5 . Amazon Fine Food Reviews Analysis_LR.ipynb

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    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews\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",
    "<b>Objective:-</b>\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 the Score/Rating. A rating of 4 or 5 could be cosnidered a positive review. A review of 1 or 2 could be considered negative. A review of 3 is nuetral and ignored. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
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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 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 id 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",
    "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 matplotlib.pyplot as pyplt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\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",
    "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",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
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    "id": "-lrJx9N9MQvT"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (525814, 10)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
      ]
     },
     "execution_count": 2,
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   "source": [
    "\n",
    "# using the SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite') \n",
    "#filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", con) \n",
    "\n",
    "\n",
    "# Give reviews with Score>3 a positive rating, and reviews with a score<3 a negative rating.\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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   "outputs": [],
   "source": [
    "sample_data = filtered_data.head(350000) #Sampled amazon fine foood reviews filtered data to 50k datapoints for time effiecieny"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "## Data Cleaning: Deduplication\n",
    "\n",
    "It is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"
   ]
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78445</td>\n",
       "      <td>B000HDL1RQ</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138317</td>\n",
       "      <td>B000HDOPYC</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138277</td>\n",
       "      <td>B000HDOPYM</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73791</td>\n",
       "      <td>B000HDOPZG</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155049</td>\n",
       "      <td>B000PAQ75C</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
       "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "\n",
       "   HelpfulnessDenominator  Score        Time  \\\n",
       "0                       2      5  1199577600   \n",
       "1                       2      5  1199577600   \n",
       "2                       2      5  1199577600   \n",
       "3                       2      5  1199577600   \n",
       "4                       2      5  1199577600   \n",
       "\n",
       "                             Summary  \\\n",
       "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "\n",
       "                                                Text  \n",
       "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
      ]
     },
     "execution_count": 4,
     "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": {
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    "id": "95Aa3bAYMQvb"
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   "source": [
    "As can be seen above the same user has multiple reviews of the with the same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text  and on doing analysis it was found that <br>\n",
    "<br> \n",
    "ProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n",
    "<br> \n",
    "ProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n",
    "\n",
    "It was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n",
    "\n",
    "The method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "end_time": "2019-02-22T20:45:17.028734Z",
     "start_time": "2019-02-22T20:45:16.414664Z"
    },
    "colab": {},
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   "outputs": [],
   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=sample_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "end_time": "2019-02-22T20:45:18.172125Z",
     "start_time": "2019-02-22T20:45:17.028734Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256438, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deduplication of entries\n",
    "final=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.184117Z",
     "start_time": "2019-02-22T20:45:18.175123Z"
    },
    "colab": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.268"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(sample_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
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   "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"
   ]
  },
  {
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       "      <th></th>\n",
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       "      <th>HelpfulnessDenominator</th>\n",
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       "      <th>0</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   ProductId          UserId              ProfileName  \\\n",
       "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     3                       1      5  1224892800   \n",
       "1                     3                       2      4  1212883200   \n",
       "\n",
       "                                        Summary  \\\n",
       "0             Bought This for My Son at College   \n",
       "1  Pure cocoa taste with crunchy almonds inside   \n",
       "\n",
       "                                                Text  \n",
       "0  My son loves spaghetti so I didn't hesitate or...  \n",
       "1  It was almost a 'love at first bite' - the per...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND Id=44737 OR Id=64422\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.626978Z",
     "start_time": "2019-02-22T20:45:18.306161Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "kThrWN9FMQvq"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.650963Z",
     "start_time": "2019-02-22T20:45:18.629978Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "lvw544BEMQvs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256436, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    215863\n",
       "0     40573\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 10,
     "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": "3-ZDuet8MQvw"
   },
   "source": [
    "## Text Preprocessing: Stemming, stop-word removal and Lemmatization.\n",
    "\n",
    "Now that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n",
    "\n",
    "Hence in the Preprocessing phase we do the following in the order below:-\n",
    "\n",
    "1. Begin by removing the html tags\n",
    "2. Remove any punctuations or limited set of special characters like , or . or # etc.\n",
    "3. Check if the word is made up of english letters and is not alpha-numeric\n",
    "4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n",
    "5. Convert the word to lowercase\n",
    "6. Remove Stopwords\n",
    "7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n",
    "\n",
    "After which we collect the words used to describe positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.785887Z",
     "start_time": "2019-02-22T20:45:18.653962Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "I2NRwIj6MQvz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "These days, when a person says, \"chicken soup\" they're probably going to follow up those words with, \"for the soul\" or maybe \"for the teenaged soul\".  Didn't used to be that way.  Why I can remember a time when if a person said, \"chicken soup\" those words were followed by an enthusiastic \"with rice!\".  Such was the power of Maurice Sendak's catchy 1962 children's book.  I am pleased to report that if you care to read this book again today, you will find it hasn't dimished a jot in terms of frolicksome fun.  In this book we are led through a whirlwind chicken soup year with our host, a boy who bears no little resemblance to Sendak's other great rhyming tale \"Pierre\" (in looks if not demeanor).  It's a catchy flouncy bouncy combo of soup and the people who love it so.<br /><br />This is ostensibly a book meant to teach your children the different months of the year.  Each month gets its own rhythmic poem and accompanying illustration.  These are fairly simple pen and ink drawings with the occasional splash of blue (in varying shades), yellow, gray, and green.  You may wonder how an author could ever hope to come up with twelve highly original soup-related poems.  I mean, honestly, how much is there to say about even the fanciest soup, let alone chicken soup with rice?  Quite a lot, as it happens.  In the cold winter months soup is supped while sliding on ice, while celebrating the birthday of a snowman, and in a gusty gale as a whale.  In the spring there's robin's nest soup, soup to cure drooping roses, and soup stolen by jealous March winds.  Our hero postulates the potential joys that could come of being a cooking pot, stewing soup or (oddly enough) as \"a baubled bangled Christmas tree\".<br /><br />Not to degrade the reading skills of parents everywhere, but I cannot recommend enough getting an audio version of this tale to accompany your child's reading.  Though I am now a wise and cultured 26 year-old (the years have been kind to me in this, my old age) I can still remember the chicken soup with rice tune.  Heck, I read this entire book recently and found I could do the song perfectly with each and every line.  Now maybe you have your own particular chicken soup with rice song style that you're just loathe to give up.  If so, fine.  I understand why you might not want to taint your already existing chicken soup melody.  But if you haven't found a jingle to accompany this book, get the audio version immediately, if not sooner.  Until you can sing \"Whoopy once, whoopy twice, whoopy chicken soup with rice\" with the correct oomph, you're missing out.<br /><br />I take my \"Chicken Soup With Rice\" readings seriously.  This book was the \"Chicka Chicka Boom Boom\" of its day, and still remains the catchiest method to teach kids the months of the year.  It is also seriously in danger of being forgotten.  So pull out your old accordion and strap on your dancing shoes.  The time for yukkin' it up to a merry dance of poultry broth is here.  It's Sendak at his finest.\n"
     ]
    }
   ],
   "source": [
    "# find sentences containing HTML tags\n",
    "import re\n",
    "i=0;\n",
    "for sent in final['Text'].values:\n",
    "    if (len(re.findall('<.*?>', sent))):\n",
    "        print(i)\n",
    "        print(sent)\n",
    "        break;\n",
    "    i += 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.896822Z",
     "start_time": "2019-02-22T20:45:18.788884Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "hSYsO2neMQv2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'were', 'such', 'an', 'too', 'no', 'you', 'we', 'how', 'over', 'shouldn', 'before', 'wouldn', 'and', 'wasn', 'has', 'with', 't', 'because', \"she's\", 'needn', 'don', 'weren', 'was', 'ours', 'both', \"mightn't\", 'yourself', 'nor', \"you'd\", 'after', \"weren't\", 'mightn', 'further', 'having', 'who', 'do', 'this', 'down', 'some', 'him', 'where', 'few', \"couldn't\", \"isn't\", 'for', 'between', \"don't\", 'd', 'does', \"needn't\", 'these', 'ourselves', 'those', \"shan't\", \"wouldn't\", 'can', 'any', 'whom', 'from', 'ma', 'have', 'your', \"hadn't\", \"you'll\", \"that'll\", 'why', 'what', 'haven', 'y', 'against', 'its', 'during', 'off', 'they', 'my', 'did', 'if', 'while', 'above', 'but', 'o', 'all', 're', 'won', \"you're\", 'through', \"you've\", 'of', 'below', 'most', 'other', 'm', \"hasn't\", 'is', \"aren't\", 'at', 'in', 'her', 'their', 'am', 'by', 'so', 'ain', 'very', 'it', 'yours', 'that', 'me', 'doesn', 'she', 'couldn', \"mustn't\", 'i', 'here', 'shan', 'to', 'will', \"haven't\", 'didn', 'more', \"didn't\", 'hasn', 'on', 'herself', 'which', \"should've\", 'each', 'up', 'theirs', \"it's\", 'same', 'isn', 'just', \"shouldn't\", 'are', 'itself', 'until', 'myself', 'doing', 'about', 'be', \"doesn't\", 'there', 'under', \"wasn't\", 'own', 'the', 'not', 'only', 'into', 'mustn', 'a', 'than', 'himself', 'he', 'his', 'now', 'had', 'them', 'our', 'll', 've', 'should', 'or', 's', 'when', 'hadn', 'as', 'being', 'aren', 'been', 'yourselves', 'then', 'again', 'out', 'once', 'hers', \"won't\", 'themselves'}\n",
      "************************************\n",
      "tasti\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stop = set(stopwords.words('english')) #set of stopwords\n",
    "sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer\n",
    "\n",
    "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    cleantext = re.sub(cleanr, ' ', sentence)\n",
    "    return cleantext\n",
    "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
    "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
    "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
    "    return  cleaned\n",
    "print(stop)\n",
    "print('************************************')\n",
    "print(sno.stem('tasty'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:18.961786Z",
     "start_time": "2019-02-22T20:45:18.899820Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "BUGzVaKbMQv5"
   },
   "outputs": [],
   "source": [
    "#Code for implementing step-by-step the checks mentioned in the pre-processing phase\n",
    "# this code takes a while to run as it needs to run on 500k sentences.\n",
    "if not os.path.isfile('final.sqlite'):\n",
    "    final_string=[]\n",
    "    all_positive_words=[] # store words from +ve reviews here\n",
    "    all_negative_words=[] # store words from -ve reviews here.\n",
    "    for i, sent in enumerate(tqdm(final['Text'].values)):\n",
    "        filtered_sentence=[]\n",
    "        #print(sent);\n",
    "        sent=cleanhtml(sent) # remove HTMl tags\n",
    "        for w in sent.split():\n",
    "            # we have used cleanpunc(w).split(), one more split function here because consider w=\"abc.def\", cleanpunc(w) will return \"abc def\"\n",
    "            # if we dont use .split() function then we will be considring \"abc def\" as a single word, but if you use .split() function we will get \"abc\", \"def\"\n",
    "            for cleaned_words in cleanpunc(w).split():\n",
    "                if((cleaned_words.isalpha()) & (len(cleaned_words)>2)):    \n",
    "                    if(cleaned_words.lower() not in stop):\n",
    "                        s=(sno.stem(cleaned_words.lower())).encode('utf8') #snoball stemmer\n",
    "                        filtered_sentence.append(s)\n",
    "                        if (final['Score'].values)[i] == 1: \n",
    "                            all_positive_words.append(s) #list of all words used to describe positive reviews\n",
    "                        if(final['Score'].values)[i] == 0:\n",
    "                            all_negative_words.append(s) #list of all words used to describe negative reviews reviews\n",
    "        str1 = b\" \".join(filtered_sentence) #final string of cleaned words\n",
    "        #print(\"***********************************************************************\")\n",
    "        final_string.append(str1)\n",
    "\n",
    "    #############---- storing the data into .sqlite file ------########################\n",
    "    final['CleanedText']=final_string #adding a column of CleanedText which displays the data after pre-processing of the review \n",
    "    final['CleanedText']=final['CleanedText'].str.decode(\"utf-8\")\n",
    "        # store final table into an SQlLite table for future.\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    c=conn.cursor()\n",
    "    conn.text_factory = str\n",
    "    final.to_sql('Reviews', conn,  schema=None, if_exists='replace', \\\n",
    "                 index=True, index_label=None, chunksize=None, dtype=None)\n",
    "    conn.close()\n",
    "    \n",
    "    \n",
    "    with open('positive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_positive_words, f)      # dump data to f\n",
    "    with open('negitive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_negative_words, f)      # dump data to f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:22.656767Z",
     "start_time": "2019-02-22T20:45:18.963784Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bU88yTG9MQv-",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if os.path.isfile('final.sqlite'):\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    final = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", conn)\n",
    "    conn.close()\n",
    "else:\n",
    "    print(\"Please the above cell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting dataset based on 'Time' feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:22.911871Z",
     "start_time": "2019-02-22T20:45:22.656767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256436, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_reviews = final.sort_values('Time', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "final_reviews.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.060785Z",
     "start_time": "2019-02-22T20:45:22.915866Z"
    }
   },
   "outputs": [],
   "source": [
    "#split data into train, cross validate and test \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = final_reviews['CleanedText']\n",
    "Y = final_reviews['Score']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0)\n",
    "X_tr, X_cv, Y_tr, Y_cv = train_test_split(X_train, Y_train, test_size=.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.089767Z",
     "start_time": "2019-02-22T20:45:23.063783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, Y_train (171812,) (171812,)\n",
      "X_test, Y_test (84624,) (84624,)\n",
      "X_tr, Y_tr (115114,) (115114,)\n",
      "X_cv, Y_cv (56698,) (56698,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train, Y_train', X_train.shape, Y_train.shape)\n",
    "print('X_test, Y_test', X_test.shape, Y_test.shape)\n",
    "print('X_tr, Y_tr', X_tr.shape, Y_tr.shape)\n",
    "print('X_cv, Y_cv', X_cv.shape, Y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with simple CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.208703Z",
     "start_time": "2019-02-22T20:45:23.092765Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from collections import Counter\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.293653Z",
     "start_time": "2019-02-22T20:45:23.212697Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# logistic Regression with penality = 'l1'\n",
    "def logistic_l1(X_train,X_cv,Y_train,Y_cv):\n",
    "    best_C=0\n",
    "    max_roc_auc=-1\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    C=[10000,5000,1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001,0.00005,0.00001] \n",
    "    for i in C[-19:]:\n",
    "        logisticl1 = LogisticRegression(C=i,penalty='l1')\n",
    "        logisticl1.fit(X_train,Y_train)\n",
    "        probs = logisticl1.predict_proba(X_cv)\n",
    "        prob = logisticl1.predict_proba(X_train)\n",
    "        probs = probs[:,1]\n",
    "        prob = prob[:,1]\n",
    "        auc_score_cv = roc_auc_score(Y_cv,probs)\n",
    "        auc_score_train = roc_auc_score(Y_train,prob)\n",
    "        print(i,\" for CV data auc score is --> \",auc_score_cv,\" and for train data auc score is --> \",auc_score_train)\n",
    "        pred_cv.append(auc_score_cv)\n",
    "        pred_train.append(auc_score_train)\n",
    "        if(max_roc_auc<auc_score_cv):\n",
    "            max_roc_auc=auc_score_cv\n",
    "            best_C=i\n",
    "    print(f\"\\n Best C Value {best_C} with highest roc_auc Score is {max_roc_auc}\")\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.xscale('log')\n",
    "    plt.plot(C, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(C,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(r'Auc Score v/s $\\lambda$')\n",
    "    plt.xlabel(r\"alpha values\",fontsize=12)\n",
    "    plt.ylabel(\"roc_auc\",fontsize=12)\n",
    "    plt.show()\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_train,prob)\n",
    "    # plot no skill\n",
    "    pyplt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    pyplt.plot(fpr, tpr, marker='.')\n",
    "    pyplt.title(\"Line Plot of ROC Curve on Train Data\")\n",
    "    pyplt.ylabel('True Positive Rate')\n",
    "    pyplt.xlabel('False Positive Rate')\n",
    "    pyplt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.400608Z",
     "start_time": "2019-02-22T20:45:23.296652Z"
    }
   },
   "outputs": [],
   "source": [
    "# logistic Regression with penality = 'l2'\n",
    "def logistic_l2(X_train,X_cv,Y_train,Y_cv):\n",
    "    best_C=0\n",
    "    max_roc_auc=-1\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    C=[10000,5000,1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001,0.00005,0.00001] \n",
    "    for i in C[-19:]:\n",
    "        logisticl2 = LogisticRegression(penalty='l2',C=i)\n",
    "        logisticl2.fit(X_train,Y_train)\n",
    "        probs = logisticl2.predict_proba(X_cv)\n",
    "        prob = logisticl2.predict_proba(X_train)\n",
    "        probs = probs[:,1]\n",
    "        prob = prob[:,1]\n",
    "        auc_score_cv = roc_auc_score(Y_cv,probs)\n",
    "        auc_score_train = roc_auc_score(Y_train,prob)\n",
    "        print(i,\" for CV data auc score is --> \",auc_score_cv,\" and for train data auc score is --> \",auc_score_train)\n",
    "        pred_cv.append(auc_score_cv)\n",
    "        pred_train.append(auc_score_train)\n",
    "        if(max_roc_auc<auc_score_cv):\n",
    "            max_roc_auc=auc_score_cv\n",
    "            best_C=i\n",
    "    print(f\"\\n Best C Value {best_C} with highest roc_auc Score is {max_roc_auc}\")\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.xscale('log')\n",
    "    plt.plot(C, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(C,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(r'Auc Score v/s $\\lambda$')\n",
    "    plt.xlabel(r\"alpha values\",fontsize=12)\n",
    "    plt.ylabel(\"roc_auc\",fontsize=12)\n",
    "    plt.show()\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_train,prob)\n",
    "    # plot no skill\n",
    "    pyplt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    pyplt.plot(fpr, tpr, marker='.')\n",
    "    pyplt.title(\"Line Plot of ROC Curve on Train Data\")\n",
    "    pyplt.ylabel('True Positive Rate')\n",
    "    pyplt.xlabel('False Positive Rate')\n",
    "    pyplt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.866186Z",
     "start_time": "2019-02-22T20:45:23.402587Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing_l1(X_train,Y_train,X_test,Y_test,optimal_C):\n",
    "    log1 = LogisticRegression(penalty='l1',C=optimal_C)\n",
    "    log1.fit(X_train,Y_train)\n",
    "    probs = log1.predict_proba(X_test) \n",
    "    # keep probabilities for the positive outcome only\n",
    "    probs = probs[:, 1]\n",
    "    print(\"AUC Score\",roc_auc_score(Y_test,probs))\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,probs)\n",
    "    # plot no skill\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    plt.plot(fpr, tpr, marker='.')\n",
    "    plt.title(\"Line Plot of ROC Curve on Test Data\")\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')    \n",
    "    plt.show\n",
    "    prediction=log1.predict(X_test)\n",
    "    skplt.plot_confusion_matrix(Y_test,prediction)\n",
    "    print(\"macro f1 score for data :\",metrics.f1_score(Y_test, prediction, average = 'macro'))\n",
    "    print(\"micro f1 scoore for data:\",metrics.f1_score(Y_test, prediction, average = 'micro'))\n",
    "    print(\"hamming loss for data:\",metrics.hamming_loss(Y_test,prediction))\n",
    "    print(\"Precision recall report for data:\\n\",metrics.classification_report(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.878146Z",
     "start_time": "2019-02-22T20:45:23.869152Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing_l2(X_train,Y_train,X_test,Y_test,optimal_C):\n",
    "    log2 = LogisticRegression(penalty='l2',C=optimal_C)\n",
    "    log2.fit(X_train,Y_train)\n",
    "    prob = log2.predict_proba(X_test) \n",
    "    # keep probabilities for the positive outcome only\n",
    "    probs = prob[:, 1]\n",
    "    print(\"AUC Score\",roc_auc_score(Y_test,probs))\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,probs)\n",
    "    # plot no skill\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    plt.plot(fpr, tpr, marker='.')\n",
    "    plt.title(\"Line Plot of ROC Curve on Test Data\")\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')    \n",
    "    plt.show\n",
    "    prediction=np.argmax(prob,axis=1)\n",
    "    skplt.plot_confusion_matrix(Y_test,prediction)\n",
    "    print(\"macro f1 score for data :\",metrics.f1_score(Y_test, prediction, average = 'macro'))\n",
    "    print(\"micro f1 scoore for data:\",metrics.f1_score(Y_test, prediction, average = 'micro'))\n",
    "    print(\"hamming loss for data:\",metrics.hamming_loss(Y_test,prediction))\n",
    "    print(\"Precision recall report for data:\\n\",metrics.classification_report(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:23.986627Z",
     "start_time": "2019-02-22T20:45:23.881143Z"
    }
   },
   "outputs": [],
   "source": [
    "#Code Reference:https://stackoverflow.com/questions/11116697/how-to-get-most-informative-features-for-scikit-learn-classifiers\n",
    "\n",
    "def imp_feature(vectorizer,classifier, n =10):\n",
    "    feature_names = vectorizer.get_feature_names()\n",
    "    coefs_with_fns = sorted(zip(classifier.coef_[0], feature_names))\n",
    "    top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])\n",
    "    print(\"\\t\\t\\tNegative\\t\\t\\t\\t\\t\\tPositive\")\n",
    "    print(\"________________________________________________________________________________________________\")\n",
    "    for (coef_1, fn_1), (coef_2, fn_2) in top:\n",
    "        print(\"\\t%.4f\\t%-15s\\t\\t\\t\\t%.4f\\t%-15s\" % (coef_1, fn_1, coef_2, fn_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques for vectorization :--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hXA02Hw0MQv_"
   },
   "source": [
    "##  Bag of Words (BoW) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:35.381991Z",
     "start_time": "2019-02-22T20:45:23.989623Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "giCwNM_rMQwA"
   },
   "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  (115114, 40518)\n",
      "CV Data Size:  (56698, 40518)\n",
      "Test Data Size:  (84624, 40518)\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "bow_train = count_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(bow_train))\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "#print(\"the number of unique words \", final_counts.get_shape()[1])\n",
    "bow_cv = count_vect.transform(X_cv)\n",
    "bow_test = count_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:45:35.793752Z",
     "start_time": "2019-02-22T20:45:35.385987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (115114, 40518)\n",
      "CV Data Size:  (56698, 40518)\n",
      "Test Data Size:  (84624, 40518)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "bow_train=preprocessing.normalize(bow_train)\n",
    "bow_cv=preprocessing.normalize(bow_cv)\n",
    "bow_test=preprocessing.normalize(bow_test)\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:46:42.409635Z",
     "start_time": "2019-02-22T20:45:35.796751Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.8591291509540182  and for train data auc score is -->  0.991692805764055\n",
      "5000  for CV data auc score is -->  0.8656010185454268  and for train data auc score is -->  0.9916012276958703\n",
      "1000  for CV data auc score is -->  0.8840879353925195  and for train data auc score is -->  0.9911459175082964\n",
      "500  for CV data auc score is -->  0.8935563225669626  and for train data auc score is -->  0.9907771726334812\n",
      "100  for CV data auc score is -->  0.9187618429164522  and for train data auc score is -->  0.9888837018317955\n",
      "50  for CV data auc score is -->  0.9294023601730566  and for train data auc score is -->  0.9870480158687965\n",
      "10  for CV data auc score is -->  0.9463781157436285  and for train data auc score is -->  0.9764085743619657\n",
      "5  for CV data auc score is -->  0.9496968652597577  and for train data auc score is -->  0.9683105263652751\n",
      "1  for CV data auc score is -->  0.9482992111337504  and for train data auc score is -->  0.9533979045545207\n",
      "0.5  for CV data auc score is -->  0.9445433673355101  and for train data auc score is -->  0.9472743424086383\n",
      "0.1  for CV data auc score is -->  0.9253868332760619  and for train data auc score is -->  0.924783387495399\n",
      "0.05  for CV data auc score is -->  0.9078447718779102  and for train data auc score is -->  0.9067627345510935\n",
      "0.01  for CV data auc score is -->  0.8174777632802185  and for train data auc score is -->  0.8164126612701808\n",
      "0.005  for CV data auc score is -->  0.7528284225070524  and for train data auc score is -->  0.750175285500776\n",
      "0.001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0005  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "5e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "1e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "\n",
      " Best C Value 5 with highest roc_auc Score is 0.9496968652597577\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal C using l1 regularization \n",
    "logistic_l1(bow_train,bow_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:31.964016Z",
     "start_time": "2019-02-22T20:46:42.412099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.9127841737630985  and for train data auc score is -->  0.9897281884728407\n",
      "5000  for CV data auc score is -->  0.9138686158632009  and for train data auc score is -->  0.9896973422809399\n",
      "1000  for CV data auc score is -->  0.927186102455792  and for train data auc score is -->  0.988041200011742\n",
      "500  for CV data auc score is -->  0.9333170829735581  and for train data auc score is -->  0.9866459363697928\n",
      "100  for CV data auc score is -->  0.9441792690434782  and for train data auc score is -->  0.9813658579150831\n",
      "50  for CV data auc score is -->  0.947290076773607  and for train data auc score is -->  0.9781252633562354\n",
      "10  for CV data auc score is -->  0.9509302358871625  and for train data auc score is -->  0.9688834001663503\n",
      "5  for CV data auc score is -->  0.9510561394782234  and for train data auc score is -->  0.9647478564309291\n",
      "1  for CV data auc score is -->  0.9479028478481482  and for train data auc score is -->  0.9546783935299155\n",
      "0.5  for CV data auc score is -->  0.9447178419717217  and for train data auc score is -->  0.9496359432678092\n",
      "0.1  for CV data auc score is -->  0.9312958267967197  and for train data auc score is -->  0.9338347383488422\n",
      "0.05  for CV data auc score is -->  0.9224168120392489  and for train data auc score is -->  0.9245104891363227\n",
      "0.01  for CV data auc score is -->  0.8956479957096144  and for train data auc score is -->  0.8973114548011847\n",
      "0.005  for CV data auc score is -->  0.8840421203975257  and for train data auc score is -->  0.8855577641860659\n",
      "0.001  for CV data auc score is -->  0.8665314967150843  and for train data auc score is -->  0.8676194665008088\n",
      "0.0005  for CV data auc score is -->  0.8553499119693051  and for train data auc score is -->  0.8560535870083745\n",
      "0.0001  for CV data auc score is -->  0.7529527490635353  and for train data auc score is -->  0.7530937109691276\n",
      "5e-05  for CV data auc score is -->  0.7010427109651568  and for train data auc score is -->  0.7020464821559146\n",
      "1e-05  for CV data auc score is -->  0.6412013459725161  and for train data auc score is -->  0.6433411388678617\n",
      "\n",
      " Best C Value 5 with highest roc_auc Score is 0.9510561394782234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal C using l2 regularization \n",
    "logistic_l2(bow_train,bow_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LR simple CV Over the unseen or Test data in BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:36.580889Z",
     "start_time": "2019-02-22T20:53:31.966994Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9484446122522991\n",
      "macro f1 score for data : 0.8399401095322441\n",
      "micro f1 scoore for data: 0.9203417470221213\n",
      "hamming loss for data: 0.07965825297787861\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.67      0.73     13407\n",
      "           1       0.94      0.97      0.95     71217\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     84624\n",
      "   macro avg       0.87      0.82      0.84     84624\n",
      "weighted avg       0.92      0.92      0.92     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimal_C=5 for l1 regularization\n",
    "testing_l1(bow_train,Y_tr,bow_test,Y_test,optimal_C=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:44.442033Z",
     "start_time": "2019-02-22T20:53:36.583886Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.950224330260219\n",
      "macro f1 score for data : 0.839190542368675\n",
      "micro f1 scoore for data: 0.9212043864624693\n",
      "hamming loss for data: 0.07879561353753073\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.65      0.72     13407\n",
      "           1       0.94      0.97      0.95     71217\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     84624\n",
      "   macro avg       0.87      0.81      0.84     84624\n",
      "weighted avg       0.92      0.92      0.92     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimal_C=5 for l2 regularization\n",
    "testing_l2(bow_train,Y_tr,bow_test,Y_test,optimal_C=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:48.629507Z",
     "start_time": "2019-02-22T20:53:44.445030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-19.7499\tunpalat        \t\t\t\t16.1082\tgorgeous       \n",
      "\t-19.4247\tundrink        \t\t\t\t15.5464\tgaug           \n",
      "\t-19.2795\tunforun        \t\t\t\t15.3827\tdownsid        \n",
      "\t-18.4574\tworst          \t\t\t\t14.6834\tchedder        \n",
      "\t-17.9760\texcruci        \t\t\t\t14.5200\tgentleas       \n",
      "\t-17.9694\tinstantto      \t\t\t\t13.9197\tskeptic        \n",
      "\t-17.8914\tweruvia        \t\t\t\t12.7032\tgustaf         \n",
      "\t-17.8532\tweakest        \t\t\t\t12.4288\tworri          \n",
      "\t-17.6276\tglorifi        \t\t\t\t12.2971\tdivin          \n",
      "\t-17.3093\tcaserol        \t\t\t\t11.9851\tfantast        \n"
     ]
    }
   ],
   "source": [
    "#top 10 important feature for l1 regularization\n",
    "clf1 = LogisticRegression(penalty='l1',C=5)\n",
    "clf1.fit(bow_train,Y_tr)\n",
    "imp_feature(count_vect,clf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:55.800901Z",
     "start_time": "2019-02-22T20:53:48.635502Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-14.7919\tworst          \t\t\t\t9.2818\tdelici         \n",
      "\t-10.8124\tterribl        \t\t\t\t9.0009\tfantast        \n",
      "\t-10.7348\taw             \t\t\t\t8.8579\texcel          \n",
      "\t-9.7915\thorribl        \t\t\t\t8.7780\tperfect        \n",
      "\t-9.3843\tyuck           \t\t\t\t8.6811\tworri          \n",
      "\t-9.1835\tdisappoint     \t\t\t\t7.8053\tamaz           \n",
      "\t-8.9161\tdisgust        \t\t\t\t7.8032\tawesom         \n",
      "\t-8.7790\tthrew          \t\t\t\t7.7341\tbest           \n",
      "\t-8.5459\ttasteless      \t\t\t\t7.3941\tbeat           \n",
      "\t-8.4010\tmediocr        \t\t\t\t7.2527\taddict         \n"
     ]
    }
   ],
   "source": [
    "#top 10 important feature for l2 regularization\n",
    "clf2 = LogisticRegression(penalty='l2',C=5)\n",
    "clf2.fit(bow_train,Y_tr)\n",
    "imp_feature(count_vect,clf2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sparsity using L1 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:53:55.840877Z",
     "start_time": "2019-02-22T20:53:55.831883Z"
    }
   },
   "outputs": [],
   "source": [
    "def sparsity(train_data,Y_train,test_data,Y_test):\n",
    "    C_value = [1000,100,10,1,0.1,0.01,0.001,0.0001]\n",
    "    for i in C_value[-8:]:\n",
    "        lr = LogisticRegression(C=i,penalty='l1')\n",
    "        lr.fit(train_data,Y_train)\n",
    "        pred = lr.predict(test_data)\n",
    "        print(\"C_value = \",i ,\" and l1_value = l1\" )\n",
    "        print(\"roc_auc score on test set: %0.3f%%\"%(roc_auc_score(Y_test,pred)*100))\n",
    "        print(\"Non Zero weights:\",np.count_nonzero(lr.coef_))\n",
    "        print(\"=+=+\"*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:20.777969Z",
     "start_time": "2019-02-22T20:53:55.844873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C_value =  1000  and l1_value = l1\n",
      "roc_auc score on test set: 78.711%\n",
      "Non Zero weights: 20661\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  100  and l1_value = l1\n",
      "roc_auc score on test set: 80.783%\n",
      "Non Zero weights: 15370\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  10  and l1_value = l1\n",
      "roc_auc score on test set: 81.976%\n",
      "Non Zero weights: 7219\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  1  and l1_value = l1\n",
      "roc_auc score on test set: 80.435%\n",
      "Non Zero weights: 1470\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  0.1  and l1_value = l1\n",
      "roc_auc score on test set: 72.841%\n",
      "Non Zero weights: 316\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  0.01  and l1_value = l1\n",
      "roc_auc score on test set: 52.250%\n",
      "Non Zero weights: 30\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  0.001  and l1_value = l1\n",
      "roc_auc score on test set: 50.000%\n",
      "Non Zero weights: 0\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "C_value =  0.0001  and l1_value = l1\n",
      "roc_auc score on test set: 50.000%\n",
      "Non Zero weights: 0\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n"
     ]
    }
   ],
   "source": [
    "sparsity(bow_train,Y_tr,bow_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertubation Test  on BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:27.859388Z",
     "start_time": "2019-02-22T20:54:20.787964Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Zero weights: 40518\n"
     ]
    }
   ],
   "source": [
    "from scipy.sparse import find\n",
    "logreg1 = LogisticRegression(C= 5, penalty= 'l2')\n",
    "logreg1.fit(bow_train,Y_tr)\n",
    "weights1 = find(logreg1.coef_[0])[2]  #Weights before adding random noise\n",
    "print(\"Non Zero weights:\",np.count_nonzero(logreg1.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:32.697512Z",
     "start_time": "2019-02-22T20:54:27.863384Z"
    }
   },
   "outputs": [],
   "source": [
    "bow_train_noise = bow_train\n",
    "#Random noise\n",
    "epsilon = np.random.uniform(low=-0.00001, high=0.00001, size=(find(bow_train_noise)[0].size))\n",
    "a,b,c = find(bow_train)\n",
    "bow_train_noise[a,b] = epsilon + bow_train[a,b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:39.290735Z",
     "start_time": "2019-02-22T20:54:32.697512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Zero weights: 40518\n"
     ]
    }
   ],
   "source": [
    "logreg2 = LogisticRegression(C= 5, penalty= 'l2')\n",
    "logreg2.fit(bow_train_noise,Y_tr)\n",
    "print(\"Non Zero weights:\",np.count_nonzero(logreg2.coef_))\n",
    "weights2 = find(logreg2.coef_[0])[2]  #Weights after adding random noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:39.604570Z",
     "start_time": "2019-02-22T20:54:39.293734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b1a5e1e5f8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentage_change_vector = (abs(weights1 - weights2)/weights1) * 100\n",
    "plt.plot(percentage_change_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:39.617547Z",
     "start_time": "2019-02-22T20:54:39.607558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(percentage_change_vector[np.where(percentage_change_vector > 10)].size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 Collinear feature after Pertubation test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:54:39.864519Z",
     "start_time": "2019-02-22T20:54:39.621547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['skeptic' 'earth' 'smooth' 'depend' 'unpleas' 'terribl' 'ruin' 'unus'\n",
      " 'bustelo' 'happier']\n"
     ]
    }
   ],
   "source": [
    "index = np.argsort(np.abs(weights1 - weights2))[::-1]\n",
    "features = count_vect.get_feature_names()\n",
    "features = np.array(features)\n",
    "a = features[index]\n",
    "print(a[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:55:19.842091Z",
     "start_time": "2019-02-22T20:54:39.867522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text TFIDF vectorizer  (115114, 1386366)\n",
      "CV Data Size:  (56698, 1386366)\n",
      "Test Data Size:  (84624, 1386366)\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "tfidf_train = tf_idf_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(tfidf_train))\n",
    "print(\"The shape of out text TFIDF vectorizer \",tfidf_train.get_shape())\n",
    "tfidf_cv = tf_idf_vect.transform(X_cv)\n",
    "tfidf_test = tf_idf_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:55:20.251852Z",
     "start_time": "2019-02-22T20:55:19.846085Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (115114, 1386366)\n",
      "CV Data Size:  (56698, 1386366)\n",
      "Test Data Size:  (84624, 1386366)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "tfidf_train=preprocessing.normalize(tfidf_train)\n",
    "tfidf_cv=preprocessing.normalize(tfidf_cv)\n",
    "tfidf_test=preprocessing.normalize(tfidf_test)\n",
    "print(\"The shape of out text BOW vectorizer \",tfidf_train.get_shape())\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T20:57:31.259628Z",
     "start_time": "2019-02-22T20:55:20.254851Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.9479051433995807  and for train data auc score is -->  0.999999998860608\n",
      "5000  for CV data auc score is -->  0.9510492273692229  and for train data auc score is -->  0.999999998860608\n",
      "1000  for CV data auc score is -->  0.9521113410663302  and for train data auc score is -->  0.9999999988606079\n",
      "500  for CV data auc score is -->  0.9515398031401737  and for train data auc score is -->  0.9999999988606079\n",
      "100  for CV data auc score is -->  0.9521489851015379  and for train data auc score is -->  0.9999999988606079\n",
      "50  for CV data auc score is -->  0.9528432170120651  and for train data auc score is -->  0.999999998860608\n",
      "10  for CV data auc score is -->  0.9561050081582787  and for train data auc score is -->  0.9999123169436174\n",
      "5  for CV data auc score is -->  0.9587180329653033  and for train data auc score is -->  0.9940252962951487\n",
      "1  for CV data auc score is -->  0.9537552706046013  and for train data auc score is -->  0.9583700603075211\n",
      "0.5  for CV data auc score is -->  0.9463278160927115  and for train data auc score is -->  0.9479286160885086\n",
      "0.1  for CV data auc score is -->  0.9119926400918575  and for train data auc score is -->  0.9103740929875098\n",
      "0.05  for CV data auc score is -->  0.881057333118687  and for train data auc score is -->  0.8790376494472788\n",
      "0.01  for CV data auc score is -->  0.6783754005783533  and for train data auc score is -->  0.6778244331992725\n",
      "0.005  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0005  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "5e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "1e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "\n",
      " Best C Value 5 with highest roc_auc Score is 0.9587180329653033\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3dKxCIWUvhBDAjWNbaXByMtq0eC57vY59+7HUcdZbOlahkbIXQti0e3ducHvraJqn/kyS3hslZBn6Sk0sHavQSdkLIWySqihE7/0Ov9+nU0NNI6LqILxf+gB7BydLRysSUvZCCJujTbnOjfXv0jbtV87rfLj23Bzqe1vf2fyDpOyFEDZDVcw4/r4at0Of4G7KJKLmv/AJHkE5O+uvQusfoRBCAPHXzqH+OIKGmacwVmtDcodPqe9ey9Kxio2UvRDCqplMmUT/ey7Nr36BCR37fT/C97m3QaOxdLRiJWUvhLBa1/84htOukbQ3XyDSuSX6l+bgW6mmpWNZhJS9EML6mA24HP0M/8hFJKou7G84HZ+g/qVq4rLCJmUvhLAq187so+bhD3FNv0SGT0/SW36Mr1tFS8eyOCl7IYRVyEhL5urW8bT+cwMJmnLceuFrtN7Pl9qJywqblL0QotS7dOxHqh/6kLbc4kDZrlTr+gmubmUtHatEkbIXQpRaGkMS9hGTaRG9lhhNFY60/gafgOcsHatEkrIXQpRK149upuGpKWjT/+Sy9xvYtxtDbWdXS8cqsaTshRClStLt69zZOprmab+QqPdB7bUc/VONLR2rxJOyF0KUCqqicH7vCvx//xRPNYOIam/i3WUs9g6Olo5WKhRJ2SuKwsSJEzl//jwODg5MnTqVmjX/90GGr776ih07dqDRaBgyZAidOnUqihhCCCuhTb7OzQ3/JCjtMOd0vhifn0v9OnI2nx9FUva7du3CaDSydu1aoqKimDFjBkuWLAHg3r17rFy5kp07d5Kenk737t2l7IUQj6WYzTicXIHbr9NxN5vYWysM3xfeRWcDE5cVtiL5jkVGRhIUFARAQEAAp0+fzlrm7OxM1apVSU9PJz09HY2NzU8hhMibW1d/J+GnMPwyT2OsHkRyh0/xK+Np6VilVpGUfUpKCnr9/27npdPpMJlM2P33t3GVKlXo0qULZrOZt99++7H70OsdsbPTFej4Op0WDw+XAm1bWsmYrZ+tjNeUaeT4+k8IvPQ5Ro0DvzWaTMBL71DGRk4Mi+p5LpKy1+v1pKamZn2tKEpW0UdERBAfH8/u3bsBGDRoEIGBgTRq1OihfaSkGAp8fA8PFxIT0wq8fWkkY7Z+tjDe6xcicdkVRmvlIked21Ct/yJqOVUkMSnd0tGKzZM8zxUrumW7rEhmBQoMDCQiIgKAqKgofHx8spa5u7vj5OSEg4MDjo6OuLm5ce/evaKIIYQoLcwGXA7Pwv8/IZRT/mR/o5l4/mM1FSrb5gyVRaFIzuw7derEgQMHCA0NRVVVpk2bxvLly/H09OTZZ5/l4MGD9OnTB61WS2BgIG3atCmKGEKIUuDKqQjqHBmHa8YVMnx7YWj5Mb768paOZXU0qqqqlg7xOAkJyQXe1hb+3P07GbP1s7bxpqfe49rW8bS+vZFbmvJog2ej9Xp4qgNrG3NeFNVlHHn/khCi2F38bQeeRz6mLfHsL9uDGt2m4KL3sHQsqyZlL4QoNpqMROwjJtHywnquaqpxpM23+DbuaOlYNkHKXghRLOKObMT/zCdo029zue6b2Ld7n9pO1v9W0pJCyl4IUaTuJsRxb9somqXv466bL/Regb6iv6Vj2RwpeyFEkVAVhXM/f03jc7OoqRrZW30IPp3fx04mLrMIKXshRKHT3ovl1obhtEs/yu92fpifn4NfbTmbtyQpeyFEoVEUM06nvqXMr9NxV1Uiao/CN/ifaLUFm/pEFB4peyFEobh55TS6n8LwM/2O0bM9ye0/pX6Z6paOJf5Lyl4I8URMRgMXfphFi9ivyNA4sL/+RHw7DAIbmbistJCyF0IU2PXo33DZPZJ2yiWOugRR5uXZ+FasZulY4jGk7IUQ+WfKwPW3+fgfX8Jd1Y39jefg27avpVOJHEjZCyHy5cqJPdQ5+hEuGddIr9cXQ8sP8XUtZ+lYIhdS9kKIPElLSSR2y0e0SQznOk9x88WV6Op0wN7SwUSeSNkLIXL1x2/bqHXkY1qpt9lfLgTPblNwdi1j6VgiH/Jc9klJSbi7uxdlFiFECaPJuIvDLxNodXETVzXViAxaiW+jZywdSxRArmV/5MgRJk+ejNlsJjg4mKpVq9K7d+/iyCaEsKC4I+todHoaGkMil33ewj5oFLVk4rJSK9fbEi5YsIDvvvuOChUqMGTIEFavXl0cuYQQFpIYH8PVr0MJ+C2MJPuK3O21A32n8ThK0ZdquZ7Za7VaPDw80Gg0ODo64urqWhy5hBDFTFUUzu35koBzc/DESESNYdTt8j52dvISrDXItew9PT2ZM2cOiYmJLF26lKpVqxZHLiFEMdLeu0b8huG0Sz/GGfsGKC/MpX6tBpaOJQpRrpdxJk2aRNWqVWnatCnOzs5MnTq1OHIJIYqB2WTCIeoryq1+Fm/jOfbWGU2FN3+gshS91cn1zH7atGmMHz8+6+vRo0czc+bMIg0lhCh6Ny6dxH5nGJXN5zB4diDlmRn4uclUB9Yq27JftWoVS5YsITExkZ07d2Y97uXlVSzBhBBFw2Q0cGHHp7SM+5o0jRMH/Kbg88zrMnGZlcu27Pv370///v35/PPPGTJkSHFmEkIUkbjzv+K2ZxTtlCv85tqesl1n4VNeXoezBblexgkNDWX79u2YTCZUVSU+Pp633367OLIJIQqLKR3XI3NpHLWUP9UyHAyYS902fSydShSjXMv+3XffpVatWkRHR+Po6Iizs3Nx5BJCFJLLUbvwPvoxLoYY0v1ewdRiHHVdylo6lihmub4bB2Dy5MnUrl2b5cuXk5SUVNSZhBCFIDX5LtGrhtH8wOukG4zc7LyKlA6zsJOit0l5mhvHYDCQnp6ORqMhLS2tqDMJIZ7QH79uoXbkhPsTl1XoTc2uk3FwcbN0LGFBuZZ9//79WbFiBW3atKF9+/Y0bdq0OHIJIQpAk34Hh73jaXUxnCuaGhxr/z2+DYMsHUuUALmW/QsvvJD17xdffFHO7IUogVRFIe7IOhr/Ph2NIYlL9Ybi2DaMmo7yGpu4L9tr9mfOnGHIkCGMGTOGO3fuALBlyxZCQkKKLZwQInd3468Rs7wvTSJHkWhfmbu9f8Dt2Q9xkKIXD8j2zP7jjz8mLCyM69evM2/ePNLS0oiPj2fVqlXFmU8IkQ1VUTi36wsCoufhSSYRNf9J3RfDZOIy8VjZlr2zszNt27YFYPHixXTv3p3Zs2ejkU/ZCWFx2qSrJGwYTruM45y290fz4jzqe9azdCxRgmVb9jqdLuvfTz31FCNGjMjzThVFYeLEiZw/fx4HBwemTp1KzZo1s5bv3buXxYsXA+Dn58eECRPkl4gQeWA2mXA59TVuR2ZSRtUR4f0Bvp2GoNXqct9Y2LRsy15VVTIzM1FVFScnp6x/Azg4OOS40127dmE0Glm7di1RUVHMmDGDJUuWAJCSksKsWbP49ttvKVeuHMuWLePu3buUKyd3pxciJ9fORZIZPpzK5vMYaj5LyjPTqa+XqQ5E3mRb9nFxcQQHBwP3iz84OBhVVdFoNOzevTvHnUZGRhIUdP/tXgEBAZw+fTpr2fHjx/Hx8eHTTz8lJiaG3r17S9ELkYNMYwZ/bJ9Oy+vfkKJxYX+Dafi2HyATl4l8ybbs9+zZU+CdpqSkoNfrs77W6XSYTCbs7Oy4e/cuhw8fJjw8HBcXF/r3709AQAC1a9d+aB96vSN2dgX701Sn0+LhYVu3UJMxW6fLJ/dhv+Nd2ilXOVqmIzVeWUiLirZzNm8Lz/HfFdWY8/QJ2vzS6/WkpqZmfa0oCnZ29w/l4eGBv78/FStWBKBZs2acPXv2kbJPSTEU+PgeHi4kJtrW5wFkzFYmMx3XI7OpG7WMBDw4GLiAp18cQGJimvWO+TGs+jnOxpOMuWLF7D8lnae5cfIrMDCQiIgIAKKiovDx8cla1rBhQ6Kjo7lz5w4mk4kTJ07g7e1dFDGEKJUuHduJ3YpncIn6gowG/VD+EUHdVvL5FvFk8nRmn5KSQlxcHDVq1MDFJfc/Lzp16sSBAwcIDQ1FVVWmTZvG8uXL8fT05Nlnn2XkyJEMHjwYgODg4Id+GQhhq1KT73B9yzhaJ20nVlMZQ5fV6GoFIe+zEYVBo/71Fpts/Pjjj3z++eeYzWaCg4PRaDQMGzasyIMlJCQXeFv50882WNOYLxzahNexiVRQ73KwYl9qd52Io7P+oXWsabx5JWPOnye6jPPNN9+wbt06PDw8GDZsGLt27SpQCCHEozTpt3H69zBaH3uXNK0bUR3XUK/v7EeKXognletlHK1Wi4ODAxqNBo1GIzcvEaIQqIpC3K/f0/jsp2iMKVyuPxzHtu/h6eBk6WjCSuVa9s2aNSMsLIxbt24xfvx4/P39iyOXEFbr9s0rpG0fSaDhMLfd/dF0n4++vK+lYwkrl2vZh4WFERERgZ+fH3Xq1KFjx47FkUsIq6MoZs7/53MCL8xHh5mIWv/CJ3gEOrsieQe0EA/J9aesZ8+ehISEEBoa+tAHpYQQeadNvMydjcNpl3GCUw6N0b44l/o15GxeFJ9cX6BdunQpGRkZDBw4kLFjxxIZGVkcuYSwCiZTJo6RSyi35jnqmC4SUfdDKg3exlNS9KKY5Vr2FSpUYNCgQXz22WcYDAaGDh1aHLmEKPXiLkRy78vnKfPrJxhrtOde/5+p//xQNNoi+SyjEDnK9TJOeHg4mzdvRlEUQkJCmD59enHkEqLUMhrSubhtGq1ufkuyxpUD/jPwCeovE5cJi8q17M+dO8eECROoU6dOceQRolSL+30fHr+Mpp0aw2G356jQ9VN8ylaydCwhsi/7n3/+mQ4dOlCrVi1+++03fvvtt6xlffv2LZZwQpQamWm4Hp5F4xNfEk85DjVbjHeLbpZOJUSWbMs+MTERgD///LPYwghRGl2K/Dc+xybgYrxOesPXUJuPwdvZ3dKxhHhItmXfo0cP4P4naB+cC2fOnDlFn0qIUiA16TbXt35A63s/EKOpguGldehqtpaJy0SJlG3Zr1+/ng0bNnDx4sWs6YoVRSEzM5ORI0cWW0AhSqLog+vxOT6ZFmoi+yr2o3bXCeicXS0dS4hsZVv23bp1o1WrVnzxxRcMGTIEuH+WX758+WILJ0RJo0lLwPHnj2hzZQcXtbWIeWYp9eq3snQsIXKVbdmfP38ef39/nn/+eS5fvpz1+MWLF2nbtm2xhBOipFAVhdiD3xFwbiaazDQuNfgXLq3fpYyDo6WjCZEn2Zb9oUOH8Pf354cffnhkmZS9sCW3b1wiY0cYgYaj3PZojKbnfNzK1bV0LCHyJdeblwCYzWZUVSUqKopGjRrh4OBQ5MHk5iX5I2MufIpi5vxPi2l6cSEaVI7WfgefF9612MRl8hzbhqK6eUmuP7WzZs2iRo0aXL9+nTNnzlCxYkVmzJhRoCBClBa6xEvc3fAO7QynOOkYgH3nedSvJmfzovTKdZKOyMhIQkNDOX78OF999RU3btwojlxCWITJlInj0cWUXdOJWuYrRPiOp/KgrVSQohelXK5n9oqicPLkSapXr47RaOTOnTvFkUuIYhcbfRT97pFUUS5iqBNMSrtPqO8qUx0I65Br2Xfr1o0pU6Ywbdo0Zs2axWuvvVYcuYQoNoaMNC5vm0rLW6u4p9FzoNEsfNqGysRlwqrk6QXau3fvEhMTQ/Xq1SlXrlxx5JIXaPNJxlwwcWciKLv3fWqqcfzq9jxPdf0UN4+KhZSwcMlzbBss9gLtDz/8wIIFC/Dy8uLChQsMHz6cbt1kgidRyhlTcT38KY1PLucW5TnUfAneT79s6VRCFJlcy37FihVs2rQJV1dXUlJSGDhwoJS9KNX++G0H9aIm4mK8Qbr/69B8DN5O2Z8RCWENci17jUaDq+v9OT/0ej2OjvKJQVE6pST9yc0tY2mV/CPXNNUwvLwBnWdLmbhM2IRcy97T05MZM2bQrFkzjh49iqenZ3HkEqJQnd+/lnonplBDvce+p16ldtfx6JxcLB1LiGKT6/vsp02bRo0aNTh48CA1atRgypQpxZFLiEKhSY3Haftg2p4YSaK2LKee20C9PjNwlKIXNibbM/u0tDQ2bdqEi4sLr7zyClq5SbKTCUz4AAAZPElEQVQoRVRFIfbACgLOz0ZjyuBywxG4thqOu0xcJmxUtmU/duxYPD09uXfvHleuXCEsLKw4cwlRYH9ev4hhRxiBxkj+9AhA23k++rLelo4lhEVlW/Z3795l4cKFKIrCG2+8UZyZhCgQRTFz/sfPaHrpMwAi6ozCN/ifqFp5CVaIbMte899PD2q1WhRFKbZAQhSE7u4fJG58h3aGM0Q5NsWxy1zqV/WydCwhSoxsL8SrqkpmZiZGo/GhfxuNxlx3qigK48ePp2/fvgwYMICrV68+dp3BgwezevXqJxuBsGkmowHH3xZSds3z1FRi2FdvIlUHbaaCFL0QD8n2zD4uLo7g4GDgfvEHBwejqioajYbdu3fnuNNdu3ZhNBpZu3YtUVFRzJgxgyVLljy0zvz580lKSiqEIQhbdenUQXTb/0kV5TIGry4kB02hnutTlo4lRImUbdnv2bOnwDuNjIwkKCgIgICAAE6fPv3Q8h9//BGNRkO7du0KfAxhuzLSU7mybTKt4leTqCnDwYA51G3T19KxhCjRiuSWOykpKej1+qyvdTodJpMJOzs7oqOj2b59OwsXLmTx4sXZ7kOvd8TOrmAvrOl0Wjw8bOt91LYy5ouRu3D56T2C1OscKdsZr37zeLpsyZy4rLDZynP8IBlz4SmSstfr9aSmpmZ9rSgKdv+9lVt4eDi3bt1i4MCBxMXFYW9vT7Vq1R45y09JMRT4+DJTnvXRGFNw/XU69U6t4LrmKX5t8QVNn+tNYmKaVY/7Qdb+HD+OjDl/nmjWS7h/ph4XF0eNGjVwccn9N05gYCA///wznTt3JioqCh8fn6xlo0ePzvr3Z599RoUKFeRyjsjRH0e2Uj9qEk6Z8aQ1egNd89F4Oepz31AIkSXXsv/xxx/5/PPPMZvNBAcHo9FoGDZsWI7bdOrUiQMHDhAaGoqqqkybNo3ly5fj6enJs88+W2jhhXW7dzeehK1jaJXyH65qqpPRdQO6Gi1yn+NDCPGIXG9eEhoayrfffsugQYP49ttvCQkJYdOmTUUeTG5ekj/WNGZVUYg+sIb6Jz+hjJrCocqv4vXShzj8bT4baxpzXtjaeEHGnF9PdBlHq9Xi4OCARqNBo9Hg7OxcoBBC5IU29RYOez6g7bWdROu8udZxBfV9mlk6lhClXq5l36xZM8LCwrh16xbjx4/H39+/OHIJG6MqCjH7l9Mkeu79icv8R+HW+h3K2tlbOpoQViHXsg8LCyMiIgI/Pz+8vLzo0KFDceQSNiQhLhrTjjCaZkaRUDYQXef56D3qWDqWEFYl19e6wsPDuXPnDhUqVCApKYnw8PDiyCVsgNlk4vcdc6i1+UW8jOeI8BqNEroZsxS9EIUu1zP7ixcvAvenTDh79iweHh507969yIMJ66a7E03SxuG0N/5OlNPTOHWZQ/0qUvJCFJVcy37kyJFZ/1ZVlbfffrtIAwnrZjIa0EctQR+5EFedC/v8puDbfiAauTmOEEUq17J/cJbLhIQEYmNjizSQsF4x537F/edRVFGukOHdlZSgydRzqWDpWELYhFzL/q8PUqmqipOTE4MGDSqOXMKKGNJTubx1Iq0T1nBH48HBJvOp27qXpWMJYVNyLft//etfdOvWrTiyCCsUe3I3FfaPJUi9wSH3zlTpOp267uUtHUsIm5PrhdL169cXRw5hZTTGZPS/fECTfQPRoXC45TK8ByzFVYpeCIvI0zX77t27U7t2bbT/fRFtzpw5RR5MlF5//LoFv5OTcMpMIK3xm+ieHkUdR1dLxxLCpuVa9qNGjSqOHMIK3Ltziz+3jqZV6m6uaGqQ1nUTdjWelonLhCgBsi379957j/nz59O8efPizCNKIVVRiN63Cr/T0/FUU4mo/A+8Xh6HnaPMoyRESZFt2d+5c6c4c4hSSpt6E8fdY2kbs4tonTcxz86lft1AS8cSQvxNtmUfExPD3LlzH7ssLCysyAKJ0kFVFGIivqLJhXlozAYuNxpNmVZDZeIyIUqobMveycmJ2rVrF2cWUUrEx5xD+XcYTTNPklC2GdrO89B7yM+KECVZtmVfoUIFevToUZxZRAlnNpk4/++5NL/6OWZ0RNT9AN9OQ0BbsBvDCyGKT7Zl37Bhw+LMIUo43e1zJG96h/bG8xxzaoHLS3OoX7mWpWMJIfIo27IfM2ZMceYQJVSmMQP98cW4HVuEq52e/X7T8Gn/qkxcJkQpk+v77IXtuvb7QcruHUVV5RoZdbuTEjQJX2f5BKwQpZGUvXiEIT2Fy1sm0PrPdfypKcuhwAV4twqxdCwhxBOQshcPiTnxHyrtH0sQtzjo8TJVu32Ct1s5S8cSQjwhKXsBgMZwD9eDnxD4+yriNJU50upr6jZ53tKxhBCFRMpecOHQRhqenIKT+Q5pAW9j9/RIaju4WDqWEKIQSdnbsHt3bnB762hap/7MJW1N0rp9ja5aoExcJoQVkrK3QaqicH7vShr+PoMaahoRVQfh/dIH6BycLB1NCFFEpOxtjDblOo57xhIUs4dzOl8MneZQ3yvA0rGEEEVMyt5GKIqZ2L3LaHJhPhrVxOXGY/FoOQSdnfwICGEL5P90G3Dr6ln4MYymplPEl2uOrvM89O41LR1LCFGMpOytmMmUSfQPc2hx7QuM2BPh8xH1nnsLRaY6EMLmSNlbKd2fv5OyaTjtM6OJdG6F28tzqP+Up6VjCSEsRMreyhgN6ZQ5vgj98cW42pVhv/+n+LR9RSYuE8LGFUnZK4rCxIkTOX/+PA4ODkydOpWaNf93jfibb75hx44dALRv357hw4cXRQybc/X0PipEjMZNjSHDp+f9icucylo6lhCiBCiS071du3ZhNBpZu3YtI0eOZMaMGVnLYmJi2Lp1K2vWrGHt2rXs37+fc+fOFUUMm5GRlszRpUMI/KUfzmoah5otJrnTQlQpeiHEfxXJmX1kZCRBQUEABAQEcPr06axllStX5ssvv0Snu393I5PJhKOjY1HEsAkxUT9R6cA4WnGLg2W7UrXrJ3i7SckLIR5WJGWfkpKCXq/P+lqn02EymbCzs8Pe3p5y5cqhqiozZ87Ez8/vsfe61esdsbMr2O3udDotHh5WPrdLRhK6XR9T8cR3xGqqcqbTGp5+2rYmLrOJ5/kBtjZekDEXpiIpe71eT2pqatbXiqJg98CHdwwGA+PGjcPV1ZUJEyY8dh8pKYYCH9/Dw4XExLQCb1/SRR9cj/+pqbiZE0kLHIZ9s/fwqVjBqsf8ONb+PP+drY0XZMz5VbGiW7bLiuSafWBgIBEREQBERUXh4+OTtUxVVYYNG4avry+TJ0/Oupwjcpf4ZxxXlvenzfERJChluNl9C6mtxqG1t60zHyFE/hXJmX2nTp04cOAAoaGhqKrKtGnTWL58OZ6eniiKwpEjRzAajezbtw+AsLAwmjRpUhRRrIKqKJz/5Rv8z86kpppBRLW3qNtlDHYO8lqHECJvNKqqqpYO8TgJCckF3taa/vTTJsfhuHs0+ri9nLWrR2anuVSp0+iR9axpzHlla2O2tfGCjDm/crqMIx+qKqEUxUzsz5/T5OJCNKrC5SYfUrb5mzJxmRCiQKQ5SqCbV86g/SmMpqYz3CrfErvOc9GXkakOhBAFJ2VfgphMmVzYMYsWMcsw4EBEvQnU6zhIJi4TQjwxKfsSQpdwhtTN79Au8w8iXdpQ5uU51K9Y3dKxhBBWQsrewowZaZQ5thB91BJcHDw40GgWPkGvWDqWEMLKSNlb0JWTv/DU/jG4qXFk1OtNSpvx+Mh8NkKIIiBlbwHpqfeI2fIRre5s5pamPIeaL8H76ZctHUsIYcWk7IvZtWM/UPXQh7RS/+RQ2e5U7zYFb72HpWMJIayclH0x0WQkoj8wmabn1nFNU43ItivwadzR0rGEEDZCyr4YnN+/lsZnPsHRnERa4HAcm/2LWvbOlo4lhLAhUvZF6G5CHPe2jaRt+n7+0NYmpcdK7Ko0LprZ54QQIgdS9kVAVRTO7fmKxudnU1M1ElF9CHU7vy8TlwkhLEbKvpBp78XgtHs07a7v43c7P8wvzKF+LX9LxxJC2Dgp+0KiKGZi9yymycVFoNFwpel4yjcfhFYr8/ULISxPyr4Q3Lx8Ct3OkTQ1/c6tCq2xe3EurmVkqgMhRMkhZf8ETEYDF36YScvYr0jXOLKv3iR8O/xDJi4TQpQ4UvYFZJdwirTNw2mXeZGjLkG4d51NvQrVLB1LCCEeS8o+nwzpqbgfW4D+xBc4O5Rlf+M5+Lbta+lYQgiRIyn7fLh8Yg+VD4zFTb1Oer2+pLb5GF8nmepACFHySdnnQVpKIrFbPqJNYjjXeYpfW3yBV7Mulo4lhBB5JmWfi2uR26n660e0Um9zoHwvanSdjJdrGUvHEkKIfJGyz4Ym4y76/ZNoen4DV7XViWy7Ch//dpaOJYQQBSJl/zeqonDhwBoan5mOo5pMarN/4dz0n9Syc7J0NCGEKDAp+wckxsdwb9tI2mQcJFrnjVuP77Gr7I/G0sGEEOIJSdnz34nLdi8j4PwcapBJhOc71O08Cjs7e0tHE0KIQmHzZa+9dw2n3e/T7voBztg3RHlhDvVrNbB0LCGEKFQ2W/Zmk4nYPYtoenkxqkbH5WaTqPD06zJxmRDCKtlk2d+4dAKHnWE0M5/nZoW22Heei96tqqVjCSFEkbGpss80ZvDH9hm0vP4NqRpn9vlNwbf9QJm4TAhh9Wym7O3iT5C+eTjtTJf5zfUZynadSb3ycjYvhLANVl/2hvRU3CPnoj+5DGeH8hzwn49P616WjiWEEMWqSMpeURQmTpzI+fPncXBwYOrUqdSsWTNr+bp161izZg12dnYMHTqUDh06FEUMLh3/D1UPjcNNvUG6Xz9SW3+Ij6N7kRxLCCFKsiIp+127dmE0Glm7di1RUVHMmDGDJUuWAJCQkMDKlSvZuHEjBoOBfv360aZNGxwcHArl2HY3I8mM3MeNU7/SInU/cVTicKsvqRMYXCj7F0KI0qhIyj4yMpKgoCAAAgICOH36dNaykydP0qRJExwcHHBwcMDT05Nz587RqFGjJz6u3c1IymzuhVbJxF+FKPeOlA9ZTB0XtyfetxBClGZFUvYpKSno9fqsr3U6HSaTCTs7O1JSUnBz+1/5urq6kpKS8sg+9HpH7Ozy95537ZmjaJVMNICq0eIf2AalaqUCj6M00em0eHi4WDpGsbK1MdvaeEHGXJiKpOz1ej2pqalZXyuKgp2d3WOXpaamPlT+f0lJMeT7uHblm+Ghc0JVMkFnz73yzTAlphVgBKWPh4cLiTYy1r/Y2phtbbwgY86vihWzv4pRJG8wDwwMJCIiAoCoqCh8fHyyljVq1IjIyEgMBgPJyclcvHjxoeVPwlS5KYnd16K0H0dit7WYKjctlP0KIURpVyRn9p06deLAgQOEhoaiqirTpk1j+fLleHp68uyzzzJgwAD69euHqqqMGDECR0fHQju2qXJTlHpBNnNGL4QQeaFRVVW1dIjHSUhILvC28qefbbC1MdvaeEHGnF/FfhlHCCFEySJlL4QQNkDKXgghbICUvRBC2AApeyGEsAEl9t04QgghCo+c2QshhA2QshdCCBsgZS+EEDag1Ja9oiiMHz+evn37MmDAAK5evfrQ8nXr1tGzZ0/69OnDzz//bKGUhSu3MX/zzTf07t2b3r17s2jRIgulLFy5jfmvdQYPHszq1astkLDw5TbmvXv30qdPH/r06cPEiROxhpfdchvzV199Rc+ePQkJCeE///mPhVIWvhMnTjBgwIBHHt+zZw8hISH07duXdevWFc7B1FLqp59+UseMGaOqqqoeP35cHTJkSNay+Ph49aWXXlINBoN67969rH+XdjmN+dq1a2qPHj1Uk8mkms1mtW/fvurZs2ctFbXQ5DTmv8yZM0ft1auX+v333xd3vCKR05iTk5PVLl26qLdv31ZVVVWXLl2a9e/SLKcxJyUlqe3bt1cNBoOamJioPvPMM5aKWaiWLl2qvvTSS2rv3r0fetxoNKrPPfecmpiYqBoMBrVnz55qfHz8Ex+v1J7Z5/UGKW5ublk3SCntchpz5cqV+fLLL9HpdGi1WkwmU6FOMGcpOY0Z4Mcff0Sj0dCuXTtLxCsSOY35+PHj+Pj48Omnn9KvXz8qVKhAuXLlLBW10OQ0ZmdnZ6pWrUp6ejrp6eloNBpLxSxUnp6efPbZZ488fvHiRTw9PXF3d8fBwYGmTZty9OjRJz5eqb3heGHcIKW0yWnM9vb2lCtXDlVVmTlzJn5+ftSuXduCaQtHTmOOjo5m+/btLFy4kMWLF1swZeHKacx3797l8OHDhIeH4+LiQv/+/QkICCj1z3VOYwaoUqUKXbp0wWw28/bbb1sqZqF64YUXiI2NfeTxouqvUlv2hXGDlNImpzEDGAwGxo0bh6urKxMmTLBExEKX05jDw8O5desWAwcOJC4uDnt7e6pVq1bqz/JzGrOHhwf+/v5UrFgRgGbNmnH27NlSX/Y5jTkiIoL4+Hh2794NwKBBgwgMDCyUW5mWREXVX6X2Mo6lbpBiSTmNWVVVhg0bhq+vL5MnT0any98tHUuqnMY8evRo1q9fz8qVK+nRowevv/56qS96yHnMDRs2JDo6mjt37mAymThx4gTe3t6Wilpochqzu7s7Tk5OODg44OjoiJubG/fu3bNU1CLn5eXF1atXSUxMxGg0cvToUZo0afLE+y21Z/aWvEGKpeQ0ZkVROHLkCEajkX379gEQFhZWKD8klpTb82yNchvzyJEjGTx4MADBwcFWcSKT25gPHjxInz590Gq1BAYG0qZNG0tHLnTbtm0jLS2Nvn37MnbsWAYNGoSqqoSEhFCp0pPfS1umSxBCCBtQai/jCCGEyDspeyGEsAFS9kIIYQOk7IUQwgZI2QshhA0otW+9FNYlNjaWrl270qBBg6zHWrRowfDhwx+7/tixY+ncuXOB31ffsWNHqlSpglarRVVVPDw8mDFjxkOf4szN0qVLadmyJb6+vmzdupXevXuzadMm3N3dC/y20Adzmc1m0tLSmDJlCv7+/tlu89133/Hqq68W6HjChjzx7DpCFIKYmJhHJoTKyZgxY9S9e/cW+HgdOnRQMzIysr6eOXOmumLFigLtK7/Z85MrIiJCfeutt3LcpnXr1oVybGHd5MxelGhms5nx48dz8+ZN7t69S7t27Xjvvfeyll++fJkPPvgAOzs7dDodM2fOpFKlSsyZM4fffvsNVVV5/fXXefHFF7M9hqIoJCcnU7t2bTIzMxk3bhwxMTGYzWb+8Y9/0LlzZ1atWkV4eHjWh3rGjBmT9dfFzp07+eOPP1i0aBGqqlKhQgWuXLlCvXr16NGjBwkJCbz99tts2rQpX7kArl+/TpkyZYD7k76tWrUqa9mCBQtYu3YtSUlJTJw4kQ8//JAJEyZw9epVFEXhvffeo0WLFk/4DAhrIWUvSow//vjjobm9Z8+eTWZmJgEBAfTu3RuDwfBI2R88eJAGDRowduxYjh49SlJSEufOnSM2NpY1a9ZgMBjo06cPbdq0ySrNv7zxxhtotVo0Gg2NGjWie/furFmzhrJlyzJr1ixSUlLo2bMnLVu2ZNOmTXz88ccEBATw/fffYzKZsvYzZMgQoqOjGT58eNYshn369GHSpEn06NGDLVu20LNnT/bu3ZvnXAaDgfj4eIKCghgzZgwAV65cYenSpTg7OzN+/Hj279/P0KFD+e6775g4cSLff/89ZcuWZdq0ady9e5dXX32VHTt2FPrzJEonKXtRYnh7e7Ny5cqHHktJSeHUqVP8+uuv6PV6jEbjQ8t79erFsmXLGDx4MG5ubowYMYLo6GjOnDmT9YvDZDI9dIb8l6+//vqRaTQuXrxI69atgfsTUnl5eRETE8P06dP5+uuvmT17NgEBAbneMMTLywuz2UxcXBw//PAD33zzDWvXrs1Xrrlz5xIbG0v58uUBKF++PGPGjMHV1ZVLly4REBDw0HbR0dFERkZy8uTJrP3fvXuXsmXL5phV2AZ5N44o0TZt2oSbmxtz5szhjTfeICMj46Gi3b17N02bNmXFihUEBwfz5ZdfUqdOHVq0aMHKlStZsWIFL774ItWrV8/T8by8vLLmDk9JSSE6Oprq1auzbt06Jk2axHfffcfZs2c5fvx41jZarRZFUR7ZV69evZg1axbe3t6UKVMm37nee+894uPj+f7770lOTmbhwoXMmzePqVOn4ujomPV9+Ou/derUoUuXLqxcuZJly5YRHByMu7t7nsYtrJ+UvSjRWrVqRUREBKGhoUycOJGaNWsSHx+ftbxhw4bMnz+ffv36sWbNGl599VU6duyIi4sL/fr1o2fPngB5fpdNnz59SExM5JVXXuG1115j+PDhlC9fHl9fX3r16sVrr71GuXLlaNy4cdY25cuXJzMzk1mzZj20r+DgYPbv30/v3r0B8p1Lq9XyySefsGTJEtLS0ggMDKRHjx70798fJyenrO+Dl5cXo0aNIjQ0lEuXLvHqq68SGhpKtWrV0Grlf3Fxn0yEJoQQNkB+7QshhA2QshdCCBsgZS+EEDZAyl4IIWyAlL0QQtgAKXshhLABUvZCCGEDpOyFEMIG/D8GYlLU3EqgzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "logistic_l1(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:00:47.210915Z",
     "start_time": "2019-02-22T20:57:31.262624Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.9620461544404055  and for train data auc score is -->  0.9999999988606081\n",
      "5000  for CV data auc score is -->  0.9623224745018848  and for train data auc score is -->  0.9999999988606079\n",
      "1000  for CV data auc score is -->  0.962310496906911  and for train data auc score is -->  0.9999999988606079\n",
      "500  for CV data auc score is -->  0.9624508657116853  and for train data auc score is -->  0.9999999988606079\n",
      "100  for CV data auc score is -->  0.9626415677213035  and for train data auc score is -->  0.9999999988606079\n",
      "50  for CV data auc score is -->  0.9627274287498786  and for train data auc score is -->  0.9999999988606079\n",
      "10  for CV data auc score is -->  0.9625154257816566  and for train data auc score is -->  0.9999464519907374\n",
      "5  for CV data auc score is -->  0.9618498662804267  and for train data auc score is -->  0.9990413753277217\n",
      "1  for CV data auc score is -->  0.9564004007347837  and for train data auc score is -->  0.9839091912402279\n",
      "0.5  for CV data auc score is -->  0.9519592792964474  and for train data auc score is -->  0.9734927549681335\n",
      "0.1  for CV data auc score is -->  0.937321707158281  and for train data auc score is -->  0.9496716956819771\n",
      "0.05  for CV data auc score is -->  0.9304281523225001  and for train data auc score is -->  0.9408330018196137\n",
      "0.01  for CV data auc score is -->  0.9198382297599657  and for train data auc score is -->  0.9283987637131135\n",
      "0.005  for CV data auc score is -->  0.9179064306670712  and for train data auc score is -->  0.9262698956485037\n",
      "0.001  for CV data auc score is -->  0.9125373024793695  and for train data auc score is -->  0.920851940498412\n",
      "0.0005  for CV data auc score is -->  0.900163287525481  and for train data auc score is -->  0.9079650954120314\n",
      "0.0001  for CV data auc score is -->  0.7919562875820183  and for train data auc score is -->  0.7969653014505786\n",
      "5e-05  for CV data auc score is -->  0.7341067392447214  and for train data auc score is -->  0.7406041906986294\n",
      "1e-05  for CV data auc score is -->  0.6631221011489569  and for train data auc score is -->  0.6726194737245547\n",
      "\n",
      " Best C Value 50 with highest roc_auc Score is 0.9627274287498786\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l2 regularization\n",
    "logistic_l2(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Logistic Regression Over the unseen or Test data in TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:00:56.292606Z",
     "start_time": "2019-02-22T21:00:47.213912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9588670935471677\n",
      "macro f1 score for data : 0.8627215526500356\n",
      "micro f1 scoore for data: 0.9302089241822651\n",
      "hamming loss for data: 0.06979107581773492\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.72      0.77     13407\n",
      "           1       0.95      0.97      0.96     71217\n",
      "\n",
      "   micro avg       0.93      0.93      0.93     84624\n",
      "   macro avg       0.88      0.85      0.86     84624\n",
      "weighted avg       0.93      0.93      0.93     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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NfPjbSRb/eYqQAG+WP96aAO+ikyv2CnunbXEs7KfKN4PwOrWlyOtl/UNXWVQkeG8lrM3VGpIx8Odb2IMQ9rMzNZPp61M4mZ5Lz6Y1ea5z1E2D3p4k7N1YtQVRN8xh5isuuMsS6BX5Y1hZVCSs3emsT7ivjFwjz67cQ3CAN/P7xnJnRDWH1CFh7yZUrwXn33xy/WvFbVvKvkoL6bKEuMU/jPTHd5ZhSyHc0/5z2dxeQ0eQnxdv9m5Kk1qB+Hk5rj+/hL2LuvGql5sF+K2EenHrLED600XvoRBC5MvINfLm5qN8vz+NOT2b0LlBCK3DgxxdloS9q7jxjs4bg7ykYC9vqEugC1F+iqKwIeUiszceISvPxLC7wmlb3zFTNjcjYe/Erj97L2u4FxfsEupC2NeMnw6zes85bq+hY/69sTQM0zm6pEIk7J1MeQP+ZiF+42sS6kLYh6IoWBTQqFW0jQwmvJofD7eqi1btfPdoS9g7geICviLhLsEuROVIzcjltZ8O065+NQbfUY+uDUMdXVKJJOwdJOjLLmjSDwPlD/ibhbtchihE5TBbFJbtPM2CX4+jUau477bqji6pTCTsK1nBWXxFA17O3IVwnL8v5TD1x0PsPZtNh6hgJvyrITUCfRxdVplI2FeSgrtWSwr5mwW8AmQlrMFUs5Vd6xNClC7rqpEzmVeZ1uM27r0tDJXK+ebmiyNhb2cF/WaKC3k5gxfCue0/l81fqZk80rouzetUZc2wOws1LnMVEvZ2Um1+BGrMZQp5CXghnM9Vo5n3t53gyx2phAZ407tZTQK8tS4Z9CBhbxfV5tct9FCNkkJeAl4I57PjVAbT16eQmnGV3s1q8mynym9cZmuuXb2TufGPrxLyQriejFwjo1ftJVTnzYJ+zZyi1YEtSNjbyPVn8xLyQrievWezaFIzkCA/L97q05QmNQNddsrmZiTsb5H/thn47PxviUEvIS+E80rPMTD356P8ePCCtXFZq3rucTZ/PQn7W1CWs3kzEvJCOCNFUVh/8AJzfj6KPs/EU+0iaBfpPI3LbE3CvoJKC3oF53qknRCisNfWH2bN3nM0qRnIK/fG0CA0wNEl2ZWEfQXcLOjlbF4I53d947J2UcFEhfozoEUdNE7YuMzWJOzLqaSgtyB3uwrhrE6l5/LaTym0jwx2icZltiZhXw4lBb2czQvhnEwWhaV/nea9rcfRqlXc37iGo0tyCAn7MpKgF8L1HL14hak/prD/XDYd/2lcVt1FGpfZmoR9GUjQC+GarhjMnMu6ymv330b3Rq7VuMzWJOxLIUEvhGvZezaLnamZDL6jHs1qV3HZxmW2JmFfAgl6IVxHrtHMe1uPs2THaWoE+tCneS2XblxmaxL2xfDfNkOCXggX8cfJdF5bf5jTmVdJaF6LZzpGunzjMluTz0Yx/Hb+F5CgF8LZZeQaGfP1PqoH+vD+gGa0rOt+rQ5sQcL+JqrNr4sKCXohnNnuM1nE1spvXPZ2QlMa13CvxmW2pnZ0Ac6mYJ7+Zo8MlKAXwvEu5xh46bsDDF2STNLRSwC0rBskQV8KObO/TpVvBnHjt0vBWb2xXufKLkcIcR1FUVh3MI25m46SYzQzon0E7SKDHV2Wy7BL2FssFqZMmcKhQ4fw9vZm+vTpREREWNd//PHHrF27FpVKxYgRI+jevbs9yig3r1NbgJtP32Q9tNgRJQkh/jHtxxS+3Xee2Fr5jcuiQty7cZmt2SXsN2zYgMFgYNmyZSQnJzNz5kwWLFgAQFZWFosWLWL9+vXk5ubSq1cvpwh7macXwvlYLApmi4JGraJjdAgNq+voH1fbIxqX2Zpdwn7Hjh107NgRgLi4OPbu3Wtd5+fnR+3atcnNzSU3N9cp7mi72fQNyDy9EI50Mj2XmSv30KZeEEPurEcXD2tcZmt2CXu9Xo9Op7MuazQaTCYTWm3+4WrVqsX999+P2Wxm+PDhN92HTueDVluxP7hoNGqCgvzLvn0x0zeWyK7l2o8jlXfM7sDTxuwp4zWZLXyy7TjzNh3BR6umf6u6HjHuAvb6Otsl7HU6HVeuXLEuWywWa9AnJSWRlpbGxo0bARg6dCgtW7akWbNmhfah1+dV+PhBQf5kZOSUaVv/bTO4/tNaaPqmx+dQxv04WnnG7C48bcyeMN4jF68wdd0hDpzXc3eDEF7rE4u32eL2477erXydw8ICi11nl0svW7ZsSVJSEgDJycnExMRY11WtWhVfX1+8vb3x8fEhMDCQrKwse5RRJjfePAUyfSOEo+QazFzQG5j54O3Meqgx1QN9HV2S27DLmX337t3ZunUriYmJKIrCjBkzWLhwIeHh4XTr1o1t27bRv39/1Go1LVu2pH379vYoo9yufwiJEKJy7D6T37hsyJ31iK1dhdXD7sRHK7cA2ZpKURSl9M0q34UL2RV+b1l/DbrxBirln/8uueBZvSf8in8jTxuzu403x2BmwdbjLPvrNDWr+LBkSKsi/WzcbcxlYa9pHI++qUrOHYRwjN+PpzPjpxTOZOXRL642T3esL43L7Ew+u/+QKRwhKkdGjpFxa/Ibl30woDkt6lZ1dEkewWPD/sabqED+MCuEPSWnZtK8ThWC/L14JyGW22sGytx8JfLYz/TNBi5n9ULY3sUrBiZ8u58nl+0i6ehlAOLqVpWgr2Qee2Z/vYIpnLwWoxxahxDuRFEU1u4/z5ubj3HVaGZUh/q0j6zm6LI8lkeGfXFTODntXnJQRUK4n1fXHWLt/jSa167CK/fEUD/Ec+6CdUYeGfbyy6MQ9mFRFCwKaNUqujQMpXHNQPrG1UbtBD2wPJ3HhX21hS2KnNEDWPzDHFGOEG7j+OUcXlufQoeoEIbcWY/ODaRxmTPxuJNcdc4F4CZX4Ty+0yH1COHqTGYLn/5+kkGf7+DYpRyqB3o7uiRxEx53Zn89ubZeiFtz5MIVpqw7xKE0Pd1iQhnXtQGhARL2zsgjw16urRfCNq6azFzOMfCfhxrTVfrNOzWPCvuCq3CEEBWXnJrJztOZPN4mnKa1qrB66J14yzXzTs+jwl6+HYWouCsGE/N/Oc6K5DPUrupL/xa1CfDWStC7CI8K++sVzNcb63V2aB1CuILfjl9mxvrDnM/OY0CL2ozqEIm/d8WeJCcco8xhn5mZSdWqrt+w6Mb5+qyHFjuqFCFcQkaOkfHf7KdGoA8fJjaneR3XzwFPVGrYb9++nalTp2I2m4mPj6d27dr069evMmqzqWoLomS+Xohy+Cs1gxZ1qlobl91WQxqXubJSv3Jvv/02X3zxBaGhoYwYMYIlS5ZURl02p7YYHF2CEC7hoj6PF7/Zz/Blu0k6egmA5nWkcZmrK/XMXq1WExQUhEqlwsfHh4CAgMqoy65kvl6IohRF4dt953lr8zHyTGae6RhJ+6gQR5clbKTUsA8PD2fu3LlkZGTwwQcfULt27cqoy+5kvl6IwqasO8T3+9NoUacKL98TQ0SwNC5zJ6WG/auvvsqKFSto1aoVfn5+TJ8+vTLqEkJUArNFQSG/cVnXhmE0rVWFhOa1pHGZGyo17GfMmMGkSZOsyy+++CKzZs2ya1FCCPv7+1IO035MoWN0MI+3CadzA5mycWfFhv3ixYtZsGABGRkZrF+/3vp6dHR0pRRmS/7bZhTpXy+EpzKZLXz+Ryof/e8E/l4aaldxj6lZUbJiw37QoEEMGjSI9957jxEjRlRmTTbnt/O/ji5BCKeQkqZnyrpDHL5whe6NwhjXNZpgf2lc5glKncZJTEzku+++w2QyoSgKaWlpDB8+vDJqswtrp0svnUPrEMIRjGYLWVdNzOnZWPrNe5hSw/7ZZ5+lfv36pKSk4OPjg5+fX2XUZVcKkP7UQUeXIUSl+Cs1g+TULJ64K5wmtarw9dA78NLINfOepkxf8alTpxIZGcnChQvJzMy0d01CCBvQ55mYueEww5ft5pu957hiMAFI0HuoMvXGycvLIzc3F5VKRU5Ojr1rsgv546zwJFuPXeb1DYdJy85jYKs6jGhfHz8vaVzmyUoN+0GDBvHZZ5/Rvn17OnfuTKtWrSqjLpuRHvbC02TkGJnw7X5qVfXl44fjiK1dxdElCSdQatjfe++91o/vu+8+lzuzl19YhSdQFIUdpzJpVS+/cdn8fs24rbpOes0Lq2K/E/bt28eIESMYP348ly9fBmDNmjUkJCRUWnG2Jj1xhDu6oM9j3Jr9jFyxm6Sj+f9Wm9WuIkEvCin2zH7ixImMGTOGM2fO8Oabb5KTk0NaWhqLF7t2PxnpiSPchaIorNlzjreTjmE0KzzXOYr2UcGOLks4qWLD3s/Pjw4dOgAwf/58evXqxZw5c1BJzwwhnMKkHw6x7kAaLetW5ZV7YqhXzfUvixb2U2zYazTX/nJfvXp1nn/++TLv1GKxMGXKFA4dOoS3tzfTp08nIiLCun7Lli3Mnz8fgMaNGzN58mS7/hCRH0/CXVzfuOyeRmG0qFOFXs2kcZkoXbGTeoqiYDQaMRgM+Pr6Wj82GEp/CMiGDRswGAwsW7aMsWPHMnPmTOs6vV7P7Nmzee+991i+fDl16tQhPT3dNqO5gfbcDgl64TYOn89m2NJkFv1xCoCO0SH0aV5bgl6USbFn9qdPnyY+Ph7ID/74+HgURUGlUrFx48YSd7pjxw46duwIQFxcHHv37rWu27lzJzExMfznP//h1KlT9OvXj+Bg+8wzBq5z7Z4+QkB+i4NPt59i4e8nCfDWUqeqr6NLEi6o2LDftGlThXeq1+vR6a71ntFoNJhMJrRaLenp6fz++++sXr0af39/Bg0aRFxcHJGRkYX2odP5oNVW7CYQjUZNUJA/6itnra9Ze+KENCIoyP0eylAwZk/iCWPefzaLF1fu5tB5PQ82q8XLPW4nJMBzGpd5wtf4RvYac5nuoC0vnU7HlStXrMsWiwWtNv9QQUFBxMbGEhYWBkDr1q05cOBAkbDX6/MqfPygIH8yMnK4sTu3AqQnboQM17pXoCwKxuxJPGHM6Rk5ZOYYmdurCQ+1qkdGRg4ZGSZHl1VpPOFrfKNbGXNYWGCx6+xyIW7Lli1JSkoCIDk5mZiYGOu6pk2bkpKSwuXLlzGZTOzatYsGDRrYowwhXNKfJzP46LcTANbGZZ2i5cEi4taU6cxer9dz+vRp6tWrh79/6b9edO/ena1bt5KYmIiiKMyYMYOFCxcSHh5Ot27dGDt2LMOGDQMgPj6+0A8DITyVPs/EvKRjfL37HOHV/BjYqi7+3hq00rhM2IBKURSlpA3WrVvHe++9h9lsJj4+HpVKxahRo+xe2IUL2RV+r3Ua55++OCryp3AU4NLTqTaq0LnIr7uu7Zejl5i54TAXrxgY2Kouw9tF4HtD4zJ3Gm9ZyZjL55amcT799FOWL19OUFAQo0aNYsOGDRUqwlHkojTh7DJyjLy89gBVfL34ZGALnuscVSTohbhVpU7jqNVqvL29UalUqFQqt3h4iRCOpigKf5zM4I7wIIL8vVjQrxkx1XXSa17YTanfWa1bt2bMmDGcP3+eSZMmERsbWxl1CeG2zmfnMWb1Pp7+ag9JRy8B+X+IlaAX9lTqmf2YMWNISkqicePGREVF0bVr18qo65YFfdlFpnCEU7EoCqv3nGPelmOYLArP3x1Fhyi5ykZUjlLDvk+fPiQkJJCYmFjoRilnp0k/7OgShChk0vcH+fHgBVqHB/Fy94bUDZIpUVF5Sg37Dz74gDVr1jBkyBAaNmxIv379XO5pVdLHXjiKyaKAoqDVqIm/vTqt6wXRM7amdI8Vla7UScLQ0FCGDh3KO++8Q15eHiNHjqyMumxO+tiLynb4gp4nvtzJ53/kX+7bISqEXs1qSdALhyj1zH716tV8/fXXWCwWEhISeP311yujLiFclsFkYeHvJ1m4/RRVfLREBMt0jXC8UsP+4MGDTJ48maioqMqoRwiXdvB8NpN+OMTfl3Lo0bg6z98dTZCfl6PLEqL4sP/555/p0qUL9evX548//uCPP/6wrhswYEClFCeEqzErkGc081afprSPlEcECudRbNhnZGQAcPHixUorxtZkZlRUhj9OppOcmsWT7SJoUjOQlU/cIf1shNMpNux79+4N5N9Be30vnLmHexwFAAAej0lEQVRz59q/KiFcQPZVE28nHWPNnvzGZYNaS+My4byKDfsVK1bw1VdfcfToUWu7YovFgtFoZOzYsZVWoBDOaMuRi8zccIT0HAOP3lGPJ9uGSz8b4dSKDfuePXvStm1b3n//fUaMyH+8n1qtJiRE7vgTni09x8DE7w9SN8iPN3o34fYaxXcaFMJZFBv2hw4dIjY2lnvuuYe///7b+vrRo0fp0KFDpRRXUaqNU4rM1yv2eU6L8BCKovD7iXTaRFSjmr83C/o3p1FYgEzZCJdRbNj/9ttvxMbG8v333xdZ5+xhr/59fpHXVCr5Rykq5lzWVV7fcJhtf6czp2cTOjcIoUlNOZsXrqXYsH/qqacAeP311zGbzSiKQnJyMs2aNau04ipMMV/78J//G+u2d0wtwmVZFIVVu87yTtLfKCiM6xJNhyi5nFK4plJvqpo9ezb16tXjzJkz7Nu3j7CwMGbOnFkZtdmMtEoQFTFx7UHWH7pAm4ggXuoeQ+2qvo4uSYgKK3VuY8eOHSQmJrJz504+/vhjzp49Wxl1CeEQJouCyWwBoEfjGky8N4Z3EmIl6IXLK/XM3mKxsHv3burWrYvBYODy5cuVUZcQlS4lTc+0H1O4u2EIQ++KoL1M2Qg3UmrY9+zZk2nTpjFjxgxmz57No48+Whl13RILoOHafL3FgbUI55dnsvDJ/07w2R+pVPXVEhkS4OiShLC5UsN+0KBB9OjRg1OnTjFy5EiCg53/bKdgbkpFfuDLdTiiOAfOZzPp+4Mcv5zL/U1q8HznKKpK4zLhhkoN+++//563336b6OhoDh8+zDPPPEPPnj0rozYhKoXRrDAvoSlt6zv/iYwQFVVq2H/22WesWrWKgIAA9Ho9Q4YMcbmwV0rfRHiQ/x2/TPLpLEa0r8/tNQL56ok70KqlbZ5wb6XOcKhUKgIC8ucwdTodPj4+di/qVlk0/tYpHABFIw+PEJB11cir6w7xfyv3sjHlAjmG/PsxJOiFJyj1zD48PJyZM2fSunVr/vzzT8LDwyujrluiNucA183Zm3MdWo9wvE2HLzJr4xEycgw8dmc9hrWNwEcrf80RnqPU7/YZM2ZQr149tm3bRr169Zg2bVpl1HWLCncftCDdCD1Zeo6BV384RIi/F58NasnTHSMl6IXHKfbMPicnh1WrVuHv78/DDz+MWu1C/zi8/cCgty6qvPwdWIxwBEVR+O14Om3r5zcue29AMxqGSuMy4bmK/c6fMGEC586dIzk5mbfeeqsya7p1hiuFFlVGfTEbCnd0Nusqz67ay3Or9pJ0NP8mwNtrBErQC49W7Jl9eno68+bNw2Kx8MQTT1RmTTZw4/U3cj2OJ7AoCl8ln+HdX/Jbcr/QNZqO0XI5pRBQQtirVPlXKKjVaiwW17kHNejLLvLsWQ/18ncH2ZBygbvqV+Ol7g2pVUX62QhRoNiwVxQFo9GIoiiFPgbw9vYucacWi4UpU6Zw6NAhvL29mT59OhEREUW2eeqpp+jWrRsPP/ywDYaST5N+uGg9cuml2ypoWqbVqHmgaQ06RAXTo3F168mKECJfsWF/+vRp4uPjgfzgj4+PR1EUVCoVGzduLHGnGzZswGAwsGzZMpKTk5k5cyYLFiwotM1bb71FZmamDYZQvILJm5yOU+x6HOEY+85k8uJXu7m7YShPto2gfaRM2QhRnGLDftOmTRXe6Y4dO+jYsSMAcXFx7N27t9D6devWoVKp6NSpU4WPUVYKkNdkkN2PIyrPVaOZj/53ki/+TCXIz4uYMGlcJkRpSr2pqiL0ej06nc66rNFoMJlMaLVaUlJS+O6775g3bx7z5xd9fGABnc4HrdY218cHBbn/pZcajdojxrk7NZNxX+3i70s59GtVl/H3NvKYxmWe8jW+nozZduwS9jqdjitXrl3+aLFY0GrzD7V69WrOnz/PkCFDOH36NF5eXtSpU6fIWb5en1ehY4eovcBivPaC2ouMjJwK7cuVBAX5e8Q4r1y5isls4d2+sdzbvA4ZGTlk5BlLf6Mb8JSv8fVkzOUTFlb8s5HLFPZ6vZ7Tp09Tr149/P1L/4nTsmVLfv75Z3r06EFycjIxMTHWdS+++KL143feeYfQ0FCbTucoFkuhq3EUF7qSSNzctr8vs+tMFiP/aVy24vE70Eg/GyHKpdSwX7duHe+99x5ms5n4+HhUKhWjRo0q8T3du3dn69atJCYmoigKM2bMYOHChYSHh9OtWzebFX8zKswlLgvXkZFr5K3NR1m7P43IYH+G3FEPf2+NBL0QFaBSCq6nLEZiYiKff/45Q4cO5fPPPychIYFVq1bZvbALF7Ir9L6Q+XVRca0JmgJcejrVhpU5J3f6dVdRFGvjssyrJobcWY+hbcLxvqGfjTuNuSw8bbwgYy6vW5rGUavVeHt7o1KpUKlU+PnJNevCvjJyjUxdl0JEsB/vJMQSU11X+puEECUqNexbt27NmDFjOH/+PJMmTSI2NrYy6qowReWFSjEWWhbOT1EUtv59mfaRwVTz9+aDAc2JDguQXvNC2EipYT9mzBiSkpJo3Lgx0dHRdOnSpTLqqjBzWBPUacnWG6rMYU0cWo8o3enMXGasP8z2kxnM7dWETtEhNKohZ/NC2FKpbQBXr17N5cuXCQ0NJTMzk9WrV1dGXRWm+FQFsF6RU7AsnI/ZorDkr9MkfrqDfeeymfCvBnSIkrtghbCHUs/sjx49CuT/mn3gwAGCgoLo1auX3QurKE3aLuBaq4SCZeF8Xll7gA0pF2kfGcyEfzWgpjQuE8JuSg37sWPHWj9WFIXhw4fbtaBbpc7LuHZW/8+ycB7XNy57KLYmnRqEEH+bNC4Twt5KDXuDwWD9+MKFC6SmOvdljBa1NyqLwRr4FnXJHTpF5TlwPptpP6bQ5Z/GZW3ry5SNEJWl1LAvuJFKURR8fX0ZOnRoZdRVYTmdphG4efy1jpedXOGZue7tqtHMh7+d4Is/UwkJ8KaRXEopRKUrNeyfe+45evbsWRm12ERek0HWsLcE1pWOlw6272wWk344xMn0XHrF1uTZTlEE+tqlJZMQogSlXo2zYsWKyqjDZqouf8B6B60mO5WqKx5wdEkeraC1wX/7xfLyPTES9EI4SJnm7Hv16kVkZCRqdf7Phrlz59q9sIrSXtwNXGuXoL2w26H1eKKtxy6TfDqTpztGcluNQJY/1lr62QjhYKWG/bhx4yqjDpu5sdVPKa1/hA1l5BiZu/ko6w6kERXiz+NtwqVxmRBOotiwHz16NG+99RZ33nlnZdZzy1QoJS4L21MUhZ8OXWD2pqNk55kYdlc4j9+kcZkQwnGKDfvLly9XZh3ChWXkGpnx02Eigv2ZeE8MDeQxgUI4nWLD/tSpU7zxxhs3XTdmzBi7FXTrCmbrC8jZpT0oisIvxy7TMeq6xmWhATJlI4STKjbsfX19iYyMrMxabMKi8UNtvtYL2uLlWc+vrAypGbm89tNh/jyZwZyeTejcIETaEAvh5IoN+9DQUHr37l2ZtdiGfwhkXwt7RVfLgcW4F7NFYelfp1mw9ThatYp/d29Ix2i5C1YIV1Bs2Ddt2rQy67AZc1AU2uxT1okcU5hz9993JS99d4BNhy/SISqYCf9qSI1AH0eXJIQoo2LDfvz48ZVZh81oLx0Arr/Ofo9D63F1xn8al3lp1PRqVpOuDUO557YwaVwmhItxq9sZted2oM5JK/SaypTroGpc375z2Uz78RDdGobxZDtpXCaEK3OrsPf7a0GR10yhrjkd5UhXjWbe23qCJX+lEhrgze015Y+vQrg6twp79ZVz1o8L5uxzW450TDEuas+ZLCZ+f5DTmVfp06wW/9cpEp2PW32bCOGR3PpfsalaQ0w1Wzm6DJfirVHjpVHxXv9mtKoX5OhyhBA24lZhr8k8UWhZnXPRQZW4lqSjl9h9JotnOkbSqIaOZY+1Ri1/gBXCrbjV7aXmqhElLovC0nMMvLL2AGNX72PrscvkGs0AEvRCuCG3OrNXGa+UuCzyKYrCjwcvMGfTEa4YzDzVLoLH7qyHl8atfvYLIa7jXmF/w8PFb1wW+dJzjczccJjIEH9euSeG6FBpXCaEu3OvsDdeLXHZk1kUhV+OXqJTdAjB/t58mNicqBBpXCaEp3Cr39tlGufmTqbnMnL5bsat2c8vx/JbVzcM00nQC+FB3OrMHiylLHsWk0VhyY5U3t92Ai+NilfuaUjHKLkLVghP5GZhL6730ncH+PnwRTpHhzD+Xw0I00njMiE8lYS9mzGYLKhU+Y3LEprVonujMP4VEyqNy4TwcHYJe4vFwpQpUzh06BDe3t5Mnz6diIhr17x/+umnrF27FoDOnTvzzDPP2OS4pupxeKUlX2tvXD3OJvt1FXvOZDFtfQrdGoYyvH192tSv5uiShBBOwi5/oN2wYQMGg4Fly5YxduxYZs6caV136tQpvvnmG5YuXcqyZcv49ddfOXjwoE2Oe7XxwyUuu6tco5nXvj/A0CXJ5BjMNK1dxdElCSGcjF3O7Hfs2EHHjh0BiIuLY+/evdZ1NWvW5KOPPkKj0QBgMpnw8bHNXHLA9rnAtV72AdvnktdkkE327ax2/9O47EzmVfo2r8XTHaVxmRCiKLukgl6vR6e71hZXo9FgMpnQarV4eXkRHByMoijMmjWLxo0b3/RZtzqdD1qtplzHVRn0RZaDgtz7GbShuSb8vbUsffIuWoV7VuMyjUbt9l/f63naeEHGbEt2CXudTseVK9eucbdYLGi11w6Vl5fHSy+9REBAAJMnT77pPvT6vPIfNyoe35RV1jn7vKh49Bk5Jb7HFW05cpHdZ7L4v05R1PbTsnhwC4KrBZDhhmMtSVCQv0eN2dPGCzLm8goLCyx2nV3m7Fu2bElSUhIAycnJxMTEWNcpisKoUaNo1KgRU6dOtU7n2MLV2CElLru6S1cM/PvbA4xbs5/fjqdL4zIhRJnZ5cy+e/fubN26lcTERBRFYcaMGSxcuJDw8HAsFgvbt2/HYDDwyy+/ADBmzBhatGhxy8cN2DYDuG7OftsMMvusvOX9OpqiKPxwII03fj5KjtHMyPb1efSOumilcZkQoozsEvZqtZqpU6cWei06Otr68Z499nkIuDrrZInLrio918isjUeIDg3glXtiiAzxrDlMIcStc6vLNszBMWiunLXO2ZuDY0rc3plZFIXNRy7RpUF+47KPHo4jMthf+tkIISrELecBXD0OT1zOYcSyXYz/5lrjsgah0qFSCFFxbnVmnxfdA+9TW65djRPdw6H1lJfJorD4z1Q+2HYcH62GSffGSOMyIYRNuFfYNxmE/9bpqI3ZmKrHudwNVf/+dn/+1E3DUF7s1oDQAG9HlySEcBNuFfa6n55FY8wGwCstGd1Pz6LvPs/BVZXs+sZlfeNqc9/t1ekaE+bosoQQbsat5ux9jq0Drs3ZFyw7q12nMxm0aAef/C//qqE2EdUk6IUQduFWZ/YWlRrNDcvOKMdg5r+//s3ynWeoEehDszrSuEwIYV9uFfaKrhakHy687GSSUzOZ9MNBzmbl0T+uNqM61ifA262+DEIIJ+RWKWMKi0WbfvhaP/uwWIfWczP+3hr8vTV8OKA5cXWrOrocIYSHcKuwV+deAq61SyhYdrRNhy+y50wWz3WOIqa6ji8fbSX9bIQQlcqtwt7ZrrO/eMXA7I1H2HT4IrdV15FrNOPnpZGgF0JUOvcK+yaD8P/tddRGPXkNHnLYdfaKovDdvvO8teUYV41mnu5Qn0daS+MyIYTjuFXY++xbjCYvAwDflFUYa7dxSOCn5xqZ+/NRGoYF8PI9MdQPlsZlQgjHcqtTTZ+j3wPXXWf/z3JlsCgKG1MuoCgKwf7efDIwjvcHNJegF0I4BbcKe4tfCIB1zr5g2d6OX8rhqaW7mPDtAX79p3FZVEiAzM0LIZyGW03jVPbVOCazhUV/pvLhbyfw89IwJb4RHaRxmRDCCblV2Ff21TgTvj3AlqOX6BYTygtdGxAijcuEEE7KrcK+Mlw1mlGrVHhr1fRvUZseTWrQtWGoo8sSQogSudWcvd+uj4Brf6AtWLaVnamZDFr0Fx//nt+47M6IahL0QgiXIGf2ZXDFYOLdpL/5atdZalfxoaW0ORBCuBi3CnvFKyD//zcs34qdqZlM/P4gadl5PNyyDiPa18ffW1P6G4UQwom4VdhrMk8A167GKVi+FQHeGqr4apnxwO00qy2tiIUQrsmt5uwNEV2Aa2f2Bcvlofxzc9Sbm48CEFNdx+LBLSXohRAuza3O7PXd5+F9dC0qFPKiHyj3Iwkv6vP4z8YjbD5yidtrXGtcppKbo4QQLs6twh4A3yBoeA/69q+X+S2KovDt3vzGZQazhWc7RfJwq7po1RLyQgj34H5hb8iBoxvwCWpa5iZo6blG3th8lJjqOl7u3pAI6WcjhHAzbhX2PvsWozZmgzGbwM3jAYoNfLMlf27+X43CCPb3ZuHAFkQE+0k/GyGEW3KrP9CWtevlsUtXeHJpMi+vPcjWfxqXRYb4S9ALIdyWW53Zl9Ybx2i28Nn2U3zy+0n8vTRM7SGNy4QQnsG9wr7JIHS/TIGAYPQtny0yhTP+m/38cuwy9zQKY2zXaIL9pXGZEMIzuFXY51MKLV3fuOzhVnXoGVuLzg0qp8+9EEI4C7uEvcViYcqUKRw6dAhvb2+mT59ORESEdf3y5ctZunQpWq2WkSNH0qVL+W9+uhmffYtRma9C1mkCN4/n5OUcRqY0p3ujMEZ2iOSO8Go2OY4QQrgau/yBdsOGDRgMBpYtW8bYsWOZOXOmdd2FCxdYtGgRS5cu5eOPP+aNN97AYDDY5Lg3dr303vkRFgVahwfZZP9CCOGq7BL2O3bsoGPHjgDExcWxd+9e67rdu3fTokULvL29CQwMJDw8nIMHD9rkuGr9uWsLCtTVprNkSCs5oxdCeDy7TOPo9Xp0Op11WaPRYDKZ0Gq16PV6AgMDresCAgLQ6/VF9qHT+aDVlq+7pEoxX7cAvhqoFRZY/BvciEajJijIs24G87Qxe9p4QcZsS3YJe51Ox5UrV6zLFosFrVZ703VXrlwpFP4F9Pq88h83Kh7flFXXLr2MikefkVPu/biioCB/MjxkrAU8bcyeNl6QMZdXWAknt3aZxmnZsiVJSUkAJCcnExMTY13XrFkzduzYQV5eHtnZ2Rw9erTQ+luh7z6PqzF9UHyrcTWmT7kboQkhhLuyy5l99+7d2bp1K4mJiSiKwowZM1i4cCHh4eF069aNwYMHM3DgQBRF4fnnn8fHx8dmx9Z3n4c2yN9jzuiFEKIsVIqiKKVvVvkuXMiu8HvlVz/P4Glj9rTxgoy5vCp9GkcIIYRzkbAXQggPIGEvhBAeQMJeCCE8gIS9EEJ4AKe9GkcIIYTtyJm9EEJ4AAl7IYTwABL2QgjhAVw27C0WC5MmTWLAgAEMHjyYEydOFFq/fPly+vTpQ//+/fn5558dVKVtlTbmTz/9lH79+tGvXz/effddB1VpW6WNuWCbYcOGsWTJEgdUaHuljXnLli3079+f/v37M2XKFNzhz26ljfnjjz+mT58+JCQk8NNPPzmoStvbtWsXgwcPLvL6pk2bSEhIYMCAASxfvtw2B1Nc1I8//qiMHz9eURRF2blzpzJixAjrurS0NOWBBx5Q8vLylKysLOvHrq6kMZ88eVLp3bu3YjKZFLPZrAwYMEA5cOCAo0q1mZLGXGDu3LlK3759lS+//LKyy7OLksacnZ2t3H///cqlS5cURVGUDz74wPqxKytpzJmZmUrnzp2VvLw8JSMjQ7n77rsdVaZNffDBB8oDDzyg9OvXr9DrBoNB+de//qVkZGQoeXl5Sp8+fZS0tLRbPp7Lntk76gEpjlTSmGvWrMlHH32ERqNBrVZjMpls2mDOUUoaM8C6detQqVR06tTJEeXZRUlj3rlzJzExMfznP/9h4MCBhIaGEhwc7KhSbaakMfv5+VG7dm1yc3PJzc1FpVIVtxuXEh4ezjvvvFPk9aNHjxIeHk7VqlXx9vamVatW/Pnnn7d8PJd94LgtHpDiakoas5eXF8HBwSiKwqxZs2jcuDGRkZEOrNY2ShpzSkoK3333HfPmzWP+/PkOrNK2Shpzeno6v//+O6tXr8bf359BgwYRFxfn8l/rksYMUKtWLe6//37MZjPDhw93VJk2de+995KamlrkdXvll8uGvS0ekOJqShozQF5eHi+99BIBAQFMnjzZESXaXEljXr16NefPn2fIkCGcPn0aLy8v6tSp4/Jn+SWNOSgoiNjYWMLCwgBo3bo1Bw4ccPmwL2nMSUlJpKWlsXHjRgCGDh1Ky5YtadasmUNqtTd75ZfLTuM46gEpjlTSmBVFYdSoUTRq1IipU6ei0ZTvkY7OqqQxv/jii6xYsYJFixbRu3dvHnvsMZcPeih5zE2bNiUlJYXLly9jMpnYtWsXDRo0cFSpNlPSmKtWrYqvry/e3t74+PgQGBhIVlaWo0q1u+joaE6cOEFGRgYGg4E///yTFi1a3PJ+XfbM3pEPSHGUksZssVjYvn07BoOBX375BYAxY8bY5JvEkUr7Oruj0sY8duxYhg0bBkB8fLxbnMiUNuZt27bRv39/1Go1LVu2pH379o4u2ea+/fZbcnJyGDBgABMmTGDo0KEoikJCQgI1atS45f1LuwQhhPAALjuNI4QQouwk7IUQwgNI2AshhAeQsBdCCA8gYS+EEB7AZS+9FO4lNTWVhx56iCZNmlhfa9OmDc8888xNt58wYQI9evSo8HX1Xbt2pVatWqjVahRFISgoiJkzZxa6i7M0H3zwAXfddReNGjXim2++oV+/fqxatYqqVatW+LLQ6+sym83k5OQwbdo0YmNji33PF198wSOPPFKh4wkPcsvddYSwgVOnThVpCFWS8ePHK1u2bKnw8bp06aJcvXrVujxr1izls88+q9C+ylt7eepKSkpSnnrqqRLf065dO5scW7g3ObMXTs1sNjNp0iTOnTtHeno6nTp1YvTo0db1f//9N//+97/RarVoNBpmzZpFjRo1mDt3Ln/88QeKovDYY49x3333FXsMi8VCdnY2kZGRGI1GXnrpJU6dOoXZbObxxx+nR48eLF68mNWrV1tv6hk/frz1t4v169dz5MgR3n33XRRFITQ0lOPHj3PbbbfRu3dvLly4wPDhw1m1alW56gI4c+YMVapUAfKbvi1evNi67u2332bZsmVkZmYyZcoUXn75ZSZPnsyJEyewWCyMHj2aNm3a3OJXQLgLCXvhNI4cOVKot/ecOXMwGo3ExcXRr18/8vLyioT9tm3baNKkCRMmTODPP/8kMzOTgwcPkpqaytKlS8nLy6N///60b9/eGpoFnnjiCdRqNSqVimbNmtGrVy+WLl1KtWrVmD17Nnq9nj59+nDXXXexatUqJk6cSFxcHF9++SUmk8m6nxEjRpCSksIzzzxj7WLYv39/Xn31VXr37s2aNWvo06cPW7ZsKXNdeXl5pKWl0bFjR8aPHw/A8ePH+eCDD/Dz82PSpEn8+uuvjBw5ki+++IIpU6bw5ZdfUq1aNWbMmEF6ejqPPPIIa9eutfnXSbgmCXvhNBo0aMCiRYsKvabX69mzZw//+9//0Ol0GAyGQuv79u3Lhx9+yLBhwwgMDOT5558nJSWFffv2WX9wmEymQmfIBT755JMibTSOHj1Ku3btgPyGVNHR0Zw6dYrXX3+dTz75hDlz5hAXF1fqA0Oio6Mxm82cPn2a77//nk8//ZRly5aVq6433niD1NRUQkJCAAgJCWH8+PEEBARw7Ngx4uLiCr0vJSWFHTt2sHv3buv+09PTqVatWom1Cs8gV+MIp7Zq1SoCAwOZO3cuTzzxBFevXi0UtBs3bqRVq1Z89tlnxMfH89FHHxEVFUWbNm1YtGgRn332Gffddx9169Yt0/Gio6OtvcP1ej0pKSnUrVuX5cuX8+qrr/LFF19w4MABdu7caX2PWq3GYrEU2Vffvn2ZPXs2DRo0oEqVKuWua/To0aSlpfHll1+SnZ3NvHnzePPNN5k+fTo+Pj7Wz0PB/6Oiorj//vtZtGgRH374IfHx8VStWrVM4xbuT8JeOLW2bduSlJREYmIiU6ZMISIigrS0NOv6pk2b8tZbbzFw4ECWLl3KI488QteuXfH392fgwIH06dMHoMxX2fTv35+MjAwefvhhHn30UZ555hlCQkJo1KgRffv25dFHHyU4OJjmzZtb3xMSEoLRaGT27NmF9hUfH8+vv/5Kv379AMpdl1qt5rXXXmPBggXk5OTQsmVLevfuzaBBg/D19bV+HqKjoxk3bhyJiYkcO3aMRx55hMTEROrUqYNaLf/ERT5phCaEEB5AfuwLIYQHkLAXQggPIGEvhBAeQMJeCCE8gIS9EEJ4AAl7IYTwABL2QgjhASTshRDCA/w/NON+HRAIYzUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l1(tfidf_train,Y_tr,tfidf_test,Y_test,optimal_C=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:01:14.566102Z",
     "start_time": "2019-02-22T21:00:56.295602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9623107060731548\n",
      "macro f1 score for data : 0.8649699182117649\n",
      "micro f1 scoore for data: 0.9331631688409907\n",
      "hamming loss for data: 0.06683683115900926\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.70      0.77     13407\n",
      "           1       0.95      0.98      0.96     71217\n",
      "\n",
      "   micro avg       0.93      0.93      0.93     84624\n",
      "   macro avg       0.90      0.84      0.86     84624\n",
      "weighted avg       0.93      0.93      0.93     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l2(tfidf_train,Y_tr,tfidf_test,Y_test,optimal_C=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:01:26.059693Z",
     "start_time": "2019-02-22T21:01:14.569101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-54.5735\ttwo star       \t\t\t\t34.8736\tpleasant surpris\n",
      "\t-29.7651\tworst          \t\t\t\t33.5165\tgreat          \n",
      "\t-26.2039\tdisappoint     \t\t\t\t31.4493\thigh recommend \n",
      "\t-25.9299\tdecent use     \t\t\t\t31.1902\tdelici         \n",
      "\t-24.6044\tleast favorit  \t\t\t\t28.2384\tfour star      \n",
      "\t-22.9339\tterribl        \t\t\t\t27.0996\tperfect        \n",
      "\t-22.2055\tcannot recommend\t\t\t\t26.5241\twont disappoint\n",
      "\t-21.6708\tundrink        \t\t\t\t26.1000\tbest           \n",
      "\t-21.6333\toat roll       \t\t\t\t25.9049\texcel          \n",
      "\t-21.6265\tcan sever      \t\t\t\t24.0695\tlove           \n"
     ]
    }
   ],
   "source": [
    "clf1=LogisticRegression(penalty='l1',C=5)\n",
    "clf1.fit(tfidf_train,Y_tr)\n",
    "imp_feature(tf_idf_vect,clf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:01:50.718890Z",
     "start_time": "2019-02-22T21:01:26.062036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-28.3874\tdisappoint     \t\t\t\t31.6732\tgreat          \n",
      "\t-26.6740\tworst          \t\t\t\t27.3465\tdelici         \n",
      "\t-22.2637\tterribl        \t\t\t\t25.1577\tbest           \n",
      "\t-19.9733\taw             \t\t\t\t24.0328\tperfect        \n",
      "\t-19.6164\thorribl        \t\t\t\t23.9598\tlove           \n",
      "\t-18.2742\treturn         \t\t\t\t21.7541\texcel          \n",
      "\t-17.9923\ttwo star       \t\t\t\t20.1395\thigh recommend \n",
      "\t-16.6708\tunfortun       \t\t\t\t18.8025\tnice           \n",
      "\t-16.1234\tdisgust        \t\t\t\t17.6408\tgood           \n",
      "\t-15.7882\tstale          \t\t\t\t17.2816\tamaz           \n"
     ]
    }
   ],
   "source": [
    "clf2=LogisticRegression(penalty='l2',C=50)\n",
    "clf2.fit(tfidf_train,Y_tr)\n",
    "imp_feature(tf_idf_vect,clf2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:02:04.899067Z",
     "start_time": "2019-02-22T21:01:50.722885Z"
    }
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sent_train=[]\n",
    "for sent in X_tr.values:\n",
    "    filtered_sentence=[]\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent_train.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:02:11.797516Z",
     "start_time": "2019-02-22T21:02:04.899067Z"
    }
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sent_CV=[]\n",
    "for sent in X_cv.values:\n",
    "    filtered_sentence=[]\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent_CV.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:02:22.307882Z",
     "start_time": "2019-02-22T21:02:11.797516Z"
    }
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "list_of_sent_test=[]\n",
    "for sent in X_test.values:\n",
    "    filtered_sentence=[]\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent_test.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:02:54.146315Z",
     "start_time": "2019-02-22T21:02:22.310860Z"
    }
   },
   "outputs": [],
   "source": [
    "import gensim\n",
    "w2v_model = gensim.models.Word2Vec(list_of_sent_train,min_count=5,size=50,workers=4)\n",
    "w2v_words = list(w2v_model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:02:54.155545Z",
     "start_time": "2019-02-22T21:02:54.146315Z"
    }
   },
   "outputs": [],
   "source": [
    "def avg_w2v(list_of_sent):\n",
    "    sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "    for sent in list_of_sent: # for each review/sentence\n",
    "        sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "        cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "        for word in sent: # for each word in a review/sentence\n",
    "            if word in w2v_words:\n",
    "                vec = w2v_model.wv[word]\n",
    "                sent_vec += vec\n",
    "                cnt_words += 1\n",
    "        if cnt_words != 0:\n",
    "            sent_vec /= cnt_words\n",
    "        sent_vectors.append(sent_vec)        \n",
    "    print(len(sent_vectors))\n",
    "    print(len(sent_vectors[0]))\n",
    "    return sent_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:05:23.616178Z",
     "start_time": "2019-02-22T21:02:54.159543Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115114\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "train_avgw2v = avg_w2v(list_of_sent_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:06:34.830490Z",
     "start_time": "2019-02-22T21:05:23.619175Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56698\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "cv_avgw2v = avg_w2v(list_of_sent_CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:08:22.764871Z",
     "start_time": "2019-02-22T21:06:34.834490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84624\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "test_avgw2v = avg_w2v(list_of_sent_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:08:23.515579Z",
     "start_time": "2019-02-22T21:08:22.768870Z"
    }
   },
   "outputs": [],
   "source": [
    "normalizer = preprocessing.Normalizer()\n",
    "train_avgw2v = normalizer.fit_transform(train_avgw2v)\n",
    "cv_avgw2v = normalizer.transform(cv_avgw2v)\n",
    "test_avgw2v = normalizer.transform(test_avgw2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:09:43.518027Z",
     "start_time": "2019-02-22T21:08:23.515579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.9137147259828023  and for train data auc score is -->  0.9122094322904706\n",
      "5000  for CV data auc score is -->  0.9137150754064579  and for train data auc score is -->  0.9122061451443395\n",
      "1000  for CV data auc score is -->  0.9137144297826174  and for train data auc score is -->  0.9122096909324713\n",
      "500  for CV data auc score is -->  0.9137144598654487  and for train data auc score is -->  0.9122089890669542\n",
      "100  for CV data auc score is -->  0.9137148139172322  and for train data auc score is -->  0.9122092944240299\n",
      "50  for CV data auc score is -->  0.913714869454767  and for train data auc score is -->  0.912209482993418\n",
      "10  for CV data auc score is -->  0.913716970624828  and for train data auc score is -->  0.9122108815971877\n",
      "5  for CV data auc score is -->  0.9137182248474857  and for train data auc score is -->  0.9122124568067288\n",
      "1  for CV data auc score is -->  0.9137381558802345  and for train data auc score is -->  0.9122195734496179\n",
      "0.5  for CV data auc score is -->  0.9137578948456762  and for train data auc score is -->  0.9122288424041332\n",
      "0.1  for CV data auc score is -->  0.91375864228833  and for train data auc score is -->  0.9121493413222279\n",
      "0.05  for CV data auc score is -->  0.9134470026688748  and for train data auc score is -->  0.9117208888846028\n",
      "0.01  for CV data auc score is -->  0.9075232211225831  and for train data auc score is -->  0.9054404589411111\n",
      "0.005  for CV data auc score is -->  0.8999251448909485  and for train data auc score is -->  0.8980472274869605\n",
      "0.001  for CV data auc score is -->  0.7953246274884657  and for train data auc score is -->  0.7914489751767622\n",
      "0.0005  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "5e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "1e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "\n",
      " Best C Value 0.1 with highest roc_auc Score is 0.91375864228833\n"
     ]
    },
    {
     "data": {
      "image/png": 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TsdmC+9RNqLGv+pAn2xygs60lfVL6mV2OiNSA387h14WGcE/bhvoxdd0tFzHigh3MuvxFRnS8oUbPaai9OFPqR2Xqxwn+OqWjC53ltNm/WM+TiTtIJp5rO4wyuxwRqSEFvpy2r1+7n8+T4faLJ+laepEgov9a5bTYtm7h6fAvcRmR/LbLLWaXI3LaZs58ih07tuF2Z1NUVETz5i1wOuOZNu2xUz73u+928Nlna7n55ltPue2rr77EypUfkZCQgNfrJTw8nAkT/kT79icf+5CZuYno6BguvLDrSbepDQW+nJY9s6fyXgeY3Pl2GjkamV2OyGn705/uAmDFivfYu3cPEyb8qcbPbdfuHNq1O6fG26el3cDQoRVf6Nq7dw/33TeJuXPn+750+r+WL3+Xvn39d8WbAl9qzPr9Lp4tWkmUYWfcRRPNLkcagIXb57Ng+zzgp+FpdTAt87cdRnN9h5pdSPBLmzZ9xaxZM3E4HAwZMozw8HCWLHmLn69rmTbtcb7//r8sW7aYhx56lLS0YXTu3JV9+/bicrmYNu3xX73aMCWlNe3bd+CbbzJJSWnNE09Mp6SkmNzcY4wdeytNmiSxYcN6du7cTteuHVmx4iM+/fQTysrKiI6O5pFHZuBwOM64L6Bz+HIa3C+m8/86w+izb8AV0djsckTqXElJCS+88ArXXDOQ/fv3MWPGMzz33GySk1P497/XV9r2xx9/YPz423nppbnk5HjYtu0/p9y/y+Xi2LEc9u7dQ1ra73j66Re4667JLFmyiA4dzuXii3swYcJEkpKacuzYMZ5++gVeeOEVysrK2Lbt21q/Px3hS41YDx3khcOLMVIs/L7Hn80uRxqI6zvc4DsaD4TLMpOTT0z0jY93MW3a34iKimLv3j106tSl0rZxcU6SkpoC0KRJEiUlp54McOjQIX7zm77ExMTw+uuvsnz5MsBCWVlZpe2sVisOh4OpU6cQGRnJkSNHqmxzJhT4UiNFLz7By+d7GdZiAMmxKad+gkgQslorJlbm5eXx6qsvsXjx+wDcddcd/O9Xlk53uuX33/+XPXu+57zzOvHgg/cxePBQevToyfLl7/LBB+/79mkYXnbs2MHatf/i5Zdfp6ioiFtuGV0H706BLzVg8biZu/018i+DO3rdb3Y5In7XqFEjOnfuyrhxo4mMjCQmJoajR7No1qz5qZ/8C2++OZ9Vqz7GZrNhs9mZNu0x7HY7ffr05ZlnnuCNN+bSpEkSOTk5AHTs2IkXX3yOJ598ksjISG65ZQxhYQ4aN07wTd2sDX3T1s8C4WNqbVme+DsdjRl0bdWD+WkfnfF+GkIv6pL6UZn6cYK+aSvmyM9n4YbnyWoEf+z9wKm3F5GApcCXX+V441X+0bWA1EYd6NG8p9nliEgtKPDl5EpK+ODjJ/neBX/sdX/Q3zRcJNQp8OWkwt96kyc6ejg7rDn92ww0uxwRqSUFvlSvvJz1b6ezqTn8ocdfsFl1vwKRYKfAl2qFrXiPJ876kaZWJ9d1+K3Z5YhIHVDgS1WGwfbXp7GqLdx60Z2E26of9CQiwUWBL1U4PlnNP5rsJJYIbuqkEcgiDYUCX6o4+PIjvN0Rbux8K7HhcWaXIyJ1RIEvldi/3MDMiI3YrTZuS635nHARCXwKfKkk74VHmXs+jDr7epIaNTW7HBGpQwp88bFt+w8vFa6h2AZ/uGiS2eWISB1T4ItP+XOP8Xx3GNDqas6Ob2d2OSJSxxT4AoB17x7e+GEpORHwx4snm12OiPiBAl8AsD//D566xODShIu4IOkis8sRET9Q4AuWw4d5Z+s8DsTCny6+1+xyRMRPFPhCxEvPMaN7GR2jz+aK5H5mlyMifqLAD3GWYzmsXjub/zSBOy6erBHIIg2YAj/ERc55mRkXFtIyPImhZ48wuxwR8SMFfigrKGDTe8+QkQwTLrobh81hdkUi4kcK/BAWMf+fPNEpF5ctlhvOvdHsckTEzxT4oaq0lD3z/8G7HWDc+X+gkaOR2RWJiJ8p8ENU+OJF/KPtISItYdzS+TazyxGReqDAD0VeL+5XZ/D/uli44byxNI5sbHZFIlIPFPghKOyD5TyX9D1em4UJ3TQCWSRUKPBDjWFQPOsxXrrIwrVnDyc5NsXsikSknijwQ4xj7b94Jfwb8hwGf0y9y+xyRKQe2c0uQOqX5bkZPNPDyhUtLqdTQmezyxGReqTADyH2TV+xIP8zjkTBHy+82+xyRKSeKfBDSPgzT/JELyvnN+5Cz+a9zS5HROqZ387he71eHnzwQa6//nrGjBnD3r17K61/9dVXGT58OCNGjGDlypX+KkN+YtuxneV7l7PL6eWPF07SkDSREOS3I/xVq1ZRUlLCwoULyczMZPr06cyaNQuA3Nxc3njjDT7++GMKCwsZOnQo/fppLK8/Rc78B49dZuWs6GQGtBlkdjkiYgK/HeFv3LiR3r0rTht069aNrVu3+tZFRkbSvHlzCgsLKSws1NGmn1n37yNj0yI2NvVyx4V3Y7PazC5JREzgtyP8vLw8oqOjfY9tNhtlZWXY7RUv2axZMwYOHEh5eTm33Vb9V/ujo8Ox24M7nGw2K05nlKk1WKfO4vFLDZpGJnJr93FE2CNMqSMQehFI1I/K1I8T/NULvwV+dHQ0+fn5vsder9cX9mvXruXIkSOsXr0agFtuuYXU1FS6dOlSaR95ecX+Kq/eOJ1R5OQUmPb6lqws9i1/mZU3G9zf9Q6K8rwUYU49Zvci0KgflakfJ9SmF4mJMSdd57dTOqmpqaxduxaAzMxM2rdv71sXFxdHREQEYWFhhIeHExMTQ25urr9KCWlhGWuZcWEJ0bYobjpvnNnliIiJ/HaE369fPzIyMkhLS8MwDNLT05k7dy7Jycn07duXzz//nFGjRmG1WklNTaVnz57+KiWk7T26k0XnwR/a/o64cKfZ5YiIiSyGYRhmF3EyWVnHzS6h1sz+mPre02O4JWwZnw5fx7lNu5pWB5jfi0CjflSmfpwQdKd0JDC4C48C0CSupcmViIjZFPgNnKc4B4C48DiTKxERsynwGzhPWS7OEht2q6ZoiIQ6BX4D5/bm4yoLM7sMEQkACvwGzm0tJN4w54tWIhJYFPgNnMdWgsvSyOwyRCQAKPAbMq+X7LByXPaTX6YlIqFDgd+AWXKPkR0JrjB94UpEFPgNWln2EXIjwBnpMrsUEQkACvwGLPfoPgDiGzUxuRIRCQQK/AbM4z4AQHxMU5MrEZFAoMBvwHI8PwAQH9/C5EpEJBAo8BswT95hAOJcmqMjIgr8Bi0nLwsAZ0KKyZWISCBQ4Ddg7iI3AK7oRJMrEZFAoMBvwDwlOdi9EO3QF69ERIHfoHnKj+MqsWOxWMwuRUQCgAK/AXMb+bjKNSlTRCoo8Bswt6UIF5FmlyEiAUKB34C57SXEW6PNLkNEAsRpBf6XX34JQE5ODqtWrfJLQVJHystxh3tx2WPNrkREAkSNA/+pp57i2WefBaCoqIjZs2fzwgsv+K0wqR3LsRyyIyE+PN7sUkQkQNQ48FevXs2cOXMAaNq0KfPmzWPFihV+K0xqp+joDxQ5wBnZ2OxSRCRA1DjwS0tLcTgcvscOh0OX+wWwnKP7AXBFa1KmiFSw13TD1NRUJk2axMiRI7FYLCxdupSuXbv6szaphRx3ReA7YzUpU0Qq1PgI/4EHHiAhIYFHH32Uxx9/nMaNGzNlyhR/1ia14Mk5CEC8U5MyRaRCjY/wo6KiuO+++/xZi9ShnJ8nZTZuZXIlIhIoahz4t99+e7XLX3zxxTorRuqOJ/8oREJ8QrLZpYhIgKhx4F999dW+f5eWlvLRRx/RqVMnvxQltecpckMkOKMSzC5FRAJEjQN/2LBhVR6PGTOmzguSuuEuPUZkmYVIu0YriEiFMx6tYBgGR44cqctapA7llB+ncWmNf5+LSAg443P4O3fupHv37nVekNSNbApwlYebXYaIBJAzOodvsVj47W9/S69evfxSlNSe21qMizizyxCRAHLG5/ANw2Dv3r20bt26rmuSOuB2lNLRpkmZInJCjQP/zTff5PHHH6ewsNC3zOVykZGR4ZfCpBZKS3GHe4m36whfRE6oceDPnj2buXPnMmvWLO68804++eQTDh065M/a5Ex5PLgjweXQpEwROaHGV+k4nU66du3KueeeS3Z2NhMmTPDNx5fAkpe1j3IrxOsafBH5hRoHvt1u59ixY6SkpPDNN98AUF5e7rfC5MzlZP80OK2RJmWKyAk1DvxRo0Zx2223cfnll7Nw4UKGDx9O27Zt/VmbnKEczwEA4p3NTa5ERAJJjc/hjxw5kgEDBhAVFcXChQvZsmWL77LM999/n0GDBvmtSDk9nmMVkzKd8ZqUKSInnNZXMaOiogBISkoiKSnJt/zVV1+tEvher5epU6eyY8cOwsLCmDZtGikpKQBs27aN9PR037aZmZk8//zzXHbZZWf8RuQET17FN6CdGpwmIr9QJ9+9NwyjyrJVq1ZRUlLCwoULyczMZPr06cyaNQuAc889lzfeeAOADz74gCZNmijs65Cn4ChEgbOxAl9ETqiTwK/uVocbN26kd+/eAHTr1o2tW7dW2aagoICZM2cyb968uihDfuIp9lQEfoQuyxSRE/w2XSsvL4/o6BPf9LTZbJSVlWG3n3jJt99+m2uuuQaXy1XtPqKjw7Hbbf4qsV7YbFaczqh6fc1j5bk4S6wkuGLr9XVPxYxeBDL1ozL14wR/9cJvgR8dHU1+fr7vsdfrrRT2AO+99x7PPvvsSfeRl1fsr/LqjdMZRU5OQb2+ZnbZcVyljnp/3VMxoxeBTP2oTP04oTa9SEyMOem6Mx6P/EvVncNPTU1l7dq1QMUfZdu3b19p/fHjxykpKaFZs2Z1UYL8gpsCXN4Is8sQkQBzWoH/8zdrc3JyWLVqlW/54MGDq2zbr18/wsLCSEtL49FHH+W+++5j7ty5rF69GoDdu3fTooUuG/QHt60Yl0UfjUWkMotR3eF5NZ566ik2bdrEG2+8waFDh5g4cSKXX345f/jDH/xWXFbWcb/tu76Y8TH14vQ4Lgo/m+cmbazX1z0VfWSvTP2oTP04wfRTOqtXr2bOnDkANG3alHnz5rFixYozKkj8qLgYd4SBy+E0uxIRCTA1/qNtaWkpDofD99jhcFR7OaaYy+vO4lgExNurv/JJREJXjQM/NTWVSZMmMXLkSCwWC0uXLqVr167+rE3OQE7WXgBcjTQpU0Qqq/EpnQceeICEhAQeffRRHn/8cRo3bsyUKVP8WZucgWM/TcqMi046xZYiEmpqfIQfFRXFfffdx4EDBygvL/fNxZHA4vH8AGhSpohUVePA37NnD3fccQdHjhzBMAycTicvvfSSRiQHmJzcnyZlulqaXImIBJoan9L5+9//zvjx4/nyyy/56quvmDBhAg899JA/a5Mz4M7LAjQpU0SqqnHgZ2dnM2zYMN/jESNG4PF4/FKUnLmcomwA4uN1hC8ildU48MvLy8nJyfE9drvdfilIasdd7MFeDjHhcWaXIiIBpsbn8EePHs31119P//79sVgsrFixgptuusmftckZ8JTl4iq16TsSIlJFjY/w58+fz0MPPURpaSlFRUX87W9/44YbbvBnbXIGPN584svCzC5DRAJQjY/wIyMjad26NZdccok/65FaclOoSZkiUq0aB35hYSF9+/aladOmvnvbQsVMewkcbnsxrSy605WIVFXjwNe3aoNDdlgZXe2BdacrEQkMNQ787t27+7MOqQuFhbgjwGnVFToiUpXfbnEo9a84+yCFDnA5NClTRKqqk1scSmDwHNkDQHx0E3MLEZGApMBvQI65DwDgjNGkTBGpSoHfgPw8KdOpSZkiUg0FfgOSc/wQAPGuViZXIiKBSIHfgHjyf5qUmdja3EJEJCAp8BsQT2HFQDuns5nJlYhIIFLgNyCe0hwiSyHSEXXqjUUk5CjwGxB3WS6NS/XVChGpngK/AfEYBcSXa1KmiFRPgd+AuC2FuIxIs8sQkQClwG9A3PYSXNZos8sQkQClE74NhWGQHVaOy6pJmSJSPQV+A2Hk5+OOBCdOs0sRkQClwG8g8rP2UW4FV3hjs0sRkQClc/h66CxoAAAOTUlEQVQNhCdrLwBOTcoUkZNQ4DcQxzw/TcqMbWpyJSISqBT4DYTn2EEA4p0tTK5ERAKVAr+B8OT+NCkzQZMyRaR6CvwGwlNwFIC4hNbmFiIiAUuB30Dk/DwpM07n8EWkegr8BsJdeoy4Ygt2q660FZHqKfAbCHd5Lo1LHWaXISIBTIHfQHgowFUebnYZIhLA/Pb53+v1MnXqVHbs2EFYWBjTpk0jJSXFt/7TTz/l+eefB6Bjx4787W9/w2Kx+KucBs9tLSYezdERkZPz2xH+qlWrKCkpYeHChUyaNInp06f71uXl5TFjxgxefPFFFi1aRIsWLfB4PP4qJSRoUqaInIrfAn/jxo307t0bgG7durF161bfuq+//pr27dvz2GOPccMNN5CQkIDL5fJXKQ2fYZAd7sXliDO7EhEJYH47pZOXl0d09IkjTpvNRllZGXa7HY/Hw4YNG1i6dClRUVH87ne/o1u3brRp08Zf5TRo5bkejkWA09CkTBE5Ob8FfnR0NPn5+b7HXq8Xu73i5ZxOJ507dyYxMRGACy+8kG3btlUJ/OjocOx2m79KrBc2mxWn0783Fc86tA2AJs6mfn+t2qiPXgQT9aMy9eMEf/XCb4GfmprKJ598woABA8jMzKR9+/a+dZ06dWLnzp243W5iY2PZvHkzo0aNqrKPvLxif5VXb5zOKHJyCvz6Gvt37wAgOqyx31+rNuqjF8FE/ahM/TihNr1ITIw56Tq/BX6/fv3IyMggLS0NwzBIT09n7ty5JCcn07dvXyZNmsT48eMBuOaaayr9QpDT4/H8AIAzrpnJlYhIIPNb4FutVh5++OFKy9q2bev798CBAxk4cKC/Xj6kHPtpUqYzXpMyReTk9MWrBsBz/DAAzsbJJlciIoFMgd8AeAorJmXGN2ltbiEiEtAU+A2Ap9iDzQsxjXQ/WxE5OY1WbAA8Jcdw2awaTSEiv0pH+A2A25uHS5MyReQUFPgNgJsCGnsjzC5DRAKcAr8BcFuLibdEml2GiAQ4BX4D4HaUEW89+bfrRERAf7QNfl4v2RFeXF5NyhSRX6fAD3JFniMUOsBp0XhpEfl1OqUT5I4d2Q1AfGSCyZWISKBT4Ae5nOz9ALhik0yuREQCnQI/yHk8BwBwxjY3uRIRCXQK/CCXk/vz4LSWJlciIoFOgR/kPHmalCkiNaPAD3KewmwAnJqUKSKnoMsyg5yn2EOkBSIj9MUrEfl1Cvwg5ynNxWUN7hu9i0j90CmdIOf25uEq06RMETk1BX6Qc1sKaezV4DQROTUFfpDz2EpwWqLMLkNEgoDO4Qe5bEcZLkN/sBWRU1PgBzGjrAx3hIGrzGl2KSISBBT4QSw/+wBlNoh36OblInJqOocfxHKy9gLgbKRJmSJyagr8IOY5ug+A+NimJlciIsFAgR/Eco79CIAzTpMyReTUFPhBLCf3EABOVwuTKxGRYKDAD2Ke/CwAnImtzS1ERIKCAj+IuQt+mpSZkGJyJSISDHRZZhDLKc0hzmLB7gg3uxQRCQIK/CDmLsvFhSZlikjN6JROEHN783GVh5ldhogECQV+EHNbinBpUqaI1JACP4h57CXEWxuZXYaIBAkFfhDLDivDZY81uwwRCRL6o22QKisuJCcC4ks1KVNEakZH+EEqN+unOTqRmpQpIjWjwA9SOUcrJmXGN2piciUiEiwU+EHKk70fAGdMksmViEiw8Ns5fK/Xy9SpU9mxYwdhYWFMmzaNlJQTIwCmTZvGpk2baNSo4iqTF154gZgY3aqvpnKOHQQgPr6lyZWISLDwW+CvWrWKkpISFi5cSGZmJtOnT2fWrFm+9d9++y2vvPIKLpfLXyU0aDnHf5qU2biVyZWISLDwW+Bv3LiR3r17A9CtWze2bt3qW+f1etm7dy8PPvggR48eZeTIkYwcObLOXnvTJ3PZvuuzOttfbYSF2SkpKavz/a47/DnEg7OJBqeJSM34LfDz8vKIjo72PbbZbJSVlWG32ykoKGD06NHcfPPNlJeXc+ONN9KpUyc6dOhQaR/R0eHY7ac/K+b+9ffxlbOg1u+hTpT4ab/x0DTfSss2Z2O1BcfVtTabFaczyuwyAob6UZn6cYK/euG3pIiOjiY/P9/32Ov1YrdXvFxkZCQ33ngjkZEVYwEuueQStm/fXiXw8/KKz+i1l0zcTu5Pt/8zW0xMBMePF/ll39Gu5uQeL8F/v1XqltMZRU5OgPwiDgDqR2Xqxwm16UVi4sn/Fuq3wE9NTeWTTz5hwIABZGZm0r59e9+6PXv2cNddd/HOO+/g9XrZtGkTw4YNq7PXjoh2EhEdGF9IcjqjiNQPsYgEAL8Ffr9+/cjIyCAtLQ3DMEhPT2fu3LkkJyfTt29fBg8ezKhRo3A4HFx77bW0a9fOX6WIiAhgMQzDMLuIk8nKOm52CbWmj6knqBeVqR+VqR8n+OuUjr54JSISIhT4IiIhQoEvIhIiFPgiIiFCgS8iEiIC+iodERGpOzrCFxEJEQp8EZEQocAXEQkRCnwRkRChwDdJdnY2w4cPN7uMgLB+/XomT57MxIkT2b59u9nlmGLTpk3ce++93HvvveTm5ppdjun0M1FVXWSGAt8EhmHwyiuv0KJFC7NLCQiFhYU89thj3H777Xz2WWDcuKa+LVq0iIcffpiRI0eyYsUKs8sxnX4mKqurzAiOO2cEuddee43169cDFXf/iouLY/DgwcyZM8fkyszxv/2YMGECBQUFvPHGG9xzzz0mV2eO8vJywsPDSUxM5IsvvjC7HNNdccUVIf8z8UsLFiyok8xQ4NeDsWPHMnbsWN/jP/7xj2zfvp0tW7bwwQcf0L9/f/OKM8H/9sPj8fDEE08wceJEGjdubF5hJoqMjKSkpISsrCwSEhLMLsd0+pmo7PPPP6+TzNApnVravHkzY8aMASru6vXggw9y/fXXM2bMGPbu3Vvtc5577jkefvhhOnfu3ODC/kz68eijj3L48GGefPJJPvzww/ost17UpCejRo3iwQcf5M0332TIkCFmlut3NelHQ/+Z+KWa9KPOMsOQMzZ79mxj0KBBxnXXXWcYhmF89NFHxr333msYhmF8/fXXxu23325mefVO/ahKPalM/aisvvuhI/xaSE5OZubMmb7HGzdupHfv3kDFuemtW7eaVZop1I+q1JPK1I/K6rsfCvxauPrqq303ZgfIy8sjOjra99hms1FWVmZGaaZQP6pSTypTPyqr734o8OtQdHQ0+fn5vsder7fS/5mhRv2oSj2pTP2ozN/9UODXodTUVNauXQtAZmYm7du3N7kic6kfVaknlakflfm7H6H7q9QP+vXrR0ZGBmlpaRiGQXp6utklmUr9qEo9qUz9qMzf/dA8fBGREKFTOiIiIUKBLyISIhT4IiIhQoEvIhIiFPgiIiFCgS8iEiIU+CIiIUKBLw3GX/7yF1599dVf3WbJkiXcdttt9VRRhQMHDnD++efX62uKVEeBLyISIjRaQYKK1+slPT2dzZs3k5+fj2EYTJs2jQsuuKDSdh07duTWW29l3bp1FBQUcPfdd3PVVVcBkJWVxe9//3sOHjyIzWbjySefpG3btmRmZjJjxgzfnacuvfTSKl9t3717N2lpaaxbt46wsDDKy8u5/PLLee211zh+/Pgpnz9z5kw8Hg8PPvhglcfHjx/nkUceYefOnZSWltKjRw8mT56M3W7n2WefZeXKlTgcDuLj43n00Udp0qSJHzstDZGO8CWobN68mSNHjrBw4UJWrFjBsGHDePnll6tsV15eTmRkJEuWLOHpp5/mr3/9K263G4D9+/czZcoU3nvvPS688ELfaaB//vOfTJw4kbfeeovly5ezZs2aKvPI27RpQ7t27VizZg0An332GS1btqRt27Y1ev6vSU9P57zzzmPJkiUsXboUj8fD3LlzOXjwIK+//jqLFy9myZIl9OzZk2+++eZMWyghTEf4ElTOP/984uLiePPNN9m/fz8bNmygUaNG1W47evRoADp06ED79u358ssvAejSpQspKSkAnHvuuaxcuRKA6dOns3btWl588UW+//57iouLKSgoqLLfkSNH8s4773DNNdewZMkSRo0a9avPdzqdNXpv//rXv9iyZQtvv/02AEVFRQAkJSXRoUMHhg0bxmWXXcZll11Gjx49atoyER8FvgSVf/3rXzzyyCPcfPPN9O3bl7POOot333232m1tNpvv316v1/f4l/PFLRYLP88PHD16NOeccw69e/emf//+bN68mepmC/bv35/p06eza9cuvvzyS6ZPn17j5//y9QBKS0sr1fjMM8/Qtm1bAHJzc7FYLFitVubNm8eWLVtYv3496enp9O7dm8mTJ59W70R0SkeCSkZGBn369OGGG26gU6dOrFq1ivLy8mq3Xbp0KQDffvstu3fv5qKLLjrpfnNzc9myZQv33HMPV111FYcOHWLfvn14vd4q24aHhzNw4ED+8pe/cNVVVxEZGVnj58fHx/Ptt99iGAZ5eXl88sknvnW9evXitddewzAMSkpKmDBhAvPmzWP79u0MGjSItm3bcttttzF27Fi2bNlyJu2TEKcjfAkqaWlpTJo0icGDB1NWVkbPnj35+OOPqw3mTZs2sWjRIrxeL0899RRxcXEn3W9sbCy///3vGTZsGFFRUSQlJZGamsrevXurPX1y3XXXMW/ePKZOnXrK57dq1cr3vCFDhrBu3TquuuoqkpKS6N69u++If8qUKTzyyCMMHjyY0tJSLr30UsaPH4/D4aB///6MGDGCqKgoIiIiuP/++2vZSQlFmocvDdI555zD+vXrcblcZpciEjB0SkdEJEToCF9EJEToCF9EJEQo8EVEQoQCX0QkRCjwRURChAJfRCREKPBFRELE/wcB2aFss5PhSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "logistic_l1(train_avgw2v,cv_avgw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:10:13.066167Z",
     "start_time": "2019-02-22T21:09:43.521024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.9137159917757798  and for train data auc score is -->  0.9122085754676316\n",
      "5000  for CV data auc score is -->  0.9137159987179717  and for train data auc score is -->  0.912208580594896\n",
      "1000  for CV data auc score is -->  0.9137160866524014  and for train data auc score is -->  0.9122086090796979\n",
      "500  for CV data auc score is -->  0.9137160935945932  and for train data auc score is -->  0.9122086797220065\n",
      "100  for CV data auc score is -->  0.9137167831856485  and for train data auc score is -->  0.9122090095760115\n",
      "50  for CV data auc score is -->  0.9137174195532332  and for train data auc score is -->  0.9122096123144183\n",
      "10  for CV data auc score is -->  0.9137219551185631  and for train data auc score is -->  0.9122136605744542\n",
      "5  for CV data auc score is -->  0.9137268586200604  and for train data auc score is -->  0.9122187120692106\n",
      "1  for CV data auc score is -->  0.9137678314362513  and for train data auc score is -->  0.9122488672198191\n",
      "0.5  for CV data auc score is -->  0.9138023133030797  and for train data auc score is -->  0.9122714522494932\n",
      "0.1  for CV data auc score is -->  0.9137158714444547  and for train data auc score is -->  0.9121475342463998\n",
      "0.05  for CV data auc score is -->  0.9132875590350109  and for train data auc score is -->  0.911713854847639\n",
      "0.01  for CV data auc score is -->  0.9095099537794422  and for train data auc score is -->  0.9079831900975454\n",
      "0.005  for CV data auc score is -->  0.9061990049728679  and for train data auc score is -->  0.90471124003854\n",
      "0.001  for CV data auc score is -->  0.8914273629119824  and for train data auc score is -->  0.8897900405994108\n",
      "0.0005  for CV data auc score is -->  0.8765159490748207  and for train data auc score is -->  0.8744883166805181\n",
      "0.0001  for CV data auc score is -->  0.7856425654346011  and for train data auc score is -->  0.7812245190629478\n",
      "5e-05  for CV data auc score is -->  0.7299715763988581  and for train data auc score is -->  0.725069863306176\n",
      "1e-05  for CV data auc score is -->  0.6490826937137175  and for train data auc score is -->  0.6446087598340302\n",
      "\n",
      " Best C Value 0.5 with highest roc_auc Score is 0.9138023133030797\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l2 regularization\n",
    "logistic_l2(train_avgw2v,cv_avgw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:10:20.431521Z",
     "start_time": "2019-02-22T21:10:13.069164Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9131383534549062\n",
      "macro f1 score for data : 0.7746668684119387\n",
      "micro f1 scoore for data: 0.8952897523161278\n",
      "hamming loss for data: 0.10471024768387219\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.52      0.61     13407\n",
      "           1       0.91      0.97      0.94     71217\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     84624\n",
      "   macro avg       0.83      0.74      0.77     84624\n",
      "weighted avg       0.89      0.90      0.89     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l1(train_avgw2v,Y_tr,test_avgw2v,Y_test,optimal_C=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-22T21:10:23.091273Z",
     "start_time": "2019-02-22T21:10:20.431521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9132104570832862\n",
      "macro f1 score for data : 0.7736040913603216\n",
      "micro f1 scoore for data: 0.895266118358858\n",
      "hamming loss for data: 0.10473388164114199\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.51      0.61     13407\n",
      "           1       0.91      0.97      0.94     71217\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     84624\n",
      "   macro avg       0.83      0.74      0.77     84624\n",
      "weighted avg       0.89      0.90      0.89     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l2(train_avgw2v,Y_tr,test_avgw2v,Y_test,optimal_C=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-24T20:43:53.856095Z",
     "start_time": "2019-02-24T20:43:40.176144Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text TFIDF vectorizer  (115114, 40518)\n",
      "CV Data Size:  (56698, 40518)\n",
      "Test Data Size:  (84624, 40518)\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer()\n",
    "tfidf_train = tf_idf_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(tfidf_train))\n",
    "print(\"The shape of out text TFIDF vectorizer \",tfidf_train.get_shape())\n",
    "tfidf_cv = tf_idf_vect.transform(X_cv)\n",
    "tfidf_test = tf_idf_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T09:57:08.523496Z",
     "start_time": "2019-02-25T08:34:09.498206Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115114\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "t = tf_idf_vect.get_feature_names()\n",
    "tfidf_sent_vectors_train = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent_train:\n",
    "    sent_vec = np.zeros(50)\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",
    "            tfidf = tfidf_train[row,t.index(word)]\n",
    "            sent_vec += (vec * tfidf)\n",
    "            cnt_words += tfidf\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    tfidf_sent_vectors_train.append(sent_vec)\n",
    "    row += 1\n",
    "print(len(tfidf_sent_vectors_train))\n",
    "print(len(tfidf_sent_vectors_train[0]))\n",
    "#return tfidf_sent_vectors_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T10:36:50.237457Z",
     "start_time": "2019-02-25T09:59:24.849353Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56698\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "t = tf_idf_vect.get_feature_names()\n",
    "tfidf_sent_vectors_CV = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent_CV:\n",
    "    sent_vec = np.zeros(50)\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",
    "            tfidf = tfidf_cv[row,t.index(word)]\n",
    "            sent_vec += (vec * tfidf)\n",
    "            cnt_words += tfidf\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    tfidf_sent_vectors_CV.append(sent_vec)\n",
    "    row += 1\n",
    "print(len(tfidf_sent_vectors_CV))\n",
    "print(len(tfidf_sent_vectors_CV[0]))\n",
    "#return tfidf_sent_vectors_CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T11:43:18.882945Z",
     "start_time": "2019-02-25T10:40:17.493919Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84624\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "t = tf_idf_vect.get_feature_names()\n",
    "tfidf_sent_vectors_test = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent_test:\n",
    "    sent_vec = np.zeros(50)\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",
    "            tfidf = tfidf_test[row,t.index(word)]\n",
    "            sent_vec += (vec * tfidf)\n",
    "            cnt_words += tfidf\n",
    "    if cnt_words != 0:\n",
    "        sent_vec /= cnt_words\n",
    "    tfidf_sent_vectors_test.append(sent_vec)\n",
    "    row += 1\n",
    "print(len(tfidf_sent_vectors_test))\n",
    "print(len(tfidf_sent_vectors_test[0]))\n",
    "#return tfidf_sent_vectors_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T11:55:23.678024Z",
     "start_time": "2019-02-25T11:55:23.672030Z"
    }
   },
   "outputs": [],
   "source": [
    "train_tfidfw2v = tfidf_sent_vectors_train\n",
    "cv_tfidfw2v = tfidf_sent_vectors_CV\n",
    "test_tfidfw2v = tfidf_sent_vectors_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:26:20.682773Z",
     "start_time": "2019-02-25T12:26:20.019745Z"
    }
   },
   "outputs": [],
   "source": [
    "normalizer = preprocessing.Normalizer()\n",
    "train_tfidfw2v = normalizer.fit_transform(train_tfidfw2v)\n",
    "cv_tfidfw2v = normalizer.transform(cv_tfidfw2v)\n",
    "test_tfidfw2v = normalizer.transform(test_tfidfw2v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:27:49.006485Z",
     "start_time": "2019-02-25T12:26:25.696847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.8878792526339879  and for train data auc score is -->  0.8865923071218754\n",
      "5000  for CV data auc score is -->  0.8878794608997429  and for train data auc score is -->  0.8865927469272157\n",
      "1000  for CV data auc score is -->  0.8878768413793583  and for train data auc score is -->  0.8865885750431397\n",
      "500  for CV data auc score is -->  0.8878792688324355  and for train data auc score is -->  0.8865926170365193\n",
      "100  for CV data auc score is -->  0.8878796460248582  and for train data auc score is -->  0.8865925395578583\n",
      "50  for CV data auc score is -->  0.887879537263853  and for train data auc score is -->  0.8865931457144411\n",
      "10  for CV data auc score is -->  0.8878812473571075  and for train data auc score is -->  0.8865953339169178\n",
      "5  for CV data auc score is -->  0.8878823511656089  and for train data auc score is -->  0.8865974258407642\n",
      "1  for CV data auc score is -->  0.8878921026310684  and for train data auc score is -->  0.8866155102717495\n",
      "0.5  for CV data auc score is -->  0.8879130472238248  and for train data auc score is -->  0.8866335451391796\n",
      "0.1  for CV data auc score is -->  0.8879537886336215  and for train data auc score is -->  0.8866543105597129\n",
      "0.05  for CV data auc score is -->  0.8876702186098059  and for train data auc score is -->  0.8863001333822493\n",
      "0.01  for CV data auc score is -->  0.8798587741122224  and for train data auc score is -->  0.878409297506769\n",
      "0.005  for CV data auc score is -->  0.8682401159109076  and for train data auc score is -->  0.8673565637435425\n",
      "0.001  for CV data auc score is -->  0.725619701464647  and for train data auc score is -->  0.7226937272223276\n",
      "0.0005  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "0.0001  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "5e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "1e-05  for CV data auc score is -->  0.5  and for train data auc score is -->  0.5\n",
      "\n",
      " Best C Value 0.1 with highest roc_auc Score is 0.8879537886336215\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "logistic_l1(train_tfidfw2v,cv_tfidfw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:30:21.885955Z",
     "start_time": "2019-02-25T12:29:48.692067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000  for CV data auc score is -->  0.8878793822215688  and for train data auc score is -->  0.8865907484335194\n",
      "5000  for CV data auc score is -->  0.8878795280075973  and for train data auc score is -->  0.8865907866031537\n",
      "1000  for CV data auc score is -->  0.8878795303216612  and for train data auc score is -->  0.8865908031243389\n",
      "500  for CV data auc score is -->  0.8878796251982828  and for train data auc score is -->  0.8865908674999909\n",
      "100  for CV data auc score is -->  0.8878799861922582  and for train data auc score is -->  0.8865913238265164\n",
      "50  for CV data auc score is -->  0.8878805739644999  and for train data auc score is -->  0.8865919772678703\n",
      "10  for CV data auc score is -->  0.8878840589447994  and for train data auc score is -->  0.8865967171388947\n",
      "5  for CV data auc score is -->  0.887887335659344  and for train data auc score is -->  0.886601549870373\n",
      "1  for CV data auc score is -->  0.8879141394620065  and for train data auc score is -->  0.886639702983639\n",
      "0.5  for CV data auc score is -->  0.8879368311730405  and for train data auc score is -->  0.8866740003941294\n",
      "0.1  for CV data auc score is -->  0.8877834966679886  and for train data auc score is -->  0.8866234330345302\n",
      "0.05  for CV data auc score is -->  0.8872799818084953  and for train data auc score is -->  0.8862182418557747\n",
      "0.01  for CV data auc score is -->  0.8828709101557823  and for train data auc score is -->  0.8821933279633938\n",
      "0.005  for CV data auc score is -->  0.8790400559748002  and for train data auc score is -->  0.8785477040194648\n",
      "0.001  for CV data auc score is -->  0.8629671532785232  and for train data auc score is -->  0.8626446910005842\n",
      "0.0005  for CV data auc score is -->  0.8476269896738322  and for train data auc score is -->  0.8470103494810363\n",
      "0.0001  for CV data auc score is -->  0.7600801470030409  and for train data auc score is -->  0.7573998792372014\n",
      "5e-05  for CV data auc score is -->  0.7107644175625087  and for train data auc score is -->  0.7073623905034534\n",
      "1e-05  for CV data auc score is -->  0.6420809656448142  and for train data auc score is -->  0.6382031215259224\n",
      "\n",
      " Best C Value 0.5 with highest roc_auc Score is 0.8879368311730405\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "logistic_l2(train_tfidfw2v,cv_tfidfw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:32:51.402038Z",
     "start_time": "2019-02-25T12:32:44.888318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8872233092123054\n",
      "macro f1 score for data : 0.7276069252646076\n",
      "micro f1 scoore for data: 0.8814757042919268\n",
      "hamming loss for data: 0.11852429570807337\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.41      0.52     13407\n",
      "           1       0.90      0.97      0.93     71217\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     84624\n",
      "   macro avg       0.81      0.69      0.73     84624\n",
      "weighted avg       0.87      0.88      0.87     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FkJAQ2rZtS3JyMoDjnFlfX1+qVavmUm1x1MGJiFOlPYrar18/Jk2axIABAzAMg9mzZ2OxWBg3bhyFhYWEhoZy66230qpVK7Zv305kZKTjnFmAGTNmuFxb5PjL40Tf4uhEX/ejE33dz7U40bfN9C3FFwG7plfNy97UwYmIU+5+qZYCTkScqsoX0rtCASciTrl5vingRMQ5dXAiYlpunm8KOBFxTh2ciJiWjqKKiGm5eQOngBMR57SJKiKm5eb5poATEefUwYmIaSngRMS0dBRVREzLzRs4BZyIOKdNVBExLTfPNwWciDhncfOEU8CJiFM6yCAipuXm+aaAExHndJBBREzLzfPNecBFRERckd52ux3DMHjzzTfLfWAiUvkM3DvhnAZcQkJCRY5DRKog0+6Du+GGG4CLT6WeN28ep0+fJiwsjJtvvtnxmYiYm7sfRbUUVxAbG8tf/vIX8vPzCQkJ4dlnn62IcYlIFWAxDJdezvz888/cfffdHDhwgPT0dAYMGMDAgQOZNm0aNtvFh4kvXryYfv36ERkZyddffw1Qotoix19cwfnz5+nQoQOGYRAcHEz16tVd+mFExP0Zhmuvq7lw4QJxcXF4eXkBEB8fT3R0NImJidjtdrZs2UJaWho7duwgKSmJhIQEZsyYUeLaohQbcJ6enmzbtg2bzcauXbvw9PQswc8jIu7MMAyXXlczd+5cIiMj+cMf/gBAWloa7du3B6Bz58589tln7Ny5k9DQUAzDoEGDBhQWFpKZmVmi2qIUG3DPPPMM69at4/Tp07zyyitMnz69JL+PiLix0nZw69atIzAwkE6dOjmmXToLA8DHx4esrCyys7Px9fV11FyaXpLaohR7Hlz9+vUZNmwYP/74I82aNePGG28s7isiYhLWUp4It3btWgzD4PPPP2fv3r3ExMRc1m3l5OTg7++Pr68vOTk5l0338/PDYrG4XFuUYju4JUuWMGPGDL788kumTJnCihUrSrKeIuLGSruJ+vrrr7Nq1SpWrlxJixYtmDt3Lp07dyY1NRWA5ORkQkJCaNu2LSkpKdhsNjIyMrDZbAQGBtKyZUuXa4tSbAeXnJxMYmIiFouFgoICBg4cyGOPPVaKn0pE3M21PEskJiaG2NhYEhISCA4OJiwsDKvVSkhICBEREdhsNuLi4kpcWxTDbrfbiyoYMWIE8+bNw8fHh3PnzjFmzBiWLl1aphXNzCko0/el4uVdsFX2EKSEGgSU/YDgoFW7XapbNejWMi+rPBR7qdbPP//sOMH3wIEDBAQEVOT4RKQSmfZaVF2qJSKmvZvIpcux0tPT2bhxIxcuXADg5MmTzJw5s2JGJyKVymr2S7ViYmIA+PLLLzly5Ahnzpwp90GJSNVguPiqqooNOC8vL4YNG0a9evWYM2cOP/30U0WMS0SqgLJei1rZij1NxG63c+rUKXJzc8nNzeXs2bMVMS4RqQKqcHa5pNgObuTIkWzatImHHnqIrl270rlz54oYl4hUAWW5FrUqKLaDu/3227n99tsB6Nq1a7kPSESqjiqcXS5xGnChoaFOv5SSklIugxGRqsXdj6I6DbjyDLEa1fWsG3dzQ+jIyh6ClNC5rxaXeR5VefPTFUoaEXGq2J30VZwCTkSccvcOzqWAzs7O5ttvvyU3N7e8xyMiVYjFcO1VVRXbwW3cuJGlS5dSWFhI9+7dMQyDESNGVMTYRKSSuftBhmI7uBUrVrBmzRoCAgIYMWIEmzdvrohxiUgVYPoOzmKx4Onp6Tihz9vbuyLGJSJVgJvvgis+4EJCQhg7diwnTpwgLi6OVq1aVcS4RKQKqMrXmbqi2IAbO3YsycnJtGzZkiZNmtClS5eKGJeIVAHufppIseNfv349mZmZ1KlTh7Nnz7J+/fqKGJeIVAFlefBzVVBsB3fgwAHg4l1F9u7dS0BAAL179y73gYlI5XP3o6jFBtzTTz/t+LvdbmfYsGHlOiARqTrcPN+KD7j8/HzH30+dOsWRI0fKdUAiUnWY/iDDpZN77XY7Xl5eDBkypCLGJSJVgJvnW/EB99RTTxEeHl4RYxGRKsbdN1GLPYqalJRUEeMQkSrIcPFPVeXSPrjevXsTFBSExXIxD//xj3+U+8BEpPJ5lPJEuMLCQqZOncoPP/yA1WolPj4eu93OxIkTMQyDZs2aMW3aNCwWC4sXL2br1q14eHgwefJkWrduTXp6usu1RY6/uIGOGzeudGsoIm6vtLdL+uSTTwB48803SU1NdQRcdHQ0d9xxB3FxcWzZsoUGDRqwY8cOkpKSOHbsGKNGjWLt2rXEx8e7XFsUpwEXHR3NwoULad++falWUETcX2n3wd13333cc889AGRkZFCnTh22bt3qyJPOnTuzfft2goKCCA0NxTAMGjRoQGFhIZmZmaSlpblcGxgY6Hz8zj7IzMws3ZqJiGmU5UoGDw8PYmJieOaZZwgLC8Nutzs6Qh8fH7KyssjOzsbX19fxnUvTS1JbFKcd3OHDh0lISLjqZ2PHji1ypiJiDmU9D27u3LmMGzeOhx9+mPPnzzum5+Tk4O/vj6+vLzk5OZdN9/Pzc+zvd6W2yPE7+8DLy4ugoKCrvkTk+mC1uPb6vfXr1/PSSy8B4O3tjWEY3HLLLaSmpgKQnJxMSEgIbdu2JSUlBZvNRkZGBjabjcDAQFq2bOlybVGcdnB16tShT58+pf1dRMQELKU8BeT+++9n0qRJPPLIIxQUFDB58mSaNGlCbGwsCQkJBAcHExYWhtVqJSQkhIiICGw2G3FxcQDExMS4XFsUw26326/2wdy5c4mJiSnVyhUnr6BcZivlqNbtemygu7kWjw1c8tmPLtWNuOumMi+rPDjt4Mor3ETEfbj7lQx6bKCIOGX6i+1F5Prl5vmmgBMR50x/w0sRuX65+zMZFHAi4lRpr0WtKhRwIuKUe8ebAk5EiqCjqCJiWu4dbwo4ESmCRUdRRcSsdBRVRExLR1FFxLTcO94UcCJSBHVwImJaVgWciJiVe8ebAk5EiuDmDZwCTkScK+0ty6sKBZyIOKUOTkRMy1AHJyJmpaOoImJabp5vCjgRcU4BJyKmpX1wImJapb1b0oULF5g8eTJHjx4lPz+f4cOH07RpUyZOnIhhGDRr1oxp06ZhsVhYvHgxW7duxcPDg8mTJ9O6dWvS09Ndri2KAk5EnCrtHX03bNhAQEAA8+bN4/Tp0/Tp04c//elPREdHc8cddxAXF8eWLVto0KABO3bsICkpiWPHjjFq1CjWrl1LfHy8y7VFUcCJiFOl3UTt3r07YWFhjvdWq5W0tDTat28PQOfOndm+fTtBQUGEhoZiGAYNGjSgsLCQzMzMEtUGBgY6HYe738+uQly4cIHHH42i6z2dCO3Qnvfe3eD4bPzTY1j+0tLL6k+dOsUtLZqRl5cHQGZmJr179eDeu0Pp3zeckydPVuj4ryfjHr+frf96mu2vT+DR3h1o86eGbFs5js0vR5MQ099xd4zJQx9g28pxfLJiLCF/bgzgtHbu033ZtnIcn/7raTrcGlxp61YZLIZrr9/z8fHB19eX7OxsRo8eTXR0NHa73fGb+vj4kJWVRXZ2Nr6+vpd9Lysrq0S1RY7/GvwGV7V7926ioqLKa/YV6o3XVxFYuzZbtm7jnfc+YMxTIzl16hThPR/g/fc2XFa76aMP6fXA/Zw8ccIx7bk5s7mrYygff5rC8L+PYtrUyRW9CteFTu2acWfrILo8lsD9f1tIw3q1WBw7kPHz13LfkIWczTpHxAMhtPlTQzq1a0qnqPkMnvgqCyc9DHDV2lbNb+DO1kF0iprP47GvMX9Cv0pey4pluPjnao4dO8bgwYMJDw+nV69eWCy/xk1OTg7+/v74+vqSk5Nz2XQ/P78S1RalXAJu+fLlTJ06lfPnz5fH7Ctc3379mTbjGcd7Dw8PcrKzmRI7nYGPXB7iFouF9z/cTK3ftM379v6P+8MeAKDDXR357LOUihn4dabbXS1I+z6D1QlPsPb5J/lg2zfc8IcAvtj9AwCf7z7IXbc14a7bmrDli30AHD5+GqvVQp1avletzTh5hty8fKp7euDv40VBQWGlrV9lMAzXXr/3008/8fjjjzN+/Hj69bv4P4WWLVuSmpoKQHJyMiEhIbRt25aUlBRsNhsZGRnYbDYCAwNLVFuUctkH16hRIxYtWsSECRPKY/YV7lJbnJWVxcCIfkybMYubgoK4KSiIjz784LLarvd1u+L7rW9tw/vvbaDNbbfx3rsbyM3NrZBxX29qB/jQ6I+B9B29lJtuqM1bC4eRnvEzoe2akrLze3p0vgUfL0/8fLzIPPtrJ5Cdex5/Xy9+PPrTFbUFBTZsdju7347F39ebvz+TWIlrWPFKe5LI0qVL+eWXX1iyZAlLliwBYMqUKcyaNYuEhASCg4MJCwvDarUSEhJCREQENpuNuLg4AGJiYoiNjXWptsjx2+12eynXoUhHjhxh7NixrFmz5orP8grKY4nl6/Dhw0T268PQJ0fw6F8fd0yfNXM69erV54lhT15Wf3PTm9j9zT68vLzIysri6ejRHDlymG5h3dmw/m0+Sd5e0atQJrVuH1nZQyjWM6Mf4qfT2Ty/8mMAUldPZOQzbzD1yQcptNnYmXaImr5e/JjxM16e1Uj412YAPn8jhp7DFxNY04f54/tdURvy58b8LW4lfjW82PLqGB4a8X9knDpbmavqknNfLS7zPL74/oxLdXc2DSjzssqDDjK44MSJE/TqcT+z4udeFm6uStmWzCNRg/n3h5u56aYgOtzVsRxGKZ99dZBud7UE4I91a+LjVZ27bmvCkzNep+/opdQO8GFL6j4+33WQ+zq0wDAMbqxfC4vF4OczOTzQ6ZYrak//kkv2uXxsNjtZuXmczy/Ap0b1Sl7TCmS4+KqidJqIC56bM5szp08T/+wzxD97cV/cO+99gLe3t0vfb978Zob8dTAADW64gaXLXi63sV7PPtj2DaFtm5CyajyGYRA9Zw2e1ay8vWg45/Ly+fQ/3/Fhyv8A2P7VAT7919NYLAZj4i9uZXx/6OQVtRaLQYc2TfhkxVisFgurP/gv36VfP0fB3f1KBm2iikvcYRNVLnctNlF3HHRtU7x9cM0yL6s8lFsH17Bhw6uGm4i4D/fu37SJKiJFcfOEU8CJiFOlvRa1qlDAiYhT7h1vCjgRKYqbJ5wCTkSccvfTRBRwIuKUm++CU8CJiHMKOBExLW2iiohpqYMTEdNy83xTwIlIEdw84RRwIuKU9sGJiGmV9rmoVYUCTkScU8CJiFlpE1VETEuniYiIabl5vingRKQIbp5wCjgRcUo3vBQR03LveFPAiUhR3Dzh9OBnEXHKcPGPM7t37yYqKgqA9PR0BgwYwMCBA5k2bRo2mw2AxYsX069fPyIjI/n6669LXFsUBZyIOGUYrr2uZvny5UydOpXz588DEB8fT3R0NImJidjtdrZs2UJaWho7duwgKSmJhIQEZsyYUeLaoijgRMSpsgRco0aNWLRokeN9Wloa7du3B6Bz58589tln7Ny5k9DQUAzDoEGDBhQWFpKZmVmi2qIo4ETEqbJsooaFheHh8etufrvdjvH/09DHx4esrCyys7Px9fV11FyaXpLaouggg4g4dS3PErFYfu2ncnJy8Pf3x9fXl5ycnMum+/n5lai2yGVeu+GLiNkYLr5c0bJlS1JTUwFITk4mJCSEtm3bkpKSgs1mIyMjA5vNRmBgYIlqi6IOTkScupYdXExMDLGxsSQkJBAcHExYWBhWq5WQkBAiIiKw2WzExcWVuLbI8dvtdvu1WwXX5BVU9BKlrGrdPrKyhyAldO6rxWWex5HT+S7VNazlWeZllQd1cCLilG54KSKm5eaXoirgRMQ53fBSRMzLvfNNAScizrl5vingRMQ57YMTEdMy3DzhFHAi4pR7x5sCTkSK4OYNnAJORJzTaSIiYlrq4ETEtBRwImJa2kQVEdNSBycipuXm+aaAE5EiuHnCKeBExCntgxMR09INL0XEvBRwImJW7r6JWikPnRERqQh6LqqImJYCTkRMSwEnIqalgLsGLj1lOyIigqioKNLT0yt7SOKi3bt3ExUVVdnDkHKio6jXwObNm8nPz2dcasxcAAAFAElEQVT16tXs2rWLOXPm8OKLL1b2sKQYy5cvZ8OGDXh7e1f2UKScqIO7Bnbu3EmnTp0AaNOmDd98800lj0hc0ahRIxYtWlTZw5BypIC7BrKzs/H19XW8t1qtFBQUVOKIxBVhYWF4eGgjxswUcNeAr68vOTk5jvc2m03/4YhUAQq4a6Bt27YkJycDsGvXLpo3b17JIxIR0EGGa6Jbt25s376dyMhI7HY7s2fPruwhiQi6VEtETEybqCJiWgo4ETEtBZyImJYCTkRMSwEnIqalgHMDqampdOjQgaioKKKionj44YdZuXJlqeY1f/581q1bx969e1m8eLHTuk2bNnHixAmX5pmcnMzEiROvGPOYMWOcfmfdunXMnz/fpfmXpFbkt3QenJu48847WbBgAQD5+fl0796d8PBw/P39SzW/Fi1a0KJFC6efv/baa0yfPp169eqVav4iVYECzg1lZ2djsViwWq1ERUVRq1YtfvnlF5YtW8b06dNJT0/HZrMRHR3NHXfcwYcffsiLL75IYGAgFy5cIDg4mNTUVN58800WLFhAUlISb7zxBjabja5du9KqVSv27t1LTEwMiYmJrF69mvfeew/DMOjRoweDBw/mwIEDTJ48GW9vb7y9valZs6bT8a5atYqPPvqIgoIC/Pz8HBe479q1i0cffZTs7GxGjRrFPffcw44dO1iwYAFWq5Ubb7yRmTNnVtTPKiakgHMTX3zxBVFRURiGQbVq1YiNjcXHxweAXr160a1bNxITE6lVqxazZ8/m9OnTDBo0iPfff5958+aRlJREQEAAQ4cOvWy+P//8s+O2QZ6ensyZM4fbb7+dFi1aMH36dA4dOsS///1vEhMTMQyDxx57jNDQUJ5//nlGjx5Nx44dWbZsGQcPHrzquG02G2fOnGHFihVYLBaGDBnCnj17APD29mbZsmVkZmbSv39/OnXqRGxsLImJidSuXZuFCxfy9ttv67peKTX9m+MmfruJ+ntBQUEA7N+/n507d/L1118DUFBQwE8//YSvry+1atUC4Lbbbrvsu4cPH6ZZs2Z4eXkBMHny5Ms+379/PxkZGTz22GMAnD17lkOHDvHdd9/RunVr4OK1uM4CzmKxUK1aNcaOHUuNGjU4fvy4404r7dq1wzAMateujZ+fH6dPn+bkyZNER0cDkJeXR8eOHWnUqFGJfiuRSxRwJmAYFx/tFhwcTP369XnyySfJy8vjxRdfxN/fn6ysLDIzMwkMDGTPnj3Ur1/f8d1GjRpx8OBB8vPz8fT0ZPTo0UyZMgXDMLDb7QQHB9O0aVP++c9/YhgGK1asoHnz5gQHB/PVV1/RuXPnIu9/t2/fPjZv3kxSUhLnzp2jb9++XLo68FInd+rUKXJzc6lVqxb169dnyZIl+Pn5sWXLFmrUqMGxY8fK8dcTM1PAmUhkZCRTp05l0KBBZGdnM3DgQDw9PYmPj2fIkCHUrFnzis29wMBAnnjiCQYNGoRhGHTp0oV69epx2223MWHCBF555RU6dOjAgAEDyM/Pp3Xr1tSrV49p06YxZswYXn75ZQIDA6levfpVx9S4cWO8vb3p27cvnp6e1K1bl5MnTwIXO7TBgweTm5vLzJkzsVqtTJkyhaFDh2K32/Hx8eG5555TwEmp6WJ7ETEtnQcnIqalgBMR01LAiYhpKeBExLQUcCJiWgo4ETEtBZyImJYCTkRM6/8B83rkGnngL70AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l1(train_tfidfw2v,Y_tr,test_tfidfw2v,Y_test,optimal_C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:33:05.758109Z",
     "start_time": "2019-02-25T12:33:02.975863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8873407204587216\n",
      "macro f1 score for data : 0.7298904011937353\n",
      "micro f1 scoore for data: 0.8818892985441482\n",
      "hamming loss for data: 0.11811070145585177\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.42      0.53     13407\n",
      "           1       0.90      0.97      0.93     71217\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     84624\n",
      "   macro avg       0.81      0.69      0.73     84624\n",
      "weighted avg       0.87      0.88      0.87     84624\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testing_l2(train_tfidfw2v,Y_tr,test_tfidfw2v,Y_test,optimal_C=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T12:35:08.152645Z",
     "start_time": "2019-02-25T12:35:07.732236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "****Final Conclusion:****"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+----------------+-------------------------+----------------+\n",
      "| Vectorizer | Regularization | Best Hyper Parameter(C) | Test Auc Score |\n",
      "+------------+----------------+-------------------------+----------------+\n",
      "|    BoW     |       L1       |            5            |     94.84      |\n",
      "|    BoW     |       L2       |            5            |     95.02      |\n",
      "|   Tf-Idf   |       L1       |            5            |     95.88      |\n",
      "|   Tf-Idf   |       L2       |            50           |     96.23      |\n",
      "|  Avg-W2V   |       L1       |           0.5           |     91.31      |\n",
      "|  Avg-W2V   |       L2       |           0.5           |     91.32      |\n",
      "| TfIdf-W2V  |       L1       |           0.1           |     88.72      |\n",
      "| TfIdf-W2V  |       L2       |           0.5           |     88.73      |\n",
      "+------------+----------------+-------------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Vectorizer\", \"Regularization\", \"Best Hyper Parameter(C)\",\"Test Auc Score\"]\n",
    "x.add_row([\"BoW\",\"L1\",5,94.84])\n",
    "x.add_row([\"BoW\",\"L2\",5,95.02])\n",
    "x.add_row([\"Tf-Idf\",\"L1\",5,95.88])\n",
    "x.add_row([\"Tf-Idf\",\"L2\",50,96.23])\n",
    "x.add_row([\"Avg-W2V\",\"L1\",0.5,91.31])\n",
    "x.add_row([\"Avg-W2V\",\"L2\",0.5,91.32])\n",
    "x.add_row([\"TfIdf-W2V\",\"L1\",0.1,88.72])\n",
    "x.add_row([\"TfIdf-W2V\",\"L2\",0.5,88.73])\n",
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "printmd('****Final Conclusion:****')\n",
    "print(x)"
   ]
  }
 ],
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