07 Amazon Fine Food Reviews Analysis_Support Vector Machines.ipynb

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   "source": [
    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n",
    "\n",
    "EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n",
    "\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [1]. Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it is easier to query the data and visualise the data efficiently.\n",
    "<br> \n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score is above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1212: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite')\n",
    "\n",
    "#filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "filtered_data = pd.read_sql_query(\"\"\"SELECT * FROM Reviews WHERE Score != 3\"\"\", con) \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 0 and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(525814, 10)\n"
     ]
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    {
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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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</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",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \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",
       "3                     3                       3      0  1307923200   \n",
       "4                     0                       0      1  1350777600   \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...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(filtered_data.shape) \n",
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  [2] Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [2.1] Data Cleaning: Deduplication\n",
    "\n",
    "It is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78445</td>\n",
       "      <td>B000HDL1RQ</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138317</td>\n",
       "      <td>B000HDOPYC</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138277</td>\n",
       "      <td>B000HDOPYM</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73791</td>\n",
       "      <td>B000HDOPZG</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155049</td>\n",
       "      <td>B000PAQ75C</td>\n",
       "      <td>AR5J8UI46CURR</td>\n",
       "      <td>Geetha Krishnan</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1199577600</td>\n",
       "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
       "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
       "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
       "\n",
       "   HelpfulnessDenominator  Score        Time  \\\n",
       "0                       2      5  1199577600   \n",
       "1                       2      5  1199577600   \n",
       "2                       2      5  1199577600   \n",
       "3                       2      5  1199577600   \n",
       "4                       2      5  1199577600   \n",
       "\n",
       "                             Summary  \\\n",
       "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
       "\n",
       "                                                Text  \n",
       "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
       "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
      ]
     },
     "execution_count": 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it can be seen above that same user has multiple reviews with same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text and on doing analysis it was found that <br>\n",
    "<br> \n",
    "ProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n",
    "<br> \n",
    "ProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n",
    "\n",
    "It was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n",
    "\n",
    "The method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=filtered_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deduplication of entries\n",
    "final = sorted_data.drop_duplicates(subset = {\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "69.25890143662969"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Observation:-</b> It was also seen that in two rows given below the value of HelpfulnessNumerator is greater than HelpfulnessDenominator which is not practically possible hence these two rows too are removed from calcualtions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>64422</td>\n",
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       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
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       "      <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",
    "display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364171, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    307061\n",
       "0     57110\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": {},
   "source": [
    "#  [3] Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [3.1].  Preprocessing Review Text\n",
    "\n",
    "Now that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n",
    "\n",
    "Hence in the Preprocessing phase we do the following in the order below:-\n",
    "\n",
    "1. Begin by removing the html tags\n",
    "2. Remove any punctuations or limited set of special characters like , or . or # etc.\n",
    "3. Check if the word is made up of english letters and is not alpha-numeric\n",
    "4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n",
    "5. Convert the word to lowercase\n",
    "6. Remove Stopwords\n",
    "7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n",
    "\n",
    "After which we collect the words used to describe positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "I set aside at least an hour each day to read to my son (3 y/o). At this point, I consider myself a connoisseur of children's books and this is one of the best. Santa Clause put this under the tree. Since then, we've read it perpetually and he loves it.<br /><br />First, this book taught him the months of the year.<br /><br />Second, it's a pleasure to read. Well suited to 1.5 y/o old to 4+.<br /><br />Very few children's books are worth owning. Most should be borrowed from the library. This book, however, deserves a permanent spot on your shelf. Sendak's best.\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'once', 'that', \"weren't\", 'both', 'had', 'a', 'o', \"doesn't\", 'the', \"didn't\", 'it', 'by', 'of', 'ma', 'their', 'at', \"aren't\", 'on', 'no', 'only', 'up', 'will', 'while', 'y', 'themselves', 'yours', 'when', 'why', 'yourself', 'being', 'herself', 'itself', 'who', 'were', \"won't\", 'yourselves', 'me', 'your', 'than', 'have', 'from', 'doesn', \"wouldn't\", 'do', 'this', 'he', 'an', 'mustn', 'does', \"haven't\", 'is', \"needn't\", 'before', 'off', 'wouldn', 'needn', 'be', 'own', 'we', 'which', 'her', 'mightn', 'such', 'weren', 'll', \"couldn't\", \"mightn't\", 'whom', 'against', 'each', 'what', 'over', 'through', 'in', 'under', 'very', \"hadn't\", 'has', \"you're\", 'theirs', 'because', \"you'll\", \"she's\", 're', 'just', 'you', 'don', 'but', 'should', \"shouldn't\", 'again', 'shouldn', 'isn', 'having', \"you've\", 'into', 'to', 'did', 'or', 'myself', 'ain', 'its', 'doing', \"mustn't\", \"isn't\", 'same', 'they', 'any', 'as', 'until', 'was', 'below', 'now', 'out', 'then', 'haven', 'more', 'too', 'other', 'here', 'wasn', 'above', 'further', 'm', 'ourselves', 'couldn', 'ours', 'd', 'with', \"hasn't\", 'down', 'some', 'about', \"it's\", 'are', 'am', 'how', 'all', 'she', 've', \"should've\", 'shan', 'his', 'my', 'him', 'between', 'so', 'hasn', 'them', 'been', 't', 'didn', 'i', 'where', 'can', 'our', 'himself', 'for', 'won', 'not', 'aren', 'few', \"wasn't\", 'and', 'during', 'nor', 'hadn', 'these', \"don't\", \"shan't\", \"that'll\", 'most', 'after', 'there', 'those', 's', \"you'd\", 'if', 'hers'}\n",
      "************************************\n",
      "tasti\n"
     ]
    }
   ],
   "source": [
    "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": {},
   "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",
    "i=0\n",
    "str1=' '\n",
    "final_string=[]\n",
    "all_positive_words=[] # store words from +ve reviews here\n",
    "all_negative_words=[] # store words from -ve reviews here.\n",
    "s=''\n",
    "for sent in final['Text'].values:\n",
    "    filtered_sentence=[]\n",
    "    #print(sent);\n",
    "    sent=cleanhtml(sent) # remove HTMl tags\n",
    "    for w in sent.split():\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')\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",
    "                else:\n",
    "                    continue\n",
    "            else:\n",
    "                continue \n",
    "    #print(filtered_sentence)\n",
    "    str1 = b\" \".join(filtered_sentence) #final string of cleaned words\n",
    "    #print(\"***********************************************************************\")\n",
    "    \n",
    "    final_string.append(str1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "final['CleanedText']=final_string #adding a column of CleanedText which displays the data after pre-processing of the review "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138706</th>\n",
       "      <td>150524</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>ACITT7DI6IDDL</td>\n",
       "      <td>shari zychinski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>939340800</td>\n",
       "      <td>EVERY book is educational</td>\n",
       "      <td>this witty little book makes my son laugh at l...</td>\n",
       "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138688</th>\n",
       "      <td>150506</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>A2IW4PEEKO2R0U</td>\n",
       "      <td>Tracy</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1194739200</td>\n",
       "      <td>Love the book, miss the hard cover version</td>\n",
       "      <td>I grew up reading these Sendak books, and watc...</td>\n",
       "      <td>b'grew read sendak book watch realli rosi movi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138689</th>\n",
       "      <td>150507</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>A1S4A3IQ2MU7V4</td>\n",
       "      <td>sally sue \"sally sue\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1191456000</td>\n",
       "      <td>chicken soup with rice months</td>\n",
       "      <td>This is a fun way for children to learn their ...</td>\n",
       "      <td>b'fun way children learn month year learn poem...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id   ProductId          UserId            ProfileName  \\\n",
       "138706  150524  0006641040   ACITT7DI6IDDL        shari zychinski   \n",
       "138688  150506  0006641040  A2IW4PEEKO2R0U                  Tracy   \n",
       "138689  150507  0006641040  A1S4A3IQ2MU7V4  sally sue \"sally sue\"   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "138706                     0                       0      1   939340800   \n",
       "138688                     1                       1      1  1194739200   \n",
       "138689                     1                       1      1  1191456000   \n",
       "\n",
       "                                           Summary  \\\n",
       "138706                   EVERY book is educational   \n",
       "138688  Love the book, miss the hard cover version   \n",
       "138689               chicken soup with rice months   \n",
       "\n",
       "                                                     Text  \\\n",
       "138706  this witty little book makes my son laugh at l...   \n",
       "138688  I grew up reading these Sendak books, and watc...   \n",
       "138689  This is a fun way for children to learn their ...   \n",
       "\n",
       "                                              CleanedText  \n",
       "138706  b'witti littl book make son laugh loud recit c...  \n",
       "138688  b'grew read sendak book watch realli rosi movi...  \n",
       "138689  b'fun way children learn month year learn poem...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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', index=True, index_label=None, chunksize=None, dtype=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlite3.connect(\"final.sqlite\")\n",
    "cleaned_data = pd.read_sql_query(\"select * from Reviews\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364171, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138706</td>\n",
       "      <td>150524</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>ACITT7DI6IDDL</td>\n",
       "      <td>shari zychinski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>939340800</td>\n",
       "      <td>EVERY book is educational</td>\n",
       "      <td>this witty little book makes my son laugh at l...</td>\n",
       "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138688</td>\n",
       "      <td>150506</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>A2IW4PEEKO2R0U</td>\n",
       "      <td>Tracy</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1194739200</td>\n",
       "      <td>Love the book, miss the hard cover version</td>\n",
       "      <td>I grew up reading these Sendak books, and watc...</td>\n",
       "      <td>b'grew read sendak book watch realli rosi movi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138689</td>\n",
       "      <td>150507</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>A1S4A3IQ2MU7V4</td>\n",
       "      <td>sally sue \"sally sue\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1191456000</td>\n",
       "      <td>chicken soup with rice months</td>\n",
       "      <td>This is a fun way for children to learn their ...</td>\n",
       "      <td>b'fun way children learn month year learn poem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138690</td>\n",
       "      <td>150508</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>AZGXZ2UUK6X</td>\n",
       "      <td>Catherine Hallberg \"(Kate)\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1076025600</td>\n",
       "      <td>a good swingy rhythm for reading aloud</td>\n",
       "      <td>This is a great little book to read aloud- it ...</td>\n",
       "      <td>b'great littl book read nice rhythm well good ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>138691</td>\n",
       "      <td>150509</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>A3CMRKGE0P909G</td>\n",
       "      <td>Teresa</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1018396800</td>\n",
       "      <td>A great way to learn the months</td>\n",
       "      <td>This is a book of poetry about the months of t...</td>\n",
       "      <td>b'book poetri month year goe month cute littl ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      Id   ProductId          UserId                  ProfileName  \\\n",
       "0  138706  150524  0006641040   ACITT7DI6IDDL              shari zychinski   \n",
       "1  138688  150506  0006641040  A2IW4PEEKO2R0U                        Tracy   \n",
       "2  138689  150507  0006641040  A1S4A3IQ2MU7V4        sally sue \"sally sue\"   \n",
       "3  138690  150508  0006641040     AZGXZ2UUK6X  Catherine Hallberg \"(Kate)\"   \n",
       "4  138691  150509  0006641040  A3CMRKGE0P909G                       Teresa   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     0                       0      1   939340800   \n",
       "1                     1                       1      1  1194739200   \n",
       "2                     1                       1      1  1191456000   \n",
       "3                     1                       1      1  1076025600   \n",
       "4                     3                       4      1  1018396800   \n",
       "\n",
       "                                      Summary  \\\n",
       "0                   EVERY book is educational   \n",
       "1  Love the book, miss the hard cover version   \n",
       "2               chicken soup with rice months   \n",
       "3      a good swingy rhythm for reading aloud   \n",
       "4             A great way to learn the months   \n",
       "\n",
       "                                                Text  \\\n",
       "0  this witty little book makes my son laugh at l...   \n",
       "1  I grew up reading these Sendak books, and watc...   \n",
       "2  This is a fun way for children to learn their ...   \n",
       "3  This is a great little book to read aloud- it ...   \n",
       "4  This is a book of poetry about the months of t...   \n",
       "\n",
       "                                         CleanedText  \n",
       "0  b'witti littl book make son laugh loud recit c...  \n",
       "1  b'grew read sendak book watch realli rosi movi...  \n",
       "2  b'fun way children learn month year learn poem...  \n",
       "3  b'great littl book read nice rhythm well good ...  \n",
       "4  b'book poetri month year goe month cute littl ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(cleaned_data.shape)\n",
    "cleaned_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    307061\n",
       "0     57110\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 11)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To randomly sample 60k points from both class\n",
    "random_sample_data = final.sample(n=100000) \n",
    "random_sample_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort data based on time\n",
    "final_sorted_time=random_sample_data.sort_values('Time',ascending=True,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138706</th>\n",
       "      <td>150524</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>ACITT7DI6IDDL</td>\n",
       "      <td>shari zychinski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>939340800</td>\n",
       "      <td>EVERY book is educational</td>\n",
       "      <td>this witty little book makes my son laugh at l...</td>\n",
       "      <td>b'witti littl book make son laugh loud recit c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346041</th>\n",
       "      <td>374343</td>\n",
       "      <td>B00004CI84</td>\n",
       "      <td>A1B2IZU1JLZA6</td>\n",
       "      <td>Wes</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>948240000</td>\n",
       "      <td>WARNING: CLAMSHELL EDITION IS EDITED TV VERSION</td>\n",
       "      <td>I, myself always enjoyed this movie, it's very...</td>\n",
       "      <td>b'alway enjoy movi funni entertain didnt hesit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70688</th>\n",
       "      <td>76882</td>\n",
       "      <td>B00002N8SM</td>\n",
       "      <td>A32DW342WBJ6BX</td>\n",
       "      <td>Buttersugar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>948672000</td>\n",
       "      <td>A sure death for flies</td>\n",
       "      <td>I bought a few of these after my apartment was...</td>\n",
       "      <td>b'bought apart infest fruit fli hour trap mani...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id   ProductId          UserId      ProfileName  \\\n",
       "138706  150524  0006641040   ACITT7DI6IDDL  shari zychinski   \n",
       "346041  374343  B00004CI84   A1B2IZU1JLZA6              Wes   \n",
       "70688    76882  B00002N8SM  A32DW342WBJ6BX      Buttersugar   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score       Time  \\\n",
       "138706                     0                       0      1  939340800   \n",
       "346041                    19                      23      0  948240000   \n",
       "70688                      0                       0      1  948672000   \n",
       "\n",
       "                                                Summary  \\\n",
       "138706                        EVERY book is educational   \n",
       "346041  WARNING: CLAMSHELL EDITION IS EDITED TV VERSION   \n",
       "70688                            A sure death for flies   \n",
       "\n",
       "                                                     Text  \\\n",
       "138706  this witty little book makes my son laugh at l...   \n",
       "346041  I, myself always enjoyed this movie, it's very...   \n",
       "70688   I bought a few of these after my apartment was...   \n",
       "\n",
       "                                              CleanedText  \n",
       "138706  b'witti littl book make son laugh loud recit c...  \n",
       "346041  b'alway enjoy movi funni entertain didnt hesit...  \n",
       "70688   b'bought apart infest fruit fli hour trap mani...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sorted_time.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data splitting\n",
    "y_Train=final_sorted_time['Score'][0:70000]\n",
    "y_train=final_sorted_time['Score'][0:49000]\n",
    "y_cv=final_sorted_time['Score'][49000:70000]\n",
    "y_test=final_sorted_time['Score'][70000:100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_data=final_sorted_time['CleanedText'][0:70000]\n",
    "train_data=final_sorted_time['CleanedText'][0:49000]\n",
    "cv_data=final_sorted_time['CleanedText'][49000:70000]\n",
    "test_data=final_sorted_time['CleanedText'][70000:100000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] Featurization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  BAG OF WORDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.1] Bi-Grams and n-Grams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vector = CountVectorizer(ngram_range=(1,2))\n",
    "bow_train = count_vector.fit_transform(train_data)\n",
    "bow_cv  = count_vector.transform(cv_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49000, 763027)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21000, 763027)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_cv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.2] TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vec = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "tf_train = tfidf_vec.fit_transform(train_data)\n",
    "tf_cv  = tfidf_vec.transform(cv_data)\n",
    "tf_test = tfidf_vec.transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.3] Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train your own Word2Vec model using your own text corpus\n",
    "list_of_sent_train = []\n",
    "for i in train_data:\n",
    "    sent = []\n",
    "    for word in i.split():\n",
    "        sent.append(word.decode('utf-8'))\n",
    "    list_of_sent_train.append(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_sent_cv = []\n",
    "for i in cv_data:\n",
    "    sent = []\n",
    "    for word in i.split():\n",
    "        sent.append(word.decode('utf-8'))\n",
    "    list_of_sent_cv.append(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_sent_test = []\n",
    "for i in test_data:\n",
    "    sent = []\n",
    "    for word in i.split():\n",
    "        sent.append(word.decode('utf-8'))\n",
    "    list_of_sent_test.append(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_of_sent_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(list_of_sent_cv))\n",
    "len(list_of_sent_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "w2v_model = Word2Vec(list_of_sent_train,min_count = 5,size = 50,workers = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of words that occured minimum 5 times  9383\n",
      "sample words  ['littl', 'book', 'make', 'son', 'laugh', 'loud', 'recit', 'car', 'drive', 'along', 'alway', 'sing', 'refrain', 'hes', 'learn', 'india', 'love', 'new', 'word', 'introduc', 'silli', 'classic', 'will', 'bet', 'still', 'abl', 'memori', 'colleg', 'enjoy', 'movi', 'funni', 'entertain', 'didnt', 'hesit', 'pick', 'edit', 'guess', 'market', 'plan', 'famili', 'someth', 'elimin', 'strong', 'element', 'usual', 'televis', 'version', 'warn', 'want', 'uncut']\n"
     ]
    }
   ],
   "source": [
    "w2v_words = list(w2v_model.wv.vocab)\n",
    "print(\"number of words that occured minimum 5 times \",len(w2v_words))\n",
    "print(\"sample words \", w2v_words[0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [4.3.1] Converting text into vectors using Avg W2V, TFIDF-W2V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4.3.1.1] Avg W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49000\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_train = []\n",
    "for sent in list_of_sent_train:\n",
    "    sent_vec = np.zeros(50)\n",
    "    cnt_word = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_word += 1\n",
    "        except:\n",
    "            pass\n",
    "    if cnt_word!=0:\n",
    "        sent_vec /= cnt_word\n",
    "    \n",
    "    sent_vectors_train.append(sent_vec)\n",
    "print(len(sent_vectors_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21000\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_cv = []\n",
    "for sent in list_of_sent_cv:\n",
    "    sent_vec = np.zeros(50)\n",
    "    cnt_word = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_word += 1\n",
    "        except:\n",
    "            pass\n",
    "    if cnt_word!=0:\n",
    "        sent_vec /= cnt_word\n",
    "    sent_vectors_cv.append(sent_vec)\n",
    "print(len(sent_vectors_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000\n"
     ]
    }
   ],
   "source": [
    "sent_vectors_test = []\n",
    "for sent in list_of_sent_test:\n",
    "    sent_vec = np.zeros(50)\n",
    "    cnt_word = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_word += 1\n",
    "        except:\n",
    "            pass\n",
    "    if cnt_word!=0:\n",
    "        sent_vec /= cnt_word\n",
    "    sent_vectors_test.append(sent_vec)\n",
    "print(len(sent_vectors_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [4.3.1.2] TFIDF weighted W2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_feat = tfidf_vec.get_feature_names()\n",
    "tfidf_sent_vec_train = []\n",
    "row = 0\n",
    "for sent in list_of_sent_train:\n",
    "    sent_vec = np.zeros(50)\n",
    "    weight_sum = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            tfidf = tf_train[row,tf_idf_feat.index(word)]\n",
    "            sent_vec += (vec*tfidf)\n",
    "            weight_sum += tfidf\n",
    "        except:\n",
    "            pass\n",
    "    if weight_sum !=0:\n",
    "        sent_vec/= weight_sum\n",
    "    \n",
    "    tfidf_sent_vec_train.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_feat = tfidf_vec.get_feature_names()\n",
    "tfidf_sent_vec_cv = []\n",
    "row = 0\n",
    "for sent in list_of_sent_cv:\n",
    "    sent_vec = np.zeros(50)\n",
    "    weight_sum = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            tfidf = tf_cv[row,tf_idf_feat.index(word)]\n",
    "            sent_vec += (vec*tfidf)\n",
    "            weight_sum += tfidf\n",
    "        except:\n",
    "            pass\n",
    "    if weight_sum !=0:\n",
    "        sent_vec/= weight_sum\n",
    "    tfidf_sent_vec_cv.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_feat = tfidf_vec.get_feature_names()\n",
    "tfidf_sent_vec_test = []\n",
    "row = 0\n",
    "for sent in list_of_sent_test:\n",
    "    sent_vec = np.zeros(50)\n",
    "    weight_sum = 0\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            tfidf = tf_test[row,tf_idf_feat.index(word)]\n",
    "            sent_vec += (vec*tfidf)\n",
    "            weight_sum += tfidf\n",
    "        except:\n",
    "            pass\n",
    "    if weight_sum !=0:\n",
    "        sent_vec/= weight_sum\n",
    "    tfidf_sent_vec_test.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49000, 50)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfw2v_train= np.array(tfidf_sent_vec_train)\n",
    "tfw2v_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfw2v_cv= np.array(tfidf_sent_vec_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21000, 50)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfw2v_cv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 50)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfw2v_test= np.array(tfidf_sent_vec_test)\n",
    "tfw2v_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"tfw2v_train\", tfw2v_train)\n",
    "np.save(\"tfw2v_cv\", tfw2v_cv)\n",
    "np.save(\"tfw2v_test\", tfw2v_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [5] Assignment 7: SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol>\n",
    "    <li><strong>Apply SVM on these feature sets</strong>\n",
    "        <ul>\n",
    "            <li><font color='red'>SET 1:</font>Review text, preprocessed one converted into vectors using (BOW)</li>\n",
    "            <li><font color='red'>SET 2:</font>Review text, preprocessed one converted into vectors using (TFIDF)</li>\n",
    "            <li><font color='red'>SET 3:</font>Review text, preprocessed one converted into vectors using (AVG W2v)</li>\n",
    "            <li><font color='red'>SET 4:</font>Review text, preprocessed one converted into vectors using (TFIDF W2v)</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Procedure</strong>\n",
    "        <ul>\n",
    "    <li>You need to work with 2 versions of SVM\n",
    "        <ul><li>Linear kernel</li>\n",
    "            <li>RBF kernel</li></ul>\n",
    "    <li>When you are working with linear kernel, use SGDClassifier’ with hinge loss because it is computationally less expensive.</li>\n",
    "    <li>When you are working with ‘SGDClassifier’ with hinge loss and trying to find the AUC\n",
    "        score, you would have to use <a href='https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html'>CalibratedClassifierCV</a></li>\n",
    "    <li>Similarly, like kdtree of knn, when you are working with RBF kernel it's better to reduce\n",
    "the number of dimensions. You can put min_df = 10, max_features = 500 and consider a sample size of 40k points.</li>                \n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Hyper paramter tuning (find best alpha in range [10^-4 to 10^4], and the best penalty among 'l1', 'l2')</strong>\n",
    "        <ul>\n",
    "    <li>Find the best hyper parameter which will give the maximum <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/receiver-operating-characteristic-curve-roc-curve-and-auc-1/'>AUC</a> value</li>\n",
    "    <li>Find the best hyper paramter using k-fold cross validation or simple cross validation data</li>\n",
    "    <li>Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning</li>          \n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Feature importance</strong>\n",
    "        <ul>\n",
    "    <li>When you are working on the linear kernel with BOW or TFIDF please print the top 10 best\n",
    "features for each of the positive and negative classes.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Feature engineering</strong>\n",
    "        <ul>\n",
    "    <li>To increase the performance of your model, you can also experiment with with feature engineering like :</li>\n",
    "            <ul>\n",
    "            <li>Taking length of reviews as another feature.</li>\n",
    "            <li>Considering some features from review summary as well.</li>\n",
    "        </ul>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Representation of results</strong>\n",
    "        <ul>\n",
    "    <li>You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.\n",
    "    <img src='train_cv_auc.JPG' width=300px></li>\n",
    "    <li>Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.\n",
    "    <img src='train_test_auc.JPG' width=300px></li>\n",
    "    <li>Along with plotting ROC curve, you need to print the <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/confusion-matrix-tpr-fpr-fnr-tnr-1/'>confusion matrix</a> with predicted and original labels of test data points. Please visualize your confusion matrices using <a href='https://seaborn.pydata.org/generated/seaborn.heatmap.html'>seaborn heatmaps.\n",
    "    <img src='confusion_matrix.png' width=300px></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <br>\n",
    "    <li><strong>Conclusion</strong>\n",
    "        <ul>\n",
    "    <li>You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library<a href='http://zetcode.com/python/prettytable/'>  link</a> \n",
    "        <img src='summary.JPG' width=400px>\n",
    "    </li>\n",
    "        </ul>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4><font color='red'>Note: Data Leakage</font></h4>\n",
    "\n",
    "1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.\n",
    "2. To avoid the issue of data-leakag, make sure to split your data first and then vectorize it. \n",
    "3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.\n",
    "4. For more details please go through this <a href='https://soundcloud.com/applied-ai-course/leakage-bow-and-tfidf'>link.</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [5.1] Linear SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.1] Applying Linear SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV ROC_AUC Score :  0.9340696053174733  Train ROC_AUC Score :  0.99293296273959 C :  1e-05  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9378926925385017  Train ROC_AUC Score :  0.9990633697214896 C :  1e-05  penalty :  l2\n",
      "CV ROC_AUC Score :  0.9306012623338092  Train ROC_AUC Score :  0.954923723430882 C :  0.0001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9405270879793477  Train ROC_AUC Score :  0.9991228984098507 C :  0.0001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.9001999438833728  Train ROC_AUC Score :  0.8988542064017053 C :  0.001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9457081414106189  Train ROC_AUC Score :  0.9932818648967179 C :  0.001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.7631182792339892  Train ROC_AUC Score :  0.7568010272385357 C :  0.01  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9246878354841237  Train ROC_AUC Score :  0.945933256047051 C :  0.01  penalty :  l2\n",
      "CV ROC_AUC Score :  0.6377108783268208  Train ROC_AUC Score :  0.6294333635051633 C :  0.1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.7733977052641398  Train ROC_AUC Score :  0.7699509371738222 C :  0.1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.5864592856893576  Train ROC_AUC Score :  0.5755198064583055 C :  1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10  penalty :  l1\n",
      "CV ROC_AUC Score :  0.5396360124857602  Train ROC_AUC Score :  0.528317294739338 C :  10  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  100  penalty :  l1\n",
      "CV ROC_AUC Score :  0.5375871434358535  Train ROC_AUC Score :  0.5263019844252116 C :  100  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.5375865281219592  Train ROC_AUC Score :  0.5263016030512991 C :  1000  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.5375871276585741  Train ROC_AUC Score :  0.5263019844252116 C :  10000  penalty :  l2\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning:\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import roc_auc_score, auc\n",
    "penalties = ['l1', 'l2']\n",
    "C = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000]\n",
    "cv_scores = []\n",
    "tr_scores = []\n",
    "i = 0\n",
    "for alpha in C:\n",
    "    for p in penalties:\n",
    "        m = SGDClassifier(loss='hinge', penalty=p, alpha=alpha, n_jobs=-1)\n",
    "        clf = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "        clf.fit(bow_train, y_train)\n",
    "        y_score=clf.predict_proba(bow_cv)[:,1]\n",
    "        scores = roc_auc_score(y_cv, y_score)\n",
    "        cv_scores.append(scores)\n",
    "        y_score=clf.predict_proba(bow_train)[:,1]\n",
    "        scores = roc_auc_score(y_train, y_score)\n",
    "        tr_scores.append(scores)\n",
    "        print(\"CV ROC_AUC Score : \", cv_scores[i], \" Train ROC_AUC Score : \", tr_scores[i], \"C : \", alpha, \" penalty : \", p)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = ['0.00001+L1', '0.00001+L2', '0.0001+L1', '0.0001+L2', '0.001+L1', '0.001+L2', '0.01+L1', '0.01+L2',\n",
    "         '0.1+L1', '0.1+L2', '1+L1', '1+L2', '10+L1', '10+L2', '100+L1', '100+L2','1000+L1','1000+L2','10000+L1','10000+L2']\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(range(len(C)*len(penalties)), tr_scores)\n",
    "plt.plot(range(len(C)*len(penalties)), cv_scores)\n",
    "plt.xticks(range(len(C)*len(penalties)), alpha, rotation = 45)\n",
    "plt.legend(['train', 'cv'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.01, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "om = SGDClassifier(loss = 'hinge', penalty='l2', alpha=0.01)\n",
    "om.fit(bow_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=SGDClassifier(alpha=0.01, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "            cv=None, method='sigmoid')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "om = SGDClassifier(loss='hinge', penalty='l2', alpha=0.01)\n",
    "model = CalibratedClassifierCV(base_estimator=om, cv=None)\n",
    "model.fit(bow_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now Lets see result on Test data i.e on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count_vect= CountVectorizer(ngram_range=(1,2))\n",
    "#bow_Train=count_vect.fit_transform(Train_data)\n",
    "#bows_test=count_vect.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bow_test= count_vector.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= model.predict(bow_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_actual, y_predicted):\n",
    "    cm = confusion_matrix( y_actual,  y_predicted)\n",
    "    labels = [\"negative\", \"positive\"]\n",
    "    df = pd.DataFrame(data=cm, index=labels, columns=labels)\n",
    "    print(\"Confusion Matrix : \")\n",
    "    plt.figure(figsize=(6,4))\n",
    "    sns.heatmap(df, annot=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2971,  2356],\n",
       "       [  603, 24070]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix( y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score on test data is: 0.9188230030781217\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_proba(bow_test)[:,1]\n",
    "score= roc_auc_score(y_test, y_pred)\n",
    "print('AUC score on test data is:', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.2] Applying Linear SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV ROC_AUC Score :  0.9502145031567496  Train ROC_AUC Score :  0.9858447231511255 C :  1e-05  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9501128659232287  Train ROC_AUC Score :  0.9939886160368129 C :  1e-05  penalty :  l2\n",
      "CV ROC_AUC Score :  0.9307157422727145  Train ROC_AUC Score :  0.9318142750000837 C :  0.0001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9483856798218253  Train ROC_AUC Score :  0.9721120189047119 C :  0.0001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.6863053089093476  Train ROC_AUC Score :  0.6841148736155714 C :  0.001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9436367660717306  Train ROC_AUC Score :  0.9629828619771665 C :  0.001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  0.01  penalty :  l1\n",
      "CV ROC_AUC Score :  0.9440153734441  Train ROC_AUC Score :  0.963068018305288 C :  0.01  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  0.1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.6398555706828111  Train ROC_AUC Score :  0.6318081084244247 C :  0.1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.639829364621824  Train ROC_AUC Score :  0.631783910077886 C :  1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10  penalty :  l1\n",
      "CV ROC_AUC Score :  0.639829364621824  Train ROC_AUC Score :  0.631783910077886 C :  10  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  100  penalty :  l1\n",
      "CV ROC_AUC Score :  0.639829364621824  Train ROC_AUC Score :  0.631783910077886 C :  100  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.639829364621824  Train ROC_AUC Score :  0.631783910077886 C :  1000  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.639829364621824  Train ROC_AUC Score :  0.631783910077886 C :  10000  penalty :  l2\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning :\n",
    "penalties = ['l1', 'l2']\n",
    "C = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100,1000,10000]\n",
    "cv_scores = []\n",
    "tr_scores = []\n",
    "i = 0\n",
    "for alpha in C:\n",
    "    for p in penalties:\n",
    "        m = SGDClassifier(loss='hinge', penalty=p, alpha=alpha)\n",
    "        clf = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "        clf.fit(tf_train, y_train)\n",
    "        scores = roc_auc_score(y_cv, clf.predict_proba(tf_cv)[:,1])\n",
    "        cv_scores.append(scores)\n",
    "        scores = roc_auc_score(y_train, clf.predict_proba(tf_train)[:,1])\n",
    "        tr_scores.append(scores)\n",
    "        print(\"CV ROC_AUC Score : \", cv_scores[i], \" Train ROC_AUC Score : \", tr_scores[i], \"C : \", alpha, \" penalty : \", p)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = ['0.00001+L1', '0.00001+L2', '0.0001+L1', '0.0001+L2', '0.001+L1', '0.001+L2', '0.01+L1', '0.01+L2',\n",
    "         '0.1+L1', '0.1+L2', '1+L1', '1+L2', '10+L1', '10+L2', '100+L1', '100+L2','1000+L1','1000+L2','10000+L1','10000+L2']\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(range(len(C)*len(penalties)), tr_scores)\n",
    "plt.plot(range(len(C)*len(penalties)), cv_scores)\n",
    "plt.xticks(range(len(C)*len(penalties)), alpha, rotation = 45)\n",
    "plt.legend(['train', 'cv'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=1e-05, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = SGDClassifier(loss = 'hinge', penalty='l2', alpha=1e-05)\n",
    "m.fit(tf_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=SGDClassifier(alpha=1e-05, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "            cv=None, method='sigmoid')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = SGDClassifier(loss='hinge', penalty='l2', alpha=1e-05)\n",
    "model = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "model.fit(tf_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(tf_test)\n",
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3298,  2029],\n",
       "       [  558, 24115]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix( y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC score on unseen data of tfidf is:  0.9475795327037592\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_proba(tf_test)[:,1]\n",
    "score=roc_auc_score(y_test, y_pred)\n",
    "print(\"ROC score on unseen data of tfidf is: \", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.3]  Applying Linear SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV ROC_AUC Score :  0.8831708399779344  Train ROC_AUC Score :  0.8845104793030371 C :  1e-05  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8700143876165776  Train ROC_AUC Score :  0.8684516931050181 C :  1e-05  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8916731053743661  Train ROC_AUC Score :  0.8915973239562903 C :  0.0001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8932563711336121  Train ROC_AUC Score :  0.8924553843372222 C :  0.0001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8948230707495489  Train ROC_AUC Score :  0.892965937496238 C :  0.001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8960573746443091  Train ROC_AUC Score :  0.8953929392310149 C :  0.001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8382824923128361  Train ROC_AUC Score :  0.832267437913289 C :  0.01  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8945131418741552  Train ROC_AUC Score :  0.8934637884989063 C :  0.01  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  0.1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8941052045394893  Train ROC_AUC Score :  0.8930201269498239 C :  0.1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8945150982567937  Train ROC_AUC Score :  0.8927725122065765 C :  1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10  penalty :  l1\n",
      "CV ROC_AUC Score :  0.7947228124502423  Train ROC_AUC Score :  0.7937905963940362 C :  10  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  100  penalty :  l1\n",
      "CV ROC_AUC Score :  0.6317370797177331  Train ROC_AUC Score :  0.6371767399129967 C :  100  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  1000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.6317379474680969  Train ROC_AUC Score :  0.6371773617929801 C :  1000  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 C :  10000  penalty :  l1\n",
      "CV ROC_AUC Score :  0.6317374110405993  Train ROC_AUC Score :  0.637177062878292 C :  10000  penalty :  l2\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning :\n",
    "penalties = ['l1', 'l2']\n",
    "C = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100,1000,10000]\n",
    "cv_scores = []\n",
    "tr_scores = []\n",
    "i = 0\n",
    "for alpha in C:\n",
    "    for p in penalties:\n",
    "        m = SGDClassifier(loss='hinge', penalty=p, alpha=alpha)\n",
    "        clf = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "        clf.fit(sent_vectors_train, y_train)\n",
    "        scores = roc_auc_score(y_cv, clf.predict_proba(sent_vectors_cv)[:,1])\n",
    "        cv_scores.append(scores)\n",
    "        scores = roc_auc_score(y_train, clf.predict_proba(sent_vectors_train)[:,1])\n",
    "        tr_scores.append(scores)\n",
    "        print(\"CV ROC_AUC Score : \", cv_scores[i], \" Train ROC_AUC Score : \", tr_scores[i], \"C : \", alpha, \" penalty : \", p)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t7U1ERMQnxzZv3syvfvUrMjIySEtLY+nSpYbllKLNQN4eZmZHBTA7KgAyPx07q7WmtWeQssYu9jZ1U9HQRlvdWQaby7EPDWGNzWRWUhKZ8UGsiwrAyyrzOoTrUkqxaU4Eq2at58kPE1m3ZzUPqLf52um3sRa/g1p0P6x5BAKijI76qaEByPs9fPBT6G4g1yOH7/XfhjUmg/930xyyEi8+b+1yrGYTZQE55HRsB9sgmOUu+EXZ7cS0HqLAN5tVI3czlVKcC13K6uaXsfd1YvLyNzikC9OakPp9HLfMZ2V44Ccvd0SvJLRyP/11xXhGGbfMthBCuIqtW7eO+7qnpyfbtm0b99jovLWwsLALtgt4+OGHHZ4PpGhzSUopQnw9CPENGflwGAeMM5lOCDfiZTXzrQ2zuH1xLD9+O4FlBet5xPct7sh7HnX8D6hl34AVfw1egZdvzFnsNsh/Cfb8GNoqOeO9gH/s/xpV1gz+4c50bl84sXlrl5WyFp9jb1JXuJfIjPXX3t4U1VKaS4juYCBh7QWve6RuxHrgBcqPbidx2e3GhHMD/Y1nCB+q42DMFy54PSxjM1T+lOrct0m5WYo2IYRwB7IShRBiUsUEefPEny3iF39xHU/6/RVr+37KAY+lsPe/4OeZcOCXMNQ/uaG0hpNvwv8uh9e+Sv2gD39h+w43dD7CsrU3svvhtdy5ONYxBRuQmLUFm1Y0HH/XIe1NVTV57wAQs2jLBa+nZW+kT1vpKHzfiFhu41zu8PsXPP+6C15fkLGAch2JvXSXEbGEEEJcBbnTJoQwxPKZYbz916t49kA8f7UjhuTBTfws4FWS3vsOHPpfWP8vMO9O526PAVC6G3b+EGry6PRL4ieWh9navJCbMqLZtSX9quatXU5CTDSFpln4VX/o8LanEo+KDzhNAqkpMy94fUZIELnW+UTV7TMomXuwleykWoeRseDCRbJ8PCyc8s1idduO4aHAFg+DEgohhJgoudMmhDCM1Wziz1cmsfvhtaQtXMm6hr/hm+bv0aZ94U9/Cf+3Gs7sGL4T5mhVh+H3N8Ozn2OgvZ5f+H+bzKYfUhC4jpe/upzHv7DIKQUbDA+Brg9fTkJfMUNdLU7pw93pgW4Se/KpDlky7h3OtqgVxAxV0ttUaUA6N2AbIrrtMMU+WQR4f7YosyWtxZs+2kr2GxBOCCHElZKiTQhhuDA/T3565wJe+8YKqoKXsLD+u/xPwD8y0NMGz90Bz9wC5/Ic01l9EbzwBfjtRmx1hfwp4lvMa/4xz/St4tE7F/L6N1aQfQ0LjUyUd/omzEpTcUSGSI6n6ugOPBjCMmvDuMeD5m8GhrcEEJ/VUXoIX93DQPyacY8nLN7MkDZRf3T8CfZCCCFcixRtQgiXkRkXxKtfX8F/3JHJc93ZzGv6EW9Gfxt7XSE8uQ5e/hI0l15d4y1n4U8Pwv8uR5d/yP6Er5HT9d88cm4FD66bzZ6H1/L5rDiHzVu7nDnZ6+jU3nQXbZ+U/txN24n36ddWUnOuH/f43AVLadBBDJXsnORk7qH26DvYtSJ60eZxj89OjOWEmiVbJwghhJuQOW1CCJdiMinuyo7j+nmR/GzHab59wMqjngv535T9zD/9LOrkm7D4y7DmH8BvxuUb7KiFD/8T8n6PNlk5M+srfLNiFadOWbkxI4pHNqcTF+KcYZCXEuTvyyHPDBKaZHjaeILrPqLQOodFocHjHvf2tJDrm8X81kNgtzt/7qOb8aj4gCKVzLyZieMeN5kUNSFLmd/yLLqnBeXj/LvLQgghrp78lBNCuKRAbyv/evNc3vnrVcRHRXJL4Rru8/k/GmbdA7m/G15pcvdPoL9z/AZ6WmD79+CxhZD3exrT7uUvAn/NpoL1WP1DeemvlvHEFxYZUrCN6opeRaStjs7a04ZlcEV9LeeIGyynJWLlJc8bTFhNkO6gpezIJCVzD7q3jbieQqqClmIxX/zHvGfaRszYqTn63iSmE0IIcTWkaBNCuLS0SH+2/uUSHv/CQsr6fMk5fgM/SnqavsT18MGjw0XZx08Or4IH0N8FH/wn/HwB7HuM3lk38e9Jz5J9dAv5HT789M4M3vjGSnKSjL+zEJY5PHStUuZlXWB0nlrgmKXqx4pZPLwVwLkj8v6dry5/JxbsWFIvvQfg7Ox1dGhvOgpliK4QYnp75plnyMjIYMGCBdx2220kJiZit9sB6OnpIS4ujsHBQUMzyvBIIYTLU0pxU0Y069Nn8Mvdpfz6wzK2mu/j37Lu5HNNv8b0zsNw4AmYexscfRa6G7Gl3sgLfvfzo8MKm9Z8Y10yX1s7Ez9P1/m2N3vuImpeDYPSXcDfGB3HZQyc3kmzDmDuwuWXPG9W8ixKiMejQuZlna+t4F0CtCdpWeMv4jIqOsSfj6wZpNXvG16hVU3OfE4hhLiobY9AXYFj24ycD1sevejhwsJCfvSjH7Fv3z7CwsJoaWnhy1/+Mh988AHr1q3jzTff5Prrr8dqtTo21xWSO21CCLfh42Hh4evT2P63q1mWEsrf7reysfnvyV/zJFh94KP/Rs+YzYdrXmRVxVf45/121qfPYOffruHvr093qYINwMNq5ox/NvHtuWAbMjqOa9CamJaDFPssxsfz0vuHmUyKqpClJPXkY+/vnqSAri+4bh/5lnnEzxh/PuD52qNWEm6ro7/hzCQkE0II17Nr1y7uvPNOwsLCAAgJCeHuu+/mD3/4AwAvvvgid999t5ERAbnTJoRwQwmhvvzmgWx2n2rgh28Wcct7vmxK/x++eJMnPzvcw5H3Wpkb7cH/3J3JkuRQo+Neki1pLf4F26grPkDk3FVGxzFc89k8QnUbffGrJ3S+ddYGPA69RMWxnSQsucXJ6VzfQFM5kUPnOBJ9x4TOD8m4Hqr+H9VH3iHlhoecnE4IIS7jEnfEnEVrjRoz0uCWW27hO9/5Di0tLRw5coT16y893HwyyJ02IYTbWpc2g3e/vYp/3JzOvrJW7n/lHBXNPfz0jgze+OZKly/YABKyb8CuFfVH3zE6ikuoOTL8PsQsvnFC56fmXEe/ttJ2QhbTAKjOHZ0POP5WCWNlzF9ElQ7HJlsnCCGmqQ0bNvDSSy/R3NwMQEtLC35+fuTk5PDQQw9x0003YTabDU4pd9qEEG7O02Lma2tTuH1RDAfLmtkwO8LlhkFeSlJcHMWmZHyr9xodxSVYyz+glFhSZ6ZO6PyI0BCOWuYQWrfPycncw2DJTup0MBmZORM639fLygGfLJa1fTA8RNfsPv/vCCGEI8ydO5fvfve7rFmzBrPZzMKFC3n66ae5++67+fznP8+ePXuMjgjInTYhxBQREeDFrZkxblWwwfAiK7Why0jsK2Sop83oOIbSg70kdh+nMnjpFW1y3hy5kvjBs/S1nHNiOjdgtxHdcogi7ywCvC89H/B8Q0lr8dU9tJ054MRwQgjhuh544AFOnDjB8ePHefrppwG488470VqzZs0aY8ONkKJNCCEM5pW+CQt2KvLeNzqKoaqO78KLAcwzr2zuQNC8TQCUT/OtEzrKcvHXXfRPcD7gqLhFm7FrRd3RbU5KJoQQ4lpJ0SaEEAZLz95Aj/aka5rvl9Wa/x4D2kzqkonNxxo1d9FKmnUAg6en97ysmrzh+YDRizZf0XXpyQkUqhQ8Kz90RiwhhBAOIEWbEEIYLCTQn0KP+YQ3TO95WUG1H1FkmUPkyLLLE+XtaaXYZxExLQeH9xubpjwq9nBSJzJ31swrus5sUlSHLCWupxDdO72H6AohjKGnwffua/07StEmhBAuoCN6JdG2c3TVlxkdxRB9rbUkDJbSHLHiqq7vj19LiG6juSzPwcncg+7vJK77BJXBOVjMV/6j3TN1PRbs1Bzf4YR0QghxcV5eXjQ3N0/pwk1rTXNzM15eXlfdhnvN2BdCiCkqJGMzVPyMysNvM+embxkdZ9JVHN5GGhA477qruj568Q1w6vucO7KN0JTFjg3nBurydxLFEOaZG6/q+rTsjXQf8KS94D1ilt7p4HRCCHFxsbGxVFdX09jYaHQUp/Ly8iI2Nvaqr5eiTQghXMCcjGzq3wjGdmYXMP2Ktv7T22nRfsxZtPKqrk+dmUopsXhU7AH+2aHZ3EFrwfsEayuzsq+uaIsJDeSAZT5J9dN7iK4QYvJZrVaSkpKMjuHyJjSGQim1WSl1Sil1Rin1yDjH45VSu5VSR5VS+UqpGxwfVQghpi5Pq4USvyzi2w6D3WZ0nMmlNdHNBznlvQgfL8+rasJkUlQFLyGx+zh6sNfBAV1fUO1e8s1zSYi4+g3l26JWEjl0jv6msw5MJoQQwhEuW7QppczAE8AWYA5wr1JqzpjT/hl4SWu9ELgH+KWjgwohxFQ3mLSOQDqpP33I6CiTqqU8nzDdQm/8te2FY565AS8GqDy2y0HJ3MNASxXRg5U0Ryy/pnaC5g8PTa2a5lsnCCGEK5rInbYc4IzWukxrPQC8CNw65hwNBIw8DgRqHBdRCCGmh4TFw4MU6vLeNTjJ5Dp3ZHip+qhFW66pndQlmxnQZtoKptd+d1W5w++f/9wr2yphrIwFOdTqEIZKplfRK4QQ7mAiRVsMUHXe8+qR1873feA+pVQ18A7TcUKGEEJco6TERE6pJLyrPjA6yqSylO/mLNGkpo4dxHFlIsJCKbLMIbB2r4OSuYeB0zto1IHMX3Rtd9p8vawU+2QR03po+g3RFUIIFzeRok2N89rYNTnvBZ7WWscCNwDPKqU+07ZS6kGlVK5SKneqrxAjhBBXSilFTchSknoLsPV1GR1nUujBPhK7jlEZtASzabwfN1emJWI5iYOl9LXVOSCdG7DbiWo+RJHXIgJ9PK65uYGE1fjrLtpKDzsgnBBCCEeZSNFWDcSd9zyWzw5//ArwEoDW+gDgBXxmd1St9a+11lla66zw8PCrSyyEEFOYZ9oGrNioyJseQ/yq8vfgTT+mmRsc0l7AvOEhguUfT495WZ0VeQTp9mueDzgqZtHwEN3avOnx/k2UHuyj6b+yqf3gKaOjCCGmqYkUbYeBWUqpJKWUB8MLjbwx5pxKYAOAUmo2w0Wb3EoTQogrlJq9iT5tpaNwehRtLfnvMajNzMzZ7JD25ixaSav2Y+D0Toe05+qqj2wDIGqhY96/2TOTOUkSHhUfOqS9qaLq0GuEdZ1mcN/jRkcRQkxTly3atNZDwDeB94CTDK8SWaiU+qFS6paR0/4O+Eul1HHgBeBLeipvay6EEE4SFhxEkXUeodNkv6zA2r2ctKQRNcMxoy98vDwp9l5EdPMBmAY/hqzluynRccxJS3NIe2aToip4KfE9J9D9nQ5pcyroyX0egPiBM3RU5hucRggxHU1onzat9Tta61StdYrW+kcjr31Pa/3GyOMirfUKrfUCrXWm1np6/IpYCCGcoDV6JXFDlXQ3VRodxan62htI6D9D04wVjm03fjVhuoWWigKHtutq9EAPcV35lAflYDVP6Mf5hFhnrcfKEDXHp8fdyssZ6mwipW0/O6xrGdImKnfLEEkhxORz3Hd5IYQQDhEyf3rMy6o4/A4mpfGfe51D241aODwv61zu1H7/6gp248mgw+YDjpqVvZFe7UFrwXsObdddle55FitD+K79Nkesi4iseAPsdqNjCSGmGSnahBDCxcxesJRGHYhtiu+X1XdqJ+3alzmLHbOIxqjUtDmUE4W5fI9D23U1LQXv0q8tzMxybNEbGx5CgWUuwXUfObRdd2UtfJkS4lics5qOWXcQZm+iPn+70bGEENOMFG1CCOFivDysnPbNIq7t0NT9jb7WRDUf4KT3Qny9PR3atMmkqAhaSlLXUfRgn0PbdiUBNR9xwpxOfORnFmu+Zq0RK4gZrKS/ZWoP0b2crtpTJPcVUhZ1Mx5WM/M23kOn9qZp/zNGRxNCTDNStAkhhAsaSlxNsG6n4cwRo6M4RWtVETPsjfTGrnJK++aZ6/Gmn6r8qblR+WB7LXEDZTTOWIFS176/3VijWydUHX7H4W27k4pdT2HXiujV9wMQFRrCEd9VJDXsQA90G5xOCDGdSNEmhBAuKGbxjQDU5k3ND81Vh98CIHJkXzBHS8nezKA205L/rlPaN1rlSDEVMHeTU9qfv2gGtrOCAAAgAElEQVQZDTpo2mydMC6tCS17jaOW+cxLn/3Jy2rBPfjQx9l9LxsYTggx3UjRJoQQLigleSalxOFZOTXvFJnL91BJBKnp853SflTEDIotaQTUTs15Wf2nttOi/Zi7eLVT2vfzsnLSezHRLVN4iO5lNJ7cS6StluaU2y+4m7l4zc3U6FAG814wMJ0QYrqRok0IIVyQUoqqkKUk9eRj7+8xOo5D6aEBEjvzKA9cgtnk+KF9oxpnrCCxv4S+9kan9WEIrYlsPkSh1yICfRw7H/B8/fGrCdLttJ3Nc1ofrqx+79P0ag/mrP+zC1738/KgMPR6Ujo/pr+t1qB0QojpRoo2IYRwUR6pG/BkkIqjU2uIWnXBh/jSh0pZ79R+AuZuwqQ05blTa4hpR2U+IfYWeuOcc5dtVPTiLQDUTNEhupeiB/uIr32PXO/lxEbO+MzxwKX3YcFO2e7fG5BOCDEdSdEmhBAuambOdfRrC20nptZ+WU357zKkTSTnbHZqP7MXr6Fd+9JXPLWWZx/dfy4y08nv36xUThOPdYpvnTCeikOvEUAXtvl3j3t80eJlnCQZn5OvTHIyIcR0JUWbEEK4qBkhoZy0ziG4bp/RURwq4NxeTplnERMZ5dR+fL29KPbOJLr5IGjt1L4mk6l8D2U6mtmz5zq1H7NJURm0hPju/Gm3UmLv4a006kAWrr1t3OMWs4mquJtJGCihvaJgktMJIaYjKdqEEMKFtUauIHGojJ6WGqOjOER/ZzOJ/adomLFiUvrrjVvDDHsjrVVFk9Kfs+nBXhI6j3I2cAlWs/N/hJtnDQ/Rrc2f2hu9n2+wq5mZ7fvID95EoK/3Rc+LX/NFhrSJyg+emsR0QojpSoo2IYRwYUEj+2WVH37b4CSOcfbwNsxK4zfHOUvVjxW5cHheVlXu1Hj/6go/xIsBTClrJ6W/WVmb6NcWmvPfn5T+XEHpnmexMoRv9n2XPC8tJYU860Iiy9+YtitsCiEmjxRtQgjhwmYvWkmr9mfw1NRYjKSveDud2pvZWesmpb9Z6fOpJAJz2e5J6c/Zmo9vY1CbScreMin9xUaEccIyh6ApunXCeKwnXqKUWBblXHqhF6UUnam3E25vpDZ/avz/KYRwXVK0CSGEC/PysHLKZxGxrYfcf16W1kQ1HaDYKxM/n4sPO3Mks0lREbiExK489NDApPTpTP7nPqLQnEZC1GdXNHSW5ogVxA2W0d82NYboXkpnXQkpfYWURd2Eh9V82fPnrv8CXdqL5v3PTEI6IcR0JkWbEEK4uP6ENYTqFprKjhsd5Zq0VhcTYa+nO9a5S9WPpWaux5c+qgs+nNR+HW2wo4G4/jM0hi+/YLNnZwuYex0AlYe3TVqfRinf9TvsWhG9+oEJnR8ZFkKe7yoSG3agB6bWfopCCNciRZsQQri4qEU3AFB9xL3nZVWN7Jc2Y+HkDO0blZy9BZtWNB5/d1L7dbTK3G2YlMZ3kuYDjpq7aAUt2p+BU1Nr64TP0Jqw0tc4bpnHnPTZE78u4x786KFsnyz/L4RwHinahBDCxc2cmU450XhUfGB0lGtiKtvNOcJJm71gUvuNjoyk2JxKYM3eSe3X0XqLd9CufZi7eO2k9uvv7UmR9yKiptjWCWPVF31ElK2G5uTbruhO5uI1N1OnQxjMe8GJ6YQQ050UbUII4eJMJkVV8BKSu49iH+gzOs5V0bZBEjuPcDYgB/MkLFU/VuOMFST2n6K/s3nS+3YIrYls2k+h50IC/bwmvfveuDWE6FbaK9x7iO6l1H/0e/q0lfT1l141cixfb08Kw65nZudB+trqnJROCDHdSdEmhBBuwDxzA14MUHHcPVdBPHfiI/zogZT1hvTvN2cTZqU566bzsjqrTxJmb6I7dpUh/UctGh7SWj0yxHWq0UP9JNS+yxHv5cRGRVzx9YFL7seCnbLdv3dCOiGEkKJNCCHcwqwlWxjUZloL3jM6ylVpPP4udq0mban6sdKz1tGlvekt3mFI/9eqKvctAGYsvMGQ/menzaaUGCzlewzp39nKD75OIJ0Mzbvrqq5fmLWcUyThU/xHBycTQohhUrQJIYQbCA8Lo9iS7rb7Zfmd28sp80xiomOM6d/Hm5NemUQ17Tek/2ulzu6mUkcwZ858Q/of3TohoesoerDXkAzO1Jv7PE06kMx1d1zV9WaTojLuZhL7T9FWWejgdEIIIUWbEEK4jeaI5SQOnKG3rcHoKFdkoKuVpL6TNIQvNzRHT9wqIu31tFYXG5rjSumhfhI68igLyMFqwHzAUaaZ6/FigJoC914QZ6zBrhZmtn1EfvBGAn2vfv/AxDVfxKYVlXt+58B0QggxTIo2IYRwEwHzrsekNGcPu9e8orO572JRdnxmT+5S9WOFZw4PLaw67F5bJ9QVfoQPfeiUdYbmmJm9mQFtpjnfvbdOGOvM7mfxYAi/7D+7pnZmzZzFUetCIstfB7vdQemEEGKYFG1CCOEmZi9aQ7v2pf+Ue83L6jm5nW7tSXq2MYuQjEqbvYBzhKPK3Gsxl8bj27BpRVLWZkNzxEaGU2ROJ6DGPYfoXoy18GXKiGXhkrXX3FZH6u3MsDdSU7Dr2oMJIcR5pGgTQgg34e3lwSmfTGKaD7jVflkRjQc46bUAf19fQ3OYzSbOBuSQ1HkEbRs0NMuV8D+3lyJTKgkx0UZHoWnGCuL7zzDQ7l5DdC+mo/YMM/sKKIu+CavFfM3tzd3wBbq1J837nnFAOiGE+JQUbUII4UZ649YwQzfRXOEeix20nysh2l5DV8xqo6MMS1mHHz2cK9xndJIJGexqIb7vFA3hy65ow2dn8Zu7CZPSVEyRpf/Ldw3PP4te9UWHtBcRGkqe7yoSG7ZjH5h6C7YIIYwjRZsQQriRyIXDS+ZX5brHvKyKkZzhmcYO7RuVlH0Ddq1oOOYe+7VV5G7DrDQ+s68zOgoAcxevpk370uemWydcQGvCyl7jmHkes9PnOKxZteAe/OmhdN8rDmtTCCGkaBNCCDcyK20+VUS6z35ZpbupI5S0OYuMTgJATHQMp80z8Tu31+goE9Jzcjud2pvZWWuNjgKAv48XJ70WEuVmQ3THU39yH9G2c7Sk3ObQu5iL1txCvQ5mMO8Fh7UphBBStAkhhBsxmRQVgTkkdeWhhwaMjnNJ2jZEYkcupf45WBwwX8hR6sOXk9x3kv6uVqOjXJrWRDTup8hzAUH+xs4HPF9P3GrC7E20VxUZHeWa1O99mj5tJW39fQ5t18fLk6LQ65nVeZC+tnqHti2EmL4mVLQppTYrpU4ppc4opR4Z5/j/KKWOjfw5rZRqc3xUIYQQAGrmenzpoyLftffLOnfyAAF0oZPXGh3lAr6zN2FRds7mvmd0lEvqrD1NhL2erphVRke5QMQnQ3TfMjjJ1dNDAyTUvkue9zJiIiMd3n7A0vuwYqN0tyxIIoRwjMsWbUopM/AEsAWYA9yrlLpg8LfW+m+01pla60zgF8CfnBFWCCEEzMy5AZtWNOe7dtHRcHQbdq1IyrnR6CgXSMteT7f2pOfk+0ZHuaTKkf3kwhe4xnzAUenp86ggEvPZPUZHuWplB18nkE5s8+5ySvuZWSspUQl4F8u8NiGEY0zkTlsOcEZrXaa1HgBeBG69xPn3AjKQWwghnCQiIoJTllQCa1x7XpZv9V5KzEnExMQZHeUC/r6+FHstIKLxgNFRLkmV7uKcDmf2vIVGR7mAxWzibMASEjrz0EP9Rse5Kr25W2nWAWSuu8Mp7ZtNisrYW0juL6a10j1WehVCuLaJFG0xQNV5z6tHXvsMpVQCkASMu6ukUupBpVSuUiq3sbHxSrMKIYQY0ThjBUn9p+jraDY6yrgGuttJ7iukLmy50VHG1RWzmhh7DW01Z4yOMj7bEPEdRyj1z3LI/mGOpmauw4c+agpd+xcH4xnoamVW214Kgjfg7+vjtH4S1n4Rm1ZU7nnKaX0IIaaPiRRt4y2pdLElo+4BXtFa28Y7qLX+tdY6S2udFR4ePtGMQgghxvCfswmz0pQdds2l688eeQ+rsuEze6PRUcY1ugVBxceuOS+r9uQ+/OjBnrzO6CjjSsnewpA20XTsXaOjXLEze57Dk0F8s//Mqf3MTEnluDWTyPI3wG53al9CiKlvIkVbNXD+2JZYoOYi596DDI0UQginS89aR5f2dtn9srqLttOrPUjL3mR0lHGlzV1MHaGost1GRxlXw7Hh+YCJ2VuMjjKu2KhITppT8XfxIbrjsZx4iXKiyVyy3ul9tc+6nQh7PecKXPPfmRDCfUykaDsMzFJKJSmlPBguzN4Ye5JSKg0IBlx7koAQQkwBPt7eFHsvGN4vywWFNx7gpGcGAX5+RkcZl9lsosw/m8SOXLRtyOg4n+FbvZdTphQSYl1rPuD5GsOXkdB3ioFO1xyiO56O2lJS+/Ipi75pUoadztvwZ/RoT5r2ySqSQohrc9miTWs9BHwTeA84CbyktS5USv1QKXXLeafeC7yotZvvtimEEG6iO3YNUfY6mquKjY5ygfa6s8TZquh0saXqx9LJ6wmgi+oi1yp8h7pbSewtojZsmUM3fXY03znXYVaaitx3jI4yYWd3Dc8vi171wKT0Fx4WylHflSQ3vI99oHdS+hRCTE0T2qdNa/2O1jpVa52itf7RyGvf01q/cd4539daf2YPNyGEEM4RObpf1sjS8K6i/OPhPGEutlT9WKNDDxuOuda8wPIj72FRdrzTXXM+4KjZ2evo1N70FO80OsrEaE1Y2ascN88lLX3u5PW74B786eHMvj9OXp9CiClnQkWbEEII1zMrfQG1hGE662LzZUp30kAwafOyjU5ySTGx8ZwyJeNX7VrzsrqKttOtPUnP2mB0lEsK8PGmyCuTyMb94AaDbOpOHiDGVk1zym2Tegdz4ZpbadDBDObJlH8hxNWTok0IIdyUyWyiLGAJSZ1H0LZBo+MAoO02EtoPU+qXhcUFl6ofqz5sOcl9hQx0txsd5RMzGg9w0mM+wYH+Rke5rO7YVUTY6+moOWV0lMuq2/s0/dpK+vr7J7VfHy9PikKvI7XjAH3tst2REOLqSNEmhBBuTKWsw58eKk/sMzoKAOdOHiKITmzJzl+ZzxF8Zm/EqmyUHXnP6CgAdNSVEm07R4eLzwccNWPB8BDTisOuPa9NDw2QUPsuR72XEh0ZOen9By67H6uyUbLr95PetxBiapCiTQgh3FhSzg3YtaLJReZl1Y/kSMi+weAkE5OavYle7UF30XajowBQOTofMMO15wOOSp+zgGrCMZXtMjrKJZUefINg2hmad5ch/WcsWsEZFY9P8SuG9C+EcH9StAkhhBuLiorhtDkFv5qPjI4CgE/Vh5SoROLiEo2OMiEBfn4Ue2Ywo3G/0VEA0KW7qNfBpM937fmAoywWM2f9c0jocJ0huuPpzX2eVu3PgnV3GNK/2WyiMvZmUvpP0lJ10pAMQgj3JkWbEEK4uYbw5aT0FdHX1WpojsHeTlJ6T1AXtszQHFeqM2YlcbZq2mrPGhtkZD5giX8OHlbXnw84Sqesw48eaopcY4juWANdraS27aUgeAP+vr6G5UhY8wB2rajY/TvDMggh3JcUbUII4eZ852zCouycPWzsvKyy3O14qCG80jcZmuNKhWWOzst6y9ActcUHCaALW+IaQ3NcqeTs4SG6jcfeNTrKuE7veQ5PBvHN/jNDc6TMTCPfmkFk+RtusdqmEMK1SNEmhBBuLj1rAz3ak57iHYbm6Cp6n35tJTXbvYq21LnZNBCMLjV264S6o6PzAW80NMeVio2Jodg8E18X2zphlPXEK1QQxYIlxm+h0J56O1H2Oqrz9xgdRQjhZqRoE0IIN+fr60uxVwYRBs/LCmvYz0mPeQQGBBia40pZLGZK/bJJaD+MttsMy+Fb9SGnVRIJ8QmGZbha9WHLSOorYrDb2CG6Y7XXlZHWd4yy6JtcYguKORvuo1d70LRfVpEUQlwZKdqEEGIK6I5ZRaz9HK3nzhjSf0d9JQm2Cjqi3WOp+rHsyWsJpoPq4o8N6X+ot4OkvhPUhC6d1I2fHcVn9sgQ3VzX2DphVNnOpwCIXvWAwUmGhYeGcdR3Jcn127EP9BkdRwjhRqRoE0KIKSB8ZF5W+eG3Dem/fGQ+WOiC6w3p/1olZA0PSWw4asx+Y2ePbMeKDa/0jYb0f63SszfQrT3pOekaWycAoDVhZa9SYJ5Davo8o9N8Qi24mwC6KNn3R6OjCCHciBRtQggxBcyam0UDwagyY+Zl2c7splkHkpqx1JD+r1VsfCJnTIn4VBkzL6ur6H36tJW07OsM6f9aBfr5ctIzg3AX2ToBhhd2ibNV0Zxym0vdvcxccxtNOpCBoy8YHUUI4UakaBNCiCnAbDZR6r+EpI7DaNvQpPat7TYS2g5R4p+F1WKZ1L4dqTZ0GSm9BQz2dU1632H1+yjymE9woHvNBzxfZ8xqYmw1dNQaM0R3rLq9T9OvLaRvuN/oKBfw9vKkMOx60tv309fRZHQcIYSbkKJNCCGmCJ28lkC6qCo6OKn91pw+Qgjt2BLXTmq/juaVthEPNURZ7uQO8etsqCDOVkVH9IpJ7dfRZmRuBqDCoCG659O2QRJqtnHMeymREVFGx/mMoCX3YVU2Tu96xugoQgg3IUWbEEJMEaNLxTdM8n5ZdXnD88Dis9xrqfqxUnOuo19b6Syc3MU0RuchhmRsntR+HS1t7mLqdKjhWycAnDnwBiG0MzT/bqOjjGv+4lWUqTi8T75sdBQhhJuQok0IIaaImNh4SkxJ+FZ/OKn9elV9SJmKIzYheVL7dbTAgACKPecR1jC587LsJTtp0oGkZyyb1H4dzWIxU+qfTWL75A/RHasn93latT8L1t1paI6LMZlNVMbewqz+Ipqrio2OI4RwA1K0CSHEFFIftoyUvhP093RMSn+Dfd3M7MmnJnS5Sy32cLXao1aRaKugvb5ycjq024lvO0yJXxYeVuP3EbtW9uS1BNBFTfEhwzL0d7eS1vYhJ4I34OfjY1iOy0lY+wB2rajY/ZTRUSaF1troCG5N3r9r094zSN+gcftwOoIUbUIIMYV4z96Eh7JRlvv+pPR3Nm8HnmoQz7QNk9Kfs4WNbFkwWVsn1J4+TDDtDLr5fMBRiTnGbp0AcHr3VrwYxDf7C4ZlmIiklDQKrPOJLH8dpvgH8vaeQW76xUf8+1tFRkdxS32DNu598iB/84dj2O1T+9+KM9jsmm++kMe9Tx506/dPijYhhJhCUrM30aetdBdNzmIaHSfep19bSM12z/3ZxkrNWEqzDsR+Zuek9Fc3UtzEZd0wKf05W1xsPKdVMj7VxmydAGA98RKVRJKxxPX3vGtPvZ1oey1V+R8YHcVp7HbN3750jMKaDn7z0Vleyq0yOpLb+f4bhRwsa+HVo+f41YelRsdxO/+z/TR5JVXcuzACk8l9R4RI0SaEEFOIv58/xZ7zmTFJ+2WFNuznlMccAoOCJqU/Z7NYLJzxzyK+7TDa7vyhNN6VH1Kq4klMTHF6X5OlNmwZyb0nGOydnCG652urPUtq73HORt+ExeL6w03nrL+fPm2lcf/UXUXyid1n+Li4nEMRj/JY+Gv882snOHGu3ehYbuPFjyt59XApuyN/wQsRz/Ff7xXzUYlsFTFRO4rq+eXu07wS/hvuOvktsNuNjnTVpGgTQogppjNmFfG2SlrrKpzaT0fTOZKGymiPWuXUfibbUNJaQmmj5vQR5/bT101SbwHnQpZOifmAo7zTN2JVNs5O0hDd85XtfgqT0kSt/uKk9301wsLCOOa7gpT697AP9hsdx+E+ON3Iz3YU82Lob4hoz+eWzpf4ktcHfPW5I7T1DBgdz+UVVLfzvTdO8GTIVpLaDrCs/R3+JWAbf/3iUWraeo2O5/LKm7r5m5eO8WjwG8zuPADz7wCT+5Y+7ptcCCHEuMIyRuZlfezceVlnPx5dqn5qDI0clTAyVLE2b5tT+zl7dAeeDOKR5vrD+K5EWs4merUHXZM0RPcTWhNW+iqF5tnMSsuY3L6vxYJ7CKSL0x/90egkDlXV0sNDLx7lRwGvM7f7IGz5KaSs5xH7b4juzOfbMj/rklq7B/jqc0f4mtcOVve8D6v/AebfxZf7n2P50Md87fk8+ofce2ENZ+odsPHV546wRe3nrt6XYPGXIOsrRse6JlK0CSHEFDNr/hKaCITSXU7tx1ayk1btT2qme28KPVZswszhPbQqnTvP6JP5gDlTq+gN9PfnpOd8whv2TWq/504eJN5WSXPKbW515zJz7e0060AG8l4wOorD9A3a+PrzeWy07eOe/peHPzDnPAh3/g5TUBy/93mM4lPFPLarxOioLslm1zz0h2PM7Mrl27anIe1GWPsduOUxiMrkZ9Yn6K4+wQ/flIVdxqO15p9eLcDUUMBPTL+CuKWw5T/Bjb4vjEeKNiGEmGIsFgulftkktH/svHlZWhPfdogSv8VYLRbn9GGgmtBlpPTmM9jX7bQ+Quv3UWydQ8gUmQ94vs7olcTZquiod+4Q3fPVffQM/dpC2gb3GBo5ysvTk6KwTaR37Ke3vdnoOA7x/TcKsdcc5z8sYz4wewfDPS/gRT8vBT3Br3YWsvtUg9FxXc7Pd5zmbMkJfu39OCosFW7/v+FhfVZvuGcrFk9fXgp4jDcPFfHKkWqj47qc5w5W8MHRk7zo/xhm31C4+1mweBgd65pJ0SaEEFOQLXktIbRTXXzYKe3XlOQRplsZmiJL1Y/llbYRLzVIWd4Op7Tf1VRN4tBZ2qJWOqV9o4Ut2AxA+cdvTUp/2jZIYs075HsvISIialL6dKTAJffjoYY4tev3Rke5Zn84XMn2wyfYerEPzDPSUbc/SXxfMU/4/55vv3CUqpYe4wK7mJ0n6/nNrhO8FPAYHmYF924FT/9PTwiMgbufJXiwnucC/4/vvXqMwhpZ2GVUXmUrP34rnxcCf4m/rQ3ufg78ZhgdyyGkaBNCiCkoIWt4v6y6o86Zl1UzMt8rdqSfqWZWzvX0awsdJ5yzmMbofMDg+VNraOSo1PlLaNRBaCcP0R1VcuAtQmljaN5dk9Kfo81bvJqzKhbvYvee11ZQ3c4PXz/O8wFPEGBvg3ueH/8Dc/oNsO6f2TCwm/t5k68+d8TtNz52hIrmbv72D3n8xv9JIgcqUJ9/CkKSP3ti/FLUjf9FRv8R/snjJb763BHaewYnP7CLaerq5+vP5fFj762k9eejbvkFxCwyOpbDSNEmhBBTUEx8MmUqHp+qD53SvlfFHspVDHGJs5zSvtECA4M47TGHsAbnbJ1gK9lFq/YnbeHUmg84ymoxc8Y/m/g2Jw7RPU9P7vO0aV8y1n3e6X05g8lsojL2ZtL6T9BUfcroOFeltXuArz1/hH/zeJb0gROoWx6H6IUXv2D1wzDnVv5OPU9o3V7+5bUT6Cm+yfilDC+ckcdf8SeWDx5AXffvkLL+4hcs/hJk/yX32V8np2MH3/7D0Wm9sMuQzc63th5lU+873Da0DZb/NWS45y9xLkaKNiGEmKJqw5Yxs7eAgV7Hzssa6u8lpec4NVNsqfqx2qJXkjRURkfjOcc2rDWxbYc47bsYT6vVsW27EFvSGoLpoPaUc4bojurraiOt7QMKgzfg6+vr1L6cKWHtlwAo3/WUsUGuwujCGeu63uZ2+3uw4iHIuEwBrRTc+kvUjDn82vuXfJyXy4uHp+fG21prvvtaAXENu/g6L8GCe2Hp1y9/4eafQMJK/sPjNzSdPsTju884P6yL+s/3TzF4dh8/sDwNMzfCxu8bnMjxJlS0KaU2K6VOKaXOKKUeucg5dymlipRShUqprY6NKYQQ4kp5pW3AUw1SesSxS6+X5e3EWw1MuaXqxwqdPzwv66yDt06oO3OUMN3KQMIah7brakaH6NY6aYjuqNN7tuLNAL7Z9zm1H2dLTEkn3zqfqPLXwc3uOP18Zwk9JXs//cC84V8ndqGnH9zzPJ4eVrb6/oyfvn6Y41VtTs3qip4/VEnB0UM87vUriF4EN/1sYisdmq1w1+8x+8/gGd+f8+yOj9kzDRd2efdELW98cJinfX+BKTgB7vgNmMxGx3K4yxZtSikz8ASwBZgD3KuUmjPmnFnAd4AVWuu5wLedkFUIIcQVGJ2X5ej9stoL32dQm5mZvdmh7bqaWZkraNX+DJ1x7LysmiPDRWDcyH5wU1VsfBKlKsFpQ3RHWQpfpopI5i/d5NR+JkPHrDuIsddQWeDc98yRdhXX8/LOgzzl8wtUcALc8dsr+8AcnIj6/NNE287xmOf/8vVnD9PSPX023j5a2crP3jzEc74/w+rtPzwP0Oo18QZ8w1D3bCWILp72+QUPv3B4Wi3sUtrYxXdfzuVZv5/jaxqEe18YXqV0CprInbYc4IzWukxrPQC8CNw65py/BJ7QWrcCaK2nX5kvhBAuJiAgiNMecwmtd+y8rJC6fZzymE1QcIhD23U1VouFEr/FJLQeRNvtDmvXs/JDyokhITnNYW26IqUUNaFLSe4pcNrWCa115aT3HOVs9I2Yze4/42P2hvvo01Ya9z1jdJQJqWzu4ZEXD/GM78/wMw+i7n0BvK9iC4vkNajNP2GV/TD39m3loRePYpsG87Oau/r51nOH+aXn48zQTai7n4OA6CtvKCoD9blfMtd2kkf4DV97LndaLOzS3T/EV5/J5d9M/0fKUCnqjt9A+NT9vjqR73AxwPmDjKtHXjtfKpCqlNqnlDqolJrav34VQgg30R69kmRbGe2NjtnLp7OllqTBUtoip+YCGmPZEtcQRis1Jccc095AL8k9x6ie4vMBR3mlbcRTDXLWwUN0R5XtegqT0kSvesAp7U+20NBw8n2Xk1L/HrbBfqPjXFLfoI2vPpvLv/J/zLSVXfsH5pwHYeF9fNP0J/xK3+Z/tp92XFgXNGSz860Xjjl2D/EAACAASURBVPLnfU+TYz+OuvG/IS7n/7d3nuFRVVsDftdMeoAQegcJAWmigCAWQLGBiIq9Y7mWa9erXnv77NeCXVQQpIm9iwVQUREREem99xp62vp+7DMwxACBJOfsSfb7PPMw58zJzMuUc/bae6+1D/wJW/WGY27jLEZx6MoPeeCTqSUnayGqyp0fTObY9e/SQ39CjrsXmnUPWqtUKUrQVthVpWD3RxyQCXQFzgfeFJF/dLWIyFUiMkFEJqxevXp/XR0Oh8Oxn1Q9xJSUnz/+yxJ5vnm/fUlIlMqty0ffXN2deVkl9P5NHEUy2cQ37VYiz2c7TSNLJ0wthaUTVKk29yOmh5vRpHmbkn/+oGhzLpXZxKyxHwVtskdUlXs+msLRq4dyCmNLpsEsAqc8C/UOp2/i63w3ZhTfTltZMsIW8sy3s6g1/yMuD30BHa6GthcX/0mPvReansxDCYNYOHEkw8cvKv5zWsqAnxewacrX/DduOLQ4HY65LWilUqcoQdsSoH7Udj1gWSHHfKKqOao6H5iJCeJ2Q1X7qWp7VW1fvXr1A3V2OBwORxHJbGPysvLnfF8iz5c7+3s2aipNDz2mRJ7Pduo3aspCqUviwh9K5Pk2ThlJjobJ7Fi2e4QjpKWlMSOhVaksnbBk+m80zFvI2owzSvy5g+SQLmeyViuRPXFY0Cp7ZOj4RayZ9AV3xZdwgzkuEc4dTHxqOgOTn+PhET+yYE3pTK0NkpFTV/DLDyN5MrE/NDoGTnq0ZJ44FILe/QhVzaBf0ou8/uloJi8pe4Vdfl+wjqFfjuLVpJeQmi3g9FeKVrglxilK0PY7kCkiB4lIAnAe8GmBYz4GjgUQkWqY6ZLzSlLU4XA4HPtPXFwccyq0o8H634qfl6VK/fXjmJ3ajoSEsluqPhoRYWmVTjTZOomcHduK/XxVVvzMzPiDqVLG8wGjyap9FI1y55NVQlN0I6z4aSDZGqZpt7IxNTJCUlISM6qewMFZP7Mta13QOv9g0uINDPz0O15JfAlqtiz5BnPFWsh5Q6ghG3iG57jund/Yll128rPmrd7MEyPG0D+5L3GVasHZA00VyJIiKQ05bxgV4pV+8c9yyzu/lKnCLquytnP74LG8lfgsSYlJyHlDISF2l/rYH/YZtKlqLnA9MBKYDoxQ1aki8rCI9PIOGwmsFZFpwGjgdlVdW1rSDofD4Sg6uQd1pRrrWVrMvKxlcyZTQ9eS3bBzCZnFBglNjyNZspk3sXhVJDevW06jnLmsLyf5gBGqtCn5pRPyc3NotPwr/k7pQI2aB1C4wXIqHXERiZLDzFF2FSRZu3kHt7/zI/0SniE5qRQbzPXaIaf2pQNTOXvta9z90d9lYuHtrdm53PjOb/SV/5Ee2moKt6RWLfkXqtaE0FkDaMIibtvWl5uGTSwThV1y8vK5YcgfPJDzHA1YTuicgZDeMGgt3yhSqSVV/VJVm6pqhqo+6u27X1U/9e6rqt6qqi1UtbWqDi9NaYfD4XAUnXrtTGn55ROL12he5v19JM+rvNCkQ3dyNMzGKSOL9Tzzxpt8wLRWJ5WQWWzQtM2RrCvBKboAs8d9TjXWk9vqnBJ7Tpto2a4rC6UuSdPfD1plJ3n5yk3DJnD39mdpyEpC5w4q3QbzoedDp+vpEzeSuMlDGDxuYem9lg+oKnd9MJmL17/AIcwm1Pt1qNWq9F4w83jk+AfpERpH6/n96ftd7Bd2eeKrGRy9tB/HykSk+5NwUPmYph8h9uvjOhwOh2Ov1PPyspIWFW/tp4SFP7BYatOgcfMSMosNKqdXYVbCwVRZ+XOxnidn1ig2airN2pavkUqzdEJ76m8YX2KLRm+dMISNmkrrY88tkeezjVA4xKJ6p3Lwjr9Zs9iOxvYz38zkiIWvc2zoT6T7E9Do6NJ/0eMfQhsfy2PxA/j0i4/5Y+H60n/NUmLgLwtIm/I254bHQOc7oEXB1bNKgSNvRFufzX/iRzBlzAi+nx67hV0+n7yMFb8M44a4j6HtJXD4lUEr+Y4L2hwOh6OMIyIsrVq8vKzc7O002foni9PLR6n6gqyvdTSNc+aStXbFgT2BKvXX/8qs1LYkJiSUrFwMkHtQV6rpepbNnljs59q+ZSPNNvzItPTjSEkpu7ksDbr2AWDB6LcD9QD4ZuoKFv04mOvjPoG2l/rXYA7HIWf1J1S5Hq/FPc+Dg79lzWa7l0IojAkL1vHdl+/zQPw7aNPu0PUuf15YBOn1ItQ6hJcSX6bvu5+zcG3sFXaZvXIT/d//lGcSXie/Xgfo8b9yUXikIC5oczgcjnJAYqbJy5p7gHlZ8yaNIYUdxDc9roTNYoP01icREmXeAS6dsHyuyQfc0aBLCZvFBvW9KbXL/ij+0gkzRg8jhR2kHH5hsZ/LZhpmNGdKXCtqLvikxEYoD4T5a7bQb8QnPJPQj/x6Hf1vMKdUIXzBMCrHZ/No9hPcOuQ3cvNKbrH70mbVpu3835CveCm+L1RtgvTuZ6o8+kV8MqHzh5KYlMqLPM1tg36MqcIum7bncMegUbwU+h/xFaoQOnewqTJaDnFBm8PhcJQDduZlHeB6WRv+HkmuhmjSoUcJm8UGTQ/rzEZNJXf2dwf090snmmClbrvy+f7Vb9iE+VKPpEXFXzohfsoIllKDVkeU/dzAjZm9qZ+/hAV//xTI62/NzuXOgaN4QZ4mXKEqofMGQ1wAI8U1mhPu3Y9DZC6nLXmSp0fO8N/hAMjJy+e2wb/wRPYTVEwIEb5gOCRV8l8krR7h8wZTP7SG69c/zr0fToqJwi6qyn/fm8idm56gVjiL8PlDoWLNoLUCwwVtDofDUQ5I8/Kyqq4Ye0B/n778Z2bHNyO9SrUSNosN4uPjmZ3alvrrfzugUY/EhWNYTC0aNWlRCnb2IyIsq3IEGVv/InfH1gN+nnUrFnLwtonMr9OTcLjsN2Gad7uYHRrPmp/9ryKpqtzzwZ/cuvFRaoaziDt/CFSo4bvHTpr3hK53c2Z4LLk/v8zXU5YH51JEnvpqOucve5xmsoTwOf2hakZwMg07EerxNF1Df5E55TkG/2b/wttv/DSPw2f+jyNC0wj1ehHqtg1aKVDK/hnP4XA4HABsOMC8rE3rV5GRM4t15axUfUFyGnahpq5h6Zy/9uvv8nJ20HjLJBandyyX+YAREpt1K/bSCfNGvU1YlNqd+5ScmMVUqVaDyRU60XjlSPJy/F1ra9CvCzl06pMcEZpO+LSX7Ggwd76dvIN7cnf8UD54bzBzV28O2miPfPn3chJ/fY4e4fHIiQ9Dk+ODVoL2l6Htr+CauM/48/N+/LnI3sIuv85dy/xvXqVP3DdopxugTdksOrQ/uKDN4XA4ygnph0Tysvav9H95LVVfkMhSB8smfrVffzfvzzGksp24zG6loRUzNOlwMtnFmKILUHXuR8wINyPj4DYlaGY5h5xHFbKYMfZj317yj4XrmPXli1wa9y3a6UY4xJKlFUIhwme8Tn7VpvxPnufhgZ+xZUdu0Fb/YM6qTXz53pv8J/498lqdA52uD1ppJ3LyE+TW68Tjcf3o+857VhZ2WbFxO28MHcrDcQPIPehY5ISHglayAhe0ORwORzkh81CTl5Uze/9GOrJnfc8mTaZp266lIxYj1G/cnCVSi8SFY/br79b/PZI8FZp06F46YjFC5cpVmJXQnCorDmzphMXTx3NQ3nzWZpxRwmZ207rLmayjItl/DvXl9VZv2sFr7wzlwbi3yTnoOOSEB3153SKTWIH4C4eTkhDHPVmPcP9746zKz9q8I5fH3v6IJ0MvkV2zDeHTXrCr0mFcAnHnvYNUqM7j2Y9z7+BRVhV2yc7N575BI3kq92k0rR5x5wyAUDhoLStwQZvD4XCUEyJ5WQ3Wjyt6XpYq9deNY3ZqWxLKYan6aESExelHkLF1ErnZ24v8d+krxjIrvhlVqgWYD2QJG2ofzUE5c8lau//5SCt+HEiOhmna7ZJSMLOXpKQkZlQ9gRYbx7I1a12pvlZuXj4PvDOSx3KeIr9SPeLP6W9ng7nKQcSfN5AmoRWcNPN+BoydF7QRYPIAH3z3Z+7f/H/EJ1Ug4YJhEJ8ctNY/qVCdhAuHUT28lSuW3c9zI6cGbbSTpz6fxPWrHyQtPpeEC9+F5PSglazBBW0Oh8NRjshp5OVlzZ1cpOOXz59KLV3Fjgbla0HoPRGf2Y1UtjP3zzFFOn7zhjU0zp7F2prlOx8wQnqrA1s6IT83l0bLv+TvlMOpXrNuKdnZS1rHi0iUHGaMeqdUX+fZr/7imhX3Uzk+l8SLLG8wN+6KnPwoJ4b/YPM3jzJ+fukGtEWh/4+z6TX7HuqH15JwwVBIs/i7WrsNcb1f4fDQLOr88gBfTznANShLkI8nLqHFH/dxSGg+8We/BTUODlrJKlzQ5nA4HOWIum1NyfmlfxQtL2vJBNO4rtPulFJziiUyO3YnV0NsmDKySMfPG/8lYVHSWp5YymaxQdPDjmGjppI3+/v9+ruZ4z6nOuvIbWlJbpXPNG93LIulNsnT3y+11/hq8jIyf7uHVqEFMdNglo7XkN36Am4Mf8D7g19hVVbRR8BLmt/mrSX/u4foHP6bUM9noUHHwFyKTKszyT3yFi6M+57x7z3NvAALu8xYkcXMjx+nd3gs+V3vhmblezp5YbigzeFwOMoR9TNa7FdeVvzCMSyjBg0yWpauWIyQVqU6c+Kbkr68aHlZ2TO/Y7Mm07Rd19IVixHMFN121N+fKbrA1glDydIUWh1XPivIhcIhFtXrRfMdk1m9ZHaJP/+cVZuZ8v6jnBH+ObYazCIknPY822q25YHcF3hy4IfkBJCftTJrO58Pfp5/hT8nu+2VSLtLfXc4UOKOv4/tBx3PXTKAl98eyNZs/wu7ZG3P4a0Bb/Kf0FC2Nz2VcJfbfXeIBVzQ5nA4HOUIEWFJekeabPmT3Oy9Vw3Ly8kmY8tEFqV3RELuchFhXa2jyciZRdb6VXs/UJW6635lVsqhJCYm+SMXA2Q36kINXVvkpRO2b8mi+foxTEs/lpSUCqVsZy/1u/YBYP6oASX6vFt25PLW2/24NTSUbZmnEhdrDea4RJIvHEo4uRI3rb6fvp/95uvL5+Tl8+zbw7k371W21ulEwilP+Pr6xSYUJunc/uRUashdm5/gyeHf+lrYJT9feXLIF9y7/X/sSG9K0pmv2VW4xSLcVdjhcDjKGXGZx5Mq+87LmjvpByqyrdyXqi9IWqsTCRchL2v5gmnU1lVsq9/FJ7PYoF47M0W3qEsnTB89lBTZQerhF5WmlvU0yGjBtLiW1FrwyQEt8F4YqsrTw77kri1Ps71yU5LPfj02G8yVapN00XDqhDfQ6Y/b+GKSfwtH9/1kLDevfYi81OqkXDgYwvG+vXaJkZRGyiUjqBiXxzlz7mTwT9N9e+k3v/+LPgvvJjEhnpRL3oXE8tsxsy9c0OZwOBzljMyO3clTYf0+8rIipeozOvTwySw2yDysK5s0mZxZe8/LWrozH9C9f9HUb9ycxVK76FN0p4xgOdVpcUT5XicQYGPT3jTIX8L8vw9s2YSCDBrzNxfO+y/x8QmkXjoCElJL5HkDoV576NmXo8JTWfvRHcxeuanUX/KzP+bTddJtVA1vJeXidyG1Wqm/ZqlRLZP4cwbQPLSI9G9vZcL8taX+kmNnraLxj7fQOLScxPMHQXqjUn/NWMYFbQ6Hw1HOSKtSndnxzaiyYuxej6u8fCxz4jNJr1bTJ7PYICExkTkph1Fv3d7zsuIWjGEZ1WmU2dpHO/sxU3SLtnTC2hWLaL5tIvPqnEI4bGHpeZ85uNslZGscq38eVOzn+m3uauqOvonGoeUkXfgOpDcsAcNgiWt3IVsOu4pL5Cs+GvAkm7bnlNprzVyexY5PbqF9aBahM16F2oeU2mv5RajZSezoch89w78y7p37WLWp9Aq7LN2wjWnD7uL48ERyT3gMaexmJOwLF7Q5HA5HOWRdraPJyJ5F1obVhT6+acMaMrJnulL1e2B7wy7U1lUsmzet0MfzcnPI2PIHiyq7fMDCiG9atKUT5n4/gLAotTtf5o+Y5aRXrcHk1E5krPyavNwDD0hWZW1nypD/cnxoItknPI4cVHaW9Ejt+Tgbah3JTdte5dXBw0slPytrew5fD3iEs0Kj2dLxZuJa9y7x1wiK5K63ktWkF//OG8pb/V8rlcIuO3LzGPxWX67S98lqfj6JR15T4q9RFnFXEofD4SiHpLU8gbAoc38rPK9o7viviJN8KrlS9YVSx1s6Yckfnxf6+LxJP1KRbYQzj/VTK2ZosnPphG/2ely1eR8xM5xJ44MP9cnMfqTNeVRlI9N++viA/j4nL5933urLFfnvs7H5+SQfeXUJGwZMOI7KlwxhR3ItLl18L0O+K9nCJKpKv4EDuW7Hm6yv143Ukx4o0ecPHBEqnfM6WZUP5rp1T/DWR1+X+Eu8PuJTbsh6lvVVDqPSmX1jM48yAFzQ5nA4HOWQzLbHslmTyZn1XaGPZ8/6ni2aRGY7F3QURoMmrVhGDRIW/lDo4+v+/pp8FRp3cOvbFUbl9Gpmiu7yPU/RXTT9dxrnzWdtxhk+mtlPq65nsp6K5Pw59ID+/q33P+XaDc+wtsphpJXVBnNKFSpeNoK00HZa/nQd42YtK7GnHvL1WC5b9iCbKjQi/aK3oSyOpCekUPmy9wjFJ3LC5FsZOWFmiT31J79M5owZt5ObUIn0y4ZDXGKJPXdZpwx+0xwOh8OxLxISE5mdcih1140r9PG6a39ldsqhJCYm+2wWG0goxOL0jmRsmUhuTvY/Hk9bNpY5cU2oWr12AHaxwfpaR9E4Z89TdJf/NJAcDZPZLXbWvPKDxMRkZlQ9nhYbf2JL1rr9+tuvx0+l57TbyE2oRNXL3i3TDWap2RI943UOC81h9bBrWbFhW7Gfc9yMRbT99TqSw0rly96DpEolYGopleuTeMEQGoZWkfTZ1cxZsaHYTzl1yRpqfn0NNUMbSLlkOFSsVQKi5QcXtDkcDkc5ZXvDLtTVlf/Iy1q+YDp1dYUrVb8P4pp2oyLbmDvpx932b964jibZM1hd88iAzGKDtFYnmaUTfvvn0gn5ubkctOxLpiQfTvWa9QKws5u0jheTJDnMGDWkyH8za9k60r+4kuqSZUqrVyz7BYaSDzmdtYffyqk6hi/evJ/s3APPz1q2fiub3r2KZqHFhM4egFRrUoKmdhLf+Gi2dHuCLvIn4/vfyuYdB77w9satOUx/+0aOCE1lR/fniKvfvgRNywcuaHM4HI5yyq68rN0bzUsmfAFA7bbdfXeKJTIO70G+Chv+3j3nY+7vXj5gC5cPuDcy2+556YTp476kBmvJbX12AGb207z9sSyW2iRNf69Ix2/ansPUAdfTUaax/eTniKvfrpQN7aFq9/tYUecELt30JsOGDTyg59iRm8eoN/7LCfor6zvdQ1Lz8vPbTjvmKlY0vYALsj9gxIBnD6iwS36+8sGbj3FW7hesbPUvKna8uBRMyz4uaHM4HI5ySoMmrVlOdeIXjN5tf9yCH1hBNRo2bROQWWxQuVpNZsdnkrZ89zWzdsz4ji2aSGa74wIyiw0SEhKYndqWeut+/cfSCdsmDGGTJtOq63kB2dmNhEIsrncqzbdPZtWSuXs9VlX56K3HOCPnC5a3uJK0I8rZIuWhELUuHcC6lEacNucevhn7634/xXtD3uCCLYNY1uBUqp14WylI2k2tc/qyPO0wzl/+NB9/+c+R8X3x/scfcNHaviyregQ1z3iiFAzLBy5oczgcjnKKhEIs8vKyIuXD83Jzydj8B4sqd3Cl6ovAuppH0SR7Bps27FqIts7accxObkNSckqAZrHBjgad/7F0wvatm2i+fgzT0o8lObVCgHZ2U79rH0KizB81YK/HffzZR5y3qi9LqnSi9plP+mRnGYkVSb/8feJCQqNvr2TmwqIXJvlmzA+cNu9BVqY2o87Fb5TNwi37Ii6BWv8awbb4ynQcfwMTpha9MMmvf/7FsX/dSlZCLWpfMQzCcaUoWrZxV2SHw+Eox4SbHEcltjLHy8ua99dPVGIL0qRbwGaxQcWWJxEn+cz93SydsGLhTOrpMrbVLzvrXpUmddtFpuh+sXPftFHDSJXtpLa/MCitmKB+Rkumxzen1oKP0fzCc7Um/PU3R/9xMxsSalH3yvLdYI6vnkHOmf3JkGWsHHQZG7fu2OffzJi/iMzRV5EXTqT6le9BfPktzCQVapB40TCqyiZC713KyvVZ+/ybJavWUvGTy0iVbCr0GYGkVPHBtOzigjaHw+Eox2R0PIV8FdZPHgnA2smmVH1Ghx4Bm8UGme26slUT2THT5GUt9vIBax3m3r+iUD+jFcukBgkLx+zcFzdlBMupRotOLqdyX2zM7E3D/MXMm/LLPx5bvnYdyR9dSqpkk9pnBJKSHoChXaS3Oomlh99D57xx/PDG7eTn7zk/a+Pm7Wx85xLqyRr07EHEVWngo6mdpDZqz7rjn6Ut05nU7+q9FnbZnp3LrDevoBVz2dTjZZLqtvLRtGzigjaHw+Eox6RXq8XcuCakeetlVVo+lrlxGVSpUSdgs9ggMTGZWSmHUXetyZMJzx/DKqrQ6ODDAjaLDSQUYnHlXUsnrFmxiJbbJrCgzimEwuGg9azn4OMuJVvjWDN20G77s3PymNnvcloylw3dXyHVNZh30qDHbcyufSq91g/k6w/eKPSY/Hzll3430DH/T5Yd+QjpzV0l3Qi1j76YOU2v5KRtXzJy4ON7PO7b/g9wXPZo5ra8iZodzvTRsOzigjaHw+Eo56ypeRSZ2dNZvXQ+mTums6aGK1W/P2yv35l6upxl86aRsXkCC9JcPuD+sHPphL9+ZO73bxMWpXbnPkFrxQSVq9VkSuoRNFk1crf1Ake9/QBdd4xmZoubqNOxd4CGFiJCk8vfYEFSczpPuY+Jv//8j0O+ffclumeNYEb9c2l44r8DkLSbJuc9xexKnTh50TP89N0n/3h89BfD6bH8ZWZVOY6MMx/0X7CMUqSrioicLCIzRWSOiPy3kMf7iMhqEZnk3a4seVWHw+FwlAYVW55AnOQz/4P7iJc8KrYsP+WsS4Lakbyszx8jjS2EmriqkftDZOmE9ZNHUm3ex8wON6HRwW2D1ood2pxLVTYw7edPARj79bucsOQlpqcfR7OzHwpYzk4kPpkaV77HjlAK1b+4jGUrdhUmmThuNF1mPMSclDY0u/SlAC0tJhSm0dXDWB1XixY/Xc/c2TN2PjRj6iQOG38rSxMakXHVIHAdWCXGPt9JEQkDLwPdgRbA+SLSopBD31XVQ73bmyXs6XA4HI5SIrPdcWzRRNqt/Zytmkhme1eEZH9okNmGlVSl3drPAWjU4ZSAjWKLytVqMic+k0aLPiQjby5rM84IWimmaNnlLDaSSvbEYcyZ8Retf72FJfGNyLxqYPmsdFhEUqrVZ3vvt6mpa1n11vnsyN7B0iULqfP1FWwMVabuv95D4hKC1rSW+NR0Ei9+l0TJJW/YBWRt2si69etIfP8iRIRKfd4jnFQxaM0yRVHC3w7AHFWdp6rZwHDgtNLVcjgcDodfJCalMCelDWFRZicfQmKSK1W/P0goxMLKHQmLMjfcmGo16wWtFHOsrXkUtVhDroZo0q1P0DoxRWJSCjOqnkDLjT8S9+4FqISo0GcEccmVglaznrqtuzDz8Ic5NGcSE167ho1vn0cam8g7ZwjJ6TWD1rOeqo1as6zbizTJm8f01/owv98F1M9fypru/ahcNzNovTJHUYK2usDiqO0l3r6CnCkik0XkfRGpXyJ2DofD4fCFSIl6V6r+wAhlmimRq6t3CtgkNqnkTcmdmtLeBb0HQFrHi0iWbOrlL2Plya9TtV7ToJVihtY9r+P3Wudy1LoPaZE7jdmdnqJO845Ba8UMTY85i4mZN9BxyyjabfuVSS3vIKOjm21QGhRlwY7CxtYL1kj9DBimqjtE5BpgIPCPSf0ichVwFUCDBq50qsPhcNhC4y4XMWXhdxzUxa2NdSA0Pao3k/8eRq0ulwetEpNktjuOSWM7EX/0jUGrxCTN2nfj91+6IwcdQ/sjXIN5fznsipf4/eUs8qsdTMeTLgtaJ+Zod8FDjHt9FcQl0vGsO4PWKbOI6p7XqAAQkU7Ag6p6krd9F4CqFlrn08uBW6eqaXt73vbt2+uECRMOSNrhcDgcDofD4XA4Yh0R+UNV2+/ruKJMj/wdyBSRg0QkATgP+LTAi9WO2uwFTN8fWYfD4XA4HA6Hw+FwFM4+p0eqaq6IXA+MBMJAf1WdKiIPAxNU9VPgRhHpBeQC64A+pejscDgcDofD4XA4HOWGfU6PLC3c9EiHw+FwOBwOh8NRninJ6ZEOh8PhcDgcDofD4QgIF7Q5HA6Hw+FwOBwOh8W4oM3hcDgcDofD4XA4LCawnDYRWQ0sDOTF9041YE3QEnvB+RUP51c8nF/xsN0P7Hd0fsXD+RUP51c8nF/xcH7Fw1a/hqpafV8HBRa02YqITChKMmBQOL/i4fyKh/MrHrb7gf2Ozq94OL/i4fyKh/MrHs6veNjuty/c9EiHw+FwOBwOh8PhsBgXtDkcDofD4XA4HA6Hxbig7Z/0C1pgHzi/4uH8iofzKx62+4H9js6veDi/4uH8iofzKx7Or3jY7rdXXE6bw+FwOBwOh8PhcFiMG2lzOBwOh8PhcDgcDotxQZvD4XBYiIhI0A4Oh8PhcDjswAVtpUAsNLZExH32+4GIJATt4ChZRCQctENhiEgVEamoFs5dj4VzW4RYcg0S9z45HA5H4dh2fnQN9xJERCoDqKra9kEDiEhTEWntNQjzbQncROQIETnW1sBIRE4EhovIiyLyr6B9IohI1aAd9oaIHC4iZ4lIRxFJCdoHQEROEpGHAFQ1z7bATUROBYYAX4nI70erVgAAIABJREFUZSKSFrQTgIg0E5G6tp7bALxzyLUichvYcx62waEwRCRDRGpiFpt1lBC2XFcLI+Jmq6OIVLTlnFcUbH0fYw1bz5EeydEbQbu6L1wJISI9gcEi0gvsaTBEEJHuwCfAXcAMEanjBW6BOopIN+AX4Fqgs22Bm/e+PQd8AIwGzhORzsFagYicASz1vnfW4QUfbwKnA9cAl1nwXTsGGAicJSIvgV2Bm4icADwB3Ov9ezZwaKBSgHdO+x64T0Qa23ZuAxCRHsDLQD5wlYj0B3MeDsint4gMEhGxdMS0JzAU8xu9T0Q6BKy0R0SksYjUCtpjT4hIVxG5WUSuE5FKNlxXC0NEjgXuEpE0Gx1F5BTgY+BzEXkwYJ29IiK1AWzq/I5GRA71zkFNbWtTRSMiXUSkhY3nSNjZYT9IRB4XkYshuGtKBOu+bLGIiDTHXPyWAyfYFrh5F+TngatU9QJgMNBPRMJBfgFFJB44CPgXJnA7G4sCN6/Hrxdwq6oOAT4D/gQCHeESkfrAFZgRmae8Bqs1iEgb4FHgYlW9CPgWOCrokx1QHbgf6AQ0EZGXwY7AzbvwHw78T1X/UNXPgZHAGd7jgZxHRKQicDkwDJgN3Ghb4Ob9Hu4GblTV1zGBboaItAjI5xDgKaAb0D8Ih73hdTo9DVwJ3AZsBA7xHrPiM40gIqcBk4FLRKRh0D4F8YLf54FKwGHAGyKSaMG5bje8z/xLoA1wm4hUtuw3fALmN/Mw5tp2qYhcFqxV4Xjtu79F5HawL3Dzgt8RwKnAAOB4b781jrCzE2E08LWItPT2WfF9BBCRk4EXMde+NUAHEckM1soFbSXFYkzD5v+AqcCp3sXGlsAtCbhbVX/ytl8FtqlqXoBOqGoO8CkwQFWfBxYBZwFdRSQxSDcAVd2IOen94fWY5wCr8E6CAbIReEFVrwAeAF6KBG7R37UAT9JrgNdVdbK3PRyoIyItg/wtqOqHwAhVzcI0WDNE5BXvsTwRSQ/QLR+zfsyHYghhvms1vcdVRJIC8NoE3AA8BIwBNmECtyYWNUy3AY+o6ndeR1AusIPgpv0lATeoal2gnogMjjzgfbZBXw8Owvw+/1bVWZigqLf33lmDmHSDXpjAtyFwuog0CNZqF14QeStwrao+DDyI+S5WCdJrD8QBdwDPAhWwKHDzOmkPAe5T1R+87+RdeOc+m/A+83uAZ4CetgVuItIa0yHTR1UvAwYBj3odCfnB2u3C+8wPBU7B+I4QkVbe99GG97EipvPgLlX9AHNtzgCOCVQMF7QVG68xvxkYqaoLMcP7EzA/6NO8w6oHJmiYAIyN2l4DNPC+mJERJV+JNBBUdRWg3v1HgSXAmUAzEfl3ZEg6AL8Ez2m8qq6JaqCuxTTKEJFzxafpiZELq4iEvKBjtOf3HnAnJnA7xTvpdRKRVD9P0lF+YVVdihl5jnzOYSDH6Kp6wZuvAUhkJE1VN3j/LgGuAhqLyKMici7w7yBHeb3v2UY15AOzMMEHInIBcEFAI4JLVHWzqv6BmWK9CbheRBJFpIOI1AjAaSequgb41buf43WuzAbyALzfQ5yPPuOB37z7JwA1RGSI99tVoK5fLtFEfqOqOhDTexzZNw3I9d47FRFbcty2AM+o6o0Y3/aYwO2g6IMCbORtAl5T1V+97WVAPeCI6IOCDooAVHUU8KqqjsPMGEkB/hMVuAV53svGnFfGRO/GdN4G/t5F47XxHsCMCt6KmWq/M3AL0s1jLfCAqv4C4M08WAJUDtSqAN5nPhT4VVVfBN7B1A04xIb30eusvAcY77VpNgHfAIFP03ZB2wESuVBEGvORUStVXQF8jgmUOovIIOADEUkNws9z2qqqK739YSABSAM2i0gf4G2/TtoicrjnlBNpSEX3rqjq/2GmIL6Jye2ZvKfnKmW/7OiGXtTFYzYw2xs6vwOY45NaJe/fyPctJ+IVFbg9LSKvAy9gPl8/ifjle347Itue62pgnYicDTyG6e0tVcQUzzjV88kr2ABQ1UVAD+Bi4DXgU+9i4gvRft52wQZKLpAnIlcA9wG/+DE6LiJpBc4fGnV/AqaBtQj4ATP1NfkfT+K/Y5a3P/IeVgJSROQ8zHRwXwLLqHPauii/kzEddy+LyJXA8yJS6t//gnjn2Uhn2crIPmAdkOr5XwLcLSK+f6be60c6fyIzG2Z5nmOB1zFTiHuJSFhEThaRCgF1ToVUdR1mpkiksyofmAlEvotdRCTZlhFpVc317o7BTJVMBi73go5bgwyQVHWO935GWIyZDaQi0sc7BwZK1G/7a1XN8zqxrgXOFpE7vGNaB9WJ5X1+y4GvvO3I+acqUNvb1yTSWR8UUR2oKzGzhlDVJzDpHsNEpKqI9BKRawP2m6WqS6KuudmY0TZE5HQR6R2EH6rqbvt5w1w4/g1U2MdxIzCNm0Nt88MMm98O/Awc4qPbt8D4qO1w1P2Q9++VmB9ziwA+2z36edtdMYHJn0BLn5zqA18ARxfymADi3X8HExy19vk926Nf1DGvYjoz/gBa+eAUhwlkXwFOKez98rbPBhb49VkW1c+7tcGMNvwKHOyTVyPMRf/oyO8x2i3q/vPAfD8+y/10jJxDngJ+9G6l+tkCrYH/K+gQ+Zyj7i8DVvp5vt2Xn7ddGxOI3wr8BTT3+zONcqlWmGPU40djCs58iBlVaGKDX9Q5+CnMFKqzgIlA/YDex0MKO2dEeQrQHNPxsho4zAa/qMfreeeYC7zvpK/XtAIulQu+fwUeb4eZ+TISGA/UCMqvwP4E79/vgDred/KLPR3vg2cboKF3P/ocGX3/X8BWTNDu63loX37AuZg0gRO833bjIN5HN9J2YMQDfTDTlXaOoEX3/IpIR8x8/B6qOsk2P8x84luAK3RX7lGpo2a60GoRiUzvy4uaipgvpgT1NuAYVZ3ml1dR/DziMCfmc1R1qk9a2zAn3v+IyM6pN17vrqqqikgrTOPreFX92yevffmFor5zNYAOwLmqOqW0hdT0KvcH5gLHiTeNVc3ZN9JbHo8ZZeju42dZJD/v3yWYQPxqVZ3hk9cCTFGg24GOsqtE+M4qiGLyTTsBvf34LPfTMTLqMhczDfFqHz7bFKC5eBXvvPNYyPPJ9dw6YKa6dvPzfLsvP+/xLEyD5ULM73O6z36RXL8awEIR6aUFcoQirmpG3AQzBbGzqvoy02FffpHfBmZK7quYWRgXq+piP/wK4V/A4yLSrOADUb/lozHBUxdV/dMGv6jvZBpwI2aK2rkBXNMiPk0wFTe7QuE1CtSMuI3DtKmuVJPyEYifty/yW4nMGpkCPInplLlLvRQBP/GuGb0xsw0aRP9+CvyWlmLOkyf5eR7amx/e7CZM5+4tmOmxl6jqPL/8diOISDGWb+zqqToCGIUpZ55a4Jj23r/1LPU7CtPg8bWnAIiPuj8SGF3g8Usw1f2SA/ps9+V3ofe+BfG5VgWuw+QjHFHgsz4KaIXXCxzQe7c3v9ZAT3zsLWVX71g1diXg94p6/GZM72ihPfqW+P0MVPTbybt/J6ZXthO7jxad651b4vzyOgDHM73zSB2fnOIwsxsGYwqiRPaHvX/rYxrIDQN6z/blVx14C59nhOzB9RzMCFqPyOfN7rMxqmNyYXwdGdqXX9S57mZMMbJmFryXz2KmnBU24paGCdTbWOrXEjMLwZcZBntxbIIJeB4laiZJge9kC8zMG19H0PflF/WdfB8zepUZ8HvZADPNfwS7RrSiz9s1MEGRr7Ne9sOvM2bmS7DfySBfPFZusNu0IIm62HXCBEY7pyJipvZNA2pa6vcvzHSrBn567cH1O7zACNMYXIGP060OwG+1HyeUAg4FGyw3YKYZdvL2XYVpRNQN6Pu2L7+rgfVALb/8vNeNPtmmAf/BNBI6YCq9zgTa+em0n36zgLZ+fpbedmLU/TswuS+Rz/JyYB7+T1vZH8crMHmmTf10wwRGHTGN0OipiDfic/C9n36RzovUIPwKuEY6Mk7HjP6dUmB/T8zslaA69fbl1w3T6MwI+H2MPre8gAlyW0R9F670ztEJlvpdjlkXtXaQ72OUYwZmWtzjFEgBAE72/q1qqd8JmHbgQUG/j55PfUyV1RHRTph2wg1AkqV+12A67QP7nHe6BC0QS7fCPjB2jWid433p5hFQ71UR/eb76YcJIs8gqpcZM4Uvcv8r7wI4h2B6qqz0o/CAMgGTPH4dpoz+88D0IL5vtvoBfdl9xOpszChgVUxg9BWmeqrv3zWb/YAT2T0Yr+j9ewemp/Zpv88dseBI1Oi8tx0Z0boZM3NgJj4E37HoV/AcggksIw33MzHVGXt621djcgJ9C4gOwG8F0Cjo97HgPu+cMxTTOXSl9xsJ+lq2N79FBJTDVsArxK6AvAkmMHoSk7oBJgVlBz4GRAfgtxmf8z4jbgW2k6Lu1wQeAd7zflNnYgqo+DbSfwB+K/z026t70AKxcvMucE/iBUaY6TcdvfudMImJC/w8GcaCH2Z9nY8wI3zJmIbVd+zeWx7Y1Bxb/TDD9L9GbT8KXO/dr46ZGubriS4W/DCJ699iRhUewSzBEUnIroqp9hXYNBEb/YAumOqZ53nbzwMPRT1+J6aATJDFAKxyBN4Fzo7avhVTkCCMGS0dhZmSFNT1wGq/Aq4tiBo9Y1cwfjqmA6M/ZvZKUJ2hVvtFeV2DybWObJ+NN6qFCYwmAgsD/E5a7RflJRQoIIMJkOpjAqO7MSOEs4Nwtd2vgOfNBfYd7/1bD5OvOBfTGeP7tcV2vz3dIr1Gjn0gZvX2SDJ5E+BgzPSIyDpKLYHNatbxcH67uzXBNBoyMZUXu6tJ9EzUXaXhA8NWPxF5G6iImSrXAtNg3eY9loaZmrhuz89Qvvy8Mtz5InIcZlH0pap6pPdYvJplJnYW1PAbW/28z6oz0BazFhaqemrBY9QsNh8ItjmKSC/gf5iRga6YEawz1SwVEsbkC61TUzTFd2z3iyAi52CCn6dV9U8ReQETWN7g/VZ6A29gCrj4XdDLer8oz8rARZh1pL7BLGXSHjMSuN075g7gE1Wd6fz2jIicjun0PlNV54vIw8BcVR3otRWuxYz6X6Sqfzm/PXqGgb8xaxjfIiKPY2Y0XeA9Xh+T3jFCAyg0Y7vfnnBB234gIi0wa8VUw8xlXuhVFswJqiEYjW1+BSrOvQGchGlIvBFp3AeJrX4ikqBe5ScRmYoZPa3lbScH/d7FgN8TwHGYkYQ71Cx2bA02+YmpQBpZhPpjTEGbR1V1gLcvTnet71SuHSMeUQF2B0wDdKaqdvSOiVdvDUW/sd1vT4jIDZgZDzWBNFXt5e2PdHKkqOpW57d3RKQSprF+O2Z9zE7e/qRIYBQktvtFELMeWx9MsL4cU5X5rKjgsg6mTbXa+e3mFd2eij5nfwekAwtVtXeBv9l5XHn3Kyqu5H8hyO6l8aPpgyn/ORw4XUQaqWp2AD3jVvp5PRM7ifqBPIwZbu4JNAVuEFPa31diwC+yqGMkIHoCM8VhtIi85z0WZDBpjZ+IHCIibaK2I2X878NUq+wIPAy8LiJd/HCKBT8psLBq1IXrBcz8/XuAeiJyjfe47wGbbY4i0jTi4QWIkaCnB2ZaXA0ROcbbF8T7ZbXfnhCRJABVfRE4DFNh86moQyLXrUDOeTb7iYd3P3JezsKsEZcI/Cwinb39vgdEtvsVhphlMMKqmquqb2JytE8D/quq28WUhUdVlwURsNnuF93OLBDoTMJ0eqyI7Ij6TvgWENnuV1Rc0FYAMetdnC1mOk70/tMwDa0umMoydYDekQ+3vPt5r79QRP5TYH8qJtfkNDXrE72FmXud/c9nKX9+ItJDRAZ5PeB5kc/La2RVUdXTVfV8IFlEhvvhZLufmDXNRgO3i8hRsNsJ+XtMoQ9V1VGY+f0L/PCy3U/MtJqBInJkVINKRCQdk5jdU1Xf9Xxqej3jvmKbo/dZThKRobArQBSRSzE5TEdiKst+LiInBtCBZ7VfAdeOItJFRA73XCMjA89gRp1fAU4Tka7e+Ua94/zqdLTaL8rzNEweXX8ROSaqU+MBzLprPTAVos8TkZP8dIsFv2hEpKGY1BFUNT/K9SlMG+AR4FEROUQDSJWw3S/Ks6uIPC4ivUXk0Kj9DwHNMOX0M0RkEPgfDNnut1+oBYl1ttwwvUD5wNeYijFpBR6PLlXbHp/XxbLVDzNtZAhm0cFJwC17OC7e+9fXUsO2+mGC7MXAj5j1zaLXidtZqSxqny/rTtnsh+ldfAVT+OQKzHTgowo5LlzaLrHkh8nXnA2M8dyOKPj5RR2bQgAl6m1zBFK9c+1VwNvA4KjHWhU433bB5ypttvsVcO3ufbb9MMV33vL2VwceizrufkwxKF9Lf9vuF/X6bYAZmMDnGszCyRcA8ZjGZ+QaVgc4H6jh/PboeiameuoYYBhmumFFTMGeD9lVJOV2bzt+T+ej8ugX5XkcZprmfzDXu0GYRcbBlMlP9e4nY5aa8Hv5H6v99vfmcto8xMwT7okZug8DJ2Maqt9oVHK7BJQTYLOf59ZJVX8SkdaYNVZeVdXnChwXUtV8P91s9vNGYVpiEtnfwCRpn6EmHyV6znVQ3zkr/USkCqbgTi3Mcg2ZmAbrT3457A0b/USkAaY3cQpwL+Yc8i7wu+6aVpcb1G/UVkcx+SFZQBKmeuUOVb0w6vHACtvEgp/nEMZ0mn2hqu94o6NfAMtU9dxCjq+i/hYwstqvwGufBFyrqqd72ycDNwHvqOpQb19gv2Xb/aI8UzHLDDyiqhNE5CagEebc0z/ym5FdeaLpqrre+RXqeiEm0HlGRGoBh2LWBh6qqsO9YwLLW7Tdb39x0yM91Ewr+Qb4zDu5jMYESSeLqXwUOS6QJG4b/USkrpi8udxIg1RNlZ0zgWtF5BbvuONEpEEAFxCr/VT1Z2CYdwK+CdMb9LGYQh953gkmyO+cNX4icqiINBeR5qq6TlW3qOpc4BNMD/lFItJYRLqJSLPS9ok1P1VdBExS1Q2YxUPzMNPmOniH1PCOC6QR5b22dY5q8kM2q+oazFpcSSIyGEBE2mJGDwLDdj/YOdXoz6jtLFU9BqgrIq9H9ouXq+13QGS7XwEmAVneVM6Qqn6NKe9+r4h08vxyvX+D+C3b7hchH6iMWZgaVe0LjMdM3+wOO4PLPO9xvwMi2/2iSQTO9XxWYGbmvAqcJKY4HgEHRLb77RflPmgTkc4icruInABUV68KlJrKZD9ihvkPFZH/iJmT7fyM11mYIfkRIvKIiJwReSwqMOojIp9h1mDxFVv9RKSniDwmIi+JSDXMiAyqugWz7MByYJCIXAvcKSLJfrnZ6ici3TGjytcB74nIZZHHVHU2ZjrTb5hczk8wFzzfsN0vymWzN/KyGZMLkYe5cD0LjBKRiiImjywobHZU1bWYwChHRGZgRgE3B+FSGLb5iVcgxWMp5nzRIGrf6UDVqIaT379bq/0iyK5cu/aquhKTz3ku0MAbafkK0wg92/ntHTGE1RTMehHo7HVugCngtgS4FAIrwmS1XwQxuXatPI/+wDhggJhlkrYCf2FSKBo6vxJGLZijGdQNU352DmZR6r6YIgFHFDimJ+YDXoz/CwVb6QekYRKJ2wJ1MUPNrwJXFDjucczio74uTGirH2Yu+hKgFyZROzJPvWBu4kRgHT4v1mqbHyBABeBLTPEOMHlOc4BrChz7GKax0NLH98taP8wISydMnkM44uv9G4o67lfMgra+LwwcC4578L4FU2nMmgVXbfPzrktbgeFR+x7xrlMNovYNBzo6vz16RufafQo86+1/DXgWOMbbvh542fnt1fU0zPqYA4CjMAXH7seM6reLOu57oLHz26NndK7dCOBUoDmmsuoQvJoA3vbdzq9kb3GUb9oAr6jqs2KqMZ4FvCEiV6rqb94xScBBmGBpmvMDTK5JLrBBVZeKyLvAaqCriKxU1c9FJBNTPr+b+r8woa1+TTE5iJ8Cn4rI1cApQJ6IfKVmnn8voBLmYjfVJy8r/dScWTeLyASgkpjcuXEich5mRGu7qr4tJiflYKC3n++ZrX5iFvx9DDN6sBSYICJvq2qWROWRiMnvPAg4we/faCw4FoaYKpY9gBNt8CmIDX5i8nGuB24GjhSRYap6vqre5w2SfiYir2DWE20DrHJ+hXqGMaMqD+uuXLuRItJPVa8Ss3zI1SJyD6aBf4Hz26NrG0zn962Y0ZXXgPuAn4DOwDUi8iOm3VAL2OD8CvVMBS4BLlSTa3cz0BXTOf4W5nf1s4iMwnzexzm/EiboqDHIG2YE5s0C+64AvgLqe9un4GPvfaz4YXp/huJVf8Jc4G4BHvC244DKAX621vkBjTHV3o6M2nctpjcozds+BsgM6D2z0s/7HQwAKkXtOxqYQIAV8Wz0w4xavYtXsRLT6/g08H/8c8Q0DZ8rksaK4z78A6kcGEt+mOqAFbzz7vuY3NjIY2d455U3gVbOb6+edwIXF9j3C/C0dz/dOyfXc3579TwJ+Dhquwem2EwvTHXpXpjKgcOAw5zfHj2TgR+Ac6P2XQg8A5zkbZ/l+TZzfqXwfwxaIIAPtS7e9AdM2ei/2L2kb1VMye5O3ravJVRt9cNMJ3kMeNm70DXFlND/H1DTO6YhMJZgGoJW+mEqFTUHWnjbj3oXuoOjjhkMPOr3e2a7X/R3G9PIH4ppxEfKRvcHGgXxvtnqhwmIvgL6eNshTLn3p/CmbAKHE+BUw1hwdLcS/byrYir2DvO2WwINg/ay1Q9oGnX/IkzFwOgpm9Uw+dotnF+RnWtiSr13xJt6jQmMprKrLZWIz8sRxZCfsGsK+1mYdlbbqMfuAD4I8PO12q8kb+WqEInsKk7xvog8jsl5OhkzReJx2JnMnQAc5m1refcTkXaY4fpxmJ6MvkALb3sT8LKYinhHeH+ytbSdYsGvQIGKESJyJmaIvjFmkdbO3qHjgS1+ONnuJyLNRKSTiMQTVShJTentEPA8cLmIXIdp6PuajG27n5pKns8CvcUsbJuP6aiYhElqT8aMAq700yvWHB0lh+4qkLJdRGZiivFYs3itTX6ya7H04Z7bYOAjzJSuBt6+NZiFlSs6vz1ThAIpX2LaDWeLiKjqDlXNdn7/8Iwslv6mmGWAfsOcm3uJSDs1PAVUFpEM51e6lJt12rycsK8xDdSVmKTPNpih1NHeYxOA9ZjExZ5qKr+Vez8ROR+TU3K5t30t0BrTWz4ZM6+9Eya/7jZVneiHl61+YhIjUjHTCl9T1U/FlDseDNyNCSYvwzTqVwJHAqeoTzkotvoVlucEvK2qWVHHXI6Z2tQGeFB9zGGz3S/KIQm4ElMeerCq/ujtH4MpxjPXb6eCxIKjo2QRs8TKnViSn1iQoP28fJwPMB23RwKJqnq+99gjmCldkVy7i4Aeqjrf+RXq2h2z1MBoTA7YHFW9VURew3TafqRm3dbrgeaqep3zK9SzDWYWSSTX7t+YXLuNmFy7epgq5rmYtTWPUX/XWLTar1QIeqjPrxtQBZPU2djbroopQfsaZh2gFMzc1+sIYFjfZj8Kz3X6N6ayViVvuwIB5VLY6gc8jLl4RabLdcD0pvX2tuthAvAGfnrZ6Md+5Dl5jyf6/F5Z7VfI66d754qvgKswHRdT8aYK23CLBUd3K9HP+lvgkKBdbPbD8lw72/08j7B37b/Y266EqUDbz9u+D9NB+bV3vvG7SrPVfgVcrc61s92vNG7lZqQNQEQexOQ63ayqq8SsQ3UxUFVV7w1UDrv8RORQzBpdoqrTRORRIAv4RFVneMcMBhar6l1+usWCn/f6/8bk5tyk3miMiBwDPIdJlA10NMEmP2+64afAu2oqLoYwCeynAPNU9TUR6QDkqupEb7qIn1OXrfbbg3MCpnT01cB2oK+q/rn3v/KXWHB0lAwikqQWL2Jrm5+IVMWU0s9W1fNFpCWwWVUXBqwG2O0nIncCy1T1nah9vwA/q+rtYqqstgLmq+oS57dHz5qYzsmXgd9VNV9Eenj7rlTVX0UkEZOpE8TUTav9SoMyndMmuxYKftkLgIZi1m+4Q0Rqqpl7/SGmFHwt57fTa39ynTb55RVDfgKgqq9gRkhfE5E0MSXhf8JM2Qwsp8NGPy1antNRwDLveF8DItv9CkNVs1V1NGaE/nIbg6FYcHSUDDYFRIVhm59alGtXGLb5yb4XS+8FZIhIC1Vdr6o/+RkQ2e4XQSzPtbPdr7Qps0Gb7H9xCl9P2Db6iaECcANwnapeD/wLU9XtcEw+TzLwkJhE5P9ggidfsNlPLC9QYbufx0/AN8DFItJZVfNUdShmSk4dVX1OVVcE4BUrfoXieeYH7bE3YsHR4fAbr+N2MqYq7RlBjroUhi1+YnmBFNv9ojy7Y6ZmXgjcLyLPqur9mM7dGzG5jACKSQHwtXPSdj8/KLPTI8Wy4hSx5CciDwOzMFPBcrxpXyOAW1X1QxGph6le+ZeqLvLLy1Y/sbxAhe1+BVzTMYte9sRc1HZgyvUe5/WqBYrtfg6Ho+zgnW9GYNoAk4P2KYgNfmJ5gRTb/aI8w8AQ4AuNWiwd+Ft3LZbezPOsD1ygqn85P38py0FbY8wP4WFV/cXb929MRZmrVDXLG7XJDWJahM1+YlGuk+1+3sjVYOAFVf3Zm6p5BKYx/7SqbixwfKKq7nB+e0Ysz3Oy3c/hcJQdxLJcu4LY4CcidTA57UmYGUw5UYHRGZgKje2A51V1ivPbo6fVuXa2+/lBmZoeKSKHikhzb07wPOAP4BgRORh25vDkAnd525v9PNnEgJ91uU4x4lcJyPTuf4SpVpQARE7KHUSkrfd4EHOrbffbDdvznGz3czgcZYegA6J9YYOfqi7z2ktrMJ1pCSIyzHt4FvClql4ZVEBks59Ynmtnu5/flJmgTewvTmGln+25Trb7qeUFKmz32xu25znZ7udMYHFHAAACeElEQVRwOBzlDbWsQEpBbPKzPdfOdr8giPnpkd7oi3ULBceCn+25Trb7RTlYvVCw7X4Oh8PhcJQk4hZz39frW51rZ7tfUMR80BbBtuIUtvvZnutku19BxPICFbb7ORwOh8NREogFBVL2hi1+tufa2e4XBGUpaLOmOEUs+InliwXb7rcHZ6sLVNju53A4HA5HSWBDgZS9YZufWLxYOtjv5xcxn9NmcXEKq/1sz3Wy3a8wbC9QYbufw+FwOBwlgU0BUWHY5mdTrl1h2O7nFzE50iZm0ekqmBynfFXNi3psOLANkysWB9wKdPG52o3VflEuVuc62e7ncDgcDofDUVYIOtduX9juV9rEBS2wvxRWnEJEdhanUNXzChSn6OVzwGa1XzSqul1EhmBWj79LzNIDO4DqwOYgnKKx3c/hcDgcDoejLODl2vUATrQxILLdzw9iaqTN9uIUtvvtCdtznWz3czgcDofD4Yh1bMu1K4jtfqVNLAZt1hansN1vX4hIGJMeZuXaU7b7ORwOh8PhcDgcpUFMFSKxvTiF7X77Qi1fLNh2P4fD4XA4HA6HozSIqZE2sL84he1+DofD4XA4HA6HI7aIuUIkthensN3P4XA4HA6Hw+FwxBYxN9IWwfbiFLb7ORwOh8PhcDgcjtggZoO2CLYXp7Ddz+FwOBwOh8PhcNhNzAdtDofD4XA4HA6Hw1GWianqkQ6Hw+FwOBwOh8NR3nBBm8PhcDgcDofD4XBYjAvaHA6Hw+FwOBwOh8NiXNDmcDgcDofD4XA4HBbjgjaHw+FwOBwOh8PhsBgXtDkcDofD4XA4HA6HxbigzeFwOBwOh8PhcDgs5v8BnwdwI5O07HQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = ['0.00001+L1', '0.00001+L2', '0.0001+L1', '0.0001+L2', '0.001+L1', '0.001+L2', '0.01+L1', '0.01+L2',\n",
    "         '0.1+L1', '0.1+L2', '1+L1', '1+L2', '10+L1', '10+L2', '100+L1', '100+L2','1000+L1','1000+L2','10000+L1','10000+L2']\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(range(len(C)*len(penalties)), tr_scores)\n",
    "plt.plot(range(len(C)*len(penalties)), cv_scores)\n",
    "plt.xticks(range(len(C)*len(penalties)), alpha, rotation = 45)\n",
    "plt.legend(['train', 'cv'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=SGDClassifier(alpha=0.01, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "            cv=None, method='sigmoid')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = SGDClassifier(loss='hinge', penalty='l2', alpha=0.01)\n",
    "model = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "model.fit(sent_vectors_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(sent_vectors_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(sent_vectors_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(sent_vectors_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826,\n",
       "        27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826,\n",
       "        27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826,\n",
       "        27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826,\n",
       "        27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826, 27826,\n",
       "        27826, 27826, 27826, 27826, 27826], dtype=int64),\n",
       " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       dtype=int64))"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(sent_vectors_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.36055383, -0.0882455 , -0.14607091, ...,  0.26498536,\n",
       "         0.34033219, -0.52324785],\n",
       "       [-0.52845144, -0.08533141, -0.12703028, ...,  0.27026715,\n",
       "         0.18334248,  0.3804581 ],\n",
       "       [-0.66585199, -0.27462157,  0.53919998, ..., -0.21668261,\n",
       "         0.60342923, -0.65822748],\n",
       "       ...,\n",
       "       [-0.41859362, -0.07878885,  0.50618823, ...,  0.25732372,\n",
       "         0.2389253 , -0.2138205 ],\n",
       "       [-0.49944421,  0.75401115, -0.71848958, ...,  0.27402208,\n",
       "         0.91889334, -0.50573799],\n",
       "       [-0.53424546, -0.59956576, -0.0218214 , ...,  0.34130721,\n",
       "         0.70372312, -0.591834  ]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan_to_num(sent_vectors_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "#avg= np.median(sent_vectors_test)\n",
    "df = DataFrame(sent_vectors_test)\n",
    "\n",
    "df2=df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 50)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(df2)\n",
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8894813010950646"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(df2)[:,1]\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.1.4]  Applying Linear SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV ROC_AUC Score :  0.8451099442866401  Train ROC_AUC Score :  0.8488966770251944 alpha :  1e-05  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8278466030034513  Train ROC_AUC Score :  0.8356456652092823 alpha :  1e-05  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8543768555263802  Train ROC_AUC Score :  0.8608091935328439 alpha :  5e-05  penalty :  l1\n",
      "CV ROC_AUC Score :  0.857429522419861  Train ROC_AUC Score :  0.8616383347766448 alpha :  5e-05  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8576266279706803  Train ROC_AUC Score :  0.8611572848409326 alpha :  0.0001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8519942654638321  Train ROC_AUC Score :  0.8554930198541472 alpha :  0.0001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8590384262580376  Train ROC_AUC Score :  0.8611205629996995 alpha :  0.001  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8649828002411526  Train ROC_AUC Score :  0.8681690847284472 alpha :  0.001  penalty :  l2\n",
      "CV ROC_AUC Score :  0.8014900267715187  Train ROC_AUC Score :  0.7906719060708075 alpha :  0.01  penalty :  l1\n",
      "CV ROC_AUC Score :  0.862874703284596  Train ROC_AUC Score :  0.8657748467921356 alpha :  0.01  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 alpha :  0.1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8609438798387258  Train ROC_AUC Score :  0.863148867513259 alpha :  0.1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 alpha :  1  penalty :  l1\n",
      "CV ROC_AUC Score :  0.8632045746410779  Train ROC_AUC Score :  0.8653130510853035 alpha :  1  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 alpha :  10  penalty :  l1\n",
      "CV ROC_AUC Score :  0.78724508413108  Train ROC_AUC Score :  0.787458095596518 alpha :  10  penalty :  l2\n",
      "CV ROC_AUC Score :  0.5  Train ROC_AUC Score :  0.5 alpha :  100  penalty :  l1\n",
      "CV ROC_AUC Score :  0.6090112104511728  Train ROC_AUC Score :  0.6162615314415099 alpha :  100  penalty :  l2\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning :\n",
    "penalties = ['l1', 'l2']\n",
    "C = [0.00001, 0.00005, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
    "cv_scores = []\n",
    "tr_scores = []\n",
    "i = 0\n",
    "for alpha in C:\n",
    "    for p in penalties:\n",
    "        m = SGDClassifier(loss='hinge', penalty=p, alpha=alpha)\n",
    "        clf = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "        clf.fit(tfw2v_train, y_train)\n",
    "        y_score=clf.predict_proba(tfw2v_cv)[:,1]\n",
    "        scores = roc_auc_score(y_cv, y_score)\n",
    "        cv_scores.append(scores)\n",
    "        scores = roc_auc_score(y_train, clf.predict_proba(tfw2v_train)[:,1])\n",
    "        tr_scores.append(scores)\n",
    "        print(\"CV ROC_AUC Score : \", cv_scores[i], \" Train ROC_AUC Score : \", tr_scores[i], \"alpha : \", alpha, \" penalty : \", p)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = ['0.00001+L1', '0.00001+L2', '0.0001+L1', '0.0001+L2', '0.001+L1', '0.001+L2', '0.01+L1', '0.01+L2',\n",
    "         '0.1+L1', '0.1+L2', '1+L1', '1+L2', '10+L1', '10+L2', '100+L1', '100+L2','1000+L1','1000+L2','10000+L1','10000+L2']\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(range(len(C)*len(penalties)), tr_scores)\n",
    "plt.plot(range(len(C)*len(penalties)), cv_scores)\n",
    "plt.xticks(range(len(C)*len(penalties)), alpha, rotation = 45)\n",
    "plt.legend(['train', 'cv'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "            cv=None, method='sigmoid')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = SGDClassifier(loss='hinge', penalty='l2', alpha=0.001)\n",
    "model = CalibratedClassifierCV(base_estimator=m, cv=None)\n",
    "model.fit(tfw2v_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on Test data(unseen data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(tfw2v_test)\n",
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8581394099815258"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(tfw2v_test)[:,1]\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [5.2] RBF SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.1] Applying RBF SVM on BOW,<font color='red'> SET 1</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data splitting for RBF kernel\n",
    "y_train=final_sorted_time['Score'][0:13000]\n",
    "y_cv=final_sorted_time['Score'][13000:16000]\n",
    "y_test=final_sorted_time['Score'][16000:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=final_sorted_time['CleanedText'][0:13000]\n",
    "X_cv=final_sorted_time['CleanedText'][13000:16000]\n",
    "X_test=final_sorted_time['CleanedText'][16000:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13000,)     (13000,)\n",
      "(3000,)     (3000,)\n",
      "(4000,)     (4000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, \"   \", y_train.shape)\n",
    "print(X_cv.shape, \"   \", y_cv.shape)\n",
    "print(X_test.shape, \"   \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_vec = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=300)\n",
    "bow_train = cnt_vec.fit_transform(X_train)\n",
    "bow_cv  = cnt_vec.transform(X_cv)\n",
    "bow_test = cnt_vec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13000, 300)\n",
      "(3000, 300)\n",
      "(4000, 300)\n"
     ]
    }
   ],
   "source": [
    "print(bow_train.shape)\n",
    "print(bow_cv.shape)\n",
    "print(bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "def svc_grid_rbf(X, y):\n",
    "    C = [10**-4, 10**-2, 10**0, 10**2, 10**4]\n",
    "    gamma = [10**-4, 10**-2, 10**0, 10**2, 10**4]\n",
    "    tuned_parameters_grid = dict(C = C, gamma = gamma)\n",
    "    model_svc_grid = GridSearchCV(SVC(kernel = \"rbf\", class_weight = \"balanced\", probability=True), tuned_parameters_grid, cv = 3, scoring = \"roc_auc\")\n",
    "    model_svc_grid.fit(X, y)\n",
    "    print(\"\\n**********GridSearchCV**********\\n\")\n",
    "    print(\"\\nOptimal C:\", model_svc_grid.best_estimator_.C)\n",
    "    print(\"\\nOptimal Gamma:\", model_svc_grid.best_estimator_.gamma)\n",
    "    print(\"\\nBest kernel:\", model_svc_grid.best_estimator_.kernel)\n",
    "    print(\"\\nBest Score:\", model_svc_grid.best_score_)\n",
    "    df_gridsearch = pd.DataFrame(model_svc_grid.cv_results_)\n",
    "    max_scores = df_gridsearch.groupby(['param_C','param_gamma']).max()\n",
    "    max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]\n",
    "    sns.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g')\n",
    "    plt.show()\n",
    "    return model_svc_grid.best_estimator_.C, model_svc_grid.best_estimator_.gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********GridSearchCV**********\n",
      "\n",
      "\n",
      "Optimal C: 1\n",
      "\n",
      "Optimal Gamma: 0.01\n",
      "\n",
      "Best kernel: rbf\n",
      "\n",
      "Best Score: 0.8634786393955619\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To choose c and gamma, call the rbf utility function\n",
    "optimal_C_bow_grid_rbf, optimal_gamma_bow_grid_rbf = svc_grid_rbf(bow_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=1, kernel='rbf', probability=True, gamma=0.01)\n",
    "model.fit(bow_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(bow_test)\n",
    "pred_prob = model.predict_proba(bow_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.8927692307692308\n"
     ]
    }
   ],
   "source": [
    "# Train accuracy\n",
    "train_acc_bow_rbf = model.score(bow_train, y_train)\n",
    "print(\"Train accuracy:\", train_acc_bow_rbf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8636865553513071"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# auc_roc score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  11,  533],\n",
       "       [   4, 3452]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix to describe the performance of classifier.\n",
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.02      0.04       544\n",
      "          1       0.87      1.00      0.93      3456\n",
      "\n",
      "avg / total       0.85      0.87      0.81      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To show main classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.2] Applying RBF SVM on TFIDF,<font color='red'> SET 2</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vec = TfidfVectorizer(ngram_range=(1,2), min_df=10, max_features=300)\n",
    "tf_train = tfidf_vec.fit_transform(X_train)\n",
    "tf_cv  = tfidf_vec.transform(X_cv)\n",
    "tf_test = tfidf_vec.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13000, 300)\n",
      "(3000, 300)\n",
      "(4000, 300)\n"
     ]
    }
   ],
   "source": [
    "print(tf_train.shape)\n",
    "print(tf_cv.shape)\n",
    "print(tf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********GridSearchCV**********\n",
      "\n",
      "\n",
      "Optimal C: 1\n",
      "\n",
      "Optimal Gamma: 1\n",
      "\n",
      "Best kernel: rbf\n",
      "\n",
      "Best Score: 0.8676664299612968\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "SyntaxError",
     "evalue": "'return' outside function (<ipython-input-72-9bd196bc1879>, line 16)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-72-9bd196bc1879>\"\u001b[1;36m, line \u001b[1;32m16\u001b[0m\n\u001b[1;33m    return model_svc_grid.best_estimator_.C, model_svc_grid.best_estimator_.gamma\u001b[0m\n\u001b[1;37m                                                                                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'return' outside function\n"
     ]
    }
   ],
   "source": [
    "C = [ 0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "gamma = [0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "tuned_parameters_grid = dict(C = C, gamma = gamma)\n",
    "model_svc_grid = GridSearchCV(SVC(kernel = \"rbf\", probability=True), tuned_parameters_grid, cv = 3, scoring = \"roc_auc\")\n",
    "model_svc_grid.fit(tf_train, y_train)\n",
    "print(\"\\n**********GridSearchCV**********\\n\")\n",
    "print(\"\\nOptimal C:\", model_svc_grid.best_estimator_.C)\n",
    "print(\"\\nOptimal Gamma:\", model_svc_grid.best_estimator_.gamma)\n",
    "print(\"\\nBest kernel:\", model_svc_grid.best_estimator_.kernel)\n",
    "print(\"\\nBest Score:\", model_svc_grid.best_score_)\n",
    "df_gridsearch = pd.DataFrame(model_svc_grid.cv_results_)\n",
    "max_scores = df_gridsearch.groupby(['param_C','param_gamma']).max()\n",
    "max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]\n",
    "sns.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g')\n",
    "plt.show()\n",
    "#return model_svc_grid.best_estimator_.C, model_svc_grid.best_estimator_.gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8648937594565509"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc_grid.score(tf_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=1, kernel='rbf', probability=True, gamma= 1)\n",
    "model.fit(tf_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(tf_test)\n",
    "pred_prob = model.predict_proba(tf_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.9375384615384615\n"
     ]
    }
   ],
   "source": [
    "# Train accuracy\n",
    "train_acc_tf_rbf = model.score(tf_train, y_train)\n",
    "print(\"Train accuracy:\", train_acc_tf_rbf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8764877153118191"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# auc_roc score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  73,  471],\n",
       "       [  15, 3441]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T3wt4F/gW8EfgOeBLBddpZtZ+7qm3LSIWVmxeVWRdZmbLoiw3Sose/fIWS89VsACYQjY15cwi6zczy63Oe+B5FZ1TP59sroLryQbjHwisCTxNNqB+p4LrNzPLpSw99aJz6rtHxKUR8VZEvJlmNtsjIm4CVi24bjOz/EqSUy86qDdKOkBSr7QcULGvHF+LZlYKsTj/Us+KDuoHk01sMxf4d1o/RFI/4LiC6zYzyy0a8y/1rOjRLzNpfQjjfUXWbWbWLnUerPNqNahLuoUqKZKI+HJbF5e0EdlLVD8aEZtK2gzYMyLO6khjzcyKUu898Lyq9dQv6oTrXwZ8B7gUICKmSbqebE4YM7O6UfqgHhF3Na1L6gsMiYgZ7bx+/4h4oNlLVuv8NoOZ9UTRUHUK9G6jzRulkr4ATAfuTNsjUmomj9ckrU9K40jaD5hT/RQzs67Xk26UTgC2Ae4GiIipkjbIef1jyd64vbGkl4DnyUbEmJnVlWgsR089T1BfFBHzm6VQ8o4xfwm4kuwLYTXgTWAM2ReFmVndqPceeF55gvqT6aGhXpKGAd8A7s95/d8C84GHyaYLMDOrSxE9p6d+HHAq2SjOW4DbgZNzXn/tiNi9g20zM+syPaannqbP/Z6kM7LNeLcd1/+7pE9FxPQOt9DMrAs0lmT0S5tBXdIWwOXA6mn738BREfFwjut/Gjhc0vPA+2QzNUZEbNbxJpuZdb6edKP0SuCb6f2iSNoplQ3Pce7ojjfNzKzr9KSgvrApoANExD2S3s5z8YiY1eGWmZl1oSjJvLHV5n5pSpH8U9LFwA1kQxm/QhqzbmZWFj2hp35xs+3KPHhJvtPMzDKlH9IYEZ/pyoaYmdVSQ08Z/QIgaTdgE2CFprKI+GFRjTIz62pl6annmdDrErJH+48H+gGHAHnnfjEz6xaiUbmXtki6QtJcSY9VlI2QdL+kqZKmSNo6lUvShZJmSJqWhpE3nTNG0rNpGZPnc+R5nd2nI+KrwOsR8QOyyb3WznNxM7PuIiL/ksMvgeZP058DnBERI8ie0j8nlY8GNkzLOLIXCyFpNeA0spi7NXCapFXbqjhPUG96gvQ9SWsC7wFDc5xnZtZtdGZPPSL+CrzRvBhYOa2vwgfzYe0FXB2Z+4GBktYCdgPujIg3ImIe2fTnbU67kien/gdJA4FzgalAA3BVjvPMzLqNhsY8fdxl8k3gdknnknWot0/lg4EXK46bncpaK6+qzU8REadHxPyIuBkYBnwK+HWeT2Bm1l20J/0iaVzKizct43JUcTTwrYhYB/gW2fQrkE2fslRzqpRXlWv0y5KrZZN5vStpKjCkPeeamdWzxnaMfomIiWQvAGqPMWRTlwPcDPwirc8G1qk4bm2y1MxsYKdm5fe0VUlHf98ox9gfM7MkQrmXDnoZ+Gxa3xl4Nq1PBg5Lo2C2BRZExByyac5HSVo13SAdlcqqaldPvYKfKDWzUunMuV8k3UDWyx4kaTbZKJajgJ9KWo5swElTyuY2YA9gBvAOMDZrT7wh6UzgwXTchIhofvN16bqjlU+SXi7d0k4BoyJiQK5P10HL9R3sLw5byrsv31vrJlgd6jNovWXOHkxZe+/cMWfk7FvrNltRrad+UQf3mZl1O10w+qVLVJv75a6ubIiZWS2VJTXQ0Zy6mVmptGf0Sz1zUDczozwTeuUO6pKWj4j3i2yMmVmtNNa6AZ0kzyyNW0uaThpTKWm4pJ8V3jIzsy4UKPdSz/Lc7r0Q+CLwOkBEPAp8rshGmZl1tcWh3Es9y5N+6RURs6QPfZCGgtpjZlYT9d4DzytPUH8xTeYeknoD44Fnim2WmVnXKktOPU9QP5osBTME+Dfwp1RmZlYaPaanHhFzgQO7oC1mZjXTY3rqki6jhYetIiLP/MFmZt1CQ0/pqZOlW5qsAOzDh9/GYWbW7eV4S123kCf9clPltqRryN6VZ2ZWGo09qKfe3DBg3c5uiJlZLfWYCb0kzeODz9uL7A3ZJxbZKDOzrtYjbpQqe+JoOPBSKmqM1t6qYWbWjTWqHOmXqtMEpAB+S0Q0pMUB3cxKqaEdSz3LM/fLA5K2KLwlZmY11Kj8Sz1rNf0iabmIWAx8GjhK0nPAQrJ3lEZEONCbWWn0hNEvDwBbAHt3UVvMzGqmLLnlakFdABHxXBe1xcysZuo9rZJXtaC+uqTjW9sZEecX0B4zs5roCUMaewMrQkkSTWZmVTSUJNJVC+pzImJCl7XEzKyGekJPvSTfW2ZmbesJQX2XLmuFmVmN1fmrR3NrNahHxBtd2RAzs1rqCT11M7Meo94f/88rzzQBZmal15nTBEi6QtJcSY9VlP1E0lOSpkm6RdLAin0nSZoh6WlJu1WU757KZkjKNTuug7qZGVn6Je+Swy+B3ZuV3QlsGhGbAc8AJwFI+iTZe6A3SedcIqm3pN7AxcBo4JPAQenYqhzUzczo3KAeEX8le/dEZdkdaT4tgPuBtdP6XsCNEfF+RDwPzAC2TsuMiJgZEf8BbkzHVuWgbmZGNvdL3qUTHAH8Ia0P5sPvfZ6dylorr8pB3cyM9uXUJY2TNKViGZe3HknfBxYD1zUVtXBYVCmvyqNfzMxo3+iXiJgITGxvHZLGAF8Edql46dBsYJ2Kw9YGXk7rrZW3yj11MzOgkci9dISk3YHvAXtGxDsVuyYDB0paXtIwYEOyqc8fBDaUNExSX7KbqZPbqsc9dTMzOvfhI0k3ADsBgyTNBk4jG+2yPHBn9vpn7o+Ir0XE45ImAU+QpWWOjYiGdJ3jgNvJJli8IiIeb6tuB3UzMzr3JRkRcVALxZdXOf5s4OwWym8DbmtP3Q7qZmZ4mgAzs1JZrHK80M5B3cyMnvGOUjOzHsPpFzOzEunoUMV646BuZobTL2ZmpeL0i5lZiTSUpK/uoG5mhnvqZmalEu6pm5mVR1l66p6lsc5cNvE8Xp79KFMfuWtJ2ak/OJ5Zz09hyoN3MOXBOxi9+841bKEV5f33/8OB//UNvjzmGPY6+P9x0S+u+dD+H55/CVvtus9S591x971susNoHnvyGQDmL3iTscd9j6123Yezz7ukS9peBkXP0thV3FOvM1dfPYlLLrmSK6/86YfKf3rhZZx/waU1apV1hb59+3DFhT+if/9+LFq8mMOOPoHPbDuS4Zt+gseefIY331641DkLF77DdTdPZrNPfrziOn0Zf9ShPDtzFjNmzurKj9Ct1Xeozs899Tpz733/5I1582vdDKsBSfTv3w+AxYsXs3jxYiTR0NDAeRdfzrePOXKpc3522dWMPXg/+i7fd0lZ/34rsMXwTVm+b9+ljrfWLSZyL/Ws0KCuzCGSTk3bQyRtXWSdZXXM0WN5+KE7uWzieQwcuEqtm2MFaWhoYN8xx7LjFw9iu602Z7NNNub6X/+Oz316W1YftNqHjn3ymRm8Mvc1dtphmxq1tlyiHf/Vs6J76pcA2wFNcwu/BVzc2sGV7/1rbFz6V82e6ueXXs1GG2/PliNH8corc/nJOafWuklWkN69e/Prqy7mrluuYfoTzzBl6nTuuPtevrrfnh86rrGxkR9fOJHvjD+qRi0tn8Z2LPWs6KC+TUQcC7wHEBHzgFZ/J4yIiRExMiJG9uo1oOCmdR9z575GY2MjEcEvLr+OrbYaUesmWcFWXmlFttpiMx54eBovzJ7DHl85glH7juG9995n9AFHsPCdd5kxcxZjj/suo/Ydw7THn2L8985YcrPU2q8sPfWib5QuktSbdA9C0urU/xdd3VlzzTV45ZW5AOy912gef/zpGrfIivDGvPkst9xyrLzSirz3/vvc/+AjHHHI/vzld9cvOWarXffhD5OuAOC+225aUn74cd/lhGP/i00/sVGXt7ssyhKYig7qFwK3AGtIOhvYDzil4Dq7tWuvuZjP7rgdgwatxr9mTuGMCefy2c9uz/DhnyQimDVrNkcf871aN9MK8Orr8/j+WefS0NhINAa77fyZDufLR+07hrcXvsOixYv5871/Z+IFZ7P+sHU7ucXl0hD13QPPS1HwB5G0MbALIOCuiHgyz3nL9R1cjr9h61TvvnxvrZtgdajPoPW0rNf46rr75I4518+6ZZnrK0qhPXVJPwVuiohWb46amdWDes+V51X0jdKHgVMkzZD0E0kjC67PzKxDPPolh4i4KiL2ALYGngF+LOnZIus0M+sITxPQPhsAGwNDgSe6qE4zs9zKkn4pOqf+Y+DLwHPAJODMiPAz8GZWd8oy+qXonvrzwHYR8VrB9ZiZLZN6T6vkVUhQl7RxRDwFPAAMkTSkcn9EPFxEvWZmHVXvN0DzKqqnfjwwDjivhX0BeEJwM6srzqlXERHj0uroiHivcp+kFYqo08xsWZQl/VL0OPW/5ywzM6upiMi91LOicuprAoOBfpI2J5siAGBloH8RdZqZLYuGTuypSxoI/ALYlCzlfATwNHAT2dDufwEHRMQ8SQJ+CuwBvAMcviz3HYvKqe8GHA6sDZxfUf4WcHJBdZqZdVgnp19+CvwxIvaT1JesM3sy2fxXP5J0InAi8D1gNLBhWrYB/jf92SFF5dSvAq6StG9E/LqIOszMOlNnpVUkrQzsSNaxJSL+A/xH0l7ATumwq4B7yIL6XsDVkTXgfkkDJa0VEXM6Un9R6ZdDIuJaYKik45vvj4jzWzjNzKxm2tNTlzSObIRfk4kRMTGtrwe8ClwpaTjwEPAN4KNNgToi5khaIx0/GHix4lqzU1n9BHWg6bVFKxZ0fTOzTtWeIY0pgE9sZfdywBbA+Ij4Z5qt9sQql2tpGt8O/9pQVPrl0vTnGUVc38yss3XiNAGzgdkR8c+0/SuyoP7vprSKpLWAuRXHr1Nx/trAyx2tvNAhjZLOkbSypD6S7pL0mqRDiqzTzKwjOmuWxoh4BXhR0sdT0S5kExlOBsaksjHAb9P6ZOAwZbYFFnQ0nw7Fz/0yKiK+K2kfsm+j/YG7gWsLrtfMrF06efTLeOC6NPJlJjCWrBM9SdKRwAtk8RDgNrLhjDPIhjSOXZaKiw7qfdKfewA3RMQb2ZBMM7P60pkPFUXEVKCllwLt0sKxARzbWXUXHdR/J+kp4F3gGEmrA++1cY6ZWZfzNAE5RMSJwHbAyIhYBCwkG5NpZlZXoh3/1bOiX5LRBzgU2DGlXf4C/LzIOs3MOqIhyjH5btHpl/8ly6tfkrYPTWX/VXC9ZmbtUu8TdeVVdFDfKiKGV2z/WdKjBddpZtZuzqnn0yBp/aYNSesBDQXXaWbWbs6p5/Md4G5JM9P2UJZxDKaZWREaS5J+Kbqn/jfgUrLX/zWm9X8UXKeZWbu5p57P1cCbwJlp+yDgGj54ksrMrC549Es+H292o/Ru3yg1s3rk9Es+j6QJagCQtA1ZSsbMrK44/ZLPNmSzj72QtocAT0qaTjblwWYF129mlktZeupFB/XdC76+mVmnqPceeF6FBvWImFXk9c3MOktDlOMRmqJ76mZm3YKnCTAzK5GyTBPgoG5mhnvqZmal4tEvZmYl4tEvZmYl4mkCzMxKxDl1M7MScU7dzKxE3FM3MysRj1M3MysR99TNzErEo1/MzErEN0rNzErE6RczsxLxE6VmZiXinrqZWYmUJaeusnw7lZmkcRExsdbtsPrinwtrSa9aN8ByGVfrBlhd8s+FLcVB3cysRBzUzcxKxEG9e3De1Frinwtbim+UmpmViHvqZmYl4qBuZlYiDurdjKSBko6p2P6YpF/Vsk3WtSR9TdJhaf1wSR+r2PcLSZ+sXeus1pxT72YkDQV+HxGb1rgpVgck3QOcEBFTat0Wqw/uqXcySUMlPSnpMkmPS7pDUj9J60v6o6SHJN0raeN0/PqS7pf0oKQJkt5O5StKukvSw5KmS9orVfEjYH1JUyX9JNX3WDrnn5I2qWjLPZK2lDRA0hWpjkcqrmVdLP17PSXpKknTJP1KUn9Ju6R/m+np32r5dPyPJD2Rjj03lZ0u6QRJ+wEjgevSz0O/9G8+UtLRks6pqPdwST9L64dIeiCdc6mk3rX4u7CCRISXTlyAocBiYETangQcAtwFbJjKtgH+nNZ/DxyU1r8GvJ3WlwNWTuuDgBmA0vUfa1bfY2n9W8AZaX0t4Jm0/kM8BHCBAAAFXUlEQVTgkLQ+EHgGGFDrv6ueuKR/rwB2SNtXAKcALwIbpbKrgW8CqwFP88Fv1APTn6eT9c4B7gFGVlz/HrJAvzowo6L8D8CngU8AvwP6pPJLgMNq/ffipfMW99SL8XxETE3rD5H9j7w9cLOkqcClZEEXYDvg5rR+fcU1BPxQ0jTgT8Bg4KNt1DsJ2D+tH1Bx3VHAianue4AVgCHt/lTWWV6MiL+l9WuBXch+Zp5JZVcBOwJvAu8Bv5D0ZeCdvBVExKvATEnbSvoI8HHgb6muLYEH08/DLsB6nfCZrE54lsZivF+x3kAWjOdHxIh2XONgst7WlhGxSNK/yIJxqyLiJUmvS9oM+Arw/9IuAftGxNPtqN+Kk+tGVkQslrQ1WeA9EDgO2Lkd9dxE9uX+FHBLRIQkAVdFxEntbLN1E+6pd403gecl7Q+gzPC0735g37R+YMU5qwBzU0D/HLBuKn8LWKlKXTcC3wVWiYjpqex2YHz6HxpJmy/rB7JlMkTSdmn9ILLfxIZK2iCVHQr8RdKKZP+Ot5GlY1rqFFT7efgNsHeq46ZUdhewn6Q1ACStJmndVs63bshBvescDBwp6VHgcaDpZuU3geMlPUCWklmQyq8DRkqaks59CiAiXgf+JukxST9poZ5fkX05TKooOxPoA0xLN1XP7NRPZu31JDAmpdZWAy4AxpKl56YDjcDPyYL179NxfyG7Z9LcL4GfN90ordwREfOAJ4B1I+KBVPYEWQ7/jnTdO/kgFWgl4CGNNSapP/Bu+tX4QLKbph6dUlIekmpFc0699rYELkqpkfnAETVuj5l1Y+6pm5mViHPqZmYl4qBuZlYiDupmZiXioG4tktSQhsk9JunmNEqno9faSdLv0/qekk6scuyHZqFsRx2nSzohb3mV67zdGfWa1YqDurXm3YgYkYbe/YdsXpol0gNU7f75iYjJEfGjKocMBNod1M0s46BuedwLbKAPZqC8BHgYWEfSKEn/SLNJ3pyegkTS7mk2wvuALzddKM0WeFFa/6ikWyQ9mpbtaTYLZTruO2mGyWmSzqi41vclPS3pT2Rzm+Qm6VZlM2Y+Lmlcs33npc9zl6TVU1mLs2ya1RsHdatK0nLAaKBpyoGPA1dHxObAQrKnE3eNiC2AKWRPx64AXAZ8CfgMsGYrl78Q+EtEDAe2IHvS9kTgufRbwnckjQI2BLYme0x+S0k7StqS7MnZzcm+NLZq50c7IiK2JJvR8Otp0iuAAcDD6fP8BTgtlU8ExqdzTiCb3dCs7vjhI2tNvzSLH2Q99cuBjwGzIuL+VL4t8EmyaQsA+gL/ADYmm3XwWQBJ1wIf6g0nOwOHAUREA7BA0qrNjhmVlkfS9opkQX4lskmq3kl1TG7n5/u6pH3S+jrpmq+TPaLfNE/KtcBv0m8fTbNsNp2/fDvrM+sSDurWmnebzyqZAtrCyiLgzog4qNlxI8g5E2EOAv47Ii5tVsc3O1qHpJ2AXYHtIuIdZW8Pam0GzCD7jba9s2ya1YTTL7Ys7gd2aJpdUNkbfDYim3xsmKT103EHtXL+XcDR6dzeklZm6VkHbweOqMjVD04zDP4V2EfZ235WIkv15LUKMC8F9I3JfuNo0gvYL61/FbgvIqrNsmlWVxzUrcPSixgOB25IM/7dD2wcEe+RpVv+L90ondXKJb4BfC7NTPgQsEnzWSgj4g6yl4f8Ix33K2CliHiYLE0yFfg1WYqoNadImt20AH8ElkttPjO1u8lCYBNJD5Glhyak8tZm2TSrK577xcysRNxTNzMrEQd1M7MScVA3MysRB3UzsxJxUDczKxEHdTOzEnFQNzMrkf8Pi6s0wbjezgcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix to describe the performance of classifier.\n",
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.13      0.23       544\n",
      "          1       0.88      1.00      0.93      3456\n",
      "\n",
      "avg / total       0.87      0.88      0.84      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To show main classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.3]  Applying RBF SVM on AVG W2V,<font color='red'> SET 3</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_w2v = np.array(sent_vectors_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49000, 50)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_w2v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgw2v_train = avg_w2v[0:13000]\n",
    "avgw2v_cv = avg_w2v[13000:16000]\n",
    "avgw2v_test = avg_w2v[16000:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000, 50)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgw2v_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000,)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********GridSearchCV**********\n",
      "\n",
      "\n",
      "Optimal C: 10\n",
      "\n",
      "Optimal Gamma: 0.01\n",
      "\n",
      "Best kernel: rbf\n",
      "\n",
      "Best Score: 0.894978506474535\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hyperparameter tuning\n",
    "C = [ 0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "gamma = [0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "tuned_parameters_grid = dict(C = C, gamma = gamma)\n",
    "model_svc_grid = GridSearchCV(SVC(kernel = \"rbf\", probability=True), tuned_parameters_grid, cv = 3, scoring = \"roc_auc\")\n",
    "model_svc_grid.fit(avgw2v_train, y_train)\n",
    "print(\"\\n**********GridSearchCV**********\\n\")\n",
    "print(\"\\nOptimal C:\", model_svc_grid.best_estimator_.C)\n",
    "print(\"\\nOptimal Gamma:\", model_svc_grid.best_estimator_.gamma)\n",
    "print(\"\\nBest kernel:\", model_svc_grid.best_estimator_.kernel)\n",
    "print(\"\\nBest Score:\", model_svc_grid.best_score_)\n",
    "df_gridsearch = pd.DataFrame(model_svc_grid.cv_results_)\n",
    "max_scores = df_gridsearch.groupby(['param_C','param_gamma']).max()\n",
    "max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]\n",
    "sns.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8978778065130533"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc_grid.score(avgw2v_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=10, kernel='rbf', probability=True, gamma= 0.01)\n",
    "model.fit(avgw2v_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.9126923076923077\n"
     ]
    }
   ],
   "source": [
    "# Train accuracy\n",
    "train_acc_avgw2v_rbf = model.score(avgw2v_train, y_train)\n",
    "print(\"Train accuracy:\", train_acc_avgw2v_rbf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(avgw2v_test)\n",
    "pred_prob = model.predict_proba(avgw2v_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8915451814406318"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# auc_roc score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 141,  403],\n",
       "       [  37, 3419]], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix to describe the performance of classifier.\n",
    "import seaborn as sns\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.79      0.26      0.39       544\n",
      "          1       0.89      0.99      0.94      3456\n",
      "\n",
      "avg / total       0.88      0.89      0.86      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To show main classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [5.2.4]  Applying RBF SVM on TFIDF W2V,<font color='red'> SET 4</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49000, 50)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfw2v_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfrbf_train = tfw2v_train[0:13000]\n",
    "tfrbf_cv = tfw2v_train[13000:16000]\n",
    "tfrbf_test = tfw2v_train[16000:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000,)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**********GridSearchCV**********\n",
      "\n",
      "\n",
      "Optimal C: 1000\n",
      "\n",
      "Optimal Gamma: 0.001\n",
      "\n",
      "Best kernel: rbf\n",
      "\n",
      "Best Score: 0.875945460503103\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = [ 0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "gamma = [0.001, 0.01, 0.1, 1, 10, 100,1000]\n",
    "tuned_parameters_grid = dict(C = C, gamma = gamma)\n",
    "model_svc_grid = GridSearchCV(SVC(kernel = \"rbf\", probability=True), tuned_parameters_grid, cv = 3, scoring = \"roc_auc\")\n",
    "model_svc_grid.fit(tfrbf_train, y_train)\n",
    "print(\"\\n**********GridSearchCV**********\\n\")\n",
    "print(\"\\nOptimal C:\", model_svc_grid.best_estimator_.C)\n",
    "print(\"\\nOptimal Gamma:\", model_svc_grid.best_estimator_.gamma)\n",
    "print(\"\\nBest kernel:\", model_svc_grid.best_estimator_.kernel)\n",
    "print(\"\\nBest Score:\", model_svc_grid.best_score_)\n",
    "df_gridsearch = pd.DataFrame(model_svc_grid.cv_results_)\n",
    "max_scores = df_gridsearch.groupby(['param_C','param_gamma']).max()\n",
    "max_scores = max_scores.unstack()[['mean_test_score', 'mean_train_score']]\n",
    "sns.heatmap(max_scores.mean_test_score, annot=True, fmt='.4g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8708191097075679"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc_grid.score(tfrbf_cv, y_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=1000, kernel='rbf', probability=True, gamma= 0.001)\n",
    "model.fit(tfrbf_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.9045384615384615\n"
     ]
    }
   ],
   "source": [
    "# Train accuracy\n",
    "train_acc_rbf = model.score(tfrbf_train, y_train)\n",
    "print(\"Train accuracy:\", train_acc_rbf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000,)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13000, 50)\n",
      "(3000, 50)\n"
     ]
    }
   ],
   "source": [
    "print(tfrbf_train.shape)\n",
    "print(tfrbf_cv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49000, 50)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfw2v_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfw2v_test_new = tfw2v_train[20000:24000]\n",
    "y_test_new=final_sorted_time['Score'][20000:24000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 50)\n",
      "(4000,)\n"
     ]
    }
   ],
   "source": [
    "print(tfw2v_test_new.shape)\n",
    "print(y_test_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(tfw2v_test_new)\n",
    "pred_prob = model.predict_proba(tfw2v_test_new)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8913441676229963"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# auc_roc score\n",
    "roc_auc_score(y_test_new, pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 100,  495],\n",
       "       [  26, 3379]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test_new, pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix to describe the performance of classifier.\n",
    "class_label = [\"negative\", \"positive\"]\n",
    "df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusiion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.79      0.17      0.28       595\n",
      "          1       0.87      0.99      0.93      3405\n",
      "\n",
      "avg / total       0.86      0.87      0.83      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To show main classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test_new, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6] Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------------------+----------------+----------------+----------------+---------------+----------------+\n",
      "| S.NO. |             MODEL             |   Best Alpha   |     Best C     |     gamma      | Testing's AUC |    Penalty     |\n",
      "+-------+-------------------------------+----------------+----------------+----------------+---------------+----------------+\n",
      "|   1   |      Linear SVM with BOW      |      0.01      | Not Applicable | Not Applicable |    0.91882    |       L2       |\n",
      "|   2   |     Linear SVM with TFIDF     |     1e-05      | Not Applicable | Not Applicable |    0.94757    |       L2       |\n",
      "|   3   |    Linear SVM with AVG W2V    |      0.01      | Not Applicable | Not Applicable |    0.88948    |       L2       |\n",
      "|   4   |   Linear SVM with TFIDF W2V   |     0.001      | Not Applicable | Not Applicable |    0.85813    |       L2       |\n",
      "|   5   |    RBF Kernel SVM with BOW    | Not Applicable |       1        |      0.01      |    0.86368    | Not Applicable |\n",
      "|   6   |   RBF Kernel SVM with TFIDF   | Not Applicable |       1        |       1        |    0.87648    | Not Applicable |\n",
      "|   7   |  RBF Kernel SVM with AVG W2V  | Not Applicable |       10       |      0.01      |    0.89154    | Not Applicable |\n",
      "|   8   | RBF Kernel SVM with TFIDF W2v | Not Applicable |      1000      |     0.001      |    0.89134    | Not Applicable |\n",
      "+-------+-------------------------------+----------------+----------------+----------------+---------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "# Creating table using PrettyTable library\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "# Names of models\n",
    "names= ['Linear SVM with BOW','Linear SVM with TFIDF', 'Linear SVM with AVG W2V','Linear SVM with TFIDF W2V',\\\n",
    "       'RBF Kernel SVM with BOW','RBF Kernel SVM with TFIDF','RBF Kernel SVM with AVG W2V','RBF Kernel SVM with TFIDF W2v']\n",
    "\n",
    "optimal_alpha = [0.01, 1e-05, 0.01, 0.001,'Not Applicable','Not Applicable','Not Applicable','Not Applicable']\n",
    "values_of_C=['Not Applicable','Not Applicable','Not Applicable','Not Applicable',1,1,10,1000]\n",
    "gammas=['Not Applicable','Not Applicable','Not Applicable','Not Applicable',0.01,1,0.01,0.001]\n",
    "#training_value=[0.94593,0.99398, 0.89346, 0.86816, 0.93066, 0.93237, 0.91101, 0.89509 ]\n",
    "#cv_value=[0.92468, 0.95011, 0.89451, 0.86498, 0.86680, 0.86274, 0.89668, 0.87736]\n",
    "test_value=[0.91882, 0.94757,0.88948,0.85813,0.86368,0.87648,0.89154,0.89134]\n",
    "pen=['L2','L2','L2','L2', 'Not Applicable','Not Applicable','Not Applicable','Not Applicable']\n",
    "\n",
    "\n",
    "\n",
    "numbering = [1,2,3,4,5,6,7,8]\n",
    "\n",
    "# Initializing prettytable\n",
    "ptable = PrettyTable()\n",
    "\n",
    "# Adding columns\n",
    "ptable.add_column(\"S.NO.\",numbering)\n",
    "ptable.add_column(\"MODEL\",names)\n",
    "ptable.add_column(\"Best Alpha\",optimal_alpha)\n",
    "ptable.add_column(\"Best C\", values_of_C)\n",
    "ptable.add_column(\"gamma\",gammas)\n",
    "#ptable.add_column(\"Training's AUC\",training_value)\n",
    "#ptable.add_column(\"CV's AUC\",cv_value)\n",
    "ptable.add_column(\"Testing's AUC\",test_value)\n",
    "ptable.add_column(\"Penalty\",pen)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Printing the Table\n",
    "ptable.align = \"c\"\n",
    "#from prettytable import MSWORD_FRIENDLY\n",
    "#ptable.set_style(MSWORD_FRIENDLY)\n",
    "\n",
    "print(ptable)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above table we can see unseen data i.e testing data's AUC value and Linear SVM with TFIDF showing pretty well among all."
   ]
  }
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