04 Amazon Fine Food Reviews Analysis_NaiveBayes.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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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
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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",
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       "  </thead>\n",
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       "      <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",
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       "      <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>"
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      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
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    "# using SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite') \n",
    "\n",
    "# filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "# SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000, will give top 500000 data points\n",
    "# you can change the number to any other number based on your computing power\n",
    "\n",
    "# filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000\"\"\", con) \n",
    "# for tsne assignment you can take 5k data points\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 100000\"\"\", con) \n",
    "\n",
    "# Give reviews with Score>3 a positive rating(1), and reviews with a score<3 a negative rating(0).\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "#changing reviews with score less than 3 to be positive and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative\n",
    "print(\"Number of data points in our data\", filtered_data.shape)\n",
    "filtered_data.head(3)"
   ]
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    "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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     "text": [
      "(80668, 7)\n"
     ]
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "               UserId   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B005ZBZLT4                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ESG  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B005ZBZLT4        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ESG           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBEV0   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
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   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
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       "      <td>AZY10LLTJ71NX</td>\n",
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       "      <td>undertheshrine \"undertheshrine\"</td>\n",
       "      <td>1296691200</td>\n",
       "      <td>5</td>\n",
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       "              UserId   ProductId                      ProfileName        Time  \\\n",
       "80638  AZY10LLTJ71NX  B001ATMQK2  undertheshrine \"undertheshrine\"  1296691200   \n",
       "\n",
       "       Score                                               Text  COUNT(*)  \n",
       "80638      5  I bought this 6 pack because for the price tha...         5  "
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   "source": [
    "display[display['UserId']=='AZY10LLTJ71NX']"
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  {
   "cell_type": "code",
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    {
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       "393063"
      ]
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     "metadata": {},
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   "source": [
    "display['COUNT(*)'].sum()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y22fy_WtJkji"
   },
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
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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:"
   ]
  },
  {
   "cell_type": "code",
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       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
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       "      <td>B000HDL1RQ</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138317</td>\n",
       "      <td>B000HDOPYC</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138277</td>\n",
       "      <td>B000HDOPYM</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73791</td>\n",
       "      <td>B000HDOPZG</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155049</td>\n",
       "      <td>B000PAQ75C</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
       "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "\n",
       "   HelpfulnessDenominator  Score        Time  \\\n",
       "0                       2      5  1199577600   \n",
       "1                       2      5  1199577600   \n",
       "2                       2      5  1199577600   \n",
       "3                       2      5  1199577600   \n",
       "4                       2      5  1199577600   \n",
       "\n",
       "                             Summary  \\\n",
       "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "\n",
       "                                                Text  \n",
       "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 7,
=======
     "execution_count": 13,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "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": "pvogJHEyJkjq"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 8,
=======
   "execution_count": 14,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LAWOrfBaJkjr"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 9,
=======
   "execution_count": 15,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dfaQob28Jkju",
    "outputId": "21104e4f-cd18-4353-a54a-5b4638fc552b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87775, 10)"
      ]
     },
<<<<<<< HEAD
     "execution_count": 9,
=======
     "execution_count": 15,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "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",
<<<<<<< HEAD
   "execution_count": 10,
=======
   "execution_count": 16,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9QVxT6oxJkjx",
    "outputId": "f292b9fe-6065-4979-830d-c79bc6785a3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.775"
      ]
     },
<<<<<<< HEAD
     "execution_count": 10,
=======
     "execution_count": 16,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "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": "qVaxONHLJkj1"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 11,
=======
   "execution_count": 17,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vLoqfOjcJkj1",
    "outputId": "db3448f8-fbda-4205-a7cc-5048e0f1fd19"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   ProductId          UserId              ProfileName  \\\n",
       "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     3                       1      5  1224892800   \n",
       "1                     3                       2      4  1212883200   \n",
       "\n",
       "                                        Summary  \\\n",
       "0             Bought This for My Son at College   \n",
       "1  Pure cocoa taste with crunchy almonds inside   \n",
       "\n",
       "                                                Text  \n",
       "0  My son loves spaghetti so I didn't hesitate or...  \n",
       "1  It was almost a 'love at first bite' - the per...  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 11,
=======
     "execution_count": 17,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "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",
<<<<<<< HEAD
   "execution_count": 12,
=======
   "execution_count": 18,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f_185ozFJkj9"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 13,
=======
   "execution_count": 19,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CofRySJyJkkC",
    "outputId": "a06c52f6-66a9-429b-8a5e-e20259f0bb21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87773, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    73592\n",
       "0    14181\n",
       "Name: Score, dtype: int64"
      ]
     },
<<<<<<< HEAD
     "execution_count": 13,
=======
     "execution_count": 19,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "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": "-fEWeywxJkkI"
   },
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tzpbt9aOJkkJ"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 14,
=======
   "execution_count": 20,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YqxBeLk8JkkL",
    "outputId": "1819632d-0129-4a13-d7ff-4e7bbf129dff"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 15,
=======
   "execution_count": 21,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lWiJ2_9gJkkP",
    "outputId": "08234806-3657-4d8f-c6d2-020dc9cda61c"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 16,
=======
   "execution_count": 22,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cpLtZ-lAJkkR",
    "outputId": "7e0bf159-4db4-4ffe-d72e-6d5952d453f1"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 17,
=======
   "execution_count": 23,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HYBbtiCMJkkW"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 18,
=======
   "execution_count": 24,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WPQSZurTJkkY",
    "outputId": "6d91fc9f-8f77-497c-95bb-96bfc9781196"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 19,
=======
   "execution_count": 25,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6bakeyaQJkkb",
    "outputId": "f7cf0096-0b7b-4ec1-84df-01e838621d62"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 20,
=======
   "execution_count": 26,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dMqqVqadJkkg",
    "outputId": "e4a6bd28-9d39-4dd2-9483-0406dd3dff50"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 21,
=======
   "execution_count": 27,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bvEoXB5NJkkk"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 22,
=======
   "execution_count": 28,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0j3jfqWCJkko",
    "outputId": "c83c59c5-fe17-4ad2-d43f-0b0947a6e6ba"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
<<<<<<< HEAD
      "100%|██████████████████████████████████████████████████████████████████████████| 87773/87773 [00:32<00:00, 2723.71it/s]\n"
=======
      "100%|██████████████████████████████████████████████████████████████████████████| 87773/87773 [00:30<00:00, 2859.33it/s]\n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "from tqdm import tqdm\n",
    "preprocessed_reviews = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(final['Text'].values):\n",
    "    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n",
    "    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n",
    "    sentance = decontracted(sentance)\n",
    "    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n",
    "    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n",
    "    preprocessed_reviews.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 23,
=======
   "execution_count": 29,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nS_7EeV7Jkkr",
    "outputId": "4d4cd05e-0730-4e52-dc30-eb97014871dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'way hot blood took bite jig lol'"
      ]
     },
<<<<<<< HEAD
     "execution_count": 23,
=======
     "execution_count": 29,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_reviews[1500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zak5yP5zJkkt"
   },
   "source": [
    "<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 24,
=======
   "execution_count": 30,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [],
   "source": [
    "# ReviewSummary No need "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important Libraries"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 25,
=======
   "execution_count": 31,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all relevant libraries\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 26,
=======
   "execution_count": 32,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Splitting\n",
    "# Train Data :  60%\n",
    "# CV Data    :  20%\n",
    "# Test Data  :  20%\n",
    "\n",
    "X=preprocessed_reviews\n",
    "y=np.array(final['Score'])\n",
    "\n",
    "#####  SPLIT  #######\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
    "X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.25, random_state=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ChHwtw1iJkky"
   },
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qyWESNvzJkky"
   },
   "source": [
    "## [4.1] BAG OF WORDS"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 27,
=======
   "execution_count": 33,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zS0niozAJkkz",
    "outputId": "e2a25e5d-2e1a-497a-f094-a7e249201001"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some feature names  ['aa', 'aaa', 'aaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaahhhhhh', 'aaaaaaarrrrrggghhh', 'aaaaaawwwwwwwwww', 'aaaah', 'aaaand', 'aaahs']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (52663, 42680)\n",
      "the number of unique words  42680\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "X_train_bow, X_test_bow, X_cv_bow = X_train, X_test, X_cv\n",
    "y_train_bow, y_test_bow, y_cv_bow = y_train, y_test, y_cv\n",
    "\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "count_vect.fit(X_train_bow)\n",
    "print(\"some feature names \", count_vect.get_feature_names()[:10])\n",
    "print('='*50)\n",
    "\n",
    "bow_counts_train = count_vect.transform(X_train_bow)\n",
    "bow_counts_cv = count_vect.transform(X_cv_bow)\n",
    "bow_counts_test = count_vect.transform(X_test_bow)\n",
    "\n",
    "print(\"the type of count vectorizer \",type(bow_counts_train))\n",
    "print(\"the shape of out text BOW vectorizer \",bow_counts_train.get_shape())\n",
    "print(\"the number of unique words \", bow_counts_train.get_shape()[1])\n",
    "\n",
    "# bow_counts_test=bow_counts_test.toarray()\n",
    "# bow_counts_train=bow_counts_train.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LChtHtLjJkk1"
   },
   "source": [
    "## [4.2] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 28,
=======
   "execution_count": 34,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xgw2pSsoJkk3",
    "outputId": "bfcb230d-1f75-413a-e30d-c2aae84cd5c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text BOW vectorizer  (87773, 5000)\n",
      "the number of unique words including both unigrams and bigrams  5000\n"
     ]
    }
   ],
   "source": [
    "#bi-gram, tri-gram and n-gram\n",
    "\n",
    "# removing stop words like \"not\" should be avoided before building n-grams\n",
    "# count_vect = CountVectorizer(ngram_range=(1,2))\n",
    "# please do read the CountVectorizer documentation http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "\n",
    "# you can choose these numebrs min_df=10, max_features=5000, of your choice\n",
    "count_vect_bi = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=5000)\n",
    "final_bigram_counts = count_vect_bi.fit_transform(preprocessed_reviews)\n",
    "print(\"the type of count vectorizer \",type(final_bigram_counts))\n",
    "print(\"the shape of out text BOW vectorizer \",final_bigram_counts.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_bigram_counts.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1fvAmu91Jkk6"
   },
   "source": [
    "## [4.3] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 29,
=======
   "execution_count": 35,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ag5-WCR8Jkk7",
    "outputId": "94a8ddc3-4bb7-4c49-d4e7-ed98560633b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some sample features(unique words in the corpus) ['aa', 'aafco', 'ability', 'able', 'able add', 'able buy', 'able chew', 'able drink', 'able eat', 'able enjoy']\n",
      "==================================================\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the shape of out text TFIDF vectorizer  (52663, 31190)\n",
      "the number of unique words including both unigrams and bigrams  31190\n"
     ]
    }
   ],
   "source": [
    "X_train_tf, X_test_tf, X_cv_tf = X_train, X_test, X_cv\n",
    "y_train_tf, y_test_tf, y_cv_tf = y_train, y_test, y_cv\n",
    "\n",
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "final_tf_idf_train = tf_idf_vect.fit_transform(X_train_tf)\n",
    "print(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\n",
    "print('='*50)\n",
    "\n",
    "\n",
    "final_tf_idf_cv = tf_idf_vect.transform(X_cv_tf)\n",
    "final_tf_idf_test = tf_idf_vect.transform(X_test_tf)\n",
    "print(\"the type of count vectorizer \",type(final_tf_idf_train))\n",
    "print(\"the shape of out text TFIDF vectorizer \",final_tf_idf_train.get_shape())\n",
    "print(\"the number of unique words including both unigrams and bigrams \", final_tf_idf_train.get_shape()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_jVNRd5hJkk-"
   },
   "source": [
    "## [4.4] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 47,
=======
   "execution_count": 30,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8jGeY1r5Jkk_"
   },
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "i=0\n",
    "list_of_sentance=[]\n",
    "for sentance in preprocessed_reviews:\n",
    "    list_of_sentance.append(sentance.split())"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 48,
=======
   "execution_count": 31,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2X9M3ybMJklB",
    "outputId": "42dc6662-8d17-41ce-be7c-12e2db687f30",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
<<<<<<< HEAD
      "[('awesome', 0.8586839437484741), ('good', 0.8472133874893188), ('fantastic', 0.8463523387908936), ('wonderful', 0.7956298589706421), ('excellent', 0.7945278882980347), ('terrific', 0.7929657101631165), ('perfect', 0.7781151533126831), ('amazing', 0.7605840563774109), ('fabulous', 0.7241662740707397), ('decent', 0.7062463164329529)]\n",
      "==================================================\n",
      "[('greatest', 0.7842147946357727), ('tastiest', 0.7440927028656006), ('best', 0.7109968662261963), ('disgusting', 0.6815571188926697), ('nastiest', 0.6631032228469849), ('horrible', 0.6390447616577148), ('terrible', 0.6163870096206665), ('awful', 0.5937999486923218), ('vile', 0.5917909741401672), ('smoothest', 0.5906369686126709)]\n"
=======
      "[('fantastic', 0.8551209568977356), ('excellent', 0.8350623846054077), ('good', 0.8209582567214966), ('awesome', 0.8076313734054565), ('terrific', 0.8050072193145752), ('wonderful', 0.7687906622886658), ('perfect', 0.7678946256637573), ('amazing', 0.7453687191009521), ('nice', 0.7096043825149536), ('fabulous', 0.7016271352767944)]\n",
      "==================================================\n",
      "[('greatest', 0.8187421560287476), ('tastiest', 0.7359920740127563), ('best', 0.7069734334945679), ('coolest', 0.673555850982666), ('nastiest', 0.6589905023574829), ('disgusting', 0.6438694000244141), ('closest', 0.6189815402030945), ('nicest', 0.6110348701477051), ('terrible', 0.6083199977874756), ('hottest', 0.5979161262512207)]\n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    }
   ],
   "source": [
    "# Using Google News Word2Vectors\n",
    "\n",
    "# in this project we are using a pretrained model by google\n",
    "# its 3.3G file, once you load this into your memory \n",
    "# it occupies ~9Gb, so please do this step only if you have >12G of ram\n",
    "# we will provide a pickle file wich contains a dict , \n",
    "# and it contains all our courpus words as keys and  model[word] as values\n",
    "# To use this code-snippet, download \"GoogleNews-vectors-negative300.bin\" \n",
    "# from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit\n",
    "# it's 1.9GB in size.\n",
    "\n",
    "\n",
    "# http://kavita-ganesan.com/gensim-word2vec-tutorial-starter-code/#.W17SRFAzZPY\n",
    "# you can comment this whole cell\n",
    "# or change these varible according to your need\n",
    "\n",
    "is_your_ram_gt_16g=False\n",
    "want_to_use_google_w2v = False\n",
    "want_to_train_w2v = True\n",
    "\n",
    "if want_to_train_w2v:\n",
    "    # min_count = 5 considers only words that occured atleast 5 times\n",
    "    w2v_model=Word2Vec(list_of_sentance,min_count=5,size=50, workers=4)\n",
    "    print(w2v_model.wv.most_similar('great'))\n",
    "    print('='*50)\n",
    "    print(w2v_model.wv.most_similar('worst'))\n",
    "    \n",
    "elif want_to_use_google_w2v and is_your_ram_gt_16g:\n",
    "    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n",
    "        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "        print(w2v_model.wv.most_similar('great'))\n",
    "        print(w2v_model.wv.most_similar('worst'))\n",
    "    else:\n",
    "        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 49,
=======
   "execution_count": 32,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jIFbq6sDJklG",
    "outputId": "56320a9c-85c4-4f09-c139-0a64bfdc6ab2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  17386\n",
      "sample words  ['dogs', 'loves', 'chicken', 'product', 'china', 'wont', 'buying', 'anymore', 'hard', 'find', 'products', 'made', 'usa', 'one', 'isnt', 'bad', 'good', 'take', 'chances', 'till', 'know', 'going', 'imports', 'love', 'saw', 'pet', 'store', 'tag', 'attached', 'regarding', 'satisfied', 'safe', 'infestation', 'literally', 'everywhere', 'flying', 'around', 'kitchen', 'bought', 'hoping', 'least', 'get', 'rid', 'weeks', 'fly', 'stuck', 'squishing', 'buggers', 'success', 'rate']\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": "bJ1C1s_eJklQ"
   },
   "source": [
    "## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "33z1pU7zJklQ"
   },
   "source": [
    "#### [4.4.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 50,
=======
   "execution_count": 33,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ce1_4JvaJklR",
    "outputId": "198ff07c-ab57-4395-bf04-4d43a9fa1205"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "100%|███████████████████████████████████████████████████████████████████████████| 87773/87773 [03:54<00:00, 374.92it/s]\n"
=======
      "100%|███████████████████████████████████████████████████████████████████████████| 87773/87773 [04:32<00:00, 322.63it/s]\n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87773\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in tqdm(list_of_sentance): # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    sent_vectors.append(sent_vec)\n",
    "print(len(sent_vectors))\n",
    "print(len(sent_vectors[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-R_AYTtwJklT"
   },
   "source": [
    "#### [4.4.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 51,
=======
   "execution_count": 34,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "il5ldoFyJklU"
   },
   "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",
<<<<<<< HEAD
   "execution_count": 52,
=======
   "execution_count": 35,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VUZMBVxpJklX",
    "outputId": "e826680b-3c3f-4c22-9de3-c7850f5ff737",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "100%|██████████████████████████████████████████████████████████████████████████| 87773/87773 [1:26:36<00:00, 16.89it/s]\n"
=======
      " 27%|████████████████████▌                                                       | 23757/87773 [16:41<41:24, 25.77it/s]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-0d742f22e115>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtfidf_sent_vectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m;\u001b[0m \u001b[1;31m# the tfidf-w2v for each sentence/review is stored in this list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0msent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_of_sentance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# for each review/sentence\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0msent_vec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# as word vectors are of zero length\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mweight_sum\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m;\u001b[0m \u001b[1;31m# num of words with a valid vector in the sentence/review\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tqdm\\_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1000\u001b[0m                                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoveto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m                             \u001b[1;31m# Print bar update\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1002\u001b[1;33m                             \u001b[0msp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1003\u001b[0m                             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m                                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoveto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tqdm\\_tqdm.py\u001b[0m in \u001b[0;36mprint_status\u001b[1;34m(s)\u001b[0m\n\u001b[0;32m    229\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mprint_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m             \u001b[0mlen_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m             \u001b[0mfp_write\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\r'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlast_len\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    232\u001b[0m             \u001b[0mlast_len\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen_s\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tqdm\\_tqdm.py\u001b[0m in \u001b[0;36mfp_write\u001b[1;34m(s)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mfp_write\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m             \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_unicode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m             \u001b[0mfp_flush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\colorama\\ansitowin32.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__convertor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0misatty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\colorama\\ansitowin32.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m    160\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 162\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_and_convert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    163\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\colorama\\ansitowin32.py\u001b[0m in \u001b[0;36mwrite_and_convert\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m    188\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_ansi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m             \u001b[0mcursor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_plain_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    191\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\colorama\\ansitowin32.py\u001b[0m in \u001b[0;36mwrite_plain_text\u001b[1;34m(self, text, start, end)\u001b[0m\n\u001b[0;32m    193\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrite_plain_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, string)\u001b[0m\n\u001b[0;32m    398\u001b[0m             \u001b[0mis_child\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_master_process\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m             \u001b[1;31m# only touch the buffer in the IO thread to avoid races\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 400\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    401\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_child\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m                 \u001b[1;31m# newlines imply flush in subprocesses\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mschedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    201\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_events\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m             \u001b[1;31m# wake event thread (message content is ignored)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 203\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    204\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\sugar\\socket.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m    390\u001b[0m                                  copy_threshold=self.copy_threshold)\n\u001b[0;32m    391\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSocket\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msend_multipart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg_parts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    }
   ],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "tfidf_feat = model.get_feature_names() # tfidf words/col-names\n",
    "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "\n",
    "tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in tqdm(list_of_sentance): # for each review/sentence \n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    weight_sum =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        if word in w2v_words and word in tfidf_feat:\n",
    "            vec = w2v_model.wv[word]\n",
    "#             tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n",
    "            # to reduce the computation we are \n",
    "            # dictionary[word] = idf value of word in whole courpus\n",
    "            # sent.count(word) = tf valeus of word in this review\n",
    "            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n",
    "            sent_vec += (vec * tf_idf)\n",
    "            weight_sum += tf_idf\n",
    "    if weight_sum != 0:\n",
    "        sent_vec /= weight_sum\n",
    "    tfidf_sent_vectors.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZFN0k_hEJklb"
   },
   "source": [
    "# [5] Assignment 4: Apply Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O1ABSoejJklc"
   },
   "source": [
    "<ol>\n",
    "    <li><strong>Apply Multinomial NaiveBayes 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",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>The hyper paramter tuning(find best Alpha)</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>Consider a wide range of alpha values for hyperparameter tuning, start as low as 0.00001</li>\n",
    "    <li>Find the best hyper paramter using k-fold cross validation or simple cross validation data</li>\n",
    "    <li>Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Feature importance</strong>\n",
    "        <ul>\n",
    "    <li>Find the top 10 features of positive class and top 10 features of negative class for both feature sets <font color='red'>Set 1</font> and <font color='red'>Set 2</font> using values of `feature_log_prob_` parameter of  <a href='https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html'>MultinomialNB</a> and print their corresponding feature names</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. Here on X-axis you will have alpha values, since they have a wide range, just to represent those alpha values on the graph, apply log function on those alpha values.\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": "YawkDx8TJklc"
   },
   "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": "RGRFxjhgJklc"
   },
   "source": [
    "# Applying Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FxGKHpZlJkle"
   },
   "source": [
    "## [5.1] Applying Naive Bayes on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 89,
=======
   "execution_count": 36,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UR0f8SfZJkle"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_bow, X_test_bow, y_train, y_test, X_cv_bow, y_cv = bow_counts_train, bow_counts_test, y_train, y_test, bow_counts_cv, y_cv\n",
    "\n",
    "auc_train=[]\n",
    "alphas=[]\n",
    "auc_cv=[]\n",
    "\n",
    "\n",
    "############ HYPER-PARAMETER TUNING ##############\n",
    "for i in range(-5,5):\n",
    "    nb = MultinomialNB(alpha=10**i, class_prior=None, fit_prior=True)\n",
    "    nb.fit(X_train_bow, y_train)\n",
    "    \n",
    "    proba = nb.predict_proba(X_train_bow)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train, proba))\n",
    "    alphas.append(10**i)\n",
    "    \n",
    "    proba = nb.predict_proba(X_cv_bow)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv, proba))\n",
    "    \n",
    "# print(\"Pre-def accuracy: \", metrics.accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 90,
=======
   "execution_count": 37,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha for Naive Bayes Model from cv_data is:  1 for cooresponding AUC value of: 0.9119404327316976\n"
     ]
    }
   ],
   "source": [
    "######## NAIVE BAYES AUC PLOTTING ########\n",
<<<<<<< HEAD
    "ax=sns.lineplot(np.log(alphas),auc_train)\n",
    "ax=sns.lineplot(np.log(alphas),auc_cv)\n",
=======
    "ax=sns.lineplot(alphas,auc_train)\n",
    "ax=sns.lineplot(alphas,auc_cv)\n",
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
    "ax.set(xlabel='Differnt Alpha values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different alpha value Curve')\n",
    "# ax.margins(x=0, y=5)\n",
    "plt.show()\n",
    "\n",
    "###############################################\n",
    "\n",
    "############## BEST HYPER-PARAMETER ###########\n",
    "best_alpha = alphas[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best alpha for Naive Bayes Model from cv_data is: \",best_alpha, \"for cooresponding AUC value of:\",max(auc_cv))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 91,
=======
   "execution_count": 38,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_k:  0.9167008578781703\n"
     ]
    },
    {
     "data": {
      "image/png": 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RT97nkRJJ+4T2JNRK4LxO59GpYSf6HNeHTo060bBWw4DFlZrqGoXnz4evv4ZNm2DVKne1XFiPHq5toGtXlxB69XLL4uMrvS3OmPC1Zo1rOGvZEv79bzjzTNclrgpZoggTKWkpzN0yl1kbZ/HNhm9Yu28tOV43FXK92Hr8rt3vOKv9WQxoOYB2Ce04rs5xREdGBzSm9HT3N/zTT65d7eOPITn56HViY6FbN3c7qHNnGDoU+vRx/eqNMSU4cADuusuNjZgzBwYNgosvDkoolihCVJYni6U7l/Lhbx/y5bovWZ2yGoDYyFhObHki53U6jxNbnsgZbc+gSe1ylW8pk4wMePVV9/e6YYPrjVeUBg3gjjugf393m+g4K/NoTNlNnepGV+/cCePGuV4YQWSJIsR4vB4+Xfkpt315G7tT3Tjbro278sgZj9C/ZX8GtRlEXFRgG69UXZvZRx+5NrMffnBXD7kaNoRTTnGjbvv3d1cIXbq4K2O7ZWRMBV1/PUycCD17wn//C/36BTsiSxShIMebw8JtC3lq3lPMWDeDLE8W7Rq049+X/ZuTEk+iVf1Wpe+kEmRmwpNPwt//fvTyRo3c7aPf/x5GjXJtacaYSpQ7JbWISwxt2sDdd4fMIB1LFEG0bPcyHprzELM3zmZ/xn6iI6IZecJIzm5/Nud3Pp+6sYHt3rN1q2szyMhwj+2+koyJiXDtta5TxYABAQ3BGLN1K9x0E4wYAVdd5V6HGEsUVWz74e188OsHfLziYxZvd+XSB7UZxI19b+TsDmfTOL5xwL573z547TXXTfXTT4/+7KST3N9pdDQ88oh7NsYEkNfr/kHefbfrPx6khmp/WKKoAqrK3C1zeXnxy0xZMYUcbw49mvbgwUEPcmO/G2lRt0XAvjsjw/0dLljgHrmiotwV7l/+Apdeam0LxlSptWtdW8T338NZZ7kaTe3aBTuqYlmiCCBVZcqKKYz/fjzLdi+jVlQtrk26ljsG3kG3Jt0C9r1er+tufe+9rgBerk6dYNgwN5gzIpCzpRtjSrZiBfz6K0ya5Br+QvxMzRJFAKRnpzNh/gQm/jyR9fvXA/DisBcZecJIGsSVNKFJ+WVkwFdfwfvvw3/+k7+8eXM30v+JJ9xVhDEmSJYuhV9+gWuugeHDXT/zhIRgR+UXO3RUso+Xf8zN/7uZfen7aFm3JS+f+zKjkkZVqDheSRYtcn93K1cevTwpCd57z410NsYEUWYmPPqo61J43HFu5rnc2vJhwhJFJVmyfQkPzH6AL9d9SYREMPHCiVzX+7qAfd/ixUePwUlIcONzrr3WjYIO8StZY2qGH390RfxWrnTlwJ9/vkqK+FU2SxQVNH3tdJ754RnmbJpDpERy76n38sCgBwJyBbFzpzspeeGFo5d/950b3W+MCSHbtrka982bw/TproEwTFmiKCeverlh6g1M+mUSjeMb8+CgBxk7YCyN4gNTE/7yy49ue+jRA557Ds4+OyBfZ4wpr5UrXaXLli1dAbQzzwz7kseWKMph2e5ljJgyguV7lnNp10v54JIPiI2KDch37djhJtzJ9dxzrpaS9VoyJsTs3+/6m7/1luv2etppbua5asASRRl9+NuHXP3Z1cRGxfLKea9wY98byzx1Z2k8Hndr85prXMeIXCtWuBMVY0yI+ewzuOUWN+PWvfcGvYhfZbNEUQYb9m9g5KcjaVO/DXNGzaFtg7aV/h3vvusSREFPPeWqDRtjQtB117mriKQk+N//XJXMasYShZ9Ss1Lp97qr4vjyeS9XepJ44w0YMyb//dlnu7EP1fBvzpjwV7CI38CBbjTrX/9abWvfWKLww6wNs7hj5h3sz9jPM0Oe4dxO51bavo8ccdVY0wpMXT1jBpxzTqV9hTGmMm3eDDfeCFde6bq8FjzDq6asSbQEqsqoz0dx1ntnsWLPCiacM4G/nvzXStv/t9+6zhC5SWLbNneiYknCmBDk9cJLL7kuh3PnQnZ2sCOqMnZFUYLRU0fzztJ36N+yPzNGziChVuWNpPz4YzdAE9xc0UuWQGRkpe3eGFOZVq92RfzmznX3hV97zU30XkPYFUUxJv40kbd+eYuLulzED9f9UGlJYsYMd1szN0lcdpkr/2JJwpgQtnq1m+7x7bfdP+IalCTAriiKtCZlDddPu56WdVsy5fdTiIyonKP4q6+6MhvgBmv+979uKlFjTAj6+Wd3FnfttXDhha6veoPAFPUMdXZFUYiqcufMOwGY9odplZIksrNdh4jcJPGnP7mBdJYkjAlBGRlw331uLMTf/+7eQ41NEmBXFMe4dfqtTF87ndtOvI3ex/Wu8P5mz4bf/S7//fjx8MADFd6tMSYQ5s1zRfxWr3ZXEs89F5ZF/CqbJYoCJi+bzCuLX+HMdmcyYeiECu/vpZfgttvc6zp1ICUlZOZKN8YUtm0bnHGGq9E0c6YVUivAbj355HhzGP/deAThw0s/rNAtp2XLXIN1bpK47z44fNiShDEhacUK99yyJXzyCfz2myWJQixR+Fwx5QpW7l3JpOGTaFq7abn3s2gR9OzpXvfu7eaNeOyxSgrSGFN59u1z05B27+6K+AFccIG7/DdHsUQB7Di8g09XfkrbBm25ptc1pW9QDI8nv4H6zjvhp5+gb99KCtIYU3k++QS6dYMPPoC//c16lpTC2iiAZ394FoAXhr5QoUqwHTvmv37++YpGZYwJiFGj4J13XCG1GTNcMT9TohqfKA5nHub5+c/TsWFHLuh8Qbn3k5EBmza51zVoZL8x4aFgEb+TT3b1+v/yF4iq8YdAvwT01pOIDBWR1SKyTkTuKeLz1iIyW0R+FpFfRaTyqu356b5Z9wHw8OkPV+hqorevJ+1VV9nfnjEhZeNG1zj97rvu/ZgxcPfd9g+1DAKWKEQkEngJGAZ0A/4gIt0KrXY/8LGq9gZGAC8HKp6iLN6+mBcXvUhCXAIjeoyo0L5WrXLP77xTCYEZYyrO44F//tMV8Zs/P/+qwpRZIK8o+gPrVHWDqmYBk4HhhdZRoJ7vdX1gewDjOcbIT0cCMP/6+URI+X8VBS9EKnmyO2NMeaxc6aYivf12GDzY1WkaNSrYUYWtQF57tQS2FnifDAwotM7fga9E5E9AbeCsonYkImOAMQCtW7eulOAWblvImpQ1nN/5fDo36lzu/fz4Y/7rgnNKGGOCaN06N7r6vfdg5Eg7g6ugQF5RFPVfpvC13x+At1U1ETgXeE/k2FN7VX1dVfupar8mTZpUODBV5cKPLgRgwjnlH4Gt6trFwJXqqFWrwqEZY8pryRKYNMm9vuAC1zbxxz9akqgEgUwUyUCrAu8TOfbW0mjgYwBV/RGIAxoHMCbAlerYlbqLm/reRIeGHcq9n4LzWJ9+esXjMsaUQ3o63HMPDBgAjzySX8SvXr2StzN+C2SiWAR0EpF2IhKDa6yeWmidLcCZACLSFZco9gQwJgCmrnFh3HXKXaWsWTxVeNYNv2B7lbasGGPyfP+9m/nrqadcG8TPP1sRvwAIWBuFquaIyG3ATCASmKSqy0VkPLBYVacCfwHeEJE7cbelRqkGtmtCSloKk5dN5sLjL6RdQrty7+eNN/JfH3dcJQRmjCmbbdvgzDOhVSv45hv32gREQDsSq+p0YHqhZQ8WeL0COCWQMRS2YNsCAIYfX7gDlv+8Xje3Orh5TYwxVei331xBtZYt4bPPXMXX2rWDHVW1VuNqPT32f65C3/mdzy/3Pvr0yX/dq1dFIzLG+GXvXjei9YQT8ov4nX++JYkqUKOGJi7fvZwft/7IOR3OKXeF2N9+g6VL3ev09EoMzhhTNFX4z39c3f79++Ghh1zDtakyNSpRPPp/j6IoL59X/gHguX+ff/6ztZkZUyWuucaNh+jXD2bNyq/jb6pMjUkU6dnpfLHmCyIkgvYJ7cu1j+zs/KuIZ56pxOCMMUcrWMRv8GB3u+mOO6w+U5DUmDaKJ+c+yZGsI7x07kvl3sc1vqkqeveGiBrzmzOmim3YAGedBW+/7d6PHg1//asliSCqMYe73N5Oo3uPLvc+fvrJPc+YURkRGWOO4vHAhAnu1tKiRXY2FkJqTIqeuX4mzes0JzoyulzbezyudEzbttC0/DOlGmOKsmIFXHcdLFgA550Hr74KiYnBjsr41IhE8f6v7wOQ1Lz8M1lddZV7bhzwAiPG1EAbN8L69fDhhzBihNVnCjES4IHQla5fv366ePFiv9dXVTr+qyN70/aSfGcydWPrlut7c/9uk5PdOB9jTAUtWuRGrN5wg3t/+DDULd+/T1M6EVmiqv3Ks221vwk4bc00NuzfwMOnP1zuJJGc7J4jIixJGFNhaWmucXrgQHjiifwifpYkQla1TxTP/fgcAJd2vbTc+7j+evf84IMlr2eMKcWcOa6r63PPuSsJK+IXFqp9G0VqVioAreq3KmXN4s2d657vvbcyIjKmhkpOhiFDoE0b+PZbV6PJhIVqf0WxZMcSzulwToX2kepyDTExlRCQMTVNbs2bxET473/h118tSYSZap0o0rPdMOr46Phy7yN3rokePSojImNqkD174MorISkJvvvOLTv3XIgv/79HExzV+tbTpgObABjQsvwFxEaMcM9//GMlBGRMTaAKkyfD2LFw8CA8/DCcdFKwozIV4Fei8M1Q11pV1wU4nkq1au8qgHLXdvJ64f/+z72+447KisqYau6qq+CDD1wFzYkToXv3YEdkKqjUW08ich7wG/C1732SiHwW6MAqw9ZDWwHo1bx8k0Zc6uso1asXxMZWVlTGVENeb34hvzPOgOefh3nzLElUE/60UYwHBgAHAFT1F6BjIIOqLO/9+h7NajejU8NO5dr+88/d89dfV2JQxlQ369a5aUjfesu9Hz0a7rwTIiODG5epNP4kimxVPVBoWVgM5/5l5y/0bNYTKUc5gHHj8l83aVKJQRlTXeTkwLPPuiJ+P/9s3QKrMX/aKFaKyOVAhIi0A24H5gc2rIrL8mSR482hbf225dr+Zd/cRmvXVl5MxlQby5bBtdfC4sUwfLj7B9OiRbCjMgHizxXFbUBfwAt8CmTgkkVI+2LNFwB0b1r2e6SPPuqqDAB0DIubbMZUsS1bYPNm17vps88sSVRz/lxRnKOqdwN35y4QkUtwSSNkfbbKtbePShpV5m0feMA9l6H2oDHV34IFbvDcmDFuPMSGDVCnTrCjMlXAnyuK+4tY9rfKDqSyLd3pRoM2iGtQpu1++y3/dd++lRmRMWEqNdVNEn/SSfD005CZ6ZZbkqgxir2iEJFzgKFASxF5vsBH9XC3oUKWqvLb7t84udXJZd72fl9afOGFSg7KmHD07beueN+GDXDzzfDkk9ZXvAYq6dbTbmAZrk1ieYHlh4F7AhlURe08shOAfseVrfS6Kkyd6l6PHVvZURkTZpKT4ZxzoF07V4Jj0KBgR2SCpNhEoao/Az+LyAeqmlGFMVXYNxu+AeDElieWabuHHw5ENMaEmZ9/ht69XRG/adNg8GCoVSvYUZkg8qeNoqWITBaRX0VkTe4j4JFVwPxk13v33E7nlmm7rW4gd14hQGNqlF274IoroE+f/CJ+Q4dakjB+JYq3gbcAAYYBHwOTAxhThc3ZPIeoiCgS4hL83ubQIZg0yb1u1ixAgRkTilTh/fehWzdXjuDRR+HksrfvmerLn0QRr6ozAVR1vareD4R0MfkVe1bQv2V/v0dkZ2dD/frudWKim/LUmBrjyitdIb/jj3dzWP/tbxAdHeyoTAjxZxxFprgj7noRuQnYBjQNbFjll1tavENCB7+3mTEj//XmzZUckDGhyOsFEfc4+2zX9fXWW60+kymSP+fOdwJ1gLHAKcANwHWBDKoiPl/lKvldePyFfm9z7bXuecECu5owNcCaNa7Ca+691muvdd38LEmYYpR6RaGqC3wvDwNXAYhIYiCDqogdh3cAcH7n8/1aPy0NUlLc6/79AxWVMSEgJ8eV/37oIYiLs0Zq47cSz59F5EQRuUhEGvvedxeRdwnhooCTfplE+4T2xEXF+bX+7Nnu+corAxiUMcH2668wcCDcfTcMGwYrVtgfvfFbsYlCRJ4APgBGAjNE5G/AbGAp0Llqwiu7vWl7/U4SAOf7Ljz9BIMxAAAgAElEQVRuuCFAARkTCpKTXf/v//wHPvkEjjsu2BGZMFLSrafhQC9VTReRhsB23/vV/u5cRIYCLwCRwJuq+mQR61wO/B03x8VSVS33aU5ativ5enKif137vAUKkZx+enm/1ZgQ9cMP7krippvyi/jVrh3sqEwYKunWU4aqpgOo6j5gVRmTRCTwEm7sRTfgDyLSrdA6nYB7gVNUtTtQoZmptx3aBkDHhv7VBk9Ods+jRlXkW40JMUeOwO23w6mnwnPP5RfxsyRhyqmkK4r2IpJbSlyAtgXeo6qXlLLv/sA6Vd0AICKTcVcpKwqscwPwkqru9+1zdxnjP8qSHUsAaNugrX/ru9UZPLgi32pMCPnqK1cGfMsW19318cetiJ+psJISxaWF3r9Yxn23BLYWeJ+Mm3u7oM4AIjIPd3vq76o6o9A6iMgYYAxA69ati/3CeVvmAdCvhX/FAC+/3D337u3X6saEtq1b4bzzoEMH+P57d0VhTCUoqSjgrAruu6hh0YXn2o4COgGnA4nA/4lIj8JzdKvq68DrAP369St2vu4DmW6zNg3a+BVgTo577tXLr9WNCU1LlrjJU1q1gunT4bTTXPdXYypJIIeXJQOtCrxPxDWIF17nv6qaraobgdW4xFEu6/ato3OjzkRF+DPg3DnttPJ+mzFBtnMn/P730K9ffhG/IUMsSZhKF8hEsQjoJCLtRCQGGAFMLbTO5/jqRvnGanQGNpT3C9Oz0/2e0W7jRvfcqlXJ6xkTclThnXdcEb9p01w7hBXxMwHkd6IQkTK1iKlqDnAbMBNYCXysqstFZLyI5NbXmAmkiMgK3BiNcaqaUpbvKWjprqW0T2jv17q5M9jZdKcm7IwY4brqdevmivjde68V8TMBVeo9GhHpD0wE6gOtRaQXcL2q/qm0bVV1OjC90LIHC7xW4M++R4XVialDZk6mX+vmJoobb6yMbzYmwAoW8Tv3XHfP9JZbrDiZqRL+/JX9EzgfSAFQ1aWEYJnxjJwMjmQdoXuT7qWuu8LXQTciwrqWmzCwapWbhnTiRPf+mmvgttssSZgq489fWoSqFi6+7QlEMBWx/bBrJ29Rt0Wp63b35ZJ33w1kRMZUUHa2a3/o1cud3dSpE+yITA3lT/egrb7bT+obbf0nIOSmQp22ehoAjeMbl7heVlb+65EjAxmRMRXwyy+u/Pcvv8Bll8G//gXNmwc7KlND+ZMobsbdfmoN7AK+8S0LKVsPubF9Fxx/QYnr3eErEjJsWKAjMqYCdu50j08+gUtKK4JgTGCJa08uYQWRhr5aTyGhX79+unjx4mOWd3+5O171svLWlSVunzs7anq6dTc3IWbuXFfE75Zb3Pu0NIiPD25MptoQkSWq6l/ZikL8aaNYJCLTReQaEalbni+pCsmHkjmuTsmlk1NT819bkjAh4/Bh1zh92mkwYUJ+ET9LEiZElJooVLUD8CjQF/hNRD4XkREBj6wMVJVDmYc4KfGkEtf74gv3/KdSO/YaU0VmzoQePeDll13F159+siJ+JuT41b9OVX9Q1bFAH+AQbkKjkLErdRcAHi25M1ZulYNrrgl0RMb4YetWN3NWfLy77TRhgvVsMiGp1EQhInVEZKSITAMWAnuAkKoXsH7feoASR2VnZcErr7jXffpURVTGFEEVFi50r1u1gi+/hJ9/thIcJqT5c0WxDBgIPK2qHVX1L6q6IMBxlcn6/S5RHN/o+GLXmTMn/7UUVdfWmEDbsQMuvRQGDMi/vD3rLGswMyHPn+6x7VXVW/pqwbNijxtq3ap+8RX+Hn7YPf/0U1VEZEwBqvD22/DnP0NGBjz1FJxySrCjMsZvxSYKEXlOVf8CfCIix/Sh9WOGuypzKPMQAK3qFZ8ofvjBPSclVUVExhRw+eUwZYrr1fTmm9C5c7AjMqZMSrqi+Lfvuawz21W5n3f+TEJcAtGRJVfQjI62206ming87o8tIgIuuAB+9ztXgdLqM5kwVOxfrar6WtzoqqqzCj6ArlUTnn+OZB3xq8bTvfdWQTDGrFzprh5yi/hdfTXcfLMlCRO2/PnLva6IZaMrO5CK2HF4R4ljKDb4pkLKyKiigEzNlJ0Njz7q7m+uXg316wc7ImMqRUltFFfgZqVrJyKfFvioLnCg6K2qXrYnm5T0FBLrJRa7zge+UR/279YEzM8/u8mEfv0VrrgC/vlPaNo02FEZUylKaqNYiJuDIhF4qcDyw8DPgQyqLHJ7PNWOKX5iiU2b3PPokLoOMtXKrl2wdy98/jkMHx7saIypVMUmClXdCGzEVYsNWb/u+hWAro2Lbzb5t69ZvlGjqojI1Bjffw+//Qa33gpDh8K6dVCrVrCjMqbSFdtGISLf+Z73i8i+Ao/9IhIy1WQXbnNt7n2OK3m4dfPmEOXPqBFjSnPokKvwOniwu8WUW8TPkoSppkpqzM6d7rQx0KTAI/d9SPhppxtB17xO0ZO6HDniqsba7WJTKaZPd1MkvvaaG0BnRfxMDVBS99jc0ditgEhV9QAnATcCITPT9M4jO2lRtwVSzACJZ591z717V2FQpnrautW1P9Sv70ZwPvecTbpuagR/usd+jpsGtQPwLm4MxYcBjaoMNuzfwAWdi5/VLrcr+6OPVlFApnpRhfnz3etWreCrr9xVxIABwY3LmCrkT6Lwqmo2cAkwQVX/BLQMbFj+ycxx94ZLmqWvpS/SxOJ7zxpTtO3b4aKL4KST8ov4nXEGxMQENy5jqpg/iSJHRH4PXAX4pv6h5FoZVSS3amxJ5cUXLIDTT6+igEz1oOpqMnXr5q4gnn3WiviZGs2ffkDXAbfgyoxvEJF2wEeBDcs/2w9vB+DElieWuN6yZVURjak2LrsMPv3U9Wp6803o2DHYERkTVKUmClVdJiJjgY4i0gVYp6qPBT600qWkpQBQN6boqbw/+8w9jxlTVRGZsFWwiN9FF8HZZ8MNN1h9JmPwb4a704B1wERgErBGRELiOvxg5kEA4qKKnvjlEl8h9MGDqyoiE5aWLXO3lnJ7Plx1lVV6NaYAf/4l/AM4V1VPUdWTgfOAFwIbln92HtkJQJPaxw7ryC3bERHhTg6NOUZWlpvRqk8fWL8eEhKCHZExIcmfNooYVV2R+0ZVV4pISHT7yG3MTog79h/4W2+556efrsqITNhYssQV8Vu2DK68EiZMgCYhM47UmJDiT6L4SUReA97zvR9JiBQF3LDf1Q+PjTp2ZOz48e55xIiqjMiEjZQUOHAApk2D888PdjTGhDR/EsVNwFjgLkCA74F/BTIof8VFxdEk/tizwOTk/NctQ2LEhwkJs2e7In5jx7r7kWvXQlzR7VvGmHwlJgoR6Ql0AD5T1ZC7iZPlyaJ70+7HLJ82zT3/9a9VHJAJTQcPwl13weuvQ5curqE6NtaShDF+Kql67H248h0jga9FpKiZ7oJqbcpaYiKPbS6ZOdM9X3ttFQdkQs+0aW7g3JtvujOHJUusiJ8xZVTSFcVI4ARVTRWRJsB0XPfYkLHjyA5a1299zPKcHPd8/PFVHJAJLVu3wqWXuquIzz+HE0semGmMKVpJ3WMzVTUVQFX3lLJu0BQ1BeqCBdCrF0RGBiEgE1yqrrIr5BfxW7zYkoQxFVDSwb+9iHzqe3wGdCjw/tMStssjIkNFZLWIrBORe0pY7zIRURHp52/gR7KOANCtSbejlmdluRkpt2zxd0+m2khOhgsvdIPncov4nX66FfEzpoJKuvV0aaH3L5ZlxyISiZtrewiQDCwSkakFx2T41quL61W1oCz737h/IwAN4hoctXzGDPdsPR5rEK8X3ngDxo1z9x2ffx5OPTXYURlTbZQ0Z/asCu67P64u1AYAEZkMDAdWFFrvEeBpoEx9lFanrAagQ0KHo5Y//LB7vvnmsgdswtSll7o2iN/9ziWM9sVXEzbGlF0g2x1aAlsLvE+m0DwWItIbaKWqX1ACERkjIotFZPGePXsA1+MJ4PjGR7dY//abex44sEKxm1CXk+OuJMAlijfegG++sSRhTAAEMlEUNTdp3gxDIhKBqyP1l9J2pKqvq2o/Ve3XxFdmYd2+dQB0bJhfAjo1FbKzoVMnVwjUVFO//uomE3rjDff+j3+E66+3/+jGBIjfiUJEytr5PBk333auRGB7gfd1gR7AHBHZBAwEpvrboJ182A2/jorIv3v2he+65MILyxipCQ+ZmfDQQ9C3L2zebLWZjKki/pQZ7y8ivwFrfe97iYg/JTwWAZ1EpJ2viOAIYGruh6p6UFUbq2pbVW0LzAcuVNXF/gS+N23vMfNQTPXt3RJFNbRokavyOn48/OEPsHJlfh15Y0xA+XNF8U/gfCAFQFWXAmeUtpGq5gC3ATOBlcDHqrpcRMaLSIUP5TuP7KRZnWZHLWvg6wBls1ZWQ/v3w5EjMH06vPsuNGoU7IiMqTH8KQoYoaqb5ej7vx5/dq6q03Ejugsue7CYdU/3Z5+5dqfuZlCbQUcte/NNqFPHBtpVG99+63on3H67K+K3Zo2V3zAmCPy5otgqIv0BFZFIEbkDWBPguErk8XrI8ebQrkG7o5Z37epOOk2YO3DATUN65pnw2muubQIsSRgTJP4kipuBPwOtgV24RuegjlJIz0kHOKbO05491j4R9v77X1fEb9IkV/HVivgZE3Sl3npS1d24huiQsT99PwB1YurkLfN6Yft22L07WFGZCtuyBX7/e3dpOHUq9PO7oosxJoBKTRQi8gYFxj/kUtUxAYnID/vS9wFQL7Ze3rJ0d5FBq1ZFbWFClirMnQunnQatW7tBcwMHWn0mY0KIP7eevgFm+R7zgKZAZiCDKs2u1F3A0XWetm1zz926FbWFCUlbtsB558GgQflF/AYNsiRhTIjx59bTvwu+F5H3gK8DFpEfDmYcBI5uo/iXb2SHzUERBrxeePVVuPtud0Xxz39aET9jQpg/3WMLawe0qexAyuJAxgHAzZmda9Uq93zFFcGIyJTJJZe4RushQ9z0pG3bBjsiY0wJ/Gmj2E9+G0UEsA8odm6JqtSwVsO81/td+zYRITm9kiEnx/3HiYhw2Xz4cBg1yuozGRMGSkwU4kbZ9QJ8LQB4VfWYhu2qtv2wKxkVG5nfbXLJEhtoF7KWLoXrrnNjI266yZXgMMaEjRLPv31J4TNV9fgeQU8SAB51A8PrxrpaT2PHuuX16hW3hQmKjAy4/37XzTU5GZo3D3ZExphy8OdGzUIR6RPwSMrA4/UQIRHERcWhmt+QPWdOUMMyBS1cCL17w2OPwciRrojfRRcFOypjTDkUe+tJRKJ8hf1OBW4QkfVAKm6eCVXVoCWPZXuWESnuPlOyqzZO69ZwwgnBisgc49AhN7hlxgw455xgR2OMqYCS2igWAn2AkDsN3J++n9wihR984JaNHh3EgIzz1VewfDnceSecdRasXm3lN4ypBkpKFAKgquurKBa/xUbFUiuqFuCmSgbXTmqCZP9++POf4e23oXt3uOUWlyAsSRhTLZSUKJqIyJ+L+1BVnw9APH7xeD30bNYTcFOfAhx3XLCiqeE+/RRuvdVVZLz3XnjwQUsQxlQzJSWKSKAORc99HVTZ3myiI6IB+OmnIAdTk23ZAiNGQI8ebkKh3r2DHZExJgBKShQ7VHV8lUVSBhk5GcTXis973717EIOpaVTh++9h8GDXg+Dbb2HAAIiODnZkxpgAKal7bMhdSeTak7qH+Oh4du50761MUBXZvBmGDYPTT88v4nfqqZYkjKnmSkoUZ1ZZFGW0+eBmjqtzHLNnu/eNGwc3nmrP64UXX3SXbnPnuoErp50W7KiMMVWk2FtPqrqvKgMpqyxPFhs3utcXXxzcWKq9iy6CadPceIjXXoM2Qa0JaYypYuWpHhtU6qtP2K5Bu7zONVZ8NACys13xrIgIV5vpssvgqqusiJ8xNVDY1VrNLTcVHRlNVpZbZvPcVLKffoL+/d2cEeASxdVXW5IwpoYKu0ThVS8AURFR3HefW2ZtqZUkPd2NhejfH3butHlljTFAGN56yk0UqVmpecvi4opb2/ht/ny45hpYs8aVBH/2WUhICHZUxpgQEHaJIvfWU8v49oDrqWkqQWqqa5f4+mtXp8kYY3zCL1H4GrOjxDVMDB0azGjC3IwZrojfX/4CZ57p5pO1Bh9jTCFh10aR480BQD3WMFFuKSnuNtOwYfDOO1ivAGNMScIuUXi8bna7batcFcCMjGBGE2ZUYcoU6NYNPvzQzT63aJElCGNMicLu1lOWx539Zu3sCFidpzLZsgWuvNLN8PTVV9CrV7AjMsaEgfC7ovDNl71ri5sg++STgxlNGFB1hfvAjaieM8f1cLIkYYzxU9glikxPJgArlruLIZuHogQbN8LZZ7uG6twifiefDFFhdyFpjAmisEsUuXNlf/+dGyVsg4WL4PHACy+4eSIWLIBXXrEifsaYcgu7U0tVpUFcAxLakVdm3BQyfDj8739w7rmuDIeNsDbGVED4JQqUqIgoIiOtauxRChbxu+oqV5/pyivtkssYU2EBvfUkIkNFZLWIrBORe4r4/M8iskJEfhWRWSJSav3qHG8OkRLJhg2BiTksLV4M/fq5W0wAV1wBI0dakjDGVIqAJQoRiQReAoYB3YA/iEi3Qqv9DPRT1ROAKcDTpe3Xox72HcrA6yVvPooaKz0d7r7bTUW6Z4/NE2GMCYhAXlH0B9ap6gZVzQImA8MLrqCqs1U1zfd2PpBY2k4jJIIWMccDNbwk0Y8/ui6uTz/tivitWAHnnx/sqIwx1VAg2yhaAlsLvE8GBpSw/mjgy6I+EJExwBiAWi1rUSe2DlDDu8amp7spSr/5xnV/NcaYAAlkoijqBrkWuaLIH4F+wOCiPlfV14HXAeq3r68RnlqVFWN4mT7dFfEbNw5+9ztYudIm4zDGBFwgbz0lAwX7ZSYC2wuvJCJnAX8DLlTVzNJ26vF68BxsBkBmqWtXE3v3wh//COedBx98kF/Ez5KEMaYKBDJRLAI6iUg7EYkBRgBTC64gIr2B13BJYrc/O83x5pD81WVADWi7VYXJk6FrV/j4Y3joIVi40Ir4GWOqVMBuPalqjojcBswEIoFJqrpcRMYDi1V1KvAMUAf4j7iunFtU9cKS9hshEcRQF3Djyaq1LVtcOfBevWDiROjZM9gRmTCQnZ1NcnIyGVZauUaKi4sjMTGR6Eq84yC5M8aFC2kpWitrF+l7mxJmoftHFWbNyu/SNX8+nHiiG0xnjB82btxI3bp1adSoEWJjaWoUVSUlJYXDhw/Trl27oz4TkSWq2q88+w27Wk9REVFEx6dSp06wIwmA9etdD6YhQ/KL+A0caEnClElGRoYliRpKRGjUqFGlX02GXaIAiI2JYHCR/aPClMcDzz/vbi0tWQKvvWZF/EyFWJKouQLx3z7saj2hkLKpBRwf7EAq0QUXwJdfugFzr7wCiaWOOzTGmCoTdolCUaJis9mxI8y7hmZluXkhIiJg1ChXyG/ECKvPZIwJOWF360mBrNR4+vcPdiQVsHAh9O0LL7/s3l9+uav2aknCVCOfffYZIsKqVavyls2ZM4fzC5WaGTVqFFOmTAFcj6177rmHTp060aNHD/r378+XXxZZsAGAxx9/vFyxXX/99axYsaJc2/pjyZIl9OzZk44dOzJ27FiK6jS0f/9+Lr74Yk444QT69+/PsmXL8j5r27YtPXv2JCkpiX798tufx40bR5cuXTjhhBO4+OKLOXDgQMB+hoLC7ooCj2vYraLfT+VKS4MHHoAJE1z9kQ4dgh2RqebumHEHv+z8pVL3mdQ8iQlDJ5S63kcffcSpp57K5MmT+fvf/+7Xvh944AF27NjBsmXLiI2NZdeuXXyX27GjCI8//jj33XffMctVFVUlIqLoc+E333zTr3jK6+abb+b1119n4MCBnHvuucyYMYNhw4Ydtc7jjz9OUlISn332GatWreLWW29l1qxZeZ/Pnj2bxo0bH7XNkCFDeOKJJ4iKiuLuu+/miSee4KmnngrozwLheEXhcbecwu6KYu5c11j9/PNwww2uFEehPxxjqosjR44wb948Jk6cyOTJk/3aJi0tjTfeeIN//etfxMbGAtCsWTMuv/zyIte/5557SE9PJykpiZEjR7Jp0ya6du3KLbfcQp8+fdi6dSs333wz/fr1o3v37jz00EN5255++uksXrwYgDp16vC3v/2NXr16MXDgQHbt2lWhn33Hjh0cOnSIk046CRHh6quv5vPPPz9mvRUrVnCmr05bly5d2LRpU6nfffbZZxPlm8p44MCBJCcnVyhWf4XdFYVmuz+gRo2CHEhZ5U4sNHs2nH56sKMxNYQ/Z/6B8PnnnzN06FA6d+5Mw4YN+emnn+jTp0+J26xbt47WrVtTr149v77jySef5MUXX+SXX9wV06ZNm1i9ejVvvfUWL/tu6z722GM0bNgQj8fDmWeeya+//soJJ5xw1H5SU1MZOHAgjz32GHfddRdvvPEG999//1HrzJ49mzvvvPOYGOLj4/nhhx+OWrZt2zYSC3RISUxMZNu2bcds26tXLz799FNOPfVUFi5cyObNm0lOTqZZs2aICGeffTYiwo033siYMWOO2X7SpElcccUVfv2uKirsEgVHXJ2nboVntghF06a5wn133QVnnOFKgUeF36/cmLL66KOPuOOOOwAYMWIEH330EX369Cm262Zldels06YNAwcOzHv/8ccf8/rrr5OTk8OOHTtYsWLFMYkiJiYmr92kb9++fP3118fs94wzzshLSKUpqj2iqJ/vnnvu4fbbbycpKYmePXvSu3fvvKuFefPm0aJFC3bv3s2QIUPo0qULgwYNytv2scceIyoqipEjR/oVU0WF31FL3H+EpKQgx1GSPXvg9tvho49coHfc4eozWZIwNUBKSgrffvsty5YtQ0TweDyICE8//TSNGjVi//79R62/b98+GjduTMeOHdmyZQuHDx+mbt265fru2rVr573euHEjzz77LIsWLSIhIYFRo0YVORAtOjo670AeGRlJTk7OMeuU5YoiMTHxqFtCycnJtGjR4pht69Wrx1tvvQW45NKuXbu80dS56zdt2pSLL76YhQsX5iWKd955hy+++IJZs2ZV2XiZsGujQDy0OH5baB5zVeHDD10RvylTYPx4WLDAiviZGmXKlClcffXVbN68mU2bNrF161batWvH3Llz6dSpE9u3b2flypUAbN68maVLl5KUlER8fDyjR49m7NixZPkqJO/YsYP333+/2O+Kjo4mOzu7yM8OHTpE7dq1qV+/Prt27Sqx91Rpcq8oCj8KJwmA4447jrp16zJ//nxUlXfffZfhw4cfs96BAwfyfs4333yTQYMGUa9ePVJTUzl8+DDgbot99dVX9OjRA4AZM2bw1FNPMXXqVOLj48v985RVKB5uS1W7VoiOodiyBa69Fnr3dkX8uncPdkTGVLmPPvqIe+6556hll156KR9++CGnnXYa77//Ptdeey0ZGRlER0fz5ptvUr9+fQAeffRR7r//frp160ZcXBy1a9dm/PjxxX7XmDFjOOGEE+jTpw+PPfbYUZ/16tWL3r170717d9q3b88pp5xS+T9sMV555RVGjRpFeno6w4YNy+vx9OqrrwJw0003sXLlSq6++moiIyPp1q0bEydOBGDXrl1cfPHFAOTk5HDllVcydOhQAG677TYyMzMZMmQI4Bq0c/cZSOFXFDC2s3buPpfVPzUNdiiO1wtffw3nnOPe546RsPpMJkhWrlxJ165dgx2GCaKi/gZqVFFAACly8rwgWLvWzTQ3dCh8/71b1r+/JQljTLUSlreeip5ltQrl5MA//gEPPgixse42kxXxMyZgBgwYQGahKS3fe+89etocLVUiDBOFEPREcf75MHMmDB/uynAU0aPBGFN5FixYEOwQarTwSxRZtcnJDMKtncxMN0d1RARcfz1cdx38/vdWn8kYU+2FYRtFBER4qvYr58+HPn3gpZfc+8suc4X8LEkYY2qAMEwUkNjKWzVflJoKd94JJ58Mhw9Dp05V873GGBNCwu/WE0pi28zSV6uo//s/uOYa2LgRbrkFnngC/KxBY4wx1UkYXlEIEVVxyycnx7VJfPedu+VkScKYMgnl+SgA3n77bbZv317u7Qt655136NSpE506deKdd94pcp2lS5dy0kkn0bNnTy644AIOHToEuJInZ5xxBnXq1OG2227LW//w4cMkJSXlPRo3bpxXP6uqheEVBRw5GKCR2Z9/7or43XuvK+K3fLnVZzJh7Y47wM9adn5LSnJTqpQmmPNR+OPtt9+mR48eRdZhKot9+/bx8MMPs3jxYkSEvn37cuGFF5KQkHDUetdffz3PPvssgwcPZtKkSTzzzDM88sgjxMXF8cgjj7Bs2bKjJi+qW7fuUYUI+/btyyWXXFKhWMsrDK8ooHP3Ywt7VciuXa5x+uKLXY0mX/0VSxLGlE8w5qMAeP/99+nfvz9JSUnceOONeDwePB4Po0aNokePHvTs2ZN//OMfTJkyhcWLFzNy5EiSkpJIT08v9886c+ZMhgwZQsOGDUlISGDIkCHMmDHjmPVWr16dV9hvyJAhfPLJJ4ArZHjqqacSFxdX7HesXbuW3bt3c1qQxmuF5ZEwQiopv6nC+++7064jR+Cxx2DcOHfLyZhqwJ8z/0AIxnwUK1eu5N///jfz5s0jOjqaW265hQ8++IDu3buzbdu2vLP1AwcO0KBBA1588UWeffbZo6YazfXMM8/wwQcfHLN80KBB/POf/zxq2bZt22jVqlXe++Lmn+jRowdTp05l+PDh/Oc//2Hr1q1+/Zzgrs6uuOKKKqsWW1hYJoqstOIzb5ls2eLGRPTr50ZXd+lSOfs1poYLxnwUs2bNYsmSJZx44okApKen07RpUy644AI2bNjAn/70J8477zzOPvvsUvc1btw4xo0b50GXqRoAAAv8SURBVNf3+jv/xKRJkxg7dizjx4/nwgsvJKYMVaUnT57Me++95/f6lS0sE0VSzwqU7fZ63ajqYcOgTRuYN89Ve7X6TMZUimDNR6GqXHPNNTzxxBPHfLZ06VJmzpzJSy+9xMcff8ykSZNK3FdZrigSExOZM2dO3vvk5GROL2IWyy5duvDVV18BsGbNGv73v//58VO52HNycujbt69f6wdE7iTk4fKAvvrZf7O0XFavVj3tNFVQnTOnfPswJsStWLEiqN//6quv6pgxY45aNmjQIP3+++81IyND27Ztmxfjpk2btHXr1nrgwAFVVR03bpyOGjVKMzMzVVV1+/bt+t577xX7XQ0aNNCsLHc8WL58uXbs2FF37dqlqqopKSm6adMm3bNnjx48eFBVVX/++Wft1auXqqqef/75+u2331b4501JSdG2bdvqvn37dN++fdq2bVtNSUk5Zr3cuDwej1511VU6ceLEoz5/66239NZbbz1mu7vvvlsffPDBMsVU1N8AsFjLe9wt74bBekBfnTbNW6ZfmmZnqz75pGpsrGqDBqpvvaXqLeM+jAkTwU4UgwcP1i+//PKoZS+88ILedNNNqqo6d+5cHTBggPbq1Uv79eunX331Vd56mZmZOm7cOO3QoYN2795d+/fvrzNmzCj2u+666y7t0qWLXnnllaqqOnnyZO3Vq5f27NlT+/Tpoz/++KP+8ssv2rt3b+3Vq5f26tVLp0+frqqqU6ZM0c6dO2uvXr00LS2tQj/zxIkTtUOHDtqhQwedNGlS3vLRo0frokWLVFV1woQJ2qlTJ+3UqZPefffd6i1wDGrTpo0mJCRo7dq1tWXLlrp8+fK8z9q1a6crV64sUzyVnSjCbz4K6adffLGY884rw0bnnANffQWXXOLGRDRvHrD4jAk2m4/CVPZ8FGHZRuGXjAzXeykyEsaMcY9LLw12VMYYE3bCMlEUGsdyrHnzYPRoV3pj7FhLEMaEOZuPIrjCMlEUe1V95Ajcdx+8+CK0bl3CisZUb6oatD73gWDzUfgvEM0JYTkyOz6+iIXffQc9ergkcdttsGwZ+CYgN6YmiYuLIyUlJSAHDBPaVJWUlJQSR3mXR1heURQ7TiU+3lV9PeWUKo3HmFCSmJhIcnIye/bsCXYoJgji4uJITEys1H2GX6+niD6q3p/cm08/hVWr3O0mAI/HBs4ZY0wRKtLrKaC3nkRkqIisFpF1InJPEZ/Hisi/fZ8vEJG2pe/UCzt3ulnmLr0UPvssv4ifJQljjKl0AUsUIhIJvAQMA7oBfxCRboVWGw3sV9WOwD+Ap0rbbyNSXCP1F1+4yYR++KGEe1HGGGMqKpBXFP2Bdaq6QVWzgMnA8ELrDAdyZ/mYApwppXTVaOPd5hqtly6Fe+6xSq/GGBNggWzMbgkUrKObDAwobh1VzRGRg0AjYG/BlURkDDDG9zZT5s5dZpVeAWhMod9VDWa/i3z2u8hnv4t8x5d3w0AmiqKuDAq3nPuzDqr6OvA6gIgsLm+DTHVjv4t89rvIZ7+LfP/f3v3GyFWVcRz//gQqrUCVNBgQZCEUtNRSazVVErEWCdZYlDTdkhZYAxiqaADrC1MTEXxBQF5YAZeKTcEAKSWgG4RUggslTRfaCN22GwQsDWlCLNHaGCgGy88X5ywzDrszd9fO330+ySRz/z/zZOaeuefOPCdyUSJp23i3rWfX017glLLpk4HKAWrfW0fSkcBU4B91jCmEEMIY1bOh2ApMl3SapEnAUqCvYp0+4PL8fDHwJ7fb73VDCKHD1a3rKd9zuAbYCBwBrLW9S9KNpHK3fcBvgN9KeoV0JbG0wK7X1CvmNhS5KIlclEQuSiIXJePORdv94S6EEEJjtWWtpxBCCI0TDUUIIYSqWrahqEv5jzZVIBfXSxqSNCjpSUmnNiPORqiVi7L1FkuypI79aWSRXEhakt8buyTd3+gYG6XAZ+TjkvolPZ8/JwubEWe9SVoraZ+knaMsl6TVOU+DkuYU2vF4x1Ct54N08/uvwOnAJGA7MKNine8Avfn5UmB9s+NuYi7mA1Py8xUTORd5vWOBTcAAMLfZcTfxfTEdeB74SJ4+odlxNzEXa4AV+fkMYE+z465TLr4IzAF2jrJ8IfA46T9s84Bni+y3Va8o6lL+o03VzIXtfttv5ckB0n9WOlGR9wXATcAtwNuNDK7BiuTiKuAO2/sBbO9rcIyNUiQXBo7Lz6fy/v90dQTbm6j+X7SLgHudDAAflnRirf22akMxUvmPj422ju3/AMPlPzpNkVyUu4L0jaET1cyFpE8Dp9h+tJGBNUGR98WZwJmSNksakHRhw6JrrCK5uAFYLmkv8BjwvcaE1nLGej4BWnfgosNW/qMDFH6dkpYDc4Hz6hpR81TNhaQPkKoQ9zQqoCYq8r44ktT99CXSVeYzkmba/medY2u0Irm4BFhn+zZJnyf9f2um7XfrH15LGdd5s1WvKKL8R0mRXCDpfGAVsMj2vyuXd4hauTgWmAk8JWkPqQ+2r0NvaBf9jPze9ju2XwX+Qmo4Ok2RXFwBPAhgewtwNKlg4ERT6HxSqVUbiij/UVIzF7m75S5SI9Gp/dBQIxe2D9ieZrvLdhfpfs0i2+MuhtbCinxGfkf6oQOSppG6onY3NMrGKJKL14AFAJI+SWooJuJYsX3AZfnXT/OAA7Zfr7VRS3Y9uX7lP9pOwVzcChwDbMj381+zvahpQddJwVxMCAVzsRG4QNIQcAj4oe2/Ny/q+iiYix8Av5Z0HamrpacTv1hKeoDU1Tgt34/5CXAUgO1e0v2ZhcArwFvAtwrttwNzFUII4TBq1a6nEEIILSIaihBCCFVFQxFCCKGqaChCCCFUFQ1FCCGEqqKhCC1H0iFJL5Q9uqqs2zVapcwxHvOpXH10ey55cdY49nG1pMvy8x5JJ5Utu1vSjMMc51ZJswtsc62kKf/vscPEFQ1FaEUHbc8ue+xp0HGX2T6HVGzy1rFubLvX9r15sgc4qWzZlbaHDkuUpTjvpFic1wLRUIRxi4YitIV85fCMpD/nxxdGWOdsSc/lq5BBSdPz/OVl8++SdESNw20CzsjbLshjGOzItf4/mOffrNIYID/P826QtFLSYlLNrfvyMSfnK4G5klZIuqUs5h5JvxxnnFsoK+gm6VeStimNPfHTPO/7pAarX1J/nneBpC05jxskHVPjOGGCi4YitKLJZd1Oj+R5+4Cv2J4DdAOrR9juauAXtmeTTtR7c7mGbuDcPP8QsKzG8b8O7JB0NLAO6Lb9KVIlgxWSjge+CZxtexbws/KNbT8EbCN9859t+2DZ4oeAi8umu4H144zzQlKZjmGrbM8FZgHnSZplezWpls982/NzKY8fA+fnXG4Drq9xnDDBtWQJjzDhHcwny3JHAbfnPvlDpLpFlbYAqySdDDxs+2VJC4DPAFtzeZPJpEZnJPdJOgjsIZWhPgt41fZLefk9wHeB20ljXdwt6Q9A4ZLmtt+QtDvX2Xk5H2Nz3u9Y4vwQqVxF+QhlSyR9m/S5PpE0QM9gxbbz8vzN+TiTSHkLYVTRUIR2cR3wN+Ac0pXw+wYlsn2/pGeBrwEbJV1JKqt8j+0fFTjGsvICgpJGHN8k1xb6HKnI3FLgGuDLY3gt64ElwIvAI7atdNYuHCdpFLebgTuAiyWdBqwEPmt7v6R1pMJ3lQQ8YfuSMcQbJrjoegrtYirweh4/4FLSt+n/Iel0YHfubukjdcE8CSyWdEJe53gVH1P8RaBL0hl5+lLg6dynP9X2Y6QbxSP98uhfpLLnI3kY+AZpjIT1ed6Y4rT9DqkLaV7utjoOeBM4IOmjwFdHiWUAOHf4NUmaImmkq7MQ3hMNRWgXdwKXSxogdTu9OcI63cBOSS8AnyAN+ThEOqH+UdIg8ASpW6Ym22+TqmtukLQDeBfoJZ10H837e5p0tVNpHdA7fDO7Yr/7gSHgVNvP5XljjjPf+7gNWGl7O2l87F3AWlJ31rA1wOOS+m2/QfpF1gP5OAOkXIUwqqgeG0IIoaq4ogghhFBVNBQhhBCqioYihBBCVdFQhBBCqCoaihBCCFVFQxFCCKGqaChCCCFU9V/0ThtBfrkN3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "######### FRESH-MODEL FOR BEST_K ######################\n",
    "nb = MultinomialNB(alpha=best_alpha, class_prior=None, fit_prior=True)\n",
    "nb.fit(X_train_bow, y_train)\n",
    "y_pred_train_data = nb.predict(X_train_bow)\n",
    "y_pred = nb.predict(X_test_bow)\n",
    "proba = nb.predict_proba(X_test_bow)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_k: \",roc_auc)\n",
    "proba = nb.predict_proba(X_train_bow)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "\n",
    "# storing values for future reference\n",
    "alpha_1 = best_alpha\n",
    "set_1_auc = roc_auc\n",
    "\n",
    "############# ROC CURVE ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.3f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.3f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of nb')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 92,
=======
   "execution_count": 39,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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\n",
=======
      "image/png": "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\n",
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### TEST DATA - CONFUSION MATRIX ############\n",
    "\n",
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in ['negative','positive']],columns = [i for i in ['negative','positive']])\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title('COnfusion Matrix on Test Data')\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### TRAIN DATA - CONFUSION MATRIX ############\n",
    "\n",
    "y_true = y_train\n",
    "y_pred = y_pred_train_data\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in ['negative','positive']],columns = [i for i in ['negative','positive']])\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title('Confusion Matrix on Train Data')\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u-ZSD3tlJklg"
   },
   "source": [
    "### [5.1.1] Top 10 important features of positive class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 94,
=======
   "execution_count": 66,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "86fu_aO8Jklg",
    "scrolled": true
   },
   "outputs": [],
   "source": [
<<<<<<< HEAD
    "# all feature names in BOW vectoriser\n",
    "a=count_vect.get_feature_names()\n",
    "# all negative features \n",
    "f0 = nb.feature_log_prob_[0]\n",
    "# all positive features \n",
    "f1 = nb.feature_log_prob_[1]\n",
    "#######################\n",
=======
    "# Please write all the code with proper documentation\n",
    "a=tf_idf_vect.get_feature_names()\n",
    "f0 = nb.feature_log_prob_[0]\n",
    "f1 = nb.feature_log_prob_[1]\n",
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
    "f_neg = np.argsort(f0)[::-1]\n",
    "f_pos = np.argsort(f1)[::-1]"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 95,
=======
   "execution_count": 68,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not\n",
<<<<<<< HEAD
      "like\n",
      "good\n",
      "great\n",
      "one\n",
      "taste\n",
      "coffee\n",
      "would\n",
      "flavor\n",
      "love\n"
=======
      "great\n",
      "good\n",
      "like\n",
      "coffee\n",
      "love\n",
      "tea\n",
      "one\n",
      "taste\n",
      "product\n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    }
   ],
   "source": [
    "for j in f_pos[:10]:\n",
<<<<<<< HEAD
    "    print(a[j])"
=======
    "   print(a[j]) \n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mquFtwF5Jklj"
   },
   "source": [
    "### [5.1.2] Top 10 important features of negative class from<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 96,
=======
   "execution_count": 69,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_RacUnTrJklj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not\n",
      "like\n",
<<<<<<< HEAD
      "taste\n",
      "would\n",
      "product\n",
      "one\n",
      "good\n",
      "coffee\n",
      "no\n",
      "flavor\n"
=======
      "product\n",
      "taste\n",
      "would\n",
      "coffee\n",
      "one\n",
      "flavor\n",
      "no\n",
      "good\n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "for j in f_neg[:10]:\n",
<<<<<<< HEAD
    "    print(a[j]) "
=======
    "   print(a[j]) \n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6bp7BbwCJkll"
   },
   "source": [
    "## [5.2] Applying Naive Bayes on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 97,
=======
   "execution_count": 42,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oL29fzPFJkll"
   },
   "outputs": [],
   "source": [
    "# Please write all the code with proper documentation\n",
    "\n",
    "X_train_tf, X_test_tf, X_cv_tf = final_tf_idf_train, final_tf_idf_test,final_tf_idf_cv\n",
    "\n",
    "auc_train=[]\n",
    "alphas=[]\n",
    "auc_cv=[]\n",
    "\n",
    "\n",
    "for i in range(-5,5):\n",
    "    nb = MultinomialNB(alpha=10**i, class_prior=None, fit_prior=True)\n",
    "    nb.fit(X_train_tf, y_train_tf)\n",
    "\n",
    "    proba = nb.predict_proba(X_train_tf)[:,1]\n",
    "    auc_train.append(roc_auc_score(y_train_tf, proba))\n",
    "    alphas.append(10**i)\n",
    "    \n",
    "    proba = nb.predict_proba(X_cv_tf)[:,1]\n",
    "    auc_cv.append(roc_auc_score(y_cv_tf, proba))\n",
    "\n",
    "# print(\"Pre-def accuracy: \", metrics.accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 98,
=======
   "execution_count": 43,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha for Naive Bayes Model from cv_data is:  0.1 for cooresponding AUC value of: 0.9521071187903292\n"
     ]
    }
   ],
   "source": [
    "######## AUC PLOTTING ########\n",
    "ax=sns.lineplot(np.log(alphas),auc_train,dashes=True)\n",
    "ax=sns.lineplot(np.log(alphas),auc_cv)\n",
    "ax.set(xlabel='Differnt alpha values',ylabel='roc_auc_score')\n",
    "ax.set_title('AUC Score vs Different alpha value Curve')\n",
    "plt.show()\n",
    "######## AUC PLOTTING ########\n",
    "\n",
    "\n",
    "######## best_alpha ##############\n",
    "best_alpha = alphas[auc_cv.index(max(auc_cv))]\n",
    "print(\"Best alpha for Naive Bayes Model from cv_data is: \",best_alpha, \"for cooresponding AUC value of:\",max(auc_cv))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 103,
=======
   "execution_count": 44,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test_data with best_k:  0.9561017882544315\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "######### FRESH-MODEL FOR BEST_K ######################\n",
    "nb = MultinomialNB(alpha=best_alpha, class_prior=None, fit_prior=True)\n",
    "nb.fit(X_train_tf, y_train_tf)\n",
    "y_pred = nb.predict(X_test_tf)\n",
    "y_pred_train_data = nb.predict(X_train_tf)\n",
    "\n",
    "proba = nb.predict_proba(X_test_tf)[:,1]\n",
    "fpr, tpr, threshold = roc_curve(y_test_tf, proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC score on test_data with best_k: \",roc_auc)\n",
    "#############################################\n",
    "proba = nb.predict_proba(X_train_tf)[:,1]\n",
    "fpr_train, tpr_train, threshold_train = roc_curve(y_train_tf, proba)\n",
    "roc_auc_train = auc(fpr_train, tpr_train)\n",
    "\n",
    "alpha_2 = best_alpha\n",
    "set_2_auc = roc_auc\n",
    "############# ROC ######################\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr_train, tpr_train, 'g', label = 'AUC_train = %0.2f' % roc_auc_train)\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC_test = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of nb')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### TEST DATA - CONFUSION MATRIX ############\n",
    "\n",
=======
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-c623ed2951b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdf_cm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;34m\"01\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;34m\"01\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_test' is not defined"
     ]
    }
   ],
   "source": [
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
    "y_true = y_test\n",
    "y_pred = y_pred\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
<<<<<<< HEAD
    "df_cm = pd.DataFrame(array, index = [i for i in ['negative','positive']],columns = [i for i in ['negative','positive']])\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title('Confusion Matrix on Test Data')\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########### TRAIN DATA - CONFUSION MATRIX ############\n",
    "\n",
    "y_true = y_train\n",
    "y_pred = y_pred_train_data\n",
    "\n",
    "array = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(array, index = [i for i in ['negative','positive']],columns = [i for i in ['negative','positive']])\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.title('Confusion Matrix on Test Data')\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3,fmt='g', linecolor='pink', cbar=False, square=True)\n",
=======
    "df_cm = pd.DataFrame(array, index = [i for i in \"01\"],columns = [i for i in \"01\"])\n",
    "\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 16}, linewidths=6.3, linecolor='pink', cbar=False, square=True)\n",
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dtpj7iMSJkln"
   },
   "source": [
    "### [5.2.1] Top 10 important features of positive class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 107,
=======
   "execution_count": 62,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {},
   "outputs": [],
   "source": [
    "a=tf_idf_vect.get_feature_names()\n",
    "f0 = nb.feature_log_prob_[0]\n",
    "f1 = nb.feature_log_prob_[1]\n",
    "f_neg = np.argsort(f0)[::-1]\n",
    "f_pos = np.argsort(f1)[::-1]"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 108,
=======
   "execution_count": 63,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g3flcTmmJklo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not\n",
      "great\n",
      "good\n",
      "like\n",
      "coffee\n",
      "love\n",
      "tea\n",
      "one\n",
      "taste\n",
      "product\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "for j in f_pos[:10]:\n",
<<<<<<< HEAD
    "   print(a[j]) "
=======
    "   print(a[j]) \n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UovIVRyEJklq"
   },
   "source": [
    "### [5.2.2] Top 10 important features of negative class from<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 109,
=======
   "execution_count": 64,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i3i3cShuJklr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not\n",
      "like\n",
      "product\n",
      "taste\n",
      "would\n",
      "coffee\n",
      "one\n",
      "flavor\n",
      "no\n",
      "good\n"
     ]
    }
   ],
   "source": [
    "# Please write all the code with proper documentation\n",
    "for j in f_neg[:10]:\n",
<<<<<<< HEAD
    "    print(a[j]) \n"
=======
    "   print(a[j]) \n"
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vst5Y7mmJklu"
   },
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 110,
=======
   "execution_count": 65,
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "De9HYtIGJklv",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-------------+-----------------+------+\n",
      "| Vectoriser |    Model    | Hyper Parameter | AUC  |\n",
      "+------------+-------------+-----------------+------+\n",
      "|    BOW     | Naive Bayes |        1        | 0.92 |\n",
      "|   TFIDF    | Naive Bayes |       0.1       | 0.96 |\n",
      "+------------+-------------+-----------------+------+\n"
     ]
    }
   ],
   "source": [
    "# Please compare all your models using Prettytable library\n",
    "\n",
    "# Reference: http://zetcode.com/python/prettytable/\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "x= PrettyTable()\n",
    "\n",
    "x.field_names = [\"Vectoriser\", \"Model\", \"Hyper Parameter\", \"AUC\"]\n",
    "\n",
    "x.add_row([\"BOW\", \"Naive Bayes\", alpha_1, \"{0:.2f}\".format(set_1_auc)])\n",
    "x.add_row([\"TFIDF\", \"Naive Bayes\", alpha_2, \"{0:.2f}\".format(set_2_auc)])\n",
    "\n",
    "print(x)"
   ]
<<<<<<< HEAD
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Naive bayes proves to be a great algorithm giving 0.96 auc-metric for TFIDF vectorisation where as BOW vectorisation also fetched 0.92 auc-metric which is quite good. \n",
    "\n",
    "\n",
    "* Hyper parameter with various experiments in this assignment remain either 0.1 or 1\n",
    "\n",
    "\n",
    "* Nature of model could be verified with the most relevant features listing. Features like, 'great', 'good', 'like' are among top postive class features and features like 'no', 'would', 'one' are among top negative class features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
=======
>>>>>>> 677429d9425f1f6d2c387ebc7f440e368d9c573b
  }
 ],
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