Amazon Fine Food - Support Vector Machines.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n",
    "\n",
    "EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n",
    "\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files(C)\\Anaconda\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "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 time import time\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.svm import SVC\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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using SQLite Table to read data.\n",
    "con = sqlite3.connect('D:/Machine Learning Notes/Python Notebook/Amazon Fine Food Reviews Analysis/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",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#oc-R115TNMSPFT9I7</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Breyton</td>\n",
       "      <td>1331510400</td>\n",
       "      <td>2</td>\n",
       "      <td>Overall its just OK when considering the price...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#oc-R11D9D7SHXIJB9</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Louis E. Emory \"hoppy\"</td>\n",
       "      <td>1342396800</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife has recurring extreme muscle spasms, u...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#oc-R11DNU2NBKQ23Z</td>\n",
       "      <td>B005ZBZLT4</td>\n",
       "      <td>Kim Cieszykowski</td>\n",
       "      <td>1348531200</td>\n",
       "      <td>1</td>\n",
       "      <td>This coffee is horrible and unfortunately not ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#oc-R11O5J5ZVQE25C</td>\n",
       "      <td>B005HG9ESG</td>\n",
       "      <td>Penguin Chick</td>\n",
       "      <td>1346889600</td>\n",
       "      <td>5</td>\n",
       "      <td>This will be the bottle that you grab from the...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#oc-R12KPBODL2B5ZD</td>\n",
       "      <td>B007OSBEV0</td>\n",
       "      <td>Christopher P. Presta</td>\n",
       "      <td>1348617600</td>\n",
       "      <td>1</td>\n",
       "      <td>I didnt like this coffee. Instead of telling y...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               UserId   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B005ZBZLT4                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ESG  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B005ZBZLT4        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ESG           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBEV0   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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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       "<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 [01:07<00:00, 1291.53it/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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███████████████████████████▌                                              | 32724/87773 [00:16<00:28, 1898.26it/s]D:\\Program Files(C)\\Anaconda\\lib\\site-packages\\bs4\\__init__.py:272: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      " 70%|███████████████████████████████████████████████████▊                      | 61402/87773 [00:33<00:12, 2137.89it/s]D:\\Program Files(C)\\Anaconda\\lib\\site-packages\\bs4\\__init__.py:272: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      " 75%|███████████████████████████████████████████████████████▎                  | 65606/87773 [00:35<00:11, 1973.26it/s]D:\\Program Files(C)\\Anaconda\\lib\\site-packages\\bs4\\__init__.py:272: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      " 96%|███████████████████████████████████████████████████████████████████████   | 84289/87773 [00:44<00:01, 2043.52it/s]D:\\Program Files(C)\\Anaconda\\lib\\site-packages\\bs4\\__init__.py:272: UserWarning: \"b'...'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
      "  ' Beautiful Soup.' % markup)\n",
      "100%|██████████████████████████████████████████████████████████████████████████| 87773/87773 [00:46<00:00, 1897.19it/s]\n"
     ]
    }
   ],
   "source": [
    "## Similartly you can do preprocessing for review summary also.\n",
    "# Combining all the above stundents \n",
    "preprocessed_summary = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(final['Summary'].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_summary.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'made china'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_summary[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.3].  Split Dataset into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection as ms\n",
    "y=final['Score'][:100000]\n",
    "dw_train,dw_test,y_train,y_test=ms.train_test_split(preprocessed_reviews, y, stratify=y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaa', 'aaaaa', 'aaaaaaaaaaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaarrrrrggghhh', 'aaaaah', 'aaah', 'aaahs']\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer(dtype='float') #in scikit-learn\n",
    "d_train =count_vect.fit_transform(dw_train)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of train-text BOW vectorizer  (61441, 46047)\n",
      "the shape of test-text BOW vectorizer  (26332, 46047)\n"
     ]
    }
   ],
   "source": [
    "print(\"the type of count vectorizer \",type(d_train))\n",
    "print(\"the shape of train-text BOW vectorizer \",d_train.get_shape())\n",
    "\n",
    "d_test = count_vect.transform(dw_test)\n",
    "print(\"the shape of test-text BOW vectorizer \",d_test.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bi-gram, tri-gram and n-gram\n",
    "\n",
    "#removing stop words like \"not\" should be avoided before building n-grams\n",
    "# count_vect = CountVectorizer(ngram_range=(1,2))\n",
    "# please do read the CountVectorizer documentation http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "\n",
    "# you can choose these numebrs min_df=10, max_features=5000, of your choice\n",
    "count_vect_gram = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=5000)\n",
    "d_train_gram = count_vect_gram.fit_transform(dw_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of train-text BOW vectorizer  (61441, 5000)\n",
      "the shape of test-text BOW vectorizer  (26332, 5000)\n"
     ]
    }
   ],
   "source": [
    "#Vectorize cv and test data\n",
    "print(\"the type of count vectorizer \",type(d_train_gram))\n",
    "print(\"the shape of train-text BOW vectorizer \",d_train_gram.get_shape())\n",
    "\n",
    "d_test_gram = count_vect_gram.transform(dw_test)\n",
    "print(\"the shape of test-text BOW vectorizer \",d_test_gram.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aafco', 'aback', 'abandon', 'abdominal', 'ability', 'able', 'able add', 'able buy', 'able chew', 'able drink']\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "d_train_tf =tf_idf_vect.fit_transform(dw_train)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of train-text BOW vectorizer  (61441, 36194)\n",
      "the shape of test-text BOW vectorizer  (26332, 36194)\n"
     ]
    }
   ],
   "source": [
    "#Vectorize cv and test data\n",
    "print(\"the type of count vectorizer \",type(d_train_tf))\n",
    "print(\"the shape of train-text BOW vectorizer \",d_train_tf.get_shape())\n",
    "\n",
    "d_test_tf = tf_idf_vect.transform(dw_test)\n",
    "print(\"the shape of test-text BOW vectorizer \",d_test_tf.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "train_list=[]\n",
    "test_list=[]\n",
    "for sent in dw_train:\n",
    "    train_list.append(sent.split())\n",
    "for sent in dw_test:\n",
    "    test_list.append(sent.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('good', 0.822709321975708), ('awesome', 0.817183792591095), ('fantastic', 0.8169326782226562), ('excellent', 0.8103747367858887), ('wonderful', 0.7806501984596252), ('terrific', 0.7770112156867981), ('amazing', 0.7562711238861084), ('perfect', 0.7422863245010376), ('decent', 0.7002018690109253), ('nice', 0.6535245180130005)]\n",
      "==================================================\n",
      "[('greatest', 0.7651511430740356), ('best', 0.7233803272247314), ('tastiest', 0.6810969114303589), ('closest', 0.6696071028709412), ('experienced', 0.6656286716461182), ('softest', 0.6455549001693726), ('disgusting', 0.6202574968338013), ('coolest', 0.6155783534049988), ('awful', 0.6050782203674316), ('smoothest', 0.5930044054985046)]\n"
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\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(train_list,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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  14742\n",
      "sample words  ['kids', 'love', 'amazon', 'great', 'deal', 'back', 'school', 'sale', 'use', 'snacks', 'go', 'live', 'long', 'commute', 'no', 'matter', 'wonderful', 'complaints', 'flavor', 'gobbled', 'right', 'flavors', 'hit', 'house', 'plus', 'currently', 'made', 'peanut', 'free', 'facility', 'allergic', 'folks', 'four', 'chihuahuas', 'seemed', 'interested', 'treats', 'not', 'seem', 'know', 'quite', 'first', 'licked', 'walked', 'away', 'finally', 'one', 'managed', 'crunch', 'ate']\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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 61441/61441 [15:20<00:00, 48.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61441\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review in train dataset.\n",
    "#train dataset\n",
    "d_train_w2 = [];\n",
    "for sent in tqdm(train_list): # 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",
    "    d_train_w2.append(sent_vec)\n",
    "print(len(d_train_w2))\n",
    "print(len(d_train_w2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 26332/26332 [06:04<00:00, 72.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26332\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review in test dataset.\n",
    "#test dataset\n",
    "d_test_w2 = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(test_list): # 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",
    "    d_test_w2.append(sent_vec)\n",
    "print(len(d_test_w2))\n",
    "print(len(d_test_w2[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "model = TfidfVectorizer()\n",
    "tf_idf_matrix = model.fit_transform(dw_train)\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": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 61441/61441 [58:23<00:00, 17.54it/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",
    "d_train_tf_wt = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(train_list): # 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",
    "    d_train_tf_wt.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 26332/26332 [24:28<00:00, 17.94it/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",
    "#test dataset\n",
    "d_test_tf_wt = [];\n",
    "row=0;\n",
    "for sent in tqdm(test_list): # 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",
    "    d_test_tf_wt.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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "def cv_sgd_grid(d_trai, y_trai):\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "\n",
    "    #defining 'alpha' for grid-search alpha = 1/C\n",
    "    param= {'alpha':[0.0001,0.001,0.01,1,10,100,1000,10000],'penalty':[\"l1\",\"l2\"]}\n",
    "\n",
    "    #Using Grid Search to get Best alpha=1/c\n",
    "    start = time()\n",
    "    #hinge loss for svm\n",
    "    clfSGD = SGDClassifier(loss='hinge', class_weight='balanced')\n",
    "    clf = GridSearchCV(clfSGD, param, cv=5,scoring='roc_auc')\n",
    "    clf.fit(d_trai, y_trai)\n",
    "    print(\"Time elapsed : %.2f Seconds\" % (time() - start))\n",
    "    print(\" optimal alpha using Grid Search is \" + str(clf.best_params_))\n",
    "    optimal_alpha_grid=clf.best_params_.get('alpha')\n",
    "    best_penal=clf.best_params_.get('penalty')\n",
    "    return optimal_alpha_grid,best_penal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotROC(d_trai,y_trai,d_tes,y_tes,clf):\n",
    "    train_score = clf.predict_proba(d_trai)[:,1]\n",
    "    test_score = clf.predict_proba(d_tes)[:,1]\n",
    "    fpr_train, tpr_train, _ = roc_curve(y_trai, train_score)\n",
    "    fpr_test, tpr_test, _ = roc_curve(y_tes, test_score)\n",
    "    auc_train = auc(fpr_train, tpr_train)\n",
    "    auc_test = auc(fpr_test, tpr_test)\n",
    "    plt.title('Receiver Operating Characteristic Curve')\n",
    "    plt.plot(fpr_train, tpr_train, label = 'AUC Train = %0.2f' % auc_train)\n",
    "    plt.plot(fpr_test, tpr_test, label = 'AUC Test = %0.2f' % auc_test)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'k--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    return auc_train,auc_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotTrainvsTestAUC(d_trai,y_trai,d_tes,y_tes,penalty):\n",
    "    import warnings\n",
    "    from math import log\n",
    "    warnings.filterwarnings('ignore')\n",
    "    auc_train_list=list()\n",
    "    auc_test_list=list()\n",
    "    param=[0.0001,0.001,0.01,1,10,100,1000,10000]\n",
    "    x=list()\n",
    "    for i in tqdm(param):\n",
    "        x.append(log(i,10))\n",
    "        model=SGDClassifier(loss='hinge',penalty=penalty,alpha=i,class_weight='balanced')\n",
    "        model.fit(d_trai,y_trai)\n",
    "        clf=CalibratedClassifierCV(model,method='sigmoid',cv='prefit')\n",
    "        clf.fit(d_trai,y_trai)\n",
    "        train_score = clf.predict_proba(d_trai)[:,1]\n",
    "        test_score = clf.predict_proba(d_tes)[:,1]\n",
    "        fpr_train, tpr_train, _ = roc_curve(y_trai, train_score)\n",
    "        fpr_test, tpr_test, _ = roc_curve(y_tes, test_score)\n",
    "        auc_train_list.append(auc(fpr_train, tpr_train))\n",
    "        auc_test_list.append(auc(fpr_test, tpr_test))\n",
    "    plt.title('Train AUC Score vs Test AUC Score')\n",
    "    plt.plot(x, auc_train_list, label = 'Train AUC Score')\n",
    "    plt.plot(x, auc_test_list, label = 'Test AUC Score')\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.xlabel('Log OF Alpha (1/C)')\n",
    "    plt.ylabel('AUC Score')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot confusion matrix and print report\n",
    "def report(x,y,model,auc_):\n",
    "    y_true, y_pred = y, model.predict(x)\n",
    "    print(classification_report(y_true, y_pred))\n",
    "    print()\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    print(\"The value of TPR is %.3f\" %(tp/(fn+tp)) +\n",
    "          \"\\nThe value of TNR is %.3f\" %(tn/(tn+fp)) +\n",
    "         \"\\nThe value of FPR is %.3f\" %(fp/(tn+fp)) +\n",
    "         \"\\nThe value of FNR is %.3f\" %(fn/(fn+tp)) )\n",
    "         \n",
    "    cm=confusion_matrix(y_true, y_pred)\n",
    "    ax=sns.heatmap(cm,annot=True,fmt=\"d\")\n",
    "    ax.set(xlabel='Predicted', ylabel='Actual')\n",
    "    print()    \n",
    "    print(\"The auc score is %f\" %auc_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 11.88 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.001, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "#getting best alpha and best regularizer\n",
    "alpha_grid,reg_bow = cv_sgd_grid(d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.001, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp=SGDClassifier(penalty=reg_bow,loss=\"hinge\",alpha=alpha_grid,class_weight='balanced')\n",
    "model_temp.fit(d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_bw=CalibratedClassifierCV(model_temp,method='sigmoid',cv='prefit')\n",
    "clf_bw.fit(d_train,y_train)\n",
    "auc_train,auc_test=plotROC(d_train,y_train,d_test,y_test,clf_bw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:02<00:00,  3.32it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(d_train,y_train,d_test,y_test,reg_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.88      0.79      9927\n",
      "           1       0.98      0.93      0.95     51514\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     61441\n",
      "   macro avg       0.84      0.91      0.87     61441\n",
      "weighted avg       0.93      0.92      0.93     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.930\n",
      "The value of TNR is 0.883\n",
      "The value of FPR is 0.117\n",
      "The value of FNR is 0.070\n",
      "\n",
      "The auc score is 0.965765\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train,y_train,model_temp,auc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.81      0.73      4254\n",
      "           1       0.96      0.92      0.94     22078\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     26332\n",
      "   macro avg       0.81      0.87      0.84     26332\n",
      "weighted avg       0.91      0.90      0.91     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.922\n",
      "The value of TNR is 0.810\n",
      "The value of FPR is 0.190\n",
      "The value of FNR is 0.078\n",
      "\n",
      "The auc score is 0.935528\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test,y_test,model_temp,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Positive Words Are::\n",
      " [(0.658084273108339, 'smooth'), (0.6605575283886033, 'pleased'), (0.6906634261675605, 'yummy'), (0.7275740495370459, 'awesome'), (0.7504814161632636, 'amazing'), (0.7742139178185786, 'excellent'), (0.781405608676179, 'best'), (0.8079721423203736, 'perfect'), (0.8096622815273081, 'wonderful'), (0.9722924479355334, 'delicious')]\n"
     ]
    }
   ],
   "source": [
    "##sorting by weights (indices) and getting the last 10 weights\n",
    "w_=model_temp.coef_\n",
    "feature_names = count_vect.get_feature_names()\n",
    "pos = sorted(zip(w_[0], feature_names))\n",
    "print(\"Top 10 Positive Words Are::\\n\",pos[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Negative Words Are::\n",
      " [(-1.1247112263439034, 'worst'), (-1.058172548669205, 'disappointing'), (-0.9003561910960796, 'disappointed'), (-0.8392041481139514, 'terrible'), (-0.8255614726422811, 'disappointment'), (-0.8084257516056869, 'awful'), (-0.7673916028977397, 'unfortunately'), (-0.718196591231438, 'weak'), (-0.7173448767698518, 'sorry'), (-0.7137345690027811, 'bland')]\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 10 Negative Words Are::\\n\",pos[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.2] Applying Linear SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 14.57 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.0001, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#getting best alpha and best regularizer\n",
    "alpha_grid_tf,reg_tf = cv_sgd_grid(d_train_tf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_tf=SGDClassifier(penalty=reg_tf,loss=\"hinge\",alpha=alpha_grid_tf,class_weight='balanced')\n",
    "model_temp_tf.fit(d_train_tf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IcNnjXG5yPNKoImXPnj3cfvvtDBo0iBtvvDFzvIisMca0Ppd1OlmimA3cZd9V2RY4mleSKM68PsP+pBT+Tk5lf1IKXp/h8Ik0tuy17uM6kJzK38kpHDyWhgiZCSGnM+SsqleIplRkOKUi3VQsFUHJiDAiw12UigwjOtxNZLibEhFuSkaGIUBUuJtSkWGEu4Uwt4twtxAZ5iYyzEVEmIsI+0AfGW59DnO7CHNJZgI49cXS4eRhSE2GE4fAm2YNe1Ig5SicPATitoYP7YTIMtZ7WCQc3m0d/A5ug/CSkH4CfOmn1n0ggC8eFg0ValvrddWntPFBmWrWATj9BOFRZSG6vLW91GRr3owDdZkYCI+2Yoguby3jjoCIktb87khwuRGR0Ci6q5BmjGHKlCmMHz+e9PR0evfunW/rDtr+KyIzsTq+qyQiCVjdG4QDGGPewbqbshfWHaAnsO46LraMMRw+kc7uxOPsPZrC7sQTxB8+wV9HTrLvaAp/Jh4nJT3ns/oaFUpQqVQEFUtFUKFEBBeVi6ZciXDKRodTOiqMcLeLC8ta48pEh1MqMowo+yAu+XGmmZ4CJw7C8YNw/AicSIS/N8PxA1Yy2P+7dTD/OxaiysCJw5AWyM3K2ShbHVKSoEpDSDsGjQdYSaJiXYgsZR30w6Ksg3hUOeugHRZpjYsoZY2PKGFN07NsVQTs2LGDsWPHsnDhQjp37szkyZOpUyfQbr7yFrREYXcQltt0g9V3TrGSku4ldm8SW/cls/3vY+w9epL4Qyf5M/E4ySmnV29UKBnBBWWiiCkfzRV1KlG7UgkuKhdtneVHuKlSOoqKpSIIdwfp4jVPKhyJg6S/4PCfViJI3HHq7Pr4ATj2t1USOLYv93VVrGuVEKq3hbTjcHFn8HmhVGWreqRCbWu4XHUIL2FVlURXsA7skaXss3O9P1Sp7GzcuJE1a9bw7rvvMmbMmPw5+fMTcl14hFIbxdGT6fx58Dhb9iWxcc9Rvv99P0dOpOGx64Mi3C5iKkRTrVw0NSuWoFbFktSsWJILy0ZRo2IJykSFBy84Y+zqnkQ4vAv2x1oJ4UQipCZB3K9WFVB2xAVlY6DUBVCyCpSoAGUushJIhTpQspJVFVOiEpSsrAd4pYLg999/57fffmP48OEAJCYmUrFixRznD9U2iiLH4/WxZV8yP23+m6/XJvBn4onMaVHhLlpUL0fVMhXp3rgqjS8qS7Xy0afX1weDz2uVCg7/aR38/1oHCausuv+swqKtA3tkaYhpYx3wa3e0EkKFi6FERav+XatrlHJMWloazz33HM899xwXXHABAwcOJCoqKtckcb40UZwHYwyb/kpixa5DrNp1iF92HCTJrj5qXr0cD7SuTp3KJal/QWlqViwZ3KSQdgL2bYB9G63XoZ2QfhL2ZCl9RZSGSvWsBFA2Bio3gPK1rHGlc+tBWynltBUrVnDzzTezadMmbrrpJl599VWioqKCvl1NFGcp8VgqGxKO8vmaeBZvPcDxNOs69wvLRtG10QW0r1uJNrUrEFO+RB5rOkfGQPJeq6roaDysnmbV/R/J5hLpyg2h+RCr8bfG5VYyKFcjOHEppYJqz549dOjQgQsuuID//e9/+XpVU140UQRg39EUvlgTz/827GXLPutKndJRYfRtUY1Lq5ejY/3KVC0bpKx+7ADEL7cSw/qPrSokf9EVrKt5rhxnfa7eFqo2saqPlFIhb9u2bdSvX59q1arx6aef0qVLF8qUKZP3gvlIE0UOklPS+XbDXuZs+ItfdiRiDDS+qAwP9riE5jHlaFWzPFHhZ/NY5gD5fLB3HWz4DP6YD4f8HidcsR7UuRqqNrWqjsrEQOVLtM1AqSLoyJEjPPjgg0yZMoVFixbRsWNH/vGPfzgSiyaKLI6nenj9pz+YuTKO5BQPVUpHckvHi7mhVXXqVimV9wrOljEQtxx2L4O4FbD9h1PTLr4KWgyxEkS1llptpFQxMXv2bG6//Xb27dvHAw88wGWXnc0TifOfJgrbjgPHmLJ0F7PX7eF4mpcejasyun1tWtUsH5xG6JSjsPxtWPT8qXEV6kDjf1gNzM2HQPmaOS+vlCqSxowZw9SpU2natCnffPMNrVuf0xWt+arYJ4pUj5eJ87cxaclOALo2rMLtV9WhVc0K+buhlCSr5LDjJ9j9i3WFEliXo1ZtBv3f0quOlCqmTnXiJ7Ru3ZqaNWsyYcIEIiIiHI7MUmwThTGGuRv38fL8rew6eJxO9SvzRN/G1K5UMv82cmgXxH4Dm2fDnjWnxle6BNrdZbU31O2Sf9tTSoWc+Ph4brvtNgYPHsywYcO47bbbnA7pDMUyUfx58DjjP1/P6t2HqV4hmknDWtG9cT6eze/+FZZNtBqjwbpPod1dUPNKqNXe6utIKVWs+Xw+Jk2axIQJE/B6vY41VAei2CWKFTsTGTV9FS4R/t2nESPa1SQsP/pK8vng59fgpydPjWs2GFqNgBrt9MokpVSmP/74gzFjxrBkyRK6du3Ku+++S+3atZ0OK0fFKlEs2vo3t364hsqlI5k+qk3+XcWUuANmXG/dDV22OtTqAD1fgKiy+bN+pVSREhsby4YNG5g2bRojR47M90788luxSRRb9iUxavoqLiwTlX9JwueFBc/Ar29azyfocD90egjCCkcDlFKq8Fi/fj3r1q1jxIgR9OvXj507d1K+fHmnwwpIsUgUK3YmMub91ZSKDGP66HxKEn+tha9vhwObrRJEl8ehurPXOiulCp/U1FSeeeYZXnjhBS688EIGDRpEVFRUyCQJKAaJYvb6vxj/2XqqV4hm8vDWXFw5H5LErqXwfh/rOQr934Hmg7UNQil1hl9//ZWbb76ZzZs3M3z4cCZOnFggnfjltyKdKOIPneC+T9fRuFpZPhjVhrIlzvP5DsbAW+2sUoQ7Avq/DU2vz59glVJFyp49e+jUqRNVq1Zl7ty59OzZ0+mQzlmRTRQn0jwMm7oCA7w2qMX5J4n0FPhqjJUkarSDoZ9qY7VS6gybN2+mYcOGVKtWjc8++4wuXbpQunRod9JZZB899ubC7fyZeIJn+zc5/5vo1s6A52Ng8xy4pBeM+k6ThFLqNIcPH2b06NE0atSIpUuXAtC/f/+QTxJQREsU2/8+xpsLd9D5ksoMbnMeHemlp8Ci5+Dn163h/m9b90Zoe4RSys/XX3/NHXfcwYEDB3j44Ycd78QvvxXJRPH0/2IBuPea+ue+kv2b4ONB1sOBqjaD66ZC5fNYn1KqSBo9ejTvvfceLVq04Ntvv6Vly5ZOh5Tvilyi+G7jXhZvO8CAltVoFlPu3Fay7mOYdbv1ufdEuOzm/AtQKRXy/Dvxu/zyy6lXrx7jx48nPPw820ILqSKVKI6eSOfRWb9TtUwUT/RtfG4r8U8Sw2fDxZ3yL0ClVMjbvXs3t956K0OHDmX48OHccsstTocUdEWqMfuDX/8k8Xgazw1oQpmoc8jsi188lSTu+V2ThFIqk8/n480336RJkyYsW7aM9PR0p0MqMEWqRPHj5v0AdL6kytkt6PPCp8Ng67fW8K1LoVz1fI5OKRWqtm7dypgxY1i2bBndunVj0qRJ1KpVy+mwCkyRSRR/HjzO+oSjdGlQ5ew72Hq9BRyNg2qtYMinUKpycIJUSoWkrVu3smnTJqZPn87w4cMLfSd++a3IJIpvN+4F4I7Odc9uwbjlVpIILwljftJLX5VSAKxdu5Z169YxatQo+vbty86dOylX7hwvkAlxRaaN4r2fd1GrYgla1TyLjraS98O07tbnYV9rklBKkZKSwiOPPMJll13GE088QUpKCkCxTRJQRBLF9r+TOXgs7eyeUvfXWpjYwPp85T1Qo21wglNKhYyff/6ZFi1a8PzzzzN8+HDWrVsXkp345bciUfU0Y0UcADe0jglsgYTVMMV+VvUN06Fx4X0EoVKqYOzZs4fOnTtTrVo15s2bR7du3ZwOqdAoEiWKrfuScQnUrRJAnypeD3x7v/V50AxNEkoVc7GxVk8O1apV48svv2Tjxo2aJLII+UTh9Rl+2ZHIkED7dPpqDOxdB1fcDQ37BDc4pVShdejQIUaOHEnjxo1ZsmQJANdeey2lSuXTI5KLkJCvetp18DhAYA8kmn03bPoaLukN3Z4OcmRKqcLqyy+/5M477yQxMZF//etftGnTxumQCrWQTxTf2ZfFtq1dIfcZN34Bv70P5WvBdZODH5hSqlAaOXIk77//Pi1btuT777+nRYsWTodU6IV8ovhlRyKlI8NofFGZ3GdcNcV6v/lHiDjP51MopUKKfyd+V1xxBQ0bNuT+++8nLCzkD4EFIqhtFCLSQ0S2ish2EXkom+k1RGShiKwVkQ0i0uts1m+MYX3CES6tWT73OyV/+wDifoWaV+pd10oVM7t27aJbt2588MEHANxyyy1MmDBBk8RZCFqiEBE38CbQE2gEDBGRRllmexT4zBhzKTAYeOtstrHpryROpHm5pmEefTt9/zC4wq1nSiiligWv18sbb7xBkyZNWL58eWapQp29YJYo2gDbjTE7jTFpwCdAvyzzGCCjzqgs8NfZbGBt3GEALqmaS7VTwmpIOwZ1u0KZC89m9UqpELV582Y6dOjAuHHj6NSpE5s2bWLkyJFOhxWygln2qgbE+w0nAFlvf34CmC8i/wRKAl2zW5GI3ALcAlCjxqnLYHfaVzw1uDCH+yeS91k31rnC4OpHz+ErKKVC0fbt29m6dSsffvghN954Y7HrxC+/OX0fxRBgujEmBugFfCgiZ8RkjHnXGNPaGNO6cuVTbQw7D1iJIsdnTyx71Xrv+1+o2iSfQ1dKFSZr1qxh2rRpgHU/xK5du7jppps0SeSDYCaKPYD/Qx1i7HH+bgY+AzDG/ApEAZUC3UDcoRNUrxCd8wx//my9Nx8c6CqVUiHm5MmTPPTQQ7Rt25ann346sxO/MmXyuBJSBSyYiWIVUE9EaotIBFZj9ews88QBXQBEpCFWojgQ6AYOHkulfd0c8sq+jbB/I1x0qfYKq1QRtWTJEpo3b85//vMfRo4cydq1a7UTvyAIWhuFMcYjIncB8wA3MM0Ys0lEngJWG2NmA/cDk0XkXqyG7ZEmwEsTjp5MJznFQ40KOdwTsdoqgtLrlfP9KkqpQmjPnj106dKF6tWr8+OPP9KlSxenQyqygnohsTFmLjA3y7jH/D7HAleey7rjD50A4KJyOZw9xM6GqLIQ0+pcVq+UKqQ2btxI06ZNqVatGl9//TWdO3emZEm9iTaYnG7MPmfLth8EoHqFEmdO/HsznDgIVRoXcFRKqWA5ePAgw4YNo1mzZpmd+PXp00eTRAEI2VsTdydaVzw1ujCbBqt5j1jvnR8uwIiUUsFgjOHzzz/nrrvu4vDhwzz++OO0basPGitIIZso0r1WU0ZUuPv0CT4f7FhgNWLX7uhAZEqp/DRixAg+/PBDWrduzU8//UTTpk2dDqnYCdlEkebxUatiNtVOf/1mvde9pmADUkrlG/9O/Dp16kSzZs245557tH8mh4RsG8X+pBQiw9xnTtj9i/Veu0PBBqSUyhc7d+6ka9euTJ8+HYCbb76Z8ePHa5JwUMgmiuNpHo6nec6cEL/Ceq/WumADUkqdF6/Xy2uvvUbTpk1ZtWoVLlfIHp6KnJBN0YIQUz7LXdnpKbDlf3BJL4jIplpKKVUoxcbGMnr0aFasWEHv3r155513iImJcTosZQvZlL1xz1EqlIw4feRvVn/z1Lm64ANSSp2zXbt2sWPHDj7++GPmzJmjSaKQCckSRarHC0CaJ8tN3EtetN6bXl/AESmlztaqVatYt24dY8eOpXfv3uzcuZPSpXPoCVo5KiRLFEknrbaJDvWy9PN0/ACUvgiiyzsQlVIqECdOnGD8+PFcfvnlPP/885md+GmSKLxCMlHssp9DUbl05KmRKUet9xZDHYhIKRWIRYsW0axZM1555RXGjh2rnfiFiJCsetqfZJ2BVPFPFJ+PtN4v0G47lCqMEhISuOaaa6hZsyYLFiygc+fOToekAhSSJYrEY6kAXFDG70wk49kTjf/hQERKqZysX78egJiYGL755hs2bNigSSLEhGSiOGq3UWQ+2e6vteBNhYs767MnlCqriP0yAAAgAElEQVQkDhw4wNChQ2nRogWLFy8GoFevXpQooZeuh5qQrHry+HwAlIm2w49fZb23vdWhiJRSGYwxfPLJJ9x9990cPXqUJ598knbt2jkdljoPASUK+wl1NYwx24McT0AOJKdSJirs1LNw92+03mtc7lxQSikAhg0bxowZM2jbti1Tp06lcWNtNwx1eVY9iUhvYCPwgz3cQkS+DnZguUlO8VDJvyHbY7VZ6GWxSjnD5/NlduTXuXNnJk6cyM8//6xJoogIpI3iKaAtcATAGLMOqBvMoPKyZV8SJSL8OgT8ax1UbuBcQEoVY9u3b6dLly689957gNWJ37333ovbnU2nnSokBZIo0o0xR7KMC+i51sGSlOIhNd13akT6SUg/4VxAShVDHo+Hl19+maZNm7J27VoiIiLyXkiFpEDaKDaLyEDAJSK1gbuB5cENK3den6FiKb+d8mgc1O3qXEBKFTO///47o0aNYvXq1fTr14+33nqLiy66yOmwVJAEUqK4C2gF+ICvgFRgXDCDykvSyXQaXVjWGkg9Zr1XqONcQEoVM3FxcezevZtPPvmEr7/+WpNEERdIiaK7MWYCMCFjhIgMwEoaBc4AHp/Ba18iS9Ie671cDSfCUarYWLFiBevXr+eWW26hV69e7Ny5k1KlSjkdlioAgZQoHs1m3L/yO5BAZVxZUa6EXfX05zLrXROFUkFx/Phx7rvvPtq1a8eLL75Iaqp1laEmieIjxxKFiHQHegDVRGSi36QyWNVQjrDzBOVK2Hdlnzhkvde8wpmAlCrCFixYwNixY9m5cye33347L7zwApGRkXkvqIqU3Kqe/gZ+B1KATX7jk4GHghlUbjIutwp324WhP+ZZ71FlHYlHqaIqISGB7t27U7t2bRYvXkzHjh2dDkk5JMdEYYxZC6wVkRnGmJQCjClXPp+VKsLd9l3ZYVGAgDvcuaCUKkLWrl3LpZdeSkxMDHPmzKFTp05ER0fnvaAqsgJpo6gmIp+IyAYR2ZbxCnpkObDzBKkeH6QkwZ9L4ZKeToWjVJGxf/9+Bg0aRMuWLTM78evRo4cmCRVQopgOvAcI0BP4DPg0iDHlytiVT1VKR8Eua2emelunwlEq5Blj+Oijj2jUqBGzZs3imWee4YortM1PnRJIoihhjJkHYIzZYYx5FCthOMMuUYS7BQ7aBZtG/RwLR6lQN3ToUIYNG8Yll1zCunXr+Ne//kV4uFblqlMCuY8iVURcwA4RuQ3YAzj2cNuMxmy3S2Ddx9ZA2epOhaNUSPL5fIgIIkK3bt1o164dd955p/bPpLIVSIniXqAkVtcdVwJjgdHBDCo3GfdRhLtdIPZO7Q7Jx2oo5Yht27bRuXNnpk2bBsCoUaO4++67NUmoHOV5hDXGrLA/JgPDAESkWjCDyk26168/woNboflQp0JRKqR4PB4mTpzI448/TlRUlDZSq4DlWqIQkctEpL+IVLKHG4vIB8CK3JYLpoxnFVVwH7c+GMfu/VMqZGzYsIHLL7+cCRMm0LNnT2JjYxk6VE+yVGByTBQi8jwwA7gR+F5EngAWAuuB+gUSXTYy7swu5bF7Pq/a1KlQlAoZCQkJxMfH8/nnn/Pll19y4YUXOh2SCiG5VT31A5obY06KSAUgHmhqjNkZ6MpFpAfwOuAGphhjXshmnoHAE1jt1OuNMbme5mRcHuvO6EWkjPZaqVR2fvnlFzZs2MBtt92W2YlfyZIlnQ5LhaDcqp5SjDEnAYwxh4BtZ5kk3MCbWJfSNgKGiEijLPPUAx4GrjTGNAbuyXPFdonCbbz2N9CGbKX8HTt2jHHjxtG+fXteeeWVzE78NEmoc5XbUfZiEcnoSlyA2n7DGGMG5LHuNsD2jOQiIp9glVJi/eYZC7xpjDlsr/PvvALOvDwWTRRKZTV//nxuueUW4uLiuPPOO3nuuee0Ez913nI7yl6XZfi/Z7nualjVVRkSsJ697a8+gIj8jFU99YQx5vusKxKRW4BbACrF1KYkEL1tljUxTB+/qBRAfHw8vXv3pk6dOixZsoT27ds7HZIqInLrFPCnAtp+PeAqIAZYIiJNsz6j2xjzLvAuQEy9JgYgLNWepVaHAghTqcJrzZo1tGrViurVqzN37lw6dOhAVFSU02GpIiSQG+7O1R7A/5bpGHucvwRgtjEm3RizC9iGlThy5LKvjw036VC2BoRpsVoVT/v27eOGG26gdevWmZ34XXPNNZokVL4LZqJYBdQTkdoiEgEMBmZnmWcWVmkC+16N+kCuDeZpXh9ul+A6shvC9R9CFT/GGN5//30aNWrEnDlzeO6557QTPxVUAbcEi0ikMSY10PmNMR4RuQuYh9X+MM0Ys0lEngJWG2Nm29O6iUgs4AUeMMYk5rVur89A8t5AQ1GqSBk8eDCfffYZV155JVOmTKFBgwZOh6SKuDwThYi0AaYCZYEaItIcGGOM+Wdeyxpj5gJzs4x7zO+zAe6zXwGrUioCjsZD8yFns5hSIcu/E79evXrRoUMH7rjjDlyuYFYKKGUJZC97A+gDJAIYY9YDnYMZVF4iXCbvmZQqIrZs2ULHjh2ZOnUqACNGjOCuu+7SJKEKTCB7mssYszvLOG8wggmEwRAmdqKolGu7t1IhLT09neeee47mzZsTGxtLqVKlnA5JFVOBtFHE29VPxr7b+p9YVyc5JsJld9+hN9upImrdunWMGjWKdevWcf311/N///d/VK1a1emwVDEVyJH2dqzqpxrAfuBHe5wzDISLXaAR7T9fFU379u1j3759fPnllwwYkFcnCEoFVyCJwmOMGRz0SAJkgGjSrAEtUagiZNmyZWzYsIE77riDHj16sGPHDkqUKOF0WEoF1EaxSkTmisgIEXHsEaj+yor9LApvmrOBKJUPkpOTueuuu+jQoQOvvfZaZid+miRUYZFnojDG1AGeAVoBG0Vklog4WsIIF4/1oUJtJ8NQ6rzNmzePJk2a8NZbbzFu3Dh+++037cRPFToBXV9njPnFGHM30BJIwnqgkSOMgYjMnmPDnQpDqfMWHx9Pnz59KFGiBMuWLeO1117TK5tUoZRnohCRUiJyo4jMAVYCBwDH+gvw+nyUxq560jYKFWKMMaxcuRKA6tWr891337F27VrtgkMVaoGUKH4HLgdeNMbUNcbcb4xx7JnZPgN1TmywBkpWdCoMpc7a3r17ue6662jbtm1mJ35du3bVTvxUoRfIKfnFxhhf0CMJkEuEklERcAKo2tzpcJTKkzGG6dOnc99995GSksJ//vMfrrzySqfDUipgOSYKEXnFGHM/8KWInNFnRgBPuAsKnzFUlUNW+4R2YaBCwMCBA/niiy/o0KEDU6ZMoX79+k6HpNRZya1E8an9frZPtgsqA1Tx7gNfutOhKJUjr9eLiOByubj22mu5+uqrufXWW7V/JhWSctxrjTEr7Y8NjTE/+b+AhgUT3plS070IBqq1cioEpXK1efNmOnTokNmJ3/Dhw7n99ts1SaiQFcieOzqbcTfndyCBCnO7qJW6BSL0MkJVuKSnp/PMM8/QokULtm7dStmyZZ0OSal8kVsbxSCsp9LVFpGv/CaVBo5kv1TBOBlWjqi0406GoNRp1q5dy8iRI9mwYQODBg3ijTfeoEqVKk6HpVS+yK2NYiXWMyhigDf9xicDa4MZVG6MMVbVU+VLnApBqTPs37+fgwcPMmvWLPr16+d0OErlqxwThTFmF7ALq7fYQkUwIFrfq5y1ZMkSNm7cyJ133kmPHj3Yvn070dHRToelVL7L8WgrIovt98MicsjvdVhEDhVciGdyGZ8mCuWYpKQk7rjjDjp16sQbb7yR2YmfJglVVOV2tM143GkloLLfK2PYEQYQfODSZ1Gogjd37lwaN27MpEmTuO+++7QTP1Us5HZ5bMbd2NUBtzHGC7QDbgVKFkBsOQSmVU/KGfHx8fTr14+yZcvyyy+/8Morr1CypHP/CkoVlECOtrOwHoNaB3gPqAd8HNSocmEAl/FqolAFwhjD8uXLAasTv/nz5/Pbb7/Rtm1bhyNTquAEcrT1GWPSgQHA/xlj7gWqBTes3EV7k/UxqCro/vrrL/r370+7du0yO/Hr3LkzERERDkemVMEKJFF4ROQGYBjwP3ucgw+CsLudSnH0Vg5VhBljmDJlCo0aNWL+/Pm8/PLL2omfKtYC6T12NHAHVjfjO0WkNjAzuGHlwtiJolwNx0JQRdv111/PV199RadOnZgyZQp169Z1OiSlHJVnojDG/C4idwN1RaQBsN0Y82zwQ8shnowP4fo8YZV//Dvx69+/P926dWPs2LHaP5NSBPaEuw7AdmAqMA3YJiKOlcMlI1VoY7bKJ7///jtXXnllZid+w4YN055elfITyH/Cq0AvY8yVxpgrgN7A68ENKwAiTkegQlxaWhpPPvkkLVu2ZMeOHZQvX97pkJQqlAJpo4gwxsRmDBhjNouI85d9aIlCnYc1a9YwcuRIfv/9d4YOHcprr71G5cqO3UeqVKEWSKL4TUTeAT6yh2/EwU4BM8sRmijUeUhMTOTIkSPMmTOHPn36OB2OUoVaIIniNuBu4EF7eCnwf0GLKE8Zzdla9aTOzsKFC9m4cSN333033bp1448//iAqKsrpsJQq9HI9LReRpkAP4GtjTF/79ZIxJqVgwsuFlihUgI4ePcqtt97K1Vdfzdtvv53ZiZ8mCaUCk1vvsY9gdd9xI/CDiGT3pLsCd6rqSUsUKm9z5syhUaNGTJkyhfHjx7NmzRrtxE+ps5Rb1dONQDNjzHERqQzMxbo8tnDQEoXKQ3x8PNdddx0NGjRg1qxZXHbZZU6HpFRIyu1om2qMOQ5gjDmQx7wFRrA7tdUShcqGMYZffvkFONWJ3+rVqzVJKHUecjv4XywiX9mvr4E6fsNf5bJcJhHpISJbRWS7iDyUy3zXiYgRkdZ5rbMEqXbkDnY3pQqlhIQE+vbty5VXXpnZid9VV12lnfgpdZ5yq3q6Lsvwf89mxSLixnrW9jVAArBKRGb735Nhz1caGAesCGS90aRZH+p2PZtwVBHm8/mYPHkyDzzwAB6Ph4kTJ9K+fXunw1KqyMjtmdk/nee622D1C7UTQEQ+AfoBsVnmexr4D/BAICs1Gc3ZZS48z/BUUXHdddcxa9Ysrr76aiZPnszFF1/sdEhKFSnBbHeoBsT7DSeQ5TkWItISqG6M+Ta3FYnILSKyWkRWAxhtyC72PB4PPp/VXnXdddcxefJkfvzxR00SSgWBY0dcEXEBE4H785rXGPOuMaa1MaY1GEzhaFdXDtmwYQPt2rVj8uTJANx0002MGTMG0QsclAqKgI+4InK2F5/vwXredoYYe1yG0kATYJGI/AlcDszOq0FbQC+NLaZSU1N5/PHHadWqFbt379a+mZQqIIF0M95GRDYCf9jDzUUkkC48VgH1RKS23YngYGB2xkRjzFFjTCVjTC1jTC1gOdDXGLM6zzXrmWOxs2rVKlq2bMlTTz3FkCFD2Lx5MwMGDHA6LKWKhUD6enoD6IN1lzbGmPUi0jmvhYwxHhG5C5gHuIFpxphNIvIUsNoYMzv3NeRCSxTFzuHDhzl27Bhz586lZ8+eToejVLESSKJwGWN2Z6n/9QaycmPMXKw7uv3HPZbDvFcFsk7rwUVaoigOFixYwMaNGxk3bhzdunVj27Zt2v2GUg4I5NQ8XkTaAEZE3CJyD7AtyHHlSq96KtqOHDnC2LFj6dKlC5MmTcrsxE+ThFLOCOSIeztwH1AD2I/V6Hx7MIPKkyaKIuubb76hUaNGTJs2jQcffFA78VOqEMiz6skY8zdWQ3ShECnpYHxOh6GCIC4ujhtuuIGGDRsye/ZsWrfOs0cXpVQByDNRiMhkTj0tKJMx5pagRJQHH4I7/ZgTm1ZBYIxh2bJldOjQgRo1avDjjz9y+eWXa/9MShUigdTh/Aj8ZL9+BqpARs98zkgtX9/Jzat8EhcXR+/evenYsWNmJ34dO3bUJKFUIRNI1dOn/sMi8iGwLGgR5UEAXG6nNq/ygc/n45133mHChAkYY3jjjTe0Ez+lCrFALo/NqjZwQX4HEjgDookilA0YMIBvvvmGa665hnfffZdatWo5HZJSKheBtFEc5lQbhQs4BOT4bIlgE8BoiSLkeDweXC4XLpeLQYMG0a9fP0aOHKn9MykVAnJNFGL9FzfnVB9NPmPMGQ3bBSkCj14eG2LWr1/P6NGjGTt2LLfddhtDhgxxOiSl1FnI9YhrJ4W5xhiv/XI0SVgM7pTDTgehApCSksKjjz5K69atSUhIoGrVqk6HpJQ6B4G0UawTkUuNMWuDHk0AIkkntUoL9EGohdvKlSsZMWIEW7ZsYcSIEUycOJEKFSo4HZZS6hzkmChEJMwY4wEuxXqM6Q7gOHYzgTGmZQHFeCa3ponCLikpiZMnT/L999/TvXt3p8NRSp2H3EoUK4GWQN8CiiVg3lIXOR2Cysb8+fPZtGkT9957L127dmXr1q3a/YZSRUBubRQCYIzZkd2rgOLLlrdMtbxnUgXm8OHDjBo1iu7duzN16lTtxE+pIia3EkVlEbkvp4nGmIlBiCcgJrykU5tWWXz11VfceeedHDhwgIcffpjHHntME4RSRUxuicINlKIwPvxB76MoFOLi4hg8eDBNmjRh7ty5XHrppU6HpJQKgtwSxV5jzFMFFsnZ0EThGGMMS5YsoVOnTtSoUYMFCxbQtm1bwsP1AgOliqo82ygKI9EuPByxe/duevbsyVVXXZXZiV/79u01SShVxOWWKLoUWBRny6V3Zhckn8/Hf//7Xxo3bsyyZcv4v//7Pzp06OB0WEqpApJj1ZMx5lBBBnI2xBfQI7tVPunfvz9z5syhe/fuTJo0iZo1azodklKqAJ1L77GO85XW+yiCLT09HbfbjcvlYsiQIVx//fUMGzZMO/FTqhgKyTocPVgF12+//UabNm145513ABgyZAjDhw/X312pYiokE4X2HhscJ0+e5OGHH6ZNmzbs27eP6tWrOx2SUqoQCMmqp4hwveopvy1fvpwRI0awbds2Ro8ezcsvv0z58uWdDkspVQiEZKJwaYki3x0/fpz09HR++OEHunbt6nQ4SqlCJCQThWiiyBfff/89mzZt4v7776dLly5s2bKFiIgIp8NSShUyIXnEFb2P4rwkJiYyYsQIevbsyfvvv09aWhqAJgmlVLZC8oirV9+cG2MMX3zxBY0aNeLjjz/m0UcfZdWqVZoglFK5Cs2qJ5cminMRFxfH0KFDadasGfPnz6d58+ZOh6SUCgEhWaII2bAdYIxhwYIFANSsWZNFixaxfPlyTRJKqYCF5BHXpW0UAdm1axfdunWjS5cumZ34XXHFFYSFhWRBUinlkJA84upVT7nzer28/vrrNGnShBUrVvD2229rJ35KqXMWkqeWLm2jyFW/fv349ttv6dWrF++8847eYa2UOi8hmSi0RHEm/078hg0bxpAhQxg6dKheIaaUOm9BPeKKSA8R2Soi20XkoWym3ycisSKyQUR+EpHA+q8uUTHfYw1lq1evpnXr1rz99tsADBo0iBtvvFGThFIqXwQtUYj1GLo3gZ5AI2CIiDTKMttaoLUxphnwBfBigCvPx0hD18mTJ5kwYQJt27blwIED+pwIpVRQBLNE0QbYbozZaYxJAz4B+vnPYIxZaIw5YQ8uB2ICW7Umil9//ZXmzZvz4osvMnr0aGJjY+nTp4/TYSmliqBgtlFUA+L9hhOAtrnMfzPwXXYTROQW4BaAVhdq+wRYpQmfz8ePP/5Ily6F96m1SqnQVygas0XkJqA10Cm76caYd4F3AVpf5DbFtepp7ty5bNq0iQceeICrr76azZs3Ex4e7nRYSqkiLpin53sA/+syY+xxpxGRrsC/gL7GmNTAVl28EsXBgwe56aab6N27NzNmzMjsxE+ThFKqIAQzUawC6olIbRGJAAYDs/1nEJFLgUlYSeLvIMYSkowxfPLJJzRs2JDPPvuMxx9/nJUrV2onfkqpAhW0qidjjEdE7gLmAW5gmjFmk4g8Baw2xswGXgJKAZ/bl3LGGWP65rnyYlL1FBcXx4gRI2jevDlTp06ladOmToeklCqGxBjjdAxnpfVFbrP6zySIKOl0KEFhjOGnn37KfMrc8uXLueyyy3C79fGvSqlzJyJrjDGtz2XZQtGYffaKZolix44djB07loULF7Jo0SI6derE5Zdf7nRYqohIT08nISGBlJQUp0NRQRQVFUVMTEy+tmGGZqIoYlVPGZ34Pfroo4SHhzNp0iTtxE/lu4SEBEqXLk2tWrX0rv0iyhhDYmIiCQkJ1K5dO9/WG5qJooi59tpr+e677+jTpw9vv/02MTEB3neo1FlISUnRJFHEiQgVK1bkwIED+breEE0Uob+jp6WlERYWhsvlYuTIkQwbNozBgwfrP7EKKt2/ir5g/I31NmcHrFy5klatWvHWW28BMHDgQIYMGaL/xEqpQik0E0WIHlBPnDjB/fffT7t27Th8+DB16tRxOiSlCtysWbMQEbZs2ZI5btGiRWf0VTZy5Ei++OILwGqIf+ihh6hXrx4tW7akXbt2fPfd6T3+/OMf/6BFixbUrVuXsmXL0qJFC1q0aMEvv/wScGxvvvkmM2bMOI9vl7vExES6dOlCvXr16N69O0ePHs12vvHjx9OkSROaNGmS+RsAzJ8/n0svvZQWLVrQoUMHdu7cGbRYT2OMCalXqwtdxqSnmlCzdOlSc/HFFxvA3HrrrebIkSNOh6SKmdjYWKdDMMYYM3DgQNO+fXvz2GOPZY5buHCh6d2792nzjRgxwnz++efGGGMmTJhghg8fblJSUowxxuzbt898+umn2a4/u3X5S09PP9+vcM7uvfde89JLLxljjHn66afNI488csY8s2bNMt27dzcej8ckJyebli1bmuTkZGOMMbVr1zbbtm0zxhjz+uuvm5tvvjnb7WT3t8a6f+2cjrsh2kYRejIeLLRw4UKuuuoqp8NRxdyTczYR+1dSvq6z0UVlePzaxrnOc+zYMZYtW8bChQu59tprefLJJ/Nc74kTJ5g8eTK7du0iMjISgAsuuICBAwcGHFtMTAw33XQT8+bN45FHHiExMZGpU6eSlpZG/fr1+eCDD4iOjubRRx+lUqVK3HPPPbRv35727duzYMECjh49ynvvvccVV1wR8Daz880337B8+XIARowYQY8ePXj22WdPmyc2NpZOnTrhdrspVaoUTZo0Yf78+QwYMAARISnJ+rsdPXqUiy666LziCVRoJooQqXqaM2cOmzdv5sEHH6Rz587ExsYSFhaaP7lS+eGbb76hR48e1K9fn4oVK7JmzRpatWqV6zLbt2+nRo0alClT5ry2XaVKFdauXQtYVUC33XYbAA899BDTp0/n9ttvP2MZYwwrV65k9uzZPPXUU3z//fenTT9y5EiOJ36ffvopl1xyyWnjEhMTqVy5MgDVqlVj7969ZyzXvHlzXnjhBe655x6OHTvG4sWLadmyJQBTp06lW7duREdHU65cucykE2x61AqCAwcOMG7cOGbOnEmLFi245557iIiI0CShCo28zvyDZebMmYwbNw6AwYMHM3PmTFq1apXjhRz5eYHHoEGDMj9v2LCBxx57jCNHjpCcnJzjs1wGDBgAQKtWrfjzzz/PmF6uXDnWrVuXbzEC9OrVi9WrV9OuXTuqVKlCu3btMntmePXVV5k3bx6tW7fm+eefZ/z48bzzzjv5uv3shOiRq3CWKIwxzJw5k7vvvpukpCSeeuopJkyYoJ34KQUcOnSIBQsWsHHjRkQEr9eLiPDSSy9RsWJFDh8+fMb8lSpVom7dusTFxZGUlHRepYqSJU91+zN8+HC+++47mjRpwpQpU3I8M8+o6nK73Xg8njOmn22JIuMeh8qVK7Nnzx4uvPDCbJd97LHHeOyxxwDrqsj69euzd+9etmzZQuvWVi8cgwYNon///rl/6XyiVz3lo7i4OEaNGkXdunVZu3Yt//73vzVJKGX74osvGDZsGLt37+bPP/8kPj6e2rVrs3TpUurVq8dff/3F5s2bAdi9ezfr16+nRYsWlChRgptvvplx48ZldrF/4MABPv/883OO5fjx41StWpX09HQ+/vjjc15PRokiu1fWJAHQt29f3n//fQDef/99+vXrd8Y8Ho+HQ4cOAbB27Vo2b95Mly5dqFixIgcPHmT79u0A/PDDDzRs2PCcYz8bIVqiKDx8Ph8//PAD3bt3p2bNmixdupRWrVppJ35KZTFz5kwmTJhw2rjrrruOmTNn0rFjRz766CNGjRpFSkoK4eHhTJkyhbJlywLwzDPP8Oijj9KoUSOioqIoWbIkTz311DnH8tRTT3HZZZdRuXJl2rRpU2D9Xz3yyCMMHDiQSZMmUbt2bT799FMAVqxYwXvvvcc777xDamoq7du3B6Bs2bLMmDEDt9uN2+3m3XffpX///rjdbipUqMB7771XIHGHZu+xCengcr4w9McffzB27FgWL17M4sWL6dixo9MhKZWjzZs3F9gZqHJWdn/r8+k91vmjbQjyeDy89NJLNGvWjHXr1jF16lTtxE8pVWSFZtWTw20Uffr0Yd68efTr14+33nqrwK5lVkopJ2iiCFBqairh4eG4XC7GjBnD6NGjueGGG7R/JqVUkadVTwFYvnw5LVu25M033wTg+uuvZ+DAgZoklFLFgiaKXBw/fpx7772XK664guTkZOrVq+d0SEopVeBCs+qpACxdupQRI0awa9cu7rjjDp5//vnz7kJAKaVCUQiWKAqmusfj8RAeHs7ixYt58803NUkolU8KczfjAAsWLMi3PpRWrVpFkyZNqFu3Lvfee2+28xw6dIi+ffvSrFkz2rZtS2xs7GnTBgwYQIMGDWjYsCErV67Ml7jOVggmiuCZNWsWzz//PACdO3dm06ZNem+EUvls5pYXgj0AAA3nSURBVMyZtG/fnpkzZwa8zL///W/27t3L77//zm+//casWbNITk4+bZ6vv/6adevWMWXKFDp06JB5h/TZ9vian4nitttu47333uOPP/5g06ZN/PDDD2fM8/TTT9O2bVs2bNjAtGnTMvvCAvjnP/9J37592bJlC+vXr8/2bu+CoFVPwP79+/nnP//J559/TsuWLbn//vu1Ez9VtH33EOzbmL/rrNoUer6Q6yxOdTO+atUqxo8fz7Fjx6hSpQrTp0/nggsu4NVXX2Xy5MmEhYXRrFkznnzySaZMmYLb7Wb69Om89dZb59y1eHx8PCkpKVx22WUADBs2jFmzZnHNNdecNl9sbCxPPPEEAI0bN2bbtm0kJiYiIqxYsSLzQUoRERGOdQkUckfC/LyP3BjDRx99lNmd77PPPssDDzxAeHh4Pm5FKZXBiW7GU1NTGTduHLNnz6ZSpUrMmDGDf//737z77ru8+OKL7N69m4iICI4cOUK5cuUYM2ZM5jMpsvrxxx8ZP378GeNLly7N0qVLTxu3Z88eqlevnjkcExPDnj17zli2efPmfPXVV7Rr145ff/2VhIQEEhISSE9Pp3LlygwfPpyNGzdy2WWX8dprr1GiRIlz+h3OR8glivxsoYiLi2PMmDG0bt2aqVOn0qBBg3xcu1KFWB5n/sHiRDfjmzdvZtOmTXTt2hWA/2/v3oPjKss4jn9/RptQrEVluCWlaSlV0qQJJRakgxaoQMtI0XRAhqI4aCxaMt6YQaqDtwFsRbmk2BYrtxFRlGqmAsWpNaWhxQa1oS21IjKSKlJqyoi9UJPHP86bZEk3uychu5vdPJ+Zndk9ey7vPtnss+d99zxvZ2cnZWVlQPQNfv78+cydOzdWJdZZs2YNeVnxRYsW0dDQQE1NDdXV1VRXV1NUVMT+/ftpbW3ljjvu4LTTTuOaa65hyZIl3HDDDUN6/DjyLlG82XOKrq4u1qxZw+zZsxk/fjwtLS2ceuqpXsTPuQzLVZlxM2Pq1KmHfeMHWLNmDc3NzTQ1NXHjjTfS1taWcl8DOaMoLS3lxRdf7Hnc3t5OaWnpYduOHTu2p6JsV1cX5eXlTJgwgY6ODk488cSesuJ1dXXceuut6V9wBoyoweydO3cyc+ZM5syZQ3NzMwC1tbWeJJzLglyVGa+oqGDXrl09vxh6/fXX2bZtG52dnbS3t3POOeewePFiXnnlFfbt28eYMWMOGyjv1n1G0feWLAmNGzeO4uJiNm/ejJlx//33Jy0rvnfvXg4dOgTA8uXLmTVrFkceeSRlZWUce+yxPWXF165dS0VFRazXPOQGO9l2rm7Vx49KOpl4KocOHbKbb77ZiouL7aijjrK7777burq6Brwf5/LZ9u3bc3r8mTNn2qOPPvqGZbfddpstWLDAzMw2bNhgp59+ulVXV1ttba09/vjjPesdPHjQrr32WjvppJNsypQpNn36dHvssceSHmfdunV24YUXvmHZ008/bTNmzLCqqiqrqKiwlStX2oEDB+zMM8+0qqoqmzJlii1evNjMzJ599lmrrKy0mpoaa2lpeVOvedOmTVZRUWETJ060hoaGnuWNjY121113mZnZ+vXrbdKkSTZ58mSrq6uzjo6OnvVaW1tt2rRpVlVVZRdffLHt3bs31nGT/a2BVhvk527elRmvPqHYtvzj4IC2Of/883smJ1+6dCnHHXdchlrn3PDlZcZHjqEuM56HYxTxBre6Jz8pKiqivr6e+vp66urqMtw255wrPAU5RtHS0kJNTU1PEb+6ujpPEs45N0gFlShee+01GhoaOOusszhw4ICfZjvXR751NbuBy8TfuGASRXNzM5WVlTQ2NrJw4UK2bt162BWQzo1kJSUl7Nmzx5NFATMz9uzZQ0lJyZDuN+/GKCzFBTijR4/miSeeYMaMGVlskXP5oaysjPb2dnbv3p3rprgMKikp6bmgcKjk3a+eppYeYW279gPw8MMPs2PHDq6//noguuLSr4lwzrnDvZlfPWW060nSBZL+LOk5Sdcleb5Y0k/D809JKo+z35deeol58+ZRV1fHqlWrei7C8SThnHNDL2OJQlIRsBSYDVQAl0nqe1nhVUCHmU0Cvg98J91+O/Z1csopp7B69WpuuukmnnzyyZxVVHTOuZEgk2cU04HnzOx5M3sdeBDoe/36XODecP/nwLlKUwVs195DVFZWsmXLFq677jqv9OqccxmWycHsUuDFhMftwOn9rWNm/5P0KvBu4JXElSTVA/Xh4cENGzZs9UqvABxNn1iNYB6LXh6LXh6LXoOe9SgvfvVkZiuAFQCSWgc7IFNoPBa9PBa9PBa9PBa9JLUOdttMdj3tAsYlPC4Ly5KuI+mtwFhgTwbb5JxzboAymSg2AydLmiBpFPAxoKnPOk3AJ8L9ecBvLd9+r+uccwUuY11PYcxhIbAGKAJ+ZGbbJH2TqNxtE7ASuF/Sc8C/iZJJOisy1eY85LHo5bHo5bHo5bHoNehY5N0Fd84557KrYGo9OeecywxPFM4551IatokiU+U/8lGMWHxR0nZJbZLWShqfi3ZmQ7pYJKxXJ8kkFexPI+PEQtIl4b2xTdID2W5jtsT4HzlR0jpJfwz/J3Ny0c5Mk/QjSS9L2trP85J0e4hTm6RpsXY82DlUM3kjGvz+KzARGAVsASr6rPNZYFm4/zHgp7ludw5jcTYwOty/eiTHIqw3BlgPbAJqc93uHL4vTgb+CLwzPD4m1+3OYSxWAFeH+xXAC7lud4Zi8QFgGrC1n+fnAI8STRV6BvBUnP0O1zOKjJT/yFNpY2Fm68xsX3i4ieialUIU530B8C2iumEHstm4LIsTi08DS82sA8DMXs5yG7MlTiwMeEe4Pxb4RxbblzVmtp7oF6T9mQvcZ5FNwFGSjk+33+GaKJKV/yjtbx0z+x/QXf6j0MSJRaKriL4xFKK0sQin0uPM7NfZbFgOxHlfTAYmS2qRtEnSBVlrXXbFicXXgfmS2oFHgGuy07RhZ6CfJ0CelPBw8UiaD9QCH8x1W3JB0luA7wFX5rgpw8VbibqfZhKdZa6XVGVme3Paqty4DLjHzG6R9H6i67cqzawr1w3LB8P1jMLLf/SKEwskzQIWAReZ2cEstS3b0sViDFAJ/E7SC0R9sE0FOqAd533RDjSZ2SEz+xuwkyhxFJo4sbgK+BmAmW0ESogKBo40sT5P+hquicLLf/RKGwtJpwLLiZJEofZDQ5pYmNmrZna0mZWbWTnReM1FZjboYmjDWJz/kV8SnU0g6Wiirqjns9nILIkTi78D5wJIOoUoUYzEOWGbgI+HXz+dAbxqZv9Mt9Gw7HqyzJX/yDsxY7EEeDvwUBjP/7uZXZSzRmdIzFiMCDFjsQY4T9J2oBO41swK7qw7Ziy+BNwl6QtEA9tXFuIXS0k/IfpycHQYj7kBeBuAmS0jGp+ZAzwH7AM+GWu/BRgr55xzQ2i4dj0555wbJjxROOecS8kThXPOuZQ8UTjnnEvJE4VzzrmUPFG4YUdSp6Q/JdzKU6xb3l+lzAEe83eh+uiWUPLiPYPYxwJJHw/3r5R0QsJzP5RUMcTt3CypJsY2n5c0+s0e241cnijccLTfzGoSbi9k6biXm1k1UbHJJQPd2MyWmdl94eGVwAkJz33KzLYPSSt723kn8dr5ecAThRs0TxQuL4Qzhyck/SHczkyyzhRJvw9nIW2STg7L5ycsXy6pKM3h1gOTwrbnhjkMngm1/ovD8pvVOwfId8Oyr0v6sqR5RDW3fhyOeUQ4E6gNZx09H+7hzKNxkO3cSEJBN0k/kNSqaO6Jb4RlDUQJa52kdWHZeZI2hjg+JOntaY7jRjhPFG44OiKh22lVWPYy8CEzmwZcCtyeZLsFwG1mVkP0Qd0eyjVcCswIyzuBy9Mc/8PAM5JKgHuAS82siqiSwdWS3g18BJhiZlOBbydubGY/B1qJvvnXmNn+hKd/Ebbtdinw4CDbeQFRmY5ui8ysFpgKfFDSVDO7naik9tlmdnYo5fFVYFaIZSvwxTTHcSPcsCzh4Ua8/eHDMtHbgMbQJ99JVLeor43AIkllwMNm9hdJ5wKnAZtDeZMjiJJOMj+WtB94gagM9XuAv5nZzvD8vcDngEaiuS5WSloNrI77wsxst6TnQ52dvwDvBVrCfgfSzlFEZVsS43SJpHqi/+vjiSboaeuz7RlheUs4ziiiuDnXL08ULl98AfgXUE10JnzYpERm9oCkp4ALgUckfYZoJq97zewrMY5xeWIBQUnvSrZSqC00najI3DxgIXDOAF7Lg8AlwA5glZmZok/t2O0EniYan7gD+KikCcCXgfeZWYeke4gK3/Ul4DdmdtkA2utGOO96cvliLPDPMH/AFUTF395A0kTg+dDd8iuiLpi1wDxJx4R13qX4c4r/GSiXNCk8vgJoDn36Y83sEaIEVp1k2/8QlT1PZhXRTGOXESUNBtrOUNDua8AZkt5LNHvbf4FXJR0LzO6nLZuAGd2vSdKRkpKdnTnXwxOFyxd3Ap+QtIWou+a/Sda5BNgq6U9E81LcF35p9FXgcUltwG+IumXSMrMDRNU1H5L0DNAFLCP60F0d9reB5H389wDLugez++y3A3gWGG9mvw/LBtzOMPZxC1FV2C1E82PvAB4g6s7qtgJ4TNI6M9tN9Iusn4TjbCSKp3P98uqxzjnnUvIzCueccyl5onDOOZeSJwrnnHMpeaJwzjmXkicK55xzKXmicM45l5InCueccyn9H8TyDK2V6jaaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve on train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_tf=CalibratedClassifierCV(model_temp_tf,method='sigmoid',cv='prefit')\n",
    "clf_tf.fit(d_train_tf,y_train)\n",
    "auc_train_tf,auc_test_tf=plotROC(d_train_tf,y_train,d_test_tf,y_test,clf_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:03<00:00,  1.91it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(d_train_tf,y_train,d_test_tf,y_test,reg_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.95      0.80      9927\n",
      "           1       0.99      0.92      0.95     51514\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     61441\n",
      "   macro avg       0.84      0.93      0.88     61441\n",
      "weighted avg       0.94      0.92      0.93     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.920\n",
      "The value of TNR is 0.948\n",
      "The value of FPR is 0.052\n",
      "The value of FNR is 0.080\n",
      "\n",
      "The auc score is 0.980943\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_tf,y_train,model_temp_tf,auc_train_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.89      0.75      4254\n",
      "           1       0.98      0.91      0.94     22078\n",
      "\n",
      "   micro avg       0.91      0.91      0.91     26332\n",
      "   macro avg       0.82      0.90      0.85     26332\n",
      "weighted avg       0.93      0.91      0.91     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.911\n",
      "The value of TNR is 0.887\n",
      "The value of FPR is 0.113\n",
      "The value of FNR is 0.089\n",
      "\n",
      "The auc score is 0.962777\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_tf,y_test,model_temp_tf,auc_test_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Positive Words Are::\n",
      " [(3.305721741609117, 'excellent'), (3.320648542927482, 'wonderful'), (3.472128475735681, 'love'), (3.4913837734105817, 'loves'), (3.5110170065220347, 'nice'), (3.7436216801968, 'perfect'), (4.302247247942115, 'good'), (4.733289610714529, 'delicious'), (4.769759838586462, 'best'), (6.028308662357402, 'great')]\n"
     ]
    }
   ],
   "source": [
    "##sorting by weights (indices) and getting the last 10 weights\n",
    "w_=model_temp_tf.coef_\n",
    "feature_names = tf_idf_vect.get_feature_names()\n",
    "pos = sorted(zip(w_[0], feature_names))\n",
    "print(\"Top 10 Positive Words Are::\\n\",pos[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Negative Words Are::\n",
      " [(-4.5891499285610164, 'not'), (-4.541580009566832, 'disappointed'), (-3.519651250932601, 'not good'), (-3.4697351400159544, 'disappointing'), (-3.450467621606705, 'not worth'), (-3.3407626215701094, 'worst'), (-3.1637384417808283, 'terrible'), (-2.93789051147833, 'unfortunately'), (-2.898663850933937, 'awful'), (-2.7915288979057107, 'not recommend')]\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 10 Negative Words Are::\\n\",pos[:10])"
   ]
  },
  {
   "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": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 20.25 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.01, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#getting best alpha and best regularizer\n",
    "alpha_grid_w2,reg_w2 = cv_sgd_grid(d_train_w2,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.01, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_w2=SGDClassifier(penalty=reg_w2,loss=\"hinge\",alpha=alpha_grid_w2,class_weight='balanced')\n",
    "model_temp_w2.fit(d_train_w2,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_w2=CalibratedClassifierCV(model_temp_w2,method='sigmoid',cv='prefit')\n",
    "clf_w2.fit(d_train_w2,y_train)\n",
    "auc_train_w2,auc_test_w2 = plotROC(d_train_w2,y_train,d_test_w2,y_test,clf_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:06<00:00,  1.38it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(d_train_w2,y_train,d_test_w2,y_test,reg_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.84      0.59      9927\n",
      "           1       0.96      0.80      0.88     51514\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     61441\n",
      "   macro avg       0.71      0.82      0.73     61441\n",
      "weighted avg       0.88      0.81      0.83     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.805\n",
      "The value of TNR is 0.845\n",
      "The value of FPR is 0.155\n",
      "The value of FNR is 0.195\n",
      "\n",
      "The auc score is 0.902817\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_w2,y_train,model_temp_w2,auc_train_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.85      0.59      4254\n",
      "           1       0.97      0.80      0.88     22078\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     26332\n",
      "   macro avg       0.71      0.83      0.73     26332\n",
      "weighted avg       0.88      0.81      0.83     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.800\n",
      "The value of TNR is 0.854\n",
      "The value of FPR is 0.146\n",
      "The value of FNR is 0.200\n",
      "\n",
      "The auc score is 0.905592\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_w2,y_test,model_temp_w2,auc_test_w2)"
   ]
  },
  {
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 35.14 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.01, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#getting best alpha and best regularizer\n",
    "alpha_grid_tf_wt,reg_tf_wt = cv_sgd_grid(d_train_tf_wt,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.01, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_tf_wt=SGDClassifier(penalty=reg_tf_wt,loss=\"hinge\",alpha=alpha_grid_tf_wt,class_weight='balanced')\n",
    "model_temp_tf_wt.fit(d_train_tf_wt,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_av=CalibratedClassifierCV(model_temp_tf_wt,method='sigmoid',cv='prefit')\n",
    "clf_av.fit(d_train_tf_wt,y_train)\n",
    "auc_train_av,auc_test_av = plotROC(d_train_tf_wt,y_train,d_test_tf_wt,y_test,clf_av)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:05<00:00,  1.42it/s]\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": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(d_train_tf_wt,y_train,d_test_tf_wt,y_test,reg_tf_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.82      0.55      9927\n",
      "           1       0.96      0.78      0.86     51514\n",
      "\n",
      "   micro avg       0.78      0.78      0.78     61441\n",
      "   macro avg       0.69      0.80      0.70     61441\n",
      "weighted avg       0.87      0.78      0.81     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.776\n",
      "The value of TNR is 0.825\n",
      "The value of FPR is 0.175\n",
      "The value of FNR is 0.224\n",
      "\n",
      "The auc score is 0.878868\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEKCAYAAADzQPVvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHq1JREFUeJzt3XmcFNW5//HPw7DLLioyg4KAC2rCEhC3KKisEVCJQu4P0EBIvHgjSdS4JJIgxg1ReQn8REFAUUCNV0xQIgYRlNWVTWQAgQEEFQTEsPTMc//oYmxwlh6cnp7u+r7zqter6tSpqlNInj48deqUuTsiIpLeKiS7ASIikngK9iIiIaBgLyISAgr2IiIhoGAvIhICCvYiIiGgYC8ikgBmlmFmH5jZP4LtJma22MzWmtl0M6sclFcJtrOD/Y1jznFHUL7GzDrHlHcJyrLN7PZ42qNgLyKSGDcDq2O2HwAecffmwC5gYFA+ENjl7s2AR4J6mFkLoA9wNtAFGBv8gGQAY4CuQAugb1C3SAr2IiKlzMyygO7AU8G2AR2BF4Mqk4FewXrPYJtg/2VB/Z7ANHc/4O4bgGygXbBku/t6dz8ITAvqFqliadxYInTIukKv9sr3zN+xKtlNkHIocnCL/dBzHPpyfdwxp/IJTX8NDI4pGu/u42O2HwVuA2oG28cDX7t7JNjOATKD9UxgM4C7R8xsd1A/E1gUc87YYzYfVX5ecW0ut8FeRKS8CgL7+IL2mdnPgB3u/p6ZXXq4uKDTFLOvsPKCMjLF/lAp2IuIAOTlltaZLgR6mFk3oCpQi2hPv46ZVQx691nA1qB+DtAIyDGzikBtYGdM+WGxxxRWXijl7EVEAHIj8S9FcPc73D3L3RsTfcD6b3f/L2Au0DuoNgB4JVifGWwT7P+3R2eonAn0CUbrNAGaA0uApUDzYHRP5eAaM4u7PfXsRUQA97xEX+KPwDQzGwF8AEwIyicAz5hZNtEefZ9oe3ylmc0AVgERYIi75wKY2U3AbCADmOjuK4u7uJXXKY71gFYKoge0UpDSeEB7MGd5/A9os879wdcra+rZi4gAJL5nn1QK9iIiUJoPaMslBXsREVDPXkQkDLyYUTapTsFeRAQgTz17EZH0pzSOiEgI6AGtiEgIqGcvIhICekArIhICekArIpL+gmln0paCvYgIKGcvIhIKSuOIiISAevYiIiGQeyjZLUgoBXsREVAaR0QkFJTGEREJAfXsRURCQMFeRCT9uR7QioiEgHL2IiIhoDSOiEgIqGcvIhIC6tmLiISAevYiIiEQ0cdLRETSn3r2IiIhoJy9iEgIqGcvIhIC6tmLiISAevYiIiGg0TgiIiHgnuwWJJSCvYgIKGcvIhIKCvYiIiGgB7QiIiGQm5vsFiRUhWQ3QESkXMjLi38pgplVNbMlZvaRma00s78G5VPNbI2ZrTCziWZWKSg3MxttZtlm9rGZtY451wAzWxssA2LK25jZ8uCY0WZmxd2egr2ICJRasAcOAB3d/cdAS6CLmbUHpgJnAucC1YBBQf2uQPNgGQyMAzCzesAw4DygHTDMzOoGx4wL6h4+rktxjVKwFxGBaM4+3qWo00R9E2xWChZ391nBPgeWAFlBnZ7AlGDXIqCOmZ0MdAbecPed7r4LeIPoD8fJQC13XxicawrQq7jbU7AXEQE8z+NezGywmS2LWQbHnsvMMszsQ2AH0YC9OGZfJaAf8HpQlAlsjjk8JygrqjyngPIi6QGtiAiUaOilu48HxhexPxdoaWZ1gJfN7Bx3XxHsHgu87e7zg+2C8u1+DOVFUs9eRASio3HiXeLk7l8DbxHk1M1sGHAC8PuYajlAo5jtLGBrMeVZBZQXScFeRARKczTOCUGPHjOrBlwOfGJmg4jm4fu6H5H4nwn0D0bltAd2u/s2YDbQyczqBg9mOwGzg317zax9MAqnP/BKcbenNI6ICJTmG7QnA5PNLINoh3qGu//DzCLARmBhMFLy7+4+HJgFdAOygW+BGwDcfaeZ3QMsDc473N13Bus3ApOIjup5LViKpGCfJL0HXU33vl1xd9Z/8hkP/OEhuvftRu9BV5HZOJOe517Dnl17ADiuZnXuHH07J2WeSEZGBtOfeJHXZ8ym5QU/ZsiwG/PPeUrTRgwfci/vzH43WbclP9CT4x+me7fL2fHFl7RsdRkAz00dx+mnNwWgTu1afL17Dz9p2yn/mEaNGrL8o7cYfs/DjHrkCQA6d7qUUaOGk1GhAhOffp4HHxpT9jeTakppIjR3/xhoVUB5gfE2GFEzpJB9E4GJBZQvA84pSbsU7JOgfoPjufqXvbi+4yAO7j/IsHF/omOPDqxYuoKFcxbx6Asjj6jfa0BPNq7dxF033E3terWZ8vZE5rz8Jh+++xG/6vwbAGrWqcmzCyaxbN57ybglKSVTpsxg7Ninefrpx/LLfvFf3/2gP/TA3ezes+eIYx4e+Rdenz03f7tChQqMfuxeunTrS07ONhYtnMWr//gXq1evTfwNpDLNjXNszOxMouNHM4k+Kd4KzHT31Ym6ZirJqJhBlapViByKUKVaFb7a/hXZK9cVWNfdqX5cNQCqHVeNvV/vJTdy5EOiS7pfzJK5Szmw/0DC2y6JM3/BYk49NavQ/b17X8kVna/N3+7RozMb1m9i37ff5pe1a9uKdes+Y8OGTQDMmPEKPa7srGBfnLz0nuI4IQ9ozeyPwDSiQ4SWEM05GfC8md2eiGumki8//4oZT7zI9MVTeen96ezbu49lbxfeI3950iuc0vwUXnxvGhPnjOfxu8fiR/2Ts0OPS3nzf+cWcgZJBxdfdB7bd3xBdvYGAKpXr8Zttwxh+IhRR9RrmNmAzTnfDc7I2bKNhg0blGlbU1ICRuOUJ4kajTMQaOvu97v7s8FyP9FXfgcWdlDsiwpb9+UUVi3l1ahdgws6nU/f8/vRu00fqlaryuVXX1Zo/baX/oTslevo3aYPgzr/ht+OuInqNarn7693Yj1OO7MJS+ctK4vmS5Jcd10vpk//btDFX+6+hUdHP8m+fd8eUa+gaVKO7hzI93leXtxLKkpUGicPaEj0yXOsk4N9BYp9UaFD1hVp+7ezzUWt+Xzz5+zeuRuA+a8t4Jw2LZjz9zcLrN/12s48N2YaAFs/28q2zZ9zSrNGfPLhGgA6XHkJC15/53upHUkfGRkZXNWrK+3ad80va9euFVdf3Z37/3YXderUIi8vj/37D/D++8tplNUwv15W5sls27Y9Gc1OLWmexklUsB8KvGlma/nudd9TgGbATQm6ZsrYsXUHLVqdRZWqVTiw/wCtL2rFmo8/LbT+9i07aH1RK5YvWUHd+nVo1LQRWzduy9/fsWcHnrx/Qlk0XZLk8ssuZs2abLZs+e6/+6Udr85fv/vPv+ebb/YxdtwkMjIyaNasCY0bN2LLls+59tqe9Otf4GAPiaX57EvO3V83s9OJpm0yiebrc4ClwWvEobb6g0+YN2s+418fS24kl7Ur1/GPqbO4+pe96HPjtdQ7oR4T3hjP4rlLGHnrKJ55bCp/HHUrE+aMx4Dxf3sqf1jmSVkncULDE/ho4cfJvSkpFc8+M4ZLfno+9evX47P1y/jr8JE8PWka117bk2nTi31vBoDc3FxuHvonZv3zOTIqVGDS5OmsWlV4Z0ICad6zt/Kay0vnNI4cu/k7ViW7CVIORQ5uKXY+9+Lsu7tP3DHnuOHTfvD1yprG2YuIgNI4IiKhkOZpHAV7ERFI2SGV8VKwFxEB9exFREJBwV5EJARSdBqEeCnYi4gQ/QZtOlOwFxEBpXFEREJBo3FEREJAPXsRkRBQsBcRSX+eqzSOiEj6U89eRCT9aeiliEgYKNiLiIRAeqfsFexFRAA8kt7RXsFeRATUsxcRCQM9oBURCQP17EVE0p969iIiYaCevYhI+vNIsluQWAr2IiKAq2cvIhICCvYiIulPPXsRkRBQsBcRCQHPtWQ3IaEqJLsBIiLlgefFvxTFzBqZ2VwzW21mK83s5qP232Jmbmb1g20zs9Fmlm1mH5tZ65i6A8xsbbAMiClvY2bLg2NGm1mxv1QK9iIigOdZ3EsxIsAf3P0soD0wxMxaQPSHALgC2BRTvyvQPFgGA+OCuvWAYcB5QDtgmJnVDY4ZF9Q9fFyX4hqlYC8iQun17N19m7u/H6zvBVYDmcHuR4DbgNjXdXsCUzxqEVDHzE4GOgNvuPtOd98FvAF0CfbVcveF7u7AFKBXcfenYC8iArhb3IuZDTazZTHL4ILOaWaNgVbAYjPrAWxx94+OqpYJbI7ZzgnKiirPKaC8SHpAKyJCyUbjuPt4YHxRdcysBvASMJRoaucuoFNBVQu6xDGUF0k9exERIC/X4l6KY2aViAb6qe7+d6Ap0AT4yMw+A7KA982sAdGeeaOYw7OArcWUZxVQXiQFexERSu8BbTAyZgKw2t1HAbj7cnc/0d0bu3tjogG7tbt/DswE+gejctoDu919GzAb6GRmdYMHs52A2cG+vWbWPrhWf+CV4u5PaRwREYhnlE28LgT6AcvN7MOg7E53n1VI/VlANyAb+Ba4AcDdd5rZPcDSoN5wd98ZrN8ITAKqAa8FS5EU7EVEAC+l6ezdfQEF59Vj6zSOWXdgSCH1JgITCyhfBpxTknYVGuzN7FWKSPq7e4+SXEhEpDwrxZ59uVRUz35kmbVCRCTJ3EMa7N19Xlk2REQkmXLTfG6cYnP2ZtYcuA9oAVQ9XO7upyWwXSIiZSrde/bxDL18mug8DBGgA9FXc59JZKNERMpaKc6NUy7FE+yrufubgLn7Rnf/C9Axsc0SESlb7vEvqSieoZf7zawCsNbMbgK2ACcmtlkiImUrVXvs8Yon2A8FqgO/Be4h2qsfUOQRIiIpJjcvvScUKDbYu/vht7e+IXizS0Qk3aRqeiZe8YzGmUsBL1e5u/L2IpI28tJ8NE48aZxbYtarAtcQHZkjIpI20n3oZTxpnPeOKnrHzPTClYikFaVxot9BPKwC0AZokLAWBZpWrJ3oS0gK+tfW+clugqQppXHgPb77OkoE2AAMTGSjRETKWuhH4wBnufv+2AIzq5Kg9oiIJEWaZ3HieoP23QLKFpZ2Q0REkinPLe4lFRU1n30Dol8sr2ZmrfhuMv5aRF+yEhFJG2EejdMZuJ7ox2wf5rtgvwe4M7HNEhEpW3nJbkCCFTWf/WRgspld4+4vlWGbRETKnBf9JcGUF0/Ovo2Z1Tm8EXzpfEQC2yQiUuYibnEvqSieYN/V3b8+vOHuu4h+CV1EJG04FveSiuIZeplhZlXc/QCAmVUDNPRSRNJKaHP2MZ4F3jSzp4PtG4DJiWuSiEjZS9Uee7zimRvnQTP7GLic6Iic14FTE90wEZGypJ591OdE/yyuJTpdgkbniEhayQ1rz97MTgf6AH2Br4DpRL9D26GM2iYiUmbS/KuERfbsPwHmA1e6ezaAmf2uTFolIlLG8tK8Z1/U0MtriKZv5prZk2Z2GaT5n4aIhJaXYElFhQZ7d3/Z3a8DzgTeAn4HnGRm48ysUxm1T0SkTOSVYElFxb5U5e773H2qu/+M6Dw5HwK3J7xlIiJlKM8s7iUVlWi2fnff6e5P6GPjIpJuckuwpKJ4h16KiKS1MI/GEREJjXQfjaNgLyJC6o6yiZeCvYgISuOIiIRCqg6pjJeCvYgIkJvmPfsSDb0UEUlXpflSlZlNNLMdZrbiqPL/MbM1ZrbSzB6MKb/DzLKDfZ1jyrsEZdlmdntMeRMzW2xma81suplVLq5NCvYiIpT6G7STgC6xBWbWAegJ/MjdzwZGBuUtiE46eXZwzFgzyzCzDGAM0BVoAfQN6gI8ADzi7s2BXcDA4hqkYC8iArjFvxR7Lve3gZ1HFd8I3H/4q3/uviMo7wlMc/cD7r4ByAbaBUu2u69394PANKCnmRnQEXgxOH4y0Ku4NinYi4hQsp69mQ02s2Uxy+A4LnE6cHGQfplnZm2D8kxgc0y9nKCssPLjga/dPXJUeZH0gFZEhJJNg+Du44HxJbxERaAu0B5oC8wws9MoeDZhp+DOuBdRv9iLi4iEXhmMs88B/u7uDiwxszygflDeKKZeFrA1WC+o/EugjplVDHr3sfULpTSOiAhlMsXx/xLNtR/+EmBlooF7JtDHzKqYWROgObAEWAo0D0beVCb6EHdm8GMxF+gdnHcA8EpxF1fPXkSE0n2pysyeBy4F6ptZDjAMmAhMDIZjHgQGBIF7pZnNAFYBEWCIu+cG57kJmA1kABPdfWVwiT8C08xsBPABMKG4NinYi4hQunPjuHvfQnb9v0Lq3wvcW0D5LGBWAeXriY7WiZuCvYgImhtHRCQUUvWjJPFSsBcRAfLSfJJjBXsRETTrpYhIKKR3v17BXkQEUM9eRCQUIpbefXsFexERlMYREQkFpXFEREJAQy9FREIgvUO9gr2ICKA0johIKOSmed9ewV5EBPXsRURCwdWzFxFJf+rZS6m4/sH/5kcd27D3q90M6/x7ANp0O58eQ6/l5GaZ3NvzDjYuXwfA8VkncM+cR/l8ffSzkus/WMuzd0W/bTx08l3UPrEuFTIyWLt0NVP//BSel0fWWafS797BVKlela9yvuDJoY+x/5v/JOdmJW4HDhxkwJBbOXjoELmRXK7ocBE3DerH4vc+ZOTjT3HoUIQWZzRj+B2/o2LFDHbv2cuf73uEzVu2UaVyZe6583c0P60xGzbmcMvd9+WfN2frNm4a1I9+113FmAnP8tLM16lbpzYAN/96AD+9oETfvQgFDb2UUvHOi3P59+TXGDjqf/LLtq7ZxNjfPET/v/36e/W/2Lid4d1u/V75/x8yKj+I3zjuFn7S/XyWvvoOA+6/kRf+NoVPF6/iwp93pPPgnrwyalribkhKReXKlZg4+n6qV6/GoUiE/jfewoXnteHOEQ8z4bH7aHxKFo8/OYVXXpvDNVd25skp0zmzeVNG33c36zdu5t6HxzBh9P00OTWLlyaPASA3N5eOvfpx2SUX5F+n33W9uOEXvQtrhpD+Qy/1wfEysnbJavbt/uaIsm3rtrB9fbEfhT/C4UCfUTGDipUqgkf/ijY4rSGfLl4FwKoFH9Gm63ml0GpJNDOjevVqAEQiESKRCBkVKlC5UiUan5IFwPltWzPnrQUArPtsE+3b/BiA005txJZt2/ly564jzrlo2Yc0yjyZhg1OKsM7SX0RPO4lFSnYl1P1G53I3f98iFun/5Xmbc86Yt/QKX9i1HsT2L/vPyybtQiALZ9upuUVbQH4SbfzqXdy/TJvsxyb3NxcrhkwhJ/+rC/nt23FuS3OIBLJZcXqTwH411sL+HzHlwCc0ew05sx7F4Dlq9awbfsOtgf7DnvtzXl0u/ySI8qef+lVrup/I3/62yh279lbBneVerwE/0tFZR7szeyGIvYNNrNlZrbsk73ry7JZ5cruHbu47YLfMLz7rcy4ZzK/euxmqtaolr//0f4j+EO7X1GxciXOuuAcACbdNoYO/brw51cfoGqNakQORZLVfCmhjIwMXpo8hjdffoblqz4le8NGHhp+Ow+OHk+fQTdzXPVqZGRE/686qN/P2bP3G64ZMISpL87kzOZNycjIyD/XoUOHeGvBYjp1vDi/7LqruvPajIm8NGkMJxxfj4cef7LM7zEV5JVgSUXJyNn/FXi6oB3uPh4YDzCoce/U/PksBZGDESIHoymfjSvW88Wm7ZzUpGH+A1yAyIFDfDRnKS2vaMuqBR/z+bqtPNL/HgBOanIyP+rQOiltl2NXq2YN2rb+EQsWLeOGX/RmyriRALyz+D02bt4CQI3jjmPEXdEH/O5O597Xk9Xwu3TN/EXLOOv0ptSvVze/LHa9d4+uDLl1WFncTspJ1R57vBLSszezjwtZlgNKJBajRr1aWIXof5r6jU7kxMYN+HLTdqpUr0rtE+oAUCGjAud2aM22ddEgUPP4WkA0B9z9pt68NfWN5DReSmTnrq/Zszf6w77/wAEWLf2AJqc24qtdXwNw8OBBJk59gWt7dQNgz95vOHToEAAvvfo6bVqeS43jjss/36w33qLbFZcecY0vvtyZv/7mvHdpdtqpibyllKWe/bE5CegM7Dqq3IB3E3TNcu1Xo4dyRvuzqVG3Jg8ufIKZj0xn3+5v6PuXgdSsV4ubJ97BptWf8Wj/EZze7ix6/r4Pebm55OXm8exd49m3+xtq1a/NTU/dTqXKlbCMCnzy7nLmTf0XAO16XESHfl0A+GD2Yt554d/JvF2J0xdf7eKuESPJzcvD85zOHS/m0gvPY+TjTzHv3SV4Xh7XXdWd89q0BGD9xs3cec9IMipU4LTGpzD8jqH55/rP/v0sXPoBw2777RHXeHjsBNasXQ8GmQ1O+t5+icr19O7ZmyfgBs1sAvC0uy8oYN9z7v6L4s4R5jSOFG7csgeT3QQphyrVP81+6Dl+cepVccec5za+/IOvV9YS0rN394FF7Cs20IuIlLV0z9nrpSoREVI3Fx8vBXsRETRdgohIKCiNIyISAuk+GkfBXkQEpXFEREJBD2hFREJAOXsRkRBQGkdEJAQSMZtAeaJgLyIC5KZ5z14fLxERIZrGiXcpjpn9zsxWmtkKM3vezKqaWRMzW2xma81suplVDupWCbazg/2NY85zR1C+xsw6/5D7U7AXESGaxol3KYqZZQK/BX7i7ucAGUAf4AHgEXdvTnRG4MNziA0Edrl7M+CRoB5m1iI47mygCzDWzDI4Rgr2IiKUbs+eaIq8mplVBKoD24COwIvB/slAr2C9Z7BNsP8yM7OgfJq7H3D3DUA20O5Y70/BXkSEkn2DNvYTqsEyOP887luAkcAmokF+N/Ae8LW7H/5eaA6QGaxnApuDYyNB/eNjyws4psT0gFZEhJJNlxD7CdWjmVldor3yJsDXwAtA14JOc/iQQvYVVn5M1LMXEaFU0ziXAxvc/Qt3PwT8HbgAqBOkdQCygK3Beg7QCCDYXxvYGVtewDElpmAvIkKpBvtNQHszqx7k3i8DVgFzgd5BnQHAK8H6zGCbYP+/PfoUeCbQJxit0wRoDiw51vtTGkdEhNJ7qcrdF5vZi8D7QAT4gGjK55/ANDMbEZRNCA6ZADxjZtlEe/R9gvOsNLMZRH8oIsAQd8891nYp2IuIULrTJbj7MGDYUcXrKWA0jbvvB35eyHnuBe4tjTYp2IuIoInQRERCIdfTe5JjBXsRETQRmohIKGiKYxGREFDOXkQkBPKUxhERSX/q2YuIhIBG44iIhIDSOCIiIaA0johICKhnLyISAurZi4iEQO6xTyiZEhTsRUTQdAkiIqGg6RJEREJAPXsRkRDQaBwRkRDQaBwRkRDQdAkiIiGgnL2ISAgoZy8iEgLq2YuIhIDG2YuIhIB69iIiIaDROCIiIaAHtCIiIaA0johICOgNWhGREFDPXkQkBNI9Z2/p/muWDsxssLuPT3Y7pHzR3wspiQrJboDEZXCyGyDlkv5eSNwU7EVEQkDBXkQkBBTsU4PyslIQ/b2QuOkBrYhICKhnLyISAgr2IiIhoGBfzplZFzNbY2bZZnZ7stsjyWdmE81sh5mtSHZbJHUo2JdjZpYBjAG6Ai2AvmbWIrmtknJgEtAl2Y2Q1KJgX761A7Ldfb27HwSmAT2T3CZJMnd/G9iZ7HZIalGwL98ygc0x2zlBmYhIiSjYl29WQJnGyopIiSnYl285QKOY7Sxga5LaIiIpTMG+fFsKNDezJmZWGegDzExym0QkBSnYl2PuHgFuAmYDq4EZ7r4yua2SZDOz54GFwBlmlmNmA5PdJin/NF2CiEgIqGcvIhICCvYiIiGgYC8iEgIK9iIiIaBgLyISAgr2UurMLNfMPjSzFWb2gplV/wHnutTM/hGs9yhq5k8zq2Nm/30M1/iLmd1yrG0USQUK9pII/3H3lu5+DnAQ+E3sTosq8d89d5/p7vcXUaUOUOJgLxIGCvaSaPOBZmbW2MxWm9lY4H2gkZl1MrOFZvZ+8C+AGpA/h/8nZrYAuPrwiczsejN7PFg/ycxeNrOPguUC4H6gafCvioeCerea2VIz+9jM/hpzrruC7wTMAc4osz8NkSRRsJeEMbOKROfiXx4UnQFMcfdWwD7gT8Dl7t4aWAb83syqAk8CVwIXAw0KOf1oYJ67/xhoDawEbgfWBf+quNXMOgHNiU4V3RJoY2Y/NbM2RKeeaEX0x6RtKd+6SLlTMdkNkLRUzcw+DNbnAxOAhsBGd18UlLcn+kGWd8wMoDLRKQDOBDa4+1oAM3sWGFzANToC/QHcPRfYbWZ1j6rTKVg+CLZrEA3+NYGX3f3b4Bqab0jSnoK9JMJ/3L1lbEEQ0PfFFgFvuHvfo+q1pPSmcTbgPnd/4qhrDC3Fa4ikBKVxJFkWAReaWTMAM6tuZqcDnwBNzKxpUK9vIce/CdwYHJthZrWAvUR77YfNBn4Z8ywg08xOBN4GrjKzamZWk2jKSCStKdhLUrj7F8D1wPNm9jHR4H+mu+8nmrb5Z/CAdmMhp7gZ6GBmy4H3gLPd/SuiaaEVZvaQu/8LeA5YGNR7Eajp7u8D04EPgZeIpppE0ppmvRQRCQH17EVEQkDBXkQkBBTsRURCQMFeRCQEFOxFREJAwV5EJAQU7EVEQuD/AEi+0osq4+FDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_tf_wt,y_train,model_temp_tf_wt,auc_train_av)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.83      0.55      4254\n",
      "           1       0.96      0.77      0.85     22078\n",
      "\n",
      "   micro avg       0.78      0.78      0.78     26332\n",
      "   macro avg       0.68      0.80      0.70     26332\n",
      "weighted avg       0.87      0.78      0.81     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.771\n",
      "The value of TNR is 0.826\n",
      "The value of FPR is 0.174\n",
      "The value of FNR is 0.229\n",
      "\n",
      "The auc score is 0.879016\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_tf_wt,y_test,model_temp_tf_wt,auc_test_av)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [5.2] RBF SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.1] Applying RBF SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of train-text BOW vectorizer  (61441, 500)\n",
      "the shape of test-text BOW vectorizer  (26332, 500)\n"
     ]
    }
   ],
   "source": [
    "#BoW with min_df=10,max_features=500\n",
    "count_vect_rbf = CountVectorizer(dtype='float',min_df=10,max_features=500) #in scikit-learn\n",
    "d_train_rbf =count_vect_rbf.fit_transform(dw_train)\n",
    "print(\"the type of count vectorizer \",type(d_train_rbf))\n",
    "print(\"the shape of train-text BOW vectorizer \",d_train_rbf.get_shape())\n",
    "\n",
    "d_test_rbf = count_vect_rbf.transform(dw_test)\n",
    "print(\"the shape of test-text BOW vectorizer \",d_test_rbf.get_shape())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "def cv_svc_grid(d_train, y_train):\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "\n",
    "    #defining 'C' for grid-search\n",
    "    param= {'C':[0.0001,0.001,0.01,1,10,100,1000,10000]}\n",
    "\n",
    "    #Using Grid Search to get Best alpha=1/c\n",
    "    start = time()\n",
    "    #hinge loss for svm\n",
    "    clfSVC = SVC( kernel='rbf', class_weight='balanced')\n",
    "    clf = GridSearchCV(clfSVC, param, cv=5,scoring='roc_auc')\n",
    "    clf.fit(d_train, y_train)\n",
    "    print(\"Time elapsed : %.2f Seconds\" % (time() - start))\n",
    "    print(\" optimal alpha using Grid Search is \" + str(clf.best_params_))\n",
    "    optimal_c_grid=clf.best_params_.get('C')\n",
    "    return optimal_c_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotTrainvsTestAUC_rbf(d_trai,y_trai,d_tes,y_tes):\n",
    "    import warnings\n",
    "    from math import log\n",
    "    warnings.filterwarnings('ignore')\n",
    "    auc_train_list=list()\n",
    "    auc_test_list=list()\n",
    "    param=[0.0001,0.001,0.01,1,10,100,1000,10000]\n",
    "    x=list()\n",
    "    for i in tqdm(param):\n",
    "        x.append(log(i,10))\n",
    "        model=SVC( kernel='rbf', C=i,class_weight='balanced',probability=True)\n",
    "        model.fit(d_trai,y_trai)\n",
    "        train_score = model.predict_proba(d_trai)[:,1]\n",
    "        test_score = model.predict_proba(d_tes)[:,1]\n",
    "        fpr_train, tpr_train, _ = roc_curve(y_trai, train_score)\n",
    "        fpr_test, tpr_test, _ = roc_curve(y_tes, test_score)\n",
    "        auc_train_list.append(auc(fpr_train, tpr_train))\n",
    "        auc_test_list.append(auc(fpr_test, tpr_test))\n",
    "    plt.title('Train AUC Score vs Test AUC Score')\n",
    "    plt.plot(x, auc_train_list, label = 'Train AUC Score')\n",
    "    plt.plot(x, auc_test_list, label = 'Test AUC Score')\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.xlabel('Log OF C')\n",
    "    plt.ylabel('AUC Score')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 753.45 Seconds\n",
      " optimal alpha using Grid Search is {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "#getting best value for C\n",
    "d_train_rbf_10000=d_train_rbf[:10000]\n",
    "y_train_10000=y_train[:10000]\n",
    "c_grid_rbf = cv_svc_grid(d_train_rbf_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with kernal = 'rbf'\n",
    "svc_bow= SVC( kernel='rbf', C= c_grid_rbf, class_weight='balanced',probability = True)\n",
    "svc_bow.fit(d_train_rbf_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [15:44<00:00, 100.90s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train auc vs test auc curve on log of C\n",
    "plotTrainvsTestAUC_rbf(d_train_rbf_10000,y_train_10000,d_test_rbf,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "auc_train_sb,auc_test_sb = plotROC(d_train_rbf_10000,y_train_10000,d_test_rbf,y_test,svc_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.91      0.68      1610\n",
      "           1       0.98      0.85      0.91      8390\n",
      "\n",
      "   micro avg       0.86      0.86      0.86     10000\n",
      "   macro avg       0.76      0.88      0.79     10000\n",
      "weighted avg       0.91      0.86      0.87     10000\n",
      "\n",
      "\n",
      "The value of TPR is 0.851\n",
      "The value of TNR is 0.912\n",
      "The value of FPR is 0.088\n",
      "The value of FNR is 0.149\n",
      "\n",
      "The auc score is 0.949659\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_rbf_10000,y_train_10000,svc_bow,auc_train_sb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.80      0.59      4254\n",
      "           1       0.95      0.83      0.89     22078\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     26332\n",
      "   macro avg       0.71      0.81      0.74     26332\n",
      "weighted avg       0.88      0.82      0.84     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.830\n",
      "The value of TNR is 0.796\n",
      "The value of FPR is 0.204\n",
      "The value of FNR is 0.170\n",
      "\n",
      "The auc score is 0.887368\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_rbf,y_test,svc_bow,auc_test_sb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.2] Applying RBF SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aafco', 'aback', 'abandon', 'abdominal', 'ability', 'able', 'able add', 'able buy', 'able chew', 'able drink']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of train-text BOW vectorizer  (61441, 500)\n",
      "the shape of test-text BOW vectorizer  (26332, 500)\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "tf_idf_vect_rbf = TfidfVectorizer(ngram_range=(1,2), min_df=10, max_features=500)\n",
    "d_train_tf_rbf =tf_idf_vect_rbf.fit_transform(dw_train)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "#Vectorize cv and test data\n",
    "print(\"the type of count vectorizer \",type(d_train_tf))\n",
    "print(\"the shape of train-text BOW vectorizer \",d_train_tf_rbf.get_shape())\n",
    "\n",
    "d_test_tf_rbf = tf_idf_vect_rbf.transform(dw_test)\n",
    "print(\"the shape of test-text BOW vectorizer \",d_test_tf_rbf.get_shape())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 901.33 Seconds\n",
      " optimal alpha using Grid Search is {'C': 100}\n"
     ]
    }
   ],
   "source": [
    "#getting best value for C\n",
    "d_train_tf_rbf_10000=d_train_tf_rbf[:10000]\n",
    "c_grid_rbf_tf = cv_svc_grid(d_train_tf_rbf_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with kernal = 'rbf'\n",
    "svc_tf = SVC( kernel='rbf', C= c_grid_rbf_tf, class_weight='balanced',probability = True)\n",
    "svc_tf.fit(d_train_tf_rbf_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data \n",
    "auc_train_st,auc_test_st = plotROC(d_train_tf_rbf_10000,y_train_10000,d_test_tf_rbf,y_test,svc_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [18:42<00:00, 120.21s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train auc vs test auc curve on log of C\n",
    "plotTrainvsTestAUC_rbf(d_train_tf_rbf_10000,y_train_10000,d_test_tf_rbf,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.88      0.64      1610\n",
      "           1       0.97      0.83      0.90      8390\n",
      "\n",
      "   micro avg       0.84      0.84      0.84     10000\n",
      "   macro avg       0.74      0.86      0.77     10000\n",
      "weighted avg       0.90      0.84      0.86     10000\n",
      "\n",
      "\n",
      "The value of TPR is 0.831\n",
      "The value of TNR is 0.884\n",
      "The value of FPR is 0.116\n",
      "The value of FNR is 0.169\n",
      "\n",
      "The auc score is 0.929887\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_tf_rbf_10000,y_train_10000,svc_tf,auc_train_st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.81      0.59      4254\n",
      "           1       0.96      0.82      0.88     22078\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     26332\n",
      "   macro avg       0.71      0.82      0.74     26332\n",
      "weighted avg       0.88      0.82      0.84     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.819\n",
      "The value of TNR is 0.814\n",
      "The value of FPR is 0.186\n",
      "The value of FNR is 0.181\n",
      "\n",
      "The auc score is 0.898992\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_tf_rbf,y_test,svc_tf,auc_test_st)"
   ]
  },
  {
   "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": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 489.23 Seconds\n",
      " optimal alpha using Grid Search is {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "#getting best value for C\n",
    "d_train_w2_10000=d_train_w2[:10000]\n",
    "c_grid_rbf_w2 = cv_svc_grid(d_train_w2_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with kernal = 'rbf'\n",
    "svc_w2=SVC(kernel='rbf',C=c_grid_rbf_w2,class_weight='balanced',probability=True)\n",
    "svc_w2.fit(d_train_w2_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "auc_train_sav,auc_test_sav = plotROC(d_train_w2_10000,y_train_10000,d_test_w2,y_test,svc_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [11:22<00:00, 103.16s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train auc vs test auc curve\n",
    "plotTrainvsTestAUC_rbf(d_train_w2_10000,y_train_10000,d_test_w2,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.90      0.67      1610\n",
      "           1       0.98      0.85      0.91      8390\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     10000\n",
      "   macro avg       0.75      0.88      0.79     10000\n",
      "weighted avg       0.91      0.85      0.87     10000\n",
      "\n",
      "\n",
      "The value of TPR is 0.845\n",
      "The value of TNR is 0.905\n",
      "The value of FPR is 0.095\n",
      "The value of FNR is 0.155\n",
      "\n",
      "The auc score is 0.943407\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_w2_10000,y_train_10000,svc_w2,auc_train_sav)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.83      0.61      4254\n",
      "           1       0.96      0.83      0.89     22078\n",
      "\n",
      "   micro avg       0.83      0.83      0.83     26332\n",
      "   macro avg       0.72      0.83      0.75     26332\n",
      "weighted avg       0.89      0.83      0.85     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.830\n",
      "The value of TNR is 0.834\n",
      "The value of FPR is 0.166\n",
      "The value of FNR is 0.170\n",
      "\n",
      "The auc score is 0.913134\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_w2,y_test,svc_w2,auc_test_sav)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.4]  Applying RBF SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 789.29 Seconds\n",
      " optimal alpha using Grid Search is {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "#getting best value for C\n",
    "d_train_tf_wt_10000=d_train_tf_wt[:10000]\n",
    "y_train_10000=y_train[:10000]\n",
    "c_grid_rbf_av = cv_svc_grid(d_train_tf_wt_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with kernal = 'rbf'\n",
    "svc_av=SVC(kernel='rbf',C=c_grid_rbf_av,class_weight='balanced',probability=True)\n",
    "svc_av.fit(d_train_tf_wt_10000,y_train_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "auc_train_stf,auc_test_stf = plotROC(d_train_tf_wt_10000,y_train_10000,d_test_tf_wt,y_test,svc_av)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████| 8/8 [18:51<00:00, 168.60s/it]\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": [
    "#train auc vs test auc curve\n",
    "plotTrainvsTestAUC_rbf(d_train_tf_wt_10000,y_train_10000,d_test_tf_wt,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.90      0.66      1610\n",
      "           1       0.98      0.84      0.91      8390\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     10000\n",
      "   macro avg       0.75      0.87      0.78     10000\n",
      "weighted avg       0.91      0.85      0.87     10000\n",
      "\n",
      "\n",
      "The value of TPR is 0.842\n",
      "The value of TNR is 0.903\n",
      "The value of FPR is 0.097\n",
      "The value of FNR is 0.158\n",
      "\n",
      "The auc score is 0.940430\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_train_tf_wt_10000,y_train_10000,svc_av,auc_train_stf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.80      0.59      4254\n",
      "           1       0.95      0.82      0.88     22078\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     26332\n",
      "   macro avg       0.71      0.81      0.73     26332\n",
      "weighted avg       0.88      0.82      0.83     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.821\n",
      "The value of TNR is 0.798\n",
      "The value of FPR is 0.202\n",
      "The value of FNR is 0.179\n",
      "\n",
      "The auc score is 0.890597\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on test dataset\n",
    "report(d_test_tf_wt,y_test,svc_av,auc_test_stf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Review Summary To the Reviews And Length Of Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "listA = [preprocessed_summary[i] +\" \" + preprocessed_reviews[i] for i in range(len(preprocessed_reviews))]\n",
    "dwA_train,dwA_test,y_train,y_test=ms.train_test_split(listA, y, stratify=y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('excellent', 0.8297293782234192), ('fantastic', 0.8061206340789795), ('awesome', 0.792291522026062), ('terrific', 0.7863889932632446), ('good', 0.7747745513916016), ('wonderful', 0.7552236318588257), ('perfect', 0.7239371538162231), ('amazing', 0.6951207518577576), ('nice', 0.6296993494033813), ('fabulous', 0.6207087635993958)]\n",
      "==================================================\n",
      "[('greatest', 0.716107964515686), ('best', 0.7120872139930725), ('nastiest', 0.6321882605552673), ('experienced', 0.6300017833709717), ('ive', 0.6201525926589966), ('smoothest', 0.6071344017982483), ('disgusting', 0.5972799062728882), ('awful', 0.591404139995575), ('horrible', 0.5855566263198853), ('closest', 0.5741324424743652)]\n"
     ]
    }
   ],
   "source": [
    "trainA_list=[]\n",
    "for sent in dwA_train:\n",
    "    trainA_list.append(sent.split())\n",
    "w2vA_model=Word2Vec(trainA_list,min_count=5,size=50, workers=4)\n",
    "print(w2vA_model.wv.most_similar('great'))\n",
    "print('='*50)\n",
    "print(w2vA_model.wv.most_similar('worst'))\n",
    "w2vA_words = list(w2vA_model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 61441/61441 [15:54<00:00, 64.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61441\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review in train dataset.\n",
    "#train dataset\n",
    "d_trainA_w2 = [];\n",
    "for sent in tqdm(trainA_list): # 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 w2vA_words:\n",
    "            vec = w2vA_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "        else:\n",
    "            sent_vec+=0\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    d_trainA_w2.append(sent_vec)\n",
    "print(len(d_trainA_w2))\n",
    "print(len(d_trainA_w2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 26332/26332 [05:58<00:00, 63.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26332\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review in train dataset.\n",
    "#test dataset\n",
    "testA_list=[]\n",
    "for sent in dwA_test:\n",
    "    testA_list.append(sent.split())\n",
    "d_testA_w2 = [];\n",
    "for sent in tqdm(testA_list): # 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 w2vA_words:\n",
    "            vec = w2vA_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "        else:\n",
    "            sent_vec+=0\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    d_testA_w2.append(sent_vec)\n",
    "print(len(d_testA_w2))\n",
    "print(len(d_testA_w2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding length of reviews\n",
    "train_ex = [np.append(d_trainA_w2[i],len(dwA_train[i])) for i in range(len(dwA_train))]\n",
    "test_ex = [np.append(d_testA_w2[i],len(dwA_test[i])) for i in range(len(dwA_test))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 100.91 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.0001, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "#getting best alpha and best regularizer\n",
    "alpha_grid_ex,reg_ex = cv_sgd_grid(train_ex,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l1',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_ex=SGDClassifier(penalty=reg_ex,loss=\"hinge\",alpha=alpha_grid_ex,class_weight='balanced')\n",
    "model_temp_ex.fit(train_ex,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "#plot roc curve on train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_ex=CalibratedClassifierCV(model_temp_ex,method='sigmoid',cv='prefit')\n",
    "clf_ex.fit(train_ex,y_train)\n",
    "auc_train_ex,auc_test_ex=plotROC(train_ex,y_train,test_ex,y_test,clf_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:23<00:00,  2.45s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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HAQf9J1DVg6p6o6oOAB53x50sPiNVna2qg1R1UMuWLYMY2ZSky8CrOBTShiap87yOYowJomAWhXVAvIh0EZEIYDKwyH8CEWkhIucy/ALnTiRTE4WEcLjbzfQv2MKO1CSv0xhjgiRoRUFVC4D7gY+BVGChqiaLyFMiMtGd7Apgu4jsAFoDzwQrj7lw3cbOpFCFg5/9zesoxpggCeoN+qr6IfBhsXFP+j1/C3grmBlM1WncqiPJDYeQ8O375OQ+R1Rk3W7mw5j6qO603maqReigO2jFcb5e8S+voxhjgsCKgqmQHsN/SKY0JTTpDa+jGGOCwIqCqZCQ8Ai+aT+Ri7NXs/+bvV7HMcZUMSsKpsI6XHkPYVLE7uV/9zqKMaaKWVEwFdaySx92RPahS9rbFBQUeh3HGFOFrCiYSsnteyudOMimVUu8jmKMqUJWFEyl9LpyKmeIJm/tXK+jGGOqkBUFUynh0Y3Y2fIqEk9/TsZRa4/KmLrCioKptBYj7iZGcklZak1qG1NXWFEwldahz3C+CetM650LUS3eAK4xpjayomAqT4RjPSbRq2gnyZu+8jqNMaYKWFEwF6TH2LvJ0zCOffmK11GMMVXAioK5IA2atWJb0xH0y1zC6TNnvI5jjLlAVhTMBYsZOp2mcoakZW96HcUYc4GsKJgL1m3INRyWVjRItqJgTG1nRcFcMAkJ5WCXG+mft4ndO1K8jmOMuQBWFEyV6Dr2HgDSP7Ve2YypzawomCrRtG1XtjUYRI9D75Gbl+d1HGNMJVlRMFVGL55KWzLZ9Pm7XkcxxlSSFQVTZXqNmMQJGiEb/+l1FGNMJVlRMFUmNCKK3e2uI/HsKg4eSPM6jjGmEqwomCrVfvRMIqSQncvmeB3FGFMJVhRMlWrTfQC7InrRcd9bFBYWeR3HGFNBVhRMlcu66Ba66H42r1nmdRRjTAVZUTBVrteYOzhLJGdXz/U6ijGmgqwomCoX2aAp22PH0P/kco4fP+Z1HGNMBVhRMEHRfPhdNJQctn7yD6+jGGMqwIqCCYpOiaPZHxpH8x0LrFc2Y2oRKwomOETI6P4jLipMZdvW9V6nMcaUkxUFEzQ9xs0gX0PJ+PzvXkcxxpSTFQUTNA1j25HaeBgXZXzI2eyzXscxxpSDFQUTVJGXTCNWTrFp2XyvoxhjysGKggmqHpddT4bEErXlDa+jGGPKwYqCCSoJDeObjj+gf+4G9u3d4XUcY0wZrCiYoOs8Zgahonyz3HplM6ams6Jggq5Fh16kRg2gW/q75BcUeB3HGFMKKwqmWhT0v404jrDpi8VeRzHGlMKKgqkWvUffwkkaUrjBmr0wpiYLalEQkatFZLuI7BKRxwK83lFEPhWRr0Vks4hMCGYe452wyBh2tR7PgDMrOfLtIa/jGGNKELSiICKhwMvAeCABmCIiCcUm+xWwUFUHAJOBPwcrj/FemytmECn5bFv6itdRjDElCOaRwiXALlXdo6p5wHzg+mLTKNDYfd4EOBjEPMZj7XsPYXd4PG33/Astsl7ZjKmJglkU2gP7/YbT3XH+ZgG3iUg68CHwk0AzEpGZIrJeRNZnZGQEI6upJqd6TSa+aB9b1n/hdRRjTADBLAoSYFzxNpSnAHNVNQ6YALwmIudlUtXZqjpIVQe1bNkyCFFNdek19k5yNJzTq+wUkjE1UTCLQjrQwW84jvNPD90FLARQ1a+AKKBFEDMZj0U3bk5q89H0O76Uk6dOeh3HGFNMMIvCOiBeRLqISATOheRFxaZJA64EEJHeOEXBzg/VcU0uu4tGks2Wpa95HcUYU0zQioKqFgD3Ax8DqTh3GSWLyFMiMtGd7GfADBFJAuYB09S66arzugwcy4GQtjRJtZZTjalpwoI5c1X9EOcCsv+4J/2epwDDgpnB1DwSEsLhrj9k4K4/sTN1E/G9E72OZIxx2S+ajSe6j51JgYZw+DNrJM+YmsSKgvFEk9YdSW04lF7fvk9Obq7XcYwxLisKxjNhg26nJSdIWrHQ6yjGGJcVBeOZnsNvIpNmhCa97nUUY4zLioLxTEh4BHvjJpKYvZb0tL1exzHGYEXBeKzDlTMJkyL2LrMLzsbUBFYUjKdad+nDtsi+dNr/NoWF1kieMV6zomA8l9v3VjrqIZJWfVj2xMaYoLKiYDzX+8qpnCGG3DXWK5sxXrOiYDwXEd2Q7S3HkXj6MzIzrekrY7xkRcHUCC1HzCBa8kj9ZI7XUYyp18osCiISIyJPiMjf3OF4Ebk2+NFMfdKxzzD2hXWh5c6FWJuIxninPEcKrwK5wKXucDrwdNASmfpJhGM9JtGzaBepm1Z5ncaYeqs8RaGbqv4XkA+gqtkE7lXNmAvSY9zd5Go4x760XtmM8Up5ikKeiETjdqUpIt1wjhyMqVINm7YktekI+mYu4UzWGa/jGFMvlaco/BpYAnQQkTeA5cCjQU1l6q2YodNpIllsWWrtIRnjhVKLgogIsA24EZiG0zvaIFX9LOjJTL0UP2QCh6QVMcnzvI5iTL1UalFwu8Z8V1UzVfUDVX1fVY9WUzZTD0lIKAc630T//E3s3ZnsdRxj6p3ynD5aLSKDg57EGFfXsTMpUiF9hTWSZ0x1K09RGAV8JSK7RWSziGwRkc3BDmbqr+btupLSYDDxhxaRl5fvdRxj6pXyFIXxQDdgNHAdcK37rzHBc/FU2pBJ0udve53EmHqlzKKgqt8ATXEKwXVAU3ecMUHTe+QkjtEYvn7N6yjG1CvlaebiQeANoJX7eF1EfhLsYKZ+Cw2PZE/ba0nMWsXhg2lexzGm3ijP6aO7gCGq+qSqPgkMBWYEN5Yx0G70TMKlkJ1L7RfOxlSX8hQFAQr9hguxZi5MNWgXP4AdEb2J2/cWRdYrmzHVorwN4q0RkVkiMgtYDdium6kWWQm30EXT2bp2qddRjKkXynOh+XlgOnAMOA5MV9X/CXYwYwB6j72DLKLIWj3X6yjG1AthZU0gIkOBZFXd6A43EpEhqrom6OlMvRfVoAkbYsfQ7+hSThw/RtNmzb2OZEydVp7TR38B/JuszHLHGVMtmg2/iwaSS/LSuV5HMabOK9eFZvXrCktViyjHEYYxVaVr4ijSQjvSfPt865XNmCArT1HYIyIPiEi4+3gQ2BPsYMb4iHCk+4/oXbidnVvXe53GmDqtPEXhXuAy4ID7GALMDGYoY4rrMe5u8jSUI19YI3nGBFOZp4FU9QgwuRqyGFOixrFtSWo8nIQjH5KdnU10dLTXkYypk0o8UhCRGSIS7z4XEZkjIifdllIvrr6IxjgiBk+juZwmafmbXkcxps4q7fTRg8A+9/kUoD/QFfgp8EJwYxlzvl7DJvKttCByixUFY4KltKJQoKrnGrO/Fvin2wPbMqBB8KMZ830SGkZahxvon7OB/Xt3eB3HmDqptKJQJCJtRSQKuBJY5veandA1nug89h4Avlk+2+MkxtRNpRWFJ4H1OKeQFqlqMoCIjMRuSTUeadmhB6nRA+ia/h4FBQVexzGmzimxKKjq+0AnoLeq+jeVvR6YVJ6Zi8jVIrJdRHaJyGMBXv+jiGxyHztE5ERFP4Cpfwr730Y7jrD5y0VeRzGmzin1dwqqWqCqx4uNy1LVMyW95xwRCQVexunOMwGYIiIJxeb1sKomqmoi8CfA+l40Zeo9egonaEjB+n94HcWYOqc8P16rrEuAXaq6R1XzgPnA9aVMPwWYF8Q8po4Ij4xhZ+sJ9D+zkowjB72OY0ydEsyi0B7Y7zec7o47j4h0AroAK0p4faaIrBeR9RkZGVUe1NQ+ba6YQaQUsP2TOV5HMaZOKe3Ha1eJyA8DjL9VRMaWY96BemcrqTWzycBbqloY6EVVna2qg1R1UMuWLcuxaFPXdeh9CbvC4mm7ZyFaZL2yGVNVSjtS+A3weYDxy4GnyjHvdKCD33AcUNKx/mTs1JGpoJO9b6Fb0TekbAj039QYUxmlFYUYVT3vXI2qHqZ8P15bB8SLSBcRicDZ8J93u4iI9ASaAV+VL7Ixjt5jp5GtEZxcZaeQjKkqpRWFKBE5r8E8EQmnHD9eU9UC4H7gYyAVWKiqySLylIhM9Jt0CjBfraF8U0ExjZuT2nw0fY8t5dQpu5vZmKpQWlF4G/ibiPiOCtznf6Wct46q6oeq2kNVu6nqM+64J1V1kd80s1T1vN8wGFMejS67i0aSzdal//Q6ijF1QmlF4VfAt8A3IrJBRDbi/Lo5w33NGM91HziG/SHtaZI63+soxtQJpf2iucDdg+8ATAPuADqq6mN+DeUZ4ykJCeFw1x9yUUEyu7dt8jqOMbVeabek3igiN+L8Ijke6A4MEpFG1RXOmPKIHzuDAg3h8KfWSJ4xF6q0nteuCzCuOdBPRO5S1YA/NDOmujVt3YGkhpfS89v3yc19nsjIKK8jGVNrlVgUVHV6oPHur48X4vTVbEyNEDrwdlp8cQ9rV/yLS8ZP9TqOMbVWhZu5UNVvgPAgZDGm0hJG3MRRmhGe9JrXUYyp1SpcFNwfm+UGIYsxlRYSFs6euOvpl72Wg/utuw9jKqvE00cispjz2ypqDrQF7Pjc1Dgdx9xD6Ny57Fn2N9pN/53XcYyplUq70PyHYsMKZAI73aawjalR2nROIDWyH53T3qaw8BlCQ4PZCLAxdVNpv1P4vNjjC7dLzsEi8nI1ZjSm3HL63kqcHmbLqg+8jmJMrVSuXSkRSRSR/xKRfcDTwLagpjKmkhKuvI3TxJC31nplM6YySrum0AOnZdMpOKeNFgCiqqOqKZsxFRYZ3ZDNLa+m35HFHM/MoFms9b9hTEWUdqSwDbgSuE5Vh6vqn4CAneAYU5O0HDGDKMkn9ZNXvI5iTK1TWlG4CTgMfCoifxORKwncm5oxNUrnvpexJ6wrLXcuxFpkN6ZiSrvQ/I6qTgJ6AZ8BDwOtReQvIjKumvIZUynHekwivmg32zet9DqKMbVKmReaVTVLVd9Q1WtxutTcBFj/B6ZG6znuLnI1nOMrrVc2YyqiQjdyq+oxVf1fVR0drEDGVIVGTVuS3HQkCUc/5mzWaa/jGFNr2K97TJ0VM3Q6TSSLzcve8DqKMbWGFQVTZ/UcMp6D0poGyW96HcWYWsOKgqmzJCSU9M430TcviX07t3odx5hawYqCqdO6jb2HQhUOfPo3r6MYUytYUTB1Wmy7zqTEXEL3g4vIz7d2HI0pixUFU+fpxVNpzTE2f/6211GMqfGsKJg6L2Hkj8ikCWy0XtmMKYsVBVPnhUVEsrvttfTL+oojB9O8jmNMjWZFwdQL7UfPJFwK2bXs715HMaZGs6Jg6oX28Ylsi0ig/d63KCos8jqOMTWWFQVTb2Ql3EInPUDK2qVeRzGmxrKiYOqNi8beThZRZK1+1esoxtRYVhRMvRHVoAkpsePoe2IFJ08c8zqOMTWSFQVTrzQffhcxkkvKJ3a0YEwgVhRMvdItcST7QjrSbMcCr6MYUyNZUTD1iwhH4ifRq2A7O7es9TqNMTWOFQVT7/Qaezd5GkrGF9ZInjHFWVEw9U7jFm1Ibnw5vTM+Iif7rNdxjKlRrCiYeili8DSacZotK6wDHmP8WVEw9VLvYRM5LC2J3GxFwRh/QS0KInK1iGwXkV0i8lgJ0/xIRFJEJFlE7C/UVIuQ0FC+6XADfXI2kr53u9dxjKkxglYURCQUeBkYDyQAU0Qkodg08cAvgGGqehHwULDyGFNcl7EzAfhmuV1wNuacYB4pXALsUtU9qpoHzAeuLzbNDOBlVT0OoKpHgpjHmO9p1aEHKdEX0zX9HQoLCryOY0yNEMyi0B7Y7zec7o7z1wPoISL/FpHVInJ1oBmJyEwRWS8i6zMyMoIU19RHBf1voy1H2fLle15HMaZGCGZRkADjtNhwGBAPXAFMAf4uIk3Pe5PqbFUdpKqDWrZsWeVBTf2VMGoKJ2hEwfp/eh3FmBohmEUhHejgNxwHHAwwzXuqmq+qe4HtOEXCmGoRERXN9tYT6HfmS45+W/y/pzH1TzCLwjogXkS6iEgEMBlYVGyad4FRACLSAud00p4gZjLmPG2vmEGEFLLDemUzJnhFQVULgPuBj4FUYKGqJovIUyIy0Z3sYyBTRFKAT4H/UNXMYGUyJpCOvQed4msoAAAXZklEQVSzI6wnbXa/hRZZr2ymfgvq7xRU9UNV7aGq3VT1GXfck6q6yH2uqvpTVU1Q1b6qOj+YeYwpyaneU+ha9A3bNn7qdRRjPGW/aDYG6D32Ds5qJKf+bf0smPrNioIxQIPGzUlpPpqLji3jzOmTXscxxjNWFIxxNbr0ThpKNslL/+F1FGM8Y0XBGFePQWNIC2lP49R5XkcxxjNWFIxxSUgIh7reTO/8FPZt+9rrOMZ4woqCMX7ix95NvoZy8FNrJM/UT1YUjPHTvHUHkhteSs9v3ycvN9frOMZUOysKxhQTMvB2YjnJlk8XeB3FmGpnRcGYYi4acSMZNCd00+teRzGm2llRMKaY0LBwdsddT9/stRxOt6a4TP1iRcGYADpeeQ+houxZOtvrKMZUKysKxgTQrktvkiP70yntbYoKC72OY0y1saJgTAly+t5Ke/2W5FUfeh3FmGoT5nUAY2qqi0bfyqn1vyFn7Vy4/Dqv4wRNfn4+6enp5OTkeB3FVIGoqCji4uIIDw+v1PutKBhTgqiYhmxueTX9jyziROYRmsa28jpSUKSnp9OoUSM6d+6MSKBedE1toapkZmaSnp5Oly5dKjUPO31kTClajLibSMkndekrXkcJmpycHGJjY60g1AEiQmxs7AUd9VlRMKYUXftexq7QbrTcuRBV9TpO0FhBqDsu9Lu0omBMGY71nET3wj3sSlrpdRRjgs6KgjFl6DXuLnI0nMwv6+4pJC9lZmaSmJhIYmIibdq0oX379r7hvLy8cs1j+vTpbN++vcLLvuaaa7j88su/N+62227j3Xff9Q0XFBTQtGlT3/C2bdsYP3488fHx9O7dm8mTJ3PkyJHvzaOwsJAf//jH9OnTh759+3LJJZfwzTffVDifF+xCszFlaNy0BeubXEHC0Y/JzjpDdIOGXkeqU2JjY9m0aRMAs2bNomHDhjzyyCPfm0ZVUVVCQgLvx776asW7Uc3MzGTLli1ERUWRlpZGx44dy3xPdnY21157LS+++CITJkwAYPny5WRmZtKq1Xc3Irz55ptkZmayefNmQkJCSEtLo3HjxhXO6K+goICwsOBvsq0oGFMOMUOn0/iTpaxd9hqXXH+f13GC5jeLk0k5eKpK55nQrjG/vu6iCr9v165d3HDDDQwfPpw1a9bw/vvv85vf/IaNGzeSnZ3NpEmTePLJJwEYPnw4L730En369KFFixbce++9fPTRR8TExPDee+99b4N9zltvvcUNN9xAkyZNWLBgAf/xH/9RZqbXXnuNESNG+AoCwJVXXnnedIcOHaJt27a+IuZfcD744AOeeOIJCgsLad26NZ988glHjx7lzjvvZN++fTRs2JDZs2fTp08ffvWrX5GRkcGePXto06YNr776Ko8++igrV64kJyeHBx54gLvvvrvC67Y0dvrImHLoPXQ8B6QNMclveh2lXklJSeGuu+7i66+/pn379vz+979n/fr1JCUlsXTpUlJSUs57z8mTJxk5ciRJSUlceumlzJkzJ+C8582bx5QpU5gyZQrz5pWvt72tW7cycODAMqebPHkyb7/9NgMGDOCRRx7xHQkdPnyY++67j3feeYekpCTmz58PwBNPPMGQIUPYvHkzs2bNYtq0ab55ff311yxevJjXXnuN2bNn06pVK9auXcu6det4+eWXSUtLK1f28rIjBWPKQUJC2N/5JobufZn9u7bSoXsfryMFRWX26IOpW7duDB482Dc8b948XnnlFQoKCjh48CApKSkkJCR87z3R0dGMHz8egIEDB/Lll1+eN98DBw6QlpbG0KFDEREKCwvZtm0bvXr1Cnj3TkXv6OnYsSPbt29nxYoVrFixglGjRvHOO+9w/PhxRo0aRadOnQBo3rw5ACtXruSDDz4AYNy4cUybNo2srCwArr/+eqKiogD45JNPSE1N9RWTkydPsnPnznKd+iovKwrGlFP3sTMp/N8/k75iNh26v+h1nHqhQYMGvuc7d+7khRdeYO3atTRt2pTbbrst4P34ERERvuehoaEUFBScN82CBQvIzMz0/cDr5MmTzJ8/n1mzZhEbG8vx48d90x47dowWLVoAcNFFF7FmzZpyZY+KimLChAlMmDCBFi1a8N5773H55ZcHLDDFb3f2H/ZfB6rKn//854CnrKqKnT4yppxatOvM1pghdD+4iIL88t0VY6rOqVOnaNSoEY0bN+bQoUN8/PHHlZ7XvHnzWLZsGfv27WPfvn2sXbvWdwrpiiuuYP78+eTn5wMwd+5cRo0aBcDUqVP5/PPPWbJkiW9eH3744XmnsTZs2MChQ4cAKCoqYsuWLXTq1Ilhw4axYsUK351Ix44dA2DEiBG88cYbACxbtoy4uLjvFYNzrrrqKv785z/7Ct327dvJzs6u9HoIxI4UjKkAvXgqLf/9Y77+/G0GjJnsdZx65eKLLyYhIYE+ffrQtWtXhg0bVqn57N69m8OHDzNo0CDfuPj4eCIjI9mwYQM33HADGzduZODAgYSEhBAfH89f//pXAGJiYli8eDEPP/wwP/nJTwgPDycxMZEXXnjhe8s4fPgwM2bMIC8vD1Xl0ksv5b777iMyMpK//OUvXH/99agq7dq146OPPuKpp55i+vTp9OvXj4YNG5Z4N9U999xDWloaiYmJALRq1Yr33nuvUuuhJFLbfqU5aNAgXb9+vdcxTD1VkJfLyWd7kBaTwIBHP/I6TpVITU2ld+/eXscwVSjQdyoiG1R1UAlv8bHTR8ZUQFhEJDvbXkffrNUcPVS1d30YUxNYUTCmgtqPupswKWLn0r95HcWYKmdFwZgK6tAjkZSIPrTf+xZaVOR1HGOqlBUFYyohK2EKHfUgqWsqfweMMTWRFQVjKqHPmNs5o9GcWT3X6yjGVCkrCsZUQnTDxqTEjqXPiU85dSLT6zjGVBkrCsZUUtPhdxMjuaQurXgLneY7VdF0NsCcOXM4fPhwia/n5eXRvHlznnjiie+Nj4uL48SJE77hZcuWccMNN/iGP/jgAwYOHEhCQgK9evXi5z//+XnzPnToEBMmTKB///4kJCQwceLEcueuaawoGFNJ8YmXszekM822L/A6Sq12runsTZs2ce+99/Lwww/7hv2brChLWUVhyZIlJCQksGBB+b+vpKQkHnroIebNm0dKSgpbt26lc+fO5033q1/9imuuuYakpCRSUlJ4+umny72MkgRqnqM62C+ajakkCQnhSPzNDNn+3+zZuoaufYZ4HenCffQYHN5StfNs0xfG/75Sb/3HP/7Byy+/TF5eHpdddhkvvfQSRUVFTJ8+nU2bNqGqzJw5k9atW7Np0yYmTZpEdHQ0a9euPa+gzJs3j5/+9Kf88Y9/ZN26dd9raK8k//mf/8kTTzxBjx49AAgLC+O++85vOv3QoUPExcX5hvv16+d7/uyzzzJv3jxCQkK49tpreeaZZ9i4cSP33Xcf2dnZxMfHM2fOHJo0acLw4cMZOXIkX375JTfeeCNTpkzhvvvuIy0tjZCQEF588UWGDh1aqXVZXnakYMwF6Dn2bvI0jCOf228WqtrWrVt55513WLVqFZs2baKgoID58+ezYcMGjh49ypYtW9i6dSu33347kyZNIjExkQULFgQ8wsjKyuLzzz9nwoQJQWkq+/777+eOO+5g9OjRPPvss752jxYvXsxHH33E2rVrSUpK4mc/+xng9O723HPPsXnzZnr27Mlvf/tb37xOnTrFF198wUMPPcQDDzzAo48+yvr161m4cGGV950QiB0pGHMBmrZow4bGl9Mr4yNyc84SGRXjdaQLU8k9+mBYtmwZ69at87VRlJ2dTYcOHbjqqqvYvn07Dz74IBMmTGDcuHFlzmvRokWMHTuWqKgobr75ZgYNGsQf/vAHQkJCqqSp7AkTJrB7926WLFnCRx99xIABA0hOTmbZsmXceeedREdHA05T2ZmZmeTk5DB8+HAA7rjjDqZOneqb1+TJ37WptWzZsu91M3r8+HGys7N98wuGoB4piMjVIrJdRHaJyGMBXp8mIhkissl9BL8MGlPFIgbfQVPOsHW5dcBTlVSVO++803d9Yfv27TzxxBPExsayefNmhg8fzosvvsg999xT5rzmzZvHkiVL6Ny5M4MHD+bIkSN88cUXAGU2lb1hw4Zy5Y2NjeXWW2/l9ddfJzExkZUrV6Kq5xWYstqbK95U9tq1a33r4MCBA0EtCBDEoiAiocDLwHggAZgiIgkBJl2gqonu4+/BymNMsFw0bCKHaEnE5te9jlKnjBkzhoULF3L06FHAuUspLS2NjIwMVJWbb77Z1z0nQKNGjTh9+vR58zl+/Dhr1qwhPT3d11T2iy+++L2msl977TXAubj7xhtv+JrKfvTRR3n66afZtWsXAIWFhTz//PPnLWP58uW+JqxPnTrF3r176dixI+PGjeOVV17xvXau4ERHR7Nq1SrA6eJz5MiRJa6Dl19+2Td8rge3YArm6aNLgF2qugdAROYD1wPn959nTC0WEhrKvo43cmna/7LvqdrXI1vB2BfIPVgzmusoOH2EgqIz5B5MpkdsCL984C6uHDmcIi0iPCycP/3+CUJDQ7n3Z0/69sKffvxhcg8mc9v1Y7hr2u1ER0Wy8oP5RESEAzD/zbcYPXwwRRk7yHWXM35oLx7/xWM898v7+MU9k/jJY0/RL6EninL16BHcOLIfuQeTSWgdwe9++QA3/2Ai2Tk5iAjXXjWK3IPJ38v91fL3+fG9MwgPD6OoSJlxy00ktI4goXUXNlw2gIGJfQkPC2PC2CuY9ehPeOX5WTxw/71k5+TQrXNHZj//NLkHkynKO0vekd3kHnSyP//ET3jgsd8y529/oaCgkMtHjGT23wN3L1pVgtZ0toj8ELhaVe92h6cCQ1T1fr9ppgG/AzKAHcDDqro/wLxmAjMBOnbsOPBcBxXG1BTHjhxg9xsPEVp4fk9gNV3o5Y/QvVM7r2OYcpAGLYhp1KzM6S6k6exgHikEulJTvAItBuapaq6I3Av8Axh93ptUZwOzwelPoaqDGnOhmrdqT/OH/+V1jEpJTU2lQZt4r2OYGiKYF5rTgQ5+w3HAQf8JVDVTVc8d0f0NKPveL2OMMUETzKKwDogXkS4iEgFMBhb5TyAibf0GJwKpQcxjjClBbeuB0ZTsQr/LoJ0+UtUCEbkf+BgIBeaoarKIPAWsV9VFwAMiMhEoAI4B04KVxxgTWFRUFJmZmcTGxlb4/nxTs6gqmZmZREVFVXoe1kezMfVcfn4+6enp5OTUvovk5nxRUVHExcURHh7+vfE14UKzMaYWCA8Pp0uXLl7HMDWEtX1kjDHGx4qCMcYYHysKxhhjfGrdhWYRyQAq+5PmFsDRKoxTVSxXxViuiqup2SxXxVxIrk6q2rKsiWpdUbgQIrK+PFffq5vlqhjLVXE1NZvlqpjqyGWnj4wxxvhYUTDGGONT34rCbK8DlMByVYzlqriams1yVUzQc9WrawrGGGNKV9+OFIwxxpTCioIxxhifelsUROQREVERaeF1FgAR+a2IbBaRTSLyiYjUiK6wROS/RWSbm+0dEWnqdSYAEblZRJJFpEhEPL91UESuFpHtIrJLRB7zOg+AiMwRkSMistXrLP5EpIOIfCoiqe53+KDXmQBEJEpE1opIkpvrN15n8icioSLytYi8H8zl1MuiICIdgLFAmtdZ/Py3qvZT1UTgfeBJrwO5lgJ9VLUfTpepv/A4zzlbgRuBL7wOIiKhwMvAeCABmCIiCd6mAmAucLXXIQIoAH6mqr2BocCPa8j6ygVGq2p/IBG4WkSGepzJ34NUQ58z9bIoAH8EHuX87kE9o6qn/AYbUEOyqeonqlrgDq7G6UHPc6qaqqrbvc7hugTYpap7VDUPmA9c73EmVPULnH5KahRVPaSqG93np3E2dO29TQXqOOMOhruPGvF3KCJxwDXA34O9rHpXFNxOfQ6oapLXWYoTkWdEZD9wKzXnSMHfncBHXoeogdoD+/2G06kBG7naQEQ6AwOANd4mcbinaDYBR4ClqlojcgH/g7MjWxTsBdXJ/hREZBnQJsBLjwO/BMZVbyJHablU9T1VfRx4XER+AdwP/Lom5HKneRznsP+N6shU3lw1RKDuymrEHmZNJiINgf8DHip2pOwZVS0EEt1rZ++ISB9V9fSajIhcCxxR1Q0ickWwl1cni4Kqjgk0XkT6Al2AJLfbwThgo4hcoqqHvcoVwJvAB1RTUSgrl4jcAVwLXKnV+MOWCqwvr6UDHfyG44CDHmWpFUQkHKcgvKGqb3udpzhVPSEin+Fck/H6Qv0wYKKITACigMYi8rqq3haMhdWr00equkVVW6lqZ1XtjPPHfHF1FISyiEi83+BEYJtXWfyJyNXAz4GJqnrW6zw11DogXkS6iEgEMBlY5HGmGkucPbJXgFRVfd7rPOeISMtzd9eJSDQwhhrwd6iqv1DVOHebNRlYEayCAPWsKNRwvxeRrSKyGef0Vo24TQ94CWgELHVvl/2r14EAROQHIpIOXAp8ICIfe5XFvRB/P/AxzkXThaqa7FWec0RkHvAV0FNE0kXkLq8zuYYBU4HR7v+pTe5esNfaAp+6f4PrcK4pBPX2z5rImrkwxhjjY0cKxhhjfKwoGGOM8bGiYIwxxseKgjHGGB8rCsYYY3ysKJgaSUTOlD1VhecZJyLvichOEdktIi+4vytARK4QkZN+t0guK2U+74nIV8XGzRKRR8pYfpnTlOMzvCUiXd3nz4jI/kDrSkTaisgn7vMeIvKh24JrqogsFJHWItJXROZeSB5T91hRMPWC+4Opt4F3VTUe6AE0BJ7xm+xLVU10HyX9Kr4pcDHQVES6BDt3sWVfBISq6h531GKcxvgCuRr4WESicH4d/xdV7e62TPoXoKWqbgHiRKRjsLOb2sOKgqk1RKSTiCx3+3ZYfm5jJiLdRGS1iKwTkadKOMoYDeSo6qvga+PmYeBOEYmpQIybcDbG83F+XRoo52ci8j8issr9QaL/hjvBfX2PiDzg9553RWSD247/zBKWfSvga/NJVVer6qESpr0ap/HCW4CvVHWx3/s+9WvPZ3FJn8PUT1YUTG3yEvBPt2+HN4AX3fEvAC+o6mBKbnPoImCD/wi3EbY0oLs76nK/00ePlzCfKcA89zGllKwNVPUy4P8Bc/zG9wKuwtnD/7XbBhDAnao6EBgEPCAisQHmOaz4ZwjE7d+hp6qmAH3KeM964PKy5mnqDysKpja5FKexQIDXgOF+4//lPn+z+JtcQuCWS/3H+58+eua8CUVa4xSQlaq6AygQkT4lLG8e+Po0aCzf9Vj3garmqupRnOaZW7vjHxCRJJw+KzoA8cVniNMMQ0YJy/M3hPI3RX0EqBG9/JmawYqCqc0q0kZLMs5euI+INMbZAO8u5zwmAc2AvSKyD+hMyadeimc7N5zrN64QCHObQx4DXOr2+vU1TmuYxWWXML648cAS93kyMLCUaaPc+RoDWFEwtcsqvtsI3wqsdJ+vxjnXDyVvpJcDMSJyO/hOsTwHzK1A669TgKv9WtkdWMryJrnLGQ6cVNWTpcy3CXBcVc+KSC+cLioDSeW7U12luRLn84Jz5HSZiFxz7kVx+pPu6w72wPumoU0NYkXB1FQxbsue5x4/BR4AprutWE7lu5ZkHwJ+KiJrcU6xnLcBdvuB+AFws4jsxOlvOgen06UyidNDWEecAnRunnuBUyIyJMBbjovIKuCvQFmtky7BOWLYDPzWfxnFfABc4Zfpv9yWYs+tq1ki0hLngvopN2M2Tl8YP3FvxU0BpuGcNgIY5c7XGMBaSTV1gHv3ULaqqohMBqaoqmd9JIvTOcsjqrq+iucbDXwKDHPvngo0zW1AnKr+vhzziwQ+B4b79cNt6rk62fOaqXcGAi+5v0U4gdOXdJ2jqtki8muc/p/TSpjm9QrMsiPwmBUE48+OFIwxxvjYNQVjjDE+VhSMMcb4WFEwxhjjY0XBGGOMjxUFY4wxPv8f8favmRWqUiEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(train_ex,y_train,test_ex,y_test,reg_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.78      0.67      9927\n",
      "           1       0.95      0.89      0.92     51514\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     61441\n",
      "   macro avg       0.77      0.84      0.80     61441\n",
      "weighted avg       0.90      0.88      0.88     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.894\n",
      "The value of TNR is 0.778\n",
      "The value of FPR is 0.222\n",
      "The value of FNR is 0.106\n",
      "\n",
      "The auc score is 0.922438\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(train_ex,y_train,model_temp_ex,auc_train_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.78      0.67      4254\n",
      "           1       0.95      0.89      0.92     22078\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     26332\n",
      "   macro avg       0.77      0.84      0.80     26332\n",
      "weighted avg       0.90      0.88      0.88     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.895\n",
      "The value of TNR is 0.778\n",
      "The value of FPR is 0.222\n",
      "The value of FNR is 0.105\n",
      "\n",
      "The auc score is 0.922786\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(test_ex,y_test,model_temp_ex,auc_test_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Length Of Reviews As A Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding length of reviews\n",
    "train_ex1 = [np.append(d_train_w2[i],len(dw_train[i])) for i in range(len(dw_train))]\n",
    "test_ex1 = [np.append(d_test_w2[i],len(dw_test[i])) for i in range(len(dw_test))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 83.92 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.001, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "#getting best alpha and best regularizer\n",
    "alpha_grid_ex1,reg_ex1 = cv_sgd_grid(train_ex1,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.001, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l1',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_ex1=SGDClassifier(penalty=reg_ex1,loss=\"hinge\",alpha=alpha_grid_ex1,class_weight='balanced')\n",
    "model_temp_ex1.fit(train_ex1,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "#plot roc curve on train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_ex1=CalibratedClassifierCV(model_temp_ex1,method='sigmoid',cv='prefit')\n",
    "clf_ex1.fit(train_ex1,y_train)\n",
    "auc_train_ex1,auc_test_ex1=plotROC(train_ex1,y_train,test_ex1,y_test,clf_ex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:21<00:00,  2.50s/it]\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": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(train_ex1,y_train,test_ex1,y_test,reg_ex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.24      0.95      0.39      9927\n",
      "           1       0.98      0.43      0.60     51514\n",
      "\n",
      "   micro avg       0.51      0.51      0.51     61441\n",
      "   macro avg       0.61      0.69      0.49     61441\n",
      "weighted avg       0.86      0.51      0.56     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.427\n",
      "The value of TNR is 0.955\n",
      "The value of FPR is 0.045\n",
      "The value of FNR is 0.573\n",
      "\n",
      "The auc score is 0.772975\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(train_ex1,y_train,model_temp_ex1,auc_train_ex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.24      0.96      0.39      4254\n",
      "           1       0.98      0.42      0.59     22078\n",
      "\n",
      "   micro avg       0.51      0.51      0.51     26332\n",
      "   macro avg       0.61      0.69      0.49     26332\n",
      "weighted avg       0.86      0.51      0.56     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.421\n",
      "The value of TNR is 0.963\n",
      "The value of FPR is 0.037\n",
      "The value of FNR is 0.579\n",
      "\n",
      "The auc score is 0.774824\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(test_ex1,y_test,model_temp_ex1,auc_test_ex1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model on Review Summary as a Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 90.67 Seconds\n",
      " optimal alpha using Grid Search is {'alpha': 0.01, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "#getting best alpha and best regularizer\n",
    "alpha_grid_ex2,reg_ex2 = cv_sgd_grid(d_trainA_w2,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.01, average=False, class_weight='balanced',\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model with best regulariser\n",
    "model_temp_ex2=SGDClassifier(penalty=reg_ex2,loss=\"hinge\",alpha=alpha_grid_ex2,class_weight='balanced')\n",
    "model_temp_ex2.fit(d_trainA_w2,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████| 8/8 [00:19<00:00,  2.58s/it]\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": [
    "#plotting train vs test auc curve on log of alpha\n",
    "plotTrainvsTestAUC(d_trainA_w2,y_train,d_testA_w2,y_test,reg_ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "#plot roc curve on train and test data\n",
    "#creating CalibrationModel on top of SGD\n",
    "clf_ex2=CalibratedClassifierCV(model_temp_ex2,method='sigmoid',cv='prefit')\n",
    "clf_ex2.fit(d_trainA_w2,y_train)\n",
    "auc_train_ex2,auc_test_ex2=plotROC(d_trainA_w2,y_train,d_testA_w2,y_test,clf_ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.89      0.64      9927\n",
      "           1       0.97      0.82      0.89     51514\n",
      "\n",
      "   micro avg       0.83      0.83      0.83     61441\n",
      "   macro avg       0.73      0.86      0.76     61441\n",
      "weighted avg       0.90      0.83      0.85     61441\n",
      "\n",
      "\n",
      "The value of TPR is 0.824\n",
      "The value of TNR is 0.889\n",
      "The value of FPR is 0.111\n",
      "The value of FNR is 0.176\n",
      "\n",
      "The auc score is 0.931287\n"
     ]
    },
    {
     "data": {
      "image/png": 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fiEjKKMHZOHPJf1w9tk3TmNcODC2g3XhgfD71i4Bji9OveDL764CqwDVAW6KfWAMK3UNEJMWUYGZfJhWZ2bv7wuDlLmBgYrsjIpIckdz0/o5pPLNxZpHPtB5317i9iKSNkhrGKaviGbO/IeZ1ZeA3RGfmiIikjdywL3Hs7osPqHrHzPSFKxFJK6Ffzz5YjGefckRv0tZLWI9ERJJAwziwmP99RTcHWA0MTmSnADKKXrFTQuj7jXOS3QVJU6EfxgFauvvu2Aozq5Sg/oiIJEW6z8aJ5+rezafuvZLuiIhIMnkxSioqbD37ekSXzaxiZifwv2+EHUr0S1YiImkjzMM43YFLia6odh//C/b/Bf6Y2G6JiJSu0M7GcfcngCfM7Dfu/lwp9klEpNTlJrsDCRbPmH1bM6ux702w3OadCeyTiEipcyzukoriCfY93H3Hvjfuvh0obLlOEZGUk+MWd0lF8Uy9zDCzSu6+B8DMqgCaeikiaSVVM/Z4xRPsJwIzzexfwfuBwBOJ65KISOlL9zH7eNbGucfMlgNdic7IeRVokuiOiYiUJmX2UZuIfvCdR3S5BM3OEZG0EtrM3sxaAP2A/sDXwDNEn0N7Rin1TUSk1ERCnNl/DMwBerl7NoCZDSuVXomIlLIUfdpg3AqbevkbosM3s8zsETPrQhEP0RURSVW5WNwlFRUY7N39eXc/HzgamA0MA+qa2Vgz61ZK/RMRKRXpvhBakV+qcvdv3X2Su59NdJ2cZcBNCe+ZiEgpyi1GSUXxzsYBwN23AQ8HRUQkbeSm+QOTihXsRUTSVSTZHUgwBXsREdJ/No6CvYgIpOwsm3gp2IuIkLqzbOKlYC8igoZxRERCIVWnVMZLwV5EBIgosxcRSX/K7EVEQiDdg308z6AVEUl7bvGXopjZeDPbYmYrDqj/nZmtMrOVZnZPTP3NZpYdbOseU58Z1GWb2U0x9c3MbL6ZfWpmz5hZxaL6pGAvIkKJr43zOJAZW2FmZwB9gOPd/Rjg3qC+FdFnhxwT7DPGzDLMLAN4COgBtAL6B20BRgH3u3tzYDswuKgOKdiLiBBdLiHeUhR3fxvYdkD1EOBud98TtNkS1PcBJrv7HndfDWQD7YOS7e6fu/teYDLQx8wM6AxMDfZ/AuhbVJ8U7EVEiM6zj7eYWZaZLYopWXGcogXwq2D45S0zOzGobwisi2m3PqgrqL4WsMPdcw6oL5Ru0IqIULwbtO4+DhhXzFOUB2oCHYATgWfN7AjyfyiUk38y7oW0L/LkIiKhVwqzcdYD/3Z3BxaYWS5QO6hvHNOuEbAxeJ1f/VaghpmVD7L72PYF0jCOiAil8qSqF4iOtWNmLYCKRAP3NKCfmVUys2ZAc2ABsBBoHsy8qUj0Ju604MNiFnBucNwBwItFnVyZvYgIJbs2jpk9DXQCapvZemAEMB4YH0zH3AsMCAL3SjN7FvgQyAGGunskOM7VwGtABjDe3VcGpxgOTDazO4GlwGNF9UnBXkSEkn14ibv3L2DTRQW0HwmMzKf+ZeDlfOo/JzpbJ24K9iIiQG6aL3KsYC8iQvovl6BgLyKCHl4iIhIKyuxFREIgx9I7t1ewFxFBwzgiIqGgYRwRkRDQ1EsRkRBI71CvYC8iAmgYR0QkFCJpntsr2IuIoMxeRCQUXJm9iEj6S/fMXg8vSZI+g/ow5vUxjH1jLH0G9wGgWvVqjJw0kkfeeoSRk0ZSrXq1/fZpfnxzpq+ezik9T8mrG/THQYx9Yyz/nPlPrrjtilK9Bik5kUiEcy8dylV/GAHA8D+P4ux+l9H3oiu59a6/8UNOzn7tP/hoFcf/6ixmzJqTV/flpi1cft0f6XVBFr0vzGLDl5sBcHceePhxzup3Gb0uyGLilCKfcxFKuXjcJRUp2CdBkxZN6N6/O8N6DWNo96G079KeBk0bcN7Q81j2zjIuP/1ylr2zjN9e9du8fcqVK8egmwex5K0leXUt27akVbtWDO02lKvOvIoWx7fguA7HJeOS5CeaOOVFjmj687z3Z3U7g+lPP8LzT45lz569PDf91bxtkUiE+8f8i1Pat9nvGDffeS8DLziX6U+NY/IjD3BYzeoAvPDy62zaspXpT41j+lPj6NH19NK5qBRTCk+qSioF+yRo3Lwxq5asYs/uPeRGclkxbwUdMzvS4cwOvDH1DQDemPoGJ3c7OW+fXgN78c4r77Dj6x15de5OhUoVKF+hPBUqRv+7Y+uOH51PyrZNW77i7XcX8Jte3fPqTuvYHjPDzDiu5VFs3rI1b9tTU6dxZqdTOKxmjby6z1avIRKJ0DH4AKhatQpVKlcG4Jnn/8OQgRdQrlz0n3utmP3kf3LwuEsqKvVgb2YDS/ucZc2aVWs49qRjOaTGIVSqXIl2Z7Sjdv3a1Khdg+1btgOwfct2qteOZma16taiY/eOvDxx/wfWfLzkY5a/u5yJiyYycdFEFr+1mHXZ60r9euSnGfXAw/z+qsGY/fif4w85OUx/bSanntQOgM1fbWXm2+9yXt+e+7X7Yt0GDqlWjWtvvoNzLx3KvQ8+SiQSffbSug1f8srMtzhv0DVcef2fWLNuQ+IvKgV5Mf6XipKR2d9W0AYzyzKzRWa2aO2utaXZp1K1LnsdU8ZOYeSkkdzx5B2s/mh13j/M/GT9OYvxfxlPbu7+t5DqN6lP4yMbc8lJl3Bx+4v5Zcdfcmz7YxPdfSlBs9+Zz2E1a3DM0c3z3X7nvQ/R9pfH0rZ19Oc66oGHGTZkEBkZGfu1i0QiLHl/BTdcfRmTHx3N+o2beOHl6F+Je3/4gUoVK/Ls+NH8plcmf7rr/sReVIrKLUZJRQmZjWNmywvaBNQtaD93HweMA+j5856p+fEZpxnPzGDGMzMAGHDjALZ+uZUdW3dQs05Ntm/ZTs06Ndm5dScAzY9rzk0P3gTAoYcdyolnnEhuTi4NmjVg1dJV7P5uNwCLZi/i6DZHs2LBiuRclBTb0uUfMnvuPOa8t5A9e3/g22+/Y/ht9zBqxI2MGT+J7Tt2MuKuW/Par/z4U/4w4m4Atu/8L3PeW0hGRgZ1D6/N0S1+QeOG9QHofNrJLF/5MdCdeofX5sxOpwLQ9fSO/Omuv5X6daaCVM3Y45WoqZd1ge7A9gPqDXg3QedMKdVrVWfn1zs5vMHhdMzsyPXnXE/dxnXpem5XpoyZQtdzuzLv9XkADDp1UN5+w+4bxoKZC3hvxnuc1us0uvfvTrmHykXHdjscxwuPvZCsS5KDMGzIQIYNiY5sLliynMeffo5RI25k6rRXeWf+Yh4b/Ze8sXaA16Y+nvf6ljvv4/RT2tPltI5EIhH++80utm3fwWE1a7Bg8ft5fy10Pu1k5i9exq/P7s7CpR/QpHHDUr3GVJGqGXu8EhXsXwKqufuyAzeY2ewEnTOl3PLwLRxa81ByfshhzJ/GsGvnLqaMmcLNY2+m2/nd+GrjV9x15V2FHmPuf+ZyfMfjGTNjDACLZy9mwRsLSqP7kmB33PsP6tetw4VZvweiGfmQQRcW2D4jI4Mbhl7G4GtvBodWRx3Jub0zARh80XkMv+0ennzmBapWqcxtN11XKteQaiKe3pm9eRm9wHQfxpGD8+KSB5PdBSmDKtQ+wn7qMS5ock7cMeepNc//5POVNn2DVkQEjdmLiISCxuxFREIgVZdBiJeCvYgIGsYREQmFdJ+No2AvIoKGcUREQkE3aEVEQkBj9iIiIaBhHBGRECirqwmUFD28REQEiOBxl6KY2TAzW2lmK8zsaTOrbGbNzGy+mX1qZs+YWcWgbaXgfXawvWnMcW4O6leZWfeCzhcPBXsREUruGbRm1hC4Bmjn7scCGUA/YBRwv7s3J7oi8OBgl8HAdnc/Erg/aIeZtQr2OwbIBMaY2f4PMigGBXsREaLDOPGWOJQHqphZeaAq8CXQGZgabH8C6Bu87hO8J9jexcwsqJ/s7nvcfTWQDbQ/2OtTsBcRoeQye3ffANwLrCUa5HcCi4Ed7p4TNFsP7HuwQENgXbBvTtC+Vmx9PvsUm4K9iAjFewZt7CNUg5K17zhmVpNoVt4MaAD8DOiR7ymDXQrYVlD9QdFsHBERirdcQuwjVPPRFVjt7l8BmNm/gY5ADTMrH2TvjYCNQfv1QGNgfTDsUx3YFlO/T+w+xabMXkSEkhvGITp808HMqgZj712AD4FZwLlBmwHAi8HracF7gu1vevTGwDSgXzBbpxnQHDjoR9EpsxcRoeS+VOXu881sKrAEyAGWEv0r4D/AZDO7M6h7LNjlMeBJM8smmtH3C46z0syeJfpBkQMMdffIwfZLwV5EhJL9UpW7jwBGHFD9OfnMpnH33cBvCzjOSGBkSfRJwV5EBC2XICISCloITUQkBCKe3oscK9iLiJD+C6Ep2IuIoDF7EZFQ0Ji9iEgI5GoYR0Qk/SmzFxEJAc3GEREJAQ3jiIiEgIZxRERCQJm9iEgIKLMXEQmByMGvHpwSFOxFRNByCSIioaDlEkREQkCZvYhICGg2johICGg2johICGi5BBGRENCYvYhICGjMXkQkBJTZi4iEgObZi4iEgDJ7EZEQ0GwcEZEQ0A1aEZEQ0DCOiEgI6Bu0IiIhoMxeRCQE0n3M3tL90ywdmFmWu49Ldj+kbNHvhRRHuWR3QOKSlewOSJmk3wuJm4K9iEgIKNiLiISAgn1q0Lis5Ee/FxI33aAVEQkBZfYiIiGgYF/GmVmmma0ys2wzuynZ/ZHkM7PxZrbFzFYkuy+SOhTsyzAzywAeAnoArYD+ZtYqub2SMuBxIDPZnZDUomBftrUHst39c3ffC0wG+iS5T5Jk7v42sC3Z/ZDUomBftjUE1sW8Xx/UiYgUi4J92Wb51Gn6lIgUm4J92bYeaBzzvhGwMUl9EZEUpmBfti0EmptZMzOrCPQDpiW5TyKSghTsyzB3zwGuBl4DPgKedfeVye2VJJuZPQ28BxxlZuvNbHCy+yRln75BKyISAsrsRURCQMFeRCQEFOxFREJAwV5EJAQU7EVEQkDBXkqcmUXMbJmZrTCzKWZW9Sccq5OZvRS87l3Yyp9mVsPMrjqIc/zZzG442D6KpAIFe0mE7929tbsfC+wFrozdaFHF/t1z92nufnchTWoAxQ72ImGgYC+JNgc40syamtlHZjYGWAI0NrNuZvaemS0J/gKoBnlr+H9sZnOBX+87kJldamYPBq/rmtnzZvZ+UDoCdwO/CP6q+GvQ7g9mttDMlpvZbTHHuiV4TsAbwFGl9v+GSJIo2EvCmFl5omvxfxBUHQVMcPcTgG+BW4Gu7t4GWAT83swqA48AvYBfAfUKOPxo4C13/yXQBlgJ3AR8FvxV8Qcz6wY0J7pUdGugrZmdZmZtiS49cQLRD5MTS/jSRcqc8snugKSlKma2LHg9B3gMaACscfd5QX0Hog9kecfMACoSXQLgaGC1u38KYGYTgax8ztEZuATA3SPATjOreUCbbkFZGryvRjT4HwI87+7fBefQekOS9hTsJRG+d/fWsRVBQP82tgp43d37H9CuNSW3jLMBf3H3hw84x3UleA6RlKBhHEmWecApZnYkgJlVNbMWwMdAMzP7RdCufwH7zwSGBPtmmNmhwDdEs/Z9XgMGxdwLaGhmdYC3gXPMrIqZHUJ0yEgkrSnYS1K4+1fApcDTZracaPA/2t13Ex22+U9wg3ZNAYe4FjjDzD4AFgPHuPvXRIeFVpjZX919BvAU8F7QbipwiLsvAZ4BlgHPER1qEklrWvVSRCQElNmLiISAgr2ISAgo2IuIhICCvYhICCjYi4iEgIK9iEgIKNiLiISAgr2ISAj8PwXDwgGZDaGiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_trainA_w2,y_train,model_temp_ex2,auc_train_ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.89      0.63      4254\n",
      "           1       0.98      0.82      0.89     22078\n",
      "\n",
      "   micro avg       0.83      0.83      0.83     26332\n",
      "   macro avg       0.73      0.86      0.76     26332\n",
      "weighted avg       0.90      0.83      0.85     26332\n",
      "\n",
      "\n",
      "The value of TPR is 0.819\n",
      "The value of TNR is 0.894\n",
      "The value of FPR is 0.106\n",
      "The value of FNR is 0.181\n",
      "\n",
      "The auc score is 0.931552\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#report on training dataset\n",
    "report(d_testA_w2,y_test,model_temp_ex2,auc_test_ex2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+----------------------------+-------------------+-----------+-----------+-----------+--------+----------+\n",
      "| Kernal |     Vectorising Method     |  Best Regularizer | Optimal C | AUC Score | Precision | Recall | F1-Score |\n",
      "+--------+----------------------------+-------------------+-----------+-----------+-----------+--------+----------+\n",
      "| Linear |        Bag Of Words        |         L2        |    1000   |    0.93   |    0.91   |  0.9   |   0.91   |\n",
      "|        |           Tf-Idf           |         L2        |   10000   |    0.96   |    0.93   |  0.91  |   0.91   |\n",
      "|        |           AV-W2V           |         L2        |    100    |   0.905   |    0.88   |  0.81  |   0.83   |\n",
      "|        |         TfIdf-W2V          |         L2        |    100    |    0.88   |    0.87   |  0.78  |   0.81   |\n",
      "|        |                            |                   |           |           |           |        |          |\n",
      "|  RBF   |        Bag Of Words        |         -         |     10    |    0.89   |    0.88   |  0.82  |   0.84   |\n",
      "|        |           Tf-Idf           |         -         |    100    |    0.9    |    0.88   |  0.82  |   0.84   |\n",
      "|        |           AV-W2V           |         -         |     10    |    0.91   |    0.89   |  0.83  |   0.85   |\n",
      "|        |         TfIdf-W2V          |         -         |     10    |    0.89   |    0.88   |  0.82  |   0.83   |\n",
      "|        |                            |                   |           |           |           |        |          |\n",
      "| Linear |   Rev+LenRev with AV-W2V   |         L1        |    1000   |    0.77   |    0.86   |  0.51  |   0.56   |\n",
      "| Linear | Rev+Des+LenRev with AV-W2V |         L1        |   10000   |    0.92   |    0.9    |  0.88  |   0.88   |\n",
      "| Linear |    Rev+Des with AV-W2V     |         L2        |    100    |    0.93   |    0.9    |  0.8   |   0.85   |\n",
      "+--------+----------------------------+-------------------+-----------+-----------+-----------+--------+----------+\n",
      "\n",
      "\n",
      " Note: Auc Score, Precision, Recall and F1- Score are on Test Data\n"
     ]
    }
   ],
   "source": [
    "# Please compare all your models using Prettytable library\n",
    "from prettytable import PrettyTable\n",
    "    \n",
    "pt = PrettyTable()\n",
    "\n",
    "pt.field_names = [\"Kernal\",\"Vectorising Method\",\" Best Regularizer\", \"Optimal C\", \"AUC Score\",\"Precision\", \"Recall\", \"F1-Score\"]\n",
    "\n",
    "pt.add_row([\"Linear\",\"Bag Of Words\",\"L2\", 1000, 0.93, 0.91, 0.90, 0.91 ])\n",
    "pt.add_row([\"\",\"Tf-Idf\",\"L2\", 10000, 0.96, 0.93, 0.91, 0.91 ])\n",
    "pt.add_row([\"\",\"AV-W2V\",\"L2\", 100, 0.905, 0.88, 0.81, 0.83 ])\n",
    "pt.add_row([\"\",\"TfIdf-W2V\",\"L2\", 100, 0.88, 0.87, 0.78, 0.81 ])\n",
    "pt.add_row([\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"])\n",
    "pt.add_row([\"RBF\",\"Bag Of Words\",\"-\", 10, 0.89, 0.88, 0.82, 0.84 ])\n",
    "pt.add_row([\"\",\"Tf-Idf\",\"-\", 100, 0.90, 0.88, 0.82, 0.84 ])\n",
    "pt.add_row([\"\",\"AV-W2V\",\"-\", 10, 0.91, 0.89, 0.83, 0.85 ])\n",
    "pt.add_row([\"\",\"TfIdf-W2V\",\"-\", 10, 0.89, 0.88, 0.82, 0.83 ])\n",
    "pt.add_row([\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"])\n",
    "pt.add_row([\"Linear\",\"Rev+LenRev with AV-W2V\",\"L1\", 1000, 0.77, 0.86, 0.51, 0.56 ])\n",
    "pt.add_row([\"Linear\",\"Rev+Des+LenRev with AV-W2V\",\"L1\", 10000, 0.92, 0.90, 0.88, 0.88 ])\n",
    "pt.add_row([\"Linear\",\"Rev+Des with AV-W2V\",\"L2\", 100, 0.93, 0.90, 0.80, 0.85 ])\n",
    "\n",
    "\n",
    "print(pt)\n",
    "print(\"\\n\\n Note: Auc Score, Precision, Recall and F1- Score are on Test Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) The highest AUC score is achieved after vectorization of reviews using *TF-IDF* method with *L2* (after getting L2 as best regulariser). The AUC score is 0.96.\n",
    "\n",
    "2) *Linear Kernal* is clearly a better kernal than *RBF Kernal* when vectorization of reviews is done using *Bag Of Words* and *TF-IDF*.\n",
    "\n",
    "3) *RBF Kernal* is better in case of *Average Word2Vec* and *TF-IDF weighted Word2Vec* but with a very close margin.\n",
    "\n",
    "4) The best value of C, the hyperparameter, remains same from grid search and from Train vs Test AUC score plot on C.\n",
    "\n",
    "5) Most of the words are common in top 10 neagtive words using Bag of Words and Tf-Idf.\n",
    "\n",
    "6) Most of the words are common in top 10 Positive words using Bag of Words and Tf-Idf.\n",
    "\n",
    "7) The lowest ROC-AUC score is achieved after vectorization by *Tf-Idf weighted Word2Vec* and when *Linear Kernal* is used. The score is 0.88.\n",
    "\n",
    "8) We can say that model is very good in predicting positive reviews looking at the Precision, Recall and F1-score.\n",
    "\n",
    "9) The model performed terribly after adding only length of reviews as a feature. So polarity of reviews is not dependent on length of reviews.\n",
    "\n",
    "10) The AUC score for Average W2V featurization has improved after adding summary and length of reviews as features by 1.8%.\n",
    "\n",
    "11) The AUC score for Average W2V featurization increased by 2.8% after adding the summary as a feature. This also implies  that polarity of reviews is not dependent on length of reviews since, there is a decrease in performance by 1% when length has been added as a feature alongwith summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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