3.Amazon Fine Food Reviews Analysis knn.ipynb

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    "# Amazon Fine Food Reviews Analysis\n",
    "\n",
    "\n",
    "Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews\n",
    "\n",
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review\n",
    "\n",
    "\n",
    "<b>Objective:-</b>\n",
    "Given a review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "<br>\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use the Score/Rating. A rating of 4 or 5 could be cosnidered a positive review. A review of 1 or 2 could be considered negative. A review of 3 is nuetral and ignored. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n",
    "\n",
    "\n"
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    "## [1.1] Loading the data\n",
    "\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "In order to load the data, We have used the SQLITE dataset as it easier to query the data and visualise the data efficiently.\n",
    "<br> \n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score id above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
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     "text": [
      "C:\\Users\\ShivamOO7\\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"
     ]
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   ],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as pyplt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
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     "text": [
      "Number of data points in our data (525814, 10)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
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    "\n",
    "# using the SQLite Table to read data.\n",
    "con = sqlite3.connect('database.sqlite') \n",
    "#filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", con) \n",
    "\n",
    "\n",
    "# Give reviews with Score>3 a positive rating, and reviews with a score<3 a negative rating.\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "#changing reviews with score less than 3 to be positive and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative\n",
    "print(\"Number of data points in our data\", filtered_data.shape)\n",
    "filtered_data.head(3)"
   ]
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   "source": [
    "sample_data = filtered_data.head(50000) #Sampled amazon fine foood reviews filtered data to 50k datapoints for time effiecieny"
   ]
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   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "## Data Cleaning: Deduplication\n",
    "\n",
    "It is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"
   ]
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       "  <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 ...  "
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND UserId=\"AR5J8UI46CURR\"\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "display.head()"
   ]
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   "source": [
    "As can be seen above the same user has multiple reviews of the with the same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text  and on doing analysis it was found that <br>\n",
    "<br> \n",
    "ProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n",
    "<br> \n",
    "ProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n",
    "\n",
    "It was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n",
    "\n",
    "The method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."
   ]
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   "source": [
    "#Sorting data according to ProductId in ascending order\n",
    "sorted_data=sample_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')"
   ]
  },
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    {
     "data": {
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       "(46072, 10)"
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     "execution_count": 6,
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   "source": [
    "#Deduplication of entries\n",
    "final=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "final.shape"
   ]
  },
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    {
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      "text/plain": [
       "92.144"
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     "execution_count": 7,
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   "source": [
    "#Checking to see how much % of data still remains\n",
    "(final['Id'].size*1.0)/(sample_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<b>Observation:-</b> It was also seen that in two rows given below the value of HelpfulnessNumerator is greater than HelpfulnessDenominator which is not practically possible hence these two rows too are removed from calcualtions"
   ]
  },
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       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id   ProductId          UserId              ProfileName  \\\n",
       "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     3                       1      5  1224892800   \n",
       "1                     3                       2      4  1212883200   \n",
       "\n",
       "                                        Summary  \\\n",
       "0             Bought This for My Son at College   \n",
       "1  Pure cocoa taste with crunchy almonds inside   \n",
       "\n",
       "                                                Text  \n",
       "0  My son loves spaghetti so I didn't hesitate or...  \n",
       "1  It was almost a 'love at first bite' - the per...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display= pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3 AND Id=44737 OR Id=64422\n",
    "ORDER BY ProductID\n",
    "\"\"\", con)\n",
    "\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:56.974418Z",
     "start_time": "2019-01-23T12:52:56.937442Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "kThrWN9FMQvq"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:57.238269Z",
     "start_time": "2019-01-23T12:52:57.223276Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "lvw544BEMQvs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46071, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    38479\n",
       "0     7592\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Before starting the next phase of preprocessing lets see the number of entries left\n",
    "print(final.shape)\n",
    "\n",
    "#How many positive and negative reviews are present in our dataset?\n",
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3-ZDuet8MQvw"
   },
   "source": [
    "## Text Preprocessing: Stemming, stop-word removal and Lemmatization.\n",
    "\n",
    "Now that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n",
    "\n",
    "Hence in the Preprocessing phase we do the following in the order below:-\n",
    "\n",
    "1. Begin by removing the html tags\n",
    "2. Remove any punctuations or limited set of special characters like , or . or # etc.\n",
    "3. Check if the word is made up of english letters and is not alpha-numeric\n",
    "4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n",
    "5. Convert the word to lowercase\n",
    "6. Remove Stopwords\n",
    "7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n",
    "\n",
    "After which we collect the words used to describe positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:57.554086Z",
     "start_time": "2019-01-23T12:52:57.544093Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "I2NRwIj6MQvz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "Why is this $[...] when the same product is available for $[...] here?<br />http://www.amazon.com/VICTOR-FLY-MAGNET-BAIT-REFILL/dp/B00004RBDY<br /><br />The Victor M380 and M502 traps are unreal, of course -- total fly genocide. Pretty stinky, but only right nearby.\n"
     ]
    }
   ],
   "source": [
    "# find sentences containing HTML tags\n",
    "import re\n",
    "i=0;\n",
    "for sent in final['Text'].values:\n",
    "    if (len(re.findall('<.*?>', sent))):\n",
    "        print(i)\n",
    "        print(sent)\n",
    "        break;\n",
    "    i += 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:57.877902Z",
     "start_time": "2019-01-23T12:52:57.841923Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "hSYsO2neMQv2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'theirs', 'are', \"you've\", 'be', 'any', 'over', 'our', 'down', 'being', 'about', 'should', \"doesn't\", 'ours', 'has', 'who', \"she's\", 'needn', 'you', 'd', 'my', 'yourselves', 'me', 'after', \"hasn't\", 'was', \"needn't\", 'have', 'at', 'had', 'off', 'all', \"haven't\", 'other', 'out', \"isn't\", 'they', 'y', \"weren't\", 'doing', 'ain', 'hasn', 'how', 'couldn', 'itself', 'having', 'or', 'own', 'will', \"won't\", 'a', 'as', 'herself', 'so', 'his', 'why', 'through', 'between', 'what', 'not', 'does', 'don', 'her', 'during', 'such', 'no', 'these', 'your', 'its', 'yourself', \"shouldn't\", 'some', 'ourselves', 'but', 'nor', 'we', 'do', 'there', 'the', 'only', 'most', 'in', 'here', 'to', 'him', \"you're\", 'against', 'their', 'under', 'isn', 'wouldn', 'once', 'yours', 'themselves', 'been', 'again', \"you'd\", 'whom', \"it's\", 'just', 'hers', 'same', 'won', 'now', \"didn't\", 'himself', 'he', 'mustn', \"wasn't\", 'by', 'i', 'where', 'll', 're', \"don't\", 'because', 'm', 'mightn', 'wasn', 'when', 't', 'until', \"you'll\", 'up', 'weren', 'with', 'aren', 'is', 'more', \"aren't\", \"hadn't\", 'few', 'she', 'which', 'and', 'it', 'than', 'that', 'this', 'each', \"should've\", 'doesn', 've', 'myself', 'didn', 'of', 'for', 'both', 'o', 'from', 'those', 'can', 'ma', 'were', 'did', 'before', 'too', 'below', 'am', 'shan', 'haven', 'an', 'while', 'above', \"mightn't\", 's', \"shan't\", \"mustn't\", \"that'll\", 'into', \"couldn't\", 'then', 'further', 'on', 'them', 'very', 'hadn', \"wouldn't\", 'if', 'shouldn'}\n",
      "************************************\n",
      "tasti\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stop = set(stopwords.words('english')) #set of stopwords\n",
    "sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer\n",
    "\n",
    "def cleanhtml(sentence): #function to clean the word of any html-tags\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    cleantext = re.sub(cleanr, ' ', sentence)\n",
    "    return cleantext\n",
    "def cleanpunc(sentence): #function to clean the word of any punctuation or special characters\n",
    "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
    "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
    "    return  cleaned\n",
    "print(stop)\n",
    "print('************************************')\n",
    "print(sno.stem('tasty'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:58.200718Z",
     "start_time": "2019-01-23T12:52:58.182729Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "BUGzVaKbMQv5"
   },
   "outputs": [],
   "source": [
    "#Code for implementing step-by-step the checks mentioned in the pre-processing phase\n",
    "# this code takes a while to run as it needs to run on 500k sentences.\n",
    "if not os.path.isfile('final.sqlite'):\n",
    "    final_string=[]\n",
    "    all_positive_words=[] # store words from +ve reviews here\n",
    "    all_negative_words=[] # store words from -ve reviews here.\n",
    "    for i, sent in enumerate(tqdm(final['Text'].values)):\n",
    "        filtered_sentence=[]\n",
    "        #print(sent);\n",
    "        sent=cleanhtml(sent) # remove HTMl tags\n",
    "        for w in sent.split():\n",
    "            # we have used cleanpunc(w).split(), one more split function here because consider w=\"abc.def\", cleanpunc(w) will return \"abc def\"\n",
    "            # if we dont use .split() function then we will be considring \"abc def\" as a single word, but if you use .split() function we will get \"abc\", \"def\"\n",
    "            for cleaned_words in cleanpunc(w).split():\n",
    "                if((cleaned_words.isalpha()) & (len(cleaned_words)>2)):    \n",
    "                    if(cleaned_words.lower() not in stop):\n",
    "                        s=(sno.stem(cleaned_words.lower())).encode('utf8') #snoball stemmer\n",
    "                        filtered_sentence.append(s)\n",
    "                        if (final['Score'].values)[i] == 1: \n",
    "                            all_positive_words.append(s) #list of all words used to describe positive reviews\n",
    "                        if(final['Score'].values)[i] == 0:\n",
    "                            all_negative_words.append(s) #list of all words used to describe negative reviews reviews\n",
    "        str1 = b\" \".join(filtered_sentence) #final string of cleaned words\n",
    "        #print(\"***********************************************************************\")\n",
    "        final_string.append(str1)\n",
    "\n",
    "    #############---- storing the data into .sqlite file ------########################\n",
    "    final['CleanedText']=final_string #adding a column of CleanedText which displays the data after pre-processing of the review \n",
    "    final['CleanedText']=final['CleanedText'].str.decode(\"utf-8\")\n",
    "        # store final table into an SQlLite table for future.\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    c=conn.cursor()\n",
    "    conn.text_factory = str\n",
    "    final.to_sql('Reviews', conn,  schema=None, if_exists='replace', \\\n",
    "                 index=True, index_label=None, chunksize=None, dtype=None)\n",
    "    conn.close()\n",
    "    \n",
    "    \n",
    "    with open('positive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_positive_words, f)      # dump data to f\n",
    "    with open('negitive_words.pkl', 'wb') as f: # 'wb' instead 'w' for binary file\n",
    "        pickle.dump(all_negative_words, f)      # dump data to f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:59.242051Z",
     "start_time": "2019-01-23T12:52:58.410597Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bU88yTG9MQv-",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if os.path.isfile('final.sqlite'):\n",
    "    conn = sqlite3.connect('final.sqlite')\n",
    "    final = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 \"\"\", conn)\n",
    "    conn.close()\n",
    "else:\n",
    "    print(\"Please the above cell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting dataset based on 'Time' feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:52:59.600534Z",
     "start_time": "2019-01-23T12:52:59.555559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46071, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_reviews = final.sort_values('Time', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')\n",
    "final_reviews.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:00.204026Z",
     "start_time": "2019-01-23T12:52:59.855388Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ShivamOO7\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#split data into train, cross validate and test \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X = final_reviews['CleanedText']\n",
    "Y = final_reviews['Score']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0)\n",
    "X_tr, X_cv, Y_tr, Y_cv = train_test_split(X_train, Y_train, test_size=.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:00.741701Z",
     "start_time": "2019-01-23T12:53:00.696725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, Y_train (30867,) (30867,)\n",
      "X_test, Y_test (15204,) (15204,)\n",
      "X_tr, Y_tr (20680,) (20680,)\n",
      "X_cv, Y_cv (10187,) (10187,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train, Y_train', X_train.shape, Y_train.shape)\n",
    "print('X_test, Y_test', X_test.shape, Y_test.shape)\n",
    "print('X_tr, Y_tr', X_tr.shape, Y_tr.shape)\n",
    "print('X_cv, Y_cv', X_cv.shape, Y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-NN using Brute Force with simple CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:01.197441Z",
     "start_time": "2019-01-23T12:53:01.179448Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from collections import Counter\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model with Brute Force and KD-Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:01.608204Z",
     "start_time": "2019-01-23T12:53:01.596210Z"
    }
   },
   "outputs": [],
   "source": [
    "#find knn to simple cross validation with Brute Force and KD-Tree\n",
    "from sklearn.metrics import roc_auc_score\n",
    "def KNN(X_train,X_cv,Y_train,Y_cv):\n",
    "    k = []\n",
    "    max_k = 0\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    max_roc_auc=-1\n",
    "    for i in range(1,24,2):\n",
    "        knn =KNeighborsClassifier(n_neighbors=i,algorithm='brute')\n",
    "        knn.fit(X_train,Y_train)\n",
    "        probs = knn.predict_proba(X_cv)\n",
    "        prob = knn.predict_proba(X_train)\n",
    "        # keep probabilities for the positive outcome only\n",
    "        probs = probs[:, 1]\n",
    "        prob  = prob[:, 1]\n",
    "        auc_score_test = roc_auc_score(Y_cv,probs)   #find AUC score\n",
    "        auc_score_train = roc_auc_score(Y_train,prob)\n",
    "        print(i,\" ------> \",auc_score_test)\n",
    "        pred_cv.append(auc_score_test)\n",
    "        pred_train.append(auc_score_train)\n",
    "        k.append(i)\n",
    "        if(max_roc_auc<auc_score_test):\n",
    "            max_roc_auc=auc_score_test\n",
    "            max_k=i\n",
    "    #print('\\nThe optimal number of neighbors is %d.' % max_k)\n",
    "    plt.plot(k, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(k,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(\"K v/s Auc Score\")\n",
    "    plt.ylabel('Auc Score')\n",
    "    plt.xlabel('K')\n",
    "    plt.show()\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_cv,probs)\n",
    "    # plot no skill\n",
    "    pyplt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    pyplt.plot(fpr, tpr, marker='.')\n",
    "    #plt.plot(k,pred_cv)\n",
    "    pyplt.title(\"Line Plot of ROC Curve\")\n",
    "    pyplt.ylabel('True Positive Rate')\n",
    "    pyplt.xlabel('False Positive Rate')\n",
    "    pyplt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Model with Brute Force and KD-Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:02.367768Z",
     "start_time": "2019-01-23T12:53:02.086930Z"
    }
   },
   "outputs": [],
   "source": [
    "import scikitplot.metrics as skplt\n",
    "def testing(X_train,Y_train,X_test,Y_test,algo,optimal_k):\n",
    "    if algo == 'brute':\n",
    "        knn =KNeighborsClassifier(n_neighbors=optimal_k,algorithm='brute') \n",
    "    else:\n",
    "        knn =KNeighborsClassifier(n_neighbors=optimal_k,algorithm='kd_tree')\n",
    "    knn.fit(X_train,Y_train)\n",
    "    probs = knn.predict_proba(X_test) \n",
    "    # keep probabilities for the positive outcome only\n",
    "    probs = probs[:, 1]\n",
    "    print(\"AUC Score\",roc_auc_score(Y_test,probs))\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_test,probs)\n",
    "    # plot no skill\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    plt.plot(fpr, tpr, marker='.')\n",
    "    plt.show\n",
    "    prediction=knn.predict(X_test)\n",
    "    skplt.plot_confusion_matrix(Y_test,prediction)\n",
    "    print(\"macro f1 score for data :\",metrics.f1_score(Y_test, prediction, average = 'macro'))\n",
    "    print(\"micro f1 scoore for data:\",metrics.f1_score(Y_test, prediction, average = 'micro'))\n",
    "    print(\"hamming loss for data:\",metrics.hamming_loss(Y_test,prediction))\n",
    "    print(\"Precision recall report for data:\\n\",metrics.classification_report(Y_test, prediction))        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques for vectorization :--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hXA02Hw0MQv_"
   },
   "source": [
    "##  Bag of Words (BoW) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:05.691022Z",
     "start_time": "2019-01-23T12:53:02.719569Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "giCwNM_rMQwA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text BOW vectorizer  (20680, 17391)\n",
      "CV Data Size:  (10187, 17391)\n",
      "Test Data Size:  (15204, 17391)\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "bow_train = count_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(bow_train))\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "#print(\"the number of unique words \", final_counts.get_shape()[1])\n",
    "bow_cv = count_vect.transform(X_cv)\n",
    "bow_test = count_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:06.338411Z",
     "start_time": "2019-01-23T12:53:06.257692Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (20680, 17391)\n",
      "CV Data Size:  (10187, 17391)\n",
      "Test Data Size:  (15204, 17391)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "bow_train=preprocessing.normalize(bow_train)\n",
    "bow_cv=preprocessing.normalize(bow_cv)\n",
    "bow_test=preprocessing.normalize(bow_test)\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T10:34:47.953537Z",
     "start_time": "2019-01-23T10:16:34.422135Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.6358434837927727\n",
      "3  ------>  0.7249810082773754\n",
      "5  ------>  0.7519663792657778\n",
      "7  ------>  0.772804535323886\n",
      "9  ------>  0.7903691502622223\n",
      "11  ------>  0.7975748036923642\n",
      "13  ------>  0.8056290891440586\n",
      "15  ------>  0.8134675008185468\n",
      "17  ------>  0.8175302216107854\n",
      "19  ------>  0.8230820514219739\n",
      "21  ------>  0.8256346969079034\n",
      "23  ------>  0.8293785793473454\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using brute force\n",
    "KNN(bow_train,bow_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:05:33.735770Z",
     "start_time": "2019-01-16T18:04:53.490012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.7783899941007902\n",
      "macro f1 score for data : 0.5862673994167019\n",
      "micro f1 scoore for data: 0.8470139436990266\n",
      "hamming loss for data: 0.15298605630097342\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.66      0.16      0.26      2526\n",
      "          1       0.85      0.98      0.91     12678\n",
      "\n",
      "avg / total       0.82      0.85      0.81     15204\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute force, optimal_k=11\n",
    "testing(bow_train,Y_tr,bow_test,Y_test,'brute',optimal_k=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T10:07:30.263606Z",
     "start_time": "2019-01-23T10:06:23.907586Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text TFIDF vectorizer  (20680, 397694)\n",
      "CV Data Size:  (10187, 397694)\n",
      "Test Data Size:  (15204, 397694)\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "tfidf_train = tf_idf_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(tfidf_train))\n",
    "print(\"The shape of out text TFIDF vectorizer \",tfidf_train.get_shape())\n",
    "tfidf_cv = tf_idf_vect.transform(X_cv)\n",
    "tfidf_test = tf_idf_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T10:08:11.479286Z",
     "start_time": "2019-01-23T10:08:11.395307Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (20680, 397694)\n",
      "CV Data Size:  (10187, 397694)\n",
      "Test Data Size:  (15204, 397694)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "tfidf_train=preprocessing.normalize(tfidf_train)\n",
    "tfidf_cv=preprocessing.normalize(tfidf_cv)\n",
    "tfidf_test=preprocessing.normalize(tfidf_test)\n",
    "print(\"The shape of out text BOW vectorizer \",tfidf_train.get_shape())\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-01-23T10:36:37.546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.6144461669031358\n",
      "3  ------>  0.7155889372677193\n",
      "5  ------>  0.7479457346529649\n",
      "7  ------>  0.7738336066609608\n",
      "9  ------>  0.7833393048957717\n",
      "11  ------>  0.7951199530125395\n",
      "13  ------>  0.8034872967275358\n",
      "15  ------>  0.8133459035321013\n",
      "17  ------>  0.8178875460253088\n",
      "19  ------>  0.8234124858549363\n",
      "21  ------>  0.8294945042478272\n",
      "23  ------>  0.8345708588710373\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xpGCi16bvXfPQwv+6AnPNu8DIu13HcZNWfkdnotTaHbk8M3M1Y45qz+1n9iaxUWw1NVpSMNElfw8sngjfvAibvnPzCvQe7foKOp5gdxSbw5JTUMTHSzYzZlAHerRJ4PM/DY2YmdBqmiUFE/lUXb2hb16EJZPcxPGt+sDpD0L/i8ovMmdMiKav2MatkxaxMTuP/h0S6doqIWYTAlhSMJEsdyd8/4ZrItr2A9RtDP0ucH0F7QfZVYE5Irty9nPvB0uZtGADXVo25r+/ic4CdtXNkoKJHFlzIXOGmyw+aw788J6bY6D90XD2P6Hv+W6dMUeopIDd2h25XDesK9cN7xq1BeyqmyUFExlWz4BXxkCg0L2u1xiOvgoGXQlt+voamqk9duwroFmjesTHCeNH9aR9s4b0aRfdBeyqW5zfAZgYV1wI85+HNy45kBCIg+P/CGc8ZAnBVAtV5a35WQz7+xe8Pm8dAKf2aWMJoRx2pWD8EQi4TuNp98PO1a7jeMdKCBS5+kNdhvkdoaklsnbmcuvkRcz8cTsZac05rnMLv0OKaJYUTM1ShZWfwqd3w5ZFLhlc8iZ0P82NMLL6Q6YaTVqwntvfWYwA957bl8syUmtdAbvqZknB1Jx1s10yWPc1JHWEMc9A3wsgzmvFtCkpTTVLblKfjE7Nuf+8frRPauh3OFHBkoIJv82L4fN7YcVHrkLpGX93Hch16vkdmallCosDPD19FcUB+OPIbgzp3pIh3Vv6HVZUsaRgwmdnJnzxV1j4FtRvCiPuhMHXuJFFxlSzxRuyuWniQn7YtIfRA9tFbTVTv1lSMNVv7xaY8RB884IrRnfCH92jUe2aocpEhvzCYv7x6Y88M3M1zRvX4+krjq41U2P6IaxJQURGAf8E4oFnVfWBMutTgReBJG+b8ao6NZwxmTDK2w1fPwazn3TzGQz6GZx8MzRt53dkphZbtzOX575czQWDOnDrGb1iroBddQtbUhCReOAJ4BRgPTBPRKao6tKgzW4H3lLVJ0WkNzAVSAtXTCZM9ufC3AluSsv83e7O42G3HZjVzJhqtje/kI8Wb+bC9BS6t05g2o1Da+1MaDUtnFcKGcBKVV0NICJvAKOB4KSgQFPveSKwMYzxmOpWXAjfvgLT/+amuex6Coy4A9oO8DsyU4tNW7aV2yYvYvOefI5KTaJrqwRLCNUonEmhPZAV9Ho9MLjMNncB/xOR3wONgZHlfZCIXA1cDZCamlrtgZoqKnvjWcpgOP85SDvB78hMLbYzZz/3vr+Uyd9uoFurJky89ngrYBcG4UwK5XX7l50Q+hLgBVV9WESOA14Wkb6qGjjoTaoTgAng5mgOS7Smcqqw8jP47C7YvAha9YZL3nBzHtsoDxNGxQHlgie/Zt3OXP4wohu/G9aF+nWsgF04hDMprAdSgl534KfNQ78ERgGo6iwRaQAkA1vDGJc5HOvmwGd3w9qvICkVzpvgyljbVJcmjLbtLaBFY1fA7tYzetG+WUN6tW1a+RvNYQtnQbx5QDcR6SQi9YCxwJQy26wDRgCISC+gAbAtjDGZqtqyFF6/BJ4/Fbb/6G48u+4bGHCxJQQTNqrKm/PWMfzhL3htritgN7J3a0sINSBsVwqqWiQi1wEf44abPq+qS0TkHmC+qk4B/gQ8IyLX45qWrlJVax6KBLvWwLS/wsI33RwGw++AY6+1G89M2K3bkcv4SQv5etUOBndqzoldk/0OKaaE9T4F756DqWWW3Rn0fClgvZORZN9Wd+PZ/P94N579AU74P7vxzNSIid+s5453FhMfJ9x/Xl8uOcYK2NU0u6PZOPnZ8FXJjWf5MOgKOHmc3XhmalTrpvU5vksL7juvL20TrYCdHywpxLrCvAM3nuXtgj5jYPjtduOZqRH7iwI8+cUqAqpcf0p3TurWkpO6WQE7P1lSiFVrZ8Gsx10569zt0HWkK1hnN56ZGvJ91m5unriQ5Vv2Muao9lbALkJYUohFa7+GF84EDYDEuRFFGb/2OyoTI/L2F/PIJ8t57stMWiU04NmfpTOyd2u/wzIeSwqxJhCAqTe5hACAQMEeX0MysSVrVy4vfr2WsRmpjD+9J00bWAG7SGJJIZaowkfjYMtiiKvjXsfXc9NfGhNGe7wCdhd5Bey+uGko7WwmtIhkSSGWfPFX16l83HXQ6xxY+6XNh2zC7vNlW7h10mK27s1nUGozurZqYgkhgllSiBWzn3TVTAdeDqfe52oVpZatT2hM9dmxr4B73l/Ku99tpEfrBJ664mi6tmrid1imEpYUYsF3r8NH46HnWXD2P614nQm74oBy4VOzyNqVy/Uju3Pt0C7UqxPOqjqmuoSUFLzaRamqujLM8ZjqtmwqvPs76DTElbeOt+8BJny27s0nuXF94uOE287sRYdmjejRxspbR5NKU7eInAksAj7xXg8UkcnhDsxUg8yZ8N+r3L0HY1+Dug38jsjUUoGA8uqctQz/+3Re9QrYjejV2hJCFArla+M9uMlxpgGo6nci0jWsUZkjt/FbV920WRpcNtEVtTMmDNZsz2H8pIXMXr2T47u04GS7IzmqhZIUClV1d5k7Da2SaSTbtgJeOR8aJsEVk6FxC78jMrXUW/OzuOOdxdSLj+OBMf24+JgUuys5yoWSFH4QkYuAOBHpBPwRmB3esMxh250FL5/n7lS+4h1IbO93RKYWa5/UkCHdW3Lv6L60SbTmydoglKRwHXAnEAAm4eZHuCWcQZnDlLPdJYSCPXDV+5BsrXymehUUFfPvaatQVW44tQcndE3mBJvvoFYJJSmcpqrjgHElC0RkDC5BmEiRv8c1GWVnuSYjK2xnqtm363Yx7u2FrNiyj/MHdbACdrVUKEnhdn6aAG4rZ5nxS2E+vHGpK18x9jXoeLzfEZlaJHd/EQ//bwXPf5VJm6YNeP6qdIb3tAJ2tdUhk4KInAaMAtqLyCNBq5rimpJMJCgugok/hzUzYcwz0P00vyMytcyGXXm8PHstlw1OZdyoniRYAbtaraIrha3AYiAfWBK0fC8wPpxBmRAFAjDlOlg+FU5/CPpf5HdEppbIzivkw0WbGJuRSrfWCUy/aajNhBYjDpkUVPVb4FsReVVV82swJhMKVfj4Vvj+dRh6Kwy+2u+ITC3xvyWbuf2dxezI2U96WnO6tmpiCSGGhNKn0F5E7gd6A6VjzlS1e9iiMpWb8RDMeRIGXwsn3+x3NKYW2L6vgLumLOH9hZvo2SaBZ69MtwJ2MSiUpPACcB/wd+B04OdYn4K/5j4D0+6H/mPhtL9YgTtzxIoDygVPfs3G3fnceGp3fnNyF+rGWwG7WBRKUmikqh+LyN9VdRVwu4jMDHdg5hAW/tfNnNb9dBj9OMTZf1xz+LbsyadlE1fA7s9n96FDs4Z0a20lUWJZKGeUAnGDkVeJyDUicjbQKsxxmfKs+B+8cw10PAEu/A/E2ygQc3gCAeXl2WsZ8fB0Xp2zFoBhPVtZQjAhXSlcDzQB/gDcDyQCvwhnUKYca7+Gt66A1n3gktehrnX8mcOzets+xk9axNzMnZzYNZmhPew7njmg0qSgqnO8p3uBKwBEpEM4gzJlbFoIr10MiSlw+SRo0NTviEyUenPeOu58dwn168Tx4AX9ufDoDnZXsjlIhUlBRI4B2gNfqup2EemDK3cxHLDEUBN2rIJXxrjS11dMhsZWZ8Ycvg7NGjG0hytg16qpFbAzP1XRHc1/Bc4Hvsd1Lk/GVUj9G3BNzYQX4/ZshJfOBQ24iqdJKX5HZKJMQVEx//rMTZh442lWwM5UrqIrhdHAAFXNE5HmwEbv9fKaCS3G5e50FU/zdsFV70FLuy3EVM03a3dy88SFrNqWw0XpVsDOhKaipJCvqnkAqrpTRJZZQqghBXvh1QtgZyZc/ja0O8rviEwUySko4qGPl/PirDW0S2zIi7/I4OTuNhuaCU1FSaGziJRUQhUgLeg1qjqmsg8XkVHAP4F44FlVfaCcbS4C7sLN5va9ql4aevi1UFEBvHEZbPwOLn4ZOp3kd0Qmymzcncdrc9fxs2M7ctOonjSpH8ogQ2Ociv61nF/m9eNV+WARiQeeAE4B1gPzRGSKqi4N2qYbbsKeE1R1l4jE9ti44iJ4+5eQOR3OfQp6nul3RCZKZOcW8sGiTVw62BWwm3nzMFpbR7I5DBUVxPvsCD87A1ipqqsBROQNXD/F0qBtfg08oaq7vH1uPcJ9Ri9VeP+P8MN7MOoBGHiJ3xGZKPHR4s3c8e5idubsZ3Dn5nRp2cQSgjls4byubA9kBb1eDwwus013ABH5CtfEdJeqflT2g0TkauBqgNTU1LAE66t1c+DTP8O6WTDkZjj2Wr8jMlFg69587pqyhKmLNtO7bVP+c9UxdGlpBezMkQlnUihvmIOWs/9uwFDcfQ8zRaSvqu4+6E2qE4AJAOnp6WU/I7plzYUXzoRAIUg8dB3pd0QmChQHlIuemsXG7HxuOq0HVw/pbAXsTLUIOSmISH1VLajCZ68HggfWd8ANay27zWxVLQQyRWQ5LknMq8J+otvKT11CKLH2S0gte0FljLMpO4/WCQ1cAbtz+pDSrJGVtzbVqtKvFiKSISKLgB+91wNE5F8hfPY8oJuIdBKResBYYEqZbd4Bhnmfm4xrTlpdhfij355N7qfEQXw9SLPRRuanAgHlha8yGfHwdF4pKWDXo5UlBFPtQrlSeAw4C3cCR1W/F5Fhlb1JVYtE5DrgY1x/wfOqukRE7gHmq+oUb92pIrIUKAZuUtUdh3ks0Sd3Jyx9x1U97TrCJYSUDL+jMhFm5dZ9jH97IfPX7mJI95YM7xnbg/RMeIWSFOJUdW2ZOyGLQ/lwVZ0KTC2z7M6g5wrc4D1iz1f/cDeqnfkwtOrldzQmAr0xdx13TllCw7rxPHzhAMYMam93JZuwCiUpZIlIBqDevQe/B1aEN6wYsGcTzJkA/S+2hGAOKbVFI0b2asXd5/SlZUJ9v8MxMSCUpHAtrgkpFdgCfOotM0di5t9dB/PQ8X5HYiJIfmExj332IwA3j+rJ8V2SOb6LFbAzNSeUpFCkqmPDHkks2ZkJ37wAg66E5p38jsZEiPlrdnLz2wtZvS2HscekWAE744tQksI8b6jom8AkVd0b5phqv+l/g7g6MOQmvyMxEWBfQREPfbSMl2avpX1SQ176RQZDrICd8UmlQ1JVtQtwH3A0sEhE3hERu3I4XFt/gO/fgIxfQ9O2fkdjIsDm7DzemJfFlcel8fH/DbGEYHwV0i2Qqvq1qv4BGATsAV4Na1S12bT7oV4TOOF6vyMxPtqVs5+XZ7v7Dbq2cgXs7jqnD42toqnxWaX/AkWkCa6Q3VigF/AucHyY46qdNixwBe+G3gKNW/gdjfGBqvLh4s3c+e5iducWcnyXFnRp2cSmxjQRI5SvJYuB94AHVXVmmOOp3T6/Dxo2h2N/63ckxgdb9+Rzx7uL+XjJFvq1T+SlXwy2AnYm4oSSFDqraiDskdR2a76EVZ/BqfdBg6Z+R2NqWHFAufDpWWzOzueW03vyyxM7UccK2JkIdMikICIPq+qfgLdF5CeVSUOZec14VOGzeyGhLRzzK7+jMTVo4+482jR1BezuGd2XlGYN6WxXByaCVXSl8Kb3s0ozrply/PgJZM2Gsx6Fug39jsbUgOKA8tKsNTz40XJuOaMnPzsuzeZJNlGhopnX5npPe6nqQYnBK3R3pDOzxYZAAD6/B5qlwVFX+B2NqQErt+7l5okLWbBuN0N7tGREr9Z+h2RMyEJp1PxFOct+Wd2B1FpL34HNi2DorRBf1+9oTJi9NmcdZ/zzSzK35/DoxQP4z1XH0D7Jrg5N9KioT+Fi3DDUTiIyKWhVArC7/HeZgxQXwbS/QMte0O8Cv6MxNSAtuRGn9mnNXef0IbmJFbAz0aeiPoW5wA7cjGlPBC3fC3wbzqBqjYVvwI4f4eJXIS7e72hMGOQXFvPopysQhPGnWwE7E/0q6lPIBDJxVVFNVRUVwBcPQLtB0PNMv6MxYTBn9Q7GT1pE5vYcLhucagXsTK1QUfPRdFU9WUR2AcFDUgU3P07zsEcXzb55AbKz4Jx/gZ0oapW9+YX87aNlvDJ7HanNG/HarwZzfFe7OjB9jc07AAAa6UlEQVS1Q0XNRyVTbtq/9qranwMzHnLTa3Ye6nc0pppt2VPAxG/W86sTO3HDqd1pVM/qFZnao6Lmo5K7mFOAjaq6X0ROBPoDr+AK45nyzHkKcrbB2NfsKqGW2Jmznw8WbuSK49Lo2qoJM28ebjOhmVoplCGp7+Cm4uwCvIQrivdaWKOKZnm74Kt/QvdRkJLhdzTmCKkq732/kVMemc497y9l9bZ9AJYQTK0VynVvQFULRWQM8A9VfUxEbPTRoXz9L8jPhuG3+x2JOUJb9uRz2+TFfPrDFvp3SOTVCwZbiQpT64U0HaeIXAhcAZzrLbO7sMqzbyvMfgr6ng9t+vkdjTkCxQHlIq+A3W1n9OLnJ6RZATsTE0JJCr8Afosrnb1aRDoBr4c3rCg18xEoynd3L5uotH5XLm0TGxIfJ9w7ui+pzRuRltzY77CMqTGhTMe5GPgDMF9EegJZqnp/2COLNruzYP5zcNRlkNzV72hMFRUHlGdnrmbkI9N5xZsRbUj3lpYQTMwJZea1k4CXgQ24exTaiMgVqvpVuIOLKtP/5n6ePM7fOEyVLd+8l5vfXsj3WbsZ0bMVp/axAnYmdoXSfPQocIaqLgUQkV64JJEezsCiyvYf4bvXYPBvILGD39GYKnhl9lrufm8JCQ3q8s+xAzlnQDu7K9nEtFCSQr2ShACgqj+ISL0wxhR9pv0F6jSAE2/wOxITopKSFF1bNeGMfm2586zetLACdsaElBQWiMjTuKsDgMuwgngHbFoISybBSTdCE5tEJdLl7S/mkU+WExcn3HJ6L47t3IJjO7fwOyxjIkYoY+yuAVYBNwPjgNXAb8IZVFT5/D5okAjH/97vSEwlZq3awah/zuCZmZnkFhSj+pNZZo2JeRVeKYhIP6ALMFlVH6yZkKLIujnw48cw4s/QMMnvaMwh7Mkv5K9Tl/H63HV0bNGI13492MpbG3MIFVVJvRU3w9oC4BgRuUdVn6+xyCKdKnx2DzRu5TqYTcTauqeAd77dwNVDOnP9yO40rGdzWxhzKBU1H10G9FfVC4FjgGur+uEiMkpElovIShEZX8F2F4iIikj0jGhaPQ3WfglDboJ6NpY90uzYV8ALX2UC0LVVE74cN4xbz+hlCcGYSlTUfFSgqjkAqrpNRKp0j7+IxONmbDsFWA/ME5EpwSOZvO0ScDfHzalS5H4quUpITIWjr/Q7GhNEVZny/UbumrKEfQVFDOneks4tm9jIImNCVFFS6Bw0N7MAXYLnalbVMZV8dgawUlVXA4jIG8BoYGmZ7e4FHgRurErgvlr2Pmz8Fkb/G+rYySZSbNydx+3vLObzZVsZmJLEgxf0twJ2xlRRRUnh/DKvH6/iZ7cHsoJerwcGB28gIkcBKar6vogcMimIyNXA1QCpqalVDKOaBYrdiKMW3aD/xf7GYkoVFQcYO2E22/YWcMdZvbnq+DTi4+wmNGOqqqJJdj47ws8u739k6RhArznqUeCqyj5IVScAEwDS09P9HUf4xQOwbZkrjR1vM275LWtnLu2SGlInPo6/nNeP1OaNSG3RyO+wjIla4awFvB43a1uJDsDGoNcJQF/gCxFZAxwLTInozuY1X7lpNgFmPAxZc/2NJ4YVFQeYMGMVIx+Zzsuz1gBwYrdkSwjGHKFwftWdB3TzSm1vAMYCl5asVNVsguZ/FpEvgBtVdX4YYzoyC16i9GKneD+smWmzq/ngh017GPf2Qhauz+aU3q05vV9bv0MyptYIOSmISH1VLQh1e1UtEpHrgI+BeOB5VV0iIvcA81V1StXD9VmRd/gSD/H1IO0kf+OJQS/PWsPd7y0lsWFdHr/0KM7s19YK2BlTjUIpnZ0BPAckAqkiMgD4lapWWtdBVacCU8ssu/MQ2w4NJWBf7fgR2h0Fvc52CcGuEmpMSQG77q0TOHtAO+44qzfNG1tdRmOqWyhXCo8BZwHvAKjq9yIyLKxRRaKc7bBlMYy4E076k9/RxIzc/UX8/eMV1IkXbj2jF4M7t2CwFbAzJmxC6WiOU9W1ZZYVhyOYiLZmpvvZ6WR/44ghX63czmn/mMHzX2WyvyhgBeyMqQGhXClkeU1I6t2l/HtgRXjDikCZM6BeArQd6HcktV52XiF/+eAH3pyfRafkxrz1m+PI6NTc77CMiQmhJIVrcU1IqcAW4FMOow5S1MucAWkn2L0JNWD7vgLeW7iRa07uwv+N7EaDulavyJiaUukZTlW34oaTxq7sDbBjJaT/wu9Iaq1tewt47/uN/OLETnRp2YQvxw23jmRjfBDK6KNnCLoTuYSqXh2WiCJRaX/CEH/jqIVUlXe+28Dd7y0lt6CYYT1b0Sm5sSUEY3wSSlvIp0HPGwDncXBNo9ovcwY0bA6t+vgdSa2yYXcet01exBfLtzEo1RWw65RsZciN8VMozUdvBr8WkZeBT8IWUaRR9foTToS4cFYFiS2ugN0sduzbz11n9+aK46yAnTGR4HB6TTsBHas7kIi1KxOys+CEP/odSa2wbkcu7Zu5AnYPjOlPavNGpDS3ekXGRIpKv/qKyC4R2ek9duOuEm4Nf2gRInOG+2n3JxyRouIAT36xipGPTuelWWsAOKFrsiUEYyJMhVcK4orKDMAVtAMIaKzdQZQ5A5q0geRufkcStZZszGbc2wtZvGEPp/VpzZlWwM6YiFVhUlBVFZHJqnp0TQUUUUr6EzoPAyu6dlhe/HoN976/lKRG9XjyskFW0dSYCBdKn8JcERmkqgvCHk2k2bYMcrbZUNTDUFLArmebBEYPbM8dZ/UiqZENMzUm0h0yKYhIHVUtAk4Efi0iq4Ac3IxqqqqDaihG/2Ta/QlVlVNQxEMfL6duvHDbmb2tgJ0xUaaiK4W5wCDg3BqKJfJkToekjtAsdgZbHYkZK7Zxy6RFbMzO48rj0kqvFowx0aOipCAAqrqqhmKJLIFiWPOlmzvBVCg7t5B7P1jKxG/W07mlK2B3TJoVsDMmGlWUFFqKyA2HWqmqj4QhnsixeRHk77ahqCHYnlPAh4s28duhXfjDCCtgZ0w0qygpxANN8K4YYk7p/Qk25WZ5tu7NZ8p3G/nVSZ1LC9g1s3pFxkS9ipLCJlW9p8YiiTSZMyC5OyS08TuSiKKqvL1gA/e+v5S8wmJG9GpNp+TGlhCMqSUq7VOIScWFsPZrGHiJ35FElKydudw6eREzf9xOesdmPHC+FbAzprapKCmMqLEoIs2GBVCYY0NRgxQVB7jkmdnsytnPvaP7cNngjsRZATtjap1DJgVV3VmTgUSUkv6ENOtPWLM9h5TmjagTH8eDF7gCdh2aWb0iY2orqwVdnszp0KYfNIrdYZWFxQGemLaSUx+dUVrA7vguyZYQjKnlbMLhsgrzIGsuZPza70h8s3hDNjdPXMjSTXs4s19bzurfzu+QjDE1xJJCWVlzobggZvsT/vNVJvd98APNG9fjqcuPZlRfG31lTCyxpFBW5gyQeEg9zu9IalRJSYo+7RIZc1R7bj+zN4mN6vodljGmhllSKCtzBrQfBA2a+h1JjdhXUMSDHy2jXnwct5/Vm4xOzcnoFLt9KcbEOutoDlawFzYuiJlRR18s38ppj87g5dlrUdzVgjEmttmVQrB1syFQVOv7E3bl7OfeD5YyacEGurZqwsRrjufojs38DssYEwEsKQTLnA7x9SBlsN+RhNWu3P38b8kW/jC8K78b3pX6dayAnTHGCWvzkYiMEpHlIrJSRMaXs/4GEVkqIgtF5DMR8XfigswZ0CED6tW+sfhb9+QzYcYqVJXOLZvw1bjh3HBqD0sIxpiDhC0piEg88ARwOtAbuEREepfZ7FsgXVX7AxOBB8MVT6Vyd8KmhbWu6UhVeWteFiMemc7D/1vBmh25ADayyBhTrnA2H2UAK1V1NYCIvAGMBpaWbKCq04K2nw1cHsZ4Krb2K0BrVVLI2pnLLZMW8eXK7WR0as4DY/pZATtjTIXCmRTaA1lBr9cDFTXW/xL4sLwVInI1cDVAampqdcV3sMwZULcRtD86PJ9fw0oK2O3OLeS+c/tyaUaqFbAzxlQqnEmhvDNQuWMeReRyIB0od5ozVZ0ATABIT08Pz7jJzBnuhrU60T0vQOb2HFK9AnYPXTCAji0a0S6pod9hGWOiRDg7mtcDKUGvOwAby24kIiOB24BzVLUgjPEc2t4tsG1ZVDcdFRYH+NdnP3LaozN48es1ABzXpYUlBGNMlYTzSmEe0E1EOgEbgLHApcEbiMhRwNPAKFXdGsZYKrZmpvsZpUlh4frd3DxxIcs27+XsAe04Z6AVsDPGHJ6wJQVVLRKR64CPcfM9P6+qS0TkHmC+qk4BHsLNA/1fEQFYp6rnhCumQ8qcDvUToe2AGt/1kXr+y0zu+2ApLRPq88zP0jmld2u/QzLGRLGw3rymqlOBqWWW3Rn0fGQ49x+yzBmQdgLERc+Y/ZICdv07JHLxMSmMP70XiQ1tmKkx5sjYHc2718GuNTD4Gr8jCcne/EIe+HAZ9evEc+fZvUlPa056mhWwM8ZUDyuIlxk9/QnTlm3l1Edn8PrcddSJFytgZ4ypdnalsHiSuz8hf6/fkRzSzpz93PPeEt75biPdWzfh35cdz1GpVsDOGFP9YvtKYd0cWPUpFObCy+e6WdciUHZeIZ/9sJU/jujG+78/yRKCMSZsYjspLA/qAy/ef2BoagTYnJ3PU9NdAbtOyY35cvxwrj+lO/XqxPafzBgTXrHdfNQo2f2UOFcyOwIm11FV3piXxV8++IHCQIBRfdqQltzYRhYZY2pEbCeFYu8G6iE3QtdTICXD13DW7shh/NuLmLV6B8d2bs4DY/qTZgXsjDE1KLaTwpbFkJQKw27zOxKKigNc+swcsvMK+ct5/Rh7TIoVsDPG1LgYTwpLoHU/X0NYtW0fHb0Cdg9f5ArYtU20ekXGGH/Ebq9lYR7sWAmt+/iy+/1FAf7x6QpG/WMGL81aC8CxnVtYQjDG+Cp2rxS2LgUNQJu+Nb7r77J2M27iQpZv2cvoge0496j2NR6DMcaUJ3aTwpYl7mfrmk0Kz32Zyf0fLKVVQgOeuzKdEb2sgJ0xJnLEblLYvBjqNoZmnWpkdyUF7AamJDI2I5Xxp/ekaQMbZmqMiSyxmxS2LIHWvSEuvN0qe/IL+evUZTSoG8efz+7D0R2bc3RHK2BnjIlMsdnRrApbFoW96ejTpVs45ZHpvDlvHfXqxFkBO2NMxIvNK4Xs9ZCfHbaRRzv2FXD3e0uZ8v1GerZJYMIV6QxISQrLvowxpjrFZlIo6WRuE557FPbmFzFt+VauH9mda4d2sXpFxpioEaNJYZH72ap3tX3kxt15TP52A78d2oW05MZ8NX64dSQbY6JObCaFzYshqSM0aHrEHxUIKK/NXccDHy6jOKCc2a8tacmNLSEYY6JSbCaFLUuqpekoc3sO499eyJzMnZzQtQV/Pa8/qS0aVUOAxhjjj9hLCvtzYecq6DvmiD6mqDjA5c/OYU9+IQ+e358L0zsgYgXsjDHRLfaSwtYfXHmLwxyOunLrXtJaNKZOfByPXjyQji0a0bppg2oO0hhj/BF7w2K2LHY/q1jzqKComEc+WcGof8zkRa+AXUan5pYQjDG1SuxdKWxZDPWaQFJayG9ZsG4X4yYu5Met+xhzVHvGWAE7Y0wtFXtJYfNiNxQ1xPIWz8xYzV8+/IG2TRvwn58fw7AercIcoDHG+Ce2koKqG3nU7/xKNw0ElLg4YVDHJC4bnMq4UT1JsGGmxphaLraSQnYWFFRc3iI7r5D7P1hKw7rx3D26rxWwM8bElNjqaC6dQ6H8exQ+XrKZUx6ZztsLNtC4fh0rYGeMiTmxdaWw2Rt51Prg8hbb9xXw53eX8MGiTfRu25TnrzqGvu0TfQjQGGP8FVtJYcsiaJYG9RMOWrwvv4iZP27jptN6cPWQztSNj60LKGOMKRFjSWFJ6U1rG3bnMXnBen43rCtpyY35+pYRNKkfW78OY4wpK6xfiUVklIgsF5GVIjK+nPX1ReRNb/0cEUkLWzD7c2DHKgKt+/LyrDWc+sh0npi2irU7cgEsIRhjDGFMCiISDzwBnA70Bi4RkbK1qn8J7FLVrsCjwN/CFQ+LJgLK699s4Y53lzCoYzP+d/0Q0pIbh22XxhgTbcL59TgDWKmqqwFE5A1gNLA0aJvRwF3e84nA4yIiWt3DfrLmoh/8CQHO3/cKbUaMYPjIDCtgZ4wxZYSz+ag9kBX0er23rNxtVLUIyAZalP0gEblaROaLyPxt27ZVPZI1M5FAMQD1JcCIBissIRhjTDnCmRTKO+uWvQIIZRtUdYKqpqtqesuWLaseSdpJUKc+SDwSX8+9NsYY8xPhbD5aD6QEve4AbDzENutFpA6QCOys9khSMuDKKbBmpksIKRnVvgtjjKkNwpkU5gHdRKQTsAEYC1xaZpspwJXALOAC4PNq708okZJhycAYYyoRtqSgqkUich3wMRAPPK+qS0TkHmC+qk4BngNeFpGVuCuEseGKxxhjTOXCOjhfVacCU8ssuzPoeT5wYThjMMYYEzqr52CMMaaUJQVjjDGlLCkYY4wpZUnBGGNMKYm2iWREZBuw9jDfngxsr8ZwooEdc2ywY44NR3LMHVW10rt/oy4pHAkRma+q6X7HUZPsmGODHXNsqIljtuYjY4wxpSwpGGOMKRVrSWGC3wH4wI45Ntgxx4awH3NM9SkYY4ypWKxdKRhjjKmAJQVjjDGlamVSEJFRIrJcRFaKyPhy1tcXkTe99XNEJK3mo6xeIRzzDSKyVEQWishnItLRjzirU2XHHLTdBSKiIhL1wxdDOWYRucj7Wy8RkddqOsbqFsK/7VQRmSYi33r/vs/wI87qIiLPi8hWEVl8iPUiIo95v4+FIjKoWgNQ1Vr1wJXpXgV0BuoB3wO9y2zzW+Ap7/lY4E2/466BYx4GNPKeXxsLx+xtlwDMAGYD6X7HXQN/527At0Az73Urv+OugWOeAFzrPe8NrPE77iM85iHAIGDxIdafAXyIm7nyWGBOde6/Nl4pZAArVXW1qu4H3gBGl9lmNPCi93wiMEKie9LmSo9ZVaepaq73cjZuJrxoFsrfGeBe4EEgvyaDC5NQjvnXwBOqugtAVbfWcIzVLZRjVqCp9zyRn87wGFVUdQYVz0A5GnhJndlAkoi0ra7918ak0B7ICnq93ltW7jaqWgRkAy1qJLrwCOWYg/0S900jmlV6zCJyFJCiqu/XZGBhFMrfuTvQXUS+EpHZIjKqxqILj1CO+S7gchFZj5u/5fc1E5pvqvr/vUrCOsmOT8r7xl923G0o20STkI9HRC4H0oGTwxpR+FV4zCISBzwKXFVTAdWAUP7OdXBNSENxV4MzRaSvqu4Oc2zhEsoxXwK8oKoPi8hxuNkc+6pqIPzh+SKs56/aeKWwHkgJet2Bn15Olm4jInVwl5wVXa5FulCOGREZCdwGnKOqBTUUW7hUdswJQF/gCxFZg2t7nRLlnc2h/tt+V1ULVTUTWI5LEtEqlGP+JfAWgKrOAhrgCsfVViH9fz9ctTEpzAO6iUgnEamH60ieUmabKcCV3vMLgM/V68GJUpUes9eU8jQuIUR7OzNUcsyqmq2qyaqapqppuH6Uc1R1vj/hVotQ/m2/gxtUgIgk45qTVtdolNUrlGNeB4wAEJFeuKSwrUajrFlTgJ95o5COBbJVdVN1fXitaz5S1SIRuQ74GDdy4XlVXSIi9wDzVXUK8BzuEnMl7gphrH8RH7kQj/khoAnwX69PfZ2qnuNb0EcoxGOuVUI85o+BU0VkKVAM3KSqO/yL+siEeMx/Ap4RketxzShXRfOXPBF5Hdf8l+z1k/wZqAugqk/h+k3OAFYCucDPq3X/Ufy7M8YYU81qY/ORMcaYw2RJwRhjTClLCsYYY0pZUjDGGFPKkoIxxphSlhRMxBGRYhH5LuiRVsG2aYeqJlnFfX7hVeL83isR0eMwPuMaEfmZ9/wqEWkXtO5ZEeldzXHOE5GBIbzn/0Sk0ZHu28QGSwomEuWp6sCgx5oa2u9lqjoAVyzxoaq+WVWfUtWXvJdXAe2C1v1KVZdWS5QH4vw3ocX5f4AlBRMSSwomKnhXBDNFZIH3OL6cbfqIyFzv6mKhiHTzll8etPxpEYmvZHczgK7ee0d4dfoXeXXu63vLH5AD81P83Vt2l4jcKCIX4OpLverts6H3DT9dRK4VkQeDYr5KRP51mHHOIqgQmog8KSLzxc2jcLe37A+45DRNRKZ5y04VkVne7/G/ItKkkv2YGGJJwUSihkFNR5O9ZVuBU1R1EHAx8Fg577sG+KeqDsSdlNd7ZQ8uBk7wlhcDl1Wy/7OBRSLSAHgBuFhV++EqAFwrIs2B84A+qtofuC/4zao6EZiP+0Y/UFXzglZPBMYEvb4YePMw4xyFK2tR4jZVTQf6AyeLSH9VfQxXF2eYqg7zSl/cDoz0fpfzgRsq2Y+JIbWuzIWpFfK8E2OwusDjXht6Ma6mT1mzgNtEpAMwSVV/FJERwNHAPK+8R0NcginPqyKSB6zBlV/uAWSq6gpv/YvA74DHcfMzPCsiHwAhl+ZW1W0istqrWfOjt4+vvM+tSpyNcWUfgmfdukhErsb9v26Lm3BmYZn3Hust/8rbTz3c780YwJKCiR7XA1uAAbgr3J9MmqOqr4nIHOBM4GMR+RWuzPCLqnpLCPu4LLhgnoiUO8eGV48nA1eEbSxwHTC8CsfyJnARsAyYrKoq7gwdcpy4GcgeAJ4AxohIJ+BG4BhV3SUiL+AKw5UlwCeqekkV4jUxxJqPTLRIBDZ5NfKvwH1LPoiIdAZWe00mU3DNKJ8BF4hIK2+b5hL6/NTLgDQR6eq9vgKY7rXBJ6rqVFwnbnkjgPbiyneXZxJwLm4egDe9ZVWKU1ULcc1Ax3pNT02BHCBbRFoDpx8iltnACSXHJCKNRKS8qy4ToywpmGjxb+BKEZmNazrKKWebi4HFIvId0BM3ZeFS3MnzfyKyEPgE17RSKVXNx1Wg/K+ILAICwFO4E+z73udNx13FlPUC8FRJR3OZz90FLAU6qupcb1mV4/T6Kh4GblTV73FzMy8Bnsc1SZWYAHwoItNUdRtuZNTr3n5m435XxgBWJdUYY0wQu1IwxhhTypKCMcaYUpYUjDHGlLKkYIwxppQlBWOMMaUsKRhjjCllScEYY0yp/wdpQH2IZNX3cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_k\n",
    "KNN(tfidf_train,tfidf_cv,Y_tr,Y_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:09:52.999042Z",
     "start_time": "2019-01-16T18:09:11.765857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.7833233847400195\n",
      "macro f1 score for data : 0.5953638628151352\n",
      "micro f1 scoore for data: 0.851223362273086\n",
      "hamming loss for data: 0.14877663772691396\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.72      0.17      0.27      2526\n",
      "          1       0.86      0.99      0.92     12678\n",
      "\n",
      "avg / total       0.83      0.85      0.81     15204\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##algorithm = brute force, optimal_k=11\n",
    "testing(tfidf_train,Y_tr,tfidf_test,Y_test,'brute',optimal_k=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V1eebHVHMQwv"
   },
   "source": [
    "## Avg W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:11:54.380957Z",
     "start_time": "2019-01-16T18:11:48.308470Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "TMrT0BciMQww"
   },
   "outputs": [],
   "source": [
    "# Train our own Word2Vec model using our own text corpus\n",
    "import gensim\n",
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in final_reviews['CleanedText'].values:\n",
    "    filtered_sentence=[]\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:12:08.447820Z",
     "start_time": "2019-01-16T18:11:59.507981Z"
    }
   },
   "outputs": [],
   "source": [
    "# min_count = 5 considers only words that occured atleast 5 times\n",
    "w2v_model=Word2Vec(list_of_sent,min_count=5,size=50, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:12:23.236293Z",
     "start_time": "2019-01-16T18:12:15.005033Z"
    }
   },
   "outputs": [],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= cnt_words\n",
    "    sent_vectors.append(sent_vec)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:22:26.131809Z",
     "start_time": "2019-01-23T11:22:25.783054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20680, 50)\n",
      "(10187, 50)\n",
      "(15204, 50)\n"
     ]
    }
   ],
   "source": [
    "# split data into Train, CV and Test\n",
    "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(sent_vectors,Y, test_size=.33, random_state=0)\n",
    "X_tr1, X_cv1, Y_tr1, Y_cv1 = train_test_split(X_train1, Y_train1, test_size=.33, random_state=0)\n",
    "train_avgw2v=preprocessing.normalize(X_tr1)\n",
    "cv_avgw2v=preprocessing.normalize(X_cv1)\n",
    "test_avgw2v=preprocessing.normalize(X_test1)\n",
    "print(train_avgw2v.shape)\n",
    "print(cv_avgw2v.shape)\n",
    "print(test_avgw2v.shape)\n",
    "#print(len(X_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:29:22.564687Z",
     "start_time": "2019-01-23T11:22:36.969683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.6932933370287612\n",
      "3  ------>  0.8052122764792722\n",
      "5  ------>  0.837527356696862\n",
      "7  ------>  0.8560801874328647\n",
      "9  ------>  0.8644852273836946\n",
      "11  ------>  0.8720550982543411\n",
      "13  ------>  0.8758348818707672\n",
      "15  ------>  0.8793182294688349\n",
      "17  ------>  0.881599641562649\n",
      "19  ------>  0.8847884918059153\n",
      "21  ------>  0.8872856340722273\n",
      "23  ------>  0.8892959922798109\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using brute force\n",
    "KNN(train_avgw2v,cv_avgw2v,Y_tr1,Y_cv1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in AvgW2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:32:17.738187Z",
     "start_time": "2019-01-23T11:31:55.880124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8391284357776148\n",
      "macro f1 score for data : 0.7079151417693392\n",
      "micro f1 scoore for data: 0.8680610365693239\n",
      "hamming loss for data: 0.13193896343067613\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.38      0.49      2526\n",
      "          1       0.89      0.96      0.92     12678\n",
      "\n",
      "avg / total       0.85      0.87      0.85     15204\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute force, optimal_k=7\n",
    "testing(train_avgw2v,Y_tr1,test_avgw2v,Y_test1,'brute',optimal_k=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Weighted Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:34:11.802924Z",
     "start_time": "2019-01-23T11:33:45.232461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'scipy.sparse.csr.csr_matrix'>\n",
      "(46071, 24780)\n"
     ]
    }
   ],
   "source": [
    "#tf-idf weighted w2v\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidfw2v_vect = TfidfVectorizer()\n",
    "tfidfw2v= tfidfw2v_vect.fit_transform(final_reviews['CleanedText'].values) \n",
    "print(type(tfidfw2v))\n",
    "print(tfidfw2v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:46:31.364428Z",
     "start_time": "2019-01-23T11:34:19.851499Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "Z0Kg_dbxMQw0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "t=tfidfw2v_vect.get_feature_names()\n",
    "tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent:\n",
    "    sent_vec = np.zeros(50)\n",
    "    cnt_words = 0;\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            tfidf = tfidfw2v[row,t.index(word)]\n",
    "            sent_vec += (vec*tfidf)\n",
    "            cnt_words += tfidf\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= cnt_words\n",
    "    tfidf_sent_vectors.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:46:43.421374Z",
     "start_time": "2019-01-23T11:46:43.243478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20680, 50)\n",
      "(10187, 50)\n",
      "(15204, 50)\n"
     ]
    }
   ],
   "source": [
    "#split dataset\n",
    "X_train2, X_test2, Y_train2, Y_test2 = train_test_split(tfidf_sent_vectors, final['Score'], test_size=0.33,random_state=0)\n",
    "X_tr2, X_cv2, Y_tr2, Y_cv2 = train_test_split(X_train2, Y_train2, test_size=.33, random_state=0)\n",
    "train_tfidfw2v=preprocessing.normalize(X_tr2)\n",
    "cv_tfidfw2v=preprocessing.normalize(X_cv2)\n",
    "test_tfidfw2v=preprocessing.normalize(X_test2)\n",
    "print(train_tfidfw2v.shape)\n",
    "print(cv_tfidfw2v.shape)\n",
    "print(test_tfidfw2v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T11:53:37.000365Z",
     "start_time": "2019-01-23T11:46:52.539582Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.4955785665273532\n",
      "3  ------>  0.492992604606353\n",
      "5  ------>  0.48719120592740456\n",
      "7  ------>  0.4869965830093083\n",
      "9  ------>  0.4848899008171667\n",
      "11  ------>  0.4874500447050506\n",
      "13  ------>  0.48559745354486794\n",
      "15  ------>  0.4879585733197485\n",
      "17  ------>  0.48605001420858196\n",
      "19  ------>  0.48485884986727107\n",
      "21  ------>  0.48303419069996323\n",
      "23  ------>  0.4793928776883677\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using brute force\n",
    "KNN(train_tfidfw2v,cv_tfidfw2v,Y_tr2,Y_cv2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in TF-IDF W2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-16T18:34:41.178840Z",
     "start_time": "2019-01-16T18:34:24.348561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.5071521245926147\n",
      "macro f1 score for data : 0.5069221511818535\n",
      "micro f1 scoore for data: 0.725203893712181\n",
      "hamming loss for data: 0.274796106287819\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.17      0.18      0.18      2475\n",
      "          1       0.84      0.83      0.83     12729\n",
      "\n",
      "avg / total       0.73      0.73      0.73     15204\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute, optimal_k= 1\n",
    "testing(train_tfidfw2v,Y_tr2,test_tfidfw2v,Y_test2,'brute',optimal_k=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-NN using KD-Tree with Simple CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:18.649438Z",
     "start_time": "2019-01-23T12:53:18.618441Z"
    }
   },
   "outputs": [],
   "source": [
    "#find knn to simple cross validation with KD-Tree\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.metrics import roc_auc_score\n",
    "def KNN_kdtree(X_train,X_cv,Y_train,Y_cv,n_components):\n",
    "    k = []\n",
    "    max_k = 0\n",
    "    pred_cv = []\n",
    "    pred_train = []\n",
    "    max_roc_auc=-1\n",
    "    svd = TruncatedSVD(n_components=n_components)\n",
    "    X_train = svd.fit_transform(X_train)\n",
    "    X_cv = svd.fit_transform(X_cv)\n",
    "    for i in range(1,24,2):\n",
    "        knn =KNeighborsClassifier(n_neighbors=i,algorithm='kd_tree')\n",
    "        knn.fit(X_train,Y_train)\n",
    "        probs = knn.predict_proba(X_cv)\n",
    "        prob = knn.predict_proba(X_train)\n",
    "        # keep probabilities for the positive outcome only\n",
    "        probs = probs[:, 1]\n",
    "        prob = prob[:,1]\n",
    "        auc_score_test = roc_auc_score(Y_cv,probs) #find AUC score\n",
    "        auc_score_train = roc_auc_score(Y_train,prob)\n",
    "        print(i,\" ------> \",auc_score_test)\n",
    "        pred_cv.append(auc_score_test)\n",
    "        pred_train.append(auc_score_train)\n",
    "        k.append(i)\n",
    "        if(max_roc_auc<auc_score_test):\n",
    "            max_roc_auc=auc_score_test\n",
    "            max_k=i\n",
    "    #print('\\nThe optimal number of neighbors is %d.' % max_k)\n",
    "    plt.plot(k, pred_cv,'r-', label = 'CV Data')\n",
    "    plt.plot(k,pred_train,'g-', label ='Train Data')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(\"K v/s Auc Score\")\n",
    "    plt.ylabel('Auc Score')\n",
    "    plt.xlabel('K')\n",
    "    plt.show()\n",
    "    # calculate roc curve\n",
    "    fpr, tpr, thresholds = roc_curve(Y_cv,probs)\n",
    "    # plot no skill\n",
    "    pyplt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    pyplt.plot(fpr, tpr, marker='.')\n",
    "    #plt.plot(k,pred_cv)\n",
    "    pyplt.title(\"Line Plot of ROC Curve\")\n",
    "    pyplt.ylabel('True Positive Rate')\n",
    "    pyplt.xlabel('False Positive Rate')\n",
    "    pyplt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:52.325959Z",
     "start_time": "2019-01-23T12:53:52.318964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for KD TREE take 30k data point\n",
    "final_review = final_reviews.head(30000) #Sampled amazon fine foood reviews final revievs data to 30k datapoints for time effiecieny\n",
    "final_review.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spliting dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:54.831440Z",
     "start_time": "2019-01-23T12:53:54.799461Z"
    }
   },
   "outputs": [],
   "source": [
    "#split data into train, cross validate and test \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X = final_review['CleanedText']\n",
    "Y = final_review['Score']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state=0)\n",
    "X_tr, X_cv, Y_tr, Y_cv = train_test_split(X_train, Y_train, test_size=.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:53:57.223120Z",
     "start_time": "2019-01-23T12:53:57.196139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train, Y_train (20100,) (20100,)\n",
      "X_test, Y_test (9900,) (9900,)\n",
      "X_tr, Y_tr (13467,) (13467,)\n",
      "X_cv, Y_cv (6633,) (6633,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train, Y_train', X_train.shape, Y_train.shape)\n",
    "print('X_test, Y_test', X_test.shape, Y_test.shape)\n",
    "print('X_tr, Y_tr', X_tr.shape, Y_tr.shape)\n",
    "print('X_cv, Y_cv', X_cv.shape, Y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques for vectorization :-- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Bag of Words (BoW) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:54:01.423927Z",
     "start_time": "2019-01-23T12:54:00.028263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text BOW vectorizer  (13467, 14215)\n",
      "CV Data Size:  (6633, 14215)\n",
      "Test Data Size:  (9900, 14215)\n"
     ]
    }
   ],
   "source": [
    "#BoW\n",
    "count_vect = CountVectorizer() #in scikit-learn\n",
    "bow_train = count_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(bow_train))\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "#print(\"the number of unique words \", final_counts.get_shape()[1])\n",
    "bow_cv = count_vect.transform(X_cv)\n",
    "bow_test = count_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:54:02.707734Z",
     "start_time": "2019-01-23T12:54:02.646773Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (13467, 14215)\n",
      "CV Data Size:  (6633, 14215)\n",
      "Test Data Size:  (9900, 14215)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "bow_train=preprocessing.normalize(bow_train)\n",
    "bow_cv=preprocessing.normalize(bow_cv)\n",
    "bow_test=preprocessing.normalize(bow_test)\n",
    "print(\"The shape of out text BOW vectorizer \",bow_train.get_shape())\n",
    "print(\"CV Data Size: \",bow_cv.shape)\n",
    "print(\"Test Data Size: \",bow_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T12:54:32.201495Z",
     "start_time": "2019-01-23T12:54:03.967124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cumulative explained variance')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explain variance of training data\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "tsvd = TruncatedSVD(n_components=1000).fit(bow_train)\n",
    "plt.plot(np.cumsum(tsvd.explained_variance_ratio_))\n",
    "plt.xlim(0,1000,10)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T14:37:50.337255Z",
     "start_time": "2019-01-23T12:55:05.356965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.5013675532438949\n",
      "3  ------>  0.49796017578590424\n",
      "5  ------>  0.4941218051361911\n",
      "7  ------>  0.5046562094212708\n",
      "9  ------>  0.5131583746997025\n",
      "11  ------>  0.5172917161693781\n",
      "13  ------>  0.5217282102558141\n",
      "15  ------>  0.5319000586703012\n",
      "17  ------>  0.5362412689148652\n",
      "19  ------>  0.5513099227127021\n",
      "21  ------>  0.5533552866850462\n",
      "23  ------>  0.55904819794221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using kd_tree\n",
    "KNN_kdtree(bow_train,bow_cv,Y_tr,Y_cv,n_components=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T14:43:11.161041Z",
     "start_time": "2019-01-23T14:42:54.853414Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.796098497993102\n",
      "macro f1 score for data : 0.5624376964956675\n",
      "micro f1 scoore for data: 0.8658585858585859\n",
      "hamming loss for data: 0.13414141414141414\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.11      0.20      1432\n",
      "          1       0.87      0.99      0.93      8468\n",
      "\n",
      "avg / total       0.85      0.87      0.82      9900\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = kd_tree, optimal_k=9\n",
    "testing(bow_train,Y_tr,bow_test,Y_test,'kd_tree',optimal_k=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T14:43:43.831591Z",
     "start_time": "2019-01-23T14:43:38.361846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "The shape of out text TFIDF vectorizer  (13467, 286236)\n",
      "CV Data Size:  (6633, 286236)\n",
      "Test Data Size:  (9900, 286236)\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2))\n",
    "tfidf_train = tf_idf_vect.fit_transform(X_tr)\n",
    "print(\"The type of count vectorizer \",type(tfidf_train))\n",
    "print(\"The shape of out text TFIDF vectorizer \",tfidf_train.get_shape())\n",
    "tfidf_cv = tf_idf_vect.transform(X_cv)\n",
    "tfidf_test = tf_idf_vect.transform(X_test)\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T14:43:44.996353Z",
     "start_time": "2019-01-23T14:43:44.934394Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of out text BOW vectorizer  (13467, 286236)\n",
      "CV Data Size:  (6633, 286236)\n",
      "Test Data Size:  (9900, 286236)\n"
     ]
    }
   ],
   "source": [
    "#Normalize Data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import Normalizer\n",
    "tfidf_train=preprocessing.normalize(tfidf_train)\n",
    "tfidf_cv=preprocessing.normalize(tfidf_cv)\n",
    "tfidf_test=preprocessing.normalize(tfidf_test)\n",
    "print(\"The shape of out text BOW vectorizer \",tfidf_train.get_shape())\n",
    "print(\"CV Data Size: \",tfidf_cv.shape)\n",
    "print(\"Test Data Size: \",tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T14:44:48.200093Z",
     "start_time": "2019-01-23T14:43:47.913505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cumulative explained variance')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "tsvd = TruncatedSVD(n_components=300).fit(tfidf_train)\n",
    "plt.plot(np.cumsum(tsvd.explained_variance_ratio_))\n",
    "plt.xlim(0,300,10)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:22:20.677076Z",
     "start_time": "2019-01-23T14:45:53.524078Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.49961051238161536\n",
      "3  ------>  0.516600183885883\n",
      "5  ------>  0.5297688710586325\n",
      "7  ------>  0.5418055725536172\n",
      "9  ------>  0.5447341444448497\n",
      "11  ------>  0.5490690478877157\n",
      "13  ------>  0.5486312365319954\n",
      "15  ------>  0.553966364633365\n",
      "17  ------>  0.5607491594056061\n",
      "19  ------>  0.5627709154851073\n",
      "21  ------>  0.5654263346811708\n",
      "23  ------>  0.565310937255505\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal_k\n",
    "KNN_kdtree(tfidf_train,tfidf_cv,Y_tr,Y_cv,n_components=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:23:23.472321Z",
     "start_time": "2019-01-23T15:23:07.455841Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.7607538435859746\n",
      "macro f1 score for data : 0.6075077811021925\n",
      "micro f1 scoore for data: 0.8665656565656565\n",
      "hamming loss for data: 0.13343434343434343\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.63      0.19      0.29      1432\n",
      "          1       0.88      0.98      0.93      8468\n",
      "\n",
      "avg / total       0.84      0.87      0.83      9900\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute force, optimal_k=11\n",
    "testing(bow_train,Y_tr,bow_test,Y_test,'kd_tree',optimal_k=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avg W2V "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:23:41.997180Z",
     "start_time": "2019-01-23T15:23:37.867069Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train our own Word2Vec model using our own text corpus\n",
    "import gensim\n",
    "i=0\n",
    "list_of_sent=[]\n",
    "for sent in final_review['CleanedText'].values:\n",
    "    filtered_sentence=[]\n",
    "    sent=cleanhtml(sent)\n",
    "    for w in sent.split():\n",
    "        for cleaned_words in cleanpunc(w).split():\n",
    "            if(cleaned_words.isalpha()):    \n",
    "                filtered_sentence.append(cleaned_words.lower())\n",
    "            else:\n",
    "                continue \n",
    "    list_of_sent.append(filtered_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:23:54.001885Z",
     "start_time": "2019-01-23T15:23:47.684192Z"
    }
   },
   "outputs": [],
   "source": [
    "# min_count = 5 considers only words that occured atleast 5 times\n",
    "w2v_model=Word2Vec(list_of_sent,min_count=5,size=50, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:24:04.964665Z",
     "start_time": "2019-01-23T15:24:00.088804Z"
    }
   },
   "outputs": [],
   "source": [
    "# average Word2Vec\n",
    "# compute average word2vec for each review.\n",
    "sent_vectors = []; # the avg-w2v for each sentence/review is stored in this list\n",
    "for sent in list_of_sent: # for each review/sentence\n",
    "    sent_vec = np.zeros(50) # as word vectors are of zero length\n",
    "    cnt_words =0; # num of words with a valid vector in the sentence/review\n",
    "    for word in sent: # for each word in a review/sentence\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            sent_vec += vec\n",
    "            cnt_words += 1\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= cnt_words\n",
    "    sent_vectors.append(sent_vec)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:24:30.878542Z",
     "start_time": "2019-01-23T15:24:30.724630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13467, 50)\n",
      "(6633, 50)\n",
      "(9900, 50)\n"
     ]
    }
   ],
   "source": [
    "# split data into Train, CV and Test\n",
    "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(sent_vectors,Y, test_size=.33, random_state=0)\n",
    "X_tr1, X_cv1, Y_tr1, Y_cv1 = train_test_split(X_train1, Y_train1, test_size=.33, random_state=0)\n",
    "train_avgw2v=preprocessing.normalize(X_tr1)\n",
    "cv_avgw2v=preprocessing.normalize(X_cv1)\n",
    "test_avgw2v=preprocessing.normalize(X_test1)\n",
    "print(train_avgw2v.shape)\n",
    "print(cv_avgw2v.shape)\n",
    "print(test_avgw2v.shape)\n",
    "#print(len(X_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:24:45.577393Z",
     "start_time": "2019-01-23T15:24:44.910743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cumulative explained variance')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "tsvd = TruncatedSVD(n_components=49).fit(train_avgw2v)\n",
    "plt.plot(np.cumsum(tsvd.explained_variance_ratio_))\n",
    "plt.xlim(0,50,10)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:31:37.910781Z",
     "start_time": "2019-01-23T15:24:54.341215Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.6009321793818039\n",
      "3  ------>  0.6740593746175436\n",
      "5  ------>  0.7054419346403981\n",
      "7  ------>  0.725512649228303\n",
      "9  ------>  0.7395923283382327\n",
      "11  ------>  0.7547860954454323\n",
      "13  ------>  0.7632049086689887\n",
      "15  ------>  0.7672090459309027\n",
      "17  ------>  0.7729624683935485\n",
      "19  ------>  0.7764031696280794\n",
      "21  ------>  0.7801910858642074\n",
      "23  ------>  0.7847310455893127\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using kd_tree\n",
    "KNN_kdtree(train_avgw2v,cv_avgw2v,Y_tr1,Y_cv1,n_components=49)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in AvgW2V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:36:17.747360Z",
     "start_time": "2019-01-23T15:36:08.872982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8400437615287788\n",
      "macro f1 score for data : 0.6723577167561443\n",
      "micro f1 scoore for data: 0.8776767676767677\n",
      "hamming loss for data: 0.12232323232323232\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.30      0.41      1432\n",
      "          1       0.89      0.98      0.93      8468\n",
      "\n",
      "avg / total       0.86      0.88      0.86      9900\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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HPwyt5BQz4X41dXZe+3Yndq25/7IeXJIayyWp0ujLTBLuVnBkFyz6A+z6BjoPgJs+gc79za5K+Imfco/x0Owsth4sZdKAeGn05SUk3H1ZXQ388FdY/hcICIZxL8CgX0GAnDcs3K+yxsbL/9vK29/tJi6iNW/dmsGotA5mlyUcJNx91d4fjAOmRVuNi5DGPm/MsQvhIblHK5j1w16mZCYxbVwv2raWthXeRMLd11Qcgf89ARveh8gkuPFj6DHG7KqEnyhxNPq6ztHo69sHh9G5nRww9UYS7r5Ca/jpQ/jiUag8ZixIfelUCAkzuzLhJ77++SCPfLqZwtIqBia1JyUuXILdi0m4+4KiHbD4fti9HBIGwRWvQsd0s6sSfuJwWTVPL8ph/sYCenaI4PVbziclLtzsskQzJNy9WV01fPcKrHjJWJB6/Mtw/i8gQNY1F55hs2uufX0luUcruH9UD347rDshQfLz5wsk3L1R7mpjTn3HV8aqSOnXwJjnIELORBCeUVhaRUxYKwIDFI+O701C+1B6dpRrJnyJhLu3yV0N/xoH9jpAGf1gLvyd2VUJP2G3a/67Zh/PLfmZqeN6ccsFXRjZWwYVvkjC3dus/Kcj2DGafdVVmVuP8Bt7isqZ9mkWq3YdYUj3aC6VK0x9moS7NzmYbaxjqgIABYEhkHyJ2VUJP/Dx2lwen7eZkMAAnp/Ul+sHJcpVpj5Owt1bVBXDR7dAm/Zw5V+hMMcIdlmoWnhAfLs2DO0Ry/SJ6XSMbG12OcIFJNy9gdYw7244ugduWwjJF0HPcWZXJSysus7GP7/ZidaaP4zuyUUpMVwk/dYtRcLdG/zwd/h5EYx+xgh2Idxow76jTJ2TxbaDZVwzMEEafVmUhLvZ9nwHXz4JvSfAhfeYXY2wsIqaOl76YhvvfL+bjm1b887tGYzoJWfCWJWEu5lKD8Anv4CorjBxBsjoSbhR/tFK3l+1l5sGJzF1bC8ipNGXpUm4m8VWC5/cDjVlcOt8aC0LBgvXK66s5bNN+5mSmURqhwiWPThMVkbyExLuZvnySdi3Eia9CR3SzK5GWNAX2Qd4bN5mDpfXkJEcRUpcuAS7H5FwN0P2PFj5Dxh0J/S7zuxqhMUUlVXz5IJsFmXtp1fHCN66LUMaffkhCXdPK9oO8++B+AwY82ezqxEWY7NrJr/2AwXHqnhgdA9+fWl3ggOl0Zc/knD3pOoy+OhmCAyG62ZBUCuzKxIWcbCkithwo9HXn67sQ0L7NqR2kEZf/kz+pHuK1rDwPji0FSa/DZEJZlckLMBu17y/ai8jX1rGf37cC8DwXnES7EJG7h6z5i3YPBtGPAbdR5hdjbCAXYfKmPbpJlbvPsLFKTEM6xlndknCi0i4e0LuGvj8YUgdAxf/0exqhAV8tGYfT8zPplVQAC9M7se15yfIVabiJBLu7lZeBJ/cBm07waQ3ZBUl4RIJ7UMZ1tNo9BXXVhp9iVNJuLuT3QZz7jAC/o4vjI6PQrRAdZ2Nv3+1A4AHxkijL9E8CXd3+vY52PUtTPg7dO5vdjXCR63be4SHZmex81A512VIoy/hHAl3d9m2FJa/CANuhoG3ml2N8EHl1XW8uHQrs1buoXNkG2b9MpNLe8jqSMI5Tk0AK6XGKqW2KqV2KKWmnWab65RSOUqpbKXUB64t08cc2Q2f3gkd+8HlfzG7GuGjCo5V8sHqfdx6QReW3j9Ugl2clWZH7kqpQGAGcBmQB6xRSi3QWuc02CYVeBi4SGt9VCnlv+dk1VbCx46R+nXvQbD08hDOK66oZfGm/dw42Gj0teKh4XSQA6aiBZyZlskEdmitdwEopT4EJgI5Dba5E5ihtT4KoLUudHWhPmPJg3AgC274yGjlK4STPt98gMfnb+ZIeQ2Du0XRPTZcgl20mDPTMvFAboP7eY7HGuoB9FBKfa+UWqWUGtvUCyml7lJKrVVKrT106FDLKvZm69+DDe/DJQ9Azyb/CYQ4RWFpFXf/Zx2/+fc6YsNbMf93F9E9Vhp9iXPjzMi9qcPyuonXSQWGAQnACqVUutb62ElfpPVMYCZARkZG49fwbft/gsUPQNdLYfgjZlcjfITNrrnu9ZUUFFfx4Jie3DW0mzT6Ei7hTLjnAYkN7icABU1ss0prXQvsVkptxQj7NS6p0ttVHoWPboGwGJj8DgQEml2R8HL7iyvpENHaaPQ1oQ+J7UOlLa9wKWeGCGuAVKVUV6VUCDAFWNBom3nAcAClVAzGNM0uVxbqtex2mPsbKCmAa2cZAS/Eadjtmne/383Il5bx7/pGXz3jJNiFyzU7ctda1yml7gGWAoHAO1rrbKXU08BarfUCx3OjlVI5gA14UGt92J2Fe43vXoZtn8O4FyFxkNnVCC+2o7CMaXOyWLv3KEN7xDKil/+eVCbcT2ltztR3RkaGXrt2rSnv7TI7v4F/T4L0a4zl8uSqQXEaH67exxMLsmkTHMgTV6QxaWC8XGUqWkQptU5rndHcdnKFaksV5xl9Y2J6wpV/lWAXZ5QUHcqo3nE8NSGd2AhZpEW4n4R7S9TVwMe3QV01XP8+hISZXZHwMlW1Nv721XYAHhrbiyHdYxjSXY7HCM+RcG+JLx6F/LXGAdSYVLOrEV5m7Z4jPDQni12HypkyKFEafQlTSLifraxPYPVMuPAe6HOV2dUIL1JWXceLn//Me6v2Et+uDe/9MpOh0g9GmETC/WwUboGF90LShTDqSbOrEV7mQHElH67J5bYLk3lwTE/CWsmvlzCP/PQ5q6oEProZQsLh2nchMNjsioQXOFpew6JN+7nlgi6kxBmNvmRlJOENJNydoTUsuMdo5XvbQojoaHZFwmRaaz7bfIAn5m/mWEUtQ7pH0z02XIJdeA0Jd2es+ifkzIfLpkPyRWZXI0xWWFLF4/M3szT7IH3jI3nvl4Ol0ZfwOhLuzdn7A3zxOPS+Eob83uxqhMlsds21b6zkQHEVD4/rxR0XdyVIGn0JLyThfialB+GTX0D7ZJg4Qy5U8mMFxyrp2NZo9PX0xHQS27ehm4zWhReTIcfp2Opg9i+gqti4UKl1pNkVCRPY7Jp/NWr0dWmPWAl24fVk5H46Xz0Fe7+Hq2dChz5mVyNMsKOwlIdmZ7F+3zGG9YxlZO8OZpckhNMk3JuSswB++Btk3AHnXW92NcIEH/y4jycXZBPWKpBXrj+Pq/pLoy/hWyTcGyvaAfPuhvjzYexzZlcjTJIcE8roPh14ckIfYsKl0ZfwPRLuDdWUw8e3GBcoXTsLguSX2l9U1dp45cttKBTTxkmjL+H75IBqPa1h0f1Gi4Fr3oJ2ic1/jbCEH3cdZtxfV/DGsl2UVtVi1hoHQriSjNzrrX0Hsj6C4Y9CykizqxEeUFpVy/99/jP/XrWPpKhQPvjVYIakyGhdWIOEO0DeOvh8GqRcBpc8YHY1wkMOllQze10ev7q4K38Y3YPQEPl1ENYhP83lh+HjWyG8I0yaCQEyU2VlR8prWJxVwC0XJpMSF86Kh0bIykjCkvw73PeuhPl3Q9lB+NX/IDTK7IqEm2itWZS1nycXZFNSVctFKTF0iw2XYBeW5b/hnrsaZl0B9joICAZbrdkVCTc5WFLFo3M38+WWg/RLiOQ/kwfLFabC8vw33PesMIIdQNuN+4mZ5tYkXM5m11znaPT16OW9+cVFydLoS/gF/w33Tuc5bigIDIHkS0wtR7hW3tEKOkW2ITBAMX1iOklRoSTHyELmwn/47xAmwPF3rf9NcNsCGbVbhM2ueWvFLka9vIx/rzIafQ3tESvBLvyO/47c89cbn8c8A23am1uLcImtB0p5aE4WP+UeY2SvOEb3kUZfwn/5b7gXbID2XSXYLeLfq/by1MJsIloH89cp/ZlwXmdp9CX8mh+H+0ZIyDC7CnGOtNYopUiJC+fyvp144oo0oqXRlxB+Gu7lRVC8DzLvNLsS0UKVNTZe/t9WAgIUD4/rzQXdormgW7TZZQnhNfzzgGrBRuNz5wHm1iFaZOXOw4z963LeXLGbimqbNPoSogn+OXIv2ACoBqdDCl9QUlXLc0t+5r+r99ElOpQP7hwsbXmFOA2nRu5KqbFKqa1KqR1KqWln2G6yUkorpbx7MrtgA8SkQuu2ZlcizkJhSTXzNuRz19BufH7fUAl2Ic6g2ZG7UioQmAFcBuQBa5RSC7TWOY22iwDuBX50R6EuVbABuspFS77gcFk1C38q4PaLupISF853U4fLAVMhnODMyD0T2KG13qW1rgE+BCY2sd104AWgyoX1uV7pASgtkPl2L6e1Zv7GfEa9vIw/L9nCrkNlABLsQjjJmXCPB3Ib3M9zPHacUmoAkKi1XuTC2tyjYIPxWcLdaxUcq+SOWWu578ONdIkOY/G9l0ijLyHOkjMHVJu6EuT46QlKqQDgFeD2Zl9IqbuAuwCSkpKcq9DVCjaACoCOfc15f3FGdTY7U2au4lBpNY9fkcbtQ5IJDJCLkYQ4W86Eex7QcEHRBKCgwf0IIB341nFFYEdggVJqgtZ6bcMX0lrPBGYCZGRkmHP+WsEGiO0FIdJrxJvkHqmgc7s2BAUG8OzVfUmKCiUpOtTssoTwWc5My6wBUpVSXZVSIcAUYEH9k1rrYq11jNY6WWudDKwCTgl2r6C1Ee6dB5pdiXCos9mZuXwno15exvsr9wBwcWqMBLsQ56jZkbvWuk4pdQ+wFAgE3tFaZyulngbWaq0XnPkVvEhJPpQfgs79za5EAFv2lzB1ThZZecVcltaBcX07mV2SEJbh1EVMWuslwJJGjz1xmm2HnXtZbnL8YKqM3M32/so9PLUwh8g2wfzjxgGM79tJGn0J4UL+dYVq/nqjj3uHPmZX4rfqG3316BDBled15vEr0ogKCzG7LCEsx7/CvWADxKVBcGuzK/E7FTV1/GXpNoICFY9c3pvB3aIZLI2+hHAb/2kcdvxgqpzf7mnf7yhizKvLeef73dTU2aXRlxAe4D8j96N7oOqYhLsHFVfW8uziLXy0NpeuMWF8/OsLyewaZXZZQvgF/wl3uTLV44rKqlmYVcBvLu3O/xuVSuvgQLNLEsJv+Fe4B7Yy5tyF2xwqNRp9/fLirnSPDee7qSPkgKkQJvCvcO+YDkESNO6gtWbexnyeWphDRbWN4b3i6BoTJsEuhEn8I9ztdtj/E/S7zuxKLCn/WCWPzt3Et1sPMTCpHS9M7kfXGGnvIISZ/CPcj+yE6hKZb3cDo9HXSg6X1fDklWnccqE0+hLCG/hHuMvBVJfbd7iC+PZGo6/nJ/UjKSqUxCjpByOEt/CP89wLNkBQG4jpaXYlPq/OZue1b3cy6pVlvLdyDwAXpcRIsAvhZfxn5N6pHwT6x+66S3ZBMVPnZLE5v4QxfTowXhp9CeG1rJ92dptxMHXgbWZX4tNm/bCH6YtyaBcawms3DZQOjkJ4OeuHe9E2qK2Q+fYWqm/01atjBBP7x/P4Fb1pFyqnNwrh7awf7nIwtUXKq+t4celWggMVj45Pk0ZfQvgY6x9QzV8PIeEQnWJ2JT5j+bZDjH5lObNW7qHWpqXRlxA+yD9G7p36Q4D1/46dq+KKWqYvzmH2ujy6xRqNvgYlS6MvIXyRtcPdVgsHNkHmnWZX4hOKyqv5bNN+7h7WnXtHSqMvIXyZtcO9cAvYqmW+/QwKS6tYsLGAX13S7Xijr/bSD0YIn2ftcJeDqaeltWbO+nymL8qhstbGyN4d6BoTJsEuhEVYP9xbR0JUN7Mr8Sq5Ryp4ZO4mVmwvIqNLe56/Rhp9CWE11g/3zgNASSOrenU2Oze8uYqj5TVMn9iHmwZ3IUAafQlhOdYN97pqOJgNQ+4xuxKvsKeonMSoUIICA3hhstHoK6G99IMRwqqse37gwc1gr/X7+fZam50Z3+xg9CvLjzf6GtI9RoJdCIuz7shdDqayOb+Yh2ZnkbO/hPF9O3FFv85mlySE8BBrh3toNEQmml2JKf71/W6eWbyFqLAQXr/5fMamdzS7JCGEB1k43Df65cHU+kZffTpHMmlAPI+NTyMyNNjssoQQHmbNcK+pMC5g6nm52ZV4TFl1HS98/jMhgQE8dkUamV2jyOwqrQOE8FfWPKB6cDNom9/Mt3+7tZAxryzn/VWUpF2PAAAMMUlEQVR70SCNvoQQFh25+8nB1KPlNUxfnMOn6/NJiQtn9m+GcH6X9maXJYTwAtYM9/z1EN4R2lp7taCjFTV8kX2Qe0ek8LsRKbQKkkZfQgiDU9MySqmxSqmtSqkdSqlpTTz/B6VUjlIqSyn1lVKqi+tLPQv1V6ZaUGFJFTOX70RrTbfYcL6fOoI/jO4pwS6EOEmz4a6UCgRmAOOANOAGpVRao802ABla637AbOAFVxfqtOpSY2k9i4W71pqP1+Qy8uVlvPTFNvYcrgCQM2GEEE1yZlomE9ihtd4FoJT6EJgI5NRvoLX+psH2q4CbXVnkWdmfBWhLhXvukQoe/nQT3+0oIrNrFM9P6iuNvoQQZ+RMuMcDuQ3u5wGDz7D9HcBnTT2hlLoLuAsgKSnJyRLPksUOptY3+jpWUcszV6VzY2aSNPoSQjTLmXBvKkmaPNdOKXUzkAFc2tTzWuuZwEyAjIwM95yvV7DBuCo1PNYtL+8pu4vKSXI0+npx8nl0iQ6lc7s2ZpclhPARzhxQzQMaXsOfABQ03kgpNQp4FJigta52TXktULABOvc37e3PVa3Nzt+/2s6YV5Yz64c9AFzYPVqCXQhxVpwZua8BUpVSXYF8YApwY8MNlFIDgDeAsVrrQpdX6azKY3BkJwy4ybQSzkVW3jEemp3FzwdKufK8zkzoL42+hBAt02y4a63rlFL3AEuBQOAdrXW2UuppYK3WegHwIhAOfKKMXi77tNYT3Fh30/ZvND774Hz7O9/t5pnFOcRGtOLNWzO4LK2D2SUJIXyYUxcxaa2XAEsaPfZEg9ujXFxXy9QfTO3kO9My9Y2++iVEcv2gRKaN601kGzm9UQhxbqx1hWrBBmifDKHe3zCrtKqW5z/7mVZBgTxxZRoZyVFkJHt/3UII32CtxmE+cmXqNz8XMvqV5fx39T6CApU0+hJCuJx1Ru7lh+HYPhj0K7MrOa0j5TU8vTCbeRsL6NEhnH/eNIQBSdLoSwjhetYJ9/31Fy8NNLeOMyiurOWrLYXcNzKV3w1PISTIWv9xEkJ4D+uEe379wdTzzK2jkQPFVczbmM+vh3aja0wY300bIQdMhRBuZ51wL9gA0anQuq3ZlQDGWTAfrsnl2cVbqLXbGdunI8kxYRLsQgiPsFa4J19sdhUA7D1czrQ5m1i56zAXdIvi+Un9SJZGX0IID7JGuJcegNICrzhTps5m58Y3f6S4spZnr+7LlEGJ0uhLCOFx1gj3AvOvTN15qIwujkZfL11nNPrqFCn9YIQQ5rDG6RoFG0AFQKd+Hn/rmjo7r365jbGvLue9lXsBuKBbtAS7EMJUFhm5b4DYXhDi2XntjbnHmDo7i60HS5nYvzNXDYj36PsLIcTp+H64a22Ee+plHn3bt7/bzZ8X5xAX0Zq3b8tgZG9p9CWE8B6+H+4l+VBe6LH59vpGX/0TI5mSmcS0cb1o21pObxRCeBffD3cPLatXUlXLc0t+pnVwAH+6sg/nd4ni/C7S6EsI4Z18/4BqwQYICIIOfdz2Fl/mHOSyl5fx0Zp9hAQFSKMvIYTXs8bIPa43BLv+7JTDZdU8tTCHBT8V0KtjBDNvyeC8xHYufx8hhHA13w73+oOpva90y8uXVtXxzdZC7h/Vg98O6y6NvoQQPsO3w/3YXqg86tL59oJjlczdkM/dw7qTHBPG99NGyAFTIYTP8e1wL3Bdm1+7XfPB6n08/9nP2Oya8X07kRwTJsEuhPBJvh3u+eshMATi0s7pZXYXlTNtThY/7j7CRSnRPHd1P5KiQ11UpBBCeJ5vh3vBBuiQDkEhLX6JOpudm9/6kZKqWl64ph/XZiSglDT6EkL4Nt8Nd7sd9v8Efa9t0ZfvKCwlOTqMoMAAXrm+P12iQ+nQtrWLixRCCHP47ukfR3ZBdclZH0ytrrPx8v+2MfbVFcxyNPrK7BolwS6EsBTfHbm34MrU9fuOMnV2FtsLy5g0IJ5J0uhLCGFRvh3uQW2MbpBOeHP5Lp79bAud2rbmX78YxPCecW4uUAghzOPb4d6pHwSeeRfsdk1AgGJgl3bcNDiJqWN7ESGnNwohLM43w91uMw6mDrzltJsUV9by58U5tAkO5KmJ6dLoSwjhV3zzgGrRNqgtP+18+9LsA1z28jLmrM8nrFWQNPoSQvgd3xy5n+ZgalFZNX+an83iTftJ69SWd24fRHp8pAkFCiGEuXw33EPCITrlpIfLqupYsf0QD47pyV1DuxEc6Jv/MRFCiHPlVPoppcYqpbYqpXYopaY18XwrpdRHjud/VEolu7rQkxRsgE7nQUAg+ccq+cfX29FakxwTxg8Pj+R3w1Mk2IUQfq3ZBFRKBQIzgHFAGnCDUqpxM5c7gKNa6xTgFeD/XF3ocbZaOLAJ3ak/76/cw+iXlzHjm53sPVwBQHgr3/zPiBBCuJIzw9tMYIfWepfWugb4EJjYaJuJwCzH7dnASOWuBi2bZkNdFR9vKubx+dkM7NKeL+4fSnJMmFveTgghfJEzw9x4ILfB/Txg8Om20VrXKaWKgWigyBVFHpe7Gr3g9yhgYvlHxIwcy4hRmdLoSwghGnFm5N5UcjY+t9CZbVBK3aWUWquUWnvo0CFn6jvZnhUouw2AVsrOyNbbJNiFEKIJzoR7HpDY4H4CUHC6bZRSQUAkcKTxC2mtZ2qtM7TWGbGxsWdfbfIlENQKVCAqMMS4L4QQ4hTOTMusAVKVUl2BfGAKcGOjbRYAtwErgcnA19odVw4lZsJtC2DPCiPYEzNd/hZCCGEFzYa7Yw79HmApEAi8o7XOVko9DazVWi8A3gbeV0rtwBixT3FbxYmZEupCCNEMp84b1FovAZY0euyJBrergJatmiGEEMLl5EofIYSwIAl3IYSwIAl3IYSwIAl3IYSwIAl3IYSwIGXWQhZKqUPA3hZ+eQyubm3g/WSf/YPss384l33uorVu9ipQ08L9XCil1mqtM8yuw5Nkn/2D7LN/8MQ+y7SMEEJYkIS7EEJYkK+G+0yzCzCB7LN/kH32D27fZ5+ccxdCCHFmvjpyF0IIcQZeHe5etzC3Bzixz39QSuUopbKUUl8ppbqYUacrNbfPDbabrJTSSimfP7PCmX1WSl3n+F5nK6U+8HSNrubEz3aSUuobpdQGx8/35WbU6SpKqXeUUoVKqc2neV4ppf7m+PfIUkoNdGkBWmuv/MBoL7wT6AaEAD8BaY22uRt43XF7CvCR2XV7YJ+HA6GO27/1h312bBcBLAdWARlm1+2B73MqsAFo77gfZ3bdHtjnmcBvHbfTgD1m132O+zwUGAhsPs3zlwOfYaxkdwHwoyvf35tH7t61MLdnNLvPWutvtNYVjrurMFbG8mXOfJ8BpgMvAFWeLM5NnNnnO4EZWuujAFrrQg/X6GrO7LMG2jpuR3Lqim8+RWu9nCZWpGtgIvCeNqwC2imlOrnq/b053JtamDv+dNtoreuA+oW5fZUz+9zQHRh/+X1Zs/uslBoAJGqtF3myMDdy5vvcA+ihlPpeKbVKKTXWY9W5hzP7/CRws1IqD2P9iN97pjTTnO3v+1lxarEOk7hsYW4f4vT+KKVuBjKAS91akfudcZ+VUgHAK8DtnirIA5z5PgdhTM0Mw/jf2QqlVLrW+piba3MXZ/b5BuBdrfVLSqkLMVZ3S9da291fnincml/ePHJ32cLcPsSZfUYpNQp4FJigta72UG3u0tw+RwDpwLdKqT0Yc5MLfPygqrM/2/O11rVa693AVoyw91XO7PMdwMcAWuuVQGuMHixW5dTve0t5c7gfX5hbKRWCccB0QaNt6hfmBncuzO05ze6zY4riDYxg9/V5WGhmn7XWxVrrGK11stY6GeM4wwSt9VpzynUJZ36252EcPEcpFYMxTbPLo1W6ljP7vA8YCaCU6o0R7oc8WqVnLQBudZw1cwFQrLXe77JXN/uIcjNHmy8HtmEcZX/U8djTGL/cYHzzPwF2AKuBbmbX7IF9/hI4CGx0fCwwu2Z373Ojbb/Fx8+WcfL7rICXgRxgEzDF7Jo9sM9pwPcYZ9JsBEabXfM57u9/gf1ALcYo/Q7gN8BvGnyPZzj+PTa5+udarlAVQggL8uZpGSGEEC0k4S6EEBYk4S6EEBYk4S6EEBYk4S6EEBYk4S6EEBYk4S6EEBYk4S6EEBb0/wH7TgaZMrdcbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute force, optimal_k=7\n",
    "testing(train_avgw2v,Y_tr1,test_avgw2v,Y_test1,'brute',optimal_k=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Weighted Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:38:10.402646Z",
     "start_time": "2019-01-23T15:38:08.929821Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'scipy.sparse.csr.csr_matrix'>\n",
      "(30000, 20418)\n"
     ]
    }
   ],
   "source": [
    "#tf-idf weighted w2v\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidfw2v_vect = TfidfVectorizer()\n",
    "tfidfw2v= tfidfw2v_vect.fit_transform(final_review['CleanedText'].values) \n",
    "print(type(tfidfw2v))\n",
    "print(tfidfw2v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:46:55.457768Z",
     "start_time": "2019-01-23T15:39:56.919317Z"
    }
   },
   "outputs": [],
   "source": [
    "# TF-IDF weighted Word2Vec\n",
    "# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n",
    "t=tfidfw2v_vect.get_feature_names()\n",
    "tfidf_sent_vectors = []; # the tfidf-w2v for each sentence/review is stored in this list\n",
    "row=0;\n",
    "for sent in list_of_sent:\n",
    "    sent_vec = np.zeros(50)\n",
    "    cnt_words = 0;\n",
    "    for word in sent:\n",
    "        try:\n",
    "            vec = w2v_model.wv[word]\n",
    "            tfidf = tfidfw2v[row,t.index(word)]\n",
    "            sent_vec += (vec*tfidf)\n",
    "            cnt_words += tfidf\n",
    "        except:\n",
    "            pass\n",
    "    sent_vec /= cnt_words\n",
    "    tfidf_sent_vectors.append(sent_vec)\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:48:03.976551Z",
     "start_time": "2019-01-23T15:48:03.867596Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13467, 50)\n",
      "(6633, 50)\n",
      "(9900, 50)\n"
     ]
    }
   ],
   "source": [
    "#split dataset\n",
    "X_train2, X_test2, Y_train2, Y_test2 = train_test_split(tfidf_sent_vectors, final_review['Score'], test_size=0.33,random_state=0)\n",
    "X_tr2, X_cv2, Y_tr2, Y_cv2 = train_test_split(X_train2, Y_train2, test_size=.33, random_state=0)\n",
    "train_tfidfw2v=preprocessing.normalize(X_tr2)\n",
    "cv_tfidfw2v=preprocessing.normalize(X_cv2)\n",
    "test_tfidfw2v=preprocessing.normalize(X_test2)\n",
    "print(train_tfidfw2v.shape)\n",
    "print(cv_tfidfw2v.shape)\n",
    "print(test_tfidfw2v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:48:08.387007Z",
     "start_time": "2019-01-23T15:48:07.834324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Cumulative wexplained variance')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "tsvd = TruncatedSVD(n_components=49).fit(train_tfidfw2v)\n",
    "plt.plot(np.cumsum(tsvd.explained_variance_ratio_))\n",
    "plt.xlim(0,50,10)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative wexplained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:54:38.255190Z",
     "start_time": "2019-01-23T15:48:15.602880Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  ------>  0.587468265707942\n",
      "3  ------>  0.6547239990168208\n",
      "5  ------>  0.6904296159225949\n",
      "7  ------>  0.707609599429456\n",
      "9  ------>  0.7240357496792906\n",
      "11  ------>  0.7325540228701676\n",
      "13  ------>  0.7438679989813664\n",
      "15  ------>  0.748139408272001\n",
      "17  ------>  0.7530697078859907\n",
      "19  ------>  0.758180092256577\n",
      "21  ------>  0.7605061089044881\n",
      "23  ------>  0.7615624981889253\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find optimal k using brute force\n",
    "KNN_kdtree(train_tfidfw2v,cv_tfidfw2v,Y_tr2,Y_cv2,n_components=49)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with simple CV Over the unseen or Test data in AvgW2V "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T15:58:23.753589Z",
     "start_time": "2019-01-23T15:57:42.797488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score 0.8095103518207224\n",
      "macro f1 score for data : 0.6548574494800348\n",
      "micro f1 scoore for data: 0.8725252525252525\n",
      "hamming loss for data: 0.12747474747474746\n",
      "Precision recall report for data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.64      0.27      0.38      1432\n",
      "          1       0.89      0.97      0.93      8468\n",
      "\n",
      "avg / total       0.85      0.87      0.85      9900\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#algorithm = brute, optimal_k= 1\n",
    "testing(train_tfidfw2v,Y_tr2,test_tfidfw2v,Y_test2,'kd_tree',optimal_k=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-23T16:00:28.177649Z",
     "start_time": "2019-01-23T16:00:28.100699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "****Final Conclusion:****"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+---------+-------------------------+----------------+\n",
      "|    Vectorizer   |  Model  | Best hyper parameter(K) | Test Auc Score |\n",
      "+-----------------+---------+-------------------------+----------------+\n",
      "|       BoW       |  Brute  |            9            |     77.83      |\n",
      "|       BoW       | KD-Tree |            15           |      79.6      |\n",
      "|      Tf-Idf     |  Brute  |            11           |     78.33      |\n",
      "|      Tf-Idf     | KD-Tree |            7            |     76.07      |\n",
      "|   Avg Word2Vec  |  Brute  |            7            |     83.91      |\n",
      "|   Avg Word2Vec  | KD-Tree |            11           |      84.0      |\n",
      "| tf idf-Word2vec |  Brute  |            1            |      51.7      |\n",
      "| tf idf-Word2vec | KD-Tree |            11           |     80.95      |\n",
      "+-----------------+---------+-------------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Vectorizer\", \"Model\", \"Best hyper parameter(K)\",\"Test Auc Score\"]\n",
    "\n",
    "x.add_row([\"BoW\",\"Brute\",9,77.83])\n",
    "x.add_row([\"BoW\",\"KD-Tree\",15,79.60])\n",
    "x.add_row([\"Tf-Idf\",\"Brute\",11,78.33])\n",
    "x.add_row([\"Tf-Idf\",\"KD-Tree\",7,76.07])\n",
    "x.add_row([\"Avg Word2Vec\",\"Brute\",7,83.91])\n",
    "x.add_row([\"Avg Word2Vec\",\"KD-Tree\",11,84.00])\n",
    "x.add_row([\"tf idf-Word2vec\",\"Brute\",1,51.7])\n",
    "x.add_row([\"tf idf-Word2vec\",\"KD-Tree\",11,80.95])\n",
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "printmd('****Final Conclusion:****')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "colab": {
   "collapsed_sections": [
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   "name": "Amazon Fine Food Reviews Analysis.ipynb",
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   "version": "0.3.2"
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   "language": "python",
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