Logistic Regression K fold.ipynb

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    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n",
    "\n",
    "EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n",
    "\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
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   "source": [
    "# [1]. Reading Data"
   ]
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    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it is easier to query the data and visualise the data efficiently.\n",
    "<br> \n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score is above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
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     "text": [
      "/opt/conda/lib/python3.6/site-packages/smart_open/ssh.py:34: UserWarning: paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress\n",
      "  warnings.warn('paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress')\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",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from time import time\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (100000, 10)\n"
     ]
    },
    {
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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>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                        ...                                                                       Text\n",
       "0   1                        ...                          I have bought several of the Vitality canned d...\n",
       "1   2                        ...                          Product arrived labeled as Jumbo Salted Peanut...\n",
       "2   3                        ...                          This is a confection that has been around a fe...\n",
       "\n",
       "[3 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "# using SQLite Table to read data.\n",
    "con = sqlite3.connect('../input/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)"
   ]
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   "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)"
   ]
  },
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   "cell_type": "code",
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    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80668, 7)\n"
     ]
    },
    {
     "data": {
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       "<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   ...    COUNT(*)\n",
       "0  #oc-R115TNMSPFT9I7   ...           2\n",
       "1  #oc-R11D9D7SHXIJB9   ...           3\n",
       "2  #oc-R11DNU2NBKQ23Z   ...           2\n",
       "3  #oc-R11O5J5ZVQE25C   ...           3\n",
       "4  #oc-R12KPBODL2B5ZD   ...           2\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
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       "  <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",
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       "              UserId   ...    COUNT(*)\n",
       "80638  AZY10LLTJ71NX   ...           5\n",
       "\n",
       "[1 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pSwF4sdoLPmM",
    "outputId": "2f3bd7f2-0eea-45f7-eab6-bf08c630d9a3"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "393063"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "display['COUNT(*)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PuNkhjz0LPmQ"
   },
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "oCJaTWrTLPmS"
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   "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:"
   ]
  },
  {
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   "execution_count": 7,
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    "scrolled": false
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   "outputs": [
    {
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       "  <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",
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       "      <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                        ...                                                                       Text\n",
       "0   78445                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "1  138317                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "2  138277                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "3   73791                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "4  155049                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "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": {
    "colab_type": "text",
    "id": "uT7p9_deLPmb"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "mdsKFc50LPme"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "2itLgX-NLPmk",
    "outputId": "ac765625-dfdc-4e4e-f886-bd3b7ca3635a"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "IBU4iZk4LPmt",
    "outputId": "ceeba223-fe3f-407a-b4e5-0d46c08f69dd"
   },
   "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": {
    "colab_type": "text",
    "id": "gFBOfSe4LPmy"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "50flE40lLPmz",
    "outputId": "b6c7e38e-25c1-4a48-be1f-70fdf31aba13"
   },
   "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                        ...                                                                       Text\n",
       "0  64422                        ...                          My son loves spaghetti so I didn't hesitate or...\n",
       "1  44737                        ...                          It was almost a 'love at first bite' - the per...\n",
       "\n",
       "[2 rows x 10 columns]"
      ]
     },
     "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": {
    "colab": {},
    "colab_type": "code",
    "id": "0YTPJMLTLPm7"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LVJ_l-x2LPnC",
    "outputId": "d4a19c7a-16fe-4b5b-c3d1-aa1ef0f948a0"
   },
   "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": {
    "colab_type": "text",
    "id": "GrTYWzYrLPnH"
   },
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zbvLitZeLPnI"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "yYDc0ZXALPnK",
    "outputId": "5403fab9-796a-493d-f9d3-2a410d004f64"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "AxYh-wPiLPnS",
    "outputId": "31bddcb2-6c52-4b16-f9fb-c03d5b3bb7c8"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "JMSSWjZqLPne",
    "outputId": "eb1bae4e-6f37-45ee-84a3-ef3bebf4e30a"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "BEtpMkhULPnt"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "GavAnzNjLPnx",
    "outputId": "517fca9a-b1b6-4c6c-ab33-4c5ac4a5bd62"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "Kl8nOoL-LPn3",
    "outputId": "16742d57-2529-4fc0-9d28-e9332dbbca97"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "EGB6X2uxLPn7",
    "outputId": "dbd754e5-eb73-4b3a-f3c3-7b1b40952851"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "CCurSJJ2LPn_"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "mFHQEvfnLPoF",
    "outputId": "5cd6bcc3-54fe-4a8e-a39d-d6dabf3f37f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 87773/87773 [00:31<00:00, 2807.12it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above cells \n",
    "preprocessed_reviews = []\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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9IuKp2HULPoL"
   },
   "source": [
    "<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qk7gYOmKLPoM"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 87773/87773 [00:22<00:00, 3936.66it/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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'made china'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_summary[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [3.].  Split Dataset into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B93jqKuhLPoQ"
   },
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mr4j2QhgLPoQ"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dZVHt3mtLPoR",
    "outputId": "ea1629cd-7c92-400e-9acd-f7caeef0f389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaa', 'aaaaa', 'aaaaaaaaaaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaarrrrrggghhh', 'aaaaah', 'aaaah', 'aaaand']\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": 27,
   "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  (70218, 49031)\n",
      "the shape of test-text BOW vectorizer  (17555, 49031)\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": {
    "colab_type": "text",
    "id": "tSf5kRdLLPoV"
   },
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yfk2d7bLLPoW",
    "outputId": "33472132-5eb3-493f-b08c-081749f99239"
   },
   "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": 29,
   "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  (70218, 5000)\n",
      "the shape of test-text BOW vectorizer  (17555, 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": {
    "colab_type": "text",
    "id": "-cEEpDx0LPoZ"
   },
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TgP61HqBLPoa",
    "outputId": "15f1c123-d93b-414d-914b-d4e3f32a6ac1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aa', 'aafco', 'aback', 'abandon', 'abdominal', 'ability', 'able', 'able add', 'able buy', 'able chew']\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": 31,
   "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  (70218, 41295)\n",
      "the shape of test-text BOW vectorizer  (17555, 41295)\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": {
    "colab_type": "text",
    "id": "m3YEHTLPLPod"
   },
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zwPZCf8cLPoe"
   },
   "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": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8ddGZNUfLPog",
    "outputId": "390d4ccb-cda2-4391-83e9-0e502b578171",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('good', 0.8297212719917297), ('awesome', 0.8250125050544739), ('fantastic', 0.821380615234375), ('excellent', 0.8032631874084473), ('wonderful', 0.793063223361969), ('terrific', 0.7889218330383301), ('amazing', 0.750499427318573), ('perfect', 0.7422112226486206), ('nice', 0.7071204781532288), ('decent', 0.688544750213623)]\n",
      "==================================================\n",
      "[('greatest', 0.7528401017189026), ('best', 0.7175250053405762), ('closest', 0.6731676459312439), ('tastiest', 0.6690865159034729), ('ive', 0.6576330661773682), ('nastiest', 0.6529994606971741), ('smoothest', 0.6471142172813416), ('softest', 0.6301393508911133), ('disgusting', 0.606341540813446), ('worse', 0.5917190909385681)]\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": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bRWXmmnWLPoo",
    "outputId": "1cc345e7-d991-49b8-f1b7-a0d81f0b97e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  15666\n",
      "sample words  ['needless', 'say', 'saw', 'instantly', 'started', 'remembering', 'gummi', 'peach', 'rings', 'local', 'pizza', 'polar', 'young', 'boy', 'even', 'tasted', 'first', 'time', 'years', 'gave', 'bag', 'one', 'cousins', 'must', 'thats', 'many', 'think', 'happy', 'thought', 'childhood', 'small', 'tear', 'eye', 'almost', 'brought', 'back', 'happiness', 'youth', 'girls', 'past', 'broken', 'hearts', 'hassle', 'worry', 'everyday', 'life', 'could', 'see', 'people', 'outside']\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": {
    "colab_type": "text",
    "id": "TtWqibqfLPos"
   },
   "source": [
    "## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JriP5nZCLPot"
   },
   "source": [
    "#### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UnXI9NHTLPot",
    "outputId": "ecbe8256-9718-4bad-de33-e5ce1ee69038"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 70218/70218 [02:22<00:00, 493.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70218\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17555/17555 [00:37<00:00, 462.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17555\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": {
    "colab_type": "text",
    "id": "2sHfb529LPow"
   },
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sb_wDjAqLPow"
   },
   "outputs": [],
   "source": [
    "# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\n",
    "model = TfidfVectorizer()\n",
    "tf_idf_matrix = model.fit_transform(preprocessed_reviews)\n",
    "# we are converting a dictionary with word as a key, and the idf as a value\n",
    "dictionary = dict(zip(model.get_feature_names(), list(model.idf_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3VrUzqx7LPoy",
    "outputId": "8480d5da-7b1d-4bbe-a4c9-fe0a5800b5d0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 70218/70218 [32:05<00:00, 36.47it/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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17555/17555 [08:05<00:00, 36.14it/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": {
    "colab_type": "text",
    "id": "Kxx72u1hLPo1"
   },
   "source": [
    "# [5] Assignment 5: Apply Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KDR9We0oLPo2"
   },
   "source": [
    "<ol>\n",
    "    <li><strong>Apply Logistic Regression 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>Hyper paramter tuning (find best hyper parameters corresponding the algorithm that you choose)</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>Pertubation Test</strong>\n",
    "        <ul>\n",
    "    <li>Get the weights W after fit your model with the data X i.e Train data.</li>\n",
    "    <li>Add a noise to the X (X' = X + e) and get the new data set X' (if X is a sparse\n",
    "matrix, X.data+=e)</li>\n",
    "    <li>Fit the model again on data X' and get the weights W'</li>\n",
    "    <li>Add a small eps value(to eliminate the divisible by zero error) to W and W’ i.e\n",
    "W=W+10^-6 and W’ = W’+10^-6</li>\n",
    "    <li>Now find the % change between W and W' (| (W-W') / (W) |)*100)</li>\n",
    "    <li>Calculate the 0th, 10th, 20th, 30th, ...100th percentiles, and observe any sudden rise in the values of percentage_change_vector</li>\n",
    "    <li> Ex: consider your 99th percentile is 1.3 and your 100th percentiles are 34.6, there is sudden rise from 1.3 to 34.6, now calculate the 99.1, 99.2, 99.3,..., 100th percentile values and get the proper value after which there is sudden rise the values, assume it is 2.5</li>\n",
    "            <li> Print the feature names whose % change is more than a threshold x(in our example it's 2.5)</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Sparsity</strong>\n",
    "        <ul>\n",
    "    <li>Calculate sparsity on weight vector obtained after using L1 regularization</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br><font color='red'>NOTE: Do sparsity and multicollinearity for any one of the vectorizers. Bow or tf-idf is recommended.</font>\n",
    "    <br>\n",
    "    <br>\n",
    "    <li><strong>Feature importance</strong>\n",
    "        <ul>\n",
    "    <li>Get top 10 important features for both positive and negative classes separately.</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": {
    "colab_type": "text",
    "id": "gilb-DOVLPo2"
   },
   "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": {
    "colab_type": "text",
    "id": "jiYocvSHLPo4"
   },
   "source": [
    "# Applying Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LE_XPVZJLPo4"
   },
   "source": [
    "## [5.1] Logistic Regression on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_iciJvj2LPo4"
   },
   "source": [
    "### [5.1.1] Applying Logistic Regression with L1 regularization on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MyOVi-xYLPo5"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "def cv_lr_grid(d_train, y_train, regularizer):\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],'penalty':[regularizer]}\n",
    "\n",
    "    #Using Grid Search to get Best C=1/alpha\n",
    "    start = time()\n",
    "    lr= LogisticRegression(class_weight='balanced')\n",
    "    clf = GridSearchCV(lr,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_alpha_grid=clf.best_params_.get('C')\n",
    "    return optimal_alpha_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot confusion matrix and print report\n",
    "def report(x,y,model):\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 roc auc score is %f\" %roc_auc_score(y_true, model.predict_proba(x)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to test multicollinearity\n",
    "def pertubation(w_old,c,d_train,y_train):\n",
    "    d_train_new=d_train\n",
    "    d_train_new.data=d_train.data+0.00001\n",
    "    lr_l2=LogisticRegression(penalty='l2',C=c)\n",
    "    lr_l2.fit(d_train_new,y_train)\n",
    "    w_new = lr_l2.coef_\n",
    "    #to eliminate division by zero\n",
    "    w_old=w_old+0.000001\n",
    "    w_new=w_new+0.000001\n",
    "    change=((abs(w_old-w_new))/w_old)*100\n",
    "    print (change.shape)\n",
    "    return change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "##plot inspired from this blog : https://medium.com/@aneesha/visualising-top-features-in-linear-svm-with-scikit-learn-and-matplotlib-3454ab18a14d\n",
    "def plot_TopWords(classifier, feature_names, top_features): \n",
    "    coef = classifier.coef_.ravel()\n",
    "    top_positive_coefficients = np.argsort(coef)[-top_features:]\n",
    "    top_negative_coefficients = np.argsort(coef)[:top_features]\n",
    "    top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])\n",
    "    # create plot\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    colors = ['red' if c < 0 else 'green' for c in coef[top_coefficients]]\n",
    "    plt.bar(np.arange(2 * top_features), coef[top_coefficients], color=colors)\n",
    "    feature_names = np.array(feature_names)\n",
    "    plt.xticks(np.arange(1, 1 + 2 * top_features), feature_names[top_coefficients], rotation=60, ha='right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotROC(d_trai,y_trai,d_tes,y_tes,model):\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",
    "    roc_auc_train = auc(fpr_train, tpr_train)\n",
    "    roc_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' % roc_auc_train)\n",
    "    plt.plot(fpr_test, tpr_test, label = 'AUC Test = %0.2f' % roc_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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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=LogisticRegression(penalty=penalty,C=i,class_weight='balanced')\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.ylim([0, 1])\n",
    "    plt.xlabel('Log OF C')\n",
    "    plt.ylabel('AUC Score')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_grid=cv_lr_grid(d_train,y_train,'l1')\n",
    "c_grid=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.98      0.85     11345\n",
      "           1       1.00      0.94      0.97     58873\n",
      "\n",
      "   micro avg       0.95      0.95      0.95     70218\n",
      "   macro avg       0.88      0.96      0.91     70218\n",
      "weighted avg       0.96      0.95      0.95     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.940\n",
      "The value of TNR is 0.976\n",
      "The value of FPR is 0.024\n",
      "The value of FNR is 0.060\n",
      "\n",
      "The roc auc score is 0.989504\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L1 regulariser\n",
    "lr_l1_bow=LogisticRegression(penalty='l1',C=1,class_weight='balanced')\n",
    "lr_l1_bow.fit(d_train,y_train)\n",
    "report(d_train,y_train,lr_l1_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [54:30<00:00, 842.89s/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(d_train,y_train,d_test,y_test,\"l1\")"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot roc curve for train and test data\n",
    "plotROC(d_train,y_train,d_test,y_test,lr_l1_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.81      0.73      2836\n",
      "           1       0.96      0.92      0.94     14719\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     17555\n",
      "   macro avg       0.81      0.87      0.83     17555\n",
      "weighted avg       0.91      0.90      0.90     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.917\n",
      "The value of TNR is 0.815\n",
      "The value of FPR is 0.185\n",
      "The value of FNR is 0.083\n",
      "\n",
      "The roc auc score is 0.934031\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test Data\n",
    "report(d_test,y_test,lr_l1_bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5h4NhywcLPo7"
   },
   "source": [
    "#### [5.1.1.1] Calculating sparsity on weight vector obtained using L1 regularization on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-mmdLzUALPo-"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#getting roc_auc_score, balanced accuracy score and sparcity of model\n",
    "def score_sparcity(c,d_train,y_train):\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "    lr_l1=LogisticRegression(penalty='l1',C=c)\n",
    "    lr_l1.fit(d_train,y_train)\n",
    "    w = lr_l1.coef_\n",
    "    y_true, y_pred = y_train, lr_l1.predict_proba(d_train)[:,1]\n",
    "    print(\"The roc auc score is %f\" %roc_auc_score(y_true, y_pred))\n",
    "    print(\"The Sparcity is %d\" %np.count_nonzero(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.999954\n",
      "The Sparcity is 20389\n"
     ]
    }
   ],
   "source": [
    "#c=1/alpha\n",
    "c=1000\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.999888\n",
      "The Sparcity is 12950\n"
     ]
    }
   ],
   "source": [
    "c=100\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.998605\n",
      "The Sparcity is 10830\n"
     ]
    }
   ],
   "source": [
    "c=10\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.983597\n",
      "The Sparcity is 5080\n"
     ]
    }
   ],
   "source": [
    "c=1\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.947652\n",
      "The Sparcity is 956\n"
     ]
    }
   ],
   "source": [
    "c=0.1\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.890877\n",
      "The Sparcity is 141\n"
     ]
    }
   ],
   "source": [
    "c=0.01\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.732088\n",
      "The Sparcity is 6\n"
     ]
    }
   ],
   "source": [
    "c=0.001\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The roc auc score is 0.500000\n",
      "The Sparcity is 0\n"
     ]
    }
   ],
   "source": [
    "c=0.0001\n",
    "score_sparcity(c,d_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aRHMa52qLPpA"
   },
   "source": [
    "### [5.1.2] Applying Logistic Regression with L2 regularization on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GBbb9lxlLPpB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 402.81 Seconds\n",
      " optimal alpha using Grid Search is {'C': 0.01, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function with penalty = l2\n",
    "c_grid_l2=cv_lr_grid(d_train,y_train,'l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.90      0.72     11345\n",
      "           1       0.98      0.89      0.93     58873\n",
      "\n",
      "   micro avg       0.89      0.89      0.89     70218\n",
      "   macro avg       0.79      0.89      0.83     70218\n",
      "weighted avg       0.92      0.89      0.90     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.888\n",
      "The value of TNR is 0.897\n",
      "The value of FPR is 0.103\n",
      "The value of FNR is 0.112\n",
      "\n",
      "The roc auc score is 0.955622\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L2 regulariser\n",
    "lr_l2_bow=LogisticRegression(penalty='l2',C=c_grid_l2,class_weight='balanced')\n",
    "lr_l2_bow.fit(d_train,y_train)\n",
    "report(d_train,y_train,lr_l2_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [01:42<00:00, 18.87s/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(d_train,y_train,d_test,y_test,\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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",
    "plotROC(d_train,y_train,d_test,y_test,lr_l2_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.87      0.70      2836\n",
      "           1       0.97      0.88      0.93     14719\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     17555\n",
      "   macro avg       0.78      0.88      0.81     17555\n",
      "weighted avg       0.91      0.88      0.89     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.882\n",
      "The value of TNR is 0.869\n",
      "The value of FPR is 0.131\n",
      "The value of FNR is 0.118\n",
      "\n",
      "The roc auc score is 0.939782\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test Data\n",
    "report(d_test,y_test,lr_l2_bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UaJkugTKLPpE"
   },
   "source": [
    "#### [5.1.2.1] Performing pertubation test (multicollinearity check) on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E1c5B1nELPpF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 49031)\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the pertubation function with \n",
    "lr_l2_bow=LogisticRegression(penalty='l2',C=c_grid_l2)\n",
    "lr_l2_bow.fit(d_train,y_train)\n",
    "w_old = lr_l2_bow.coef_\n",
    "change=pertubation(w_old,c_grid_l2,d_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Creating a dictionary of features and the corresponding weights\n",
    "feature_names = count_vect.get_feature_names()\n",
    "feature_dict = dict()\n",
    "for weight, feature in zip(w_old[0], feature_names):\n",
    "    feature_dict[weight] = feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 0th percentile is -2.301190 and difference is -2.301190\n",
      "\n",
      "The 10th percentile is -0.000932 and difference is 2.300258\n",
      "\n",
      "The 20th percentile is -0.000492 and difference is 0.000440\n",
      "\n",
      "The 30th percentile is 0.000004 and difference is 0.000496\n",
      "\n",
      "The 40th percentile is 0.000174 and difference is 0.000170\n",
      "\n",
      "The 50th percentile is 0.000343 and difference is 0.000169\n",
      "\n",
      "The 60th percentile is 0.000514 and difference is 0.000170\n",
      "\n",
      "The 70th percentile is 0.000694 and difference is 0.000181\n",
      "\n",
      "The 80th percentile is 0.000899 and difference is 0.000205\n",
      "\n",
      "The 90th percentile is 0.001211 and difference is 0.000312\n",
      "\n",
      "The 100th percentile is 0.499700 and difference is 0.498489\n"
     ]
    }
   ],
   "source": [
    "#getting 10th, 20th, .... Percentiles\n",
    "x_0=0\n",
    "for i in range(0,101,10):\n",
    "    x_1=np.percentile(change,i)-x_0\n",
    "    print()\n",
    "    print(\"The %dth percentile is %f and difference is %f\"%(i,np.percentile(change,i),x_1))\n",
    "    x_0=np.percentile(change,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 90th percentile is 0.001211 and difference is 0.001211\n",
      "\n",
      "The 91th percentile is 0.001263 and difference is 0.000052\n",
      "\n",
      "The 92th percentile is 0.001324 and difference is 0.000061\n",
      "\n",
      "The 93th percentile is 0.001394 and difference is 0.000070\n",
      "\n",
      "The 94th percentile is 0.001494 and difference is 0.000100\n",
      "\n",
      "The 95th percentile is 0.001609 and difference is 0.000115\n",
      "\n",
      "The 96th percentile is 0.001778 and difference is 0.000169\n",
      "\n",
      "The 97th percentile is 0.002044 and difference is 0.000266\n",
      "\n",
      "The 98th percentile is 0.002482 and difference is 0.000439\n",
      "\n",
      "The 99th percentile is 0.004079 and difference is 0.001597\n",
      "\n",
      "The 100th percentile is 0.499700 and difference is 0.495621\n"
     ]
    }
   ],
   "source": [
    "#getting 90th, 91st, 92nd .. percentiles\n",
    "#since there is a sudden change from 90th to 100th percentile\n",
    "x_0=0\n",
    "for i in range(90,101):\n",
    "    x_1=np.percentile(change,i)-x_0\n",
    "    print()\n",
    "    print(\"The %dth percentile is %f and difference is %f\"%(i,np.percentile(change,i),x_1))\n",
    "    x_0=np.percentile(change,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 99.0th percentile is 0.004079 and difference is 0.004079\n",
      "\n",
      "The 99.1th percentile is 0.004485 and difference is 0.000405\n",
      "\n",
      "The 99.2th percentile is 0.005148 and difference is 0.000663\n",
      "\n",
      "The 99.3th percentile is 0.005719 and difference is 0.000571\n",
      "\n",
      "The 99.4th percentile is 0.006641 and difference is 0.000922\n",
      "\n",
      "The 99.5th percentile is 0.008052 and difference is 0.001411\n",
      "\n",
      "The 99.6th percentile is 0.010323 and difference is 0.002271\n",
      "\n",
      "The 99.7th percentile is 0.013851 and difference is 0.003528\n",
      "\n",
      "The 99.8th percentile is 0.019639 and difference is 0.005787\n",
      "\n",
      "The 99.9th percentile is 0.039889 and difference is 0.020250\n",
      "\n",
      "The 100.0th percentile is 0.499700 and difference is 0.459811\n"
     ]
    }
   ],
   "source": [
    "#getting 99.0th, 99.1th, 99.2th, 99.3th Percentiles\n",
    "x_0=0\n",
    "for i in range(990,1001):\n",
    "    x_1=np.percentile(change,i/10)-x_0\n",
    "    print()\n",
    "    print(\"The %.1fth percentile is %f and difference is %f\"%(i/10,np.percentile(change,i/10),x_1))\n",
    "    x_0=np.percentile(change,i/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating list of weights whose %change in weight is \n",
    "#more than the threshold value i.e. 0.039889\n",
    "list_=list()\n",
    "for i,j in zip(change[0],w_old[0]):\n",
    "    if(i > 0.039889):\n",
    "        list_.append(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bragged', 'sw', 'handed', 'nectar', 'amidst', 'enhancers', 'tmj', 'redken', 'crispness', 'magic', 'ammonia', 'orgainic', 'saffron', 'resounding', 'caloriesi', 'labrador', 'mauritius', 'choir', 'eve', 'supremely', 'vitamins', 'squeak', 'beers', 'purports', 'infuse', 'random', 'malted', 'jamaica', 'ing', 'condenser', 'pectin', 'fur', 'gobs', 'increments', 'rightfully', 'push', 'coughing', 'ohh', 'hoo', 'seasonings', 'clearer', 'youtube', 'zipfizz', 'aw', 'israel', 'tasts', 'teabag', 'lifted', 'dinos', 'oxidized'}\n"
     ]
    }
   ],
   "source": [
    "##features whose %change in weight is more than the threshold value\n",
    "fea=set()\n",
    "for i in list_:\n",
    "    fea.add(feature_dict.get(i))\n",
    "print(fea)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eB1rF0BQLPpG"
   },
   "source": [
    "### [5.1.3] Feature Importance on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JZTP19dqLPpI"
   },
   "source": [
    "#### [5.1.3.1] Top 10 important features of positive class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oGRpCVJgLPpI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0.6114088905170614, 'highly'), (0.6204213399892601, 'amazing'), (0.6457291750363358, 'nice'), (0.6987319405092132, 'wonderful'), (0.7474323615936173, 'excellent'), (0.7753629608468008, 'loves'), (0.8219826824207692, 'best'), (0.8321915383804527, 'perfect'), (0.9469996563341293, 'great'), (0.9886581019312854, 'delicious')]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "##mapping features and weights\n",
    "##sorting by weights (indices) and getting the last 10 weights\n",
    "pos = sorted(zip(w_old[0], feature_names))\n",
    "print(pos[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UJLYBE1gLPpJ"
   },
   "source": [
    "#### [5.1.3.2] Top 10 important features of negative class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c9r7Zjx1LPpK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-0.9407378041343881, 'disappointed'), (-0.8353100302464732, 'worst'), (-0.7798317335530787, 'disappointing'), (-0.7695282039722928, 'terrible'), (-0.7664644826647105, 'awful'), (-0.7203113139346708, 'horrible'), (-0.6638935781233972, 'money'), (-0.6430922110271099, 'waste'), (-0.6404387467407188, 'unfortunately'), (-0.6072880167497035, 'threw')]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "##mapping features and weights\n",
    "##sorting by weights (indices) and getting the first 10 weights\n",
    "pos = sorted(zip(w_old[0], feature_names))\n",
    "print(pos[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_TopWords(lr_l2_bow,feature_names,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nKn3VXcZLPpN"
   },
   "source": [
    "## [5.2] Logistic Regression on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "naTBSzm6LPpN"
   },
   "source": [
    "### [5.2.1] Applying Logistic Regression with L1 regularization on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PEhaagXXLPpS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 50.48 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_tf as training dataset\n",
    "#and regulariser as L1\n",
    "c_grid_tf_l1=cv_lr_grid(d_train_tf,y_train,'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.93      0.80     11345\n",
      "           1       0.99      0.92      0.95     58873\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     70218\n",
      "   macro avg       0.84      0.93      0.87     70218\n",
      "weighted avg       0.94      0.92      0.93     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.921\n",
      "The value of TNR is 0.934\n",
      "The value of FPR is 0.066\n",
      "The value of FNR is 0.079\n",
      "\n",
      "The roc auc score is 0.978268\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L1 regulariser\n",
    "lr_l1_tf=LogisticRegression(penalty='l1',C=c_grid_tf_l1,class_weight='balanced')\n",
    "lr_l1_tf.fit(d_train_tf,y_train)\n",
    "report(d_train_tf,y_train,lr_l1_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:13<00:00,  2.12s/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(d_train_tf,y_train,d_test_tf,y_test,\"l1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
    "plotROC(d_train_tf,y_train,d_test_tf,y_test,lr_l1_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.89      0.76      2836\n",
      "           1       0.98      0.91      0.94     14719\n",
      "\n",
      "   micro avg       0.91      0.91      0.91     17555\n",
      "   macro avg       0.82      0.90      0.85     17555\n",
      "weighted avg       0.93      0.91      0.91     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.911\n",
      "The value of TNR is 0.889\n",
      "The value of FPR is 0.111\n",
      "The value of FNR is 0.089\n",
      "\n",
      "The roc auc score is 0.962558\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test Data\n",
    "report(d_test_tf,y_test,lr_l1_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NdFShHQJLPpV"
   },
   "source": [
    "### [5.2.2] Applying Logistic Regression with L2 regularization on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jyhq3JDzLPpW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 75.89 Seconds\n",
      " optimal alpha using Grid Search is {'C': 10, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_tf as training dataset\n",
    "#and regulariser as L2\n",
    "c_grid_tf=cv_lr_grid(d_train_tf,y_train,'l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95     11345\n",
      "           1       1.00      0.98      0.99     58873\n",
      "\n",
      "   micro avg       0.98      0.98      0.98     70218\n",
      "   macro avg       0.95      0.99      0.97     70218\n",
      "weighted avg       0.98      0.98      0.98     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.978\n",
      "The value of TNR is 0.998\n",
      "The value of FPR is 0.002\n",
      "The value of FNR is 0.022\n",
      "\n",
      "The roc auc score is 0.998795\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L2 regulariser\n",
    "lr_l2_tf=LogisticRegression(penalty='l2',C=c_grid_tf,class_weight='balanced')\n",
    "lr_l2_tf.fit(d_train_tf,y_train)\n",
    "report(d_train_tf,y_train,lr_l2_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:22<00:00,  4.23s/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(d_train_tf,y_train,d_test_tf,y_test,\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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",
    "plotROC(d_train_tf,y_train,d_test_tf,y_test,lr_l2_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.84      0.79      2836\n",
      "           1       0.97      0.94      0.96     14719\n",
      "\n",
      "   micro avg       0.93      0.93      0.93     17555\n",
      "   macro avg       0.85      0.89      0.87     17555\n",
      "weighted avg       0.93      0.93      0.93     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.943\n",
      "The value of TNR is 0.842\n",
      "The value of FPR is 0.158\n",
      "The value of FNR is 0.057\n",
      "\n",
      "The roc auc score is 0.964570\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test Data\n",
    "report(d_test_tf,y_test,lr_l2_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xeeJ8eVvLPpX"
   },
   "source": [
    "### [5.2.3] Feature Importance on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cj9XEmqQLPpY"
   },
   "source": [
    "#### [5.2.3.1] Top 10 important features of positive class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RG10txygLPpb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(10.691800064577436, 'korr'), (10.799203403467859, 'immaculate'), (11.690776618810952, 'spraying'), (12.076925918748461, 'alginate'), (12.675770668052566, 'mired'), (13.9505875794831, 'fantabulous'), (14.678643916081539, 'avocados'), (15.790187242933294, 'lens'), (17.35913405815587, 'compromises'), (18.14717715046899, 'fitzroy')]\n"
     ]
    }
   ],
   "source": [
    "w_ = lr_l2_tf.coef_\n",
    "feature_names_tf= tf_idf_vect.get_feature_names()\n",
    "##mapping features and weights\n",
    "##sorting by weights (indices) and getting the last 10 weights\n",
    "pos = sorted(zip(w_[0], feature_names))\n",
    "print(pos[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PowEfns4LPpg"
   },
   "source": [
    "#### [5.2.3.2] Top 10 important features of negative class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G7jrma95LPpg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-0.9407378041343881, 'disappointed'), (-0.8353100302464732, 'worst'), (-0.7798317335530787, 'disappointing'), (-0.7695282039722928, 'terrible'), (-0.7664644826647105, 'awful'), (-0.7203113139346708, 'horrible'), (-0.6638935781233972, 'money'), (-0.6430922110271099, 'waste'), (-0.6404387467407188, 'unfortunately'), (-0.6072880167497035, 'threw')]\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "##mapping features and weights\n",
    "##sorting by weights (indices) and getting the first 10 weights\n",
    "pos = sorted(zip(w_old[0], feature_names))\n",
    "print(pos[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EM1JmGRErR8QS7eO7AWsBP56J55IkSZKkxdHCHk2wU0RcDmwMnBMRn2hf2gz4TkR8C/gAsE9mXr1woUqSJEmSJha2m+VZwFkLePwM4IyF+dm6idybJkmSJC2WFvacucWDCZMkSZKkKTOTRxNIkiRJkmaIyZwkSZIkjZDJnCRJkiSNkMmcJEmSJI2QyZwkSZIkjZDJnCRJkiSNkMmcJEmSJI2QyZwkSZIkjZDJnCRJkiSNkMmcJEmSJI2QyZwkSZIkjVBk5tAx/FtEXAVcNnQci5HbA78ZOogbMK2xGdfNN62xTWtcML2xTWtcML2xGdfNN62xTWtcML2xTWtcML2xGdfNN62xLUxcd8nMlW/KN05VMqd+RcTczJwzdBwLMq2xGdfNN62xTWtcML2xTWtcML2xGdfNN62xTWtcML2xTWtcML2xGdfNN62x9RWXZZaSJEmSNEImc5IkSZI0QiZzi7fjhg7gRkxrbMZ1801rbNMaF0xvbNMaF0xvbMZ1801rbNMaF0xvbNMaF0xvbMZ1801rbL3E5Z45SZIkSRohV+YkSZIkaYRM5iRJkiRphEzmJEm6hYqIGDoGSePg9WKcTOZuYSJiqYhYaeg4xiYiZrX/3i4ilhs6nomIWDsi7jx0HP/N5PWbBhFx36Fj0KIxGVhExJJDxzImEbHE5OOc0o3xDhpvvml7zSJimaFjGIvu724Kf4//vn9P6/Vi2sz/Oxz6dzo1AzAtMs8Gjo+Ih0bEskMH03VDb/ZpSAQy81/tw+OBHYaMZSIiZgNPB54ZEY+ethtnZ6C9TOf1G1RE3B84JCIOiIi7DR1PV2fCYFZErDB0PF2d2FYcOpauzsDi2RGx/qDBdHRerztHxNQdVJuZ1wFExIdajFMxeOzGMS2Dxs51bHZE3Lddd6fC/L+3aXnNACJiT+C1EbFuRCw1dDwTk4mMiFgxIu4w39eG/DuYvM+eAjx0wDj+w+T+HRGHRsQdJ48Pfd3o/C53iIh7dx4f/Ho2+VuMiB26nw9l8EG0FrljgPOAlwEHR8R6UzSrPfnD3DQito+IHeF6F5JB/kA7N/MHAX/NzFPa50O/bv8C3g9cCzyBGtA+YNiQSkQskZkZERsDH4yIWw8dU/Nd4GRgJeDAiHhaRCw/cEzA9SYMDgfeHBHfioiNJl8f8gbVie2giHhvN66hdBKmJwCPAb49bETzdF6vJwEPguuvhg35u+y8bjsAf87MnzMl9/rOAGiXiHhi+/uclomNpwCvAx4XEXcdOBbg+gPGiDgiInablusZ8AVgKeBVwL4RscqUDLKvax++BbgvzPubGGrAHRGzMvNfEXFP4KXUfYqIeMDQk1SdhGlfYL3MvCIiloiIdYZMUNprdl1ErAwcQruGRcQqnb+LQa+zbbLgiIh4aVRV3GDX2am4wGvRaG/+azLzNcCewLLAm6mVnTsNvQKWmf+MiHWAM4AHA7tHxOkRsUn7+iAXjs7zbg7sGhGvao9fC8OsHEZEZOa/MvMbwHrACsCjgSdGxJ4RcZe+Y+rq3DAPB96VmX+NiK0j4hkRseoQMUXE04HdM/Ns4LXAV4ANgcMi4tFDxNSJbXIj2hq4P/AMYHXg91GWHHCgMbmZbwOsAqxGrW6+LiLuNERMzeT1eCxwaJs8WBogIu7ariW9i4hNOrPEc4F7w/VWw5YdchDUBo1BJZp3iYjlO7EtceP/98zpvM/2AHamkuC92mPLDHSdndXeV+sBjwf+TlVD7BURWwyZaHauGTsDewOXA8cCtxoqpol2vboEOA24LXAwcCKwYwxYQdJ5zXYFbpuZn4rapvDiiHjZUJOOncmfFwFHZ+ZvI+JA4AjgFRGx5hBxtdiuaxPXTwV2awnn64BvRcRbhho3dl6zVwEnZuZ3I+KJwAUR8f6oiqBBr7OZeSWwLXBHYK0hK5RM5m4hOjM/S0XECpn5k8x8NvAS6qb5buCRA8V264j4cERsCGwCvCwzDwCeTw24D42Id8QApRrdmZ3MPJxKmB4UEZ+LiEe0x3v/A+3MPO0JzM7MnaiZ4x8Dz6NuAOv1HVdXG0z/MTPfFxEHUIOgPYH9o+dSpTZQ/DPwyIg4CbhnZp5ATWb8GHh8RBweA622dt5D21I3ymcAZ2XmD4FHACcO8f5vsV3XbtjHAG8EdqNW9u8EvDMidh8iCejcqC8GHt5u3te0xw4Bek/mIuJ21N/fUyNiW+A64LqW4G0ZEYcCZ0bEIAPuzsDrkdQ1/y/AaRGxC1xvEqZ3ned+OlVp8EfgC5n5e2qCo/dy1c7f5cuBEzJze+pvcwUqOdl1Cq4Zu1HvuWupibOrImLHiHj8EHG12K5tr8sbgWdm5u2oSdojqff/hgPFNXnNHg18LCK2AA4C7kpVawxSet8ZZ3wduH9EXEBdO54F/ATYeIi4OpYHLqN+f68GvkO9Xndu/wbRxhG/BlaKiDdS14htqb+FBw8U06zOx0u0SY2vUu/7B87/PX2ZmtpwLbTJwOelwD3bhfZjwIcyc9c2G/r7voNqK0x/jYhvA6dQN/BTATLzsoh4G3AuNfj+R8+xTRLgpYEtqLLGXwI7UTPHx0TEp1tSPJR/Ate2i8ZlwFsiYjVgxcy8cMC4AH4FLB0RXwG+CexOzdKe2HcgLSE5g1op2Rp4SURcDByVmW+KiIcDsyarrQM6BdgReDjz9k3sAXy37/f/fFYDvpWZ3wSIiF9QsT4e2BL4ATUQmXERsRVV7vO69tAZ1IBs24j4S4t1g8x8ah/xdGXm1RHxPGrV6wnUQGwz6nd5NrV6cmBm/r3v2DrXs7WoFfOHU3+XmwPbtMH/yzPzOwPEFm0FbCngS1RCt1VmPrB9yyup60Yv77FuXFSp4G+Bu0bE0pl5KfCsqEZiewD3iYh9+57Um7xm1H18M2qibHLNeCrwqT7jWYB1qfv5tQCZeXxEfBP4AHD7voPpvF5QK5j7tX9Py8wvR8Q5VJVL7/fNTlxnA1cBn8nMU6LK9B4KvKbvmLqvV7uuvYiaYH97Zn4iIrYDVmjjjkG0aq5jqUnZn1ELAX+NiPu0z4eI6V/t9/Yx4KMRsQbwDmp70zOB84ZYAIgBVym1iHRu4htRF7Etge9TswXXAGcBHxx4sDiZ1T6ESpaOAd405OC6M8B4KzVLtgnwtcx8VptZWQ24XVve794o+oxxRar2/3zg/Mz8XER8nLrgnjVAPLNy3h7HFYE/ABsAF7T34BnUbPtR3e+d4Zj+43cTEfcDdgXuRU0WHNVZ1elNRNwmM//YPn4W8EVqtXwtahb0wVRiMsgs40RbSToVuA3wf5n5taiyy12phOAOVJIy4ys7EXHb9uHTqD0v+wDrUysmSwJ/BU5qA7Ql+lptmv/9HNVgZ2cqWfoccE5mfquPWG5MRLwSWCIzX9o+X4ZamdgBOKOtBg8R1+2pCeT1gTcAnwVeDGwFPDczNxsirhbbA6lVkjOAK4C/Uc2wtgPeBjwrM6/qKZalMvMfbUL23tTfwAHU4PEIajJor04iPJiIOIxa/T0hM3/Rqll2z8w9BopnZSo5+iSVpN8qMy+P2nf77Mx8SM/xLNEmGjehJox/Q02Cfgm4GngT8JvMfHmfcc0X465UQv4zakLvmohYnbqmPSUzv9pzPJPXbANqDHZX2laO9vX3Aldm5vP6GmPMF99k3Lgx8A/gPtR77mrgicDXgN0y82+9xmUyd8vRkpIPA8tRN+59qNm7K6iL/9UDxDS7za4sl5l/bo+tR5WarUQNsk/uO65OfGtRA8ONI+LTVIL5kYh4FJXY/aHneCYXslsBq2XmT9pFbUfqorEK8KPMfEqfcbXYJpMGd6H2pCV1sd0uM//YSlqelZmPGSiuzVo8S2Tmqe1rW1JJwNGZ+YU+42rP/1DgIdTF/oLMfHF7/PlUnf03qVW53ldLOjFOBo+zqQRqF6pZ0a2oZGVnYPnMfEnPcd0bOJBKyI/IzNP7fP4FxDO5iT+KKj1aHjgauCfwHODWVMJ7xYAxrkaVST2Cmjg7ZTKJF1Wm2u8Ao1a+tgc+RA1cv0uVf+5JTWhsS81ovzMzv9hjXJPr7IrAqpn5/aiy2e2BlYHbAUdRk1UHZ2Zv3QdbFcGS1OrvpzLzPW1V9dnUvfwv1B6iL/Y5mTFfjMtR23RWo/Y0/bV9fh/ghZn5mb5janFtBpwEfB44JjPnthXW/ahJxs/3GMvkerECNcA/kdon/SdqBf9E4O4DrZRP7pnbUKteX6K2KfyOmvz8LHD/zJzbc1z/npiNiPOA44D9gSMz853terId9Xfxtz4n2TvXjDWoVel1qUmM38/3fW+nrme9VhmQmf4b8T9qlnOp9vHa1IDizcD27bFXA88bOMYVgTPbv32Bu7bHn0D9UQ4Z20btNdqfmrWGKhW8CLjHgHG9myo5mgs8jEribgusASw7UEyTyZ/3UIOxnalyEYC7tP/euv13iZ5imtX+uw41UNwd+AW1F3PL9rVbDfh7XJHaV3INNYHxgM7X1unrdbqR120T4ONUB9AnUw1jVqJW91cH1mzvwVv3HN/szsePBj5NDdC27b4XB3i9HkDtJ3lBu579mpo4WwrYZqj3WTdOaoD9LCoZORJ4+MDx7EuVsP+q8/hS7Xq2wgAxTa5jq1IdGc9r19r12uPLUyvRy1MVERv2GNsywKbAR6jEbZv5vn7vAX+XS7T/bk4Nsn9E7a29NbVHcxOqymCQ32c3Tqps93vAW9s9c/kB32f7UJPWAEFNtHwb2Hyo32UnxiOB+7WPH0FVjbwVeGHf19j54jqo/Q7XBL5BregvQ618zV7Q773H2L7UXrcPA5dQkxfde8THgaf3HZcNUEaszcLunjWrfl/g0qyl6K8Bx0bE0dRA47iB4tulffhcagn//dRs7IsiYnfg45m55RCxTWTNnvyDuuCe0h5+CfD5zLykzQT1IuZ14tqSSo42At5FlSQdRiWXP8vMv/QVU1dmZtQZNEtm5jupAeOr2pefFhG7t/cf2dNscc4rsXgttRLxF6qU8WTglIg4mxpI9ioiVoqIN2fm76hk7rnAz6nOsi+Kakd9JDWg7F3ndTuI2uPydapR0uOolcTvZOYvqFWK509+rzMp5nU7fBR1ftU5EbFeZn4sM7cAzqE1Pcl21+xL5/V6MfCWzHxD1gr0k6j9S5GZ5/QZ00TndVuLmiDbgBpQnEGVde0bEZsOEVtWx7djqQHiLyLi8ojYO2u18I/U8SFDHQHzAmq15oHA+4BzI+I0YLnMvDIz/0RVtJzfV0BZK6ffoJo2fZVq6HRYRKzbvuUJMVxHxsk1/WBqwvhMamL2r8A3MvMrmXnBAHFNVnJ2jGqnf11mHkxt59gKOJ3aRjFIXNTY54ERcf8sn6Heb4M0iZloq3L7UUk4La7jqYnQ8/u+xs7n+9Qk4qFUtdQ/qRW5J7ePe78HALRy3T9n5v6ZuR01of3kiNg+a6VzNvDGzDy+79gGybr9t+j+UTMWc6gGBQcyb9XrUVQzlEcOFNc9qcHXe6la/2Xb4ytRm99Po7pg9R1XdD5eqf13BWqv4XFUInwqbcaYNtvSc4yHAa/qfL4MlRBcACwzQDy3BdbpfH4kNaP9+vb57amOg2sM9F67LdXtbTaVyD2wPX4s8IIB4pnV+bvcoL2vJu+n7agVui9RScEQr9dkZnMbqqPm5PG7U62zPwA8bIjYWhwXU6uEh1Flbu+c/33PcLOy+81/3QI+SFsxHCCeyez/ilQ1wZFUYv5OqupgNrDFkLG1j5dp/92WWtH5BpVsvm6g2O5CDVy37zy2dHvvnzFQTP9xr6EanxxGVWp8F3jzELF14tkAOIEqM/4GsHp7/MzuazlAXOu2uF5FHWVyh/b4S4E9e47ltu36vk3nsUOoxOT51MrmD+hUaQz0mi1D7cX8IVXyeZch4+nENbvdiy4GrqTGZ8tQSeYj2/f0Pi5rz3v/dm1dnnkVcXsAhw39urlnbqQ69btLZrUJvh+1NL4ylYx8LDN/M2B8S1JL5JtRqxJXAs/JzO+3r68DXJ2Zvx4ovudRM1IbAgdl5mlRDVpuC/wuM38/0Obae1Gz/2tTg43PZeaP29dWzFrp6VVE7AXcj6ql/xRV8vlWKok7mhqgXZCZrxjiNWvOTJgQAAAgAElEQVQxLkGVbh1JDa5/Bbyd6pj3p55jWSMzf9Y+vjPVtGAd4N2ZeXR7fAXgL9lzA6Cobn3XtI+fT7VkP5nqEvbb9vic7H+vxGQPxyOBx2TmPu3x21ENKB5HlXD12mBksp+wfbwp1SDmLVQi8gaqOcXhwIOzh9XLG4nzEGpF6YD2+XOo68immfnTAeKZ7Bdakhrs3B34SGZ+uX19b+rv9S19XS+iGuuskpk/iogdqQmgK6lmXBdP7kWde2qfzXUmr9csKgFZmVrJP4ram7wR1Qji9KxKnD73Ck1im02VCT6XeRNBR7V71nuAOUNc+ztxrgk8hkrUr6HKLbegrht9/x73pl6nudT14bcttvWoCe1PZ+a7+4jpBuJbnVqtvJYqk30eVVr/6cx8YZ/vrxbXZDx7X2ri+PSIuAc18T9pKnJlZj6vr5g6sU3uTXtQq6z7UeWVb6USz6OB0zLzxKH2sIINUEar80d5GPClzPxYe/wR1EUkgD1ymKYn17sQtBKRp1KNDL4MvG2gpGRd6iD1X1Hdrh5DJZxHU+VIL87McweIa/7XawWq4ckG1HESFwKfyJ6bsXTiuS9VT39XaoP0R6h9Q4+nkuFPAx9t78debgIxr7HOXaiZu39ldR3dkyp9+ztV+vOymY5lAbHtRQ0Qn56tuU9UU4MDqBnGt7XJg947pEbEp6jS7F+2zx9KNTxZHTg7q3x2EBGxPDWBsQaVAH+xk2DeLzO/PUBMc6hJi52o5PsFUQepH9se/zbwlcx8zwCxdY9W2YeaADoo24b8iDgCmJuZ7x8wtmOphg93p87SOovqTPfNAWJ6LNV44jKq2cOy1H6+NanVzAuowfe1A/xdTl6vV1KD/W9Rq0y/A16RraFT+96+B9qTscahwKXUtfVgas/QX6l7w7sy86S+YmpxTV6zjalJlaSSynWoidpVgK9OxkZ9xtQ+fgO133cNqhrj9dk6Gw+hkzCtT60UTs5mfW5m/iYiHkStfB06YIxfp5pdndEmQlehVg5nUWfaZp8Txp33/l2ohG3jqIY6b6QamP0U+GdmPqOPeG6MydwIdS5iDwPekJkbtDr6bagZoM9RHQY/NHCcm1BL9+9tn29BlU8tC+w6wKrEa6jmAN8H7pSZ+3W+9gLqgrvRAKsS3S55k/0QH6RmGHemkuBXZJ1/NJio9t1bUa/hxcD7M/Pyztf7HmSsRDUv+DxV9nMBtWl6OeoC2/tExnyxnUTtiXtmts5WEfFMqiHFEwaM6+9Uc4UXZOa72qrmY6kVlPMz85AB4ppc0zakEqc7UnuGzgN+mK0LY88rErOBBwJ7U+WxL6G6Q0668q5DregMchPtDM7eQU2obE2tnF9ErUx8ANil79XMTnx3B96bmRtFxPuplf0HU4P/Z2TmGT3Hc4fMvDIiDgbuQf0uP9mua/tQe/ieP9TqUtTZVZ+kJvFOp64fq1D7096fA7T777zHVqFery3b4/egOt9eBvw8Mz/ac1yT68XaVKfUd1D9AZYFXpKZn+wznk5ck3v5y6gxxl7t7+BtVKfPV2TmW4eYyOvE+BkqGd+cavqzc1SHxitzmON7Jq/ZE6g9cdtHnXG3L5VwvjYzBz1TMSL2oV6vPTr3ortR5xL/M2slf5CqpH/HaDI3XhFxKrVH6GxqM/e9qD0mL85W5jVATCtklSgeSnXluhU10H5RWzpfGrhbtnLLAeLbg/qjXIvay/T5bGVIk9WenuOZ3Cw3osq3TqAuYpdQifrnImLdIV6vTmzLUStKq7bVrwdTLbxXolZzep00aKvRr6FWVtfOzIPabONTqBnaj2XmkX3GdENaXB+guuLtk5m/G6oUo/u87WZ5JDWz/ozM/EZErErtK7+yxxXWyXtsJaqa4LaZeWlEbE5NZCxDleL1etbRfDE+lUqULqXOH/sydd09nGqZ/asBYlo+W/lwRLyJum6sRCWef2wff7Xvv4P5VibuRe1l+ik1IHtE1Jl35wD79nlN6wwYV6VKGCcl9r+nkqbvAWtmHQUz1Jmiq1Orq7+kSrI3iSozPpBqdX7xUAPGiHgxNcnyVqq8crAVphbP5LrxNuCnmfma9vjOVAXEjgOOgWZRr9NFmfnmzuNnUufWPqzneLolvHeiErmDqH2OT8/MH0TE8cC5A6yuLputoVu75u/GvAn3z1GT2xtm5v59xtXimUwY3I6a6L8X1QPik9TxUL2OFf8bu1mOTMzrXDY5guCh1MDiAqqT2bXAwweKbWNgr7YityG192VTquzz1RFxIXXD7DUxiYjZ7Q8SqmzlcKoBy+bAbhGxeZu1/WdEf90r4Xodwg6nNkdfS5VufQx4W0ScQCV2vevEdiJVNnhyRHyVOhzz5VSC0tusf5RlqX0HX6dWSy5qX/4uVTryRmovzlRoqyJrUSutV0bErgMlcrPa4OfBEbFfZn44M9eh9teeE1V++cfMvLLF3ctgtvNaHE/9/t7UVtCvoCY1PkM1zOhV5zobwHsy8/HUoH8JagXgI8C6AyVyawLfiYgD2kNfptqcf4Y6U/FL1ETVG/qODXh5RJweEatn5kVt9W0yk70r9Tv+Xt/3gDaYXYHaH7piZr6d+n1eRQ1u98jMn0y+t6+4uvebzPxF1jlovwS+H7V/9GXUeaMXt+/pLZGLiF3ayhdUw6u51D1z64hYq+97ZSeuWZ3rxveApdq9YXZmnkad3TlYp8j2O3of8OCIuFvndfordY/v250ncbUE9/vUdfW7LZG7E9Ws6+wBYjshIlaN2pv8WWqiZy61gvkhar/cpM9C32Ozyd/aE6nO3YdTq+ZPA7aN6uw9PXIKutf476b9Y95K6jLUAH9D6s21aXt8DtVhaqizqzanbpBvp2Z9Hknr+NO+/loG6K5JzajsTq18faXz+P2pM+bOpBoYDPV7XY0auC7Zfn/rUqsUJ1FlUkPENDlT6P5U6daS7fPnU3v41hkirk58T6dm7i4GHt15fNZQ7/+bEPPSwO0HeN5uV8FdqVXNYN65OLeiJoZ6ja1zPXtme6+vQ7VkP5xqrX8Q87ogDtW98l3tunEi87qkbkhNmK004Htp83bd+gJ1RuZX27Xsi9RepqcMFNdKVOfFH1ErSpPryO5UIvUFWqfBgeLbgxq03qN9vjq1wn/XoWJqcTyWKvXcrn2+F7WifwrzzvDsrYNfuybs1D5+HjW2CGrC+CRq0myrvv8uqfLrD3Vep7Wp8t0XtI83pyb2Vu05rgV1It2fSjbfTk3mfWig99ZeVNn1Hu3z1dr76khqUvscBuj63GJZiVp9uxR42nxf25U6OmSIuCbXrckZmfu1z1elOj6fw4BjxgX9s8xyRDrL5c+ibj77t6VzqC6MB1PnQ50wYIxLUxf8baiSz7OomdifDxjTnaib+MHU4Ow44JLM/HNELEWVj5yRPS6bx392yfsRdUbakVQHsyWoQdGDJ983hIh4N3BVZh4Q87q8vQT4W2a+qedYJqU1Twbu22LahWoU8Amqsch3+oxpDNrq7tcy822tnPFkYOfM/GPUXpj7U+XG1/RdxtWuX2+mJnr2owYdh1ODn4sZYA9Tp7zmCdRA9oVUArcDNVB8a2b2vlo4v6g9fTtTyfDG1J6+I/t+vbrxTK6hMe8cxZWAVwAfbq/pctn2HPYU03+8nyNif6qZ07NzoM5zLY7J/XxfatB4KtW18tfAy/P6+5GH6Kw8i2ou9VyqFfuVVDnvNcBzgG9nZq+rOa1M9nFUl+yrqWtFUhMZt22PfSVr5bV3EfEKagXuNlRjtX8CD6O6IP44B+ow3q77JwJ3oJrR/Yjau7oyNRb68gAxdUuyd6DKY/8FHJK1veSe1DjjZzFMZ9kVqYmx46iO3Xtl5h/aCuG6mXnRjf6gnpnMjUwrMzuRqvnfNassg4i4FTXjedmA4f1b1D6rp1MzehdTs2dfHHCgcWeqy+Esanblh1QCcAhwdGZ+vc+9EjGvS95jqEMoX9BKu15O3awuAT6bmW/sI54FxLcEdQM/ldq4/RrgHVltsT9A3chfOVBs5wF7Z+a3o5rG7Erd3P8OrJ8DbOKeZlFd/F5MlbvtQw3+r6Fu5LenjuLYbcD4VqX2eb2eOkfrB1GHvb8xMz8/0EB2Seo1+1pmfjpqr9eawJ5U2eyOfV0r/ptWQr4bFduXqYH2dUNca9t1fytqL+21LSE+iEpQnp+ZF/YdU4trT6qE/Wqq+uEc4LPAKzPzT31e+1s83Xb/L6caxXwvqqnCLtTEwYcy81V9xdSJbTKZcWuqAdAjqWMSlqDe+98BTsyeG5h14lua+lvctcV3DrX6NSsz/z5APJPX6ynUxPCpVHJ5BZUMnJSZv+r7PbYgbZLlDKpp2PNzoL2PnYnZ21HXqj+0x/eiJtAmK3VDHq/1eupM1udG9ac4JDMviYhzqT2/3x0qtgUxmRuhNmPxJGpG/bvAsZl5xUCx/PsCFRFPpPYx/Z6qWz+FWtI/gErkTr3BHzQzsU0usreiZspWoeqvt6f2Gq5MNWPZuOe45u+SdyB1wf97+/pGwK9zwPOh5ntsDrV6chuqbOQHmfnyG/r+GY5vY2qm/9VUSc19qLKbWcAHsrVlV5nv7/M5wLOpNvFHUYOOH1LduP7cV9IU199YvgN1rMWVLb7Jftb7ZeYDZjqWG4lxe6rM7ePUKuZf2+PLU6Wfg5yPeWMiYj1q79xRA8awKbXKegH19/iF9vgRVMlU710PqdXBw6l75ROpfb6rUatg+2frtjyEqM6auwMHZOZZ7bFlgPWpIxLmDpUERMR7qdWuC6kzyL5HldyvRXXX7PWokM51Y+vJ+ygiNqOSutWAT2XmMUO8Xm215gvUcT3Ppc5wO5cq0f4hdaD6YKvAXS3WnalFgaflAMeqdGI5myqd/RpwTmZ+rI3XDgNe3XcyF+0c1nZvWpc62uWaiPi/9i2XU70gtu8zrpvCZG4EOrMYdwDuRjXxuIa6gG1HJSavyMxPDBDb2pn5w/bxV6kB4u+pRHMF4MzM/MiQs1KtzGwJ4CFUkvliqqXy7YG/Z+Zv+1zG78Q1f5e8L7V/h1KrEkM0V5jMGO9Hte9ekkrET4sqbzyYGqg9JzOv6ju+FuPzqJWIszLzVRGxNTVr9qAh4plWnevGOsADMvPktrL/Umr1993UathQqzgvokp+PkUNxM6JOvdudeDLQ5XXdD6fHPi+HnX20Sl9xDE2C3jdVqHKBjekyrnenpk/GDimbln7usAvqAOlD6X2EvV2HE1Ug7DfZnWnXIUqE3841b34rX3FcQOxTa7/y1DNVw5q15ANqTLoLahB9lsGiu9W1GrXHalS1NPb5OiuVHO1Qc5Ia9fVh1CNuT4MbJ1VkvcO4LjM/MYQcd2YtsK5/FCrXxHxJOocvudR1SL3pKq4PpmZ57Xv6fNMuSWpSfarqZXe12UrI44qufwEdWbg5pl50RBjxhtjMjciUeeDXEStyu3ekqQ7UG/Azw+xZB7VFvvZ1AzUrzLzsHbBvQdVYrkddZPqvSa7xbcptY9kk/b5S6mBxhMz80sDxDMZYAe1hH9tVAvvnYEVqSYQ12bm1gPENpn5vDd1ztEbqCTzYVQp3osj4jbU5vfNqNWTvw4Q59JUY50/tZn3rwMHZ4+Hw45Ju26cmp0DwSPiftTvd5/scf9X5z22HTU4PItaKVyW6ix4ymRyaCgR8XhqEmN2Zp4UETtS5dj/ALbIHvd8jUmrzDg/My9pnz+Amtz7EfCkzPxdj7FM3meHUA0WNqH2Rb95vu87hjon7Yie4grq/vNOas/Sj9vAcGOqlH1p4FWZ+ZE+4rkhUS3+t6LasH8ka39tUI1a5g5RNTJffDtR5bu/oY49urDvwXXnPbYCsFxmXt4Sy2OohkRLUce+zOkrpmnXec2CSn7XmtyXWkXSU4FrMvN5A8S2dHv+p1FbTLYBLst5x/k8n2oSdlCfSeZNZTI3EhGxN9X0Yb+I+D51c/oTdejjIIfCdmLblBoY3g/YLTPf3x5fAbhzDlhbHNUOe8vMfGpn1vHpVKnU0QPG9S5qj9ey1Dla57XZz9tQTWx+O2Bsr28xnNhmHO9GzdK+aZIAR8QqQ5eatRvnRtQh3IcNGcu06dw0H0ztC3p4e3wpau/Qapn5i/bYEGVJ7wNObhNSy1DlSQdTJbMn931N60yybE01PPkYdWN/VLZGFBHxaCcMFiwiVqMSXqjJlTPbysSB1Mpvb+fdda7z61LNfh5PtYqHKsd+bVvRWYoqiTste27Q1SY830H9LZ5LVRn8IaoZyhqZ+eI+42kxTa4ZT6Sa6fySmmD5LFUGd0kOcLZWJ66VgX/kvP1Vs6nOmpPDpk/rO7YWxzupNv9vap/vRF1DLqKONTl3iLimUedv82hqlWsbqlHSyzor56tn5i96XpW7G5UPXRoRh1PbN35AVSF9ktqSsw01BrrOZE7/szYrcCVV5nBxZh7RZpD3zZ4PoezEtATwr8lAMCKeQe1N+AbV+WeQDpZx/S5Jy1J7906j6vyva6UPv8zMQ27s58xUXDHFXfIiYg2q1POvVKnIj9vj76NmZI+cpgtZm+HrnjukjojYAHgl1U750qwGNmtTpV1Pzh47pc73d/kcamXihZ2VnJOp2exZ1F6OP/UVWyfGL1CVD7sCd83MZ7ZVk1lDVRdMq+4kQEtO7kZVidyP2mv1c2olZ5PJALzn+I6gzuz8LbUCvVNEfIiaCN00q8xxqT7/BjqxrU4lSzsBO1JbJz6TdbbW5HuGOiD8GKop2MVRpfWbUxUan+jGN0Bcp1G/y7dTieVf22TVllSC/pcBYnoYVXm0Qd/PPTad8c/dqMmVbYG7UGOhu1GTeMcONLl4AXW8xV+o1bePRcTuwKOoLTlbA6dn5uF9xnVzeGj4FGsD1UlCcg514V+XapUKVWc8VO36pKThjhGxfUTcMTPfnpkrURumL4uIpw0RW7tgLBURL6Ba3R5BzcCeGdVq/x5UB7F/v8Y9xrUkdR7OIZn5Varc5jktztf1Gc9ERMxprxVZh4puQp1X9bGIeGubqb0VdROFagU9FbKYyHVExLFtFpvMvICaYXw8sF5UN7MjqMT8HzHvaJOZjina+3+lNit7OrU/4okRsX9bLV87M59IzYKu0Edc88W4JHWY7sZUl9nJAb/7U3uAdX2zACLiZdTq70XAe6nSynOpUqWD+0zkJu/nqIPVT6OOuNgJmDReOY+aaLy4DS57ncxo/30YVaq+PbUPZz+qFPVZUXtGgX4PCO/EuC01rtihxfAeqkHXVdQ+/d517on7U8ncm4HJJMuhwOVDJHLNitR7/d8i4p4R8Zq28qum837enhoj/i4zv56Zu1L3pN0j4m4DJHKPbbF8nuo/sUvU8V8fpu4B51PjtcPb9/c+RrspXJmbcq185blU04KdgGdQB+uuCVyRmXsOENPS2dq/R8TngaBmYs+gWixf0lZ4/pY9N8mI6+/JeTXVSeq4zPx4u1H+FvhTZl4WwzQ9mboueVFn0CxNtaBeI9sm8oh4IFU+e3fgXZn50r5j003XKWHZlLoBnUHtK7mEmry4I7Xy9bPMfP4N/qCZjW1/6giVF0XEg6gkaT1gNnAsVTb7qMx8fE9xPZDq5nl++3wbaoJsbmY+NiIeSSUqD+wjnrGJauDxSarD24+HjmciIs6kmoJ9OyIeRzVa+DK1L+3emXlVnysAcf39VadQnZV/SbX5/3hmfqkNZAd9DVsC8hRqgvFHVJORXrtWdmKZnZn/bJPZa1MN375GjTVeTE2ALpOZTx0ivhbjWtQExtuovYW/apU/P8uBju6ZZlFdIl9D9QY4k7p2/Cird8BSbYKx7w7ZO1Jdbq8D5lIdz7ek3mcfp/4+/9K+d/DjJW6IydyUizrw+iTqnJ6nUQOyuwA/odrXD1GK9Fxq8P9b6lDrp0a1r98buB21+fcYqr69zz/KbgOPY4GzqS6WG1DL52/JzG/2FU8nrqntkhfz9gqtRtX4/5x6rQ7PzA+379mOunleA2ybmX8bKl7dsDbzv0obUAQ1mfFIavb/ldTf4z9jXvvlvg8IX5W6OX4yMw9oj3UPml4ReBfwzOypk2ublT0PuC9V6vYNqvxnM6rj52eAD2bPhyOPRUQ8gmomtXcnYZ9NJQPHUM2c+k6YHkE1nnhCe3wVanXpN1R53ulDTOS1WI6lGoW9sk267EhtnTgJOCEzf9f33+UNxHk74FnUiv75wF50tlT0HMvJ1GHXl1OTPe/MzDdG7bX9e89jjG5p8eT9thNwb6rh2z+pc2w3G/p3OC0WlABF9QfYlyrf/QJ1nufPhoivxXMCldC9gToWYUXq73JL4NzMPO6G/+/pYDI3EhHxWuCn2WkJPMQsQVTHn8dRg587UaU2u3UGZE+g6oyfPtQMRkQcRZVevDaq++J9qdmgq6mN5u8e6LWbui55nQHYC6l22SdExD7UavD3mTe7vQLV3v5Tfceomyaq4cMFwFHZGihE7U84iDq36uzMPHSowWJL5nan9qPNpSZXLpjve1bIns8KbInvUdR14sPt3xVUF8Src4CmD2PRJqY+R01KvaM99mxqdXXbAeKZNBZZh7q2nrugWfUB752vo1Yijuo8fjp1Xzg/Bzgk/Ma0idEtcr4uoD087/9Rr8lbqJLUPaiVk7tQ5xieNJls7DmuSQK3O9Xt8/fUCubvqXL22wLfzsxf9h3btOpMGD8OuBeVkB+fmR+MOp7gSVQH168MGOMcagvTo6k9v6+htgKsRXW7/cM0r8qBydxUayVAt87Mz7U325HA+zLzbQPHFdRFdUtqNuNCain645OvD/mmj9q0Pelw9cf22GupxHM5qulCLzX2MYIueRFxR+r8vW9k5jbtsVnUjXTJHKCUV/+bqH1fL6H2vbwkM09oj29Ora7uOXnf9RRPtxX1rTLzbxFxf6qpwkOokvFDqNVghpplb5+vBxxAOx8T+HAO2FV2LKL2gO1D7XX8HHXtfUL2f7bc5L32cGpLwjItnrlUAjXEqtLtgEfkvA7PD6XeY6cCX8nMn0TE19tjzwWel5mX9R3ntIlq3PQuqhJpLrBdZ8J4D2rV5GmZeW2PMXW7pH6EKkddnxpTLAUcOs2D/SF0XrNVqCqHI6jVy5dRye8TqY7nlw4Y5vVExJbAK6hO4zvkwMdw3FQmc1Oq3SD3pVZt7gscTZVMbQc8aKBywUlickfqwO3vUaUFD6a6Ef2NOiB26HOilqJKfO5AtVU+j+qedC+q9fn/ZTUf6TOmqe6S10qTXkHNhh6ZmR9oj0/2LUzVAZm6cVH7IE+iSn72yc6htX2tzM23EnIEdV7hBdQA+2vUNWM7qklGr+cVdq5lG1FdNS8GzsvMKyJiK2qP4fGZeXyfcU27zus2B3gAVf7/A+o+9QBqZeIr2eM+q05MK1DlUatl5lfaBNoTqc68787Mr/cVUye2B1JNWOZSey+/HtVQag7V+fNfwKeBd1PdItfrO8ZpM9914/HUvfxnwK6Z+aOIeDWwbGY+Z6D4ngXcpVP9MGlKd3Rmnj5ETNOuVf7cqzsxHBFvpxYnPjtcZDcsIvaiSp97mzBYGCZzU2QBM8XLZuZf2g3hftS5ZI/JzB0HC7Li+iDVIOBVUccTLAtsSs22vyUzf9JzPJOb+QOoJPNS6iZ5D6rd7M+omfY/Ui2MN+k5vslqyfep1bmHZebfozbpfzYzj+kznhvSfpe7UHsfZ1Ez7FeaxI1X1OHgH6MGir02CujMyh5MTaQcQHWJ3IA6x/D1EbFMW60bqvRzLlVZsBy1X/Q8qnPwX6hy6N7b1k+rzu9zFrWSfzJ1Pfs41dHvzBzgCIJOfJ+gSunvTq3KPY1K0l8IvKPPFekFxLY/tVr+QWov66RKZLkW46lU87D3DxXjNOi8x643edgqa55DddU8heouONSxKveiOqX+H3VdvSbqbLKrM/N1fcU07Tor5UsD96T+DvfKeefJvRpYItv+6Wk11L3p5jKZmyKdC9m+wMOpGvEPU3tdem90siBttvPAzNwsIu5DzZr9nEpWfjvADPvkNVuB6lZ2JfArqpHBuTmvS92y1Nla7+1jhjZG3CWvlQbtSR2QOYpZKd2wVuJ4t6wDUfvuFLYUtepwUmZ+oj12f2qP2m5DlLB0BhlPAu6TmQdG7ed7LFUF8U9qD9hgG/KnWUS8hEqWjqCuue+gSng/R+2x7a0j43wTef+XmTu0x3enJvJ2npR7DvDen8S2KpW8Lc28xlfHUOeKZvsb2X5SDbE4i+s3FdmYmlQ5JatD9u2p0suzM/OdA8X3f1TFw5bUvsxlqDHHrsDm6V65/xARb6D2nz2X+p2+imqedyjwuJx3TMjUJ0zTzGRuSnQuYhtTg58dqUYi61At/l8waIBNK0F6GNXyf1PqPLL7AH+m2hj3val8ksy9iOrWd1SL8ZHU2WgXU231/xQRd+prdjZuIV3yvMjqf9X523w6tcflcOCiVrZ7HrBvZs4dKLZbAe+hrq+PmZSGt5XMh0zLavm0aav3z6aOvTgA+HpmnhwRrwKWz8znDhTXe6mSz+dT+3yviYgDqTHOqweIZ3I/vyO1ivMnqgT1tVQCcBDwvczcue/YplXnNVuT6sD7DGqV/Dyqhf1xmXn1gPGtTO3vPS8z39MmZO9OdfD+QmZ+aajYpk1nImMTavJ/+/b4jlTlz5ep4xtOdoyxaJjMTZmoA1iXyMxD2udrUIOOQzPz0wPFNLnI3oZaLXwvlby9LjMviIi3AD8eqsSgzXx+FPhMZu7fHlsGeDKwXGa+caC47JKnxU5cf2/tb6gztZ5DrXgtT5WW3SYzHzNgjLOprmpPpc6v+hxVIu6xGzdRW6G7H/W7PZMatPU2oO3clzagus+tTZ339VZqS8LpwBcz83V9r8p1Yjyear3+A6o8cLJq+Xrg0sz8cXSO5xBExInU3+P3qL2rR1FjjnOpBk6DNUxf8j4AABMTSURBVCWK6mVwDPV+f5Vl2DesjX/eQzWHeTGVvP3H+3yov81bmllDB6B/dw6czHqeATwoIjaLiOVbqc9FVIvUQWJrN8zbUmdwRJtleXJL5HYHNpiCWvH3AA+NiOMjYqPM/FtWq+yjYd5r3Id2ESPLs6lGNvelupE+DrjOm7duqTp7XU6nymh+Tr33L6IaQXye6n44ueb1YvJcEbE8sCbVUvww6kzKewDnRMRmfcUzFp3X7R4RsXNE3DfmnQl4DXAWtQdyiETujsDBVCOdbahS2Y9T+/m+OuR9KSLuANyGen1eS5VSbkV1aNx4Uo7qveA/7s8nUa/Z86hS/09QCdS3h0rkIuKeAJn5earqB6qBjRagXTNmU9f8Vanr/dqtIuJ6TOQWjdlDB7C4a7MS/2ozxZ+kDur8PFWSt2VU84yNqPr/IUz+0I4BfpKZf25lEFtHxNuo5iIH9h1U52Y+C/hjZr4hIj5LlYC+NCIuB15KrSDS1zJ+3HCXvN1jXpe82YBd8nSL0ymtfApwVWaeEhFrUTOz3wDek51jQbLH5jqd53on1eHw4cCzMvOciLiI2gdzSV/xjEXndTueWvFaAvgK1Sn4pVQZea9NkjrX80OBL7WyytlUZ8itqYY7t46ItTPzh0MMGDPzyvZ3sBzVfGuFdr/6OZUIW8ZONQjLtjc7Ig4B3px1rtd3gP3aRPL2VLI+RHyrAs9pq3KnUPu9HgDsGBH75IDno02bzvs5MvPaiHgTNaF+EHVe4Ccj4uTM/PWggd4CmcxNj6cD383MH0TEscAOVI39EtTekl4bi0y0gdmqwOqZuVtEbAvsTN0stxiiVGqSALdPjwQ2i4hvUoOLM6n259sC1/Z9o+wMfN5CdcnbEHhI2yN0DrAJ/t3pFqozaF4FeH9UG+97UoP9hwJ/oPYQ9aqTZO5CrYw/JSJ+Akxa6Ed6DMENijqK4NLM3DMi7kt1vd0duIxq5HHVADHdDlgD+FBEPB94EPB1akX4eGpP39kRsX5mXtN3fADtvv3XiPgo8ErqDMPPZ+bl893HFmcPiWoDfwU1zph0RH0flQjvBJyamVcMFN9dqSONTqBWfn9DrUivRB0Wrqbzfn5T1PE4K1MT2i+hqsuOoLorm8wtYu6ZG1BngLEstfK1HtXhbdJ9aypq6SPi/9u78yi/y+qO4+8PBGQJkFI0LLIIUggYdhUIyKo0LJFVFtnDctKDpBhAekAFWaXAYa+EXSlhsSKmFKQiBpGglhQJAi2g0oAF4gkta8CET/+4z4/5ZgySQOf7/Gbmvv7JzG/mwD0zv/l+v/d57nPvKGJncCPiMPIVtqdIupcoo2q19KHxc/s6cU5iAnGR/TTRYOEcSUs42v+3tvKp7JKXEvBOx8pvErMnv2H7IUm3EE1/bqwY18nEw8WmxP3vJEmjiWvITm3uFHa7xvVsCLETMQE4xvbz5eu7Egn6ibVKpRRdD8cSc+6OICpJfgGMsv2ipGVtv1wjtiZFe/b1iWRuSqneyLNCRTl/eRpxLu5z7hkDsA4x8L3VpLdRYXMgsSO4IfAQsRB1f9k5bK2hWn/Q+JltSJxdPYLYyT+CeP7Zy/bsmjEOZJnMdQHFxPkPE7XYb1FWF2vehBp/mAcQK57HE901H3e0CT4HWN72US3G9M4MGuJQ7ZVE2dZd5esjifMvB7tCy/MSQ3bJS4NO48F/SeJM0GLAb4nOgq9IOogYXj6qQmzN68baxBmrEcAOtl+SdDtwp+1vtR1bN2v83CYSZwoXJTr3XePSVKp2QlJ+px8iNoXfUDQcecn2Cc3yvW5T++fWLRrvseFENdLGRJv/S22fLGkycLHtf60U3wMlrvHA1kTzsheBM7O8cv7K3+Ajjs7ii5eXLyCS4Jvyvd83sgFKJZJGSBpV6rA/X1arzyZatq4PXCxp01rxNVaoxxMDV98iWip/VTFrZVvghJbDkqQVbc91dJ27DzhM0oblxj29xLh8y3E1zSEuXFOBCyRNUAxG/lUmcmmgaqycX0t0iPw1ML4kcosBw4m28a02PSmxWdGw4wTgaWKcyizgpJLIORO5eZXk3JI+QpTMjra9DXE/2F7SdElb1X4oK/eC14HZkj5GlOqdXL5cvarl3dT+uXWDxntsKLCI7Ym2xxEVNtsrzrG+UTGR2wF4kFg43rW8/48kFoKWrRFTN5O0SLlebA+cLWln22+VZ8fhxIJQvvf7SJ7dqUCSiET6MGLV51sAjuGJTxHti0cSDx7VKGbe/S+wgqTTiYvYT4j24qMbte1tWQeYJukS2yfanijp48BBwNKlXPV3tqe1GVRjF3MZ4qLV6ZI3gji4fYekU23f12ZcKbWhsSv3GWBYWVHflmjJDrCRG10FK5UyrkCUO69IlIw/Suwgfp+edvGpaCTnhxGLY7tI+qHtH0v6KTEr6qVqAfZSHhB/K+kMR+OFQd9YpNs1fj+XAxtIeg2YSFQlbVGS82rvMdv3lJ25jYHnyiLUUOCBTjVQ6tldLb/PF4E1JY0FJinOJd8NPGP7jOb3Vwx5QMqduQrKG//XxC7cC8AISZdIWq886KwGPGS76uFa21OJ+W3nAW/a3ptIVMbYbv0ia/txYkXsFUm/l7SX7ROJA7UPEZ3MxkG7q/+et0veKUT79fVt30nMP7qR7JKXBqjGQ9mawJWSxhPNMh6UtDpwXtkZq8b2k8BWxM79Ibbvt32z7am2X6gZWzcqC44QDWKmE4tSu0laC5hj+7JyD+sqnbLKTOS6m3rGMW0DrGB7I2I+4BjgIkmfB57rgmegN4g+AU8STVCuJe7nqYcAJO0l6XhJW9q+2vZyRCJ3DLHIDeTOXF/JM3Mt670qUS5qawBHE4ds/0CUWW7SDW/6cnB78VIutQhxnu+rJVGpGddfEmfTliHO4jxaKY5ml7wxtvcvq1FbOzqWreFK5/dS6muS9gEetv2kotnPtcAnbK9avn4FMMv239XaLZG0LnENe0TSVkRntYm2b287lm7XvD9JWgpY2vZMSXsCuxM7JXcBd3XD/Sn1b5IuAha1fUz5fGmilHcLYD83xpjUpOicuhYw1Pa9tePpNopGNbcRZal/QcyXm1wW9FYihr6vD6zRLb/TgSaTuZY1SpL2AzYhZqR1tp+3JmaTTXUMyuwaio5mnwK2s31m7Xg6JG1E7MzdbfuQinFkl7w0qJTzEacQZ0p+CVwPjCJaeE8HZgKrAn9drnmtl9eUlf/x5dNhxOr/QcBngG1tP9xmPN2ucX86kWjINZOoHrmofDweeDQT4fRBSVqV2LXZGfgnorzysfK15W3PqhlfWnCKDt5L2r5K0extP+La/3jneVHSx21nhVIfyWSuRY1dnHWAe4hh239DnN04zvb3qgb4Hjpn/botMSlxrWn76TYfGBu/z+ySlwYlSesBmxFNC+YC19mepmgZ/zvgRdvPdc6VthRT7+qHZYkh4YcQXYPfJGakbZrleH+qVGPcBpwBvEq0Zt+SSNj/vpSepbTQ5vO3KaJhxo7E2czHgG/XOMaRFk6jV8B6RAn7PsC+nSRc0hii4/l1eYa172UyV4GkY4nSgk575y8A5xPNRj5FdHDKX0w/UM4CHU38/sYCexPn99YlysN3rxheSn2iVzneEKJUfAzRuOkp4Ae2p1fajevsLu1L7JQ3qx/yoeJdNBanRgB7Eo25ZgFLE0cAjiA6G2dL9vS+qIyLkLQL0YxoiO0rSknvHsRu8AW2f1U10LTAJP0HcAfxzLMScL3tC3t9TzY96WOZzLVMMQvtfGLF8+vENvSc8rU9u313Ls1L0trEgeifEV3ytiR2Wv8L+E02V0gDmaTNiHLxh4jSys2A0UQzlAkuA6ZbjGd+1Q/jgNWB423f1GY8/UUjAd6YaNq0MXFdu7Bz5lfScm6/g3EaYCRtQpytPR24FHgeOM32bZKG5z2z+zWuF9sDX7Q9try+A/C3RKXSrkQjrEwyWpDJXMtKCcsY4jD5s0T77um2Z1QNLL1v5Xd6MfCg7Wtrx5NSX2rcyPcgHsjuJUrxphALGgLWsv3LijH2rn7Yh1hE+x/g01kqOH+SriManPwbUTa+AnGeabLtmRVDS/2YYij44bbPlnQnMYt1RaLE8m7i/vkwMc/tzXqRpgUlaQngKqL7+qnE88/rirmie9ueVDO+wSZHE7SgnKnqNAxYA3gEOJTYvdkfGCfpo7XiS++PpHUlbVBuPt8B9i4tlVMasBplip8Fvmz7S0Snt7lEE4NZlRO5kcSq8NaSRkoaYvtW26sBp2Yi16OcWep8vDlRlvqw7adKQ6mriDLy3SqFmAaG04mRQsOBB4h5teOAS8pD/z8S145M5PqBsqA3G7iGSML3B3ZXz+iSSeX79Gf+M+n/USZzfay86edKGkYcKj+RmN12K9Gu9ZvEDLdnK4aZFlLpkncWcKqkHxO14nOB60uHzZQGrLLTtQGwjqShDkcAc8qB+Jr+E7gSeAM4EPhc6ZxHlrH/idUbH4soeZsk6TAA27cBmxOLVSkttLJT8xzwMeK55/uOeYA/B1aSNIrogntDvSjTglDP/N6lJa0CvGb7WOLZdifgeOCTne/PEsv2ZJllSyRdBrxq+yvl8/OJeUw71Y0sLajskpdSkLQF0Yn3o0SJ1Azib+B8260nc43Oah8h5hwB/AY4ikhGZgCX56LZvCQdTCS/M20/XV47gDgK8EfgJtt3ZOOY9EGUhG4asXhwlO2bynvvEKJ/wE9tn1czxrTgJN1CPO/8FbAUcBhxHZlANEnK62zLMpnrQ706vn0NeMz2dxuH9L8NXGH7Z3UjTQsiu+Slwaxx3RpCnEd7U9KOxGrsSkQTlOts39fm30Pj73IY0VXtCWBboiHL4cDKwF62T2sjnv5I0mTiQfvQMlpiReJnt6rtcXWjSwOBpIOA2USDjDeB44gZhi/bfr1mbGnBlbPSx9rernx+IJHE7eMyRy67V7Yvyyz7UCORO5wYuHqSpM3LA9FyRLvnnKfSD5SL09ulS975xLnH0ZKelbRfJnJpICsJkxUDYe8gyomnEG3rRwMTiTKqz0pat82/h8b/60zgfttjba8FPA1Msv1oJnJ/nu3diGHqt0i6kRiPcxZwQt3I0kBh+zvl7Ooooizvu8DlQD709y9/JGZOImlx2zcAtxDD34Esr6whk7k+0mh68gViVfgfiAee2yTdUT6+1/ZjFcNMC6hxcdqJKCW7odyUjgPOlfSIpCXrRZhS32kkTMcCPyJa/p8GfAk42vZlRKOMEUBrZ0Z7HbB/gZ6HDNmeALxQzuSk9+CY7bU2MBmYKWlf269WDisNQLYvIRazf5gNibqfpEXKv3sASwDbSRpn+63yLZsSSV6qJMss+5ikqUTHt6mN144ibpgv2p5bLbi0UHJGYBrMFIN+DwbG2Z5VXtsWGAscU3MGWal++FAjlgdL9cN9wP65aLZwyriVZWz/oXYsKaV6GmXsKwM/IDoFrw5cTZTL/hxYx/YOFcMc9IbUDmAgk7QU8BQxq6fpk8Qwxf9uP6r0AXS65O1OdMmbImm67RmZyKVBYDVgH2JltjOC4/dEJ7o5bQfTaHrSqX7YRdJcovphGrHoktUP70NpEZ9t4lMa5BpVGV8B/tn280TX209I2hN4hng2eueaXCfSwS3LLPtQOdR7F3CApN0lrSxpZ2Ck7Xsqh5cWQM4ITINZo7xmFeB7RBnecpJeljQJOJI4DP+apMXbjK3x0HAccEZ5baLtlYDbiUYLE9qMKaWUBqhngMV6vfZhYLTtV2Cea3JqWe7M9b2biZXsrYFziPa8p1SNKC2Q+cwI7N0lbwqxI5BteNOA0yivWQW4idjpGkpcx1YgulhuQ5Te0Dg/0WaMWf2QUkp9727gLEmHAv9OLGh/GdgFsoNlbXlmriWShhKd32bXPFuSFl7OCEyDmaSriIWLJ4AbiS6RbwMXAuuWf4+1fWml+L5InOO4GfgF0YDla7Y3rxFPSikNRGUUzfbAjsCzwIO2z83RTPVlMpfSfOSMwJRA0nDgEmInejLR/OQJSdOBW21/o5RiLlbOWdWIcQgxfHg9YpV4GnCN7R/ViCellAaqUg2xODCk0yApd+XqyzLLlObjXWYEPtvokpczAtOAZ/uFUlYzFHgZGFaStxnAteV73pbUeollI8Y5wNWl+uFcsvohpZT6ROkF8Xqv1zKRqyyTuZR6yS55KfXo3Lwl/QtwOjAM+IntGZ0V2W64mZeZaDkXLaWU0qCSZZYpvYucEZhSjzJ7bH0imZtSFjyyvCallFKqKHfmUpqP7JKX0rzKmbhpnc8zkUsppZTqy2Qupfmw/bqkzozARenpkjfS9pF1o0upvkzkUkoppfqyzDKld5Fd8lJKKaWUUjfLZC6l95AzAlNKKaWUUjfKZC6llFJKKaWU+qFFageQUkoppZRSSmnhZTKXUkoppZRSSv1QJnMppZRSSiml1A9lMpdSSimllFJK/VAmcymllFJKKaXUD2Uyl1JKKaWUUkr9UCZzKaWUUkoppdQP/R8TkcHkZxTQRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_TopWords(lr_l2_tf,feature_names,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sqYadGOgLPpi"
   },
   "source": [
    "## [5.3] Logistic Regression on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kByBCEA1LPpj"
   },
   "source": [
    "### [5.3.1] Applying Logistic Regression with L1 regularization on AVG W2V<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yCQ_4iJkLPpj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 367.96 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_w2 as training dataset\n",
    "#and regulariser as L1\n",
    "c_grid_l1_w2=cv_lr_grid(d_train_w2,y_train,'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.85      0.60     11345\n",
      "           1       0.96      0.81      0.88     58873\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     70218\n",
      "   macro avg       0.71      0.83      0.74     70218\n",
      "weighted avg       0.88      0.82      0.84     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.810\n",
      "The value of TNR is 0.846\n",
      "The value of FPR is 0.154\n",
      "The value of FNR is 0.190\n",
      "\n",
      "The roc auc score is 0.905789\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L1 regulariser\n",
    "lr_l1_w2=LogisticRegression(penalty='l1',C=c_grid_l1_w2,class_weight='balanced')\n",
    "lr_l1_w2.fit(d_train_w2,y_train)\n",
    "report(d_train_w2,y_train,lr_l1_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [01:28<00:00, 14.32s/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(d_train_w2,y_train,d_test_w2,y_test,\"l1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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",
    "plotROC(d_train_w2,y_train,d_test_w2,y_test,lr_l1_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.85      0.60      2836\n",
      "           1       0.97      0.81      0.88     14719\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     17555\n",
      "   macro avg       0.71      0.83      0.74     17555\n",
      "weighted avg       0.88      0.82      0.83     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.809\n",
      "The value of TNR is 0.852\n",
      "The value of FPR is 0.148\n",
      "The value of FNR is 0.191\n",
      "\n",
      "The roc auc score is 0.907432\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test\n",
    "report(d_test_w2,y_test,lr_l1_w2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O_8Gu2SfLPpn"
   },
   "source": [
    "### [5.3.2] Applying Logistic Regression with L2 regularization on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zy3s4jk1LPpn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 49.99 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_w2 as training dataset\n",
    "#and regulariser as L2\n",
    "c_grid_w2=cv_lr_grid(d_train_w2,y_train,'l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.85      0.60     11345\n",
      "           1       0.96      0.81      0.88     58873\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     70218\n",
      "   macro avg       0.71      0.83      0.74     70218\n",
      "weighted avg       0.88      0.82      0.84     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.810\n",
      "The value of TNR is 0.846\n",
      "The value of FPR is 0.154\n",
      "The value of FNR is 0.190\n",
      "\n",
      "The roc auc score is 0.905787\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L2 regulariser\n",
    "lr_l2_w2=LogisticRegression(penalty='l2',C=c_grid_w2,class_weight='balanced')\n",
    "lr_l2_w2.fit(d_train_w2,y_train)\n",
    "report(d_train_w2,y_train,lr_l2_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:11<00:00,  1.62s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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iqY82fQ7zfgfdroZb3vH7eM7GmLrNEkl9kxgLX9wLnS5x+s8KaRjoiIwxtZxfE4mIjBKRJBHZISKPlbC8s4gsEZGNIrJcRCI9lk0Wke3ua7JHeQMRmSEi20QkUURu9uc51Ck7lsCnk6H9BU5Pvg3CAx2RMaYO8FtfWyISDEwHhgMpwGoRmaeqWzxWewV4X1XfE5Grgb8Ak0SkFfAMEA0osMbd9jDwBHBQVXuISBDQyl/nUKfs/g/MuR0iesDtn0HDpoGOyBhTR/jzimQIsENVd6lqDjAHuKHYOlHAUnd6mcfykcAiVc10k8cioLD72btwEg6qWqCqh/x4DnVDyhqnO/gWHWHSVxBuudcYU3X8mUg6AHs95lPcMk8bgJvc6RuBpiLSurRtRaSFO/+8iKwVkU9FpF1JBxeRe0UkXkTi09PTK3sutVdaAnx4IzSOgDvmQpM2gY7IGFPHBLqx/WFgqIisA4YC+4D8MtYPASKBFao6CPgBp3rsLKo6Q1WjVTW6TZt6+uWZnuQMkdugCdwxD5qdG+iIjDF1kD/HI9kHdPSYj3TLiqjqftwrEhFpAtysqlkisg8YVmzb5UAGkA184ZZ/CvzaD7HXfpm74P0bQIKcJNKyc6AjMoCqcvx0HlnZuWRl53Iy98zfTYUdC0gJZcWXeJafub6UUu65fsn7KSkWbwm+94pQkY4UrPMF3/Ro25SgIP++af5MJKuB7iLSFSeBjAfOGNxCRCKATFUtAB4HZrqLFgIviUhLd34E8Liqqoh8jZNklgLXAJ6N9wbgSAq8dwPknYI7YyHi/EBHVCedys13EsLJHA6fyCUrO4fD7nxWdi6HT+SQddKjPDuXIydzyM3XQIdu6pHE50cR5ucHjv2WSFQ1T0QewEkKwcBMVd0sItOAeFWdh5MQ/iIiCnwL3O9umykiz+MkI4BpqprpTj8KfCAi/wOkA1P8dQ610rED8N71cCoLJs+DdlGBjqjGy8sv4MjJXA67X/SHT+RyODvHLStMAm5y8JgufjXhqWFIEC3DG9AiPJQW4aF0b9uEFu58y/BQZ7pRKOENQop+YaubX5SfE4165BzP9KMeC85IS2esX8p+fN2nF7RCudH3jSp2nPotNNj/LRii9eCTiY6O1vj4+ECH4X/ZmfDudXB4N0z6EjpdHOiIqpWqcux0HlluIii6Gii6Mvg5MRxx/x7OzuHYqbxS9xkcJLRoFOomgMLE0ODnZFBY3sgtb+zMh4ValzOm9hORNaoaXd56NmZ7XXHqCHxwI2TshNs/qfVJ5GRO/hlVRlnu1UFWtmdV0ZlXC1knc8kvKP2HUdOwEFp6JIEuEY1/Tg6NQmnZuEHRlULL8Aa0aBxK04YhZ7Q5GGPOZomkLjh9HGbdAgc2w/hZcN6wQEdUJDe/oKht4HAp7QbOdE5RA/Th7BxO5xWUus9GocFnXBn0OqcZzd0qo5bhDWjuJoKWjUNp3shZp3mjUEKq4RLfmPrIEkltl3sS5kyAlNXwy3egx8iAhfLx6mTmb0wtSgZHsnM5drr0aqOQIDmj3aBjq3D6dSi8Mji7yqhFI6fcqo2MqVkskdRmeTnwyR3w03dw4z+hz7iAhRKXkMqjnydwXpvGdGoVzvltmxS7MnCnPRqgm1i1kTF1giWS2io/Dz7/NWz/Bsb+HS4YH7BQtqYe5aFPNjCgYwvm3HuxXTEYU89YpXFtVFAAc++HrfNg5EsQfVfAQsk4fpq734unWaMQZkwabEnEmHrIrkhqo8VPw8Y5cNWTcMn9AQsjN7+A385aS/rx03z6m0to2ywsYLEYYwLHrkhqm9xTsHom9P0lXPlwQEN57uvN/PhTJv91c38u6Nii/A2MMXWSJZLa5qd/Q+4Jp00kgA3VH67cw4crk/nN0PMYN7B4p87GmPrEEkltkzjf6c2365UBC2HlrgyenbeZq3q24ZGRvQIWhzGmZrBEUpsU5ENSHHQfHrCx1vdmZvPbWWvp1Dqc1yYMJNjPvYoaY2o+SyS1SUo8nEiHXmMDcvgTp/O45/14cvML+L87omkWFhqQOIwxNYslktokcT4EhcD511b7oQsKlIc/3cC2A8d487ZBnNemSbXHYIypmSyR1BaqkBgDXa6ARtV/h9QbS3cQtymNx0f3ZmiPejripDGmRJZIaotD2yBzJ/S6rtoPvWBTGn9fvI2bBnXg7iu6VvvxjTE1myWS2iJxvvO355jqPWzaUR76ZD0DOrbgpRv7Wd9YxpizWCKpLRJj4dyB0Lz6ntnIPJHD3e/F0zQshP+17k+MMaWwRFIbHE2FffHVWq3ldH+yhoPHTjNjUjTtrPsTY0wpLJHUBkmxzt+e1ZdIpn29hZW7Mvnrzf2s+xNjTJkskdQGSbHQsiu07V0th5v14x4+WLmH31x5HjcOjKyWYxpjai9LJDXdqaOw699OtVY1NHT/uCuDZ+ZuZljPNjwyyro/McaUzxJJTbdjERTkVsvT7CmHs5la2P3JeOv+xBjjHUskNV1iDIRHQMchfj1Mdk4e97y/htz8At6+I5rmjaz7E2OMdyyR1GR5ObB9EfQcBUH+u/VW1en+JCntKG9MGEg36/7EGOMDSyQ12e7v4PRRv1drvbF0B7EJaTw2uhfDerb167GMMXWPXxOJiIwSkSQR2SEij5WwvLOILBGRjSKyXEQiPZZNFpHt7mtyCdvOE5FN/ow/4BJjIDQczhvmt0Ms2JTG3xZt46aBHbjnivP8dhxjTN3lt0QiIsHAdGA0EAVMEJGoYqu9Aryvqv2BacBf3G1bAc8AFwFDgGdEpKXHvm8Cjvsr9hqhoMAZe6Tb1RDayC+HKOz+5IKOLXjpJuv+xBhTMf68IhkC7FDVXaqaA8wBbii2ThSw1J1e5rF8JLBIVTNV9TCwCBgFICJNgIeAF/wYe+ClroNj+/1WrZV5Iod73o+nScMQZlj3J8aYSvBnIukA7PWYT3HLPG0AbnKnbwSaikjrcrZ9HngVyK7qgGuUxBiQYOgxssp3nZtfwP2z1nLg6Glm3GHdnxhjKifQje0PA0NFZB0wFNgH5Je2sogMALqp6pfl7VhE7hWReBGJT09Pr7KAq01iLHS+FMJbVfmun5+/hR92ZfDyTf0YYN2fGGMqyZ+JZB/Q0WM+0i0roqr7VfUmVR0IPOGWZZWx7SVAtIjsBr4HeojI8pIOrqozVDVaVaPbtKllAzFl7IT0rX7ppPGjH5N5/4c93Hvledw0yLo/McZUnj8TyWqgu4h0FZEGwHhgnucKIhIhIoUxPA7MdKcXAiNEpKXbyD4CWKiqb6nquaraBbgc2Kaqw/x4DoGRGOP8reKxR1b9lMnTczcxtEcbHrXuT4wxVcRviURV84AHcJLCVuATVd0sItNE5Hp3tWFAkohsA9oBL7rbZuK0hax2X9PcsvohKRba9YOWnatslymHs5n64Ro6tQrn9QnW/YkxpuqE+HPnqhoLxBYre9pj+jPgs1K2ncnPVyglLd8N9K2SQGuS4+mQvBKGPlplu8zOyePe99eQk1/A25Ot+xNjTNXyayIxFbAtDtAqax9RVf7fpxvZmnaUmXdeaN2fGGOqXKDv2jLFJcZA805wTr8q2d2bS3cQk5DKY6N6cZV1f2KM8QNLJDXJ6eOwcxn0GlMlY498szmNVxdt48aBHbj3Suv+xBjjH5ZIapKdSyH/dJVUayWlHePBj9dzQWRz/mLdnxhj/MgSSU2SGANhLaDTpZXazeETOdz9/moaNwzhfydFW/cnxhi/ssb2miI/D7YtgB6jILjiH0tufgG/dbs/+fjeizmnuXV/YozxL7siqSmSV8CprEpXa73gdn/ylxv7MbBTy/I3MMaYSrJEUlMkxkBIGJx/TYV3MWdVMu/9sId7rujKzYOt+xNjTPUoN5GISLiIPCUib7vz3UXEv0P21TeqTieN5w2DBo0rtIvVuzN5au4mruzRhsdG967S8IwxpizeXJG8A5zG6TARnM4T6/ZYINUtLQGOJFe4Wmtf1knu+2ANkS3DeWO8dX9ijKle3iSSbqr6X0AugKpmA/ZNVZUSYwCBHqN93jQ7J4973osnJ6+At++Ipnm4dX9ijKle3twelCMijQAFEJFuOFcopqokxUDHi6CJb93dn9H9yeQLOb+tdX9ijKl+3lyRPAMsADqKyCxgCfCIX6OqTw7vcaq2KlCtNX2Z0/3Jo6N6cVUv6/7EGBMYZV6RiPM4dCLOcLgX41Rp/UFVD1VDbPVDkts5so+J5JvNabzyzTbGDTiX31j3J8aYACozkaiqikisqvYDYqoppvolMQba9IbW3bzepLD7k/6RzXn55v7W/YkxJqC8qdpaKyIX+j2S+ig7E/ascDpp9MEjn28kvGEIM6z7E2NMDeBNY/tFwO0isgc4gVO9para36+R1QfbFoLm+1StlZyRzYa9Wfx5TC/r/sQYUyN4k0hG+j2K+ipxPjQ9F9oP9HqT2E2pAIzu295fURljjE/KrdpS1T1AC+AX7quFW2YqI/ek0218z9EQ5H1PNXEJqfSPbE7HVuF+DM4YY7znTRcpfwBmAW3d14ci8jt/B1bn7VoOudk+VWvtzcxmQ8oRuxoxxtQo3lRt/Rq4SFVPAIjIX4EfgDf8GVidlzgfGjaDLld4vcmCTWkAjOl3jr+iMsYYn3lTpyJAvsd8PtZFSuUU5EPSAug+HEIaeL1ZTEIqfc5tRufWFevY0Rhj/MGbK5J3gB9F5Et3fhzwL/+FVA/sXQXZh3yq1tqXdZL1e7P4fyN7+jEwY4zxXbmJRFX/JiLLgcvdoimqus6vUdV1ifMhKBTOH+71Jj9Xa1n7iDGmZik3kYjIxcBmVV3rzjcTkYtU9Ue/R1cXqTpPs583FMKaeb1ZbEIqvds3o2uEVWsZY2oWb9pI3gKOe8wfd8tMRaQnwuGfoKf3T7OnHTnFmj2HGdPXGtmNMTWPV43tqqqFM6pagHdtK4jIKBFJEpEdIvJYCcs7i8gSEdkoIstFJNJj2WQR2e6+Jrtl4SISIyKJIrJZRF72Jo4aJXG+89eHRBLnPoQ4pr9Vaxljah5vEskuEfm9iIS6rz8Au8rbSESCgenAaCAKmCAiUcVWewV43+1uZRrwF3fbVjjd118EDAGeEZGWhduoai9gIHCZiPg+GlQgJcZCh2ho5n1SiEtIo2e7pnRrY+ONGGNqHm8SyX3ApThD7O7D+XK/14vthgA7VHWXquYAc4Abiq0TBSx1p5d5LB8JLFLVTFU9DCwCRqlqtqouA3D3uRaIpLY4sg/2r/Wpk8aDR0+xek+mNbIbY2osb7pIOaiq41W1rfu6TVUPerHvDsBej/kUt8zTBpyxTgBuBJqKSGtvthWRwm5blpR0cBG5V0TiRSQ+PT3di3CrQdHYI2O93mTB5jRU7SFEY0zNVWoiEZF7RKS7Oy0iMlNEjrjtGYOq6PgPA0NFZB0wFOeKJ7/sTUBEQoDZwOuqWmI1m6rOUNVoVY1u08a3IWz9JikWWp8PET283iRmYyrd2zahe7umfgzMGGMqrqwrkj8Au93pCcAFwHnAQ8BrXux7H9DRYz7SLSuiqvtV9SZVHQg84ZZlebHtDGC7qv6PF3HUDCez4KdvnUZ2LweiSj92mlW7Mxlt1VrGmBqsrESSp6q57vRYnEbxDFVdDHjzMMNqoLuIdBWRBsB4YJ7nCiISISKFMTwOzHSnFwIjRKSl28g+wi1DRF4AmgN/9CKGmmPHYijIs2otY0ydU1YiKRCR9iISBlwDLPZY1qi8HatqHvAATgLYCnyiqptFZJqIXO+uNgxIEpFtQDvgRXfbTOB5nGS0Gpimqpnu7cFP4DTSrxWR9SJyt/enG0CJ86FxW4iM9nqTuIRUzmvTmJ5WrWWMqcHKeh7kaSAeCAbmqepmABEZihe3/wKoaiwQW6zsaY/pz4DPStl2Jj9foRSWpVAbO4zMOw3bF0PfmyDIu6FxM46fZuWuDH477Hwbk90YU6OVmkhUdb6IdAaaurfgFooHbvV7ZHXJT99BzjGfOmlcuPkABWp9axljar4yn1B3q6cOFys74deI6qLE+RDaGLoO9XqTuE2pdGkdTu/2Vq1ljKnZvB/j1VRMQQEkxUH3ayE0zKtNMk/ksGJnBmO+8eXNAAAfq0lEQVT6tbdqLWNMjWeJxN/2r4XjadDT+2qtRVvSyC9Qq9YyxtQKZT2QOFJEfllC+S9FxPuBNOq7xPkgwdBjhNebxCSk0alVOH3O9b6beWOMCZSyrkieBv5dQvlynA4WjTcSY6HL5dCoZfnrAlnZOazYcYjR/c6xai1jTK1QViJpqKpndVKlqofw7oFEc2g7HEry6W6tb7YcIK9Auc6qtYwxtURZiaSZ26fVGUQkFC8eSDQ4IyGCb2OPJKTSoUUj+nVo7qegjDGmapWVSL4A3haRoqsPEWkC/NNdZsqTFAvtL4AWHctfFzhyMpfvdxxijFVrGWNqkbISyZPAAWCPiKwRkbXAT0C6u8yU5dgB2LvKp761Fm85QG6+3a1ljKldynqyPQ94TESeA853i3eo6slqiay22xYHqM9D6p7bPIwBHVv4Ly5jjKlipSYSEbmpWJECLURkvaoe829YdUBiLLToDO36eLX60VO5fLvtEJMu6WzVWsaYWqWsLlJ+UUJZK6C/iPxaVZeWsNwAnD4Gu5bDhXd7PfbI0q0HyckvsC7jjTG1TllVW1NKKnc7cvwEZ+x2U5IdSyD/tE9js8ckpHJOszAGdvTueRNjjKkpfO4iRVX3AKF+iKXuSIyBRq2g48VerX78dB7/3pbOqL7nEBRk1VrGmNrF50QiIj2B036IpW7Iz4XtC6HnaAgus3PlIku2HiAnr4Dr+tvdWsaY2qesxvavcRrYPbUC2gOT/BlUrbbnP3DqiI8PIabRtmlDBneyai1jTO1T1k/mV4rNK5ABbFfVHP+FVMslxkBII+h2tVernzidx7Kkg4y/sKNVaxljaqWyGttL6rAREblcRCao6v3+C6uWUnVu++12NTQI92qTZUkHOZ1XwGh7CNEYU0t51UYiIgNF5L9FZDfwPJDo16hqq9QNcDTFp04a4xLSiGjSkAu7tPJjYMYY4z9ltZH0ACa4r0PAx4Co6lXVFFvtkxgDEgQ9Rnm1+smcfJYmHuTmwR0ItmotY0wtVVYbSSLwHTBWVXcAiMiD1RJVbZUUC50ugcatvVp9edJBTubmM6avVWsZY2qvsqq2bgJSgWUi8raIXAPYz+bSZP4EBzb5VK0Vk5BK68YNGNLVqrWMMbVXqYlEVb9S1fFAL2AZ8EegrYi8JSLejxtbXyTFOn+9vO33VK5TrTWizzmEBPv8OI8xxtQY5X6DqeoJVf1IVX8BRALrgEf9HlltkxgLbftAq65erb48KZ3snHwbCdEYU+v59FNYVQ+r6gxVvcZfAdVKJzIgeYVvd2ttSqVleCgXn2fVWsaY2s2vdSoiMkpEkkRkh4g8VsLyziKyREQ2ishyEYn0WDZZRLa7r8ke5YNFJMHd5+tSE/pc37YAtMDrRHIqN58lWw8y0qq1jDF1gN++xUQkGJgOjAaigAkiElVstVeA91W1PzAN+Iu7bSvgGZwehocAz4hIYf8hbwH3AN3dl3f32vpTUiw0i3SG1fXCd9sPcfx0nj2EaIypE/z5c3gIzoiKu9wuVeYANxRbJwooHNdkmcfykcAiVc1U1cPAImCUiLQHmqnqSlVV4H1gnB/PoXw52U638b3GeD32SFxCKs0bhXJpN+9uEzbGmJrMn4mkA7DXYz7FLfO0Aec2Y4AbgaYi0rqMbTu402XtEwARuVdE4kUkPj09vcInUa5dyyDvpNfVWqfz8lm05QAjotoRatVaxpg6INDfZA8DQ0VkHTAU2AfkV8WO3ZsColU1uk2bNlWxy5IlxkBYc+h8mVer/2fHIY6dzmOMVWsZY+oI7wbMqJh9QEeP+Ui3rIiq7se9IhGRJsDNqpolIvuAYcW2Xe5uH1ms/Ix9Vqv8PEiKg+4jIdi7sb5iNqbRNCyEy86P8HNwxhhTPfx5RbIa6C4iXUWkATAemOe5gohEiEhhDI8DM93phcAIEWnpNrKPABaqaipwVEQudu/WugOY68dzKNveH+FkptfVWjl5BSzaksbwqHY0CAn0xaAxxlQNv32bqWoe8ABOUtgKfKKqm0Vkmohc7642DEgSkW1AO+BFd9tMnF6GV7uvaW4ZwG+B/wN2ADuBOH+dQ7kSYyC4AZzv3WM1/9l5iKOn8uwhRGNMneLPqi1UNRaILVb2tMf0Z8BnpWw7k5+vUDzL44G+VRtpBahCUgycNwwaNvVqk7iEVJo2DOHy7latZYypO6x+paIOboHDu72u1srNL+CbLQe4NqodDUOC/RubMcZUI0skFZUYAwj0GO3V6j/szCArO5fRfc/xb1zGGFPNLJFUVGIMRF4ITdt5tXrcplQaNwjmyh5+vBXZGGMCwBJJRRxJgdT1Xldr5eUXsHDzAa7p3Y6wUKvWMsbULZZIKiLRvX+g11ivVv/xp0wyT+TYQ4jGmDrJEklFJMVARA+ION+r1WMSUglvEMywnlatZYypeyyR+OrkYdj9vdfVWvkFysJNaVzdq61Vaxlj6iRLJL7avggK8nyo1sogw6q1jDF1mCUSXyXGQJNz4NxBXq0el5BGWGiQVWsZY+osSyS+yD0FOxZDz9EQVP5bl1+gLNjsVGuFN/BrJwLGGBMwlkh88dO3kHPc62qt+N2ZpB87zei+Vq1ljKm7LJH4InE+NGgKXa/wavW4TWk0DAni6l5t/RyYMcYEjiUSbxUUuGOPXAshDb1YXYnblMqwnm1o3NCqtYwxdZclEm/ti4cTB72u1lqbfJgDR0/b3VrGmDrPEom3EudDUCh0H+7V6jEJqTQICeKa3t71xWWMMbWVJRJvJcZCl8ud8dnLUVCgLNiUxtAebWhi1VrGmDrOEok30rdBxnavn2ZftzeL1COnGNPPuow3xtR9lki8kTjf+dtzjFerxyWk0iDYqrWMMfWDJRJvJMXCuQOheYdyV1VV4jalcUX3CJqFhVZDcMYYE1iWSMpzLA1SVntdrbUh5Qj7sk4y2u7WMsbUE5ZIypPk29gjcQmphAYLw61ayxhTT1giKU9iLLQ6D9r0KndVVSUmIZXLzo+gebhVaxlj6gdLJGU5dRR++rfTyC5S7uqb9h0l5fBJewjRGFOvWCIpy47FkJ/jdbVWTEIqIUHCiCir1jLG1B+WSMqSFAvhEdBxSLmrOndrpXLp+RG0CG9QDcEZY0zNYImkNKqQngQ9R0FQ+UPkbt5/lD0Z2Yzpaw8hGmPqF78mEhEZJSJJIrJDRB4rYXknEVkmIutEZKOIjHHLG4jIOyKSICIbRGSYxzYT3PKNIrJARCL8FDz85lsY/V9erR63KZXgIGFEH0skxpj6xW+JRESCgenAaCAKmCAiUcVWexL4RFUHAuOBf7jl9wCoaj9gOPCqiASJSAjwGnCVqvYHNgIP+OscEIEGjctdTVWJTUjjkvNa06qxVWsZY+oXf16RDAF2qOouVc0B5gA3FFtHgWbudHNgvzsdBSwFUNWDQBYQDYj7aiwi4m67nwBLTDvGT4dO2N1axph6yZ+JpAOw12M+xS3z9CwwUURSgFjgd275BuB6EQkRka7AYKCjquYCU4EEnAQSBfyrpIOLyL0iEi8i8enp6VV0SiWLS0glSGBEH7tbyxhT/wS6sX0C8K6qRgJjgA9EJAiYiZN44oH/AVYA+SISipNIBgLn4lRtPV7SjlV1hqpGq2p0mzZt/HYChQ8hXtS1NRFNyh850Rhj6hp/JpJ9QEeP+Ui3zNOvgU8AVPUHIAyIUNU8VX1QVQeo6g1AC2AbMMBdd6eqqrvtpX48h3JtP3icneknGNPfqrWMMfWTPxPJaqC7iHQVkQY4jenziq2TDFwDICK9cRJJuoiEi0hjt3w4kKeqW3ASUZSIFF5iDAe2+vEcyhWzMRURGGnVWsaYespvw/epap6IPAAsBIKBmaq6WUSmAfGqOg/4E/C2iDyI0/B+p6qqiLQFFopIAU7ymOTuc7+IPAd8KyK5wB7gTn+dgzfiNqUypEsr2jYNC2QYxlSr3NxcUlJSOHXqVKBDMVUgLCyMyMhIQkMr1kegX8eBVdVYnEZ0z7KnPaa3AJeVsN1uoGcp+/wn8M8qDbSCdhw8xrYDx3nu+j6BDsWYapWSkkLTpk3p0qUL4kU/dKbmUlUyMjJISUmha9euFdpHoBvba7XYhDREYJQ9zW7qmVOnTtG6dWtLInWAiNC6detKXV1aIqmE2IRUoju3pF0zq9Yy9Y8lkbqjsp+lJZIK2pV+nMS0Y/YQojGm3rNEUkFxm9IAq9YyJhAyMjIYMGAAAwYM4JxzzqFDhw5F8zk5OV7tY8qUKSQlJfl87LFjx3L55ZefUTZx4kS++uqrovm8vDxatGhRNJ+YmMjo0aPp3r07gwYNYvz48Rw8ePCMfeTn53P//ffTt29f+vXrx5AhQ9izZ4/P8QWCXxvb67KYjakM7tyS9s0bBToUY+qd1q1bs379egCeffZZmjRpwsMPP3zGOqqKqhIUVPLv5Xfeecfn42ZmZrJx40bCwsJITk6mU6dO5W6TnZ3NddddxxtvvMGYMWMAWLJkCRkZGbRt27ZovY8++oiMjAw2btxIUFAQycnJNGvWrLTdeiUvL4+QEP9/zVsiqYDdh06wJfUoT17XO9ChGBNwz329mS37j1bpPqPObcYzv/D9bsgdO3Zw/fXXM3DgQNatW8eiRYt47rnnWLt2LSdPnuTWW2/l6aedG0cvv/xy3nzzTfr27UtERAT33XcfcXFxhIeHM3fu3DO+5At99tlnjBs3jubNmzNnzhweeeSRcmP68MMPGTp0aFESAbjmmmvOWi81NZX27dsXJT7PJBUTE8NTTz1Ffn4+7dq145tvvuHQoUPcdddd7N69myZNmjBjxgz69u3Lk08+SXJyMjt37qRr1668++67PPLII3z//fecOnWK3//+99x9990+v7dlsaqtCojdlArAaGsfMabGSUxM5MEHH2TLli106NCBl19+mfj4eDZs2MCiRYvYsmXLWdscOXKEoUOHsmHDBi655BJmzpxZ4r5nz57NhAkTmDBhArNnz/Yqnk2bNjF48OBy1xs/fjxffPEFAwcO5OGHHy664kpLS2Pq1Kl8+eWXbNiwgTlz5gDw1FNPcdFFF7Fx40aeffZZ7rzzzjPegyVLlvDhhx8yY8YM2rZty6pVq1i9ejXTp08nOTnZq9i9ZVckFRCXkMaAji3o0MKqtYypyJWDP3Xr1o3o6Oii+dmzZ/Ovf/2LvLw89u/fz5YtW4iKOnNEi0aNGjF69GgABg8ezHfffXfWfvfv309ycjKXXHIJAAUFBSQmJtKrV68S73ry9U6oTp06kZSUxNKlS1m6dClXXXUVX375JYcPH+aqq66ic+fOALRq1QqA77//npiYGABGjBjBnXfeyYkTJwC44YYbCAtz7ib95ptv2Lp1a1ECOnLkCNu3b/eqWs5blkh8lJyRTcK+I/x5TK9Ah2KMKUHjxj+PIbR9+3Zee+01Vq1aRYsWLZg4cWKJz0s0aPDzOELBwcHk5eWdtc7HH3/MoUOH6NKlC+B8Ic+ePZvnnnuO1q1bc/jw4aJ1MzMziYhwxtzr06cPP/74o1exh4WFMWbMGMaMGUNERARz587lyiuv9GpbT57vgaryj3/8o8TqtKpiVVs+iius1upr1VrG1HRHjx6ladOmNGvWjNTUVBYuXFjhfc2ePZvFixeze/dudu/ezapVq4qqt4YNG8acOXPIzc0F4N133+Wqq64CYNKkSSxfvpwFCxYU7WvZsmVs3XpmN4Fr1qwhNdX5fikoKCAhIYHOnTtz6aWXsmzZsqI7uDIzMwG44oormDVrFgCLFy+mQ4cOZySQQiNHjuQf//hHUXJMSkri5MmTFX4fSmJXJD6K3ZRG/8jmdGwVHuhQjDHlGDRoEFFRUfTq1YvOnTtz2WVn9cjklZ07d5KamnpGlVn37t0JCwtjzZo1jBs3jrVr1zJ48GCCgoLo3r07//yn05NTeHg48+fP58EHH+R3v/sdoaGhDBgwgNdee+2MY6SlpXHPPfeQk5ODqnLJJZcwdepUGjZsyFtvvcUNN9yAqnLuuecSFxfHtGnTuOuuu+jfvz9NmjQp9S603/zmNyQnJzNgwAAA2rZty9y5cyv0PpRGnN7Y67bo6GiNj4+v9H5SDmdz+V+X8djoXtw3tFsVRGZM7bR161Z697a7FuuSkj5TEVmjqtGlbFLEqrZ8sMB9CHGMVWsZY0wRSyQ+iElIpW+HZnRqbdVaxhhTyBKJl/ZnnWRdcpY1shtjTDGWSLxU2LeWddJojDFnskTipbiEVHq3b0bXiLNvrzPGmPrMEokX0o6cIn7PYcZYT7/GGHMWSyReWOA+hDimv1VrGVMTVEU38gAzZ84kLS2t1OU5OTm0atWKJ5988ozyyMhIsrKyiuYXL17MuHHjiuZjYmIYPHgwffr0YcCAATz66KNn7Ts1NZUxY8ZwwQUXEBUVxfXXX+913DWNJRIvxCak0bNdU7q1aRLoUIwx/NyN/Pr167nvvvt48MEHi+Y9uzspT3mJZOHChURFRfHxxx97vc8NGzbwxz/+kdmzZ7N582bWrFlT1K2KpyeffJLrrruODRs2sGXLFl544QWvj1Gakrp2qQ72ZHs5Dh49xeo9mfzxmh6BDsWYminuMUhLqNp9ntMPRr9coU3fe+89pk+fTk5ODpdeeilvvvkmBQUFTJkyhfXr16Oq3HvvvbRr147169dz66230qhRI1atWnVWEpo9ezYPPfQQf//731m1ahVDhgwp9/h//etfeeqpp+jRw/nOCA4OZurUqWetl5qaSmRkZNF8//79i6ZfeuklZs+eTVBQEGPHjuXFF19k7dq1TJ06lZMnT9K9e3dmzpxJ8+bNufzyy7nwwgv57rvvmDhxIhMmTGDq1KkkJycTFBTE66+/zsUXX1yh99JblkjKsXBzGqowpp+1jxhT023atIkvv/ySFStWEBISwr333sucOXPo1q0bhw4dIiHBSXhZWVm0aNGCN954gzfffLOo+xBP2dnZLF++vOiqZfbs2V4lkk2bNvHEE0+Uu94DDzzAbbfdxqBBg7j22muZMmUK7du35+uvvyYuLo5Vq1bRqFGjor61Jk6cyNtvv81ll13Gn//8Z55//nleeeUVwBldsbD3jltvvZVHHnmEiy++mN27dzN27Fg2bdrk9XtYEZZIyhGTkEr3tk3o3q5poEMxpmaq4JWDPyxevJjVq1cX9Yl18uRJOnbsyMiRI0lKSuL3v/891113HSNGjCh3X/PmzWP48OGEhYVxyy23MHjwYF599VWCgoKqpNv4MWPGsHPnThYsWEBcXBwDBw5k8+bNLF68mLvuuotGjZxhKlq1akVGRganTp0q6its8uTJTJo0qWhft9566xnvgecQwocPH+bkyZNF+/MHSyRlSD92mlU/ZfLA1d0DHYoxxguqyl133cXzzz9/1rKNGzcSFxfH9OnT+fzzz5kxY0aZ+5o9ezYrV64sat9IT0/n3//+N1dddVVRt/GF47IX7zZ+zZo19OlT/jgtrVu35vbbb+f2229n1KhRfP/99z6esaN4t/ElVdP5kzW2l2Hh5jQKFK6zhxCNqRWuvfZaPvnkEw4dOgQ4d3clJyeTnp6OqnLLLbcwbdo01q5dC0DTpk05duzYWfvJyspi5cqVpKSkFHUb//rrr5/RbfwHH3wAOA3cs2bNKuo2/pFHHuH5559nx44dgFPtVNgTsKclS5YUded+9OhRfvrpJzp16sTw4cOZOXNm0bLMzExat25No0aNWLFiBQAffPABQ4cOLfU9mD59etF84UiL/uTXRCIio0QkSUR2iMhjJSzvJCLLRGSdiGwUkTFueQMReUdEEkRkg4gM89imgYjMEJFtIpIoIjf7K/64Tamc16YxPdrZ3VrG1Ab9+vXjmWee4dprr6V///6MGDGCAwcOsHfvXq688koGDBjAlClTeOmllwCYMmUKd99991m3DX/++ecMHz6c0NDQorJx48bx1VdfkZuby7PPPsuWLVu44IILGDRoEL1792bChAkADBw4kFdffZVf/epXREVF0a9fv6KxRDytXr2aQYMG0b9/fy699FKmTp3KwIEDGTt2LKNGjSI6OpoBAwbw97//HXCSx4MPPkj//v3ZsmXLWbckF5o+fTr/+c9/6N+/P1FRUbz99ttV9v6Wxm/dyItIMLANGA6kAKuBCaq6xWOdGcA6VX1LRKKAWFXtIiL3A9GqOkVE2gJxwIWqWiAizwHBqvqkiAQBrVT1UFmxVKQbeVXlxZittG/RiF9f3tWnbY2p66wb+bqnMt3I+7ONZAiwQ1V3uQHNAW4Atniso0Azd7o5sN+djgKWAqjqQRHJAqKBVcBdQC93WQFQZhKpKBHhybFR5a9ojDH1nD+rtjoAez3mU9wyT88CE0UkBYgFfueWbwCuF5EQEekKDAY6ikgLd/nzIrJWRD4VkXYlHVxE7hWReBGJT09Pr6JTMsYYU1ygG9snAO+qaiQwBvjAra6aiZN44oH/AVYA+ThXUJHAClUdBPwAvFLSjlV1hqpGq2p0mzZt/H8mxtQz9WF01fqisp+lPxPJPqCjx3ykW+bp18AnAKr6AxAGRKhqnqo+qKoDVPUGoAVOe0sGkA184W7/KTDIf6dgjClJWFgYGRkZlkzqAFUlIyODsLCwCu/Dn20kq4HubtXUPmA8cFuxdZKBa4B3RaQ3TiJJF5FwnBsBTojIcCCvsJFeRL4GhuG0oVzDmW0uxphqEBkZSUpKClZtXDeEhYWd0V2Lr/yWSFQ1T0QeABYCwcBMVd0sItOAeFWdB/wJeFtEHsRpeL9TVdW9U2uhiBTgJKFJHrt+FKcK7H+AdGCKv87BGFOy0NBQuna1uxmNw2+3/9YkFbn91xhj6jtvb/8NdGO7McaYWs4SiTHGmEqpF1VbIpIOnN1HgXci8NNDj5VkcfnG4vKNxeWbuhpXZ1Ut9/mJepFIKkNE4r2pI6xuFpdvLC7fWFy+qe9xWdWWMcaYSrFEYowxplIskZSv7NFvAsfi8o3F5RuLyzf1Oi5rIzHGGFMpdkVijDGmUiyRGGOMqRRLJD4QkT+JiIpIRKBjARCR590hiteLyDcicm6gYwIQkf92h0HeKCJfeowjE1AicouIbBaRAhEJ+K2a5Q1FHQgiMlNEDorIpkDH4klEOrrDcm9xP8M/BDomABEJE5FV7pDgm90RXGsMEQl2hzKf78/jWCLxkoh0BEbg9FhcU/y3qvZX1QHAfODpQAfkWgT0VdX+ON3/Px7geAptAm4Cvg10IO5Q1NOB0Tgjgk5wh5sOtHeBUYEOogR5wJ9UNQq4GLi/hrxfp4GrVfUCYAAwSkQuDnBMnv4AbPX3QSyReO/vwCM4vRTXCKp61GO2MTUkNlX9RlXz3NmVOGPRBJyqblXVpEDH4SoailpVc4DCoagDSlW/BTIDHUdxqpqqqmvd6WM4X47FR1ytduo47s6Guq8a8f9QRCKB64D/8/exLJF4QURuAPap6oZAx1KciLwoInuB26k5VySe7gLiAh1EDeTNUNSmBCLSBRgI/BjYSBxu9dF64CCwSFVrRFw4o8s+AhT4+0D+HNiqVhGRxcA5JSx6AvgzTrVWtSsrLlWdq6pPAE+IyOPAA8AzNSEud50ncKokZlVHTN7GZWovEWkCfA78sdgVecCoaj4wwG0L/FJE+qpqQNuYRGQscFBV14jIMH8fzxKJS1WvLalcRPoBXYENIgJONc1aERmiqmmBiqsEs4BYqimRlBeXiNwJjAWu0Wp8WMmH9yvQvBmK2ngQkVCcJDJLVb8ob/3qpqpZIrIMp40p0DcrXAZcLyJjcEaebSYiH6rqRH8czKq2yqGqCaraVlW7qGoXnCqIQdWRRMojIt09Zm8AEgMViycRGYVzSX29qmYHOp4aqmgoahFpgDMU9bwAx1RjifMr7l/AVlX9W6DjKSQibQrvShSRRsBwasD/Q1V9XFUj3e+s8cBSfyURsERS270sIptEZCNO1VuNuCUSeBNoCixyb03+Z6ADAhCRG0UkBbgEiBGRhYGKxb0ZoXAo6q3AJ6q6OVDxFBKR2cAPQE8RSRGRXwc6JtdlOENuX+3+m1rv/toOtPbAMvf/4GqcNhK/3mpbE1kXKcYYYyrFrkiMMcZUiiUSY4wxlWKJxBhjTKVYIjHGGFMplkiMMcZUiiUSY7wkIsfLX8vnfUaKyFwR2S4iO0XkNfe5EkRkmIgc8bjddXEp+xgtIvFuz7jrROTVqo7TmLJYIjEmQNyH7L4AvlLV7kAPoAnwosdq36nqAPd11lP7ItIX57mdiW7PuNHADv9Hb8zPLJEYUwki0kVElrpjrywRkU5ueTcRWSkiCSLyQilXM1cDp1T1HSjqs+lB4C4RCfcyhEeAF1U1sXAfqvpW5c/MGO9ZIjGmct4A3nPHXpkFvO6Wvwa8pqr9cLrVKUkfYI1ngdsRYTJwvlt0hUfV1hMl7KNv8X0YU90skRhTOZcAH7nTHwCXe5R/6k5/VHwjH3hWbb1Y/urGVD9LJMYEzhZgsGeBiDQDOuF9O8fm4vswprpZIjGmclbg9K4KzuBi37nTK4Gb3enxxTdyLQHCReQOKBp+91XgXR96Tf5v4M8i0sPdR5CI3OfbKRhTOZZIjPFeuNsjbuHrIeB3wBS399dJ/NwD8x+Bh9zy84EjxXfmjtNyI3CLiGzHGd/+FM5Aal5R1Y3usWaLyFaccTDOq/AZGlMB1vuvMX7g3nV1UlVVRMYDE1Q14GOyG+MPNkKiMf4xGHjTfVYkC2fsemPqJLsiMcYYUynWRmKMMaZSLJEYY4ypFEskxhhjKsUSiTHGmEqxRGKMMaZS/j9WS0Zpf71KBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train auc vs test auc curve\n",
    "plotTrainvsTestAUC(d_train_w2,y_train,d_test_w2,y_test,\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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",
    "plotROC(d_train_w2,y_train,d_test_w2,y_test,lr_l2_w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.85      0.60      2836\n",
      "           1       0.97      0.81      0.88     14719\n",
      "\n",
      "   micro avg       0.82      0.82      0.82     17555\n",
      "   macro avg       0.71      0.83      0.74     17555\n",
      "weighted avg       0.88      0.82      0.83     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.809\n",
      "The value of TNR is 0.852\n",
      "The value of FPR is 0.148\n",
      "The value of FNR is 0.191\n",
      "\n",
      "The roc auc score is 0.907430\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test\n",
    "report(d_test_w2,y_test,lr_l2_w2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pdXTwVIPLPpp"
   },
   "source": [
    "## [5.4] Logistic Regression on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WP9NjwNSLPpp"
   },
   "source": [
    "### [5.4.1] Applying Logistic Regression with L1 regularization on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "207gYymwLPpq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 320.52 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_tf_wt as training dataset\n",
    "#and regulariser as L1\n",
    "c_grid_t_wt=cv_lr_grid(d_train_tf_wt,y_train,'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.82      0.56     11345\n",
      "           1       0.96      0.79      0.86     58873\n",
      "\n",
      "   micro avg       0.79      0.79      0.79     70218\n",
      "   macro avg       0.69      0.80      0.71     70218\n",
      "weighted avg       0.87      0.79      0.81     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.787\n",
      "The value of TNR is 0.819\n",
      "The value of FPR is 0.181\n",
      "The value of FNR is 0.213\n",
      "\n",
      "The roc auc score is 0.883075\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L1 regulariser\n",
    "lr_l1_tf_wt=LogisticRegression(penalty='l1',C=c_grid_t_wt,class_weight='balanced')\n",
    "lr_l1_tf_wt.fit(d_train_tf_wt,y_train)\n",
    "report(d_train_tf_wt,y_train,lr_l1_tf_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [01:18<00:00, 12.32s/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(d_train_tf_wt,y_train,d_test_tf_wt,y_test,\"l1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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",
    "plotROC(d_train_tf_wt,y_train,d_test_tf_wt,y_test,lr_l1_tf_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.82      0.56      2836\n",
      "           1       0.96      0.79      0.86     14719\n",
      "\n",
      "   micro avg       0.79      0.79      0.79     17555\n",
      "   macro avg       0.69      0.80      0.71     17555\n",
      "weighted avg       0.87      0.79      0.81     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.786\n",
      "The value of TNR is 0.822\n",
      "The value of FPR is 0.178\n",
      "The value of FNR is 0.214\n",
      "\n",
      "The roc auc score is 0.883645\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test\n",
    "report(d_test_tf_wt,y_test,lr_l1_tf_wt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ynUK2tYDLPps"
   },
   "source": [
    "### [5.4.2] Applying Logistic Regression with L2 regularization on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hVYuiXl2LPpt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 55.23 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_train_tf_wt as training dataset\n",
    "#and regulariser as L2\n",
    "c_grid_tf_wt=cv_lr_grid(d_train_tf_wt,y_train,'l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.82      0.56     11345\n",
      "           1       0.96      0.79      0.86     58873\n",
      "\n",
      "   micro avg       0.79      0.79      0.79     70218\n",
      "   macro avg       0.69      0.80      0.71     70218\n",
      "weighted avg       0.87      0.79      0.81     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.787\n",
      "The value of TNR is 0.819\n",
      "The value of FPR is 0.181\n",
      "The value of FNR is 0.213\n",
      "\n",
      "The roc auc score is 0.883076\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L2 regulariser\n",
    "lr_l2_tf_wt=LogisticRegression(penalty='l2',C=c_grid_tf_wt,class_weight='balanced')\n",
    "lr_l2_tf_wt.fit(d_train_tf_wt,y_train)\n",
    "report(d_train_tf_wt,y_train,lr_l2_tf_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:13<00:00,  1.83s/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(d_train_tf_wt,y_train,d_test_tf_wt,y_test,\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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",
    "plotROC(d_train_tf_wt,y_train,d_test_tf_wt,y_test,lr_l2_tf_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.82      0.56      2836\n",
      "           1       0.96      0.79      0.86     14719\n",
      "\n",
      "   micro avg       0.79      0.79      0.79     17555\n",
      "   macro avg       0.69      0.80      0.71     17555\n",
      "weighted avg       0.87      0.79      0.81     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.786\n",
      "The value of TNR is 0.823\n",
      "The value of FPR is 0.177\n",
      "The value of FNR is 0.214\n",
      "\n",
      "The roc auc score is 0.883644\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Test\n",
    "report(d_test_tf_wt,y_test,lr_l2_tf_wt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Review Summary To the Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('fantastic', 0.817060649394989), ('terrific', 0.8103036284446716), ('excellent', 0.8079220056533813), ('awesome', 0.8047534227371216), ('wonderful', 0.7861284613609314), ('good', 0.7792019844055176), ('perfect', 0.7489495873451233), ('nice', 0.6825402975082397), ('amazing', 0.6794854402542114), ('decent', 0.6775164008140564)]\n",
      "==================================================\n",
      "[('greatest', 0.775356113910675), ('best', 0.71474289894104), ('closest', 0.6507704257965088), ('experienced', 0.6453039050102234), ('nastiest', 0.6343936920166016), ('ive', 0.6297587156295776), ('foul', 0.6113804578781128), ('horrible', 0.610868513584137), ('awful', 0.6074503660202026), ('terrible', 0.6055221557617188)]\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": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 70218/70218 [02:23<00:00, 488.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70218\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed : 56.69 Seconds\n",
      " optimal alpha using Grid Search is {'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "#calling the function cv_lr_grid with d_trainA_w2 as training dataset\n",
    "#and regulariser as L1\n",
    "c_gridA_l1_w2=cv_lr_grid(d_trainA_w2,y_train,'l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.88      0.66     11345\n",
      "           1       0.97      0.85      0.91     58873\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     70218\n",
      "   macro avg       0.75      0.86      0.78     70218\n",
      "weighted avg       0.90      0.85      0.87     70218\n",
      "\n",
      "\n",
      "The value of TPR is 0.847\n",
      "The value of TNR is 0.876\n",
      "The value of FPR is 0.124\n",
      "The value of FNR is 0.153\n",
      "\n",
      "The roc auc score is 0.935414\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##confusion matrix and report on Train Data\n",
    "#L2 regulariser\n",
    "lr_l2_tf_=LogisticRegression(penalty='l1',C=c_gridA_l1_w2,class_weight='balanced')\n",
    "lr_l2_tf_.fit(d_trainA_w2,y_train)\n",
    "report(d_trainA_w2,y_train,lr_l2_tf_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 17555/17555 [00:38<00:00, 461.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17555\n",
      "50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:14<00:00,  1.98s/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(d_trainA_w2,y_train,d_testA_w2,y_test,\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.88      0.66      2836\n",
      "           1       0.97      0.85      0.91     14719\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     17555\n",
      "   macro avg       0.75      0.86      0.78     17555\n",
      "weighted avg       0.90      0.85      0.86     17555\n",
      "\n",
      "\n",
      "The value of TPR is 0.846\n",
      "The value of TNR is 0.879\n",
      "The value of FPR is 0.121\n",
      "The value of FNR is 0.154\n",
      "\n",
      "The roc auc score is 0.936046\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report(d_testA_w2,y_test,lr_l2_tf_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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",
    "plotROC(d_trainA_w2,y_train,d_testA_w2,y_test,lr_l2_tf_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nOvsBNf2LPpv"
   },
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T7VxD0q2LPpw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+----------------+-----------+-----------+-----------+--------+----------+\n",
      "|  Vectorising Method | Regularization | Optimal C | AUC Score | Precision | Recall | F1-Score |\n",
      "+---------------------+----------------+-----------+-----------+-----------+--------+----------+\n",
      "|     Bag Of Words    |       L1       |     1     |   0.934   |    0.91   |  0.9   |   0.9    |\n",
      "|                     |       L2       |    0.01   |    0.94   |    0.91   |  0.88  |   0.89   |\n",
      "|        Tf-Idf       |       L1       |     1     |   0.962   |    0.93   |  0.91  |   0.91   |\n",
      "|                     |       L2       |     10    |   0.964   |    0.93   |  0.93  |   0.93   |\n",
      "|        AV-W2V       |       L1       |     1     |   0.906   |    0.88   |  0.81  |   0.83   |\n",
      "|                     |       L2       |     1     |   0.906   |    0.88   |  0.82  |   0.83   |\n",
      "|      TfIdf-W2V      |       L1       |     1     |   0.881   |    0.87   |  0.79  |   0.82   |\n",
      "|                     |       L2       |     1     |   0.881   |    0.87   |  0.79  |   0.82   |\n",
      "| Rev+Sum with AV-W2V |       L2       |     1     |   0.934   |    0.9    |  0.85  |   0.86   |\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 = [\"Vectorising Method\",\"Regularization\", \"Optimal C\", \"AUC Score\",\"Precision\", \"Recall\", \"F1-Score\"]\n",
    "\n",
    "pt.add_row([\"Bag Of Words\",\"L1\", 1, 0.934, 0.91, 0.90, 0.90 ])\n",
    "pt.add_row([\"\",\"L2\", 0.01, 0.940, 0.91, 0.88, 0.89])\n",
    "pt.add_row([\"Tf-Idf\",\"L1\", 1, 0.962, 0.93, 0.91, 0.91 ])\n",
    "pt.add_row([\"\",\"L2\", 10, 0.964, 0.93, 0.93, 0.93 ])\n",
    "pt.add_row([\"AV-W2V\",\"L1\", 1, 0.906, 0.88, 0.81, 0.83 ])\n",
    "pt.add_row([\"\",\"L2\", 1, 0.906, 0.88, 0.82, 0.83 ])\n",
    "pt.add_row([\"TfIdf-W2V\",\"L1\", 1, 0.881, 0.87, 0.79, 0.82 ])\n",
    "pt.add_row([\"\",\"L2\", 1, 0.881, 0.87, 0.79, 0.82 ])\n",
    "pt.add_row([\"Rev+Sum with AV-W2V\",\"L2\", 1, 0.934, 0.90, 0.85, 0.86 ])\n",
    "\n",
    "\n",
    "print(pt)\n",
    "print(\"\\n\\n Note: Auc Score, Precision, Recall and F1- Score are on Test Data\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) We achieved the highest AUC score after vectorization by *TF-IDF* method and using *L2* regularizer. The score is 0.964.\n",
    "\n",
    "2) The sparcity is *decreasing* as Lambda is *increasing* in the sparcity test as expected.\n",
    "\n",
    "3) 50 features are found to be multi-collinear in the pertubation test done after vectorising with BOW and using L2 regulariser.\n",
    "\n",
    "4) Most of the words are common in top 10 neagtive words using Bag of Words and Tf-Idf.\n",
    "\n",
    "5) Most of the words are common in top 10 Positive words using Bag of Words and Tf-Idf.\n",
    "\n",
    "6) The lowest ROC-AUC score is achieved after vectorization by *Tf-Idf weighted Word2Vec*. The score is 0.887.\n",
    "\n",
    "7) We can say that model is very good in predicting positive reviews looking at the Precision, Recall and F1-score.\n",
    "\n",
    "8) The model performance improved drastically after including the parameter *class-weight='balanced'* in the model.\n",
    "\n",
    "9) The AUC score for Average W2V featurization has improved after adding the summary as feature by 3%.\n",
    "\n",
    "10) The best value of C, the hyperparameter, remains same from grid search and from Train vs Test AUC score plot on C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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