the maximum <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/receiver-operating-characteristic-curve-roc-curve-and-auc-1/'>AUC</a> value</li>\n",
    "    <li>Find the best hyper paramter using k-fold cross validation or simple cross validation data</li>\n",
    "    <li>Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning</li>          \n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Feature importance</strong>\n",
    "        <ul>\n",
    "    <li>When you are working on the linear kernel with BOW or TFIDF please print the top 10 best\n",
    "features for each of the positive and negative classes.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Feature engineering</strong>\n",
    "        <ul>\n",
    "    <li>To increase the performance of your model, you can also experiment with with feature engineering like :</li>\n",
    "            <ul>\n",
    "            <li>Taking length of reviews as another feature.</li>\n",
    "            <li>Considering some features from review summary as well.</li>\n",
    "        </ul>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Representation of results</strong>\n",
    "        <ul>\n",
    "    <li>You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.\n",
    "    <img src='train_cv_auc.JPG' width=300px></li>\n",
    "    <li>Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.\n",
    "    <img src='train_test_auc.JPG' width=300px></li>\n",
    "    <li>Along with plotting ROC curve, you need to print the <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/confusion-matrix-tpr-fpr-fnr-tnr-1/'>confusion matrix</a> with predicted and original labels of test data points. Please visualize your confusion matrices using <a href='https://seaborn.pydata.org/generated/seaborn.heatmap.html'>seaborn heatmaps.\n",
    "    <img src='confusion_matrix.png' width=300px></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Conclusion</strong>\n",
    "        <ul>\n",
    "    <li>You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library<a href='http://zetcode.com/python/prettytable/'>  link</a> \n",
    "        <img src='summary.JPG' width=400px>\n",
    "    </li>\n",
    "        </ul>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "27e8fe6f1d2091ddac350221569ff48cdde046af"
   },
   "source": [
    "<h4><font color='red'>Note: Data Leakage</font></h4>\n",
    "\n",
    "1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.\n",
    "2. To avoid the issue of data-leakag, make sure to split your data first and then vectorize it. \n",
    "3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.\n",
    "4. For more details please go through this <a href='https://soundcloud.com/applied-ai-course/leakage-bow-and-tfidf'>link.</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "477cf4ec0d4eb7200b8d48bdbe63f3681e168513"
   },
   "source": [
    "# Applying SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7e08e317c520fc1ec33ff693102cf49662de63fd"
   },
   "source": [
    "## [5.1] Linear SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "413431f1367ae69de097b4da7c55b87ea8357d6d"
   },
   "source": [
    "### [5.1.1] Applying Linear SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_uuid": "645b3e21c3f69be68dbc0fcc0159f0e8ed4dfaac",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "c=[10**4,10**3,10**2,10**1,1,10**-1,10**-2,10**-3,10**-4]\n",
    "\n",
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "\n",
    "for i in c:\n",
    "    clf= SGDClassifier(loss='hinge',alpha=i,class_weight='balanced')\n",
    "    calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "    # fitting the model on crossvalidation train\n",
    "    calibrated_clf.fit(final_counts, y_tr)\n",
    "    \n",
    "    # predict the response on the crossvalidation train\n",
    "    pred = calibrated_clf.predict_proba(final_counts_cv)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_cv, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_cv.append(auc)\n",
    "    tpr_cv.append(tpr)\n",
    "    fpr_cv.append(fpr)\n",
    "    \n",
    "    # predict the response on the train\n",
    "    pred = calibrated_clf.predict_proba(final_counts)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_tr, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_tr.append(auc)\n",
    "    tpr_tr.append(tpr)\n",
    "    fpr_tr.append(fpr)\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_uuid": "97dc3264df81b589e270464be713d1a5f06e0118"
   },
   "outputs": [],
   "source": [
    "lamb=[0.0001,0.001,0.01,0.1,1,10,100,1000,10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_uuid": "d2bbd1263f6e339918d16b80db8a0527e94ad299"
   },
   "outputs": [],
   "source": [
    "data1={'alpha':c,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "   alpha=10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alpha    auc_cv    auc_tr\n",
       "0  10000.0  0.525467  0.749607\n",
       "1   1000.0  0.525467  0.749607\n",
       "2    100.0  0.525467  0.749607"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log10(lamb),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log10(lamb),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of alpha')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0000</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.525467</td>\n",
       "      <td>0.749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.525348</td>\n",
       "      <td>0.749663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.507507</td>\n",
       "      <td>0.800189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.510501</td>\n",
       "      <td>0.897451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.490750</td>\n",
       "      <td>0.957218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.489045</td>\n",
       "      <td>0.982967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.470955</td>\n",
       "      <td>0.982934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        alpha    auc_cv    auc_tr\n",
       "0  10000.0000  0.525467  0.749607\n",
       "1   1000.0000  0.525467  0.749607\n",
       "2    100.0000  0.525467  0.749607\n",
       "3     10.0000  0.525348  0.749663\n",
       "4      1.0000  0.507507  0.800189\n",
       "5      0.1000  0.510501  0.897451\n",
       "6      0.0100  0.490750  0.957218\n",
       "7      0.0010  0.489045  0.982967\n",
       "8      0.0001  0.470955  0.982934"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TAKEN THE VALUE OF LAPHA AS 10000 BUT IT IS BIASED TOWARDS 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5315062505509636"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SGDClassifier(loss='hinge',alpha=100)\n",
    "calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "calibrated_clf.fit(final_counts, y_tr)\n",
    "\n",
    "\n",
    "pred_final = calibrated_clf.predict_proba(final_counts_test)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5658199610712646"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=calibrated_clf.predict_proba(final_counts)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f"
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999"
   },
   "outputs": [],
   "source": [
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "#             pred_final_class.append(1)\n",
    "            pred_final_class.append(np.random([0,1]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,  2532],\n",
       "       [    0, 11290]], dtype=int64)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.03      0.05      2532\n",
      "           1       0.82      0.98      0.89     11290\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     13822\n",
      "   macro avg       0.54      0.51      0.47     13822\n",
      "weighted avg       0.72      0.81      0.74     13822\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=1000, average=False, class_weight=None,\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "clf= SGDClassifier(loss='hinge',alpha=1000)\n",
    "clf.fit(final_counts, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "w=clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w_=np.argsort(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_name=count_vect.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 important features of positive class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exited   0.0001411370633772643\n",
      "companyis   0.00015801503611634723\n",
      "thermite   0.0001640397192988026\n",
      "evenovershadows   0.00016514719782498885\n",
      "lawsuit   0.00018791695632337744\n",
      "oquendo   0.0002034216556899895\n",
      "thing   0.00021959084217232496\n",
      "flexible   0.0002600359579485586\n",
      "kibbles   0.0002682755981834161\n",
      "fish   0.00027660383670031894\n",
      "nirvana   0.0005577261857876789\n"
     ]
    }
   ],
   "source": [
    "for i in w_[0][-11:]:\n",
    "    print(\"{0}   {1}\".format(features_name[i],w[0][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 important features of negative class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "woke   -5.050102079412199e-06\n",
      "glitter   -3.9426235532253094e-06\n",
      "avg   -3.012341591228328e-06\n",
      "repulsed   -3.0123415912283268e-06\n",
      "tombstone   -2.8351450270384233e-06\n",
      "ritz   -2.7465467449434715e-06\n",
      "tight   -2.3478544755161892e-06\n",
      "designated   -2.037760488183861e-06\n",
      "detergent   -1.8605639239939586e-06\n",
      "designates   -1.6833673598040625e-06\n"
     ]
    }
   ],
   "source": [
    "for i in w_[0][:10]:\n",
    "    print(\"{0}   {1}\".format(features_name[i],w[0][i]))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "653b2b56ac300d5d488e600174d67929774d1e3e"
   },
   "source": [
    "### [5.1.2] Applying Linear SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "_uuid": "a2053a0800fc532a4d222daac3cf9945dac7eee6",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "c=[10**4,10**3,10**2,10**1,1,10**-1,10**-2,10**-3,10**-4]\n",
    "\n",
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "\n",
    "for i in c:\n",
    "    clf= SGDClassifier(loss='hinge',alpha=i)\n",
    "    calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "    # fitting the model on crossvalidation train\n",
    "    calibrated_clf.fit(final_tf_idf, y_tr)\n",
    "    \n",
    "    # predict the response on the crossvalidation train\n",
    "    pred = calibrated_clf.predict_proba(final_tf_idf_cv)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_cv, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_cv.append(auc)\n",
    "    tpr_cv.append(tpr)\n",
    "    fpr_cv.append(fpr)\n",
    "    \n",
    "    # predict the response on the train\n",
    "    pred = calibrated_clf.predict_proba(final_tf_idf)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_tr, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_tr.append(auc)\n",
    "    tpr_tr.append(tpr)\n",
    "    fpr_tr.append(fpr)\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "_uuid": "0edddb2c4f7e31b21a7bbf19501d2b3cfa894433"
   },
   "outputs": [],
   "source": [
    "data1={'alpha':c,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.569488</td>\n",
       "      <td>0.966863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha    auc_cv    auc_tr\n",
       "7  0.001  0.569488  0.966863"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(lamb),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(lamb),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of alpha')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0000</td>\n",
       "      <td>0.507913</td>\n",
       "      <td>0.603006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>0.507913</td>\n",
       "      <td>0.603006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.507913</td>\n",
       "      <td>0.603006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0000</td>\n",
       "      <td>0.507913</td>\n",
       "      <td>0.603006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.507913</td>\n",
       "      <td>0.603006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.507912</td>\n",
       "      <td>0.603036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.567550</td>\n",
       "      <td>0.967666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.569488</td>\n",
       "      <td>0.966863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.554355</td>\n",
       "      <td>0.979017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        alpha    auc_cv    auc_tr\n",
       "0  10000.0000  0.507913  0.603006\n",
       "1   1000.0000  0.507913  0.603006\n",
       "2    100.0000  0.507913  0.603006\n",
       "3     10.0000  0.507913  0.603006\n",
       "4      1.0000  0.507913  0.603006\n",
       "5      0.1000  0.507912  0.603036\n",
       "6      0.0100  0.567550  0.967666\n",
       "7      0.0010  0.569488  0.966863\n",
       "8      0.0001  0.554355  0.979017"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6638521346604036"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "clf= SGDClassifier(loss='hinge',alpha=0.01)\n",
    "calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "calibrated_clf.fit(final_tf_idf, y_tr)\n",
    "pred_final = calibrated_clf.predict_proba(final_tf_idf_test)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.967695005879887"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=calibrated_clf.predict_proba(final_tf_idf)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f"
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999"
   },
   "outputs": [],
   "source": [
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random(1,[0,1]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  486,  2046],\n",
       "       [  693, 10597]], dtype=int64)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.19      0.26      2532\n",
      "           1       0.84      0.94      0.89     11290\n",
      "\n",
      "   micro avg       0.80      0.80      0.80     13822\n",
      "   macro avg       0.63      0.57      0.57     13822\n",
      "weighted avg       0.76      0.80      0.77     13822\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "clf= SGDClassifier(loss='hinge',alpha=0.001)\n",
    "clf.fit(final_tf_idf, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "w=clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w_=np.argsort(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_name=count_vect.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 important features of positive class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clicker   0.30769277054895044\n",
      "beheaded   0.31492674033176227\n",
      "cheesecake   0.31998610261576216\n",
      "dysfunctional   0.33622115038697814\n",
      "consisting   0.3383943033927569\n",
      "cheating   0.37715918436952556\n",
      "coatings   0.40267451891965594\n",
      "awesomely   0.45388058615797827\n",
      "agreeable   0.4570663890722345\n",
      "bottles   0.47871566166329366\n",
      "brazilian   0.5748713331196078\n"
     ]
    }
   ],
   "source": [
    "for i in w_[0][-11:]:\n",
    "    print(\"{0}   {1}\".format(features_name[i],w[0][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 important features of negative class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "easier   -0.9408833796317975\n",
      "cabernet   -0.8068011282452342\n",
      "balanced   -0.7395085124704477\n",
      "advanced   -0.6869036356822764\n",
      "disguises   -0.6570596946215869\n",
      "cnn   -0.6265660800306233\n",
      "crunchy   -0.6215259402327683\n",
      "disagreement   -0.6111071182164813\n",
      "drinkable   -0.5958174097797918\n",
      "comes   -0.5828611939833354\n"
     ]
    }
   ],
   "source": [
    "for i in w_[0][:10]:\n",
    "    print(\"{0}   {1}\".format(features_name[i],w[0][i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "35fdcefc7626721318fa6a2f51929f72ee3704ac"
   },
   "source": [
    "### [5.1.3]  Applying Linear SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "_uuid": "c8bfe5f4bf3ec0f2f04aeb776a3247f017e3a826",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankit Mishra\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "c=[10**4,10**3,10**2,10**1,1,10**-1,10**-2,10**-3,10**-4]\n",
    "\n",
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "\n",
    "for i in c:\n",
    "    clf= SGDClassifier(loss='hinge',alpha=i)\n",
    "    calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "    # fitting the model on crossvalidation train\n",
    "    calibrated_clf.fit(sent_vectors, y_tr)\n",
    "    \n",
    "    # predict the response on the crossvalidation train\n",
    "    pred = calibrated_clf.predict_proba(sent_vectors_cv)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_cv, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_cv.append(auc)\n",
    "    tpr_cv.append(tpr)\n",
    "    fpr_cv.append(fpr)\n",
    "    \n",
    "    # predict the response on the train\n",
    "    pred = calibrated_clf.predict_proba(sent_vectors)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_tr, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_tr.append(auc)\n",
    "    tpr_tr.append(tpr)\n",
    "    fpr_tr.append(fpr)\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "_uuid": "86352d1f3630aa4affba006b6fa1de5d8c01a49c"
   },
   "outputs": [],
   "source": [
    "data1={'alpha':c,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.77998</td>\n",
       "      <td>0.874989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha   auc_cv    auc_tr\n",
       "5    0.1  0.77998  0.874989"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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feMLtJL7vw00fcjn+Mi+1fon1P62n9ZTWdJvZjTVH1rgdTSTfU/EmIl4hNhYm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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f49850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(lamb),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(lamb),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of alpha')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83084735054718561"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SGDClassifier(loss='hinge',alpha=.1)\n",
    "calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "calibrated_clf.fit(sent_vectors, y_tr)\n",
    "pred_final = calibrated_clf.predict_proba(sent_vectors_test)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87653520874843549"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=calibrated_clf.predict_proba(sent_vectors)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w4otw003g0uqH2qTAJyIiASl9Xzr5pfl8s+0bvt3+LZ9t/Ix8d3759dCgUM5o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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f49ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random(1,[0,1]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  528,  2004],\n",
       "       [  302, 10988]], dtype=int64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewHvp3KXp+NVy0+s5p8mK/lBVRPwnIi4Efkw25v53xS7TzKyporY2/yViTEQMylnG1OUj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21deb470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.64      0.21      0.31      2532\n",
      "          1       0.85      0.97      0.91     11290\n",
      "\n",
      "avg / total       0.81      0.83      0.80     13822\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "be168fa670dff8a2cf835cf807991a3ef4cf1afc"
   },
   "source": [
    "### [5.1.4]  Applying Linear SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "_uuid": "e6efd468cf22dabb2621d4766716dfdcc6a4fd4d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "1000\n",
      "100\n",
      "10\n",
      "1\n",
      "0.1\n",
      "0.01\n",
      "0.001\n",
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "c=[10**4,10**3,10**2,10**1,1,10**-1,10**-2,10**-3,10**-4]\n",
    "\n",
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "\n",
    "for i in c:\n",
    "    clf= SGDClassifier(loss='hinge',alpha=i)\n",
    "    calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "    # fitting the model on crossvalidation train\n",
    "    calibrated_clf.fit(tfidf_sent_vectors, y_tr)\n",
    "    \n",
    "    # predict the response on the crossvalidation train\n",
    "    pred = calibrated_clf.predict_proba(tfidf_sent_vectors_cv)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_cv, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_cv.append(auc)\n",
    "    tpr_cv.append(tpr)\n",
    "    fpr_cv.append(fpr)\n",
    "    \n",
    "    # predict the response on the train\n",
    "    pred = calibrated_clf.predict_proba(tfidf_sent_vectors)[:,1]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_tr, pred)\n",
    "    auc=metrics.auc(fpr, tpr)\n",
    "    auc_tr.append(auc)\n",
    "    tpr_tr.append(tpr)\n",
    "    fpr_tr.append(fpr)\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "_uuid": "97dc3264df81b589e270464be713d1a5f06e0118",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lamb=[0.0001,0.001,0.01,0.1,1,10,100,1000,10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "_uuid": "502ae1c93bb80bafa0904716395f6f6fd826d9ee",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1={'alpha':c,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.633855</td>\n",
       "      <td>0.844126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha    auc_cv    auc_tr\n",
       "4    1.0  0.633855  0.844126"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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PPx5uV7Wqd3JEYaDr2hWuuqpCP8JxqQQn5vpr8+bNDB06lLZt29K5c2cuvfRSVq1axbp1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x74ffed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(lamb),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(lamb),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of alpha')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76572660031315709"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SGDClassifier(loss='hinge',alpha=1)\n",
    "calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid')\n",
    "calibrated_clf.fit(sent_vectors, y_tr)\n",
    "pred_final = calibrated_clf.predict_proba(sent_vectors_test)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87613004856258925"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=calibrated_clf.predict_proba(sent_vectors)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZT2BAIKt2ryIsMIzlGcvZk7+HPfl72JG9A/vI9fwOt9kU/wgJDKFhVEP2u/fTOq518cza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\nJEmSJNWUhU+SJEmSasrCJ0mSJEk19X/VyEf0cfgrdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x74ff9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random(1,[0,1]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  841,  1691],\n",
       "       [ 1094, 10196]], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PPs3O2/8MgG233JzhL40E4IMPx/PdzJl0KeLLoxq5Zz9/LgbuBpaWdCbZK7dO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\nqoCDvZlZFXCwNzOrAg72ZmZV4P8BjV+x463VyXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cb8b410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.43      0.33      0.38      2532\n",
      "          1       0.86      0.90      0.88     11290\n",
      "\n",
      "avg / total       0.78      0.80      0.79     13822\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8f7d174a87c76a4bd93eae2e27752230827cf584"
   },
   "source": [
    "## [5.2] RBF SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "af77db91160d197b7013f07cbfde789343aa9c06"
   },
   "source": [
    "### [5.2.1] Applying RBF SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "f2bd5d0d4d596cdf6b757f7ca2be405a6d36733c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "0b9148ed2e1678bc16d67d5d89fc88cfc37098f6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gamma= [10**-2,1,10**2]\n",
    "c= [10**-2,1,10**2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_uuid": "e6efd468cf22dabb2621d4766716dfdcc6a4fd4d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.01\n",
      "0.01 1\n",
      "0.01 100\n",
      "1 0.01\n",
      "1 1\n",
      "1 100\n",
      "100 0.01\n",
      "100 1\n",
      "100 100\n"
     ]
    }
   ],
   "source": [
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "hypermeter=[]\n",
    "for i in c:\n",
    "    for j in gamma:\n",
    "        \n",
    "        clf=SVC(gamma=j,C=i,probability=True)\n",
    "        # fitting the model on crossvalidation train\n",
    "        clf.fit(final_counts_ker, y_ker_tr)\n",
    "\n",
    "        # predict the response on the crossvalidation train\n",
    "        pred = clf.predict_proba(final_counts_cv_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_cv, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_cv.append(auc)\n",
    "        tpr_cv.append(tpr)\n",
    "        fpr_cv.append(fpr)\n",
    "\n",
    "        # predict the response on the train\n",
    "        pred = clf.predict_proba(final_counts_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_tr, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_tr.append(auc)\n",
    "        tpr_tr.append(tpr)\n",
    "        fpr_tr.append(fpr)\n",
    "        hypermeter.append([i,j])\n",
    "        print(i,j)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=[]\n",
    "gamma=[]\n",
    "for i in range(len(hypermeter)):\n",
    "    c.append((hypermeter[i])[0])\n",
    "    gamma.append((hypermeter[i])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_uuid": "502ae1c93bb80bafa0904716395f6f6fd826d9ee",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1={'hyperparameter(c,gamma)':hypermeter,'c':c,'gamma':gamma,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "      <th>c</th>\n",
       "      <th>gamma</th>\n",
       "      <th>hyperparameter(c,gamma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.670859</td>\n",
       "      <td>0.940879</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[1, 0.01]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     auc_cv    auc_tr    c  gamma hyperparameter(c,gamma)\n",
       "3  0.670859  0.940879  1.0   0.01               [1, 0.01]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VpaVw+HDNQ6Gvvlp9KLRPn/NTdmza1Dz1i4jURoFMRFqEsDAYMMC3TJ9effvp0zUPhb7/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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f1f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(c),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(c),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of c')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70366938167527648"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SVC(gamma=0.01,C=1,probability=True)\n",
    "clf.fit(final_counts_ker, y_ker_tr)\n",
    "pred_final = clf.predict_proba(final_counts_test_ker)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_ker_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94087937819960332"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=clf.predict_proba(final_counts_ker)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_ker_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pb9XK6apqNAU+ES+SXZDNmyve5JN1nzBvx7wK7xMbGkuzWs04r+l53HvOvcRH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc80b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random.randint(2))\n",
    "\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 487, 1783],\n",
       "       [ 642, 9088]], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_ker_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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538M44ehD6dChA/3XWp0N1lubLXbciy123ItNN1qPlfr0rualtQstdaO02vLO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\nCuT/AyvtQhAtiYQcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164a6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.43      0.21      0.29      2270\n",
      "          1       0.84      0.93      0.88      9730\n",
      "\n",
      "avg / total       0.76      0.80      0.77     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_ker_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6b5ed4ff5d0bd508d6ffeac3eb1840191ccd9f88"
   },
   "source": [
    "### [5.2.2] Applying RBF SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "f2bd5d0d4d596cdf6b757f7ca2be405a6d36733c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "0b9148ed2e1678bc16d67d5d89fc88cfc37098f6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gamma= [10**-2,1,10**2]\n",
    "c= [10**-2,1,10**2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_uuid": "e6efd468cf22dabb2621d4766716dfdcc6a4fd4d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.01\n",
      "0.01 1\n",
      "0.01 100\n",
      "1 0.01\n",
      "1 1\n",
      "1 100\n",
      "100 0.01\n",
      "100 1\n",
      "100 100\n"
     ]
    }
   ],
   "source": [
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "hypermeter=[]\n",
    "for i in c:\n",
    "    for j in gamma:\n",
    "        \n",
    "        clf=SVC(gamma=j,C=i,probability=True,)\n",
    "        # fitting the model on crossvalidation train\n",
    "        clf.fit(final_tf_idf_ker, y_ker_tr)\n",
    "\n",
    "        # predict the response on the crossvalidation train\n",
    "        pred = clf.predict_proba(final_tf_idf_cv_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_cv, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_cv.append(auc)\n",
    "        tpr_cv.append(tpr)\n",
    "        fpr_cv.append(fpr)\n",
    "\n",
    "        # predict the response on the train\n",
    "        pred = clf.predict_proba(final_tf_idf_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_tr, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_tr.append(auc)\n",
    "        tpr_tr.append(tpr)\n",
    "        fpr_tr.append(fpr)\n",
    "        hypermeter.append([i,j])\n",
    "        print(i,j)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=[]\n",
    "gamma=[]\n",
    "for i in range(len(hypermeter)):\n",
    "    c.append((hypermeter[i])[0])\n",
    "    gamma.append((hypermeter[i])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "502ae1c93bb80bafa0904716395f6f6fd826d9ee",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1={'hyperparameter(c,gamma)':hypermeter,'c':c,'gamma':gamma,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "      <th>c</th>\n",
       "      <th>gamma</th>\n",
       "      <th>hyperparameter(c,gamma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5871</td>\n",
       "      <td>0.911225</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[1, 0.01]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   auc_cv    auc_tr    c  gamma hyperparameter(c,gamma)\n",
       "3  0.5871  0.911225  1.0   0.01               [1, 0.01]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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RBn/4Q+TLXN++Xu1Zx5GeAwf6cx0iR0KBTLqVfv3grLO8LaS+HhYujIS0f/wDnnnGO2bm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15228450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(c),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(c),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of c')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.67365620656401248"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SVC(gamma=0.01,C=1,probability=True)\n",
    "clf.fit(final_tf_idf_ker, y_ker_tr)\n",
    "pred_final = clf.predict_proba(final_tf_idf_test_ker)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_ker_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9112218733031614"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=clf.predict_proba(final_tf_idf_ker)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_ker_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P1B60rdVWbQ5ERA6zYYOZEALT9HzBgiPPGTgQHnwQtm79hX79aldugXJ0Ho/Z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\nraK8L/gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf9a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random.randint(2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 285, 1985],\n",
       "       [ 327, 9403]], dtype=int64)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_ker_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+7f9v11rcrIlPt3UTrAJ17rmSmj6qcQessHvZMefW8cPmub485NpDl/Q3YEhE\nNPgg1MysElRybrxceefQXwXOlDRO0p8l9cu5PjOzuVKEHHquAT0iBkfEDsBGwH+AP0l6N886zczm\nRg1R9lKpWmsul1WANYC+wJutVKeZWdmKkHLJO4f+J2AP4D1gKHBeRPhNGDOrOEUY5ZJ3D/0DYNOI\n+KzJI83M2lAlp1LKlUsOPQ1VBHgJWF7S+qVLHnWamc2LlnooKmk5SU9IekvSG5J+lcoXl/SopHfT\nn4ulckm6LA0eea00RkoamI5/V9LApu4hrx76ScCRwMX17Atgm5zqNTObKy2YQ68CTo6IVyUtDLwi\n6VHgULJ3cS6UdBpwGvAbYHtg1bRsDPwd2FjS4sBZQD+yuPmKpOFpCpV65RLQI+LItLp9RMwu3Sep\nax51mpnNi5ZKuUTEJGBSWp8h6S2gD7ArsFU6bDAwkiyg7wrcFNlbni9IWlTS0unYRyPic4D0obAd\ncFtDdec9Dv25MsvMzNpURJS9lM4Mm5Yj67umpL7AesCLQO8U7GuDfu23lfQBJpSc9lEqa6i8Qbn0\n0CUtlSruJmk9stf+ARYBFsyjTjOzeVHdjB56RAwCBjV2jKSFgLuAEyLiS6nB2QLq2xGNlDcorxz6\nz8nyRcsCl5SUzwBOz6lOM7O51pKjXCR1Jgvm/4yIu1PxZElLR8SklFKZkso/ApYrOX1ZYGIq36pO\n+cjG6s0l5ZLeEN0aODQiti5Zdim5OTOzitGclEtjlHXFrwfeiojSDu1woHakykDg3pLyQ9Jol02A\n6Skl8zAwQNJiaUTMgFTWoLxSLgdFxC1AX0kn1d1f5ybNzNpcC/bQfwocDIyVNCaVnQ5cCAyVdATw\nX2DvtO8BYAdgHDALOAwgIj6XdB7wcjru3NoHpA3JK+VS+3VDC+V0fTOzFtVSwxYj4hnqz39DNpV4\n3eMDOLaBa90A3FBu3XkNW7wm/XlOHtc3M2tpRXj1P9dhi5IukrSIpM6SRkj6TNJBedZpZjY3ijDb\nYt7j0AdExJfATmRPbFcDTs25TjOzZitCQM97cq7O6c8dgNtSkj/nKs3Mmi/vr+NsDXkH9H9Jehv4\nGjhGUi9gdhPnmJm1ukrueZcr728sOg3YFOgXEXOAmWTzFpiZVZRoxn+VKu8vuOhMNh5zy5RqeRK4\nOs86zczmRnVU8reFlifvlMvfyfLoV6Xtg1PZ/+Zcr5lZsziH3rQNI2Kdku3HJf075zrNzJrNOfSm\nVUtauXZD0kpAdc51mpk1m3PoTTsVeELS+2m7L2meAjOzSlJTgJRL3j30Z4Fr+O6r+K4Bns+5TjOz\nZnMPvWk3AV8C56Xt/YGb+W7xJnppAAAHoUlEQVSWMTOziuBRLk1bvc5D0Sf8UNTMKpFTLk0bnSZs\nB0DSxmRpGDOziuKUS9M2Jvsmjv+m7eWBtySNJZsGeO2c6zczK0sReuh5B/Ttcr6+mVmLqOSed7ly\nDegRMT7P65uZtZTqaP+vyOTdQzczaxf86r+ZWUEU4dV/B3QzM9xDNzMrDI9yMTMrCI9yMTMrCL/6\nb2ZWEM6hm5kVhHPoZmYF4R66mVlBeBy6mVlBuIduZlYQHuViZlYQfihqZlYQTrmYmRWE3xQ1MysI\n99DNzAqiCDl0FeFTqegkHRkRg9q6HVZZ/HNhdXVo6wZYWY5s6wZYRfLPhX2PA7qZWUE4oJuZFYQD\nevvgPKnVxz8X9j1+KGpmVhDuoZuZFYQDuplZQTigtzOSFpV0TMn2MpLubMs2WeuSdJSkQ9L6oZKW\nKdl3naQft13rrC05h97OSOoL3BcRa7VxU6wCSBoJnBIRo9q6Ldb23ENvYZL6SnpL0rWS3pD0iKRu\nklaW9JCkVyQ9LWmNdPzKkl6Q9LKkcyV9lcoXkjRC0quSxkraNVVxIbCypDGS/pzqez2d86KkNUva\nMlLSBpK6S7oh1TG65FrWytK/19uSBkt6TdKdkhaU1D/924xN/1Zd0vEXSnozHfuXVHa2pFMk7QX0\nA/6Zfh66pX/zfpKOlnRRSb2HSro8rR8k6aV0zjWSOrbF34XlICK8tOAC9AWqgHXT9lDgIGAEsGoq\n2xh4PK3fB+yf1o8CvkrrnYBF0npPYBygdP3X69T3elo/ETgnrS8N/Cet/wE4KK0vCvwH6N7Wf1fz\n45L+vQL4adq+ATgTmACslspuAk4AFgfe4bvfpBdNf55N1isHGAn0K7n+SLIg3wsYV1L+ILA58CPg\nX0DnVH4VcEhb/714aZnFPfR8fBARY9L6K2T/E28G3CFpDHANWcAF2BS4I63fWnINAX+Q9BrwGNAH\n6N1EvUOBvdP6PiXXHQCcluoeCXQFlm/2XVlLmRARz6b1W4D+ZD8z/0llg4EtgS+B2cB1kvYAZpVb\nQUR8CrwvaRNJSwCrA8+mujYAXk4/D/2BlVrgnqwCeLbFfHxTsl5NFoinRcS6zbjGgWS9rA0iYo6k\nD8kCcYMi4mNJUyWtDewL/CLtErBnRLzTjPotP2U9uIqIKkkbkQXd/YBfAts0o54hZB/sbwPDIiIk\nCRgcEb9tZputHXAPvXV8CXwgaW8AZdZJ+14A9kzr+5Wc0wOYkoL51sAKqXwGsHAjdd0O/BroERFj\nU9nDwHHpf2YkrTevN2TzZHlJm6b1/cl+A+sraZVUdjDwpKSFyP4dHyBLwdTXIWjs5+FuYLdUx5BU\nNgLYS9KSAJIWl7RCA+dbO+OA3noOBI6Q9G/gDaD2weQJwEmSXiJLw0xP5f8E+kkalc59GyAipgLP\nSnpd0p/rqedOsg+GoSVl5wGdgdfSA9TzWvTOrLneAgamdNriwF+Bw8hScmOBGuBqskB9XzruSbJn\nJHX9A7i69qFo6Y6I+AJ4E1ghIl5KZW+S5ewfSdd9lO/Sf9bOedhiG5O0IPB1+nV4P7IHpB6FUlAe\ndmp5cg697W0AXJHSIdOAw9u4PWbWTrmHbmZWEM6hm5kVhAO6mVlBOKCbmRWEA7rVS1J1Ggr3uqQ7\n0micub3WVpLuS+u7SDqtkWO/N5tkM+o4W9Ip5ZY3cp2vWqJes7bggG4N+Toi1k3D6/6PbJ6Zb6WX\no5r98xMRwyPiwkYOWRRodkA3Mwd0K8/TwCr6bibJq4BXgeUkDZD0fJoV8o70diOStkuzCj4D7FF7\noTTr3xVpvbekYZL+nZbNqDObZDru1DRT5GuSzim51hmS3pH0GNlcJWWTdI+ymS/fkHRknX0Xp/sZ\nIalXKqt3tkyzSuKAbo2S1AnYHqidRmB14KaIWA+YSfbW4c8iYn1gFNlbr12Ba4GdgS2ApRq4/GXA\nkxGxDrA+2Ru0pwHvpd8OTpU0AFgV2Ijs1fcNJG0paQOyN2LXI/vA2LCZt3Z4RGxANjPh8WkCK4Du\nwKvpfp4Ezkrlg4Dj0jmnkM1SaFZR/GKRNaRbmo0Psh769cAywPiIeCGVbwL8mGwqAoAFgOeBNchm\nD3wXQNItwPd6wck2wCEAEVENTJe0WJ1jBqRldNpeiCzAL0w24dSsVMfwZt7f8ZJ2T+vLpWtOJXvt\nvnbek1uAu9NvHbWzZdae36WZ9ZnlzgHdGvJ13dkhUzCbWVoEPBoR+9c5bl3KnFGwDAL+GBHX1Knj\nhLmtQ9JWwM+ATSNilrJv/WloJssg+022ubNlmrU6p1xsXrwA/LR2lkBl37yzGtlEYitKWjkdt38D\n548Ajk7ndpS0CD+cPfBh4PCS3HyfNFPgU8Duyr6lZ2Gy9E65egBfpGC+BtlvGrU6AHul9QOAZyKi\nsdkyzSqGA7rNtfQlCocCt6WZ+14A1oiI2WQplvvTQ9HxDVziV8DWaYbBV4A1684mGRGPkH3xx/Pp\nuDuBhSPiVbLUyBjgLrK0UEPOlPRR7QI8BHRKbT4vtbvWTGBNSa+QpYTOTeUNzZZpVjE8l4uZWUG4\nh25mVhAO6GZmBeGAbmZWEA7oZmYF4YBuZlYQDuhmZgXhgG5mVhD/H5X3JRrxnKtNAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a0fe50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.47      0.13      0.20      2270\n",
      "          1       0.83      0.97      0.89      9730\n",
      "\n",
      "avg / total       0.76      0.81      0.76     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_ker_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "764457c9f531495a45cd5b4a745a6e840dfae3ab"
   },
   "source": [
    "### [5.2.3]  Applying RBF SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_uuid": "f2bd5d0d4d596cdf6b757f7ca2be405a6d36733c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "0b9148ed2e1678bc16d67d5d89fc88cfc37098f6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gamma= [10**-2,1,10**2]\n",
    "c= [10**-2,1,10**2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "e6efd468cf22dabb2621d4766716dfdcc6a4fd4d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.01\n",
      "0.01 1\n",
      "0.01 100\n",
      "1 0.01\n",
      "1 1\n",
      "1 100\n",
      "100 0.01\n",
      "100 1\n",
      "100 100\n"
     ]
    }
   ],
   "source": [
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "hypermeter=[]\n",
    "for i in c:\n",
    "    for j in gamma:\n",
    "        \n",
    "        clf=SVC(gamma=j,C=i,probability=True)\n",
    "        # fitting the model on crossvalidation train\n",
    "        clf.fit(sent_vectors_ker, y_ker_tr)\n",
    "\n",
    "        # predict the response on the crossvalidation train\n",
    "        pred = clf.predict_proba(sent_vectors_cv_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_cv, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_cv.append(auc)\n",
    "        tpr_cv.append(tpr)\n",
    "        fpr_cv.append(fpr)\n",
    "\n",
    "        # predict the response on the train\n",
    "        pred = clf.predict_proba(sent_vectors_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_tr, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_tr.append(auc)\n",
    "        tpr_tr.append(tpr)\n",
    "        fpr_tr.append(fpr)\n",
    "        hypermeter.append([i,j])\n",
    "        print(i,j)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=[]\n",
    "gamma=[]\n",
    "for i in range(len(hypermeter)):\n",
    "    c.append((hypermeter[i])[0])\n",
    "    gamma.append((hypermeter[i])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_uuid": "502ae1c93bb80bafa0904716395f6f6fd826d9ee",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1={'hyperparameter(c,gamma)':hypermeter,'c':c,'gamma':gamma,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "      <th>c</th>\n",
       "      <th>gamma</th>\n",
       "      <th>hyperparameter(c,gamma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.643319</td>\n",
       "      <td>0.968153</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1, 1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     auc_cv    auc_tr    c  gamma hyperparameter(c,gamma)\n",
       "4  0.643319  0.968153  1.0    1.0                  [1, 1]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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BeshEpFGKioKJE93xxBOwdasLaNV7b77xhmvXv7+b2Xm61jwTEamLeshEpEno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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d0f830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(c),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(c),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of c')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65550300854344834"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SVC(gamma=1,C=1,probability=True)\n",
    "clf.fit(sent_vectors_ker, y_ker_tr)\n",
    "pred_final = clf.predict_proba(sent_vectors_test_ker)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_ker_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96815232758109571"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=clf.predict_proba(sent_vectors_ker)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_ker_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mUyexTlko8vg8FHuLXR9+FhMZQ1REVFl48fg8FHmKyno9Su8r8hSRmZ9JnaQ6xEfFlwWe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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16bc9ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random.randint(2))\n",
    "\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   4, 2266],\n",
       "       [   1, 9729]], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_ker_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p7VSyQD5S0mHAW8Dead99wM7ABOBj4NBUn+mSzgGeS8cNi4jp5QrOqw99RbLO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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c90aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.00      0.00      2270\n",
      "          1       0.81      1.00      0.90      9730\n",
      "\n",
      "avg / total       0.81      0.81      0.73     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_ker_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2021854d5324d62299b300d6572f56798f4fa260"
   },
   "source": [
    "### [5.2.4]  Applying RBF SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_uuid": "f2bd5d0d4d596cdf6b757f7ca2be405a6d36733c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_uuid": "0b9148ed2e1678bc16d67d5d89fc88cfc37098f6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gamma= [10**-2,1,10**2]\n",
    "c= [10**-2,1,10**2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "_uuid": "e6efd468cf22dabb2621d4766716dfdcc6a4fd4d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.01\n",
      "0.01 1\n",
      "0.01 100\n",
      "1 0.01\n",
      "1 1\n",
      "1 100\n",
      "100 0.01\n",
      "100 1\n",
      "100 100\n"
     ]
    }
   ],
   "source": [
    "auc_cv=[]\n",
    "tpr_cv=[]\n",
    "fpr_cv=[]\n",
    "tpr_tr=[]\n",
    "fpr_tr=[]\n",
    "auc_tr=[]\n",
    "hypermeter=[]\n",
    "for i in c:\n",
    "    for j in gamma:\n",
    "        \n",
    "        clf=SVC(gamma=j,C=i,probability=True)\n",
    "        # fitting the model on crossvalidation train\n",
    "        clf.fit(tfidf_sent_vectors_ker, y_ker_tr)\n",
    "\n",
    "        # predict the response on the crossvalidation train\n",
    "        pred = clf.predict_proba(tfidf_sent_vectors_ker_cv)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_cv, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_cv.append(auc)\n",
    "        tpr_cv.append(tpr)\n",
    "        fpr_cv.append(fpr)\n",
    "\n",
    "        # predict the response on the train\n",
    "        pred = clf.predict_proba(tfidf_sent_vectors_ker)[:,1]\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_ker_tr, pred)\n",
    "        auc=metrics.auc(fpr, tpr)\n",
    "        auc_tr.append(auc)\n",
    "        tpr_tr.append(tpr)\n",
    "        fpr_tr.append(fpr)\n",
    "        hypermeter.append([i,j])\n",
    "        print(i,j)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=[]\n",
    "gamma=[]\n",
    "for i in range(len(hypermeter)):\n",
    "    c.append((hypermeter[i])[0])\n",
    "    gamma.append((hypermeter[i])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "_uuid": "502ae1c93bb80bafa0904716395f6f6fd826d9ee",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1={'hyperparameter(c,gamma)':hypermeter,'c':c,'gamma':gamma,'auc_cv':auc_cv[:],'auc_tr':auc_tr[:]}\n",
    "data_f=pd.DataFrame(data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "255e70604ef0241b2115327289c12a498997a986"
   },
   "source": [
    "#### MAXIMUM AUC OF CROSS VALIDATION\n",
    "     aplha=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "_uuid": "16756b4cb7e76c68cad50be2e7d46e69970ea32d",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc_cv</th>\n",
       "      <th>auc_tr</th>\n",
       "      <th>c</th>\n",
       "      <th>gamma</th>\n",
       "      <th>hyperparameter(c,gamma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.608269</td>\n",
       "      <td>0.858691</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[0.01, 0.01]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     auc_cv    auc_tr     c  gamma hyperparameter(c,gamma)\n",
       "0  0.608269  0.858691  0.01   0.01            [0.01, 0.01]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f[data_f['auc_cv']==data_f['auc_cv'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bba46aab8acd3b10833f1e852b2207f56ad21733"
   },
   "source": [
    "### AREA UNDER CURVE Vs Neighbours PLOT FOR TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_uuid": "bdf9270ddc7f9ac8e3aae0b055f3527cc3b42af4",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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v0K6d+zctDVq0gCuucC1p+fkeBywi1VKdOu5acs89MH26G+H90UcwejQsXgw3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\nJkmS1JgFmSRJUmPvAlNy+U0Wz9jWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x171ebe70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.log(c),auc_tr,'b',label='TRAIN')\n",
    "plt.plot(np.log(c),auc_cv,'g',label='CROSS VALIDATION')\n",
    "plt.xlabel('log of c')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('Area Under Curve')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_uuid": "efb93630d1dd2ee6ec47069e84e941756ffa74a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76398780283513901"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TEST DATA\n",
    "clf= SVC(gamma=0.01,C=0.01,probability=True)\n",
    "clf.fit(tfidf_sent_vectors_ker, y_ker_tr)\n",
    "pred_final = clf.predict_proba(tfidf_sent_vectors_ker_test)[:,1]\n",
    "fpr_f, tpr_f, thresholds_f = metrics.roc_curve(y_ker_test, pred_final)\n",
    "auc_final = metrics.auc(fpr_f, tpr_f)\n",
    "auc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_uuid": "6ddeeaf9d0fcd4cd6ac677a8343eff2ccd865d01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8586901460843035"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TESTING THE MODEL ON TRAIN DATA\n",
    "pred_train=clf.predict_proba(tfidf_sent_vectors_ker)[:,1]\n",
    "fpr_ft, tpr_ft, thresholds_ft=metrics.roc_curve(y_ker_tr, pred_train)\n",
    "auc_finalt=metrics.auc(fpr_ft, tpr_ft)\n",
    "auc_finalt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b516caf038fbbc506702ac2a716dc293ef39bbbd"
   },
   "source": [
    "### ROC CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "_uuid": "9779629031b5b079dc0fc12494b792fb7366e58f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_a=[0,0.5,1]\n",
    "x_a=y_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "_uuid": "44549168c2088d4a39dcdfa2d843c3f13d6eac90",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l/J494d57ldzJKVq6FO66C1auhL594amnnI5IUMInIiKVlG3bbMvaxpKdS/gh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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16462310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(fpr_f,tpr_f,'b',label='TEST')\n",
    "plt.plot(x_a,y_a,'--r')\n",
    "plt.plot(fpr_ft,tpr_ft,'g',label='TRAIN')\n",
    "plt.xlabel('tpr_f')\n",
    "plt.ylabel('fpr_f')\n",
    "plt.title('AUC ')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb1b9703925f588a3e8d33f25ca7e408d89ee0a9"
   },
   "source": [
    "### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_uuid": "536b403605690078952025907389eb004b030999",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "pred_final\n",
    "pred_final_class=[]\n",
    "for i in range(len(pred_final)):\n",
    "    if pred_final[i]>0.5:\n",
    "        pred_final_class.append(1)\n",
    "    elif pred_final[i]<0.5:\n",
    "            pred_final_class.append(0)\n",
    "    else:\n",
    "            pred_final_class.append(np.random.randint(2))\n",
    "\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_uuid": "5a66042c6c9374dc11641ae913283511272b4888",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  83, 2187],\n",
       "       [  68, 9662]], dtype=int64)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_ker_test, pred_final_class)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_uuid": "2fdbb3368eee62536010071515e4fedb99defef8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    11290\n",
       "0     2532\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "_uuid": "d3d820a6a99a5923b68a9f3fec0258e6bd662fc5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tb5EPAt4ANoyIXfIs08xsbtRGlD01owYYEBHfAzYBjpX0feA0so4hqwLD0jLA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16462350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89878768564bbe87b7d83c8323c9b2b80d2eb472"
   },
   "source": [
    "## CONCLUSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "_uuid": "953b0ccdc4c39d90a85023ee4ec3f409be901585",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.55      0.04      0.07      2270\n",
      "          1       0.82      0.99      0.90      9730\n",
      "\n",
      "avg / total       0.77      0.81      0.74     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_ker_test, pred_final_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2120c4c7ebc8ed2e2b4e5f10786c8f45d3f30509"
   },
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from prettytable import PrettyTable\n",
    "    \n",
    "x = PrettyTable()\n",
    "\n",
    "x.field_names = [\"VECTORIZER\",\"Model\", \"HYPERMETER  [gamma,C]\", \"AUC\"]\n",
    "\n",
    "x.add_row([\"BOW\",'SGD', 1000, 0.531507])\n",
    "x.add_row([\"BOW\", 'RBF KERNEL', [0.01,1], 0.70367])\n",
    "\n",
    "x.add_row([\"TFIDF\", 'SGD',0.001 ,0.669541])\n",
    "x.add_row([\"TFIDF\", 'RBF KERNEL',[0.01,1] ,0.6736])\n",
    "\n",
    "x.add_row([\"AVG W2V\", 'SGD', 0.1 ,0.830847])\n",
    "x.add_row([\"AVG W2V\", 'RBF KERNEL',[1,1],0.6555])\n",
    "\n",
    "x.add_row([\"TFIDF W2V\", 'SGD',0.1 , 0.8178])\n",
    "x.add_row([\"TFIDF W2V\", 'RBF KERNEL',[0.01,0.01] ,0.76398])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+------------+-----------------------+----------+\n",
      "| VECTORIZER |   Model    | HYPERMETER  [gamma,C] |   AUC    |\n",
      "+------------+------------+-----------------------+----------+\n",
      "|    BOW     |    SGD     |          1000         | 0.531507 |\n",
      "|    BOW     | RBF KERNEL |       [0.01, 1]       | 0.70367  |\n",
      "|   TFIDF    |    SGD     |         0.001         | 0.669541 |\n",
      "|   TFIDF    | RBF KERNEL |       [0.01, 1]       |  0.6736  |\n",
      "|  AVG W2V   |    SGD     |          0.1          | 0.830847 |\n",
      "|  AVG W2V   | RBF KERNEL |         [1, 1]        |  0.6555  |\n",
      "| TFIDF W2V  |    SGD     |          0.1          |  0.8178  |\n",
      "| TFIDF W2V  | RBF KERNEL |      [0.01, 0.01]     | 0.76398  |\n",
      "+------------+------------+-----------------------+----------+\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  }
 ],
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