07 Amazon Fine Food Reviews Analysis_Support Vector Machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [1]. Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (100000, 10)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\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>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": {},
     "output_type": "execute_result"
    }
   ],
   "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 500000\"\"\", con) \n",
    "# for tsne assignment you can take 5k data points\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 100000\"\"\", 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80668, 7)\n"
     ]
    },
    {
     "data": {
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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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393063"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display['COUNT(*)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>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": 7,
     "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": {},
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=filtered_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87775, 10)"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.775"
      ]
     },
     "execution_count": 10,
     "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": {},
   "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": 11,
   "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>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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87773, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    73592\n",
       "0    14181\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 13,
     "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": "markdown",
   "metadata": {},
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n",
      "==================================================\n",
      "The Candy Blocks were a nice visual for the Lego Birthday party but the candy has little taste to it.  Very little of the 2 lbs that I bought were eaten and I threw the rest away.  I would not buy the candy again.\n",
      "==================================================\n",
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n",
      "My dog LOVES these treats. They tend to have a very strong fish oil smell. So if you are afraid of the fishy smell, don't get it. But I think my dog likes it because of the smell. These treats are really small in size. They are great for training. You can give your dog several of these without worrying about him over eating. Amazon's price was much more reasonable than any other retailer. You can buy a 1 pound bag on Amazon for almost the same price as a 6 ounce bag at other retailers. It's definitely worth it to buy a big bag if your dog eats them a lot.\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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n",
      "==================================================\n",
      "The Candy Blocks were a nice visual for the Lego Birthday party but the candy has little taste to it.  Very little of the 2 lbs that I bought were eaten and I threw the rest away.  I would not buy the candy again.\n",
      "==================================================\n",
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n",
      "My dog LOVES these treats. They tend to have a very strong fish oil smell. So if you are afraid of the fishy smell, don't get it. But I think my dog likes it because of the smell. These treats are really small in size. They are great for training. You can give your dog several of these without worrying about him over eating. Amazon's price was much more reasonable than any other retailer. You can buy a 1 pound bag on Amazon for almost the same price as a 6 ounce bag at other retailers. It's definitely worth it to buy a big bag if your dog eats them a lot.\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": 17,
   "metadata": {},
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was way to hot for my blood, took a bite and did a jig  lol\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "sent_1500 = decontracted(sent_1500)\n",
    "print(sent_1500)\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was way to hot for my blood took a bite and did a jig lol\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": 21,
   "metadata": {},
   "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": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 87773/87773 [00:33<00:00, 2630.36it/s]\n"
     ]
    }
   ],
   "source": [
    "# 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(final['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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'way hot blood took bite jig lol'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_reviews[1500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all relevant libraries\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Splitting\n",
    "# Train Data :  60%\n",
    "# CV Data    :  20%\n",
    "# Test Data  :  20%\n",
    "\n",
    "X=preprocessed_reviews\n",
    "y=np.array(final['Score'])\n",
    "\n",
    "#####  SPLIT  #######\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.25, random_state=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qyWESNvzJkky"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zS0niozAJkkz",
    "outputId": "e2a25e5d-2e1a-497a-f094-a7e249201001"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaahhhhhh', 'aaaaaaarrrrrggghhh', 'aaaaaawwwwwwwwww', 'aaaah', 'aaaand', 'aaahs']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (52663, 42680)\n",
      "the number of unique words  42680\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "X_train_bow, X_test_bow, X_cv_bow = X_train, X_test, X_cv\n",
    "y_train_bow, y_test_bow, y_cv_bow = y_train, y_test, y_cv\n",
    "\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "count_vect.fit(X_train_bow)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "bow_counts_train = count_vect.transform(X_train_bow)\n",
    "bow_counts_cv = count_vect.transform(X_cv_bow)\n",
    "bow_counts_test = count_vect.transform(X_test_bow)\n",
    "\n",
    "print(\"the type of count vectorizer \",type(bow_counts_train))\n",
    "print(\"the shape of out text BOW vectorizer \",bow_counts_train.get_shape())\n",
    "print(\"the number of unique words \", bow_counts_train.get_shape()[1])\n",
    "\n",
    "# bow_counts_test=bow_counts_test.toarray()\n",
    "# bow_counts_train=bow_counts_train.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.2] TFIDF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ag5-WCR8Jkk7",
    "outputId": "94a8ddc3-4bb7-4c49-d4e7-ed98560633b6",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aa', 'aafco', 'ability', 'able', 'able add', 'able buy', 'able chew', 'able drink', 'able eat', 'able enjoy']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (52663, 31190)\n",
      "the number of unique words including both unigrams and bigrams  31190\n"
     ]
    }
   ],
   "source": [
    "X_train_tf, X_test_tf, X_cv_tf = X_train, X_test, X_cv\n",
    "y_train_tf, y_test_tf, y_cv_tf = y_train, y_test, y_cv\n",
    "\n",
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "final_tf_idf_train = tf_idf_vect.fit_transform(X_train_tf)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "\n",
    "final_tf_idf_cv = tf_idf_vect.transform(X_cv_tf)\n",
    "final_tf_idf_test = tf_idf_vect.transform(X_test_tf)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_train))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_train.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_train.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "\n",
    "X_train_w2v, X_test_w2v, X_cv_w2v = X_train, X_test, X_cv\n",
    "y_train_w2v, y_test_w2v, y_cv_w2v = y_train, y_test, y_cv\n",
    "\n",
    "\n",
    "i=0\n",
    "list_of_sentance_test=[]\n",
    "for sentance in X_test_w2v:\n",
    "    list_of_sentance_test.append(sentance.split())\n",
    "\n",
    "i=0\n",
    "list_of_sentance_cv=[]\n",
    "for sentance in X_cv_w2v:\n",
    "    list_of_sentance_cv.append(sentance.split())\n",
    "\n",
    "i=0\n",
    "list_of_sentance=[]\n",
    "for sentance in X_train_w2v:\n",
    "    list_of_sentance.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('good', 0.8232482671737671), ('awesome', 0.795657753944397), ('excellent', 0.7831848859786987), ('fantastic', 0.781952977180481), ('wonderful', 0.7579724192619324), ('terrific', 0.7570738792419434), ('perfect', 0.738157331943512), ('amazing', 0.7291131615638733), ('nice', 0.6525628566741943), ('decent', 0.6209871172904968)]\n",
      "==================================================\n",
      "[('greatest', 0.8210939168930054), ('best', 0.7460691928863525), ('closest', 0.7050689458847046), ('coolest', 0.6468416452407837), ('experienced', 0.6438528895378113), ('toughest', 0.6366000771522522), ('tastiest', 0.6348387598991394), ('nastiest', 0.6305350661277771), ('disgusting', 0.6203147768974304), ('nicest', 0.6058691143989563)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  13744\n",
      "sample words  ['love', 'changes', 'colors', 'night', 'underside', 'leaves', 'turn', 'purple', 'perfect', 'plant', 'great', 'coffee', 'price', 'ease', 'saving', 'subscribe', 'save', 'getting', 'fresh', 'free', 'shipping', 'people', 'gloria', 'jeans', 'k', 'cup', 'bolds', 'best', 'found', 'flavor', 'good', 'chips', 'nice', 'crunchy', 'would', 'purchase', 'bought', 'nacho', 'regularly', 'kids', 'last', 'delivery', 'came', 'not', 'non', 'gmo', 'label', 'package', 'anymore', 'still']\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": {},
   "source": [
    "## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 52663/52663 [01:19<00:00, 662.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52663\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# TRAINING W2V DATA VECTORS\n",
    "\n",
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors_train = []; # 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_train.append(sent_vec)\n",
    "print(len(sent_vectors_train))\n",
    "print(len(sent_vectors_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 17555/17555 [00:27<00:00, 629.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17555 17555\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# CV W2V DATA VECTORS\n",
    "\n",
    "sent_vectors_cv = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance_cv): # 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_cv.append(sent_vec)\n",
    "print(len(sent_vectors_cv), len(y_cv_w2v))\n",
    "print(len(sent_vectors_cv[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 17555/17555 [00:27<00:00, 635.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17555\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# TEST W2V DATA VECTORS\n",
    "\n",
    "sent_vectors_test = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance_test): # 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_test.append(sent_vec)\n",
    "print(len(sent_vectors_test))\n",
    "print(len(sent_vectors_test[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "model = TfidfVectorizer()\n",
    "tf_idf_matrix = model.fit_transform(X_train_w2v)\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_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 52663/52663 [20:17<00:00, 43.75it/s]\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "tfidf_sent_vectors_train = []; # 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_train.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 17555/17555 [06:46<00:00, 38.79it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tfidf_sent_vectors_cv = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(list_of_sentance_cv): # 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_cv.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 17555/17555 [06:48<00:00, 42.99it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tfidf_sent_vectors_test = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(list_of_sentance_test): # 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_test.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [5] Assignment 7: SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "source": [
    "# Applying SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [5.1] Linear SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.1] Applying Linear SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "\n",
    "X_train_bow, X_test_bow, y_train, y_test, X_cv_bow, y_cv = bow_counts_train, bow_counts_test, y_train, y_test, bow_counts_cv, y_cv\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "############ HYPER-PARAMETER TUNING ##############\n",
    "for i in range(-4, 4):\n",
    "    sgd = SGDClassifier(loss='hinge', alpha=10**i)\n",
    "#   sgd.fit(X_train_bow, y_train)\n",
    "    cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "    cccv.fit(X_train_bow, y_train)\n",
    "    proba = cccv.predict_proba(X_train_bow)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train, proba))\n",
    "    Cs.append(i)\n",
    "    \n",
    "    proba = cccv.predict_proba(X_cv_bow)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Z148Hg+RIXI9f2E+7N/iJomNbtL4BvZ+C/lHT5ar1/jkGUVy55NJIz6tWtotsg6f4PynPqVjSkNe/9lAq0YyJsRY9VFpOdlw/MDJHXtUbPUHF0qKiuDwLr9E4ff32L6T5UraLUqdXTRuB5HRVVrlm8sz+d+Zq/jdmG7cOKhtNW+QMeZMWFIw5Tu674dVUOW1WzRqW+rsouJ2C1XlpleWsmjzXv59+xB7IJAxIcSSgqm68tot9m8pv90iqaOTMJr1hNgEvj98gvOf/ITOzeJ4/ZaB1n23MSHCGppN1cU0hNS+zuCvMB/2bz317GL5qyfbLWIT4aYFNE3qwP2XdOeON1bxyuJt3HCuVSMZE07sTMGcvuJ2i6x18NatEJ8KN32IRsby05eXsHjLPv5z+xDaWDWSMZ4L9EzBbkM1p8/ng8SW0OlCuPwF+H4tzLsDEeGxy3sRFeHjN7NXU1QUXgcextRllhRM9eg4EobcASumwoqpNEuI5XdjuvHV1v1M+WK719EZYwJkScFUn2H3QNsh8N6vYc/XTExPY2inZB7/9wZ27DvmdXTGmABYUjDVxxcB419yGp1n/hjJPcJjl/ck0if8ZvYqq0YyJgxYUjDVq2EKTPwXHNgGc39JakIsvx3TlS+27Gfal1aNZEyoC2pSEJFRIrJRRDaJyN1lvN9aRBaIyGoR+VhE0oIZj6khrc+B8+6HdXPgq+e5IqMlgzsm8di/N7Bzv1UjGRPKgpYURCQCeAYYDXQDrhKRbqWKPQG8qqq9gIeAx4IVj6lh59wGnUbD/PuQXct4fHwvfCL8ZpZdjWRMKAvmmcIAYJOqblHVPGAGMK5UmW7AAvf1wjLeN+HK54PL/gHxzWHmT2gRfZz7Lu7K4i37mP7VDq+jM8aUI5hJoQXg15kOme40f6uA8e7ry4A4EWlSekEicouILBWRpdnZ2UEJ1gRBvUYw8RU4mgVv3sKVGS0Y1CGJx+att2okY0JUMJNCWZ3elK43uAMYKiIrgKHALuCUp9Oo6vOqmqGqGcnJydUfqQmeFv1g1GOw6QPks6d4fHxPAO5+czXhdje9MXVBMJNCJtDSbzwN2O1fQFV3q+rlqtoXuM+ddiiIMRkvZNwIPSbAwkdIO7iUey/uyqJN+3jtq52Vz2uMqVHBTApLgI4i0lZEooErgbn+BUQkSUSKY7gHmBzEeIxXROCSp50n0826kau7RnNO+yY88t46Mg9YNZIxoSRoSUFVC4BJwHxgPTBTVdeKyEMiMtYtNgzYKCLfAE2BR4IVj/FYTEO44lXIy0Fm38gfL+uGAve8ucaqkYwJIdZLqqlZq2bAWz+DQf/DlIY38Ls5X/P45T25ckArryMzplazXlJNaOp9JaRfD589xTWJ6xjYrgmPvLee3QePex2ZMQZLCsYLo/4IzXrhm3MrT5zfiIIitWokY0KEJQVT86Ji4YpXQJUWH9zKvRe245NvsnljWabXkRlT51lSMN5o3A4ufQZ2L+faQ88zoG1j/vDuOr47ZNVIxnjJkoLxTtdLYOAkZMkLPNNrK/mFRdxr1UjGeMqSgvHWyAeg5VkkL7yTRwbHsnBjNrOX7/I6KmPqLEsKxlsRUTDhXxAZw+Wb7mVQ6/o8+M5a9hw64XVkxtRJlhSM9xJawPgXkaz1/KPRdPILC7n3LatGMsYLlhRMaGg/AobeRdyGmbzQYwMfbcjirRVWjWRMTbOkYELH0N9Au2EM+uZxJqTu54G5a8k6bNVIxtQkSwomdPgi4PIXkfqNeazgCaIKcqwayZgaZknBhJaGyTDxZaKO7GRW86l8uP573l65u/L5jDHVwpKCCT2tzobzH6Rt9kfcn/QJv5+7lqwjVo1kTE2wpGBC08BJ0GUMNxybTOf89dz31tdWjWRMDbCkYEKTCIx7BklIY3KDZ1i67lvmrrJqJGOCzZKCCV31EmHiKzQoOMhL8S/wwNtryD6S63VUxtRqlhRMaEvtg4x+nH55y/hJwSx+O8euRjImmCwpmNCXfgP0+hG3R8ziyPoFvLv6O68jMqbWsqRgQp8IjHkKkjrzbMyz/G3Op+zNsWokY4LBkoIJD9ENkB+9SnxEHo8WPsmDc1Z5HZExtZIlBRM+kjvjG/tXMnwb6b7had6zaiRjqp0lBRNeek2kKP0Gbo18lw/n/It9Vo1kTLWypGDCjm/U45xI6skDhX/nr7M/9DocY2oVSwom/ETFEnvNVGKifIzffB/zV27zOiJjag1LCiY8NWpD5OXP0cu3lcNv/4b9R/O8jsiYWsGSgglbkd3GsK/3rUzU+bwz9WmvwzGmVrCkYMJak7EPsyu+DxN2/5n/fr7I63CMCXsBJwURaRDMQIw5LRFRpNwwjXxfLKnv38KBAwe8jsiYsFZpUhCRc0RkHbDeHe8tIs8GPTJjAhTVKI1DFz1HW93Fpn/dDNY3kjGnLZAzhaeAC4F9AKq6ChgSzKCMqarW/S/ii9a30P/wB6x9969eh2NM2Aqo+khVd5aaVBiEWIw5IxnXPcrSyH50XPYQR7Ys8TocY8JSIElhp4icA6iIRIvIHbhVScaEkuioSBpePZl9Gk/ea9fB8YNeh2RM2AkkKdwK/AJoAWQCfdxxY0JOl3Zt+bjXn4nPyyJr6o3WvmBMFVWYFEQkArhOVa9R1aaqmqKq16rqvhqKz5gqGz/ucibXu56UXR9y/BO7f8GYqqgwKahqITDudBcuIqNEZKOIbBKRu8t4v5WILBSRFSKyWkQuOt11GVMsOtLHudfez3+KBhD98UOw4wuvQzImbARSfbRIRP4uIoNFpF/xUNlM7lnGM8BooBtwlYh0K1Xst8BMVe0LXAnYpa6mWvRIS+Tbsx9nR1ESJ177MeRkex2SMWEhkKRwDtAdeAj4izs8EcB8A4BNqrpFVfOAGZx61qFAvPs6AdgdSNDGBOKWC/rwp/h7keP7yX/jRiiyi+aMqUylSUFVh5cxjAhg2S0A/0tZM91p/h4ArhWRTGAe8MuyFiQit4jIUhFZmp1tR3wmMDGREfz8ykt5oOB6orZ/Ap/8yeuQjAl5gdzRnCAiTxbvlEXkLyKSEMCypYxppS8FuQp4WVXTgIuAKSJySkyq+ryqZqhqRnJycgCrNsbRKy2RxoNuZHbhYPSTP8KmBV6HZExIC6T6aDJwBLjCHQ4D/wpgvkygpd94GqdWD90IzARQ1cVALJAUwLKNCdhtIzvxcuIv2SItKZp9Mxza5XVIxoSsQJJCe1X9vds2sEVVHwTaBTDfEqCjiLQVkWichuS5pcrsAM4DEJGuOEnB6odMtYqJjODhK87iltzbyM89BrNugMJ8r8MyJiQFkhSOi8ig4hERORc4XtlMqloATALm49wBPVNV14rIQyIy1i32a+BmEVkFvAZcr2p3G5nq17tlIhcMGcyvT9wEO7+EL+xCN2PKIpXtg0WkD/AKztVBAAdwdt6rghxbmTIyMnTp0qVerNqEuRP5hYz522f86cg99Ek8hu+2FSBlNX0ZU/uIyDJVzaisXCBXH61U1d5AL6CXqvb1KiEYcyZioyL484ReTMsbjO/AVtix2OuQjAk5gVx99KiIJKrqYVU9LCKNROThmgjOmOrWt1UjjrS7iKPUo2j5FK/DMSbkBNKmMFpVS7qbVNUDOJePGhOWxvbvyNsFA9Gv34ITh70Ox5iQEkhSiBCRmOIREakHxFRQ3piQNrJrU+ZFjiSi8DisfcvrcIwJKYEkhanAAhG5UUR+CnyA0/BsTFiKjYqgbe8hfKtpFCyzKiRj/AXS0Pwn4GGgK04fSH9wpxkTtib2b8nrBUOJ3L0Esjd6HY4xISOQhuYGwPuqegfwPBAjIlFBj8yYIOrZIoHVjS+kgAhYYWcLxhQLpProUyBWRFoAHwI3AC8HMyhjgk1EOL9/Tz4s7EfBitfsDmdjXIEkBVHVY8DlwN9U9TKc5yMYE9bG9U1lVtEwIo/vhW/f9zocY0JCQElBRAYC1wDvudMigxeSMTUjJS4WX8eRZNOIomWveh2OMSEhkKRwO3AP8Jbbd1E7YGFwwzKmZlye0Zo3CgbDpg/gyB6vwzHGc4FcffSpqo5V1T+641tU9bbi90Xkb8EM0JhgGtGlKfOjzsOnhbBqhtfhGOO5QM4UKnNuNSzDGE9ER/ro27c/S4s6U7jsVbBOek0dVx1JwZiwNiE9jRmFw4g4sNnpVtuYOsySgqnzerRIYHPySI5JPbtnwdR51ZEUrEN6E/bGZHRkbv5ZFK15E3JzvA7HGM9UR1J4uhqWYYynLu2Tymwdjq/gmHWSZ+q0QLq5+EBEEv3GG4nI/OJxVX05SLEZU2OaNIyhUadz2UoLiqwKydRhgZwpJJXxPIWU4IVkjDcm9m/Fa/lD8O38EvZ+63U4xngikKRQJCKtikdEpDVg1+2ZWmdY52Q+iR1BIT5rcDZ1ViBJ4T7gMxGZIiJTcDrIuye4YRlT86IifAzu24OPivpRtGK6dZJn6qRA7mj+D9APeB2YCaSr6vyK5zImPE3ISOP1gqH4jmXDpg+9DseYGhdIQ/MQnF5RDwOHgG7uNGNqnS7N4tnbbAj7JRFWTPU6HGNqXCC9nd7p9zoWGAAsA0YEJSJjPHZZRhtmzhvEzzb+B8nJgoZ2XYWpOwKpPrrEbzgf6AF8H/zQjPHG2N6pzNHhiBZYJ3mmzjmdm9cycRKDMbVSowbRtOvWl5V0RldMtU7yTJ1SafWR2zV28X+FD+gDrApmUMZ4bWJ6S6avG0KfvS9A5hJoOcDrkIypEYGcKSzFaUNYBiwG7lLVa4MalTEeG9wxia/qDeWExNo9C6ZOqfRMQVVfqYlAjAklkRE+LkzvwDufn8X4NbPxjXocoht4HZYxQRfIJakdRWSWiKwTkS3FQ00EZ4yXJqa79yzkH4V1b3sdjjE1IpDqo38B/wAKgOHAq4CdT5tar0NKHAUtzmKnrwW6/FWvwzGmRgSSFOqp6gJAVHW7qj6A3aNg6ogJGS2ZljsY2bEY9m7yOhxjgi6QpHBCRHzAtyIySUQuw3pJNXXEJb1TeUeGUkgErJzmdTjGBF0gSeFXQH3gNiAduBb4STCDMiZUJNSLol/3rnxGH3TldCgs8DokY4IqkDual6hqjqpmquoNqjpeVb8oft+9j6FMIjJKRDaKyCYRubuM958SkZXu8I2IHCxrOcZ4aWJ6GtPzhiA5e2DzAq/DMSaoquNxnOeWNVFEIoBngNE4HepdJSLd/Muo6v+oah9V7QP8DXizGuIxplqd2yGJtQ0HcsiXaPcsmFqvOpJCeQYAm1R1i6rmATOAcRWUvwp4LYjxGHNaInzCuPTWzMw7F934bzi61+uQjAmaYCaFFsBOv/FMd9op3Ke5tQU+Kuf9W0RkqYgszc7OrvZAjanM+H5pvF44FCmyTvJM7VYdSUGqML28nsWuBGapamFZb6rq86qaoaoZycnJpxOjMWekXXJDElv1ZJ2vM7piinWSZ2qt6kgKT5czPRNo6TeeBuwup+yVWNWRCXET0tN4NXcwkr0Bdi33OhxjgiKQbi4+EJFEv/FGIlLyOE5VfbmcWZcAHUWkrYhE4+z455ax/M5AI5zO9owJWRf3as4HvnPIkxhrcDa1ViBnCkmqWnKpqKoeIICb11S1AJgEzAfWAzNVda2IPCQiY/2KXgXMULXzcRPa4mKjGNKjPf/Ws9CvZ0HeMa9DMqbaBZIUikSkVfGI2ygc0A5cVeepaidVba+qj7jT7lfVuX5lHlDVU+5hMCYUTUhPY1ruUCT3CKw/5cTXmLAXSFK4D/hMRKaIyBTgU+Ce4IZlTGga2K4Ju+L7sicyFVZM9TocY6pdIHc0/wfoB7zuDumqOr/iuYypnXw+YXx6GlNODIJt/4X91ou8qV0CvfroHGCYO5wdrGCMCQfj09OYVTCEInywwjrJM7VLIFcfPQ7cDqxzh9tF5LFgB2ZMqGrdpAGt23bgq4i+Tid5RWXeXmNMWArkTOEi4HxVnayqk4FRwMXBDcuY0DYxPY2Xjw9GjuyGzQu9DseYahNo9VGi3+uEYARiTDi5qGdzFkf2JyciAVbYU9lM7VFhUhARAZ4AVojIyyLyCrAMeLQmgjMmVDWIieT8ni2ZXTAI3TAPju48A4DNAAATzklEQVTzOiRjqkWFScG9oex2nMblN91hoKpaj2CmzpuQnsa0vCFIUT6smel1OMZUi0Cqj74A0lR1rqq+rap7gh2UMeFgQJvGnGjUmc1RnWC5dZJnaodAksJwYLGIbBaR1SKyRkRWBzswY0KdzyeM75fGS8cGQ9Za2L3C65CMOWOBJIXRQHtgBHAJMMb9a0ydNz69Be8UDqTAF2N3OJtaIZA7mreXNdREcMaEurRG9enZviUL5Gx0zRuQf9zrkIw5I8F88poxdcLEjDRePj4IyT0M69/xOhxjzoglBWPO0Kjuzfk6qid7o1LtOQsm7FlSMOYM1YuO4OJeLZiWOwi2fgoHtnkdkjGnzZKCMdVgQnoaM/IGo4h1kmfCmiUFY6pBeutGxCa1YlVMP7BO8kwYs6RgTDUQESakp/H8kXPhcCZs+djrkIw5LZYUjKkml/drwQJN53hkvN2zYMKWJQVjqknzhHoM6NCcd3QwuuFdOLbf65CMqTJLCsZUo4kZLfnXsUFIYR6secPrcIypMksKxlSjC7o1JTOmPTtjO9s9CyYsWVIwphrFRkUwtncqk4+dC3vWwO6VXodkTJVYUjCmmk1IT2N23tkU+qKtwdmEHUsKxlSzPi0TSUlpxufR5zgP38k/4XVIxgTMkoIx1az4noV/HD4HThyCDe96HZIxAbOkYEwQXNa3BV9qNw7GNLcGZxNWLCkYEwRN42MZ0qkpMwuGols+gQP2CBITHiwpGBMkE9Jb8srRgc7Iqte8DcaYAFlSMCZIRnZLIadeKhvqpzs9pxYVeR2SMZWypGBMkMRERjCuTyrPHzkHDu2ArZ94HZIxlbKkYEwQTUxvybz8fuRGWSd5JjxYUjAmiHq0iKdN0yZ8GDHUeX7z8QNeh2RMhSwpGBNEIsLEjDSePTQQCnNhzSyvQzKmQkFNCiIySkQ2isgmEbm7nDJXiMg6EVkrItODGY8xXhjXpwUbpC176neyexZMyAtaUhCRCOAZYDTQDbhKRLqVKtMRuAc4V1W7A78KVjzGeCU5LobhnVN49cRg+G4VfLfa65CMKVcwzxQGAJtUdYuq5gEzgHGlytwMPKOqBwBUNSuI8RjjmQnpaUw7dhZFvihrcDYhLZhJoQWw0288053mrxPQSUQWicgXIjKqrAWJyC0islRElmZnZwcpXGOCZ0SXFCIaNGZ5/UGw+nXrJM+ErGAmBSljmpYajwQ6AsOAq4AXRSTxlJlUn1fVDFXNSE5OrvZAjQm26Egf4/qk8veDA+HEQdg4z+uQjClTMJNCJtDSbzwN2F1GmbdVNV9VtwIbcZKEMbXOxPSWfFrQjZzY5laFZEJWMJPCEqCjiLQVkWjgSmBuqTJzgOEAIpKEU520JYgxGeOZbqnxdGmeyDsyDDZ/BAd3VjqPMTUtaElBVQuAScB8YD0wU1XXishDIjLWLTYf2Cci64CFwJ2qui9YMRnjtYkZaTxz8CxArZM8E5JEtXQ1f2jLyMjQpUuXeh2GMadl/9E8znr0Q95v/BfaRmTDbSvBZ/eQmuATkWWqmlFZOfs1GlODGjeI5rwuTXnx6LlwcDts/8zrkIz5AUsKxtSwCelpzDrWl/yoOFhudzib0GJJwZgaNrRzMnEN41hUbzisnwvHD3odkjElLCkYU8OiInxc1jeVp/adBQUn4OvZXodkTAlLCsZ4YEJ6S1YVtmF/Q+skz4QWSwrGeKBzszh6pSUys3Ao7F4Be772OiRjAKebCWOMByamp/GXtzO4pf4r+FZOg1GPeR1SraOqFBQpeQVFzlDo/M11xwuLwuuS/NTEWJo0jAnqOiwpGOORS3qn8od3E1gfP4juq2bAyAchMtrrsM5IZTvh3ILCH0wvfl38fsl4fhF5hYUBlCkqZ12FJfOE2a1YFXr40h5ce3broK7DkoIxHkmsH8353Zvy7Ldn84x+5HSS1/1Sr8M6LYeO5TN50VZeWbyNg8fyq2WZPoGYyAiiI33OEOEjpvi133h8bKQ7LYLoCOe9GL8yPxgvNS3C5yuz585Q1blZXNDXYUnBGA9NSE/jxtXdON6oGfVWTA27pLAvJ5cXP9vKlMXbycktYGTXpvRKSyh3J+yMR5S5oy49HhlxBk2eRYVQkOs8ArUgz7nKqzDPb5o7FBVW34dRE3ydgfpBXYUlBWM8NKRjMsnx9fgw5jwu2fwaHNoFCaUfOxJ6sg6f4PlPtzDtyx2cKCjkop7NmTS8A12TYyEv5+QO+JQds/v6eG6p94tfl56vrGWUsXMvdN8ryHOmFxV4/REFx8VPQv8bg7oKSwrGeCjCJ1zWN40n/9ufS6KnwarpMOROr8Mq166Dx/nnJ5uZsWQnhUXKuN6pTBqYRLv9n8LCP8HmBc4O+kz4oiAyBiKiITLWaWeJiHH+RsY6r2Pj3dfRpcpWMl/Ja3c+XyRlP/olRDUKbnsCWFIwxnMT0tN47pMUdidmkLpiKgz6dch1krd931GeXbiZ2cszEYHrejXkF8030mTHS/Dyx86ReXwaZNzo7LhOdycdERNy217XWFIwxmMdUhrSt1UiU44M5q7jT8GOz6HNIK/DAmBTVg7PLtzE26t2k+I7xJPtN3EBXxK74XNYXwiJreHs/wfdLoUW/UDC6KjblMmSgjEhYEJ6Gn94qxd3xMURsWKq50lh/XeH+fvCTSxbs5YxUUv5qPFKWuWsQnYqNOkAg34F3cZBs16WCGoZSwrGhIAxvVJ56J11LIsfwYC1c2D0HyE2ocbjWJ15kGnzF9Fwy3vcFLmEZ2K+cd6o1xXS73ISQUpXSwS1mCUFY0JAQr0oLuzejP/beDbTeRu+fhMybqix9a9Zs4J1C6bSef9H/NG3BaKgIKUHdP8tdBsLyZ1rLBbjLUsKxoSIiRlpXLeqFYdTOhK/YmrQk4Jmb2THohno12/Ts2AzPYHvE7qTm34/MT0vJbJJ+6Cu34QmSwrGhIhz2ifRPKEe70acx9W7noOs9U5VTXVRhaz16Lo5HFv5Jg0OfUtrYLV05ouO/0vv86+jaUq76lufCUuWFIwJERE+YXy/NJ78uA9X1YtCVkyFCx85s4Wqwp7VsO5tdN3byL5NKMLXRZ1ZFH0jrc79EWMGZRAbFVE9G2HCniUFY0LI+PQ0/r5wE1saD6H9qhlw3u+r3kmeKuxaDuvmOE92O7CNIolgVUQPZuX/lLXxg7lyeAaT+qURHWn3BJgfsqRgTAhpm9SA/m0a8eKhc3js+AL4dj50vaTyGYuKIPMrWPc2rH8HDu1EfZF8n3Q2U2PGMO1QDxolN2fSRR14sHfqmfUrZGo1SwrGhJgJ6WncO7sTDzZuSvTyKeUnhaJC2LHYSQTr5kLOHoiIpqjdCJa0+RkPfduGtTt8dGkWxx8u7sDoHs2J8NmlpKZilhSMCTEX90rlgbnr+LzhBQzbNA0OfwfxzZ03C/Nh22dOItjwLhzNdrqJ6DCSvM5jeTOnO39blMWug8fp2SKBf17XgfO7NsVnycAEyJKCMSGmYUwko3s040/r+zOMKbD8VacLiXVzYMN7cPwARDWAThdAt3Ecbz2CaSv28fy8LWQd2U6/Vok8fFkPhnVKRuwmM1NFlhSMCUETMtK4esUu9rbIIOnjR52J0XHQebRzV3GH88gpiuLVxdt46c2v2Hc0j4HtmvB/P+rDwPZNLBmY02ZJwZgQdHbbJqQ1qsezEddwf0Zv6HgBtB8OkTEcOpbPy59sY/KirRw6ns/QTsn8ckQHMto09jpsUwtYUjAmBPncexb++tFxbrr6KlIT67H/aB4vLdjAq59v50huAed3a8qk4R3o3TLR63BNLWJJwZgQNSE9jacXfMuL/91KhA+mfuE+5axHc34xvAPdUuO9DtHUQpYUjAlRLRvX5+x2jZm8aCs+gXF9WvCL4e3pkBL8h7ebusuSgjEh7K5RXZi35juuOas1bZIaeB2OqQMsKRgTwvq2akTfVo28DsPUIXavuzHGmBKWFIwxxpQIalIQkVEislFENonI3WW8f72IZIvISne4KZjxGGOMqVjQ2hREJAJ4BjgfyASWiMhcVV1XqujrqjopWHEYY4wJXDDPFAYAm1R1i6rmATOAcUFcnzHGmDMUzKTQAtjpN57pTittvIisFpFZItKyrAWJyC0islRElmZnZwcjVmOMMQQ3KZTVI5eWGn8HaKOqvYAPgVfKWpCqPq+qGaqakZycXM1hGmOMKRbMpJAJ+B/5pwG7/Quo6j5VzXVHXwDSgxiPMcaYSgTz5rUlQEcRaQvsAq4ErvYvICLNVfU7d3QssL6yhS5btmyviGw/zZiSgL2nOa8XwinecIoVwivecIoVwivecIoVzize1oEUClpSUNUCEZkEzAcigMmqulZEHgKWqupc4DYRGQsUAPuB6wNY7mnXH4nIUlXNON35a1o4xRtOsUJ4xRtOsUJ4xRtOsULNxBvUbi5UdR4wr9S0+/1e3wPcE8wYjDHGBM7uaDbGGFOiriWF570OoIrCKd5wihXCK95wihXCK95wihVqIF5RLX2VqDHGmLqqrp0pGGOMqYAlBWOMMSXqbFIQkTtEREUkyetYKiIif3C7AVkpIu+LSKrXMZVHRP4sIhvceN8SkZB9oryITBSRtSJSJCIhe0liZT0NhxIRmSwiWSLytdexVEZEWorIQhFZ7/4Obvc6pvKISKyIfCUiq9xYHwzm+upkUnD7WDof2OF1LAH4s6r2UtU+wLvA/ZXN4KEPgB5utyXfENqXG38NXA586nUg5fHraXg00A24SkS6eRtVhV4GRnkdRIAKgF+ralfgbOAXIfzZ5gIjVLU30AcYJSJnB2tldTIpAE8Bv+HUvphCjqoe9httQAjHrKrvq2qBO/oFTtcmIUlV16vqRq/jqERY9TSsqp/i3IQa8lT1O1Vd7r4+gtObQlkddnpOHTnuaJQ7BG0/UOeSgnsH9S5VXeV1LIESkUdEZCdwDaF9puDvp8C/vQ4izAXa07A5AyLSBugLfOltJOUTkQgRWQlkAR+oatBiDeodzV4RkQ+BZmW8dR9wL3BBzUZUsYriVdW3VfU+4D4RuQeYBPy+RgP0U1msbpn7cE7Pp9VkbKUFEmuIC6SnYXMGRKQhMBv4Vamz8pCiqoVAH7ed7i0R6aGqQWm7qZVJQVVHljVdRHoCbYFVIgJO9cZyERmgqntqMMQfKC/eMkwH3sPDpFBZrCLyE2AMcJ56fBNMFT7XUFVpT8Pm9IlIFE5CmKaqb3odTyBU9aCIfIzTdhOUpFCnqo9UdY2qpqhqG1Vtg/NP18/LhFAZEenoNzoW2OBVLJURkVHAXcBYVT3mdTy1QElPwyISjdPT8FyPY6oVxDkqfAlYr6pPeh1PRUQkufhKPhGpB4wkiPuBOpUUwtTjIvK1iKzGqfYK2UvngL8DccAH7iW0z3kdUHlE5DIRyQQGAu+JyHyvYyrNbbQv7ml4PTBTVdd6G1X5ROQ1YDHQWUQyReRGr2OqwLnAdcAI97e6UkQu8jqocjQHFrr7gCU4bQrvBmtl1s2FMcaYEnamYIwxpoQlBWOMMSUsKRhjjClhScEYY0wJSwrGGGNKWFIwYUFECt3LBte6vUX+r4j43PcyROSv7usYEfnQLfsjERnszrPSvcb7TOO4tKKO00Tkx+4lxGtFZJ2I3FEN63ygOpZjTCBq5R3NplY67vYUi4ik4NzdnQD8XlWXAkvdcn2BKL+yzwFPqOq/AlmJe1OTqGpROUUuxemtdl0Z844GfgVcoKq7RSQW51p4Y8KGnSmYsKOqWcAtwCRxDBORd91kMRWnj5iVIvIz4ArgfhGZBiAid4rIEveZDw+609q4/eo/CywHWopIjtsR4SoR+UJEmorIOTh3lf/ZXX77UqHdA9yhqrvdOE+o6gv+BUQkQUS2+Z3l1BeRnSISJSI3u7GtEpHZIlK/9LaLyMfiPv9BRJJEZJv7OkKc51kUb9vP3OnNReRTN96vRWRwNXwFphazpGDCkqpuwfn9pvhNywJuAv6rqn1U9Z843ULcqarXiMgFQEecLqn7AOkiMsSdvTPwqqr2VdXtON2Uf+H2Yf8pcLOqfu63vD6qurlUWD2AZZXEfQhYBQx1J10CzFfVfOBNVe3vrnM9UJU7gm8EDqlqf6A/cLOItAWudpffB+gNrKzCMk0dZNVHJpyV1YtoRS5whxXueEOcJLED2K6qX/iVzcOpJgJnR3/+GcRZ2uvAj4CFOP0ZPetO7yEiDwOJbmxV6XrjAqCXiExwxxNwtm0JMNnt/G2OqlpSMBWyMwUTlkSkHVCI0798wLMBj7lH+X1UtYOqvuS+d7RU2Xy/Xl4LCewAai2QHkC5ucBoEWnslv/Inf4yMElVewIPArFlzFvAyf9b//cF+KXftrV1H3r0KTAE2AVMEZEfBxCfqcMsKZiwIyLJwHPA36vYPfd84KduH/qISAu3HaIqjuB0+leWx4A/iUgzd/kxInJb6ULuU7S+Ap4G3nX7ysdd7nfuUf015axjGycTzwS/6fOBn7vzIiKdRKSBiLQGsty2jZeAfoFtpqmrrPrIhIt64jx5KgrnaHkKUKUuj1X1fRHpCix2LjIiB7gW50wgUDOAF9yd/QT/dgVVnSciTYEP3auYFJhcznJeB94AhvlN+x3O07+2A2soO/k8AcwUkes4eYYB8CLQBuf5IAJk41wpNQy4U0Ty3e21MwVTIesl1RhjTAmrPjLGGFPCkoIxxpgSlhSMMcaUsKRgjDGmhCUFY4wxJSwpGGOMKWFJwRhjTIn/D2alnD2RTHvMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C for Linear SVM Model from cv_data is:  0.001 for cooresponding AUC value of: 0.9373193638465902\n"
     ]
    }
   ],
   "source": [
    "######## LINEAR SVM Model AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt C values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different C value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best C for Linear SVM Model from cv_data is: \",10**best_c, \"for cooresponding AUC value of:\",max(auc_cv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.9426517607933227\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "sgd = SGDClassifier(loss='hinge', alpha=10**best_c)\n",
    "sgd.fit(X_train_bow, y_train)\n",
    "cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "cccv.fit(X_train_bow, y_train)\n",
    "\n",
    "\n",
    "y_pred = cccv.predict(X_test_bow)\n",
    "proba = cccv.predict_proba(X_test_bow)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = cccv.predict_proba(X_train_bow)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_21 = best_c\n",
    "set_21_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of cccv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 positive features:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delicious\n",
      "perfect\n",
      "excellent\n",
      "amazing\n",
      "wonderful\n",
      "best\n",
      "highly\n",
      "pleased\n",
      "complaint\n",
      "great\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "# feature-names\n",
    "a= count_vect.get_feature_names()\n",
    "# weight vector\n",
    "w = sgd.coef_\n",
    "#positive vector\n",
    "pos = np.argsort(w[0])[::-1]\n",
    "#negative vector\n",
    "neg = np.argsort(w[0])\n",
    "\n",
    "# TOP-TEN-POSITIVE-FEATURES \n",
    "for i in pos[:10]:\n",
    "    print(a[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 negative features: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "worst\n",
      "disappointing\n",
      "awful\n",
      "horrible\n",
      "disappointed\n",
      "disappointment\n",
      "terrible\n",
      "threw\n",
      "stale\n",
      "beware\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "# TOP-TEN-NEGATIVE-FEATURES\n",
    "for i in neg[:10]:\n",
    "    print(a[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.2] Applying Linear SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "X_train_tf, X_test_tf, X_cv_tf = final_tf_idf_train, final_tf_idf_test,final_tf_idf_cv\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "\n",
    "for i in range(-5,5):\n",
    "    sgd = SGDClassifier(loss='hinge', alpha=10**i)\n",
    "    cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "    cccv.fit(X_train_tf, y_train)\n",
    "    proba = cccv.predict_proba(X_train_tf)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_tf, proba))\n",
    "    Cs.append(i)\n",
    "    \n",
    "    proba = cccv.predict_proba(X_cv_tf)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_tf, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C for Linear SVM Model from cv_data is:  1e-05 for cooresponding AUC value of: 0.9590604229387553\n"
     ]
    }
   ],
   "source": [
    "######## LINEAR SVM Model AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt C values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different C value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best C for Linear SVM Model from cv_data is: \",10**best_c, \"for cooresponding AUC value of:\",max(auc_cv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.9639321879500121\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "sgd = SGDClassifier(loss='hinge', alpha=10**best_c)\n",
    "sgd.fit(X_train_tf, y_train_tf)\n",
    "cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "cccv.fit(X_train_tf, y_train_tf)\n",
    "\n",
    "y_pred = cccv.predict(X_test_tf)\n",
    "proba = cccv.predict_proba(X_test_tf)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_tf, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = cccv.predict_proba(X_train_tf)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_tf, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_22 = best_c\n",
    "set_22_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of cccv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "\n",
    "y_true = y_test_tf\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3, linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 positive features:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "great\n",
      "delicious\n",
      "not disappointed\n",
      "best\n",
      "perfect\n",
      "good\n",
      "amazing\n",
      "wonderful\n",
      "loves\n",
      "excellent\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "# feature-names\n",
    "a= tf_idf_vect.get_feature_names()\n",
    "# weight vector\n",
    "w = sgd.coef_\n",
    "#positive vector\n",
    "pos=np.argsort(w[0])[::-1]\n",
    "#negative vector\n",
    "neg=np.argsort(w[0])\n",
    "\n",
    "# TOP-TEN-POSITIVE-FEATURES \n",
    "for i in pos[:10]:\n",
    "    print(a[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 negative features: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not worth\n",
      "worst\n",
      "disappointed\n",
      "two stars\n",
      "not recommend\n",
      "not good\n",
      "disappointment\n",
      "terrible\n",
      "disappointing\n",
      "horrible\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "for i in neg[:10]:\n",
    "    print(a[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.3]  Applying Linear SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_avgw2v, X_cv_avgw2v, X_test_avgw2v = sent_vectors_train, sent_vectors_cv, sent_vectors_test\n",
    "\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "\n",
    "for i in range(-5,5):\n",
    "    sgd = SGDClassifier(loss='hinge', alpha=10**i)\n",
    "    cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "    cccv.fit(X_train_avgw2v, y_train_w2v)\n",
    "    proba = cccv.predict_proba(X_train_avgw2v)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_w2v, proba))\n",
    "    Cs.append(i)\n",
    "    \n",
    "    proba = cccv.predict_proba(X_cv_avgw2v)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_w2v, proba))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C for Linear SVM Model from cv_data is:  0.001 for cooresponding AUC value of: 0.9019207377673062\n"
     ]
    }
   ],
   "source": [
    "######## LINEAR SVM Model AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt C values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different C value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best C for Linear SVM Model from cv_data is: \",10**best_c, \"for cooresponding AUC value of:\",max(auc_cv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.9028306482577366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "\n",
    "sgd = SGDClassifier(loss='hinge', alpha=10**best_c)\n",
    "cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "cccv.fit(X_train_avgw2v, y_train_w2v)\n",
    "\n",
    "y_pred = cccv.predict(X_test_avgw2v)\n",
    "proba = cccv.predict_proba(X_test_avgw2v)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_w2v, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = cccv.predict_proba(X_train_avgw2v)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_w2v, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_23 = best_c\n",
    "set_23_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of cccv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.4]  Applying Linear SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_tfw2v, X_cv_tfw2v, X_test_tfw2v = tfidf_sent_vectors_train, tfidf_sent_vectors_cv, tfidf_sent_vectors_test\n",
    "\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "\n",
    "for i in range(-5,5):\n",
    "    sgd = SGDClassifier(loss='hinge', alpha=10**i)\n",
    "    cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "    cccv.fit(X_train_tfw2v, y_train_w2v)\n",
    "    proba = cccv.predict_proba(X_train_tfw2v)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_w2v, proba))\n",
    "    Cs.append(i)\n",
    "    \n",
    "    proba = cccv.predict_proba(X_cv_tfw2v)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_w2v, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C for Linear SVM Model from cv_data is:  0.001 for cooresponding AUC value of: 0.8782091156779188\n"
     ]
    }
   ],
   "source": [
    "######## LINEAR SVM Model AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt C values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different C value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best C for Linear SVM Model from cv_data is: \",10**best_c, \"for cooresponding AUC value of:\",max(auc_cv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.8798255289269099\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "\n",
    "sgd = SGDClassifier(loss='hinge', alpha=10**best_c)\n",
    "cccv = CalibratedClassifierCV(sgd) # reference: https://stats.stackexchange.com/questions/263393/scikit-correct-way-to-calibrate-classifiers-with-calibratedclassifiercv\n",
    "cccv.fit(X_train_tfw2v, y_train_w2v)\n",
    "\n",
    "y_pred = cccv.predict(X_test_tfw2v)\n",
    "proba = cccv.predict_proba(X_test_tfw2v)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_w2v, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = cccv.predict_proba(X_train_tfw2v)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_w2v, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_24 = best_c\n",
    "set_24_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of cccv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [5.2] RBF SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=preprocessed_reviews\n",
    "y=np.array(final['Score'])\n",
    "\n",
    "X=X[:40000]\n",
    "y=y[:40000]\n",
    "#####  SPLIT  #######\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.25, random_state=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.1] Applying RBF SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BoW\n",
    "X_train_bow, X_test_bow, X_cv_bow = X_train, X_test, X_cv\n",
    "y_train_bow, y_test_bow, y_cv_bow = y_train, y_test, y_cv\n",
    "\n",
    "count_vect = CountVectorizer(min_df=10, max_features=500)\n",
    "count_vect.fit(X_train_bow)\n",
    "\n",
    "bow_counts_train = count_vect.transform(X_train_bow)\n",
    "bow_counts_cv = count_vect.transform(X_cv_bow)\n",
    "bow_counts_test = count_vect.transform(X_test_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 8/8 [1:02:01<00:00, 709.57s/it]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_bow, X_test_bow, y_train, y_test, X_cv_bow, y_cv = bow_counts_train, bow_counts_test, y_train, y_test, bow_counts_cv, y_cv\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "Gs=[]\n",
    "\n",
    "auc_cv=[]\n",
    "\n",
    "############ HYPER-PARAMETER TUNING ##############\n",
    "for i in tqdm(range(-4, 4)):\n",
    "    svc = SVC(C= 10**i ,kernel='rbf' ,gamma='auto', probability=True)\n",
    "    svc.fit(X_train_bow, y_train)\n",
    "\n",
    "    proba = svc.predict_proba(X_train_bow)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train, proba))\n",
    "    Cs.append(i)\n",
    "\n",
    "    proba = svc.predict_proba(X_cv_bow)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K for KNN Model from cv_data is:  2 for cooresponding AUC value of: 0.9022580222099397\n"
     ]
    }
   ],
   "source": [
    "######## KNN BRUTE-FORCE AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt K values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different K value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best K for KNN Model from cv_data is: \",best_c, \"for cooresponding AUC value of:\",max(auc_cv))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.9032403237621742\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "svc = SVC(C=10**best_c, kernel='rbf', gamma='auto', probability=True)\n",
    "svc.fit(X_train_bow, y_train)\n",
    "\n",
    "y_pred = svc.predict(X_test_bow)\n",
    "proba = svc.predict_proba(X_test_bow)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = svc.predict_proba(X_train_bow)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_11 = best_c\n",
    "set_11_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of svc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3, linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.2] Applying RBF SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "X_train_tf, X_test_tf, X_cv_tf = X_train, X_test, X_cv\n",
    "y_train_tf, y_test_tf, y_cv_tf = y_train, y_test, y_cv\n",
    "\n",
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10, max_features=500)\n",
    "final_tf_idf_train = tf_idf_vect.fit_transform(X_train_tf)\n",
    "\n",
    "final_tf_idf_cv = tf_idf_vect.transform(X_cv_tf)\n",
    "final_tf_idf_test = tf_idf_vect.transform(X_test_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████| 8/8 [37:27<00:00, 292.35s/it]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_tf, X_test_tf, X_cv_tf = final_tf_idf_train, final_tf_idf_test,final_tf_idf_cv\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "############ HYPER-PARAMETER TUNING ##############\n",
    "for i in tqdm(range(-4, 4)):\n",
    "    svc = SVC(C= 10**i ,kernel='rbf' ,gamma='auto', probability=True)\n",
    "    svc.fit(X_train_tf, y_train_tf)\n",
    "\n",
    "    proba = svc.predict_proba(X_train_tf)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_tf, proba))\n",
    "    Cs.append(i)\n",
    "\n",
    "    proba = svc.predict_proba(X_cv_tf)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_tf, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K for KNN Model from cv_data is:  3 for cooresponding AUC value of: 0.9028125407578975\n"
     ]
    }
   ],
   "source": [
    "######## KNN BRUTE-FORCE AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt K values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different K value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best K for KNN Model from cv_data is: \",best_c, \"for cooresponding AUC value of:\",max(auc_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.9073051842778399\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "svc = SVC(C=10**best_c, kernel='rbf', gamma='auto', probability=True)\n",
    "svc.fit(X_train_tf, y_train_tf)\n",
    "\n",
    "y_pred = svc.predict(X_test_tf)\n",
    "proba = svc.predict_proba(X_test_tf)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_tf, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = svc.predict_proba(X_train_tf)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_tf, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_12 = best_c\n",
    "set_12_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of svc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.3]  Applying RBF SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#####  SPLIT  #######\n",
    "X_train_w2v, X_test_w2v, X_cv_w2v = X_train, X_test, X_cv\n",
    "y_train_w2v, y_test_w2v, y_cv_w2v = y_train, y_test, y_cv\n",
    "\n",
    "i=0\n",
    "list_of_sentance_test=[]\n",
    "for sentance in X_test_w2v:\n",
    "    list_of_sentance_test.append(sentance.split())\n",
    "\n",
    "i=0\n",
    "list_of_sentance_cv=[]\n",
    "for sentance in X_cv_w2v:\n",
    "    list_of_sentance_cv.append(sentance.split())\n",
    "\n",
    "i=0\n",
    "list_of_sentance=[]\n",
    "for sentance in X_train_w2v:\n",
    "    list_of_sentance.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2v_model=Word2Vec(list_of_sentance,min_count=5,size=50, workers=4)\n",
    "w2v_words = list(w2v_model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 24000/24000 [00:31<00:00, 767.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24000\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 8000/8000 [00:10<00:00, 770.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000 8000\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 8000/8000 [00:10<00:00, 763.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# TRAINING W2V DATA VECTORS\n",
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors_train = []; # 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_train.append(sent_vec)\n",
    "print(len(sent_vectors_train))\n",
    "print(len(sent_vectors_train[0]))\n",
    "\n",
    "#####################\n",
    "# CV W2V DATA VECTORS\n",
    "sent_vectors_cv = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance_cv): # 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_cv.append(sent_vec)\n",
    "print(len(sent_vectors_cv), len(y_cv_w2v))\n",
    "print(len(sent_vectors_cv[0]))\n",
    "\n",
    "#####################\n",
    "# TEST W2V DATA VECTORS\n",
    "sent_vectors_test = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance_test): # 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_test.append(sent_vec)\n",
    "print(len(sent_vectors_test))\n",
    "print(len(sent_vectors_test[0]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████| 8/8 [34:38<00:00, 402.70s/it]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_avgw2v, X_cv_avgw2v, X_test_avgw2v = sent_vectors_train, sent_vectors_cv, sent_vectors_test\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "############ HYPER-PARAMETER TUNING ##############\n",
    "for i in tqdm(range(-4, 4)):\n",
    "    svc = SVC(C= 10**i ,kernel='rbf' ,gamma='auto', probability=True)\n",
    "    svc.fit(X_train_avgw2v, y_train_w2v)\n",
    "    \n",
    "    proba = svc.predict_proba(X_train_avgw2v)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_w2v, proba))\n",
    "    Cs.append(i)\n",
    "\n",
    "    proba = svc.predict_proba(X_cv_avgw2v)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_w2v, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K for KNN Model from cv_data is:  2 for cooresponding AUC value of: 0.8974663111280989\n"
     ]
    }
   ],
   "source": [
    "######## KNN BRUTE-FORCE AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt K values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different K value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best K for KNN Model from cv_data is: \",best_c, \"for cooresponding AUC value of:\",max(auc_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.899252080556256\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "svc = SVC(C=10**best_c, kernel='rbf', gamma='auto', probability=True)\n",
    "svc.fit(X_train_avgw2v, y_train_w2v)\n",
    "\n",
    "y_pred = svc.predict(X_test_avgw2v)\n",
    "proba = svc.predict_proba(X_test_avgw2v)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_w2v, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = svc.predict_proba(X_train_avgw2v)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_w2v, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_13 = best_c\n",
    "set_13_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of svc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### [5.2.4]  Applying RBF SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TfidfVectorizer()\n",
    "tf_idf_matrix = model.fit_transform(X_train_w2v)\n",
    "dictionary = dict(zip(model.get_feature_names(), list(model.idf_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 24000/24000 [07:46<00:00, 51.48it/s]\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "tfidf_sent_vectors_train = []; # 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 = 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_train.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 8000/8000 [02:34<00:00, 51.73it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tfidf_sent_vectors_cv = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(list_of_sentance_cv): # 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_cv.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 8000/8000 [02:36<00:00, 50.96it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tfidf_sent_vectors_test = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(list_of_sentance_test): # 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_test.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_tfw2v, X_cv_w2v, X_test_tfw2v = tfidf_sent_vectors_train, tfidf_sent_vectors_cv, tfidf_sent_vectors_test\n",
    "\n",
    "auc_train=[]\n",
    "Cs=[]\n",
    "auc_cv=[]\n",
    "\n",
    "for i in range(-4,4):\n",
    "    svc = SVC(C= 10**i ,kernel='rbf' ,gamma='auto', probability=True)\n",
    "    svc.fit(X_train_tfw2v, y_train_w2v)\n",
    "    \n",
    "    proba = svc.predict_proba(X_train_tfw2v)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_w2v, proba))\n",
    "    Cs.append(i)\n",
    "    \n",
    "    proba = svc.predict_proba(X_cv_w2v)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_w2v, proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K for KNN Model from cv_data is:  2 for cooresponding AUC value of: 0.8824982315370721\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "######## KNN BRUTE-FORCE AUC PLOTTING ########\n",
    "ax=sns.lineplot(Cs,auc_train)\n",
    "ax=sns.lineplot(Cs,auc_cv)\n",
    "ax.set(xlabel='Differnt K values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different K value Curve')\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_c = Cs[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best K for KNN Model from cv_data is: \",best_c, \"for cooresponding AUC value of:\",max(auc_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_C:  0.8816543574593796\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######### FRESH-MODEL FOR BEST_C ######################\n",
    "svc = SVC(C=10**best_c, kernel='rbf', gamma='auto', probability=True)\n",
    "svc.fit(X_train_tfw2v, y_train_w2v)\n",
    "\n",
    "y_pred = svc.predict(X_test_tfw2v)\n",
    "proba = svc.predict_proba(X_test_tfw2v)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_w2v, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_C: \",roc_auc)\n",
    "proba = svc.predict_proba(X_train_tfw2v)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_w2v, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "# storing values for future use\n",
    "c_14 = best_c\n",
    "set_14_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of svc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jHTTi7VclSYmJierTd5AmTPgk4PPGbxcVHaMT0TH616ixerp7BxUrGqZFy1fr1WGj5EtOVrvWzRQVHauzZxP03JA31b1DW915exmtj9yi9z+apLi4eD33dHdJ0qnTccqXNzTdPvLny5u6PiPLV61T/Jmf1LTR9XEL4ve64pA55zp6nvfhtZzM9Sgo6Nwl/sRJMzV4yFuSpJWr1ik4OFivvzZQ5cqV0c6de1LHT5s+R19+uVkFCxZQ48YN9Pbwl5WcnKwxYydkyvxxeSmepzM//axXBj6j+vX+JEm6u1pl/XDshMaOn6ZHWzVVSkqKEhIT9VT39mrf5iFJUs2qFfXjqdOaPHOeenZ+VHlD88jzzt1XvdDlbmPMXrBUBW7Ir7/UqnHtD9CAq7m0HHKxFc65bs65SOdc5Oi5f+xHB2JjT0qSli1bnWb5kqUrJUmVKpVPszw6OlabNm/TosUr9ORTAzRh4gy9OfT/FBLCyXNWdcP5s6XaNaqkWV67RlXFxJ5UVHRs6uVerQvH1Kwqn8+nvQcOSTp35pXRWdfpuPjU9ReKio7V+sgtimhwr0JCgq/+gAy6ZMicc9su8vW1pEIX287zvNGe51X3PK96tyYPXfNJW7Ljm92S0n+iOp371E1JSbnk9ps2bVXevKEqVOhm/0wQV61UyRIZLvd07u88KMip9PkxF55t/fK2CDq/vHTJ4jp89Lh+Pns2zbh9B79TtmwhKh4elm4/cxctV3Jyyh/2slK6/BlZIUmPSWqSwVeMf6d2fVi48HOdPXs23TNmv3y/adO2S25fp04txcXF68SJaD/NEFfrvrq1JUlfbNiUZvkXX25SoVsKquBNBfTXOrUkSWvWR6YZs3bDJuXInl1lbrtVknTvn++Rz+fT4uX/O4P3+ZK1cNkq1a5ZVdmzZ0+3/7kLl6ls6ZKpz5j9EV3uemWepFDP8766cIVzboVfZmTMQw+d+5V71aoVJUkN//ZXRUXHKDoqRqtWr1ds7EkNffMdDRzQW3Fx8fr88y9UrVpFvTiwt8Z9PE379h2UJHXt8qjuvruqli1frSOHj6rATTeqVcsmatmisV4Y8KqSkpIuNgVksjq1aqhm1Uoa8uZInfzxtMKLFNaSFWu0dsNmvTKgrySpzG23qtkD9fWfsRPkeZ7KlS2t9ZFbNGPuInXv0Fa5c+eSJN1RtpQa3ldHQ0eMVpIvWeFFCmnqrPk6cvSYhg56Nt2+v/l2r/bsP6j+T3YN6DFnNTwQe5V8iUcyXL5y5VrdV79V6ve9n+6mHt0fU/HiRXX06AmNnzBdr7w6XD7fuWfEat1TXQNeeEqVK1dQgQI3KDo6Vrt27dXbI8boswXLAnIsgXA9PhArSfFnzmj4ex9p8edrdDouXiVLhKvLo60V0eDe1DFJSUl698NJmr1gqWJif1TRsEJq81BjtWvdLM1rnU1I0Ij3x2n+khWKi4/X7aVvU5/HO6nm+Q/LX3t9+HuaOnOeln46XgUL3Oj34/S3K30glpAhoK7XkOHa4Ml+AH9YhAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5hAyAeYQMgHmEDIB5IYHeoW/J7EDvEoZk2xGb2VOAQZyRATCPkAEwj5ABMM95npfZc/hDcc518zxvdGbPA1kT748rwxlZ4HXL7AkgS+P9cQUIGQDzCBkA8whZ4HH/A5fC++MKcLMfgHmckQEwj5ABMI+QBZBzrqFz7lvn3F7n3POZPR9kHc65D5xzJ5xz2zN7LhYRsgBxzgVL+o+kRpLulNTWOXdn5s4KWchHkhpm9iSsImSBU1PSXs/z9nuelyhpiqSmmTwnZBGe562SxP/64woRssApKun7X31/+PwyAFeJkAWOy2AZz74A1wAhC5zDkor96vtwST9k0lyA6wohC5yNkso450o657JLaiNpTibPCbguELIA8TzPJ+kJSYsk7ZQ0zfO8HZk7K2QVzrnJktZJut05d9g51zmz52QJ/0QJgHmckQEwj5ABMI+QATCPkAEwj5ABMI+QATCPkAEw7/8BNXLnYITuO6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### CONFUSION MATRIX ############\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+--------+-----------------+------+\n",
      "| Vectoriser | Kernel | Hyper Parameter | AUC  |\n",
      "+------------+--------+-----------------+------+\n",
      "|    BOW     | Linear |        -3       | 0.94 |\n",
      "|   TFIDF    | Linear |        -5       | 0.96 |\n",
      "|  AVG-W2V   | Linear |        -3       | 0.90 |\n",
      "| TFIDF W2V  | Linear |        -3       | 0.88 |\n",
      "|    ----    |  ----  |       ----      | ---- |\n",
      "|    BOW     |  RBF   |        2        | 0.90 |\n",
      "|   TFIDF    |  RBF   |        3        | 0.91 |\n",
      "|  AVG-W2V   |  RBF   |        2        | 0.90 |\n",
      "| TFIDF W2V  |  RBF   |        2        | 0.88 |\n",
      "+------------+--------+-----------------+------+\n"
     ]
    }
   ],
   "source": [
    "# Please compare all your models using Prettytable library\n",
    "\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "x= PrettyTable()\n",
    "\n",
    "x.field_names = [\"Vectoriser\", \"Kernel\", \"Hyper Parameter\", \"AUC\"]\n",
    "\n",
    "x.add_row([\"BOW\", 'Linear', c_21, \"{0:.2f}\".format(set_21_auc)])\n",
    "x.add_row([\"TFIDF\", 'Linear', c_22, \"{0:.2f}\".format(set_22_auc)])\n",
    "x.add_row([\"AVG-W2V\", 'Linear', c_23, \"{0:.2f}\".format(set_23_auc)])\n",
    "x.add_row([\"TFIDF W2V\", 'Linear', c_24, \"{0:.2f}\".format(set_24_auc)])\n",
    "x.add_row([\"----\", \"----\", \"----\", \"----\"])\n",
    "x.add_row([\"BOW\", \"RBF\", c_11, \"{0:.2f}\".format(set_11_auc)])\n",
    "x.add_row([\"TFIDF\", \"RBF\", c_12, \"{0:.2f}\".format(set_12_auc)])\n",
    "x.add_row([\"AVG-W2V\", \"RBF\", c_13, \"{0:.2f}\".format(set_13_auc)])\n",
    "x.add_row([\"TFIDF W2V\", \"RBF\", c_14, \"{0:.2f}\".format(set_14_auc)])\n",
    "\n",
    "print(x)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}