6 . Amazon Fine Food Reviews Analysis_SVM.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...  "
      ]
     },
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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(100000) #Sampled amazon fine foood reviews filtered data to 100k 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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       "  <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": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:25.278748Z",
     "start_time": "2019-03-15T09:39:25.063871Z"
    },
    "colab": {},
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    "id": "0wUj2BDzMQvc"
   },
   "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": "2019-03-15T09:39:25.777462Z",
     "start_time": "2019-03-15T09:39:25.448650Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87775, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "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": "2019-03-15T09:39:25.994338Z",
     "start_time": "2019-03-15T09:39:25.987341Z"
    },
    "colab": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.775"
      ]
     },
     "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": "2019-03-15T09:39:26.491053Z",
     "start_time": "2019-03-15T09:39:26.444080Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "kThrWN9FMQvq"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:26.738910Z",
     "start_time": "2019-03-15T09:39:26.723919Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "lvw544BEMQvs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87773, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    73592\n",
       "0    14181\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 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": "2019-03-15T09:39:26.964782Z",
     "start_time": "2019-03-15T09:39:26.954787Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "I2NRwIj6MQvz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "I wish I'd read the reviews before making this purchase. It's basically a cardsotck box that is sticky all over the OUTSIDE. Those pink-ish things that look like entrances \"into\" the trap? They're just pictures. There *is no* inside of the trap. All the flies will be stuck to the OUTSIDE. It's basically fly paper, just horribly, horribly HORRIBLY overpriced.<br /><br />Do yourself a favor and just get fly paper or fly strips. Same yuck factor, but much cheaper.\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": "2019-03-15T09:39:27.218635Z",
     "start_time": "2019-03-15T09:39:27.203645Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "hSYsO2neMQv2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'should', 'her', 'here', 'there', 'other', 'where', 'don', 'haven', 'through', 'against', 'it', 'are', 'about', 'himself', 'y', 'just', 'will', 'i', \"weren't\", \"haven't\", 'very', 'they', 'm', \"you'll\", 'which', 'of', 'between', \"mightn't\", 'any', 'when', 'a', \"you've\", 'so', 'its', 'own', 'same', 'after', 'as', 'aren', 'further', 'you', 'll', 'now', 'mustn', 'do', 'for', 'by', 'themselves', 'while', 'on', 'does', 'their', 'some', 'was', 'be', 'hadn', 'in', 'mightn', 't', 'wasn', 've', 'but', 'or', \"don't\", 'ain', 'above', 'few', 'how', 'before', 'then', \"wouldn't\", 'most', 'ma', 'nor', \"isn't\", 'won', \"won't\", 'shan', 'hers', 'these', 'during', 'd', 'weren', \"doesn't\", \"needn't\", 'our', \"you'd\", \"should've\", 'yourself', 'into', 'being', 'again', 'itself', 'below', 'am', 'didn', 'doesn', 'needn', 'because', \"didn't\", \"it's\", 'than', 'each', 'until', 'hasn', 'me', 'this', \"mustn't\", 'to', \"shan't\", 'can', 'once', 'that', 'and', 'such', 'out', 'my', 'more', 'he', \"you're\", \"shouldn't\", \"aren't\", 'both', 'doing', 'isn', 'o', 'ourselves', 'no', 'ours', \"couldn't\", 'yourselves', 'has', \"hadn't\", 'only', 're', 'we', 'were', 'is', 'if', 'too', 'what', 'myself', 'from', 's', \"wasn't\", 'been', 'at', 'did', \"that'll\", 'down', 'under', 'herself', 'have', 'wouldn', 'with', 'your', 'yours', 'who', 'over', 'all', 'his', 'up', 'off', \"hasn't\", 'not', 'shouldn', 'those', 'whom', 'theirs', 'him', 'couldn', 'them', 'why', 'she', 'an', 'had', 'the', \"she's\", 'having'}\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": "2019-03-15T09:39:27.471491Z",
     "start_time": "2019-03-15T09:39:27.458499Z"
    },
    "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": "2019-03-15T09:39:28.866796Z",
     "start_time": "2019-03-15T09:39:27.764323Z"
    },
    "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": "2019-03-15T09:39:29.124773Z",
     "start_time": "2019-03-15T09:39:29.071803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87773, 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": "2019-03-15T09:39:29.462759Z",
     "start_time": "2019-03-15T09:39:29.389706Z"
    }
   },
   "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": "2019-03-15T09:39:30.531816Z",
     "start_time": "2019-03-15T09:39:30.507834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, Y_train (58807,) (58807,)\n",
      "X_test, Y_test (28966,) (28966,)\n",
      "X_tr, Y_tr (39400,) (39400,)\n",
      "X_cv, Y_cv (19407,) (19407,)\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": [
    "# SVM with SGD Classifier and simple CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:32.598794Z",
     "start_time": "2019-03-15T09:39:32.587820Z"
    }
   },
   "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": "2019-03-15T09:39:34.132417Z",
     "start_time": "2019-03-15T09:39:34.043466Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "# SVM with penality = 'l1'\n",
    "def Sgd_Classifier(X_train,X_cv,Y_train,Y_cv):\n",
    "    best_alpha=0\n",
    "    max_roc_auc=-1\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    alpha = [10 ** x for x in range(-5, 3)]\n",
    "    for i in alpha:\n",
    "        clf = SGDClassifier(alpha=i, penalty='l2', loss='hinge', random_state=42)\n",
    "        clf.fit(X_train,Y_train)\n",
    "        sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "        sig_clf.fit(X_train, Y_train)\n",
    "        probs = sig_clf.predict_proba(X_cv)\n",
    "        prob = sig_clf.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_alpha=i\n",
    "    print(f\"\\n Best C Value {best_alpha} with highest roc_auc Score is {max_roc_auc}\")\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.xscale('log')\n",
    "    plt.plot(alpha, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(alpha,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(r'Auc Score v/s $\\alpha$')\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": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:35.748573Z",
     "start_time": "2019-03-15T09:39:35.518960Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing(X_train,Y_train,X_test,Y_test,optimal_alpha):\n",
    "    clf = SGDClassifier(alpha=optimal_alpha, penalty='l2', loss='hinge', random_state=42)\n",
    "    clf.fit(X_train,Y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, Y_train)\n",
    "    prob = sig_clf.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": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:37.012999Z",
     "start_time": "2019-03-15T09:39:37.006999Z"
    }
   },
   "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": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:44.864915Z",
     "start_time": "2019-03-15T09:39:40.960195Z"
    },
    "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  (39400, 23389)\n",
      "CV Data Size:  (19407, 23389)\n",
      "Test Data Size:  (28966, 23389)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:46.402471Z",
     "start_time": "2019-03-15T09:39:46.256559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (39400, 23389)\n",
      "CV Data Size:  (19407, 23389)\n",
      "Test Data Size:  (28966, 23389)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = preprocessing.Normalizer()\n",
    "bow_train = normalizer.fit_transform(bow_train)\n",
    "bow_cv = normalizer.transform(bow_cv)\n",
    "bow_test = normalizer.transform(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": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:39:55.430354Z",
     "start_time": "2019-03-15T09:39:50.023530Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-05  for CV data auc score is -->  0.9384973497565663  and for train data auc score is -->  0.9677565976063742\n",
      "0.0001  for CV data auc score is -->  0.9374923483391848  and for train data auc score is -->  0.9523590622788936\n",
      "0.001  for CV data auc score is -->  0.9275878306706952  and for train data auc score is -->  0.9371385481947502\n",
      "0.01  for CV data auc score is -->  0.9132040277622846  and for train data auc score is -->  0.9206019720818753\n",
      "0.1  for CV data auc score is -->  0.6537251843295586  and for train data auc score is -->  0.649733638047308\n",
      "1  for CV data auc score is -->  0.6327270679075652  and for train data auc score is -->  0.6283983104764067\n",
      "10  for CV data auc score is -->  0.6327270679075652  and for train data auc score is -->  0.6283983104764067\n",
      "100  for CV data auc score is -->  0.6327270679075652  and for train data auc score is -->  0.6283983104764067\n",
      "\n",
      " Best C Value 1e-05 with highest roc_auc Score is 0.9384973497565663\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 alpha using l2 regularization \n",
    "Sgd_Classifier(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": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:12.206906Z",
     "start_time": "2019-03-15T09:40:11.117722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9236407263341346\n",
      "macro f1 score for data : 0.8005466141461386\n",
      "micro f1 scoore for data: 0.9044742111441\n",
      "hamming loss for data: 0.09552578885590002\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.57      0.66      4649\n",
      "           1       0.92      0.97      0.94     24317\n",
      "\n",
      "   micro avg       0.90      0.90      0.90     28966\n",
      "   macro avg       0.85      0.77      0.80     28966\n",
      "weighted avg       0.90      0.90      0.90     28966\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(bow_train,Y_tr,bow_test,Y_test,optimal_alpha=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:13.253837Z",
     "start_time": "2019-03-15T09:40:13.007002Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-1.4038\tdisappoint     \t\t\t\t0.7243\tgreat          \n",
      "\t-1.1305\treturn         \t\t\t\t0.6529\tbest           \n",
      "\t-0.9896\twast           \t\t\t\t0.6205\tdelici         \n",
      "\t-0.9153\tmoney          \t\t\t\t0.5369\tlove           \n",
      "\t-0.8590\thorribl        \t\t\t\t0.5361\tperfect        \n",
      "\t-0.8034\tbad            \t\t\t\t0.5050\texcel          \n",
      "\t-0.7940\taw             \t\t\t\t0.4905\tgood           \n",
      "\t-0.7759\tworst          \t\t\t\t0.4614\tnice           \n",
      "\t-0.7423\tterribl        \t\t\t\t0.4489\tamaz           \n",
      "\t-0.6507\tstale          \t\t\t\t0.4138\twonder         \n"
     ]
    }
   ],
   "source": [
    "#top 10 important feature for l2 regularization\n",
    "clf = clf = SGDClassifier(alpha=0.001, penalty='l2', loss='hinge', random_state=42)\n",
    "clf.fit(bow_train,Y_tr)\n",
    "imp_feature(count_vect,clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:30.096132Z",
     "start_time": "2019-03-15T09:40:14.871075Z"
    }
   },
   "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  (39400, 642970)\n",
      "CV Data Size:  (19407, 642970)\n",
      "Test Data Size:  (28966, 642970)\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": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:31.077751Z",
     "start_time": "2019-03-15T09:40:30.959146Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (39400, 642970)\n",
      "CV Data Size:  (19407, 642970)\n",
      "Test Data Size:  (28966, 642970)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = preprocessing.Normalizer()\n",
    "bow_train = normalizer.fit_transform(tfidf_train)\n",
    "bow_cv = normalizer.transform(tfidf_cv)\n",
    "bow_test = normalizer.transform(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": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:45.822278Z",
     "start_time": "2019-03-15T09:40:33.487932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-05  for CV data auc score is -->  0.9530388238572687  and for train data auc score is -->  0.999998310731674\n",
      "0.0001  for CV data auc score is -->  0.9461727402161696  and for train data auc score is -->  0.9889607018752229\n",
      "0.001  for CV data auc score is -->  0.9431398437721779  and for train data auc score is -->  0.984472456705179\n",
      "0.01  for CV data auc score is -->  0.9432064347118918  and for train data auc score is -->  0.9844321723480141\n",
      "0.1  for CV data auc score is -->  0.6432032760903141  and for train data auc score is -->  0.6564944277419603\n",
      "1  for CV data auc score is -->  0.6432032760903141  and for train data auc score is -->  0.6564944277419603\n",
      "10  for CV data auc score is -->  0.6432032760903141  and for train data auc score is -->  0.6564944277419603\n",
      "100  for CV data auc score is -->  0.6432032760903141  and for train data auc score is -->  0.6564944277419603\n",
      "\n",
      " Best C Value 1e-05 with highest roc_auc Score is 0.9530388238572687\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",
    "Sgd_Classifier(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Over the unseen or Test data in TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:49.598862Z",
     "start_time": "2019-03-15T09:40:47.753411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9408478788711514\n",
      "macro f1 score for data : 0.8371454017399507\n",
      "micro f1 scoore for data: 0.9183180280328661\n",
      "hamming loss for data: 0.08168197196713388\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.66      0.72      4649\n",
      "           1       0.94      0.97      0.95     24317\n",
      "\n",
      "   micro avg       0.92      0.92      0.92     28966\n",
      "   macro avg       0.87      0.81      0.84     28966\n",
      "weighted avg       0.91      0.92      0.92     28966\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(tfidf_train,Y_tr,tfidf_test,Y_test,optimal_alpha=0.001 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:53.530944Z",
     "start_time": "2019-03-15T09:40:50.622616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tNegative\t\t\t\t\t\tPositive\n",
      "________________________________________________________________________________________________\n",
      "\t-0.6497\tdisappoint     \t\t\t\t0.4180\tgreat          \n",
      "\t-0.5858\treturn         \t\t\t\t0.3435\tlove           \n",
      "\t-0.5083\twast money     \t\t\t\t0.3263\tbest           \n",
      "\t-0.4843\twast           \t\t\t\t0.3048\tgood           \n",
      "\t-0.4825\thorribl        \t\t\t\t0.2900\tdelici         \n",
      "\t-0.4722\tworst          \t\t\t\t0.2430\tperfect        \n",
      "\t-0.4356\taw             \t\t\t\t0.2171\texcel          \n",
      "\t-0.4152\tmoney          \t\t\t\t0.2148\tnice           \n",
      "\t-0.4094\tterribl        \t\t\t\t0.2052\tamaz           \n",
      "\t-0.3658\tbad            \t\t\t\t0.1860\twonder         \n"
     ]
    }
   ],
   "source": [
    "#top 10 important feature for l2 regularization\n",
    "clf = clf = SGDClassifier(alpha=0.001, penalty='l2', loss='hinge', random_state=42)\n",
    "clf.fit(tfidf_train,Y_tr)\n",
    "imp_feature(tf_idf_vect,clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:40:59.248707Z",
     "start_time": "2019-03-15T09:40:54.484742Z"
    }
   },
   "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": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:41:04.463738Z",
     "start_time": "2019-03-15T09:41:01.994648Z"
    }
   },
   "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": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:41:09.699374Z",
     "start_time": "2019-03-15T09:41:06.208034Z"
    }
   },
   "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": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:41:26.059397Z",
     "start_time": "2019-03-15T09:41:12.445522Z"
    }
   },
   "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": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:41:28.693160Z",
     "start_time": "2019-03-15T09:41:28.676236Z"
    }
   },
   "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": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:42:10.515337Z",
     "start_time": "2019-03-15T09:41:30.887173Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39400\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "train_avgw2v = avg_w2v(list_of_sent_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:43:15.673989Z",
     "start_time": "2019-03-15T09:42:56.261114Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19407\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "cv_avgw2v = avg_w2v(list_of_sent_CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:44:51.732060Z",
     "start_time": "2019-03-15T09:44:22.729403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28966\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "test_avgw2v = avg_w2v(list_of_sent_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:46:33.455517Z",
     "start_time": "2019-03-15T09:46:33.090318Z"
    }
   },
   "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": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:48:30.341121Z",
     "start_time": "2019-03-15T09:48:25.889753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-05  for CV data auc score is -->  0.8879980276607289  and for train data auc score is -->  0.8896426247746141\n",
      "0.0001  for CV data auc score is -->  0.8963869067538927  and for train data auc score is -->  0.8971240094130536\n",
      "0.001  for CV data auc score is -->  0.8958555786254879  and for train data auc score is -->  0.8954531588041361\n",
      "0.01  for CV data auc score is -->  0.8949378991008323  and for train data auc score is -->  0.8943654436213828\n",
      "0.1  for CV data auc score is -->  0.8949705248629498  and for train data auc score is -->  0.8943836314103604\n",
      "1  for CV data auc score is -->  0.8949490542327327  and for train data auc score is -->  0.8943629988747221\n",
      "10  for CV data auc score is -->  0.8880172392767796  and for train data auc score is -->  0.8881438073750603\n",
      "100  for CV data auc score is -->  0.6195776663064239  and for train data auc score is -->  0.6174849050736311\n",
      "\n",
      " Best C Value 0.0001 with highest roc_auc Score is 0.8963869067538927\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_alpha using l2 regularization\n",
    "Sgd_Classifier(train_avgw2v,cv_avgw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:48:33.259947Z",
     "start_time": "2019-03-15T09:48:32.305478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8843041628413222\n",
      "macro f1 score for data : 0.6797784321516265\n",
      "micro f1 scoore for data: 0.8738521024649589\n",
      "hamming loss for data: 0.12614789753504108\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.30      0.43      4649\n",
      "           1       0.88      0.98      0.93     24317\n",
      "\n",
      "   micro avg       0.87      0.87      0.87     28966\n",
      "   macro avg       0.83      0.64      0.68     28966\n",
      "weighted avg       0.86      0.87      0.85     28966\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(train_avgw2v,Y_tr,test_avgw2v,Y_test,optimal_alpha=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Weighted W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:48:50.814065Z",
     "start_time": "2019-03-15T09:48:46.639690Z"
    }
   },
   "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  (39400, 23389)\n",
      "CV Data Size:  (19407, 23389)\n",
      "Test Data Size:  (28966, 23389)\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": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T09:59:01.858291Z",
     "start_time": "2019-03-15T09:49:02.948095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39400\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": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:04:33.617659Z",
     "start_time": "2019-03-15T09:59:48.105603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19407\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": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:12:41.561360Z",
     "start_time": "2019-03-15T10:05:40.949391Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28966\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": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:14:19.469633Z",
     "start_time": "2019-03-15T10:14:19.465633Z"
    }
   },
   "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": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:17:11.326762Z",
     "start_time": "2019-03-15T10:17:11.082290Z"
    }
   },
   "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": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:17:24.216770Z",
     "start_time": "2019-03-15T10:17:19.765909Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e-05  for CV data auc score is -->  0.8575708260914936  and for train data auc score is -->  0.8607274563763347\n",
      "0.0001  for CV data auc score is -->  0.8679976958055504  and for train data auc score is -->  0.8711656940610432\n",
      "0.001  for CV data auc score is -->  0.8677412877020468  and for train data auc score is -->  0.8699069169982137\n",
      "0.01  for CV data auc score is -->  0.8676660605311841  and for train data auc score is -->  0.869778457526839\n",
      "0.1  for CV data auc score is -->  0.8676383326316396  and for train data auc score is -->  0.8697655205469086\n",
      "1  for CV data auc score is -->  0.867616522149734  and for train data auc score is -->  0.8697585288530033\n",
      "10  for CV data auc score is -->  0.8639473833619009  and for train data auc score is -->  0.867165037423675\n",
      "100  for CV data auc score is -->  0.6102945174725749  and for train data auc score is -->  0.6092823295550782\n",
      "\n",
      " Best C Value 0.0001 with highest roc_auc Score is 0.8679976958055504\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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4qqIk7IUQDsdUyJc4iy9n2+BbeXo+2nOOr/YnEejlRr+WtfFyc3HIoAcJeyGEA7l9ucbSs/gC+y+kMXNbIklpt+jXsjYv3h+Gl5tjx6VjVy+EcAq3z+RNhXxp++LNkZaTx0sbjhDo7caSmJZ20erAEiTshRB2S/WWPwHYJuSPXM6geW0ffD1cWdC/Bc1r+zjskk1JJOyFEHan3DN5tRupz5+u0LlSs7XM33WK74+lGBuXtW1QNWbzhUnYCyHsQnkbkin8r51wRSiKwrZjKczbdYrMXB3PdgihQ6j9NC6zNAl7IUSlMjWLhxJC3gI35H5r2wk2HrlC89o+vNYzgkaBXnd0PHsnYS+EqBQV2SOf03oM2R2mVPichRuXdQjzJyzQk0Gt66Gxw8ZlliZhL4SwqfKsx4PphmTmupCaw1s/JNIx1N8hGpdZmoS9EMLq/JaEoTZoAfPX4xVAGb6VVO8Wd3RunUFh9Z8X+XD3WVzUKh5pVuuOjueoJOyFEFZTkaUaAypSx14AwNfXE9KyK3z+U9eymPF9In9fuUnnfxqX1XSQxmWWJmEvhLC4CoX8HeysKU2WVs+VjFu89UhTejRxrMZlliZhL4SwCJcr+/FZ36dc6/GW2llT2JHLGRxISmfo3Q1oWbe6wzYuszQJeyHEHSnoIV/ezpN3urPmdjl5ej7cfZZV+y9Sy8ed/q3qOHTjMkuTsBdCVEjF1uMr3s6gLH+cT+WtbSe4mH6L6FZ1eKFzqMM3LrM0+W4IIcrF1Ey+pKUac+4CVVFpOXmM++Yvavq489GglrSpX/VaHViChL0Qwiy2ai9srkOXMoisk9+4bGF0C5rVqlqNyyxNwl4IUaby3CjEmks1BW5ka5m38xQ/HE9hXp9mdGkUKLN5M0jYCyFK5Lc4BDV6m7QXNoeiKGw9lsz8nafIztMzumMIHUL9rXa+qsYqYW8wGJg+fTrHjx/Hzc2NmTNnEhISYnz8k08+YcuWLahUKkaPHk2PHj2sUYYQogJs2V64PN78PpFv/7pKZJ38xmVhAVW7cZmlWSXst2/fjlarZc2aNSQkJDB79myWLFkCQEZGBitWrGDbtm3k5OTQt29fCXshKpnvV13RpJ4AbHOjEHMZDAp6g4JGraJzeACNa3ozMKquUzQuszSrhP3+/fvp3LkzAFFRURw5csT4mIeHB3Xr1iUnJ4ecnBynvqJNiMrmuWcW7gc+oPDbmqZDXkPq2HNWr+18ag6z1x+mfQNfht/TgK5O1rjM0qwS9pmZmXh7exs/12g06HQ6XFzyT1enTh0eeeQR9Ho9zz33XInH8PZ2x8WlYu+sazTq/J4aTkTGXPVZeryqt/yNIW9WD/l7X0TpPh0Aa74dqtMb+HTPWRbtPIm7i5qBbevLz9kCrBL23t7eZGVlGT83GAzGoI+Pjyc5OZkdO3YAMHLkSNq0aUPLli2LHCMzM7fC5/f19STtDponOSIZc9VnqfGWtSZfYjsDCi3XWPn7ffJaFjO2Hufo1UweaBTAW/0jcdMb5OdspqAgn1Ifs0rYt2nThl27dtG7d28SEhKIiIgwPlajRg2qVauGm5sbKpUKHx8fMjIyrFGGEOIfBcs15oS8LfbIlyZHqyclU8vsx+6iW+NA/HyqOVXQW5NVwr5Hjx7s3r2b2NhYFEVh1qxZLF++nODgYLp3786ePXsYOHAgarWaNm3a0LFjR2uUIYQgfyZvzpq8rd50vd2hS/mNy4bf04DIutWJG3UP7i5q0y8U5aJSFOX239zsQkrKzQq/1tl+vQcZszMoz3hNzeSh6Gxej+1DPlurZ8nus6z58yK1q7uzanjbYv1snO1nDA62jCOEqDxlzeRLWpO3dPdJc/x+NpVZPyRyKSOXmKi6jO3cUBqXWZl8d4WoIgpu/WdqJl/wf0v3kTdXWnYeEzbmNy5bOqgVrevXsHkNzkjCXogqoLTZ/O0z+cpYrimQkJROq3rV8fV05b3oSO6q7SNr8zYkYS+EAyvcv8bUTN6abYbLci1Ly7ydJ9mReI15fZrTpVEAUTKbtzkJeyEckLl75StzJq8oClv+vsq7P57mVp6eMZ0a0jHUr1JqERL2QjiU228cYm/bKAt7Y+txtvydTKu61XntoQgaBjjPVbD2SMJeCAfgcmU/6sX5N/MG+9tGWcCgKBgUcFGr6No4kGa1fRgQVRe19MCqdBL2Qtixgm6U5tw4xABkRG9EV7utbYq7zdkb2by1LZFOYQEMv6cBXRpJ4zJ7ImEvhB2yt7tDlUWnN/DlviSW/XqOaq4a+reqUyl1iLJJ2AthRxwp5AFOpmQxfetxjidn0j0ikAndGhHo5VZp9YjSSdgLYQccLeQL3NLpuZGt5T+PN6Ob9Ju3axL2QlQic3fXFHxsDyGfkJTOgYvpPN0+mBZ1qhM38h7c5OIouydhL0Ql8FveGnV2SrlCXnn1RqU2BcvS6lj881nWJVyibo1qDGxdFy83Fwl6ByFhL4SNFW5tUJ6ZvDXvDmXKr2dvMGvbCa7ezGVQ67qM6RSKp1vF7iQnKofZYZ+enk6NGnKJsxAVZe5Vrwpg8Awi9ekDNqutLGnZeUza9De1fNxZFtuKVvUkBxyRybDfu3cvM2bMQK/X06tXL+rWrUtMTIwtahOiSiirG+XtIV9Z/WtK8mdSGq3r1TA2LmtaSxqXOTKTP7mFCxfy5ZdfEhgYyOjRo1m1apUt6hLC4fktb03A4vpo/gn625uVFW5toAeuj02yi6C/lpnLxE1/89yaQ8Sfug5Aq3o1JOgdnMmZvVqtxtfXF5VKhbu7O15eXraoSwiHZu4NRCqztcHtFEXh27+usuDH0+Tq9LzQOZSOYQGVXZawEJNhHxwczPz580lLS2Pp0qXUrVvXFnUJ4ZDKsy5fGTf0Lsv0rcf57u9kWterzqsPRRDiL43LqhKTYf/GG2+wbt062rZti4eHBzNnzrRFXUI4lLL2yxffYaMhdew52xVXBr1BQSG/cVm3xkG0qFOd6FZ1pHFZFWQy7GfNmsXUqVONn0+cOJE5c+ZYtSghHIXf4gaoUUxe+WovF0QVduZ6Nm9+n0jncH+ebh9Ml0ayZFOVlRr2K1euZMmSJaSlpbFt2zbj18PDw21SmBD27PaLosAx1uUhv3HZF38k8fFv5/B01VC3uizNOoNSw37IkCEMGTKEDz/8kNGjR9uyJiHsmrlvvipU3k29S5OYnMn0rcc5kZJFjyZBTOgWjr+nNC5zBiaXcWJjY9m8eTM6nQ5FUUhOTua5556zRW1C2I3qm4bgeuEnwMyLouxoXb6wPL2BjFs65vVpJv3mnYzJsH/xxRdp2LAhiYmJuLu74+HhYYu6hLALnntm4X7gA/Nn8tjXkg3kXxyVkJTBiHuDaV6nOt+MvBtXjeyZdzZm/cRnzJhBaGgoy5cvJz093do1CWEX/BbXx7NQ0JtzUZQ9BX1mro7Z20/w3JpDbDpyhSytDkCC3kmZ1RsnNzeXnJwcVCoV2dmV13VPCFvwWxyCGr3J5RqwvzdfC+w+fYO3t58g+WYug9vWY3THhni4SuMyZ2Yy7IcMGcLnn39Ox44d6dKlC23bVs79LYWwtvJcEKVQufd7LUtadh6Tv/2bOjWq8ckTUUTWrV7ZJQk7YDLse/bsafz44Ycflpm9qHLKCnmw773yBRRFYf+FdNo2yG9ctjimJU1rekuveWFU6p+Ev/76i9GjRzNp0iRu3LgBwMaNG4mOjrZZcUJYk+9XXfMblUGpjcoKgt4e1+QLpGTmMmHj3zy/7hDxp/L/rrasW12CXhRR6sz+9ddfZ9y4cVy6dIl3332X7OxskpOTWbnSfnp5CFFR5bmBiD21HS5MURQ2Hr7CwvjT5OkV/tUljI5h/pVdlrBTpYa9h4cHnTp1AmDx4sX07duXefPmoZKeGcKBla+HjX0u2RSY+t/jbD2aTJv6NXjtoQga+Mm2aFG6UsNeo/nfO/c1a9bk5ZdfNvugBoOB6dOnc/z4cdzc3Jg5cyYhISHGx3/66ScWL14MQLNmzZg2bZr8IyKsrrQrXx1lJg9FG5c91CSI1vWq07elNC4TppUa9oqikJeXh6IoVKtWzfgxgJtb2ZdXb9++Ha1Wy5o1a0hISGD27NksWbIEgMzMTObOncsXX3yBv78/y5YtIzU1FX9/+fVTWE9pyzZF9srbccgDnLh6k4nrD3F/eABPtw+mc7g0LhPmKzXsL168SK9evYD84O/VqxeKoqBSqdixY0eZB92/fz+dO3cGICoqiiNHjhgfO3DgABEREfznP//hwoULxMTESNALq7m9YVlJs3l73StfIE9v4LO9F1j++3m83FyoV6NaZZckHFCpYb9z584KHzQzMxNvb2/j5xqNBp1Oh4uLC6mpqfz+++/ExcXh6enJkCFDiIqKIjQ0tMgxvL3dcXGp2EUgGo0aX1/nuvGCjLk41Vv+JmfzBkB59Qa+1inxjv19OYOJ6w9x/Gomj7Wsw6u97yLAy3kal8mfa8sx6wra8vL29iYrK8v4ucFgwMUl/1S+vr5ERkYSFBQEQLt27Th69GixsM/MzK3w+X19PUlLc67rAWTMRZlatikym7fj71tqWjbp2XnM79ucx9s2IC0tm7Q0XWWXZTPy57p8goJ8Sn3MKhtx27RpQ3x8PAAJCQlEREQYH2vRogWJiYncuHEDnU7HwYMHadSokTXKEE7Ic88sY9AX3jd/+555e1622Xc+jY9/ze+YWdC47H5Znxd3yKyZfWZmJhcvXqRBgwZ4epr+9aJHjx7s3r2b2NhYFEVh1qxZLF++nODgYLp378748eMZNWoUAL169Sryj4EQFVVj3aO4JicAJc/mDdhviwPIb1y2KP403xy6QrCfB4Pb1sfTTYOLNC4TFqBSCrbYlGLr1q18+OGH6PV6evXqhUqlYsyYMVYvLCXlZoVfK7/6OYfCYy7Xso0d+vnUdWZvP8G1LC2D29bnuQ4hVLutcZmz/4ydRaUt43z22WesXbsWX19fxowZw/bt2ytUhBDW4Le4vrHlAZSybONT366DPi07j1e3HKV6NVc+Hdyaf3UJKxb0Qtwpk8s4arUaNzc3VCoVKpVKbl4i7II5HSoVIKf1GLI7TLFpbeZQFIU/zqdxd7Avvp6uLIlpSURNb+k1L6zGZNi3a9eOcePGcfXqVaZOnUpkZKQt6hKiRAW3BzTVodKel22u3sxl9vYT/HL6hvH2gM3rSBtiYV0mw37cuHHEx8fTrFkzwsLC6Natmy3qEqIYv+Wt0WSnAGU3L7PXoDcoCnGHr7Dop9PoDAovPxBGpzDZZSNsw2TY9+/fn+joaGJjY4tcKCWELfktCUNj0AJlNy+z12UbgKnfHeP7Yym0C/bl1R6Nqe8rS6LCdkyG/dKlS9m4cSPDhw+ncePGxMTEyN2qhE2VtNOmeIdKDaljz9m2MDPoDAooCi4aNb3uqkm7Br70iawtjf+EzZncelng0qVLzJ07l927d7N3715r1yVbL8upKo65tHbERdsdqEgde8H2xZnhREomb36fyAONAhlxb/AdH68q/oxNkTGXT1lbL03O7OPi4vjmm28wGAxER0fz9ttvV6gIIcrDnH3zyqs37DIItDoDy38/z/K9F6ju7kKIvyzXiMpnMuyPHTvGtGnTCAsLs0U9wsl57pmF+4EPzLpAyh6blx27epOp/z3OmevZ9G5Wk5cfCMfXw7WyyxKi9LDftWsXXbt2pWHDhvzxxx/88ccfxscGDRpkk+KEczE1mwf73WlTQK9Abp6eBf1b0DFUWncL+1Fq2KelpQFw7do1mxUjnJPvV13RpJ4weYGUwdWb1GeP2bY4M/xxPpWEpAye6RBC89o+rB9xt/SzEXan1LDv168fkH8FbeFeOPPnz7d+VcJp+C0ORoMBcLzZ/M1bOhbGn2bj4fzGZUPaSeMyYb9KDft169bx9ddfc+rUKWO7YoPBQF5eHuPHj7dZgaLqMudWgfZ60++fTl5j9vaTpGZrGXZ3A565L1j62Qi7VmrY9+nTh/vuu4+PPvqI0aNHA/mz/IAAueJP3DlTe+ftdTYPkJqt5fXvjlHf14N3+jXnrlqlb3cTwl6UGvbHjx8nMjKShx56iDNnzhi/furUKTrwgnZ0AAAdqElEQVR16mST4kTVVFbQ2+vavKIo/H4ulfYhfvh5urFkYCuaBHnJko1wGKWG/a+//kpkZCTfffddscck7EVFFO5U6Ug9569k3OLt7SfYcyaVeX2a06VRAM1ry2xeOJZSw/7ZZ58F4O2330av16MoCgkJCbRs2dJmxYmqwZzdNvYY9AZFYcPBy7wXfwYFhQldw+kUJtsphWMyeVHV3LlzadCgAZcuXeKvv/4iKCiI2bNn26I2UQX4fdAQjZJ/g2xHa0f8+pZjbDueQvsQX6b0iKBujWqVXZIQFWZywXH//v3ExsZy4MABPvnkEy5fvmyLukQV4Le4folBb883/9YZFHT6/K2gvZvV4vWeEbwXHSlBLxyeyZm9wWDg0KFD1K9fH61Wy40bN2xRl3BgfkvCUBu0pTYwK/g4r0EXMh5fadviypCY/E/jssYBjLw3hI6yZCOqEJNh36dPH958801mzZrF3LlzGTZsmC3qEg7KrL3zdrbbJldn4NPfzvH5H0nUqOZCaIBXZZckhMWZ1eI4NTWVCxcuUL9+ffz9bTPbkRbH5WMPYzanU6Ull2wsMeajV28y9btjnL2RwyPNa/FylzBq2GnjMnv4GduajLl87qjF8XfffcfChQsJDw/nxIkTvPDCC/Tp06dChYiqqbSbfzvKRVJ5eoVF0S24r6Es24iqy2TYf/7552zYsAEvLy8yMzMZPny4hL0ASg95uG3ZxjOI1KcP2LS2svx29gYJFzMY3bEhd9Xy4esRd+OiljtHiarNZNirVCq8vPLXML29vXF3d7d6UcL+lbZkA/a70ybjVh7v/niazX9dpaG/B8PubpDfuEyCXjgBk2EfHBzM7NmzadeuHfv27SM4+M5vryYclzk3FwH7C/qdJ64xZ8dJ0rK1PHVPA0bdF4K7i7Q6EM7DZNjPmjWLNWvWsGfPHsLDw6XjpRMz5+YiBiAjeiO62vZzU/rUbC1v/Pc49X2rsbBfC5rU8q7skoSwuVLDPjs7mw0bNuDp6ckTTzyBWi2zIGdmzpZKvV9j0gbvsm1hpVAUhV/PpnJfw/zGZR8OaknjQGlcJpxXqWE/efJkgoODycjI4OzZs4wbN86WdQk7YdZOG5ULqWPO2rSuslzOuMWsH07w29n/NS6TNsTC2ZUa9qmpqSxatAiDwcCIESNsWZOwA9U3DcH1wk9l7rSxtyUbg6LwdcIl3v85vyX3K93C6Rwu2ymFgDLCXqXK/+utVqsxGAw2K0hUvsJLNuA4XSpf3XyM7Ykp3NvQjyk9GlOnuvSzEaJAqWGvKAp5eXkoilLkYwA3N7cyD2owGJg+fTrHjx/Hzc2NmTNnEhISUuw5zz77LN27d+eJJ56wwFCEJZjzJqw9BX1B0zIXjZpHW9SiU5g/vZvVNE5WhBD5Sg37ixcv0qtXLyA/+Hv16oWiKKhUKnbs2FHmQbdv345Wq2XNmjUkJCQwe/ZslixZUuQ5CxYsID093QJDEJbgt7Qp6rxMk83LclqPIbvDFNsWV4q/LqUz8etDPNA4kGfuC6FjqCzZCFGaUsN+586dFT7o/v376dy5MwBRUVEcOXKkyONbt25FpVJx//33V/gcwjJcruzHZ30fh7rx9608PR//dp4v9yXh6+FKRJA0LhPCFJP77CsiMzMTb+//7WXWaDTodDpcXFxITExk8+bNLFq0iMWLF5d6DG9vd1xcNKU+XhaNRo2vr2eFXuuoKjJm1Vv+Zq3NK6/mt7X2vZMCLeRQUjoTvj7ImevZxLStz6SeTey2cZmlyZ9r52CtMVsl7L29vcnKyjJ+bjAYcHHJP1VcXBxXr15l+PDhXLx4EVdXV+rVq1dslp+ZmVvh80unPNPKtTZvR9/LrKxb6PQG3h8QSc9W9UhLyyYtN6+yy7IJ+XPtHCqt6yXkz9QvXrxIgwYN8PQ0/S9OmzZt2LVrF7179yYhIYGIiAjjYxMnTjR+/N577xEYGCjLOTZUWruD29fm7ekCqT1nbnDwUgbP/9O4bN3Td6ORfjZClIvJsN+6dSsffvgher2eXr16oVKpGDNmTJmv6dGjB7t37yY2NhZFUZg1axbLly8nODiY7t27W6x4UT6F7wcLxYPe3tbm03LyWPDjKbb8nUyovyfD/2lcJkEvRPmZvHlJbGwsX3zxBSNHjuSLL74gOjqaDRs2WL0wuXlJ+Zgas61vLHInFEUxNi5Lv6Vj+D0NGNk+GLfbGpc528/Z2cYLMubyuqNlHLVajZubGyqVCpVKhYeHR4WKEJXD96uuaFJPONSNRdJy8pixNZEQfw/ei44koqY0LhPiTpkM+3bt2jFu3DiuXr3K1KlTiYyMtEVdwgIc6X6wiqKw+8wNOob64+fpxtJBrQgP8pJe80JYiMmwHzduHPHx8TRr1ozw8HC6du1qi7rEHXKkZZuL6TnM2naCvefTmN+3OfeHB0gbYiEszGTYx8XFARAYGEh6ejpxcXH07dvX6oWJivFb3AA1Cirsv92B3qCwNuESH/x8Bo1axeQHG9EpTK6CFcIaTIb9qVOngPxfs48ePYqvr6+EvZ0yNZu3ty6Vr205yvbEa3QM9Wfyg42oLY3LhLAak2Ff+M5UiqLw3HPPWbUgUTGFr4a152Wbwo3LHo+szf2NAujVVBqXCWFtJsNeq9UaP05JSSEpqfIDQxRV0ozeHpdtjl69yZvfJ9L1n8Zl9zWUJRshbMVk2BdcSKUoCtWqVWPkyJG2qEuYwdT6vL3strmVp2fZr+f4cl8SAV5uNJGtlELYnMmw/9e//kWfPn1sUYswkzmdKu1lNv/X5Qym/vc451Nz6BtZmxfvD8OnmlVaMgkhymDyb926desk7O2I38ct0OSmGT+356AHjK0NPoiJ5O5gv0quRgjnZdaafd++fQkNDUWtzr9cff78+VYvTBTnKHeR2n36BgkX0xnbOZSmtXxY+1Q76WcjRCUzGfYTJkywRR3CBFNvwtrDXaTSsvOY/+Mpth5NJizAk6fbB0vjMiHsRKlh/9JLL7FgwQLuueceW9YjSlBW0Bd0qlRevUF2JTWMUhSFH46nMHfnKW7m6hh1bzBPl9C4TAhReUoN+xs3btiyDlGKsoK+8JJNZd5FKi0nj1k/nCDE35PXH4qgkdwmUAi7U2rYX7hwgXfeeafEx8aNG2e1gkS+wjtu7PFNWEVR+Pn0DTqHFWpcFuglSzZC2KlSw75atWqEhobashbxD3tvYpaUlsNbP5xg3/k05vVpTpdGAdKGWAg7V2rYBwYG0q9fP1vW4vT8lrdGnZ1SrPc82EfQ6w0Kq/+8yJLdZ3FRq/h3j8Z0DperYIVwBKWGfYsWLWxZh9Mzb1ulhtSx52xYVVFTNh9l54lrdArzZ/KDjanl415ptQghyqfUsJ80aZIt63Bq5txkRFczivSYzbYtDMj7p3GZq0ZN35a16dY4kIeaBknjMiEcjFy3XsnsuYnZX1du8ub3x+neOIhnOkjjMiEcmYR9JTK9f15F6tgLNq/rVp6eD3efY9WfSQR6uXFXbXnzVQhHJ2FfSQqC3t7ehD18KYPXvzvGxfRb9G9Zh/+7PxRvd/ljIoSjk7/FNlZ4x429BT2Am0aNq0bFhwNb0rZBZV6qJYSwJAl7G/Jb3hpNdgpgX0Eff+o6hy5l8ELnUJrU8mbNU+1QyxuwQlQp0rzERgpm9GA/QZ+areW1LUcZH/cXu0/fICdPDyBBL0QVJDN7G/D7oCEaRQfYx44bRVH4/lgK83aeJEur59kOITx1TwNcNfJvvxBVlYS9lZW2hx7yA1/v15i0wbtsWlNqTh6zt58gNMCT1x6KIDxQGpcJUdVJ2FuRqT306dEb0dVua5NaDIrCz6euc394AP6ebiyLbUVYgDQuE8JZyO/tVlJW0BuANBsG/fnUHJ5fe4gJG//m59P5rasbB3lL0AvhRGRmbwX2sodeZ1BYtT+Jj/acw1Wj4rWHGtM5TK6CFcIZSdhbmO9XXY0z+gKVteNmyuaj7DpxjS7hAUx6sBFB3tK4TAhnJWFvYZrUE0DZd5WyJq3OgEqV37gsumUdejQJ4sGIQGlcJoSTs0rYGwwGpk+fzvHjx3Fzc2PmzJmEhIQYH//ss8/YsmULAF26dOGFF16wRhk257e4fpErY20d9IcvZfDmtkS6Nw7kuY4Nad/Qz+rnFEI4Bqu8Qbt9+3a0Wi1r1qxh/PjxzJ492/jYhQsX2LRpE6tXr2bNmjX88ssvHDt2zBpl2FThN2QLU7B+0Ofk6Xnru6OMXJVAtlZPi7rVrXo+IYTjscrMfv/+/XTu3BmAqKgojhw5Ynysdu3afPzxx2g0+dGo0+lwd3fsteQyd964Wrdj5KF/GpddSr/FgFZ1GNtZGpcJIYqzSipkZmbi7f2/kNNoNOh0OlxcXHB1dcXf3x9FUZgzZw7NmjUr8V633t7uuLiUNFc2TaNR4+vrWeH6y0P1ln+pQa8HlInnsWY7scAcHZ5uLqx+5l7aBjtX4zJb/pztgbONF2TMlmSVsPf29iYrK8v4ucFgwMXlf6fKzc1lypQpeHl5MW3atBKPkZmZW+Hz+/p6kpaWXeHXm8usLZZWqOOnk9c4dCmD/7s/jLoeLqwc2hp/Py+bjNme2OrnbC+cbbwgYy6voCCfUh+zypp9mzZtiI+PByAhIYGIiAjjY4qiMGbMGJo0acKMGTOMyzmOpjL20l/P0vLvb48yYePf/Ho2VRqXCSHMZpWZfY8ePdi9ezexsbEoisKsWbNYvnw5wcHBGAwG9u7di1ar5eeffwZg3LhxtG7d2hqlWIWtg15RFP57NJl3dp0iO0/P8x0bMuzu+rhI4zIhhJmsEvZqtZoZM2YU+Vp4eLjx48OHD1vjtDZRGTP61Jw85uw4SXigF689FEFogHOtYQoh7pxs2ygHWwa9QVH48eR1ujbKb1z28RNRhPp7Sj8bIUSFyDqAmcoMeo2HRYP+3I1sRq85yKRN/2tc1ihQOlQKISpOZvZmKOmCKWPQ+9QnddhvFjmPzqCwcl8SS/ecxd1Fw9SeEdK4TAhhERL2JphcurFQ0AP8+9u/85duGgcysXsjAr3cLHZsIYRzk7AvQ5kzeiyzRl+4cdmAqLo8fFdNukUE3fFxhRCiMFmzL4XnnlllXhlriaA/eDGdISv28+lv5wFoH+InQS+EsAqZ2ZfC48AHgHWCPlur54NfzrD2wCVq+bjTsp40LhNCWJeEfQms2ao4ISmdqf89xuWMXAZG1WVM54Z4ucmPQQhhXZIytynpDVmw3NKNp5sGTzcNywa1Iqp+jTs+nhBCmEPCvpCy3pC9Gb2xwsfdeeIahy9l8K8uYUTU9OarYW2ln40QwqYk7P9R5hZLd190tduW+5jXsrTM3XGSnSeu0bSmNzl5ejxcNRL0Qgibk7DHjL30o44Uf1EZFEVh819XWfDTaW7l6RnbqSFPtpPGZUKIyuP0Ye+3uIHF99Kn5uQxf9cpGgd58epDETT0l8ZlQojK5dRh77lnFup/ov1Od94YFIVdJ67RrXEg/p5ufDo4iob+nrJkI4SwC067ruD9w4t4HvjAIlssz17P5tnVB5n87VF++adxWViAlwS9EMJuOOXM3uXKfqolbgCKb7FUMD/odXoDK/YlsezXc3i4apjeqwmdpHGZEMIOOWXYV1/fByg+owfQ+zU2+ziTvz3KT6eu0z0ikFe6NSJAGpcJIeyU04W93+L6qCke9Ar5QZ82eFeZr7+Vp0etUuHmomZg67r0bl6Lbo0DrVixEELcOacK+4KdNyUt3Vw3Y+nmQFI6M7cl8mCTIJ7v2JB7QvysUaYQQlicU4W9usiCTaE3ZE0s3WRpdbwff4avD16mbnV32kibAyGEg3GasC+1uZnKpcylmwNJ6bz+3TGSb+byRJt6jO7YEE+323fmCyGEfXOKsC9t+UYPpI45W+Zrvdw0VK/mwqxH76JlXWlFLIRwTE6xz16NUmIrhJKamymKwo7EFN798RQAETW9WTm0jQS9EMKhVfmZfcHyTYHCNwq/vbnZtcxc/rPjJD+evM5dtf7XuEwlF0cJIRxclQ57vyVhxfrewD8XThW6UbiiKHx7JL9xmVZv4MX7Q3mibX1c1BLyQoiqoUqHvdqgBYq/KZvXoEuR56Xm5PHOj6eIqOnNqz0aEyKNy4QQVUyVDXvPPbNK7XuT8fhK9Ib8tfkHmwTh7+nG8sGtCfH3kH42QogqqcqGfcENwwsr6Htz+noWM79P5PDlm3i4augcHkBogMzmhRBVV5UMe9WO6SW+KWvwCOLjX8/x6e/n8XTVMKO3NC4TQjiHKhn26t8WAcXvPPW03wp+3nOOh5oEMb5bOP6e0rhMCOEcqlzYl3alrAF4om09+kTWoUujgMopTgghKolVwt5gMDB9+nSOHz+Om5sbM2fOJCQkxPj42rVrWb16NS4uLjz//PN07drVIuct2Gp5+1usBvLX6u+2yFmEEMLxWCXst2/fjlarZc2aNSQkJDB79myWLFkCQEpKCitWrGD9+vXk5uYyePBgOnbsiJvbnS+pFGy1LKAogAqyvMLu+NhCCOHIrNIuYf/+/XTu3BmAqKgojhw5Ynzs0KFDtG7dGjc3N3x8fAgODubYsWMWr6Eg6PVA7lPxFj++EEI4EqvM7DMzM/H29jZ+rtFo0Ol0uLi4kJmZiY+Pj/ExLy8vMjMzix3D29sdF5c76C6pyl+vV169gW/Fj+IwNBo1vr7OtX3U2cbsbOMFGbMlWSXsvb29ycrKMn5uMBhwcXEp8bGsrKwi4V8gMzO33OetUTMK1+QE45uyuppRpKdll/s4jsjX15M0JxlrAWcbs7ONF2TM5RUUVDxLC1hlGadNmzbEx+cvnSQkJBAREWF8rGXLluzfv5/c3Fxu3rzJqVOnijx+J9JjNpNXMwpF7UJezSjSYzZb5LhCCOHorDKz79GjB7t37yY2NhZFUZg1axbLly8nODiY7t27M3ToUAYPHoyiKLz88su4u7tb7NzpMZvx9fV0mhm9EEKYQ6UoimL6abaXknKzwq+VX/2cg7ON2dnGCzLm8rL5Mo4QQgj7ImEvhBBOQMJeCCGcgIS9EEI4AQl7IYRwAna7G0cIIYTlyMxeCCGcgIS9EEI4AQl7IYRwAg4b9gaDgalTpzJo0CCGDh3KuXPnijy+du1a+vfvz8CBA9m1a1clVWlZpsb82WefERMTQ0xMDO+//34lVWlZpsZc8JxRo0axatWqSqjQ8kyN+aeffmLgwIEMHDiQ6dOnUxXedjM15k8++YT+/fsTHR3NDz/8UElVWt7BgwcZOnRosa/v3LmT6OhoBg0axNq1ay1zMsVBff/998qkSZMURVGUAwcOKKNHjzY+lpycrDz66KNKbm6ukpGRYfzY0ZU15vPnzyv9+vVTdDqdotfrlUGDBilHjx6trFItpqwxF5g/f74yYMAA5auvvrJ1eVZR1phv3rypPPLII8r169cVRVGUpUuXGj92ZGWNOT09XenSpYuSm5urpKWlKQ888EBllWlRS5cuVR599FElJiamyNe1Wq3y4IMPKmlpaUpubq7Sv39/JTk5+Y7P57Aze3u4QYqtlTXm2rVr8/HHH6PRaFCr1eh0Oos2mKssZY0ZYOvWrahUKu6///7KKM8qyhrzgQMHiIiI4D//+Q+DBw8mMDAQf3//yirVYsoas4eHB3Xr1iUnJ4ecnBxUqttvPOqYgoODee+994p9/dSpUwQHB1OjRg3c3Nxo27Yt+/btu+PzOewNxy1xgxRHU9aYXV1d8ff3R1EU5syZQ7NmzQgNDa3Eai2jrDEnJiayefNmFi1axOLFiyuxSssqa8ypqan8/vvvxMXF4enpyZAhQ4iKinL4n3VZYwaoU6cOjzzyCHq9nueee66yyrSonj17kpSUVOzr1sovhw17S9wgxdGUNWaA3NxcpkyZgpeXF9OmTauMEi2urDHHxcVx9epVhg8fzsWLF3F1daVevXoOP8sva8y+vr5ERkYSFBQEQLt27Th69KjDh31ZY46Pjyc5OZkdO3YAMHLkSNq0aUPLli0rpVZrs1Z+OewyTmXdIKUylTVmRVEYM2YMTZo0YcaMGWg0d3BLRztS1pgnTpzIunXrWLFiBf369eOpp55y+KCHssfcokULEhMTuXHjBjqdjoMHD9KoUaPKKtViyhpzjRo1qFatGm5ubri7u+Pj40NGRkZllWp14eHhnDt3jrS0NLRaLfv27aN169Z3fFyHndlX5g1SKktZYzYYDOzduxetVsvPP/8MwLhx4yzyh6Qymfo5V0Wmxjx+/HhGjRoFQK9evarERMbUmPfs2cPAgQNRq9W0adOGjh07VnbJFvftt9+SnZ3NoEGDmDx5MiNHjkRRFKKjo6lVq9YdH1/aJQghhBNw2GUcIYQQ5pOwF0IIJyBhL4QQTkDCXgghnICEvRBCOAGH3XopqpakpCQef/xxmjdvbvxa+/bteeGFF0p8/uTJk+ndu3eF99V369aNOnXqoFarURQFX19fZs+eXeQqTlOWLl3KvffeS5MmTdi0aRMxMTFs2LCBGjVqVHhbaOG69Ho92dnZvPnmm0RGRpb6mi+//JInn3yyQucTTuSOu+sIYQEXLlwo1hCqLJMmTVJ++umnCp+va9euyq1bt4yfz5kzR/n8888rdKzy1l6euuLj45Vnn322zNd06NDBIucWVZvM7IVd0+v1TJ06lStXrpCamsr999/PSy+9ZHz8zJkz/Pvf/8bFxQWNRsOcOXOoVasW8+fP548//kBRFJ566ikefvjhUs9hMBi4efMmoaGh5OXlMWXKFC5cuIBer+fpp5+md+/erFy5kri4OONFPZMmTTL+drFt2zZOnjzJ+++/j6IoBAYGcvbsWZo2bUq/fv1ISUnhueeeY8OGDeWqC+DSpUtUr14dyG/6tnLlSuNjCxcuZM2aNaSnpzN9+nReffVVpk2bxrlz5zAYDLz00ku0b9/+Dn8CoqqQsBd24+TJk0V6e8+bN4+8vDyioqKIiYkhNze3WNjv2bOH5s2bM3nyZPbt20d6ejrHjh0jKSmJ1atXk5uby8CBA+nYsaMxNAuMGDECtVqNSqWiZcuW9O3bl9WrV+Pn58fcuXPJzMykf//+3HvvvWzYsIHXX3+dqKgovvrqK3Q6nfE4o0ePJjExkRdeeMHYxXDgwIG88cYb9OvXj40bN9K/f39++ukns+vKzc0lOTmZzp07M2nSJADOnj3L0qVL8fDwYOrUqfzyyy88//zzfPnll0yfPp2vvvoKPz8/Zs2aRWpqKk8++SRbtmyx+M9JOCYJe2E3GjVqxIoVK4p8LTMzk8OHD/Pbb7/h7e2NVqst8viAAQNYtmwZo0aNwsfHh5dffpnExET++usv4z8cOp2uyAy5wKefflqsjcapU6fo0KEDkN+QKjw8nAsXLvD222/z6aefMm/ePKKiokzeMCQ8PBy9Xs/Fixf57rvv+Oyzz1izZk256nrnnXdISkoiICAAgICAACZNmoSXlxenT58mKiqqyOsSExPZv38/hw4dMh4/NTUVPz+/MmsVzkF24wi7tmHDBnx8fJg/fz4jRozg1q1bRYJ2x44dtG3bls8//5xevXrx8ccfExYWRvv27VmxYgWff/45Dz/8MPXr1zfrfOHh4cbe4ZmZmSQmJlK/fn3Wrl3LG2+8wZdffsnRo0c5cOCA8TVqtRqDwVDsWAMGDGDu3Lk0atSI6tWrl7uul156ieTkZL766itu3rzJokWLePfdd5k5cybu7u7G70PB/8PCwnjkkUdYsWIFy5Yto1evXtSoUcOscYuqT8Je2LX77ruP+Ph4YmNjmT59OiEhISQnJxsfb9GiBQsWLGDw4MGsXr2aJ598km7duuHp6cngwYPp378/gNm7bAYOHEhaWhpPPPEEw4YN44UXXiAgIIAmTZowYMAAhg0bhr+/P61atTK+JiAggLy8PObOnVvkWL169eKXX34hJiYGoNx1qdVq3nrrLZYsWUJ2djZt2rShX79+DBkyhGrVqhm/D+Hh4UyYMIHY2FhOnz7Nk08+SWxsLPXq1UOtlr/iIp80QhNCCCcg/+wLIYQTkLAXQggnIGEvhBBOQMJeCCGcgIS9EEI4AQl7IYRwAhL2QgjhBCTshRDCCfw/tJdxrb/eTf8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "Sgd_Classifier(train_tfidfw2v,cv_tfidfw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:17:35.266635Z",
     "start_time": "2019-03-15T10:17:34.157006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8618425971868506\n",
      "macro f1 score for data : 0.6375804969699151\n",
      "micro f1 scoore for data: 0.8646343989504937\n",
      "hamming loss for data: 0.13536560104950632\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.23      0.35      4649\n",
      "           1       0.87      0.99      0.92     24317\n",
      "\n",
      "   micro avg       0.86      0.86      0.86     28966\n",
      "   macro avg       0.82      0.61      0.64     28966\n",
      "weighted avg       0.85      0.86      0.83     28966\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(train_tfidfw2v,Y_tr,test_tfidfw2v,Y_test,optimal_alpha=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:17:40.324588Z",
     "start_time": "2019-03-15T10:17:40.294607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_reviews_sample = final_reviews.sample(20000)\n",
    "final_reviews_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:17:48.810082Z",
     "start_time": "2019-03-15T10:17:48.784078Z"
    }
   },
   "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_sample['CleanedText']\n",
    "Y = final_reviews_sample['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": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T16:01:09.906148Z",
     "start_time": "2019-03-15T16:01:09.890157Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# SVM with kernel = 'rbf'\n",
    "def SVM_Classifier(X_train,X_cv,Y_train,Y_cv):\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    C = [10 ** x for x in range(-3, 4)]\n",
    "    gamma = [10** x for x in range(-4,2)]\n",
    "    for i in C:\n",
    "        for j in gamma:\n",
    "            clf = SVC(kernel='rbf',probability=True,C=i,gamma=j)\n",
    "            clf.fit(X_train,Y_train)\n",
    "            probs = clf.predict_proba(X_cv)\n",
    "            prob = clf.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",
    "            pred_cv.append(auc_score_cv)\n",
    "            pred_train.append(auc_score_train)\n",
    "    cmap=sns.light_palette(\"green\")\n",
    "    # representing heat map for auc score\n",
    "    print(\"-\"*30, \"AUC Score for train data\", \"-\"*30)\n",
    "    pred_train = np.array(pred_train)\n",
    "    pred_train = pred_train.reshape(len(C),len(gamma))\n",
    "    plt.figure(figsize=(10,5))\n",
    "    sns.heatmap(pred_train,annot=True, cmap=cmap, fmt=\".3f\", xticklabels=gamma,yticklabels=C)\n",
    "    plt.xlabel('gamma')\n",
    "    plt.ylabel('C')\n",
    "    plt.show()\n",
    "    print(\"-\"*30, \"AUC Score for CV data\", \"-\"*30)\n",
    "    pred_cv = np.array(pred_cv)\n",
    "    pred_cv = pred_cv.reshape(len(C),len(gamma))\n",
    "    plt.figure(figsize=(10,5))\n",
    "    sns.heatmap(pred_cv, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=gamma, yticklabels=C)\n",
    "    plt.xlabel('gamma')\n",
    "    plt.ylabel('C')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:18:09.176033Z",
     "start_time": "2019-03-15T10:18:09.169038Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing_SVM(X_train,Y_train,X_test,Y_test,optimal_C,optimal_gamma):\n",
    "    clf = SVC(C=optimal_C,kernel='rbf',probability=True,gamma=optimal_gamma)\n",
    "    clf.fit(X_train,Y_train)\n",
    "    prob = clf.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",
    "    # plot 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",
    "    predicted =np.argmax(prob,axis=1)\n",
    "    skplt.plot_confusion_matrix(Y_test,predicted)\n",
    "    print(\"macro f1 score for data :\",metrics.f1_score(Y_test, predicted, average = 'macro'))\n",
    "    print(\"micro f1 scoore for data:\",metrics.f1_score(Y_test, predicted, average = 'micro'))\n",
    "    print(\"hamming loss for data:\",metrics.hamming_loss(Y_test,predicted))\n",
    "    print(\"Precision recall report for data:\\n\",metrics.classification_report(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques for vectorization :-- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Bag of Words (BoW)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:18:15.650240Z",
     "start_time": "2019-03-15T10:18:14.673184Z"
    }
   },
   "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  (8978, 12047)\n",
      "CV Data Size:  (4422, 12047)\n",
      "Test Data Size:  (6600, 12047)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T10:18:19.474221Z",
     "start_time": "2019-03-15T10:18:19.426249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (8978, 12047)\n",
      "CV Data Size:  (4422, 12047)\n",
      "Test Data Size:  (6600, 12047)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = preprocessing.Normalizer()\n",
    "bow_train = normalizer.fit_transform(bow_train)\n",
    "bow_cv = normalizer.transform(bow_cv)\n",
    "bow_test = normalizer.transform(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": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T17:33:39.706536Z",
     "start_time": "2019-03-15T16:18:43.134850Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for train data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for CV data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal C\n",
    "SVM_Classifier(bow_train,bow_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T18:09:24.306842Z",
     "start_time": "2019-03-15T18:08:08.879855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9141608010571078\n",
      "macro f1 score for data : 0.786813201720312\n",
      "micro f1 scoore for data: 0.8968181818181818\n",
      "hamming loss for data: 0.10318181818181818\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.56      0.63      1056\n",
      "           1       0.92      0.96      0.94      5544\n",
      "\n",
      "   micro avg       0.90      0.90      0.90      6600\n",
      "   macro avg       0.83      0.76      0.79      6600\n",
      "weighted avg       0.89      0.90      0.89      6600\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=10\n",
    "testing_SVM(bow_train,Y_tr,bow_test,Y_test,optimal_C=10,optimal_gamma=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T11:55:56.246551Z",
     "start_time": "2019-03-15T11:55:53.147093Z"
    }
   },
   "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  (8978, 211885)\n",
      "CV Data Size:  (4422, 211885)\n",
      "Test Data Size:  (6600, 211885)\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": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T11:56:02.864435Z",
     "start_time": "2019-03-15T11:56:02.812466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text tfidf vectorizer  (8978, 211885)\n",
      "CV Data Size:  (4422, 211885)\n",
      "Test Data Size:  (6600, 211885)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "normalizer = preprocessing.Normalizer()\n",
    "tfidf_train = normalizer.fit_transform(tfidf_train)\n",
    "tfidf_cv = normalizer.transform(tfidf_cv)\n",
    "tfidf_test = normalizer.transform(tfidf_test)\n",
    "print(\"The shape of out text tfidf 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": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T19:24:15.290226Z",
     "start_time": "2019-03-15T18:09:48.040426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for train data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for CV data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C \n",
    "SVM_Classifier(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-16T06:49:30.155333Z",
     "start_time": "2019-03-16T06:48:24.605274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.9151821584349994\n",
      "macro f1 score for data : 0.7843451587830337\n",
      "micro f1 scoore for data: 0.8972727272727272\n",
      "hamming loss for data: 0.10272727272727272\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.54      0.63      1056\n",
      "           1       0.92      0.96      0.94      5544\n",
      "\n",
      "   micro avg       0.90      0.90      0.90      6600\n",
      "   macro avg       0.83      0.75      0.78      6600\n",
      "weighted avg       0.89      0.90      0.89      6600\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": "iVBORw0KGgoAAAANSUhEUgAAATMAAAEPCAYAAAAu4COIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHBdJREFUeJzt3XmYFNW9//F398wAI9tVAUXzUwKYr8YgxhURBBFFRCOoN3pdcVdwIxpUxO26G/eAUXHBFY2oMYoIroiA+4ro113jBoJBBNmnf39UDXcyTjUNMz3dVf158fQz01Wnqk7PMJ/nnFPLSWUyGURE4i5d6AqIiDQEhZmIJILCTEQSQWEmIomgMBORRFCYiUgilBe6AqXKzMqAU4GDCX4PTYDHgPPcfWk99vkwsAVwg7uPWsPttwPOcvcD1ub4dezvc6AtsIG7L6yxfDBwB/Df7j4+y/atgUfcvU/E+reA3u4+vyHqK/GmMCucvwHrAru5+49m1hy4F7gVOGwt97kx0A9o7u4r13Rjd38NaJAgq2EusB9wV41lhwOzc9h2XWCHqJXuvnX9qiZJojArADPrABwCtHf3BQDuvsjMTgB2Dsu0BkYDWwMZYCIwwt1XmNkS4HJgD6A9cCVwD/AkUAG8bmb7Ax8Dbd19brjPDEFLaQlBy2gzoAp4HTge2AUY5e6/W9Pju/vfIj7uPcChhGFmZpsCLYAPavw8jgqP3wRYD7g83N8dQGXYAtsW+Bl4FOga/vxeDT/PUIIQ7xm+fwM4xN2fy+HXIQmhMbPC2BZ4rzrIqrn7d+7+UPj2BmAe0AXYjuAP+IxwXVNgrrt3J2hJXQssB/YCFrv71u7+SZbjDwJahi2b7cNlHWuVWaPjm1mziGNNALqaWfvw/WHUaKWZWQvgWGAvd/89cCBBOAMcWePzrCTsiru7ha3IaheHn//PwN0EgawgKzEKs8KoYvU/+/4Ef5SZcAztpnBZtUfDr28QhEvzNTj+i8CWZvY8cBZwnbt/nKfjLwPGE4wNQhBW91WvDMfS9gYGmNlFwDkELbcoU2svCIPuEOBMIAVclmV7SSiFWWG8DGxhZi1rLjSzjc1sgplVEvxuat44myboQlZbDODu1WVSEcdKhftuUr3A3T8DOhP80bcCnjazfWpt11DHh6AldqiZdQ828R+qV5jZr4C3gE0JQnZklv0ALIxYvmlYp04EY21SYhRmBeDu3xAM9t9uZq0Awq83AvPcfTEwCTjJzFJm1hQ4DnhqDQ/1PUEXEf6vZYSZnUgwHjXZ3c8Mj7VNrW0b4vgAuPvLQCVwKTC21urtwnpeDEwmaKVVn5ldAZSZWbagxMz+i+DnORgYB9y2NvWUeFOYFc4QYBYwPRzgfjl8f0y4/hSgHfBu+HLgkjU8xinAaDN7g+ByjW/D5XcBZcAsM3sdaE0wRlZ72/oev6a7ASM4SVHTZOCrcP/vA5sQhFvnsL6vAO+Z2fpZ9j0GeNzdJwMXAB3NbEg96ioxlNIjgEQkCdQyE5FEUJiJSCIozEQkERRmIpIICjMRSYSC3Js5e8FynUKNmfJ01ku9pAit36K83r+0yt+flNPf6uI3RxX8P4huNBeRaKn4dN4UZiISLVXwBlfOFGYiEk0tMxFJBLXMRCQR0mWFrkHOFGYiEk3dTBFJBHUzRSQR1DITkURQy0xEEkEtMxFJhHqczTSzN4Efw7efATcD1xM8Dn2yu19oZmmCx8V3BZYCx7j7x2bWrXbZ1R1PYSYi0dayZVY99aC7966x7C1gf+BTYIKZbQN0AJq5+05hgF0N7EswG9h/lHX3N7IdU2EmItHW/gEDXYF1zGwyQc5cADStns/VzCYBuxFMIv0kgLu/ZGbbhZP71FVWYSYia2ntx8x+Bq4CbgU2AyYC82us/4lg4ulW/F9XFGBluGxBHWWzUpiJSLS1P5v5IfBxOK/qh2b2I7BejfUtCcJtnfD7ammCIGtZR9ms4nOqQkQaX7ost9cvHUUw/oWZbUQQWovMrFM4D2o/gtnppwF7heW6Ae+6+wJgWR1ls1LLTESirX038zZgrJm9CGQIwq2KYLLmMoIzlC+b2avA7mY2HUgBR4bbn1C77GqrWoh5M/Wk2fjRk2bjp0GeNNvvqtyeNDvpjIL/B1HLTESi6aJZEUkE3c4kIomglpmIJIIezigiiaCWmYgkgsbMRCQR1DITkURQy0xEEkEtMxFJglRaYSYiCZBSN1NEEiE+WaYwE5FoapmJSCIozEQkEdI6ASAiiRCfhpnCTESiqZspIomgMBORRFCYiUgiKMxEJBFSMZrIRmEmIpHUMhORRFCYiUgyxCfLFGYiEk0tMxFJBIWZiCSC7s0UkWSIT8NMYSYi0dTNFJFEUJiJSCIozBKub4/tadm6NQCbbNqBL7/4fNW6jz90Djz4MM4+7yJOP+UEPvnoQ8rKyrhu9Bg6dOxUoBrL99/PYbeeO/LQoxNZsnQJZ5w6lPLycjp13ozrRt9COp3mtltuZNy9d5FKpTjjzJH06z+g0NUuuJK/ncnM0sCNQFdgKXCMu3+cj2M1tiVLlgDwyISnf7Hui88+5djBBzPszyOYPPFxAB6bPIVpU6dw/jl/5s5xDzdqXSWwfPlyTj9lCM2aVQLwl8su5oyzRrJ7v/4cf/RhTH7yCbbfoRu3j7mZ56e/xpIlS9h5+63YY8+9YtUyyYc4ff58nXcdCDRz952As4Cr83ScRjdr5jssXvwzBw7ci/333oPXX3151bpzzz6dcy+8lOYtWtB/73256vq/AfDVv76gbdsNClXlknf+OcMZfPRxbNi+PQBdum7Nv//9A5lMhoU/LaSiooL127RhyozXqaioYM7s72jdunWs/pDzJZVK5fQqBvkKsx7AkwDu/hKwXZ6O0+gqKys58eQ/cf8jE7jyulEMOeYIVqxYwayZ7/DTTz/Rs3efVWXLy8s5+YSjOGf4MPbed78C1rp0jbvnTtZv05Y+ffdYtaxjp86MGD6Mnbbtwvffz2bnnr2A4Pd1682j2bNPD/YZuH+hqlxU4hRmqUwm0+A7NbNbgYfcfWL4/kugo7uvAJi9YHnDH7SRLF26lKqqKiorgy7Lnrt257a7H+C2m0ezZZet2P+PB/9imzmzv6P/bj144eW3ad68eWNXuUGUx2jspKa9++266g9u5rtv06nzZsx8522em/Yqm2+xJbfdciP+wftcec1fV22zbNkyDtxvb/40fAQ9d+lduMrX0/otyuv9S/v1sAk5/a1+du2AOo9lZu2A14HdgRXAWCADzASGunuVmZ0PDAjXn+bur5hZ57rKZqtDvlpmC4CWNY9THWRxN+7usVxwznAAvvv2Gxb+tIANNmzPi1OeY9e+/VaVe/D+e7jh6isAqKxch3QqTVlZWUHqXMoen/Qcjz35LP+c+Ay/69KVG2++g007dKRly1YAbLjhRsyfP5+PPnSOOPi/yWQyVFRU0LRp01hd/Z4v9WmZmVkFcDOwOFx0DTDS3XsSXI67r5ltA/QCdgQOAkZHlV1dXfN1NnMasA/wdzPrBrybp+M0uoMPP5JTTzyaP/TrTSqV4trRYygvL2fOnNmst976q8rttc8gThtyDAP792H58uVcdPlVNGvWrIA1l2rXjbqJYwcfQll5OU2aNOHav97EJpt2YMsuW7Fnnx6kUil222NPdu6xS6GrWnDp+rXIrwJuAs4O328LTAm/nwjsATgw2d0zwJdmVm5mbSPKPpLtYPkKs0eA3c1sOkGqHpmn4zS6Jk2a8Lfb7v7F8rc++Pw/3jdv3pwxd45rpFpJLv458RkANgOeePqFX6wffva5DD/73EauVXFb2/EwMxsMfO/uk8ysOsxSYWgB/AS0BloB82psWr28rrJZ5SXMwr7tCfnYt4g0nnqM7R8FZMysL7A1cBfQrsb6lsB8fjkkVb28qo5lWWlQQEQire2Ymbvv4u693L038BZwODDRzHqHRfoDUwmGpPqZWdrMNiEYX58LvFlH2ax0B4CIRGrgqy5OB8aYWRPgfWC8u680s6nADILG1dCosqutaz4uzVidOF+aUariemlGKWuISzN+O2JyTn+rsy7do+D/QdQyE5FI9Tyb2agUZiISqUgu7s+JwkxEIhXLrUq5UJiJSCSFmYgkQoyyTGEmItF0AkBEEkHdTBFJhBhlmcJMRKKpZSYiiRCjLFOYiUg0tcxEJBF0NlNEEiFGDTOFmYhEUzdTRBIhRlmmMBORaGqZiUgiKMxEJBF0NlNEEiFGDTOFmYhEUzdTRBIhRlmmMBORaOkYpZnCTEQi6QSAiCRCjLJMYSYi0XQCQEQSIUZZFh1mZjYDqD01ewrIuHv3vNZKRIpCivikWbaW2UGNVgsRKUqJGDNz9y8AzGxj4AqgLTAeeAf4olFqJyIFFaezmekcytwC3A40AV4Ars9rjUSkaKRTqZxexSCXMGvm7s8SjJU5sCTPdRKRIpFK5fYqBrmczVxqZv2AMjPrhsJMpGTE6dKMXFpmxwFHAm2AM4AT81ojESkaiWqZuftXZnYp8Btgprt/lv9qiUgxKCuWpMrBasPMzEYCewKvAsPM7EF3vy7vNRORglvbbqaZlQFjAANWEvTuUsBYgutXZwJD3b3KzM4HBgArgNPc/RUz61xX2WzHzKWbuRewi7sPA3qh689ESkY6ldurDvsAuPvOwHnANeFrpLv3JAi2fc1sG4Jc2ZEgW0aH2/+i7GrrmsPnmQOsE37fBPg+h21EJAFSqVROr9rc/R8E4+0AmwKzgW2BKeGyiUBfoAcw2d0z7v4lUG5mbSPKZpXL7UztgI/M7G3gt8C81e1URJKhPkNm7r7CzO4EBgEHAHu7e/Utkj8BrYFW/GemVC9P1VE2K93OJCKR6ntphrsfYWZnAi8DlTVWtQTmAwvC72svr6pjWVaR3Ux3/yK8pakCOBg4AhgMjMjpU4hI7JWlUzm9ajOzw8zs7PDtzwTh9JqZ9Q6X9QemAtOAfmaWNrNNgLS7zwXerKNsVrlcNHsX8BhB3/YboEUO24hIAtSjXfYwcIeZvUDQIDoNeB8YY2ZNwu/Hu/tKM5sKzCBoXA0Ntz+9dtnVHTCXMPvZ3S8zs83c/ajwwCJSAtb2vkt3XwT8sY5VveooewFwQa1lH9ZVNptcwixlZhsCLcysObDemhxAROIrRtfM5hRmFxKcjbgH+Iyg2ykiJSBO92bmcjvTCwSP/oHgMg0RKRExyrKs15l9yy8fmw2Au2+UtxqJSNGo60xlscr2pNn2+Tpo63Uq8rVryZN1tz+p0FWQNbT4zVH13keiupkiUrpyud+xWCjMRCRS4lpmZtaK4GbRT8PrR0SkBMRoyGz1rUgzO4Dg7vX7gD+FzzcTkRKwtrczFUIuXeJhQDdgLnAxwTVnIlIC6vE8s0aXS5hVuftSgtmZMoC6mSIlIlFzAABTzWwc8Cszu4ng8dkiUgKKZU7MXORyB8AIM9sTeAN4390fz3+1RKQYxOnSjFxOABxOcBvTbGC98L2IlICkdTO3CL+mgK2BH9DN5iIloVjOVOYil25m9dMiMbMUoG6mSImIUZblNG9mkxpv2wO/zl91RKSYJOoEAOAET89IAYuBv+S1RiJSNGKUZTmF2bnufk/eayIiRSdO3cxczrwem/daiEhRSuX4rxjk0jJramZvEnQ3qwDc/eC81kpEikJ5jC40yyXMzsx7LUSkKCXiEUBm9oC7H+juUxqzQiJSPOI0ZpatZda20WohIkUpRg2zrGHWycwurWuFu4/IU31EpIgk5TqznwkG/UWkRJUl5ATAd+5+Z6PVRESKTrpILrvIRbYwe73RaiEiRSlGvcys82ae0ZgVEZHik5SzmSJS4pJyAkBESlyMskxhJiLREvVwRhEpXTG6MkNhJiLREnFvpojI2kaZmVUAtwMdgKYEE4jPAsYSPOx1JjDU3avM7HxgALACOM3dXzGzznWVzXbMOLUiRaSRpVOpnF51OBSY5+49gf7AKOAaYGS4LAXsa2bbAL2AHYGDgNHh9r8ou9q61vOzikiCpXJ81eFB4Nwa71cA2wLVT+GZCPQFegCT3T3j7l8C5WbWNqJsVupmikik9FqezXT3hQBm1hIYD4wErnL3TFjkJ6A10AqYV2PT6uWpOspmr+ta1VRESkI6x1ddzOz/Ac8Bd7v7fYRPqg61BOYDC8Lvay+vq+xq6yoiUqdUKpXTqzYz2wCYDJzp7reHi980s97h9/2BqcA0oJ+Zpc1sEyDt7nMjymalbqaIRKrHhRkjgHWBc82seuzsVOCGcC7e94Hx7r7SzKYCMwgaV0PDsqcDY2qWXW1dM5nM6so0uCUraPyDSr2su/1Jha6CrKHFb46q90Vi49/+Nqe/1QO6ti/4BWlqmYlIpDJdNCsiSRCfKFOYiUgWMWqYKcxEJFpSHpstIiVOLTMRSYSUWmYikgQ6mykiiRCjLFOYiUg0hZmIJILGzEQkEWI0n4nCTESiad5MEUkEdTMT7pWXX2bkiDOZ/MzzvPnGG5w89ASaNm3KVl235uprryedTnPAoD8wb948KioqqKys5NHHJxa62iVnxrgzWbBwCQCffz2Pux6dwWXDBpHJZHhy2ntcdsuTq8pWNqvgubGnc+4Nj/LU9PfZdKP1ufWiw0gBX377A0MvHsfiJcsL9EkKR91MwMx2BK5w9975OkYhXH3VlYy7527Wad4cgJNOPI6rrr2Bnbp354LzRvLAuPv4n0MO5ZNPPuaNt9+L1VRdSdK0SfBfu9+x169aNu3e4Rz859v44pt5PHnLKTwxZSZv+1cAXHfWgdR8HNZlwwZy64Mv8sCTrzF40E6ccmgfrrh1UuN+iCIQp5ZZXp40a2bDgVuBZvnYfyF17NiJ+x98eNX7r7/+ip26dwdgp+47M33ai8yePZv58+ez/8B96NOrB09MeLxQ1S1ZW/1mY9Zp1oTHbhzKxJtPZocuHdjl8Kv44pt5NK9sQqsWlfzw4yIATjtsN156+1Pe/fDrVdtv3nFDJk17D4AZb31K9607FeRzFFoqldurGOTrsdmfAPvlad8FNWi//amoqFj1vsOvOzL1hWASmScef4xFixaxbNkyTh12On9/6B/c/+DDDD9jGHPmzClUlUvSz0uWc91dz7DPkNGcfMkD3HHJEQDs0KUDr48/h9nzFvD9vxfSe4ff0GmTttzxyPT/2P4d/5oBvbcCYO9eXWhe2aTRP0MxqMfsTI0uL2Hm7g8BJTHAcMutd/CXKy5j0B8G0LZdO9Zv04YNN9yQY487gfLyctq1a0fXrX/Phx96oataUj76Yg7jnngVgI+/nMMPPy6ifZtWvPLu52w+4Hzeev9fnHHk7gwe2J0tO7dn0phT2b37b7nk1IFs9ZuNOeuah9m7VxceHTWEqqoM8+YvKvAnKoyyVCqnVzHQhCb1NPGJCdw05nYe+ecE5s2bx259d+fZZ57m0P/5IwALFy5k1nsz2XzzLQpc09JyxMBuXP6nQQC0b9uaVi0quefKo/mvlpUALPx5CVVVGQaPGEufI6+l37HX89T0WZxz/T9458Ov6dNtcy65+Qn2PelGqjIZnnnpg0J+nMKJUdNMZzPrqfNmmzFon72oXGcdevXelT377wXAU5MnscvO3Uin01x40aW0adOmwDUtLWMfmcGY/z2MZ24fRiaT4fgL7qHNui14dNQQli5fwXdzF3DihfdGbv/R57O56YJDWbZsObM++Y7TLn+gEWtfPOJ0AiBvE5qYWQfgfnfvVnudJjSJH01oEj8NMaHJK5/+mNPf6g4dWxc89fLWMnP3z4FfBJmIxEfBE2oNqJspItFilGYKMxGJpHszRSQR4hNlCjMRySZGaaYwE5FIcbo0Q2EmIpFiNGSmMBORaAozEUkEdTNFJBHUMhORRIhRlinMRCSLGKWZwkxEImnMTEQSob4TmtScC8TMOgNjgQwwExjq7lVmdj4wAFgBnObur0SVzVrX+lVVRBKtHg9nrGMukGuAke7eM9xqXzPbBugF7AgcBIyOKru6qirMRCRSKsd/EWrPBbItMCX8fiLQF+gBTHb3jLt/CZSbWduIslkpzEQkUn1mZ6pjLpCUu1c/7PEnoDXQCvixRpnq5XWVzUpjZiISqYGH/2uOebUE5gMLwu9rL6+rbFZqmYlItIad0ORNM+sdft8fmApMA/qZWdrMNgHS7j43omxWapmJSKQGfjjj6cAYM2sCvA+Md/eVZjYVmEHQuBoaVXZ1O8/bhCbZaEKT+NGEJvHTEBOafDJncU5/q53aVRb8gjS1zEQkWsEjKncKMxGJpDsARCQR9NQMEUkEhZmIJIK6mSKSCGqZiUgixCjLFGYiEk0tMxFJiPikmcJMRCLV9+GMjUlhJiKR1M0UkUTQpRkikgzxyTKFmYhEi1GWKcxEJJrGzEQkEVIxSjOFmYhEik+UKcxEJIsYNcwUZiISTZdmiEgiqGUmIomgMBORRFA3U0QSQS0zEUmEGGWZwkxEsohRminMRCSSxsxEJBH0cEYRSQaFmYgkQZy6malMJlPoOoiI1Fu60BUQEWkICjMRSQSFmYgkgk4ANAAzSwM3Al2BpcAx7v5xYWsluTCzHYEr3L13oesi9aOWWcMYCDRz952As4CrC1wfyYGZDQduBZoVui5SfwqzhtEDeBLA3V8CtitsdSRHnwD7FboS0jAUZg2jFfBjjfcrzUxd+CLn7g8BywtdD2kYCrOGsQBoWeN92t1XFKoyIqVIYdYwpgF7AZhZN+DdwlZHpPSoK9QwHgF2N7PpBHezHVng+oiUHN3OJCKJoG6miCSCwkxEEkFhJiKJoDATkURQmIlIIujSjBgws97A34FZQAaoBO5197+uxb4uBz4A3gL+4O7/G1FuEPCyu3+Twz73BA5y98G16nyCux8Usc1gYHN3PyuH/edcVkqXwiw+nq0OBjNrCriZ3e3u89dmZ+7+FkGgRTkVOAFYbZiJFAOFWTy1BFYCK8zseeB7YF1gAMGjiDYjGEIY6e7Pm9n+wMiwXBPgg5otJzM7GjgRKAMeBV4FtgbuMrMewPHAwQStwvvd/QYz2wK4HVgUvv4dVVkzO4nghu4KgntYq2/u3snMniG4t/UCd59gZr2AS8LP90l4bJHV0phZfPQxs+fN7FngXuBkd18YrrvP3fsCRwFz3X0XYF9gdLj+SqAv0A/4ueZOzawdwWOLegLbAq2BKQSttsOBzsCBBE8G6QEMNDMDLgLOC487ParS4bPe1gf6untPgkDbPly9KKzXAGCUmZUBY4D93L0X8DUweA1/TlKi1DKLj2ejxp8AD792AXqGDxwEKDezDYAF7j4PILzlqqaOwEx3Xxy+HxaWq17/O2BT4Jnw/boEAbcl8Eq4bBqwRZ0Vc68ys2XAODNbCPyKINAAXnT3DDDHzH4E2gDtgb+Hx68EJhO00ESyUsssGarCrx8A48KnpvYHHiTo/rU2s7Zhme1rbfsJsHk4DoeZjTezjcN9pgmC8j1g13C/YwlupP8A2Clin6uY2VbAQHc/EDg53Geq5nZmtiHQApgLfAXsGx7rEuC53H8MUsoUZslyM0EwTSHo+n3h7ssIbnyfZGZPE4yZreLu3wNXAFPMbAbwhrt/HW5/F/AvglbZi2b2GsF43NfAEGBEOOa1I9E+BhaF2z4FfAtsFK6rDLvN/wSOd/eVBCceJoQtyCHAzHr9RKRk6EZzEUkEtcxEJBEUZiKSCAozEUkEhZmIJILCTEQSQWEmIomgMBORRFCYiUgi/H9aovzHEgj5AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimal_C=100\n",
    "testing_SVM(tfidf_train,Y_tr,tfidf_test,Y_test,optimal_C=0.1,optimal_gamma=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:23:52.070434Z",
     "start_time": "2019-03-15T14:23:50.915681Z"
    }
   },
   "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": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:25:31.529307Z",
     "start_time": "2019-03-15T14:25:30.868912Z"
    }
   },
   "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": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:27:10.632271Z",
     "start_time": "2019-03-15T14:27:09.756392Z"
    }
   },
   "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": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:28:50.740579Z",
     "start_time": "2019-03-15T14:28:48.848303Z"
    }
   },
   "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": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:30:36.247374Z",
     "start_time": "2019-03-15T14:30:28.994274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8978\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "train_avgw2v = avg_w2v(list_of_sent_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:32:27.922334Z",
     "start_time": "2019-03-15T14:32:24.047493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4422\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "cv_avgw2v = avg_w2v(list_of_sent_CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:34:26.019567Z",
     "start_time": "2019-03-15T14:34:20.719377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "test_avgw2v = avg_w2v(list_of_sent_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:36:27.061486Z",
     "start_time": "2019-03-15T14:36:26.997524Z"
    }
   },
   "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": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T19:41:06.446196Z",
     "start_time": "2019-03-15T19:24:38.385507Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for train data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vn7jaTmQqRwghhBBX4xacz8FDVdUvgMwL2noDW1VVbVZVtRk4BKQDA4EMRVE+UxTlA0VR4q/UiRQmQgghhLgaYTgfzntOq6Io52ZezMAwRVFCFUXpDNwMBAMHgWdVVR0O/Dfwtyt1ct1M5TgcDnLn5lKiluDn78esebNI6JYAQPE3xbz6wquubQ/sPcC8RfMYNGwQAPu+2sf8mfNZ+dlKak7WkDMjx7Vt8TfFPPq7R5nw4ATvBuQh7soTwP+u/l8+WP4Ber2eO++5k7sm3+X9gDzI4XDwwUsfcKzkGEY/Iw/OepDoxGhX+6f//JT8zfnodXpG/r+R9Mvuh7XFyor5K6g/XU9AUAAPPf0QoRGhmHea2fjmRvQGPUPGDOHmcTf7MDL30hwaecvzqDlag95PT/avsgmPC3e1F6wtoGRnCTq9jv539af7Td1dbbXHavl4zsc89NpDGP2NVB2q4vO3Pkdn0JHYN5GMezJ8EJFntCVPDoeDXSt2cbL0JA6bg4x7MkjKSKK8oJyv3vsKo8lIYr9EBvx0gA8jcz8dOqb0nUJSWBJ2h52l+5ZSZalytY9JGcOQLkPQ0FhdvJr8inyC/YL5zYDfEGAMoMHawLKCZdRZ68hKyGJM6hiabE1sL9/OZ2Wf+TCydq8OCL1gWa+qqh1AVdVvFEV5FfgEKAZ2AaeALwHL2e1XATlcwXVTmOzYtAOr1cqi9xdxYO8BFv95MfNfmw9Aj949yF2RC8DWT7bSOaaz62JbdaKKlctWYrfbAYiMjnRtu3/Pfl5/6XXG3j/WBxF5hrvyBPD3F//O8rXLCQwK5Odjf85Pxv6E0PDQSzu9Rpl3mLFZbcxYPIPS/aWsem0Vj85/FABLvYVtH23jj+/8EWuzlQWPLKBfdj92fLyD+OR4Hs55mPxP8/mfFf/DxN9MZNWrq5j5XzPxD/An9/Fc0oamEdY5zMcRuseR3UdotbUyIWcCVYeq2PXOLkb9bhQALY0t7N+4n/tfuh97s51Vv1/lKkysFiu73tmFwc/gOtaOZTu4ffrthMaEsvHFjZwqPUVUcpQvwnK7tuSpeHsxjlYHE+ZOoLGmkdJdpWgOje1LtjP2mbGExYaxZdEWKg5WEHdDnI8jdJ+BcQPx1/uTk5dDakQqk/tMJne3870pyBjEyOSRzNw8E5PRxPzs+eRX5DOhxwTUGpU1xWu4MepG7rvhPt7/5n3uVe7lme3PYLFZeGrIU+w/tZ9TTad8HGG7lQeMB1aevcfEfK5BUZRoIEpV1VsURQkH/gfnDa/vAv8CVgK3AflX6uS6mcox55sZlO28iPbp34eiwqJLtmmyNPHG395g2h+mAWBtsbLw2YVMnzv9km01TeOV519h+tzpGAyGS9qvVe7MU4qSQmN9I1arFTTQ6XSeD8CLSswl9B7UG4DkG5MpU8tcbaZAE51iO2FtttLS1OKK/bD5sGufPoP7oOarVBytICohiqDQIIx+RlL6plBiLvF+QB5SqVaSmJ4IQEzPGE4dPv+m72fyIyQqBHuzHXuLHc6+RDRNY8fSHWROysTo7/z8ZLVYabW1EhYbhk6nIzE9keOFx70ej6e0JU/lBeUERwaz8cWNbF+ynaSMJJrrmzEFmwiLdRa2sb1iqSyq9Ho8ntQrshcFJwsAKKktITki2dXW0tpCtaUak9GEyWBCQwMgITSBgirnPkU1RfSK7EVMcAxH647SaGtEQ+Nw7WF6dOrh/YCuHauAZkVRdgIvAb9VFGWGoigTcI6OpCiK8hWwHpilqmor8BTwG0VRtgK/Bv7jSp1cNyMmlgYLwSHBrmW9QU+rvRWD8XxRsf7D9QwfPZzwSOfw6cs5LzPpl5OIjo2+5Hg7N++ke8/uJKUkef7kvcideUrumcxj9zxGQGAA2SOzCQkL8U4QXtLc2ExgSKBrWa+/OFedYjrxwpQXcDgcjHxo5CX7mIJMNDU0XXKcc+s7CmuTFf8gf9eyTq/D0epAb3B+LgrpHMKHT36I5tDoN6EfAF//62u6DuhK526dXfvZmmz4B54/jl+gH/VV9V6KwvPakqfm+mbOVJxh1KxRVBysYNt/bWPsH8dib7FTe6yWsPgwyvaWXZTHjiDQGIjFZnEtOzQHep0eh+YAoLq5mgXDF6DX6VlTvAaAo2eOkhGbwdE659/+Bn8qGitIDE0kzD+MZnszfaL6UNFY4ZOYrgWqqjpwFhcXOnjBvx/7nn1KgVt/TD/XTWESFBKEpfGCF7LDcdHFFmDTmk0898pzAJyqPIV5t5lj3x7jzUVvUn+mnpzf5jDnpTmA8/6Je/7tHu8F4CXuytNDjz3EF1u/4N1P3yUwKJAXZr3A1k+2MuLOEd4Mx6MCggNotjS7li/M1YFdBzhTfYZn33sWgMVPLiYlLYWA4ABaLC0AtFhaCAoJumjdufUXFirXOv9Af2zNNteypmmui23ZvjIstRYm5U4CYMOfNxDbK5aSvBKCIoNQt6o0nWliw583MGrmqIuOY2uyXXQhv9a1JU8BIQEkZSSh0+mI7x3PmRNn0Ol0jJg6gh3LdmAKNhHRJYKA0ACfxOQpTfYmAoznY9JzvihJj0knwhTBjM0zAHhy8JMUnS5iTfEafpb2M2YPno35pJmaphosNgvv7H+HaZnTON10mqNnjlJv7TjF7rXK7YWJoigv/FCbqqq/d3d/VystI43Pt3zOrWNu5cDeA6T0SrmovaG+AZvVRkx8DABRsVG8tfEtV/vdWXe7ihKAQ/sPkZaR5p2T9yJ35aniWAWmABMmkwmDwUBEZAT1dR3rP3xKWgqFOwvJuDWD0v2ldEnp4moLCg3C3+SP0d+ITqcjMCQQS4OFlLQU9n+xn269u3Fg1wFS0lOI6xbHyfKTNNY1Ygo0UVxQzE8m/cSHkblXrBLLt19/S8qQFKoOVRHZNdLVZgo2YfAzYPAzoNPp8A/2x2qxcv9L97u2+ee0fzL6qdEY/Y3ojXrqKusIjQmlvKC8Q9382pY8xSqxlO0tI3lQMtVHqwnp7ByVLNtXxh2z7sBoMrLppU30Gt7LV2F5RFFNERmxGXx54ktSI1Ipqz8/jdpoa8TmsGFz2FzLQcYglM4KO8p38E31N2TGZXLo9CH0Oj2pnVKZv3M+ep2ep4Y8xcqDK30VljjLEyMmVcBvgPm4ZkJ9L3tkNvl5+Tz+wONomsbsF2azcvlKEpISyLoti/LScuISru7msNqaWgKDAzvcPRPgvjzFJcQxftJ4pk2ehtHPSJekLoz+6WgvROA96dnpqLtVFv77QtDgodkPsXnlZqIToumb1Rc1X2Xh1IXodDpS+qZwQ+YNpPZN5e0/vU3u47kY/AxMeWYKBqOBif8+kddmvYZDczDkziFEREf4Ojy36Z7ZnWPmY6x+djVoMOyxYZjXmQmLC6PbwG5EF0azes5qdDodsUosCX0TfvBYt/zyFrYs2oLm0Ejom0BMjxgvRuJZbclTfO948pbl8fGcj0GDrIezAAiODGbNc2sw+hlJzUqlU2InH0fnXvkV+aRFpzHn5jmggyV7lzA6eTSVlkr2VO7hcO1h5mbNxYGDopoiCk8VEhMUw6/7O2chTjefZknBEhyag1ZHKznZOdgcNj45/AkNtgYfRyd0mqa5/aCKoqwA3lRVddPVbH+c4+4/CXHdMp8wX3kjQcHxAl+fguhgCk7Ia+pqrRi3wqufbPv9o1+brrP7Ht3n9U/gnrrH5FdAx5rUFEIIIYTHeaowaQVSz36XuRYoVFXV6qG+hBBCCNFBuP13TBRFGYvzB1R+D0wBngH2Kooy0d19CSGEEKJj8cSIyR+AW1RVrTu34uzIySacv5MvhBBCCPG9PPHLr36c/138c5oAucFVCCGEEJfliRGTfwBfK4qyA+dTCMNwPir5FQ/0JYQQQogOxO2FiaqqSxRFWQ0MwlmU1AE5qqp2rIc1CCGEEMLtPPKtnLNFyJoL1ymKMk5V1bWe6E8IIYQQHYNHny6sKMqFx+/pyb6EEEIIce3zxLNyUoCFQCZgP1ucmIHfursvIYQQQnQsnpjKWQo8rarqrnMrFEUZAiwHsjzQnxBCCCE6CE9M5QRcWJQAqKr6hQf6EUIIIUQH44kRk32KoiwDNuD8unAoMAaQpzsJIYQQ4rI8UZhMBSbi/O2Sc18XXgus8kBfQgghhOhAPPE7JhrOIkQKESGEEEL8KB79urAQQgghxI/hkR9YE55hPmH29SlcEwqOy+1Mwr0KTshrSghvkRETIYQQQrQbUpgIIYQQot2QwkQIIYQQ7YYUJkIIIYRoN6QwEUIIIUS7IYWJEEIIIdoNKUyEEEII0W5IYSKEEEKIdkMKEyGEEEK0G1KYCCGEEKLdkMJECCGEEO2GFCZCCCGEaDekMBFCCCFEuyFPFxZCCCE6uPQu6b4+hat23RQmDoeD3Lm5lKgl+Pn7MWveLBK6JQBQ/E0xr77wqmvbA3sPMG/RPAYNGwTAvq/2MX/mfFZ+thKAgwUHWfznxWiaRmR0JH/4yx/wN/l7PygPcDgcfPDSBxwrOYbRz8iDsx4kOjEagPJD5Xz06keubY8cOMIj8x4hNimWt//0tjMfsZE8MPMB/AP8ObDrAJ+88QkAXXt15b7p96HT6XwSlydoDo285XnUHK1B76cn+1fZhMeFu9oL1hZQsrMEnV5H/7v60/2m7tiabWxZtIWWhhb8TH4MnzqcwLBAwJn7za9sRrlVoWu/rr4Ky+3akieHw8GuFbs4WXoSh81Bxj0ZJGUkAR03Tzp0TOk7haSwJOwOO0v3LaXKUuVqH5MyhiFdhqChsbp4NfkV+YxLHUd6jPOCE2QMItwUzhObnmBIlyGMTh6NQ3Pwbf23vGl+Ew3NV6G5XVtyFewXzG8G/IYAYwAN1gaWFSyjzlpHVkIWY1LH0GRrYnv5dj4r+8yHkQm4jgqTHZt2YLVaWfT+Ig7sPcDiPy9m/mvzAejRuwe5K3IB2PrJVjrHdHYVJVUnqli5bCV2ux0ATdP4zz/+J8+98hwJ3RJY98E6Ko5VkJSS5JvA3My8w4zNamPG4hmU7i9l1WureHT+owAk9kxk2svTANizdQ/hUeH0GdyH1+e8TtaELDJvz2Tn2p1sWbmF4fcO5+PXPuaJ3CcIiQhh03ubaDjTQGhEqC/Dc6sju4/QamtlQs4Eqg5VseudXYz63SgAWhpb2L9xP/e/dD/2Zjurfr+K7jd1R92iEpUcRcbdGRR9VsTeVXsZOmUodZV1fPbaZzTWNKLcqvg4MvdqS56KtxfjaHUwYe4EGmsaKd1VCtCh8zQwbiD+en9y8nJIjUhlcp/J5O52vi8FGYMYmTySmZtnYjKamJ89n/yKfNaWrGVtyVoAZtw0g/cPvo+f3o97lXv5/We/x+qwMnXAVPrH9mdP5R5fhudWbcnVhB4TUGtU1hSv4caoG7nvhvt4/5v3uVe5l2e2P4PFZuGpIU+x/9R+TjWd8nGE17fr5h4Tc76ZQdnOYqNP/z4UFRZdsk2TpYk3/vYG0/7gvPhaW6wsfHYh0+dOd21TVlpGWEQYH775If/x//6Dutq6DlOUAJSYS+g9qDcAyTcmU6aWXbJNS1ML65ev554n7gGg4mgFfQb3ASClbwol5hJKC0uJT4nnv1/7b3KfyCW0U2iHKkoAKtVKEtMTAYjpGcOpw+ffzPxMfoREhWBvtmNvscPZgaK0O9PoP7E/AA3VDQSGO0dLbM02bvnVLcT3ifduEF7QljyVF5QTHBnMxhc3sn3JdtdoSUfOU6/IXhScLACgpLaE5IhkV1tLawvVlmpMRhMmg+mS0Y/MuEwabY2YT5qxO+zk5OVgdVgBMOgM2Fpt3gvEC9qSq4TQBAqqnPsU1RTRK7IXMcExHK07SqOtEQ2Nw7WH6dGph/cDEhe5bkZMLA0WgkOCXct6g55WeysGo8G1bv2H6xk+ejjhkc5h5pdzXmbSLycRHRvt2ubM6TPs37OfaX+cRmK3RJ7+9dP0SuvFwKEDvReMBzU3NhMYEuha1usvzdMX679gwIgBhESEAJDYIxFznpnBowdjzjNjbbbScKaBQ3sOMXvpbEyBJnKn5ZJ8YzIxXWO8HpOnWJus+Aedn8LT6XU4Wh3oDc56P6RzCB8++SGaQ6OO8N3+AAAgAElEQVTfhH6u7fR6PevmreN02WnufPpOADp36+zdk/eituSpub6ZMxVnGDVrFBUHK9j2X9sYN2dch85ToDEQi83iWnZoDvQ6PQ7NAUB1czULhi9Ar9OzpnjNRfuO7zGeRV8vAkBDo85aB8DI7iMxGU0Unir0UhTe0ZZcHT1zlIzYDI7WOf/2N/hT0VhBYmgiYf5hNNub6RPVh4rGCp/EJM5ze2GiKMoP3myhqqrV3f1draCQICyNF7yQHY6LLrYAm9Zs4rlXngPgVOUpzLvNHPv2GG8uepP6M/Xk/DaHnz/+cxK6JdC9R3cABmUPoqiwqMMUJgHBATRbml3L35en3Zt288vnfulanjh1Ih++/CH5n+ajDFQICQ8hOCyYpBuSCOscBkCPfj0oLy7vUIWJf6A/tubzn0Q1TXNdbMv2lWGptTApdxIAG/68gdhescT0cMY/9pmx1B6rZeNfNrq26ajakqeAkACSMpLQ6XTE947nzIkzPjl3b2qyNxFgDHAt6zl/oU2PSSfCFMGMzTMAeHLwkxSdLuJw7WG6hHTBYrNcdI+FDh0P9H6AuOA4Xtn9incD8YK25GpN8Rp+lvYzZg+ejfmkmZqmGiw2C+/sf4dpmdM43XSao2eOUm+t90lM4jxPTOUUAlXAQUD9zt8+k5aRxq5tuwDnza0pvVIuam+ob8BmtRET77xwRMVG8dbGt8hdkUvuilxCw0OZ89Ic4rvG09TYxLGjxwAw7zbTvWd3r8biSSlpKRz44gAApftL6ZLS5aL2poYm7FY7nWI6udapu1VGTxnN1L9MRafToWQqdFW6cqL0BA21DbTaWzly4Ahx3eK8GounxSqxlO11TnVVHaoismukq80UbMLgZ8DgZ8Dob8Q/2B+rxcrej/dyaPshAIwBRnT6jnMz8A9pS54u3Kf6aDUhnUN8cu7eVFRTRP8Y5zRfakQqZfXnp1EbbY3YHDbXn0ZbI0HGIADSotLYV7XvomP9Iv0X+Bn8yN2d65rS6Ujakiuls8KO8h0s2LWAKksVh04fQq/Tk9oplfk75/P3vX8nPiSeoppLp/mFd3liKicL2AjcpqrqaQ8cv02yR2aTn5fP4w88jqZpzH5hNiuXryQhKYGs27IoLy0nLuHKF04/fz9mzZ/FvN/NQ9M0bhxwI0NHDPVCBN6Rnp2Oultl4b8vBA0emv0Qm1duJjohmr5ZfakqryIyLvKifWK6xvDugncx+huJ7x7PfdPvw2A0MP5X41n85GIABowYcEmRc63rntmdY+ZjrH52NWgw7LFhmNeZCYsLo9vAbkQXRrN6zmp0Oh2xSiwJfROI7BbJtte2oW5V0Rwawx4b5uswPK4teYrvHU/esjw+nvMxaJD1cJavw/C4/Ip80qLTmHPzHNDBkr1LGJ08mkpLJXsq93C49jBzs+biwEFRTZFreiY+JJ7Ck+enarqFdWN41+EU1RTx9NCnAdhYupH8inyfxOUJbclVTFAMv+7/awBON59mScESHJqDVkcrOdk52Bw2Pjn8CQ22Bh9HJ3Sa5v6vkCmKMgpoVVX106vZ/jjHO8732DzIfMLs61O4JhQcL/D1KYgOpuCEvKaEe60Yt8Krw6U/W/uzNl1nvX2e4KGbX1VV/R9PHFcIIYQQHZvXvpWjKMo4VVXXeqs/IYQQQriPoih6YDHQD2gBHlFVtfiC9tnAg0Ad8KKqqmsVRYkC3gUCgePAL1RVtVxy8At49HdMzgZxTk9P9iWEEEIIj5oIBKiqOhR4CvjruQZFUfoCk4EhwCggR1GUIGAO8K6qqtnAHuCxK3Xiia8LpwALgUzAfrY4MQO/dXdfQgghhPCaW4ANAKqqfqEoSuYFbb2BraqqNgMoinIISD+7zwtnt/nk7L9fulwnnpjKWQo8rarqrnMrFEUZAizH+Y0dIYQQQlx7woALf1SoVVEUo6qqdpwDEE8rihIK+AM3A//4zj71QDhX4ImpnIALixJwVlYe6EcIIYQQ3lMHXPhsEf3ZogRVVb8BXsU5KvJXYBdw6jv7hAK1V+rEEyMm+xRFWYZzuOfM2RMZA8j37YQQQohrVx4wHlh5dibE9RsWiqJEA1Gqqt6iKEo48D84f3A1D2cN8AZwJ7D9Sp14ojCZivMGmVtwDuHUAWuBVR7oSwghhBDesQoYqSjKTpyP3PyFoigzgGJgDZCiKMpXgBWYpapqq6Io84A3FUX5Fc4RlMlX6sTthYmqqtrZk5dCRAghhOggVFV1AL/+zuoLHzdzyTduVFWtBEb/mH48+nVhIYQQQogfQwoTIYQQQrQbUpgIIYQQot2QwkQIIYQQ7YYUJkIIIYRoN6QwEUIIIUS74bWnC1+O+YT5yhsJCo7Lb9QJIYTo2GTERAghhBDthhQmQgghhGg3pDARQgghRLshhYkQQggh2g0pTIQQQgjRbkhhIoQQQoh2QwoTIYQQQrQbUpgIIYQQot2QwkQIIYQQ7YYUJkIIIYRoN6QwEUIIIUS7IYWJEEIIIdoNKUyEEEII0W5IYSKEEEKIdkMKEyGEEEK0G0Zfn4C3OBwOPnjpA46VHMPoZ+TBWQ8SnRgNQPmhcj569SPXtkcOHOGReY8QmxTL2396G03TiIyN5IGZD+Af4M+2Vdv4csOXoIPR/zaatJvTfBWW22kOjbzledQcrUHvpyf7V9mEx4W72gvWFlCyswSdXkf/u/rT/abu2JptbFm0hZaGFvxMfgyfOpzAsEAqDlaw651doIOu/buScXeGDyNzv7bkStM03nv8PcLiwgCI7RnLTQ/cBIC9xc76P61n2K+GEZEQ4ZOYPKEteXI4HOxasYuTpSdx2Bxk3JNBUkYS5QXlfPXeVxhNRhL7JTLgpwN8GJl76dAxpe8UksKSsDvsLN23lCpLlat9TMoYhnQZgobG6uLV5FfkMy51HOkx6QAEGYMIN4XzxKYnGNJlCKOTR+PQHHxb/y1vmt9EQ/NVaG53pVyNTR3L0C5DabI3sa5kHXur9hLiF8LUjKn46/053XKaJXuXYHVYvzevwreum8LEvMOMzWpjxuIZlO4vZdVrq3h0/qMAJPZMZNrL0wDYs3UP4VHh9Bnch9fnvE7WhCwyb89k59qdbFm5hawJWez4eAezl87GZrXxwpQXuHHojeh0Ol+G5zZHdh+h1dbKhJwJVB2qYtc7uxj1u1EAtDS2sH/jfu5/6X7szXZW/X4V3W/qjrpFJSo5ioy7Myj6rIi9q/YydMpQPn/rc26ffjuhMaGsm7eOUxmniOoe5eMI3actuaqrrKNz987cMeuOi4518vBJ8l7Po7Gm0ReheFRb8lS8vRhHq4MJcyfQWNNI6a5SNIfG9iXbGfvMWMJiw9iyaAsVByuIuyHOxxG6x8C4gfjr/cnJyyE1IpXJfSaTuzsXcBYdI5NHMnPzTExGE/Oz55Nfkc/akrWsLVkLwIybZvD+wffx0/txr3Ivv//s91gdVqYOmEr/2P7sqdzjy/Dc6nK5SgxN5OaEm5m7Yy4Ac7LmcODUAX7a66d8fuxztpdvZ1zqOH7S7SdsK9v2vXkVvnXFqRxFUR5VFMV49t/ZiqL82vOn5X4l5hJ6D+oNQPKNyZSpZZds09LUwvrl67nniXsAqDhaQZ/BfQBI6ZtCibmEkIgQZi+djcFooK6mjsCQwA5TlABUqpUkpicCENMzhlOHT7na/Ex+hESFYG+2Y2+xw9mw0+5Mo//E/gA0VDcQGB4IwF3P30VoTCi2ZhtWi5WAkADvBuNhbcnVqdJTWE5bWDdvHRsWbKD2eC0ArbZWbp9xO+Fdwi/p51rXljyVF5QTHBnMxhc3sn3JdpIykmiub8YUbCIs9uxoU69YKosqvR6Pp/SK7EXByQIASmpLSI5IdrW1tLZQbanGZDRhMpguGf3IjMuk0daI+aQZu8NOTl4OVocVAIPOgK3V5r1AvOByueoS0oVvqr/B5rBhc9ioaKyga1jXi/YpqCrgxqgbr5hX4RuXHTFRFGUukAa8DdiBMuC3iqJEq6r6vOdPz32aG5sJDAl0Lev1elrtrRiMBte6L9Z/wYARAwiJCAEgsUci5jwzg0cPxpxnxtp89j+60cC2j7ax/o31DL97uHcD8TBrkxX/IH/Xsk6vw9HqQG9w1rAhnUP48MkP0Rwa/Sb0c22n1+tZN28dp8tOc+fTdzrXGfRUHapi8982E5EQQUBYxypM2pKroIgg+k3oR8qQFCoOVrB18VYmzptInNIxPvV/n7bkqbm+mTMVZxg1axQVByvY9l/bGPvHsdhb7NQeqyUsPoyyvWV07tbZJzF5QqAxEIvN4lp2aA70Oj0OzQFAdXM1C4YvQK/Ts6Z4zUX7ju8xnkVfLwJAQ6POWgfAyO4jMRlNFJ4q9FIU3nG5XJXXlzO+x3gCDAEY9UZ6durJFsOWi/Zpam0i0M95PbhcXoVvXGnE5E7gPlVVLQCqqh4BJgETPHxebhcQHECzpdm17HA4LipKAHZv2s3QsUNdyxOnTqRwZyGLZy1Gr9cTEh7iaht29zDm/WseJQUlFO0p8nwAXuIf6I+t+fynK03TXBeQsn1lWGotTMqdxAOvPMDR3UepKr5gXveZsYybM45NuZtc62J6xvDAKw8QlRzFvtX7vBeIF7QlV9Ep0XTL7AZA3A1xWGosaFrH/pTWljwFhASQlJGETqcjvnc8Z06cQafTMWLqCHYs28GnuZ8S0SWCgNCOU+w22ZsIMJ6PR8/5oiQ9Jp0IUwQzNs9g+qfTGRg3kJSIFMA5QmCxWS66x0KHjgd7P0haVBqv7H7Fu4F4weVydbzhOJuObGLm4JlM7jOZktoSGqwNNNmbCDQ6i5FAg7NIuVxehe9c6R6TBlVVL3rXVFXVpihK/Q/toCjKFsD0ndU6QFNV9ea2neb/XUpaCoU7C8m4NYPS/aV0SelyUXtTQxN2q51OMZ1c69TdKqOnjCYhNYHN729GyVSo/LaSNUvW8HDOwxiMBox+xg41lROrxPLt19+SMiSFqkNVRHaNdLWZgk0Y/AwY/AzodDr8g/2xWqzs/XgvwZHB9MzuiTHAiE6vQ9M01uasZdTvRmEKMeEX4EerrdWHkblfW3L19UdfYwox0W98P6qPVhMcFdyhXj/fpy15ilViKdtbRvKgZKqPVhPS2fmhoGxfGXfMugOjycimlzbRa3gvX4XldkU1RWTEZvDliS9JjUilrP78dHOjrdE1NXFuOcgYBEBaVBr7qi4u+n+R/gvsDju5u3M75PTE5XIV6h9KiH8I83bOI9AYyOzBsymrL6Oopoh+Mf3YXr6d9Jh01Br1snntaNLj0319ClftSoVJk6IoKaqqHj63QlGUFLjsK/0pYAnwU5zTP+1CenY66m6Vhf++EDR4aPZDbF65meiEaPpm9aWqvIrIuMiL9onpGsO7C97F6G8kvns8902/D4PRQEJqAgunLkSn09F7cG969u/po6jcr3tmd46Zj7H62dWgwbDHhmFeZyYsLoxuA7sRXRjN6jmr0el0xCqxJPRNILJbJNte24a6VUVzaAx7bBg6nY70selseHEDBqOBoIggsh/N9nV4btWWXEWnRLN18VbK9pShN+gZ/ljHmgr8Pm3JU3zvePKW5fHxnI9Bg6yHswAIjgxmzXNrMPoZSc1KpVNipyv0fu3Ir8gnLTqNOTfPAR0s2buE0cmjqbRUsqdyD4drDzM3ay4OHBTVFLmmZ+JD4ik8eX6qpltYN4Z3HU5RTRFPD30agI2lGzvUTZ1XylVMUAzP3fIcdoed9755Dw2Njw99zGP9H2NE0gjqrfW8tuc1WlpbfjCvwnd0lxtGVhTlRuA94FPgMJAE3AFMUVX1B2/xVhRlFlCsquqqqzmJjSc2dryS3gMKjhf4+hSEuC4VnJD/e8K9Voxb4dWh0r/k/6VN19lZA2d5fUj3siMmqqruVxQlG7gL6AJ8DeSoqvqDUzln9/uL+05RCCGEENeLK/6OiaqqZ4C3vHAuQgghhLjOyU/SCyGEEKLdkMJECCGEEO2GFCZCCCGEaDekMBFCCCFEuyGFiRBCCCHaDSlMhBBCCNFuSGEihBBCiHZDChMhhBBCtBtSmAghhBCi3ZDCRAghhBDthhQmQgghhGg3pDARQgghRLshhYkQQggh2o0rPl3YGwqOF/j6FIQQQvwfvZ3/tq9P4ZqxYtwKX5/Cj6Yoih5YDPQDWoBHVFUtvqB9JvAg4ABeUFV1laIoOqAcOHR2s89VVX36cv20i8JECCGEEO3eRCBAVdWhiqIMAf4K3AWgKEoEMA3oAQQDe4FVQCrwtaqq46+2E5nKEUIIIcTVuAXYAKCq6hdA5gVtjcBRnEVJMM5RE4CBQIKiKFsURVmvKIpypU6kMBFCCCHE1QgDzlyw3KooyoUzL2XAAeBr4JWz604Af1JV9VbgBeCK831SmAghhBDiatQBoRcs61VVtZ/9951APJAMJAETFUUZBOwGPgZQVXUHztET3eU6kcJECCGEEFcjDxgDcPYeE/MFbaeBJqBFVdVmoBaIAJ4Fpp/dpx/wraqq2uU6kZtfhRBCCHE1VgEjFUXZCeiAXyiKMgMoVlV1taIotwNfKIriAHYA/wt8BbytKMpYwA78/EqdSGEihBBCiCtSVdUB/Po7qw9e0P4szhGSC50Gxv6YfmQqRwghhBDthhQmQgghhGg3pDARQgghRLshhYkQQggh2g0pTIQQQgjRbkhhIoQQQoh2QwoTIYQQQrQbUpgIIYQQot24bn5gTXNo5C3Po+ZoDXo/Pdm/yiY8LtzVXrC2gJKdJej0Ovrf1Z/uN3XH1mxjy6IttDS04GfyY/jU4QSGBbL2+bWu/c4cP0PPYT0Z9OAgX4Tldm3Jk6ZpvPf4e4TFhQEQ2zOWmx64idIvS9m3eh86dCi3Kdxw6w2+Cssj2pIrh8PBrhW7OFl6EofNQcY9GSRlJFH6ZSlfvvslwZ2DARh470Die8f7KjS3cmeeygvK+eq9rzCajCT2S2TATwf4MDL30qFjSt8pJIUlYXfYWbpvKVWWKlf7mJQxDOkyBA2N1cWrya/IZ1zqONJj0gEIMgYRbgrniU1PcHPCzdyZcicOzcG2sm18evRTX4XlETp0LB67mH6x/WhpbeGR1Y9QcrrE1f5k1pM8mPYgdS11vJj3IusOraNTQCeKniiisKoQgFUHV/HZkc/IHZ3r2m9I4hAm/nMiG0s2ej0mcd51U5gc2X2EVlsrE3ImUHWoil3v7GLU70YB0NLYwv6N+7n/pfuxN9tZ9ftVdL+pO+oWlajkKDLuzqDosyL2rtrL0ClDGffHcQDUVdax+ZXNHerNsS15qquso3P3ztwx6w7XcRwOB1+99xUT50/EGGDkX7P+RfeB3QkIC/BVaG7XllwVby/G0epgwtwJNNY0UrqrFIDqI9UMmjyI5EHJvgzJI9yVJ82hsX3JdsY+M5aw2DC2LNpCxcEK4m6I83GE7jEwbiD+en9y8nJIjUhlcp/J5O52XjSDjEGMTB7JzM0zMRlNzM+eT35FPmtL1rK2xPlBacZNM3j/4PsAPNj7QZ767Cma7c0sGLGAz49/jsVm8Vls7jbxhokEGAO4ednNDE4YzF9H/ZWJ708EIC0mjclpkxm8dDAAOx/eyebSzWTEZ/Be4XtM+2TaRce69c1bAbi3z70crz8uRUk7cN0UJpVqJYnpiQDE9Izh1OFTrjY/kx8hUSHYm+3YW+zOJwAAaXem4XA4AGiobiAwPPCiY36x4gtuevAm/AL8vBOEF7QlT6dKT2E5bWHdvHUY/AwM+dkQIrpEcO9/3oveoKfpTBOapmEM6Fgvt7bkqrygnMiukWx8cSOapnHzz28GnDmsPlJN4SeFRKdGM+jBQegNHWOm1V15aq5vxhRsIiz27Mhcr1gqiyo7TGHSK7IXBScLACipLSE54nyR2tLaQrWlGpPRhMlgQuPiZ6BlxmXSaGvEfNL5TLVv674lyBhEq9bq3OCyj0y79tySdAsbijcAsOvYLjK7ZLraekf1ZuvRrbS0tgBwqPoQ6bHpDOwykIy4DLZO2UpVYxXTNkyjoqECgCC/IJ4b8RzDlg/zfjDiEh3rSnEZ1iYr/kH+rmWdXoej1eF68w/pHMKHT36I5tDoN6Gfazu9Xs+6ees4XXaaO5++07W++ttqrE1WEtISvBeEF7QlT0ERQfSb0I+UISlUHKxg6+KtTJw3Eb1BT+mXpex8Yydd+3dFb+wYF9pz2pKr5vpmzlScYdSsUVQcrGDbf21j3JxxJKQl0C2zG6ExoeS9nsc3m77hxjtu9Elc7uauPI3941jsLXZqj9USFh9G2d4yOnfr7JOYPCHQGHjRqIZDc6DX6XFozg9H1c3VLBi+AL1Oz5riNRftO77HeBZ9vci1XF5fTk52Di2tLeyu2I3F3nFGSwDCTGGcaTnjWm7VWjHoDLRqrZirzDx9y9OE+Ifgb/Dn5q4384+v/8HBUwfJP57Pp6WfMrnvZP5259+474P7AHh4wMN8cOADqpuqfRWSuIBHChNFUe4CbgfCcT76eDvw4ZUedexJ/oH+2JptrmVN01xvjGX7yrDUWpiUOwmADX/eQGyvWGJ6xAAw9pmx1B6rZeNfNrq2Kd5R3OHumYC25Sk6JRqdwflRN+6GOCw1FjRNQ6fTkTwome6Z3fns759RvK2YXiN6eT8oD2lLrgJCAkjKSEKn0xHfO54zJ5xvrr1G9MIUbAKg28BulH5V6uVoPMddedLpdIyYOoIdy3ZgCjYR0SWCgNCOMzXYZG8iwHg+Hj3ni5L0mHQiTBHM2DwDgCcHP0nR6SIO1x6mS0gXLDaL636UrqFd6R/bnxmbZ9Bsb+Y3A37DoPhBfHniS+8H5SF1LXWE+oe6lvU6vWt06OCpg7z61at88tAnFNcUs+vYLk5ZTvHlsS9dhd+qb1aRMyLHtf9DfR/i3g/u9W4Q4ge5/SOsoiiLgNE4H3e8HNgE/ARY4u6+foxYJZayvWUAVB2qIrJrpKvNFGzC4GfA4GfA6G/EP9gfq8XK3o/3cmj7IQCMAUZ0ep1rn+OFx0nsl+jdILygLXn6+qOvKfzEeUNZ9dFqgqOCsTXZWJuzllZbKzq9DqPJ2OG+A9aWXF24T/XRakI6h6BpGh899RGN1Y0AHNt/jKjkKO8H5CHuyhM4C5k7Zt3B7b+9nbrKOhL6dpwRy6KaIvrH9AcgNSKVsvoyV1ujrRGbw+b602hrJMgYBEBaVBr7qva5trXYLVhbrVhbrWho1FnrCPYL9m4wHpZXlseYnmMAGJwwGHOl2dUWFRRFVFAU2cuz+Y8N/0HXsK4UVhWydPxS7ul9DwC3pdxG/ol8wDn6YjKaKK8r934g4nt5YsQkTVXV4d9Zt1pRlDwP9HXVumd255j5GKufXQ0aDHtsGOZ1ZsLiwug2sBvRhdGsnrManU5HrBJLQt8EIrtFsu21bahbVTSHxrDHzs8/Np1p6lCf1s5pS56iU6L5/+3deXxU5b3H8c9MJgvZIUDCYmR/RNkxsi9aWQSL2L6sV6pVqtaqXVHUqkVF7b3oy6W31dqCuFQvtLVFAQW0AhKCAgY0QeWBYAxhSQIJSTAhy2Tm/jEhRASRdCYzDN/368ULznnOOc/5/V7DzG/Oc84za59dS+HWQpwRTsbdMo6o2Ch6jerF8rnLcUY4aZfejl6jewU7PL9qSa469e1E1sIs3pjzBnhh1I2jcDgcjLl5DP9+6t9EREWQ3CU5rK7G+StPAHHt4lj20DJckS56jupJ265tgxyd/2QXZdOvQz/mjJwDDpj/0Xwmd59McXUxW4u38nn55zw46kE8eNhRtoNtB31fBjrFd2LbgW1Nxyk9UsqagjX8dtRvcXvclFSVsK5wXbDCCoglny1hQo8JZP04CwcOZr4xk18P/zV5ZXks27GMHm17sOmmTdQ11DH7ndl4vB7uefceFk5byG0Zt1FVV8VNy24CoE9KH74o/yK4AclXOLxe/46uGGMygXuttZnN1o0F5lprx59on8ezHw+zW7NEJJzk7M8J9imcEV7JfiXYp3DG8D7gdZx6K/9p6efs7KGzW/U8ITBXTG4AnjTGLMJ3j70H2ALcHIC+REREJIz4vTCx1u4CrvD3cUVERCT8+b0wMcasAaJP1GatHenv/kRERCR8BGIo5x58T+BcCbgDcHwRERE5DQM6Dwj2KXxrgRjK2WiM+SswwFq7xN/HFxERkfAVkAnWrLWPB+K4IiIiEt7CbMorEREROZOpMBEREZGQocJEREREQoYKExEREQkZKkxEREQkZKgwERERkZChwkRERERChgoTERERCRkqTERERCRkqDARERGRkKHCREREREKGChMREREJGSpMREREJGSoMBEREZGQocJEREREQoYKExEREQkZKkxEREQkZKgwERERkZChwkRERERChgoTERERCRkqTERERCRkqDARERGRkOEK9gmIiIhI6DPGOIFngYFALXCTtTavWfudwDWAB/idtXaJMaYN8ArQETgMXG+tPfBN/eiKiYiIiHwb04EYa+0I4B7giaMNxphk4BfACGAi8HRj061ArrV2DPAycP+pOlFhIiIiIt/GaGAlgLX2A+DCZm1VQAEQ1/jHc/w+wArg0lN1ctYM5Xg9XrJeyKKsoAxnpJMxN48hKS2pqT1neQ67NuzC4XQw6IpBdMvohtfrZdHPFpGYlghAau9UMv4rgwO7DvDBKx+AF9okt2H8beNxRYVHKluSJ4/Hw8a/buRA/gE89R6GfH8I6UPS2bttL9l/z8bpchKTGMP4W8fjig6PPIF/c1VRVEHW81l4Gjw4XU4u+fklxCTEBDE6//Fnnvbk7GHzos24ol10HdiVwVcODmJk/uXAwfX9ryc9MR23x82CjxdQUl3S1D6lxxSGdx6OFy9L85aSXZRNXGQctw6+lRhXDF/WfcnCnIVU1lUysstILutxGVphx0QAABtCSURBVB6vh3WF63i34N0gRuZ/Dhw8O/VZBqYOpLahlpuW3sSuQ7ua2u8adRfX9LuGytpKHst6jDd3vkm35G68NP0lHDgoqCjgJ8t+whH3EX4/+feMOmcUh+sOA3DF4iuorK0MVmihLhGoaLbcYIxxWWvdjcuFwKdABPDfJ9jnMJDEKYTPp8QpfPHhFzTUNzBt7jRKdpaw8dWNTLxjIgC1VbV8suoTfvDUD3DXuFly7xK6ZXSjsriSlG4pTJo9qek4Xq+XzAWZfOeX3yEpLYnta7bz5cEvSe6cHKzQ/KolecrLzMPT4GHag9OoKqsif2M+ABte2MDUOVOJTYpl8+LNbF+znX6T+wUzPL/yZ67WL1hPxtUZdOzdkfxN+VTsrwibwsRfefJ6vGTOz2Tq/VNJTE1kzTNrKNpeRNp5aUGO0D+Gpg0lyhnF3Ky59EzuyYzzZ/D0h76r4bGuWCZ0n8Cdq+8k2hXNo2MeJbsom2m9pmHLLMvylnFB+wu46ryreD7nea7pew33vHcPNe4a5o2fx/v73qe6vjrIEfrP9POmE+OKYeTCkQzrMownJj7B9L9NB6Bfx37M6DeDYQuGAbDhxg2szl/N4xMe57kPn2PRtkXcOPhGZo2YxaOZjzKk0xAmvTKJ0iOlwQzpTFEJJDRbdjYrSi4DOgHdG5dXGWOyjtsnASg/VSdnzVBOsS2m64CuAHTs3ZGDnx9saouMjiS+fTzuGjfuWjc4fOsP5h+k+lA1bz7yJivnraR8X7nvAyM+hk9WfMLyucup/bI2bIoSaFme9uTsIa5dHKseW0Xm/EzSh6QDMPV+X1EC4Gnw4IoMrzrYX7ly17mpqayhYEsByx9eTsnOEjr06hCMkALCX3mqOVxDdFw0iamNVzD7pFK8o7jV4wmUPu36kHMgB4Bd5bvonty9qa22oZbS6lKiXdFER0TjxQtAl4Qu5JT49tlRtoM+7foAsLtyN7GuWCIjIn0H8LZiIK1gdPpoVub5Rgc27t3IhZ2PjSj0bd+XtQVrqW2opbahlp2lOxmQOoDzO5zPirwVAGQVZjE6fTQOHPRu15u/fPcvrJ+5npmDZgYlnjNIFjAFwBgzHMht1nYIOALUWmtr8BUgyc33wVe8ZJ6qk4B9UhhjUvBdsim31pYFqp9vq+5IHVGxUU3LDqfDd9k8wlebxafE89pdr+H1eBk4bSAAscmxDJw2kB7De1C0vYi1z65l+HXDKd5RzIjrR5CUlsSqx1fRvnt7uvTrEpS4/K0leao5XENFUQUTZ0+kaHsR6/68jsvnXE5sW19R8sXmL9j/6X6GXjW09QMKIH/l6uKfXcyhPYcYcf0ILvzBhWTOz2Tnup2Y8SYocfmbv/I09bdTcde6Kd9bTmKnRAo/KiTl3JSgxBQIbVxtvnJVw+P14HQ48Xh9Q/WlNaXMGzcPp8PJsrxlABRUFDAkdQgFlb6/oyJ8ed5zeA9zx8yltqGWD4s+pNodPldLABKjE6moPTai0OBtIMIRQYO3gdySXH4z+jfER8UTFRHFyHNG8pctf+Gjoo+YZqbx8scvM81MIy4yjrioOP6w6Q88+f6TRDgjWHP9Gj7c9yG5Jbnf0PtZbQkwwRizAd/XiJnGmFlAnrV2qTHmUuADY4wHWA+80/j3S8aY9UAdMONUnfi9MDHGZADP4Btj+hJIMMY4gNuttRv83d+3FdUmivqa+qZlr9fb9MZY+HEh1eXVXP301QCs/J+VpPZJpUOPDjgifF/h0s5Lo7qsmuj4aBLTEmnbtS0AXQd25WD+wbApTFqSp5j4GNKHpONwOOjUtxMV+4+9YeS+lUv+pnwm3z05bO7DOcpfuYqOjyayTSSdL+gMQPrgdPbm7g2bwsRfeXI4HIy/bTzrF64nOi6a5M7JYTPcBXDEfYQY17F4nBwrSgZ0HEBydDKzVs8C4K5hd7Hj0A6W5S3jun7Xcfewu8k9kEvZkTLOSTiHQamDmLV6FjXuGm4dfCsXdbqITfs3BSWuQKisrSQh6tiIgtPhpMHbAMD2g9v54+Y/suKHK8gry2Pj3o0crD7IHW/fwR8v+yPX9LuGd/Pf5WD1Qarrq/n9xt9zxH0EgNX5qxmYNlCFyUlYaz3AT49bvb1Z+wPAA8e1VwNXnU4/gRjKeQr4vrV2qLV2nLV2CHBF4/qgSTWpFH5UCEDJzhLandOuqS06LpqIyAgiIiNwRbmIiouirrqOLf/awrYV2wAoLSglrn0ciamJ1NfUU1Hk+/AtskVNRUo4aEmemu9TWlBKfEo8AFtf30qRLWLKvVOISQyfD5Cj/JUrV5SLpLQkirYXAVC0Xa+pk72mCj8uZNLsSVz660upLK6kS//w+EIAvqGYQR0HAdAzuSeFhwub2qrqq6j31Df9qaqvItYVi0kxrN+znnkb51FSXcLOQzupdldT11BHXUMdXrxU1lUSFxkXrLACIqswiym9faMDw7oMI7f4WCHRPrY97WPbM+aFMfxy5S85J/EctpVsY0KPCTz03kNc9qrvpuB3Pn+HPil9WD9zPU6HE5fTxej00WzZvyVYYUkjh9fr38FHY8xGa+2w49Y5gPettcNPtM/j2Y8HfAS06cmA3WXghbG3jKXwo0IS0xI5d+i5ZL+WzZ6P9+BwOEg1qVw04yLqqupY++xa6mvqcUY4GXnDSJK7JLPvk31sXrQZL15Se6cy4voRgT79VtOSPHncHrIWZnFo7yHwwqgfjyK2XSyLf7aYlO4pRERGANBjeA/On3B+kCP0H3/lqn339pQWlLLhhQ14PB4SOiQw7tZxRLgigh2iX/gzT9tXb+fTdz7FFemi56ieXDDpglaJIWd/TsD7aHoqJyEdHDD/o/kM7DiQ4upithZv5Xt9vseADgPw4GFH2Q4Wf7aYjrEd+ekg3xfYQzWHmJ8znxp3DZekX8LY9LG4PW5Kqkp4Puf5pisKgfRK9isB7wOOPZUzIHUADhzMfGMmU3pPIa8sj2U7lvHc5c8xJG0IdQ11/Obd35C5O5OLulzEM1OeodZdyycHPuH2t27H7XEze+Rsrjr/Kuo99bz88cv8OfvPrRKD9wGvo1U6arRq/6oWfc5O6jSpVc8TAlOYzAHGAm/je0QoEd9kK5nW2rkn2qc1ChMRkZZqjcIkHLRWYRIOVJicnN+HchqLj9n47s5Nafz77pMVJSIiIiJHBepx4XTAAH2BPkDPxuEcERERkZMKxFM5z+AreFbgm+UtAd+zy5OAm/zdn4iIiISPQDy/2c9aO+64dUsbZ4ATEREROalADOU4jTFjmq8wxowF6k+yvYiIiAgQmCsmNwBPGmMW4ZsZzgNsAW4OQF8iIiISRvxemFhrd+GbUE1ERETktATi5tc1QPSJ2qy1I/3dn4iIiISPQAzl3APMB64E3KfYVkRERKRJIIZyNhpj/goMsNYu8ffxRURE5PT079Q/2KfwrQXk516ttY8H4rgiIiIS3gI186uIiIjIaVNhIiIiIiFDhYmIiIiEDBUmIiIiEjICcvOriIS+nP05wT6FM0bOPuVKpLXoiomIiIiEDBUmIiIiEjJUmIiIiEjIUGEiIiIiIUOFiYiIiIQMFSYiIiISMlSYiIiISMhQYSIiIiIhQ4WJiIiIhAwVJiIiIhIyVJiIiIhIyFBhIiIiIiFDhYmIiIiEDBUmIiIiEjJUmIiIiEjIcAX7BFqL1+Ml64UsygrKcEY6GXPzGJLSkprac5bnsGvDLhxOB4OuGES3jG54vV4W/WwRiWmJAKT2TiXjvzLYmbmTnOU5RMVG0WdsH8zFJlhh+V1L8uTxeNj4140cyD+Ap97DkO8PIX1IOnu37SX779k4XU5iEmMYf+t4XNHh85LzZ64qiirIej4LT4MHp8vJJT+/hJiEmCBG5z8OHFzf/3rSE9Nxe9ws+HgBJdUlTe1TekxheOfhePGyNG8p2UXZXN7zcgZ0HABArCuWpOgk7s+8n9uH3N60X3piOn//7O+s3r261WMKBAcO7ht9H31S+lDXUMdD6x6isLKwqX3mwJlM7jmZqvoqXvz4RdbtXkdaXBoPjXuICGcEDhzMzZxLQUUB1/W/jivNlZTVlAHwcObDFFQUBCu0gLmoy0XMu3QeF7908VfWX97ncuaMnYPb42bhRwtZsGUBMa4YXrnyFTrGdeRw3WGuf/16DlYfPOG2Elzh8ylxCl98+AUN9Q1MmzuNkp0lbHx1IxPvmAhAbVUtn6z6hB889QPcNW6W3LuEbhndqCyuJKVbCpNmT2o6Tk1lDdn/yGb676YTHRvNW797i879OpPQISFYoflVS/KUl5mHp8HDtAenUVVWRf7GfAA2vLCBqXOmEpsUy+bFm9m+Zjv9JvcLZnh+5c9crV+wnoyrM+jYuyP5m/Kp2F8RNoXJ0LShRDmjmJs1l57JPZlx/gye/vBpwFd0TOg+gTtX30m0K5pHxzxKdlE2y3ctZ/mu5QDMypjF37b/jYraCn73/u8A6JXci6vOu4o1u9cELS5/u6TbJURFRPGjN35E/479uWP4Hfzq7V8B0KttLy7rdRnXvn4tAC9f8TKb9m7i9ozbWfzJYtYUrGFk15H88qJfMuudWfRt35f71t7HZwc/C2ZIATV75GyuG3AdVfVVX1nvcrp4atJTZMzPoKquiqwfZ7HMLmNG/xnkluTy0HsPcfUFV3P/2Pu58+07T7htcVVxkKISOIuGcoptMV0HdAWgY++OHPz8YFNbZHQk8e3jcde4cde6weFbfzD/INWHqnnzkTdZOW8l5fvKqSyppN257YiJj8HhdNChZwdKdpacqMszUkvytCdnD3Ht4lj12Coy52eSPiQdgKn3+4oSAE+DB1dkeNXB/sqVu85NTWUNBVsKWP7wckp2ltChV4dghBQQfdr1IedADgC7ynfRPbl7U1ttQy2l1aVEu6KJjojGi/cr+16YdiFV9VXkHsj9yvrr+l3HC7kvfG37M9ngtMFs2LMBgNySXC7ocEFTW4+2Pfhw34fUNdRR11DH7ord9E7pzRPvP0Hm7kwAIhwR1DbUAtC3fV9uHHQjL057kR8P+nHrB9MKdh3axff+/r2vre/bvi95ZXmU15RT76lnfeF6xpw7htHpo1mZtxKAFXkruLT7pSfdVoLrrClM6o7UERUb1bTscDrwNHialuNT4nntrtdYct8SLpjke0OITY5l4LSBTL1/KoOuGMTaZ9eSlJZE+Z5yqiuqcde62bdtn++DJ0y0JE81h2uoKKpg4uyJDJw2kHV/XgdAbFtfUfLF5i/Y/+l+eo3p1YqRBJ6/clX7ZS2H9hyiS78uTL1/KrVVtexct7PV4wmUNq42VNdXNy17vB6cjmNvPaU1pcwbN49HxjzC2/lvf2Xf7/b6Lkt2LPnKusGpg9l7eC9FVUWBPfFWFhcVx+G6w03LDd4GIhwRAOws28nQTkOJjfQNaw1MHUgbVxvKa8txe92cm3Qus4bP4rns5wBYuWslj2Q+wk3Lb2Jw2mDGpo8NSkyB9K/P/kV9Q/3X1idGJ1JRU9G0fLj2MEnRSb71tRXH1sUknXRbCa6AfIU1xlwBXAokAeVAJvCatTZoX2+i2kRRX3PsRez1enFG+N4cCz8upLq8mqufvhqAlf+zktQ+qXTo0QFHhO+rbtp5aVSXVRMVF8Wwa4fx7lPvEtcujpRuKWFzyR1alqeY+BjSh6TjcDjo1LcTFfuP/UfPfSuX/E35TL57Mq6o8Lpi4q9cRcdHE9kmks4XdAYgfXA6e3P3YsaHx71LR9xHiHEd+z/ixInH6yvgBnQcQHJ0MrNWzwLgrmF3sePQDj4v/5zO8Z2prq/+yv0oAKO6jGJV/qrWC6CVVNVVERcZ17TsxEmDtwGA/PJ8Fn+ymGcve5bdFbvJLcmlvKYcgIxOGdw7+l7uW3Nf030kr+a+ypf1XwKQuTuT81LOY93uda0cUXBU1laSEH1saD0hOoHymnLf+qiEr687wbYSXH6/YmKMeQaYDLwDvAD8G7gEmO/vvk5Hqkml8CPfjWQlO0tod067prbouGgiIiOIiIzAFeUiKi6Kuuo6tvxrC9tWbAOgtKCUuPZxeD1eSvJKuHzO5Yy7bRwV+ytI7ZMalJgCoSV5ar5PaUEp8SnxAGx9fStFtogp904hJjF8irej/JUrV5SLpLQkirb7rgAUbS+ibde2rR9QgOwo28GgjoMA6Jnck8LDx27orKqvot5T3/Snqr6KWJfvSlu/9v34uOTjrx2vW1I3dh4KnytKR20t3sroc0YD0L9jf3aWHYuxbUxbkmOSuWHpDTy24THS4tPIO5RHRqcM7hp5F7etuI1PD34KQHxkPP+86p+0cbUB4KLOFzW1nQ0+O/gZvdv1pm1MWyKdkYxNH8v7e94nqzCLKb2nAHBZr8vI3J150m0luALxFbaftXbcceuWGmOyAtDXt9btwm7szd3L0geWghfG3jKW3DdzSUxL5Nyh59JhWweWzlmKw+Eg1aTSpX8XOvTowNpn11K4tRBnhJNxt4zDGeEkwhXB6/e9TkRkBP2n9g+rD92W5KlT305kLczijTlvgBdG3TiK6opqtv5zKyndU1g5zzeu22N4D86fcH6QI/Qff+UKYMxPxrDhhQ14PB4SOiSQcU1GkKPzn+yibPp16MeckXPAAfM/ms/k7pMpri5ma/FWPi//nAdHPYgHDzvKdrDtoO/LQKf4Tmw7sO0rx0qISqDGXROMMAJudf5qRnQZwUvTXsLhcDBn7Ryu638duyt3817Be3RN7Mqr01+l3lPPkx88icfrYfbI2URGRPLw+IcBKKgo4OHMh/nD5j+w4PIF1Hvq2bh3I+sL1wc5usC7pt81xEfFM3/LfGa9PYtV167C6XCy8KOF7Du8jz9t/hMvTX+JzJmZ1DXUMeOfM3B73CfcVoLL4fX6d3TFGJMJ3GutzWy2biww11o7/kT7PJ79ePjcwSZyhsjZnxPsUzhj5OxTrr4Nvaa+Pe8DXkdr9rePfS36nO1M51Y9TwjMFZMbgCeNMYsalz3AVuDmAPQlIiIiYcTvhYm1dhdwhTGmPY03v1prS/3dj4iIiIQfvxcmxpgM4BkgAjgMJBhjnMBt1lrdVSQiIiInFYihnKeA71trm269N8akA/8AhgWgPxEREQmwxosMzwIDgVrgJmttXmPbIODpZpsPB6YDm4AdwNE72ZdYa3//Tf0EojCJbF6UNCqEMJqiUURE5OwzHYix1o4wxgwHngCuALDWfgSMBzDGXAXss9auNMZcCiyy1v7823YSiMLkTWPMv4G3gQogEZgIvBWAvkRERKR1jAZWAlhrPzDGXHj8BsaYOOAh4Oh0w0OBIcaY94AS4BfW2v3f1InfJ1iz1s4FZgNHgJTGv+9uXC8iIiJnpkR8FxyOajDGHH+B40bgH9baoz8eth14oHF+s9eBP5yqk0D9Vk46YIC+QB+gpzGm1Z+FFhEREb+pBBKaLTuttcf/WNwPgQXNllcDR38GfAkw+FSdBHJK+reBhYTIlPQiIiLyH8kCpgA03mPylZ/9NsYkAdHH3We6APh+47+/A2SfqpOzZkp6ERER+Y8sASYYYzYADmCmMWYWkGetXYpvhOSL4/a5B1hojLkNqAJuOlUngShMnMaYMSeYkv7rv08tIiIiZwRrrQf46XGrtzdr34zvyZ3m++QDF59OP4Gekt6Bb0r6LWhKehERETmFgE1J7+/jioiISPgLxJT0a4DoE7VZa0f6uz8REREJH4EYyrkH3xM4VwLHP0YkIiIiclIOr9f/M8UbY2bju0t3id8PLiIiImErIIWJiIiISEsEauZXERERkdOmwkRERERCRiBufg0Zxhgn8CwwEKgFbrLW5jVrvxm4Bd9Nuo9Ya5cbY9oD/we0AfYBM6211SfattlxfgWkWWvvaaXQAsafOWvcvgOwAehvra1p1WBaSUty1qwtbF47p+tUeWvcJuxfPy1ljBkGzLPWjg/2uYSa5rkxxvQCXgS8wDbg9saJwiREhfsVk+lAjLV2BL6nhZ442mCMSQN+AYwCJgH/bYyJBuYA/2etHQNsBW452bbGmDbGmFeA21szqADzS84at5+E7zeTUls1gtZ32jkL09fO6Tpp3uCsev2cNmPMXfh+gyQm2OcSak6QmyeB+xvfnxxonq2QF+6FyWhgJYC19gPgwmZtFwFZ1tpaa20FkAcMaL4PsAK49Bu2jQFeBh5thVhai79yBr5Zfy8FylrhvIOpJTkLx9fO6fqmvMHZ8/ppiV3A94J9EiHq+NwMBd5r/Hfz9ycJUeFemCQCFc2WG4wxrpO0HQaSjlt/onVN6621h6y1bwfixIPIXznDWvuOtbY0sKcbEk47Z2H62jld35S3s+n1c9qstf9Evz92QifIjcNae/Tx06b3Jwld4V6YVAIJzZad1lr3SdoSgPLj1p9oXfP14chfOTubtCRn8s15E/GX5veT6P/fGSDcC5MsYAqAMWY4kNusbRMwxhgTY4xJAvriuzGqaR/gMiDzG7YNR/7K2dmkJTmTb86biL9sNcaMb/z32fj+dMYJ66dygCXABGPMBnw3Pc00xszCNyvtUmPM/+J7kTqB+6y1NcaYR4CXGp+kOAjMsNZWnWjboEQUeH7JWbBOPkhOO2dBPNdQ8o15C+6pSRi5A5hvjIkCPgNeC/L5yClo5lcREREJGeE+lCMiIiJnEBUmIiIiEjJUmIiIiEjIUGEiIiIiIUOFiYiIiIQMFSYiIiISMlSYiIiISMgI9wnWRMKWMaYNvh8C7AwUAmOBa4AHGjeJBX4E1AF/a9ymG7AY6AcMBt601t5rjFkLfNy4/kt8E8JNApKBiUADvl9sTQbaA/OttX8KdIwicvbRFRORM9dPgHxr7SjgQSAVuAC41lp7CbAUuKpx2x7AjcDlwMPALGBY47qjNllrvwNEA9XW2gnAp8A4oBew2Fo7sfEYswIbmoicrVSYiJy5+gIbAKy124EDwF7gf40xLwIXA5GN235ura3A9wNmxdbassap8ZtP/byl8e9yfAUJwCEgBigCphtjXgHub3ZcERG/UmEicubaBowAMMb0xDfEsgCYaa29AdiH7zdo4KsFyMl80zZ3Au9ba68F/tHsuCIifqV7TETOXM8DLxpj1gEFQA3wV2CjMeYQUIzv/hN/WAb8yRjzQ6AUcBtjoq21tX46vogIoB/xEzljGWNGAvHW2reNMb2BldbansE+LxGR/4SumIicuT4HFhljHsB3z8ftQT4fEZH/mK6YiIiISMjQza8iIiISMlSYiIiISMhQYSIiIiIhQ4WJiIiIhAwVJiIiIhIyVJiIiIhIyPh/DP8sD+6+TJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for CV data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C\n",
    "SVM_Classifier(train_avgw2v,cv_avgw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-16T06:52:13.526087Z",
     "start_time": "2019-03-16T06:51:48.304712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8532966467980673\n",
      "macro f1 score for data : 0.6723337781716502\n",
      "micro f1 scoore for data: 0.8631818181818183\n",
      "hamming loss for data: 0.1368181818181818\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.31      0.42      1056\n",
      "           1       0.88      0.97      0.92      5544\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      6600\n",
      "   macro avg       0.77      0.64      0.67      6600\n",
      "weighted avg       0.84      0.86      0.84      6600\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_SVM(train_avgw2v,Y_tr,test_avgw2v,Y_test,optimal_C=10,optimal_gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Weighted W2V "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:56:22.334049Z",
     "start_time": "2019-03-15T14:56:20.806231Z"
    }
   },
   "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  (8978, 12047)\n",
      "CV Data Size:  (4422, 12047)\n",
      "Test Data Size:  (6600, 12047)\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": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T14:59:16.499049Z",
     "start_time": "2019-03-15T14:58:00.758184Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8978\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": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T15:00:59.039413Z",
     "start_time": "2019-03-15T15:00:20.345181Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4422\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": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T15:02:42.818117Z",
     "start_time": "2019-03-15T15:01:47.104416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600\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": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T15:03:05.930188Z",
     "start_time": "2019-03-15T15:03:05.914926Z"
    }
   },
   "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": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T15:03:51.544413Z",
     "start_time": "2019-03-15T15:03:51.475456Z"
    }
   },
   "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": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-15T19:58:55.606499Z",
     "start_time": "2019-03-15T19:41:29.195918Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for train data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ AUC Score for CV data ------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_C using l1 regularization\n",
    "SVM_Classifier(train_tfidfw2v,cv_tfidfw2v,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-16T07:01:27.643293Z",
     "start_time": "2019-03-16T07:00:57.624612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8289556482028073\n",
      "macro f1 score for data : 0.6146437341560083\n",
      "micro f1 scoore for data: 0.8556060606060606\n",
      "hamming loss for data: 0.1443939393939394\n",
      "Precision recall report for data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.20      0.31      1056\n",
      "           1       0.87      0.98      0.92      5544\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      6600\n",
      "   macro avg       0.76      0.59      0.61      6600\n",
      "weighted avg       0.83      0.86      0.82      6600\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_SVM(train_tfidfw2v,Y_tr,test_tfidfw2v,Y_test,optimal_C=100,optimal_gamma=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-16T07:12:14.545214Z",
     "start_time": "2019-03-16T07:12:14.527228Z"
    }
   },
   "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 |    Model     | Best Hyper Parameter(C) | Test Auc Score |\n",
      "+------------+--------------+-------------------------+----------------+\n",
      "|    BoW     | Liner kernel |          0.001          |     92.36      |\n",
      "|   Tf-Idf   | Liner kernel |          0.001          |     94.08      |\n",
      "|  Avg-W2V   | Liner kernel |            10           |     88.46      |\n",
      "| TfIdf-W2V  | Liner kernel |            10           |     86.24      |\n",
      "+------------+--------------+-------------------------+----------------+\n",
      "+------------+------------+---------------------------------+----------------+\n",
      "| Vectorizer |   Model    | Best Hyper Parameter(C & gamma) | Test Auc Score |\n",
      "+------------+------------+---------------------------------+----------------+\n",
      "|    BoW     | RBF kernel |           (10, 0.001)           |     91.41      |\n",
      "|   Tf-Idf   | RBF kernel |           (0.1, 0.001)          |     91.51      |\n",
      "|  Avg-W2V   | RBF kernel |            (10, 0.1)            |     85.32      |\n",
      "| TfIdf-W2V  | RBF kernel |           (100, 0.01)           |     82.89      |\n",
      "+------------+------------+---------------------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Vectorizer\", \"Model\", \"Best Hyper Parameter(C)\",\"Test Auc Score\"]\n",
    "x.add_row([\"BoW\",\"Liner kernel\",0.001,92.36])\n",
    "x.add_row([\"Tf-Idf\",\"Liner kernel\",0.001,94.08])\n",
    "x.add_row([\"Avg-W2V\",\"Liner kernel\",10,88.46])\n",
    "x.add_row([\"TfIdf-W2V\",\"Liner kernel\",10,86.24])\n",
    "\n",
    "y = PrettyTable()\n",
    "y.field_names = [\"Vectorizer\", \"Model\", \"Best Hyper Parameter(C & gamma)\",\"Test Auc Score\"]\n",
    "y.add_row([\"BoW\",\"RBF kernel\",(10,0.001),91.41])\n",
    "y.add_row([\"Tf-Idf\",\"RBF kernel\",(0.1,0.001),91.51])\n",
    "y.add_row([\"Avg-W2V\",\"RBF kernel\",(10,0.1),85.32])\n",
    "y.add_row([\"TfIdf-W2V\",\"RBF kernel\",(100,0.01),82.89])\n",
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "printmd('****Final Conclusion:****')\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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