Apply Naive Bayes on Amazon reviews/Naive Bayes.ipynb

{
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   },
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "import re\n",
    "# Tutorial about Python regular expressions: https://pymotw.com/2/re/\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "colab": {
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     "timestamp": 1.550765717873E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
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    "id": "e1yJEc-QSUpR",
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    {
     "ename": "ImportError",
     "evalue": "No module named 'google.colab'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-229-6db90e9297aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/gdrive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'google.colab'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 261.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 7743.0,
     "status": "ok",
     "timestamp": 1.550769111493E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
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    },
    "id": "XgKMt0qhCj8j",
    "outputId": "6bced987-bd3c-4cd2-cfbc-4e81dd90b59e"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in our data (525814, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#connecting to sqlite db\n",
    "# con = sqlite3.connect('/content/gdrive/My Drive/Colab Notebooks/Assignment 4/database.sqlite')\n",
    "con = sqlite3.connect('database.sqlite')\n",
    "                      \n",
    "# filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "# SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000, will give top 500000 data points\n",
    "# you can change the number to any other number based on your computing power\n",
    "\n",
    "# filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000\"\"\", con) \n",
    "# for tsne assignment you can take 5k data points\n",
    "\n",
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3\"\"\", con) \n",
    "\n",
    "# Give reviews with Score>3 a positive rating(1), and reviews with a score<3 a negative rating(0).\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0\n",
    "    return 1\n",
    "\n",
    "#changing reviews with score less than 3 to be positive and vice-versa\n",
    "actualScore = filtered_data['Score']\n",
    "positiveNegative = actualScore.map(partition) \n",
    "filtered_data['Score'] = positiveNegative\n",
    "print(\"Number of data points in our data\", filtered_data.shape)\n",
    "filtered_data.head(3)"
   ]
  },
  {
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    "id": "9PzG3hgQCxuA"
   },
   "outputs": [],
   "source": [
    "display = pd.read_sql_query(\"\"\"\n",
    "SELECT UserId, ProductId, ProfileName, Time, Score, Text, COUNT(*)\n",
    "FROM Reviews\n",
    "GROUP BY UserId\n",
    "HAVING COUNT(*)>1\n",
    "\"\"\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221.0
    },
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    "executionInfo": {
     "elapsed": 2589.0,
     "status": "ok",
     "timestamp": 1.550769160678E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "3Clg7vqUC22C",
    "outputId": "9186d981-37fa-4658-ffdb-98e0c9fcec91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80668, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>Text</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#oc-R115TNMSPFT9I7</td>\n",
       "      <td>B007Y59HVM</td>\n",
       "      <td>Breyton</td>\n",
       "      <td>1331510400</td>\n",
       "      <td>2</td>\n",
       "      <td>Overall its just OK when considering the price...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#oc-R11D9D7SHXIJB9</td>\n",
       "      <td>B005HG9ET0</td>\n",
       "      <td>Louis E. Emory \"hoppy\"</td>\n",
       "      <td>1342396800</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife has recurring extreme muscle spasms, u...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#oc-R11DNU2NBKQ23Z</td>\n",
       "      <td>B007Y59HVM</td>\n",
       "      <td>Kim Cieszykowski</td>\n",
       "      <td>1348531200</td>\n",
       "      <td>1</td>\n",
       "      <td>This coffee is horrible and unfortunately not ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#oc-R11O5J5ZVQE25C</td>\n",
       "      <td>B005HG9ET0</td>\n",
       "      <td>Penguin Chick</td>\n",
       "      <td>1346889600</td>\n",
       "      <td>5</td>\n",
       "      <td>This will be the bottle that you grab from the...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#oc-R12KPBODL2B5ZD</td>\n",
       "      <td>B007OSBE1U</td>\n",
       "      <td>Christopher P. Presta</td>\n",
       "      <td>1348617600</td>\n",
       "      <td>1</td>\n",
       "      <td>I didnt like this coffee. Instead of telling y...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               UserId   ProductId             ProfileName        Time  Score  \\\n",
       "0  #oc-R115TNMSPFT9I7  B007Y59HVM                 Breyton  1331510400      2   \n",
       "1  #oc-R11D9D7SHXIJB9  B005HG9ET0  Louis E. Emory \"hoppy\"  1342396800      5   \n",
       "2  #oc-R11DNU2NBKQ23Z  B007Y59HVM        Kim Cieszykowski  1348531200      1   \n",
       "3  #oc-R11O5J5ZVQE25C  B005HG9ET0           Penguin Chick  1346889600      5   \n",
       "4  #oc-R12KPBODL2B5ZD  B007OSBE1U   Christopher P. Presta  1348617600      1   \n",
       "\n",
       "                                                Text  COUNT(*)  \n",
       "0  Overall its just OK when considering the price...         2  \n",
       "1  My wife has recurring extreme muscle spasms, u...         3  \n",
       "2  This coffee is horrible and unfortunately not ...         2  \n",
       "3  This will be the bottle that you grab from the...         3  \n",
       "4  I didnt like this coffee. Instead of telling y...         2  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(display.shape)\n",
    "display.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 2151.0,
     "status": "ok",
     "timestamp": 1.550769179085E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "SFRiGUBmC7f9",
    "outputId": "6604b89e-4715-40fd-ad83-047bd00deb77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364173, 10)\n"
     ]
    }
   ],
   "source": [
    "# Removing duplicate reviews\n",
    "final=filtered_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\n",
    "print(final.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 1084.0,
     "status": "ok",
     "timestamp": 1.550769193941E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "njjTvKgwC_fs",
    "outputId": "0c23a3ae-2583-4fa4-d784-e49ffac5f8e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.25890143662969"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "icJxMrXoDCaV"
   },
   "outputs": [],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 1056.0,
     "status": "ok",
     "timestamp": 1.550769220216E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "rS-TqenXDF8m",
    "outputId": "ae0e680a-ad3a-4951-f354-058586944c1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(364171, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    307061\n",
       "0     57110\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 236,
     "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": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final[\"cleanReview\"] = final[\"Summary\"].map(str) + \". \" + final[\"Text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Good Quality Dog Food. I have bought several o...\n",
       "1    Not as Advertised. Product arrived labeled as ...\n",
       "2    \"Delight\" says it all. This is a confection th...\n",
       "3    Cough Medicine. If you are looking for the sec...\n",
       "4    Great taffy. Great taffy at a great price.  Th...\n",
       "Name: cleanReview, dtype: object"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cleanReview'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    52\n",
       "1    34\n",
       "2    98\n",
       "3    43\n",
       "4    29\n",
       "Name: lengthOfReview, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['lengthOfReview'] = final['cleanReview'].str.split().str.len()\n",
    "final['lengthOfReview'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 3699.0,
     "status": "ok",
     "timestamp": 1.550769244025E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "98Qlu7hKDK7R",
    "outputId": "e6ac4c3e-662f-45dd-8fd1-104cba1bb1f8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [00:01<00:00, 348689.13it/s]\n"
     ]
    }
   ],
   "source": [
    "#remove urls from text python\n",
    "from tqdm import tqdm\n",
    "lst = []\n",
    "removed_urls_list = []\n",
    "for text in tqdm(final['cleanReview']):\n",
    "  removed_urls_text = re.sub(r\"http\\S+\", \"\", text)\n",
    "  lst.append(removed_urls_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 3533.0,
     "status": "ok",
     "timestamp": 1.550769263468E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "Hk4AYZLLDPsd",
    "outputId": "069a3e83-0345-4136-846e-7a06cac6c519"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [00:00<00:00, 545983.46it/s]\n"
     ]
    }
   ],
   "source": [
    "#remove urls from text python\n",
    "removed_urls_list = []\n",
    "for text in tqdm(lst):\n",
    "  removed_urls_text = re.sub(r\"http\\S+\", \"\", text)\n",
    "  removed_urls_list.append(removed_urls_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 97417.0,
     "status": "ok",
     "timestamp": 1.55076937329E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "5EM9yQX_DTtc",
    "outputId": "07a280eb-abe0-4dc8-fc2a-e92c3b9f5bd4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [02:07<00:00, 2863.46it/s]\n"
     ]
    }
   ],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "text_lst = []\n",
    "for text in tqdm(removed_urls_list):\n",
    "  soup = BeautifulSoup(text, 'lxml')\n",
    "  text = soup.get_text()\n",
    "  text_lst.append(text)\n",
    "# print(text)\n",
    "# print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 841.0,
     "status": "ok",
     "timestamp": 1.550769382297E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "h4kMN4PyDYqh",
    "outputId": "8f543749-f6a4-4d09-879a-6652d45133e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364171\n"
     ]
    }
   ],
   "source": [
    "print(len(final['cleanReview']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "REc3-mEUDbsg"
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/47091490/4084039\n",
    "import re\n",
    "\n",
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won't\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 6088.0,
     "status": "ok",
     "timestamp": 1.550769390659E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "MSaFaRqVDgbU",
    "outputId": "d8cb91d4-4276-498b-d7f3-30b1bb46961b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [00:05<00:00, 67496.57it/s]\n"
     ]
    }
   ],
   "source": [
    "decat_lst = []\n",
    "for decat_text in tqdm(text_lst):\n",
    "  text = decontracted(decat_text)\n",
    "  decat_lst.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 14902.0,
     "status": "ok",
     "timestamp": 1.550769420182E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "oc7floi8DzNI",
    "outputId": "f8def560-4301-4568-f4b2-1e5c2efc3a95"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [00:19<00:00, 18494.66it/s]\n"
     ]
    }
   ],
   "source": [
    "strip_list = []\n",
    "for to_strip in tqdm(decat_lst):\n",
    "  text = re.sub(\"\\S*\\d\\S*\", \"\", to_strip).strip()\n",
    "  strip_list.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 9692.0,
     "status": "ok",
     "timestamp": 1.550769430693E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "LQStix5MD2tQ",
    "outputId": "c6dab596-05eb-4875-9adb-cc1786addaca"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [00:09<00:00, 36464.50it/s]\n"
     ]
    }
   ],
   "source": [
    "spatial_list = []\n",
    "for to_spatial in tqdm(strip_list):\n",
    "  text = re.sub('[^A-Za-z0-9]+', ' ', to_spatial)\n",
    "  spatial_list.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "KJPIpmqYD8QZ"
   },
   "outputs": [],
   "source": [
    "stopwords= set(['br', 'the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
    "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 144687.0,
     "status": "ok",
     "timestamp": 1.550769602159E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "GjPsp7hUEADC",
    "outputId": "909fe554-5306-484d-dffc-30ebc110f01c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 364171/364171 [02:01<00:00, 2999.66it/s]\n"
     ]
    }
   ],
   "source": [
    "# Combining all the above stundents \n",
    "preprocessed_reviews = []\n",
    "# tqdm is for printing the status bar\n",
    "for sentance in tqdm(spatial_list):\n",
    "    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n",
    "    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n",
    "    sentance = decontracted(sentance)\n",
    "    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n",
    "    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n",
    "    # https://gist.github.com/sebleier/554280\n",
    "    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n",
    "    preprocessed_reviews.append(sentance.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 872.0,
     "status": "ok",
     "timestamp": 1.550769609774E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "3ssesro1EGoY",
    "outputId": "55c379ca-9765-4f12-d77b-154526ff51cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364171\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'great honey satisfied product advertised use cereal raw vinegar general sweetner'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(preprocessed_reviews))\n",
    "preprocessed_reviews[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "RK6YWo6HENP6"
   },
   "outputs": [],
   "source": [
    "final['cleanReview'] = preprocessed_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 802.0,
     "status": "ok",
     "timestamp": 1.550769614923E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "1eFLmTYlEPu5",
    "outputId": "2ff2fcef-985b-4f76-9358-a7edb55fe30b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364171\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>cleanReview</th>\n",
       "      <th>lengthOfReview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1331251200</td>\n",
       "      <td>disappointed</td>\n",
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       "      <td>Perfect for our maltipoo</td>\n",
       "      <td>These stars are small, so you can give 10-15 o...</td>\n",
       "      <td>perfect maltipoo stars small give one training...</td>\n",
       "      <td>40</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1331596800</td>\n",
       "      <td>Favorite Training and reward treat</td>\n",
       "      <td>These are the BEST treats for training and rew...</td>\n",
       "      <td>favorite training reward treat best treats tra...</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
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       "      <td>568454</td>\n",
       "      <td>B001LR2CU2</td>\n",
       "      <td>A3LGQPJCZVL9UC</td>\n",
       "      <td>srfell17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1338422400</td>\n",
       "      <td>Great Honey</td>\n",
       "      <td>I am very satisfied ,product is as advertised,...</td>\n",
       "      <td>great honey satisfied product advertised use c...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id   ProductId          UserId              ProfileName  \\\n",
       "525809  568450  B001EO7N10  A28KG5XORO54AY         Lettie D. Carter   \n",
       "525810  568451  B003S1WTCU  A3I8AFVPEE8KI5                R. Sawyer   \n",
       "525811  568452  B004I613EE  A121AA1GQV751Z            pksd \"pk_007\"   \n",
       "525812  568453  B004I613EE   A3IBEVCTXKNOH  Kathy A. Welch \"katwel\"   \n",
       "525813  568454  B001LR2CU2  A3LGQPJCZVL9UC                 srfell17   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "525809                     0                       0      1  1299628800   \n",
       "525810                     0                       0      0  1331251200   \n",
       "525811                     2                       2      1  1329782400   \n",
       "525812                     1                       1      1  1331596800   \n",
       "525813                     0                       0      1  1338422400   \n",
       "\n",
       "                                   Summary  \\\n",
       "525809                 Will not do without   \n",
       "525810                        disappointed   \n",
       "525811            Perfect for our maltipoo   \n",
       "525812  Favorite Training and reward treat   \n",
       "525813                         Great Honey   \n",
       "\n",
       "                                                     Text  \\\n",
       "525809  Great for sesame chicken..this is a good if no...   \n",
       "525810  I'm disappointed with the flavor. The chocolat...   \n",
       "525811  These stars are small, so you can give 10-15 o...   \n",
       "525812  These are the BEST treats for training and rew...   \n",
       "525813  I am very satisfied ,product is as advertised,...   \n",
       "\n",
       "                                              cleanReview  lengthOfReview  \n",
       "525809  not without great sesame chicken good not bett...              15  \n",
       "525810  disappointed disappointed flavor chocolate not...              24  \n",
       "525811  perfect maltipoo stars small give one training...              40  \n",
       "525812  favorite training reward treat best treats tra...              22  \n",
       "525813  great honey satisfied product advertised use c...              11  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(final))\n",
    "final.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'good quality dog food bought several vitality canned dog food products found good quality product looks like stew processed meat smells better labrador finicky appreciates product better'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cleanReview'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['lengthOfReview'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "zae_uyJESXDu"
   },
   "outputs": [],
   "source": [
    "dir_path = os.getcwd()\n",
    "# conn = sqlite3.connect(os.path.join(dir_path, '/content/gdrive/My Drive/Colab Notebooks/Assignment 4/final.sqlite'))\n",
    "conn = sqlite3.connect(os.path.join(dir_path, 'final.sqlite'))\n",
    "final.to_sql('Reviews', conn, if_exists='replace', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 1117.0,
     "status": "ok",
     "timestamp": 1.550769622453E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "BABvD-PcScwh",
    "outputId": "2f570c2b-7b29-4d0d-95d3-84b7e264fcdd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count(*)\n",
      "0    364171\n"
     ]
    }
   ],
   "source": [
    "review_3 = pd.read_sql_query(\"\"\" SELECT count(*) FROM Reviews\"\"\", conn)\n",
    "print(review_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "EZe-zkC2SuoY"
   },
   "outputs": [],
   "source": [
    "filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 2374.0,
     "status": "ok",
     "timestamp": 1.550769627116E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "Ac-PQW4dSwdn",
    "outputId": "deca4d8e-b8b0-4c9b-de6b-67578b6413f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364171, 12)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "r5vlYfvoSyZ3"
   },
   "outputs": [],
   "source": [
    "filtered_data[\"Time\"] = pd.to_datetime(filtered_data[\"Time\"], unit = \"s\")\n",
    "filtered_data = filtered_data.sort_values(by = \"Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 547.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27331.0,
     "status": "ok",
     "timestamp": 1.550769653793E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "6IcfGK5jS0XG",
    "outputId": "2859c315-bceb-461e-f2c0-0bb05773e46c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>cleanReview</th>\n",
       "      <th>lengthOfReview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117924</th>\n",
       "      <td>150524</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>ACITT7DI6IDDL</td>\n",
       "      <td>shari zychinski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1999-10-08</td>\n",
       "      <td>EVERY book is educational</td>\n",
       "      <td>this witty little book makes my son laugh at l...</td>\n",
       "      <td>every book educational witty little book makes...</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117901</th>\n",
       "      <td>150501</td>\n",
       "      <td>0006641040</td>\n",
       "      <td>AJ46FKXOVC7NR</td>\n",
       "      <td>Nicholas A Mesiano</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1999-10-25</td>\n",
       "      <td>This whole series is great way to spend time w...</td>\n",
       "      <td>I can remember seeing the show when it aired o...</td>\n",
       "      <td>whole series great way spend time child rememb...</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298792</th>\n",
       "      <td>451856</td>\n",
       "      <td>B00004CXX9</td>\n",
       "      <td>AIUWLEQ1ADEG5</td>\n",
       "      <td>Elizabeth Medina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1999-12-02</td>\n",
       "      <td>Entertainingl Funny!</td>\n",
       "      <td>Beetlejuice is a well written movie ..... ever...</td>\n",
       "      <td>entertainingl funny beetlejuice well written m...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169281</th>\n",
       "      <td>230285</td>\n",
       "      <td>B00004RYGX</td>\n",
       "      <td>A344SMIA5JECGM</td>\n",
       "      <td>Vincent P. Ross</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1999-12-06</td>\n",
       "      <td>A modern day fairy tale</td>\n",
       "      <td>A twist of rumplestiskin captured on film, sta...</td>\n",
       "      <td>modern day fairy tale twist rumplestiskin capt...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298791</th>\n",
       "      <td>451855</td>\n",
       "      <td>B00004CXX9</td>\n",
       "      <td>AJH6LUC1UT1ON</td>\n",
       "      <td>The Phantom of the Opera</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>FANTASTIC!</td>\n",
       "      <td>Beetlejuice is an excellent and funny movie. K...</td>\n",
       "      <td>fantastic beetlejuice excellent funny movie ke...</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id   ProductId          UserId               ProfileName  \\\n",
       "117924  150524  0006641040   ACITT7DI6IDDL           shari zychinski   \n",
       "117901  150501  0006641040   AJ46FKXOVC7NR        Nicholas A Mesiano   \n",
       "298792  451856  B00004CXX9   AIUWLEQ1ADEG5          Elizabeth Medina   \n",
       "169281  230285  B00004RYGX  A344SMIA5JECGM           Vincent P. Ross   \n",
       "298791  451855  B00004CXX9   AJH6LUC1UT1ON  The Phantom of the Opera   \n",
       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score       Time  \\\n",
       "117924                     0                       0      1 1999-10-08   \n",
       "117901                     2                       2      1 1999-10-25   \n",
       "298792                     0                       0      1 1999-12-02   \n",
       "169281                     1                       2      1 1999-12-06   \n",
       "298791                     0                       0      1 2000-01-03   \n",
       "\n",
       "                                                  Summary  \\\n",
       "117924                          EVERY book is educational   \n",
       "117901  This whole series is great way to spend time w...   \n",
       "298792                               Entertainingl Funny!   \n",
       "169281                            A modern day fairy tale   \n",
       "298791                                         FANTASTIC!   \n",
       "\n",
       "                                                     Text  \\\n",
       "117924  this witty little book makes my son laugh at l...   \n",
       "117901  I can remember seeing the show when it aired o...   \n",
       "298792  Beetlejuice is a well written movie ..... ever...   \n",
       "169281  A twist of rumplestiskin captured on film, sta...   \n",
       "298791  Beetlejuice is an excellent and funny movie. K...   \n",
       "\n",
       "                                              cleanReview  lengthOfReview  \n",
       "117924  every book educational witty little book makes...              38  \n",
       "117901  whole series great way spend time child rememb...              39  \n",
       "298792  entertainingl funny beetlejuice well written m...              15  \n",
       "169281  modern day fairy tale twist rumplestiskin capt...              25  \n",
       "298791  fantastic beetlejuice excellent funny movie ke...              26  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27906.0,
     "status": "ok",
     "timestamp": 1.55076965474E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "s2At8aIwS24T",
    "outputId": "6f384358-b6aa-4a10-9465-0fcf72eb2143"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364171\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 364171 entries, 117924 to 107253\n",
      "Data columns (total 12 columns):\n",
      "Id                        364171 non-null int64\n",
      "ProductId                 364171 non-null object\n",
      "UserId                    364171 non-null object\n",
      "ProfileName               364171 non-null object\n",
      "HelpfulnessNumerator      364171 non-null int64\n",
      "HelpfulnessDenominator    364171 non-null int64\n",
      "Score                     364171 non-null int64\n",
      "Time                      364171 non-null datetime64[ns]\n",
      "Summary                   364171 non-null object\n",
      "Text                      364171 non-null object\n",
      "cleanReview               364171 non-null object\n",
      "lengthOfReview            364171 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(5), object(6)\n",
      "memory usage: 36.1+ MB\n",
      "100000\n"
     ]
    }
   ],
   "source": [
    "print(len(filtered_data))\n",
    "filtered_data.info()\n",
    "filtered_data = filtered_data.head(100000)\n",
    "print(len(filtered_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27459.0,
     "status": "ok",
     "timestamp": 1.550769654742E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "u22e0QpbS47q",
    "outputId": "fc2d5618-481e-44ad-ec94-c9934bee2403"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    87729\n",
       "0    12271\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27042.0,
     "status": "ok",
     "timestamp": 1.550769654744E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "2JlyqmfIS8Fp",
    "outputId": "f2ff174c-5eab-4ba3-eaa4-0bcb8d90d993"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X: 117924    every book educational witty little book makes...\n",
      "117901    whole series great way spend time child rememb...\n",
      "298792    entertainingl funny beetlejuice well written m...\n",
      "169281    modern day fairy tale twist rumplestiskin capt...\n",
      "298791    fantastic beetlejuice excellent funny movie ke...\n",
      "Name: cleanReview, dtype: object\n",
      "None\n",
      "shape of y: 117924    1\n",
      "117901    1\n",
      "298792    1\n",
      "169281    1\n",
      "298791    1\n",
      "Name: Score, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = filtered_data[\"cleanReview\"]\n",
    "print(print(\"shape of X:\", X.head(5)))\n",
    "y = filtered_data[\"Score\"]\n",
    "print(\"shape of y:\", y.head(5))\n",
    "X_len = filtered_data['lengthOfReview']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_data['lengthOfReview'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train = X[0:60000]\n",
    "Y_train = y[0:60000]\n",
    "X_val = X[60000:80000]\n",
    "Y_val = y[60000:80000]\n",
    "X_test = X[80000:100000]\n",
    "Y_test = y[80000:100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 20000 20000\n",
      "60000 20000 20000\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), len(X_test), len(X_val))\n",
    "print(len(Y_train), len(Y_test), len(Y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 26231.0,
     "status": "ok",
     "timestamp": 1.55076965475E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "TxvqsXUATBAv",
    "outputId": "d5002573-dbc3-4488-eaf6-dfe0c354354c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,) (60000,) (20000,)\n",
      "(60000,) (60000,) (20000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, Y_train.shape, X_test.shape)\n",
    "print(X_train.shape, Y_train.shape, X_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ge1xNyvcUi-I"
   },
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 31125.0,
     "status": "ok",
     "timestamp": 1.550769660839E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "Dfaq1nJBTDIu",
    "outputId": "77177048-fa73-46ba-eb84-1f4875b9350d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 47535)\n",
      "['aa', 'aaa', 'aaaa', 'aaaaa', 'aaaaaa', 'aaaaaaaaaaaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaaaagghh', 'aaaaaagh', 'aaaaaah', 'aaaaah', 'aaaah', 'aaah', 'aaahs', 'aaamazing', 'aachen', 'aad', 'aadp', 'aafco', 'aagh', 'aahhed', 'aahing', 'aand', 'aardvark', 'aash', 'ab', 'aback', 'abacore', 'abaloo', 'abandon', 'abandoned', 'abandoning', 'abattoir', 'abba', 'abbey', 'abbreviated', 'abbreviation', 'abby', 'abc', 'abcs', 'abcstores', 'abd', 'abdomen', 'abdominal', 'abeneficial', 'aberdeen', 'abernook', 'aberration', 'abhor', 'abi', 'abid', 'abide', 'abigirl', 'abilities', 'ability', 'abit', 'abj', 'abk', 'ablaze', 'able', 'ablity', 'abnormal', 'abnormalities', 'abnormally', 'abnormalties', 'aboard', 'abode', 'abominable', 'abomination', 'abominations', 'abondanza', 'aborio', 'abortions', 'aboslutely', 'abosolutely', 'abot', 'aboth', 'abound', 'abounding', 'abounds', 'abour', 'aboutamazon', 'aboutjust', 'aboutthese', 'abouy', 'abouyt', 'abovementioned', 'abovethirst', 'abp', 'abpve', 'abraham', 'abrasion', 'abrasive', 'abreviating', 'abroad', 'abrooks', 'abruptly', 'abruzzo', 'abs', 'abscess', 'absconding', 'absence', 'absensce', 'absent', 'absinthe', 'abso', 'absoloutely', 'absoluely', 'absoluetly', 'absolut', 'absolutaly', 'absolute', 'absoluteky', 'absolutel', 'absolutelly', 'absolutelty', 'absolutely', 'absolutelydelectable', 'absolutelydelicious', 'absolutelyscrumptious', 'absolutley', 'absolutly', 'absolutuley', 'absolves', 'absorb', 'absorbable', 'absorbance', 'absorbancy', 'absorbant', 'absorbed', 'absorbency', 'absorbent', 'absorbing', 'absorbs', 'absorption', 'absouloutely', 'absoulte', 'absoultely', 'absoulutely', 'absoutely', 'abstain', 'abstained', 'abstaining', 'abstinence', 'abstract', 'abstraction', 'abstracts', 'absulutely', 'absurd', 'absurdity', 'absurdly', 'abt', 'abudance', 'abuela', 'abuelita', 'abums', 'abundance', 'abundant', 'abundantly', 'abundunce', 'abuse', 'abused', 'abuses', 'abusing', 'abusive', 'abut', 'abuts', 'abutted', 'abysmal', 'abyssinian', 'abyssinians', 'ac', 'acacia', 'academy', 'acai', 'acaia', 'acau', 'accasion', 'accedient', 'accelerade', 'acceleradetests', 'accelerate', 'accelerated', 'accent', 'accented', 'accenting', 'accents', 'accentuate', 'accentuated', 'accepeted', 'accept', 'acceptable', 'acceptably', 'acceptance', 'acceptd', 'accepted', 'accepting', 'accepts', 'accesesible', 'accesible', 'access', 'accessable', 'accessed', 'accessibility', 'accessible', 'accessories', 'accessorize', 'accessory', 'accident', 'accidental', 'accidentally', 'accidently', 'accidents', 'acciughe', 'acclaim', 'acclaimed', 'acclamated', 'acclimate', 'acclimated', 'acclimating', 'accolades', 'accomadating', 'accomidate', 'accommodate', 'accommodates', 'accommodating', 'accomodate', 'accomondating', 'accompanied', 'accompanies', 'accompaniment', 'accompaniments', 'accompany', 'accompanying', 'accomplish', 'accomplished', 'accomplishes', 'accomplishment', 'accord', 'accordance', 'accordian', 'according', 'accordingly', 'accordion', 'accordng', 'accostumed', 'account', 'accountant', 'accounting', 'accounts', 'accoutrement', 'accoutrements', 'accredit', 'accredited', 'accross', 'accrue', 'accturall', 'accummilate', 'accumstom', 'accumulate', 'accumulated', 'accumulating', 'accumulation', 'accupuncturest', 'accuracy', 'accurate', 'accurately', 'accuring', 'accusation', 'accusatory', 'accuse', 'accused', 'accusingly', 'accustom', 'accustomed', 'accustomized', 'accustume', 'ace', 'acerbic', 'acerola', 'acertain', 'aces', 'acess', 'acesuflamek', 'acesulfame', 'acesulfate', 'acetaminophen', 'acetate', 'acetic', 'aceto', 'acetone', 'acetylated', 'ache', 'acheivement', 'aches', 'achey', 'achievable', 'achieve', 'achieved', 'achievement', 'achiever', 'achieves', 'achieving', 'aching', 'achva', 'achy', 'acia', 'acid', 'acides', 'acidez', 'acidic', 'acidification', 'acidifies', 'acidify', 'acidifying', 'acidity', 'acidolphilus', 'acidophilus', 'acids', 'acidsas', 'acidsfacilitate', 'acidsome', 'acidxantham', 'acidy', 'acini', 'aciphex', 'acis', 'ack', 'ackee', 'ackie', 'ackkkk', 'acknowleded', 'acknowledge', 'acknowledged', 'acknowledgement', 'acknowledging', 'ackward', 'acme', 'acne', 'acommodate', 'acordingly', 'acorn', 'acorns', 'acostly', 'acoustic', 'acpuncturist', 'acquaint', 'acquaintance', 'acquaintances', 'acquainted', 'acquarium', 'acquire', 'acquired', 'acquires', 'acquiring', 'acquisition', 'acre', 'acres', 'acrid', 'acronym', 'across', 'acrylic', 'acspect', 'act', 'acted', 'acting', 'action', 'actions', 'activa', 'activate', 'activateand', 'activated', 'activates', 'activating', 'activation', 'activations', 'active', 'actively', 'activia', 'activin', 'activism', 'activites', 'activities', 'activity', 'actly', 'actor', 'actors', 'actress', 'acts', 'actua', 'actual', 'actuality', 'actually', 'actualy', 'actully', 'acuity', 'acupressure', 'acupuncture', 'acupuncturist', 'acura', 'acuracy', 'acutally', 'acute', 'acutely', 'ad', 'adabi', 'adage', 'adagio', 'adam', 'adamant', 'adamantly', 'adament', 'adamo', 'adams', 'adamson', 'adapt', 'adaptable', 'adaptation', 'adaptations', 'adapted', 'adapter', 'adapting', 'adaption', 'adaptogen', 'adaptogenic', 'adaptor', 'adapts', 'adavance', 'add', 'adda', 'addage', 'addams', 'addded', 'added', 'addedcons', 'addedfrom', 'addedno', 'addedthe', 'addedthis', 'addendum', 'adder', 'addes', 'addextra', 'addicated', 'addicitive', 'addicitve', 'addict', 'addicted', 'addictedamanda', 'addicting', 'addictingly', 'addiction', 'addictions', 'addictive', 'addictively', 'addictiveness', 'addicts', 'adding', 'additative', 'addition', 'additiona', 'additional', 'additionally', 'additionof', 'additions', 'additive', 'additives', 'additivesas', 'additiveso', 'additon', 'additonal', 'addittional', 'addives', 'addmixure', 'address', 'addressed', 'addresses', 'addressing', 'adds', 'addtion', 'addtional', 'addy', 'ade', 'aded', 'adelaide', 'adele', 'adelicate', 'adem', 'adept', 'adepts', 'adequae', 'adequate', 'adequately', 'adhd', 'adhere', 'adhered', 'adherent', 'adherents', 'adheres', 'adhering', 'adhesion', 'adhesive', 'adhesives', 'adiabetic', 'adiago', 'adicting', 'adictive', 'aditional', 'adivce', 'adjacent', 'adjective', 'adjectives', 'adjunct', 'adjust', 'adjustable', 'adjusted', 'adjusting', 'adjustment', 'adjustments', 'adjusts', 'adm', 'administators', 'administer', 'administered', 'administering', 'administers', 'administration', 'administrative', 'adminster', 'admirable', 'admirably', 'admiral', 'admiration', 'admire', 'admired', 'admirehow', 'admirers', 'admires', 'admiring', 'admission', 'admissions', 'admit', 'admits', 'admitt', 'admittance', 'admitted', 'admittedly', 'admitting', 'admmitted', 'admonishments', 'adn', 'ado', 'adobe', 'adobo', 'adolescence', 'adolescent', 'adolf', 'adolph', 'adominal', 'adonis', 'adopt', 'adopted', 'adoptee', 'adoptees', 'adopting', 'adoption', 'adoptions', 'adorable', 'adore', 'adored', 'adores', 'adoring', 'adorn', 'adorned', 'adorning', 'adornment', 'adornments', 'adorns', 'adovada', 'adrenal', 'adrenaline', 'adrenals', 'adress', 'adrian', 'adrift', 'ads', 'adt', 'adujstment', 'adult', 'adulterate', 'adulterated', 'adulteration', 'adulthood', 'adults', 'adulturated', 'advance', 'advanced', 'advancement', 'advancements', 'advancing', 'advantage', 'advantageously', 'advantages', 'advantix', 'advenst', 'advent', 'adventist', 'adventure', 'adventuremonger', 'adventurers', 'adventures', 'adventurous', 'adventurously', 'adverized', 'adverse', 'adversely', 'adversion', 'advert', 'advertise', 'advertised', 'advertisement', 'advertisements', 'advertises', 'advertising', 'advertisment', 'advertize', 'advertized', 'advertizement', 'advertizements', 'advertizing', 'adverts', 'advice', 'advid', 'advils', 'advion', 'advisable', 'advise', 'advised', 'advises', 'advising', 'advisor', 'advocacy', 'advocado', 'advocate', 'advocates', 'advocating', 'adzuki', 'adzukis', 'ae', 'aegeans', 'aer', 'aerate', 'aerated', 'aeration', 'aerator', 'aero', 'aerobars', 'aerobic', 'aerobics', 'aerocrap', 'aerodynamic', 'aerogarden', 'aerogardener', 'aerogardens', 'aerogrow', 'aerogrows', 'aerolatte', 'aeropress', 'aerosolized', 'aesome', 'aesthetic', 'aesthetically', 'aesthetics', 'aesthics', 'aestivum', 'af', 'afar', 'afater', 'afb', 'afer', 'afew', 'affable', 'affaid', 'affair', 'affairs', 'affecionado', 'affect', 'affected', 'affectedby', 'affectianco', 'affecting', 'affection', 'affectionados', 'affectionate', 'affects', 'afficiado', 'afficianado', 'afficianados', 'afficiando', 'afficionada', 'afficionado', 'afficionados', 'afficionda', 'affies', 'affiliate', 'affiliated', 'affiliates', 'affiliation', 'affinity', 'affinois', 'affix', 'affixed', 'afflicted', 'affliction', 'afflicts', 'affluent', 'afforable', 'afford', 'affordability', 'affordable', 'affordablility', 'affordably', 'afforded', 'affordible', 'affording', 'affords', 'affortable', 'affraid', 'afghan', 'afghanistan', 'afi', 'aficianado', 'aficianados', 'aficienado', 'aficionado', 'aficionadoes', 'aficionados', 'afloat', 'aforementioned', 'aforesaid', 'afr', 'afraid', 'afriad', 'africa', 'africabushmen', 'african', 'africanredteacompany', 'africans', 'africian', 'afrikaaners', 'afrikaans', 'afrikaner', 'aftera', 'afteraffects', 'afterall', 'afterbite', 'afterburn', 'afterdinner', 'aftereffects', 'afterlastly', 'afterlife', 'aftermath', 'afternoon', 'afternoons', 'afternoony', 'afternooon', 'afternotes', 'afteroon', 'afterschool', 'aftershave', 'aftertase', 'aftertast', 'aftertaste', 'aftertastes', 'afterthought', 'aftertones', 'afterward', 'afterwards', 'afterwardscons', 'afterwords', 'aftificial', 'ag', 'againblack', 'againgst', 'againso', 'againvery', 'againwatermelon', 'agan', 'agao', 'agar', 'agava', 'agave', 'agaves', 'age', 'aged', 'ageing', 'ageless', 'agencies', 'agency', 'agenda', 'agendas', 'agent', 'agents', 'ager', 'ages', 'agey', 'aggravate', 'aggravated', 'aggravates', 'aggravation', 'aggree', 'aggresively', 'aggression', 'aggressive', 'aggressively', 'aggrevation', 'aghast', 'agian', 'agility', 'agin', 'aging', 'agitate', 'agitated', 'agitation', 'agni', 'ago', 'agog', 'agonize', 'agonizing', 'agony', 'agood', 'agotamiento', 'agra', 'agras', 'agreat', 'agree', 'agreeable', 'agreed', 'agreeing', 'agreement', 'agrees', 'agressive', 'agri', 'agribusiness', 'agricultural', 'agriculture', 'agrumato', 'agrumento', 'agua', 'aguave', 'aguilar', 'ah', 'aha', 'ahbnyc', 'ahead', 'ahealthy', 'ahem', 'ahh', 'ahhh', 'ahhhh', 'ahhhhh', 'ahhhhhh', 'ahhhhhhh', 'ahhhhhhhh', 'ahhhhhhhhhhh', 'ahhhhhhhhhhhhhh', 'ahhhs', 'ahhs', 'ahi', 'ahing', 'ahint', 'ahmad', 'ahmed', 'ahnd', 'ahold', 'aholic', 'aholics', 'ahoy', 'ahuge', 'ahve', 'ai', 'aid', 'aide', 'aidell', 'aidells', 'aides', 'aiding', 'aids', 'aig', 'aikido', 'ail', 'ailed', 'ailing', 'ailly', 'ailment', 'ailments', 'ails', 'aim', 'aimed', 'aiming', 'aint', 'aioli', 'air', 'airbag', 'airbags', 'airborn', 'airborne', 'aired', 'airedale', 'aires', 'airfare', 'airforce', 'airfreight', 'airhead', 'airheads', 'airiness', 'airline', 'airlines', 'airplane', 'airplanes', 'airpop', 'airport', 'airports', 'airtight', 'airtightly', 'airtran', 'airwaves', 'airways', 'airy', 'aisian', 'aisle', 'aisles', 'aj', 'ajanuary', 'aji', 'ajika', 'ajinomoto', 'ajiwaeru', 'ajo', 'ajvar', 'ajwright', 'ak', 'aka', 'akai', 'akbb', 'akc', 'akes', 'akhdar', 'akin', 'akita', 'akmak', 'akroger', 'akron', 'aktins', 'akways', 'al', 'ala', 'alabama', 'alabamian', 'alaca', 'alacrity', 'alaea', 'alai', 'alameda', 'alamosa', 'alarm', 'alarmed', 'alarming', 'alarmingly', 'alas', 'alaska', 'alaskan', 'alaskanblonde', 'alasken', 'alba', 'albacore', 'albanese', 'albania', 'albanians', 'albano', 'albanses', 'albany', 'albas', 'albeit', 'alberquerque', 'alberta', 'albertson', 'albertsons', 'albicans', 'albiet', 'albondigas', 'album', 'albumen', 'albums', 'albuquerque', 'alcat', 'alchemy', 'alcho', 'alchohol', 'alchol', 'alcholic', 'alcohol', 'alcoholic', 'alcohols', 'alder', 'alderwood', 'aldi', 'aldrich', 'aldridge', 'ale', 'alec', 'aleightenment', 'aleister', 'alena', 'alene', 'alergic', 'alergies', 'alergy', 'alernative', 'alert', 'alerted', 'alertnative', 'alertness', 'alerts', 'ales', 'alessi', 'aleve', 'alex', 'alexander', 'alexandre', 'alexandria', 'alexis', 'alf', 'alfa', 'alfalfa', 'alford', 'alfred', 'alfreda', 'alfredo', 'algae', 'algaecide', 'algave', 'algebra', 'algerian', 'algin', 'alginate', 'algorithms', 'ali', 'alia', 'alias', 'alice', 'alicia', 'alien', 'alieve', 'align', 'aligned', 'aliitle', 'alike', 'alimentari', 'alimentary', 'alimentum', 'alimony', 'alison', 'alittle', 'alive', 'alka', 'alkali', 'alkaline', 'alkalinic', 'alkalinity', 'alkalinize', 'alkalize', 'alkalizing', 'alkaloid', 'alkaloids', 'alkylamides', 'allay', 'allays', 'allday', 'alldirect', 'allegation', 'alleged', 'allegedly', 'allegen', 'allegic', 'allegies', 'allegorical', 'allegro', 'allegy', 'allen', 'allergen', 'allergenic', 'allergens', 'allergic', 'allergies', 'allergiesthat', 'allergist', 'allergy', 'alleries', 'allert', 'allery', 'allessi', 'alleviate', 'alleviated', 'alleviates', 'alleviating', 'allexceptthe', 'alley', 'allhis', 'alli', 'alliance', 'alliances', 'allie', 'allieviate', 'alligator', 'alligators', 'allimentum', 'allison', 'allister', 'allk', 'allll', 'alllll', 'allmy', 'allnatural', 'allnighter', 'allocate', 'allocation', 'allot', 'alloted', 'allotment', 'allotted', 'allover', 'allow', 'allowable', 'allowance', 'allowances', 'allowed', 'allowing', 'allows', 'allready', 'allrecipes', 'allright', 'alls', 'allsorts', 'allspice', 'allstar', 'allstarhealth', 'alltea', 'allthough', 'alltime', 'alltogether', 'alluded', 'alludes', 'alluminum', 'allure', 'alluring', 'allusion', 'allusions', 'allways', 'ally', 'almighty', 'almolds', 'almond', 'almondine', 'almonds', 'almondspaprika', 'almondy', 'almost', 'almostany', 'almsot', 'alnatura', 'alo', 'aloe', 'aloha', 'alohaisland', 'alohaislandcoffee', 'alollipop', 'alomonds', 'alomost', 'alomst', 'alone', 'along', 'alongside', 'aloo', 'aloof', 'alook', 'alos', 'alot', 'aloud', 'alow', 'alpen', 'alpenmilch', 'alpha', 'alphabet', 'alphabetically', 'alphabets', 'alpine', 'alpo', 'alps', 'alpsnack', 'alpsnacks', 'already', 'alreday', 'alredy', 'alri', 'alright', 'als', 'also', 'alsogetting', 'alsolimits', 'alsosalt', 'alsoterrible', 'alt', 'alta', 'altar', 'altenative', 'alter', 'alteranative', 'alterantive', 'alterate', 'alteration', 'alterations', 'alterative', 'altered', 'altering', 'alternaives', 'alternatative', 'alternate', 'alternately', 'alternates', 'alternating', 'alternation', 'alternative', 'alternatively', 'alternatives', 'alternativwe', 'alternatve', 'alternitive', 'alternitives', 'alterra', 'alters', 'altertness', 'altheimers', 'altho', 'althog', 'althogh', 'althos', 'although', 'althoughshipping', 'althought', 'althugh', 'altinbas', 'altiods', 'altioids', 'altitude', 'altitudes', 'alto', 'altogether', 'altoid', 'altoids', 'alton', 'altria', 'altruistic', 'altura', 'alturnitive', 'alum', 'aluminium', 'aluminized', 'aluminum', 'alumnus', 'alune', 'alvin', 'alvita', 'alwadi', 'alwasy', 'alwasydirect', 'alway', 'always', 'alwayscarries', 'alwaysdirect', 'alwaysfind', 'alwaysorder', 'alwyas', 'alyson', 'alzheimer', 'alzheimers', 'ama', 'amac', 'amacaroni', 'amade', 'amadei', 'amadeus', 'amalfi', 'amalgamation', 'amalgem', 'amalted', 'amamazed', 'amamzing', 'aman', 'amanda', 'amaranth', 'amaranthtaste', 'amarena', 'amaretti', 'amarettini', 'amaretto', 'amarettobut', 'amaron', 'amaryllis', 'amas', 'amason', 'amateur', 'amateurs', 'amatriciana', 'amatuer', 'amaxon', 'amazamazing', 'amazaon', 'amaze', 'amazed', 'amazement', 'amazes', 'amaziing', 'amazin', 'amazing', 'amazinggg', 'amazingly', 'amazingness', 'amazment', 'amazom', 'amazon', 'amazonbox', 'amazonclearly', 'amazone', 'amazong', 'amazonians', 'amazonoffers', 'amazonor', 'amazons', 'amazoo', 'ambassador', 'amber', 'ambiance', 'ambien', 'ambient', 'ambigious', 'ambiguous', 'ambitions', 'ambitious', 'ambivalent', 'ambootia', 'ambrosia', 'ambrosial', 'ambrosoli', 'ambulance', 'amco', 'amd', 'amde', 'ameal', 'ameat', 'ameatballs', 'ameatier', 'ameature', 'ameaty', 'ameda', 'amedei', 'amedicines', 'ameds', 'ameerican', 'ameliorate', 'ameliorated', 'amelt', 'ameltability', 'amen', 'amenable', 'amend', 'amended', 'amending', 'amendments', 'amends', 'amentally', 'amerasian', 'amercian', 'america', 'american', 'americana', 'americanb', 'americanize', 'americanized', 'americanizing', 'americano', 'americanos', 'americans', 'americansare', 'americas', 'americolor', 'americolors', 'ametallic', 'amhuff', 'amid', 'amide', 'amidst', 'amilk', 'amilker', 'amilkshake', 'amill', 'amincemeat', 'amini', 'amino', 'aminos', 'amint', 'amiracle', 'amish', 'amishpopcorn', 'amiss', 'amix', 'amixer', 'amixers', 'amizon', 'amjad', 'amkes', 'amla', 'ammark', 'ammazon', 'amminos', 'ammish', 'ammonia', 'ammoniate', 'ammonium', 'ammount', 'ammounts', 'ammunition', 'amnimals', 'amoazon', 'amolinillo', 'amom', 'amoma', 'amon', 'amond', 'among', 'amongst', 'amonia', 'amont', 'amoount', 'amore', 'amores', 'amoreskraft', 'amorning', 'amorous', 'amorphous', 'amortize', 'amos', 'amoth', 'amother', 'amound', 'amoung', 'amoungst', 'amount', 'amounted', 'amounts', 'amour', 'amout', 'amouts', 'amozon', 'amp', 'ampa', 'amped', 'amphetamine', 'ample', 'ampliamente', 'amplified', 'amplifies', 'amplify', 'amply', 'ampong', 'amputated', 'amputation', 'amri', 'amsterdam', 'amt', 'amuck', 'amukhwas', 'amuse', 'amused', 'amusement', 'amusements', 'amusing', 'amusingly', 'amust', 'amy', 'amylose', 'amyone', 'amyr', 'amys', 'amystery', 'amytime', 'amz', 'amzaing', 'amzaon', 'amzazon', 'amzing', 'amzn', 'amzon', 'ana', 'anadama', 'anaesthetic', 'anaheim', 'anair', 'anal', 'analize', 'analog', 'analoges', 'analogs', 'analogues', 'analogy', 'analyse', 'analysing', 'analysis', 'analysisserving', 'analyze', 'analyzed', 'analyzing', 'anamorphic', 'anand', 'anaphylactic', 'anaphylax', 'anasazi', 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'xlear', 'xlge', 'xlichew', 'xlitol', 'xlr', 'xlyitol', 'xlylitol', 'xlyo', 'xmas', 'xo', 'xocolatl', 'xox', 'xoxo', 'xoxox', 'xplode', 'xplosion', 'xplosive', 'xrays', 'xtra', 'xtreme', 'xv', 'xvi', 'xxx', 'xxxbucks', 'xxxtra', 'xxxxtra', 'xy', 'xylan', 'xylichew', 'xylitol', 'xylitoli', 'xylitolwhatever', 'xylo', 'xyloburst', 'xylosweet', 'xyltol', 'ya', 'yaa', 'yaawwwwnn', 'yaayy', 'yachts', 'yada', 'yadayadayada', 'yadda', 'yaer', 'yagart', 'yah', 'yahoo', 'yaki', 'yakimando', 'yakisoba', 'yakitori', 'yam', 'yama', 'yamamoto', 'yamamotoyama', 'yamasa', 'yamate', 'yamatoyama', 'yammy', 'yams', 'yan', 'yancyd', 'yang', 'yangtse', 'yangtze', 'yank', 'yanked', 'yankee', 'yankees', 'yanks', 'yanky', 'yaohan', 'yap', 'yappy', 'yaps', 'yard', 'yardin', 'yards', 'yardstick', 'yare', 'yarn', 'yarrow', 'yars', 'yawn', 'yawned', 'yawning', 'yay', 'yaya', 'ybrki', 'ybut', 'yc', 'ycca', 'ye', 'yea', 'yeaaaaah', 'yeah', 'yealds', 'yeap', 'year', 'yearly', 'yearn', 'yearned', 'yearning', 'yearnings', 'yearout', 'years', 'yearsago', 'yearsback', 'yeasr', 'yeast', 'yeasted', 'yeastiness', 'yeasts', 'yeasty', 'yeat', 'yeay', 'yech', 'yeck', 'yeee', 'yeeeeeeeeeeeeeeeeeeehhhhhhhhhhowwwwwwwwwwww', 'yeeeooowww', 'yeees', 'yeesh', 'yeh', 'yehuda', 'yeild', 'yeilded', 'yeilding', 'yell', 'yelled', 'yelling', 'yello', 'yellow', 'yellowcake', 'yellowed', 'yellower', 'yellowfin', 'yellowing', 'yellowish', 'yellowjacket', 'yellowrose', 'yellows', 'yellowspotted', 'yellowtail', 'yellowy', 'yells', 'yelp', 'yelped', 'yemini', 'yen', 'yeni', 'yenshaw', 'yeowww', 'yep', 'yer', 'yerba', 'yerbamate', 'yerbe', 'yers', 'yes', 'yeshua', 'yesrs', 'yessiree', 'yesss', 'yesssss', 'yessssss', 'yesterday', 'yesterdays', 'yesteryear', 'yet', 'yetclif', 'yhis', 'yi', 'yick', 'yicky', 'yield', 'yielded', 'yielding', 'yields', 'yikes', 'yimothy', 'yin', 'yip', 'yipee', 'yippee', 'yipper', 'yippie', 'yippy', 'yipyaps', 'yixing', 'ymakeda', 'ymca', 'ymmm', 'ymmv', 'ymmw', 'ymmy', 'yo', 'yobaby', 'yocheese', 'yoda', 'yodel', 'yodels', 'yoed', 'yoga', 'yogart', 'yogert', 'yoghurt', 'yogi', 'yogic', 'yogitea', 'yogourmet', 'yogurettes', 'yogurt', 'yogurts', 'yogurty', 'yohay', 'yoke', 'yokes', 'yoki', 'yokosuka', 'yoks', 'yokshire', 'yolk', 'yolkless', 'yolks', 'yolky', 'yom', 'yommy', 'yoo', 'yoohoo', 'yooooouuuuuuu', 'yoooorrk', 'yoplait', 'yopur', 'yor', 'yore', 'york', 'yorker', 'yorkers', 'yorkie', 'yorkiepoo', 'yorkies', 'yorkipoo', 'yorkshie', 'yorkshire', 'yorkshiremonm', 'yorksire', 'yorshire', 'yoru', 'yosemite', 'yoshida', 'youalready', 'youamazon', 'youcan', 'youdiane', 'yougart', 'yougert', 'yougonna', 'yougrt', 'yougurt', 'youkan', 'youletta', 'youlinda', 'youll', 'yound', 'young', 'younger', 'youngest', 'youngman', 'youngster', 'youngsters', 'youngstown', 'yourdaily', 'yourdigestive', 'youre', 'yourfavorite', 'yourfoods', 'yourn', 'youronly', 'yourselfer', 'yous', 'youse', 'youself', 'youth', 'youthful', 'youtom', 'youtube', 'youtubegarden', 'youur', 'youv', 'youve', 'youwill', 'yow', 'yowl', 'yowled', 'yoyo', 'ypsilanti', 'yr', 'yrs', 'ys', 'ytwelve', 'yu', 'yuban', 'yucatan', 'yucateco', 'yucca', 'yucchhh', 'yucchhhh', 'yuch', 'yuchk', 'yuck', 'yuckier', 'yuckiest', 'yuckiness', 'yuckkk', 'yuckkkkkkkkkkkkkkkk', 'yucko', 'yucks', 'yucky', 'yugort', 'yugoslavia', 'yuh', 'yujmmy', 'yuk', 'yukkin', 'yukon', 'yulong', 'yum', 'yuma', 'yumbay', 'yumbo', 'yumi', 'yumm', 'yumma', 'yummas', 'yummee', 'yummeeee', 'yummers', 'yummi', 'yummie', 'yummier', 'yummies', 'yummiest', 'yummilicious', 'yummily', 'yumminess', 'yummm', 'yummmm', 'yummmmiiiii', 'yummmmm', 'yummmmmeeeeee', 'yummmmmm', 'yummmmmmm', 'yummmmmmmm', 'yummmmmmmmm', 'yummmmmmmmmm', 'yummmmmmmmmmm', 'yummmmmmmmmmmmmmmmm', 'yummmmmmmmmmmmmmmmmmmmmmmmm', 'yummmmmmmmmmmmy', 'yummmmmmmmmmmy', 'yummmmmmmmmmy', 'yummmmmmmmmy', 'yummmmmmmy', 'yummmmmmy', 'yummmmmy', 'yummmmmyyyyyy', 'yummmmy', 'yummmmyyy', 'yummmy', 'yummmyyyy', 'yummo', 'yummy', 'yummydeliciouscolorfulcons', 'yummyearth', 'yummylicious', 'yummynees', 'yummyness', 'yummypat', 'yummyy', 'yumo', 'yums', 'yumscrumarama', 'yumy', 'yumyum', 'yumyumyumyumyum', 'yunah', 'yunann', 'yung', 'yunnan', 'yup', 'yuppie', 'yuppies', 'yuppy', 'yurgh', 'yuricka', 'yuu', 'yuuki', 'yuumy', 'yuuuck', 'yuuummmmm', 'yuuummmmmmmmmy', 'yuuummmmmmyyyyy', 'yuuumy', 'yuuuum', 'yuuuumy', 'yuuuuucccckkkk', 'yuuuuummmmmmm', 'yuuuuummy', 'yuuuuuummy', 'yuuuuuuuuuum', 'yuzu', 'yves', 'yvonne', 'yyou', 'zaar', 'zaba', 'zabuca', 'zach', 'zachte', 'zack', 'zacky', 'zactly', 'zag', 'zagnut', 'zags', 'zaloga', 'zambezi', 'zamori', 'zamouri', 'zango', 'zantac', 'zante', 'zany', 'zap', 'zapp', 'zapped', 'zapper', 'zapping', 'zaps', 'zarafina', 'zaraphina', 'zat', 'zatairain', 'zatarain', 'zatarains', 'zatarian', 'zatarin', 'zatarins', 'zawadi', 'zazz', 'zbar', 'zbars', 'zd', 'zea', 'zeal', 'zealand', 'zeb', 'zebra', 'zede', 'zehr', 'zelben', 'zellweger', 'zellwood', 'zen', 'zena', 'zenith', 'zensoy', 'zeolite', 'zeppelin', 'zerg', 'zermatt', 'zero', 'zeros', 'zest', 'zested', 'zestful', 'zestiness', 'zestnon', 'zesty', 'zeus', 'zevia', 'zhean', 'zhejiang', 'zhen', 'zhena', 'zhenathis', 'zhenazhena', 'zhu', 'zicam', 'zico', 'ziconot', 'zig', 'zigelhofer', 'ziggie', 'ziggies', 'ziggy', 'zigzag', 'zilch', 'zilla', 'zillion', 'zimbabwe', 'zimmern', 'zimtsterne', 'zinc', 'zinfandel', 'zing', 'zinged', 'zinger', 'zingerman', 'zingers', 'zingiber', 'zinging', 'zingy', 'ziol', 'zion', 'zip', 'zipfizz', 'ziploc', 'ziplock', 'ziplocked', 'ziplocks', 'ziplocs', 'zipp', 'zipped', 'zipper', 'zippered', 'zippers', 'zippfizz', 'zippo', 'zippy', 'zit', 'zita', 'ziti', 'zito', 'ziwi', 'ziwipeak', 'ziyad', 'zodiac', 'zoe', 'zoey', 'zoj', 'zojirushi', 'zojishuri', 'zola', 'zoloft', 'zombie', 'zomg', 'zon', 'zone', 'zoo', 'zoom', 'zoomies', 'zoster', 'zots', 'zotz', 'zotzs', 'zour', 'zours', 'zout', 'zowie', 'zreport', 'zuc', 'zucchini', 'zuccini', 'zuchini', 'zuchinni', 'zuke', 'zukes', 'zulka', 'zumis', 'zummy', 'zupreem', 'zwieback', 'zwiebeck', 'zydeco', 'zz', 'zzzzz', 'zzzzzz', 'zzzzzzzz', 'zzzzzzzzzz']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "\n",
    "count_vect = CountVectorizer()\n",
    "X_train_vect = count_vect.fit_transform(X_train)\n",
    "X_test_vect = count_vect.transform(X_test)\n",
    "X_val_vect = count_vect.transform(X_val)\n",
    "feature_names = count_vect.get_feature_names()\n",
    "# BoW_dict = {'X_train_vect':X_train_vect, 'X_test_vect': X_test_vect, 'X_val_vect': X_val_vect}\n",
    "print(X_train_vect.shape)\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 47535)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_vect.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364171"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final['lengthOfReview'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import hstack\n",
    "# len_review = final['lengthOfReview'].to_sparse()\n",
    "concat_data = hstack((X_train_vect,np.array(final['lengthOfReview'][0:60000])[:,None]))\n",
    "concat_data_val = hstack((X_val_vect,np.array(final['lengthOfReview'][60000:80000])[:,None]))\n",
    "concat_data_test = hstack((X_test_vect,np.array(final['lengthOfReview'][80000:100000])[:,None]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 47536)\n",
      "(20000, 47536)\n",
      "(20000, 47536)\n"
     ]
    }
   ],
   "source": [
    "print(concat_data.shape)\n",
    "print(concat_data_val.shape)\n",
    "print(concat_data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 30732.0,
     "status": "ok",
     "timestamp": 1.550769660849E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "kPdrDSLxzC56",
    "outputId": "5a0f5859-0403-495d-fbb0-60916f987388"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47535\n"
     ]
    }
   ],
   "source": [
    "print(len(feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 47536)\n"
     ]
    }
   ],
   "source": [
    "BoW_dict = {'X_train_vect':concat_data, 'X_test_vect': concat_data_test, 'X_val_vect': concat_data_val}\n",
    "print(BoW_dict['X_train_vect'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": false,
    "id": "fT1u1ikbTTbe"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('BoW.pkl', 'wb') as handle:\n",
    "    pickle.dump(BoW_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WZEY4oJlUpgW"
   },
   "source": [
    "## TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 37237.0,
     "status": "ok",
     "timestamp": 1.550769675618E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "1P0H-O8BTbYA",
    "outputId": "a8d5b056-895a-488d-c5c3-c73f6326bdd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the shape of out text TFIDF vectorizer  (60000, 35873)\n",
      "the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\n",
      "the number of unique words including both unigrams and bigrams  35873\n"
     ]
    }
   ],
   "source": [
    "tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\n",
    "train_tf_idf = tf_idf_vect.fit_transform(X_train)\n",
    "cv_tf_idf = tf_idf_vect.transform(X_val)\n",
    "test_tf_idf = tf_idf_vect.transform(X_test)\n",
    "\n",
    "print(\"the shape of out text TFIDF vectorizer \",train_tf_idf.get_shape())\n",
    "print(\"the type of count vectorizer \",type(train_tf_idf))\n",
    "print(\"the number of unique words including both unigrams and bigrams \", train_tf_idf.get_shape()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfidf_concat_data_train = hstack((train_tf_idf,np.array(final['lengthOfReview'][0:60000])[:,None]))\n",
    "tfidf_concat_data_val = hstack((cv_tf_idf,np.array(final['lengthOfReview'][60000:80000])[:,None]))\n",
    "tfidf_concat_data_test = hstack((test_tf_idf,np.array(final['lengthOfReview'][80000:100000])[:,None]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "_YVXgsYCUs_h"
   },
   "outputs": [],
   "source": [
    "tf_idf_dict = {'train_tf_idf': tfidf_concat_data_train, 'cv_tf_idf': tfidf_concat_data_val, 'test_tf_idf': tfidf_concat_data_test}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "oOJKYF-3Uw6Y"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('tf_idf.pkl', 'wb') as handle:\n",
    "    pickle.dump(tf_idf_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t4Wjl_tdVEW4"
   },
   "source": [
    "## Naive Bayes on BoW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": false,
    "id": "L5aJdacNUy_v"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# with open(r\"/content/gdrive/My Drive/Colab Notebooks/Assignment 4/BoW.pkl\", \"rb\") as input_file:\n",
    "with open(r\"BoW.pkl\", \"rb\") as input_file:\n",
    "    BoW_dict = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/26976362/how-to-get-most-informative-features-for-scikit-learn-classifier-for-different-c\n",
    "neg_features_labels = []\n",
    "neg_features_coeff = []\n",
    "neg_features_feat = []\n",
    "\n",
    "pos_features_labels = []\n",
    "pos_features_coeff = []\n",
    "pos_features_feat = []\n",
    "def most_informative_feature_for_binary_classification(vectorizer, classifier, n=10):\n",
    "    class_labels = classifier.classes_\n",
    "    feature_names = vectorizer.get_feature_names()\n",
    "    topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]\n",
    "    topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]\n",
    "\n",
    "    for coef, feat in topn_class1:\n",
    "        neg_features_labels.append(class_labels[0])\n",
    "        neg_features_coeff.append(coef)\n",
    "        neg_features_feat.append(feat)\n",
    "    \n",
    "    for coef, feat in reversed(topn_class2):\n",
    "        pos_features_labels.append(class_labels[1])\n",
    "        pos_features_coeff.append(coef)\n",
    "        pos_features_feat.append(feat)\n",
    "        \n",
    "    neg_df = pd.DataFrame({'Labels': neg_features_labels,'Coeff':neg_features_coeff ,'Negative features':neg_features_feat})\n",
    "    pos_df = pd.DataFrame({'Labels': pos_features_labels,'Coeff':pos_features_coeff ,'Negative features':pos_features_feat})\n",
    "    print(\"Top 10 featues for negative class \\n\", neg_df)\n",
    "    print(\"Top 10 featues for positive class \\n\", pos_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 32910.0,
     "status": "ok",
     "timestamp": 1.55076967994E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "AeeuXzD5VLRC",
    "outputId": "f1db152b-debb-4980-ee10-fb6b9dcbfe11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 featues for negative class \n",
      "        Coeff  Labels Negative features\n",
      "0 -10.770155       0            aachen\n",
      "1 -10.770155       0             aafco\n",
      "2 -10.770155       0            ablaze\n",
      "3 -10.770155       0        abominable\n",
      "4 -10.770155       0         abortions\n",
      "5 -10.770155       0       abreviating\n",
      "6 -10.770155       0       abstraction\n",
      "7 -10.770155       0            abuses\n",
      "8 -10.770155       0              acau\n",
      "9 -10.770155       0        accelerade\n",
      "Top 10 featues for positive class \n",
      "        Coeff  Labels Negative features\n",
      "0 -10.392870       1               not\n",
      "1 -10.541618       1             great\n",
      "2 -10.563572       1              good\n",
      "3 -10.582523       1              like\n",
      "4 -10.590362       1               tea\n",
      "5 -10.628716       1             taste\n",
      "6 -10.633982       1               one\n",
      "7 -10.641234       1           product\n",
      "8 -10.644660       1              love\n",
      "9 -10.644730       1              best\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from tqdm import tqdm\n",
    "\n",
    "bow_auc_train = []\n",
    "bow_auc_cv = []\n",
    "bow_auc_test = []\n",
    "\n",
    "for alpha_value in [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]:\n",
    "    nb = MultinomialNB(alpha=alpha_value)\n",
    "    nb.fit(BoW_dict['X_train_vect'],Y_train)\n",
    "    \n",
    "    train_proba = nb.score(BoW_dict['X_train_vect'], Y_train)\n",
    "    bow_auc_train.append(train_proba)\n",
    "    cv_proba = nb.score(BoW_dict['X_val_vect'], Y_val)\n",
    "    bow_auc_cv.append(cv_proba)\n",
    "\n",
    "f = most_informative_feature_for_binary_classification(count_vect, nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 32210.0,
     "status": "ok",
     "timestamp": 1.550769679941E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "UXFcZRzOWvts",
    "outputId": "41d9a764-daa1-40bd-a16a-aeaf580f4eda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.1: 0.94884999999999997, 1: 0.92926666666666669, 100: 0.8858166666666667, 1000: 0.8858166666666667, 10000: 0.8858166666666667, 1e-05: 0.96484999999999999, 0.0001: 0.96388333333333331, 10: 0.88816666666666666, 100000: 0.8858166666666667, 0.01: 0.95794999999999997, 0.001: 0.96193333333333331}\n",
      "{0.1: 0.91659999999999997, 1: 0.90749999999999997, 100: 0.86280000000000001, 1000: 0.86280000000000001, 10000: 0.86280000000000001, 1e-05: 0.90534999999999999, 0.0001: 0.90834999999999999, 10: 0.86485000000000001, 100000: 0.86280000000000001, 0.01: 0.91574999999999995, 0.001: 0.91300000000000003}\n"
     ]
    }
   ],
   "source": [
    "alpha_list = [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]\n",
    "train_alpha_dict = dict(zip(alpha_list, bow_auc_train))\n",
    "val_alpha_dict = dict(zip(alpha_list, bow_auc_cv))\n",
    "print(train_alpha_dict)\n",
    "print(val_alpha_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 31608.0,
     "status": "ok",
     "timestamp": 1.550769679942E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "ewylYXj-pXZm",
    "outputId": "e3196ea2-d18b-48c7-d45f-330a4d18b040"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_best_alpha = max(val_alpha_dict, key=val_alpha_dict.get)\n",
    "bow_best_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 31040.0,
     "status": "ok",
     "timestamp": 1.550769679943E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "zMc-NRlLuxVh",
    "outputId": "c66f0b51-a671-41d1-912a-ad572f32a2a4"
   },
   "outputs": [
    {
     "data": {
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AAADyBSEJAADIUIfaHfRcxHN6dvWz+u+v/3W6HAAAgDxH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\nopcqPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4134358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "plt.figure(figsize=(13, 10))\n",
    "neighbors_settings = [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]\n",
    "plt.plot(neighbors_settings, bow_auc_train, label=\"training accuracy\", color='green')\n",
    "plt.plot(neighbors_settings, bow_auc_cv, label=\"validation accuracy\", color='yellow')\n",
    "# plt.plot(neighbors_settings, auc_test, label=\"test accuracy\", color='red')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.xscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 31673.0,
     "status": "ok",
     "timestamp": 1.550769681168E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "zCeFVcoT1c2H",
    "outputId": "7f817124-9403-4736-9a66-6ff368e97aca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[  9.99996662e-01   4.36918808e-07   1.00000000e+00 ...,   9.94959440e-01\n",
      "   9.99990491e-01   4.60863281e-02]\n"
     ]
    }
   ],
   "source": [
    "nb = MultinomialNB(alpha=bow_best_alpha)\n",
    "nb.fit(BoW_dict['X_train_vect'],Y_train)\n",
    "test_predict_bow= nb.predict_proba(BoW_dict['X_test_vect'])\n",
    "train_predict_bow = nb.predict_proba(BoW_dict['X_train_vect'])\n",
    "\n",
    "bow_test_conf = nb.predict(BoW_dict['X_test_vect'])\n",
    "bow_train_conf = nb.predict(BoW_dict['X_train_vect'])\n",
    "\n",
    "print(type(test_predict_bow))\n",
    "print(test_predict_bow[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.927312069814\n",
      "0.973071057077\n"
     ]
    }
   ],
   "source": [
    "fpr_train, tpr_train, _ = roc_curve(Y_train, train_predict_bow[:, 1])\n",
    "fpr_test, tpr_test, _ = roc_curve(Y_test, test_predict_bow[:, 1])\n",
    "bow_test_auc = auc(fpr_test, tpr_test)\n",
    "bow_train_auc = auc(fpr_train, tpr_train)\n",
    "print(bow_test_auc)\n",
    "print(bow_train_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 30704.0,
     "status": "ok",
     "timestamp": 1.550769681172E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "EjMP7fzj-Zb5",
    "outputId": "d36add8b-3248-473f-b074-194deda03743"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JIEGprhSB6kC42vPaDt6O3gh1CxXrRsilcugMOng6eEIlVyHMIwwquQqOCkcoZUo42Tnd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\nMMlwqPbOZHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3daf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "plt.figure(figsize=(13, 10))\n",
    "plt.plot([0,1], [0,1], color='black', lw=2, linestyle='--')\n",
    "plt.plot(fpr_test,tpr_test,label=\"Test, auc=\"+str(bow_test_auc), color = 'red')\n",
    "plt.plot(fpr_train,tpr_train,label=\"Train, auc=\"+str(bow_train_auc), color = 'green')\n",
    "\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 30249.0,
     "status": "ok",
     "timestamp": 1.550769681174E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "jXopZxwVqeRr",
    "outputId": "915372f3-4278-43b1-a3f3-f2d0ed407226"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1878   798]\n",
      " [  900 16424]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.70      0.69      2676\n",
      "          1       0.95      0.95      0.95     17324\n",
      "\n",
      "avg / total       0.92      0.92      0.92     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "bow_train_conf_matrix = confusion_matrix(Y_train, bow_train_conf)\n",
    "bow_test_conf_matrix = confusion_matrix(Y_test, bow_test_conf)\n",
    "class_report = classification_report(Y_test, bow_test_conf)\n",
    "print(bow_test_conf_matrix)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 29784.0,
     "status": "ok",
     "timestamp": 1.550769681177E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "J8bWWR28rC4u",
    "outputId": "53908bb8-6418-4016-e42c-4e0ff7526fc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0xc62814a8>, <matplotlib.text.Text at 0xbb2f4e10>]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S8uslnVXR4MzMrPmoqyv+qDFVkyQAlwPrA78EpueUvwDsV4mAzMysGXKSkFk1dTfsDuwX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac76d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(bow_test_conf_matrix, annot=True, ax = ax, fmt='g')\n",
    "\n",
    "ax.set_ylabel('Predicted labels')\n",
    "ax.set_xlabel('True labels')\n",
    "ax.set_title('Test Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(['negative', 'positive']) \n",
    "ax.yaxis.set_ticklabels(['negative', 'positive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0xbbc1a3c8>, <matplotlib.text.Text at 0x1084a65f8>]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P4DTSvbfNzMzqzC0JmVVSS8KeQKFlOG8Hdk+fDwLWbLSIzMzMqlgltSTMADYl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3c61dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(bow_train_conf_matrix, annot=True, ax = ax, fmt='g')\n",
    "\n",
    "ax.set_ylabel('Predicted labels')\n",
    "ax.set_xlabel('True labels')\n",
    "ax.set_title('Train Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(['negative', 'positive']) \n",
    "ax.yaxis.set_ticklabels(['negative', 'positive'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jF2m_dZ4r0cu"
   },
   "source": [
    "## Naive Bayes on TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": false,
    "id": "d78evIY0rOYK"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# with open(r\"/content/gdrive/My Drive/Colab Notebooks/Assignment 4/tf_idf.pkl\", \"rb\") as input_file:\n",
    "with open(r\"tf_idf.pkl\", \"rb\") as input_file:\n",
    "    tfidf_dict = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 28936.0,
     "status": "ok",
     "timestamp": 1.550769681941E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "38cWhOP8sZzZ",
    "outputId": "3f295fff-f1cb-4caf-868a-883bd629a180"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 featues for negative class \n",
      "        Coeff  Labels  Negative features\n",
      "0 -10.488483       0       cancel order\n",
      "1 -10.488483       0          give zero\n",
      "2 -10.488483       0     lesson learned\n",
      "3 -10.488483       0           no stars\n",
      "4 -10.488483       0         threw rest\n",
      "5 -10.488483       0       worst coffee\n",
      "6 -10.488483       0      worst tasting\n",
      "7 -10.488482       0  false advertising\n",
      "8 -10.488482       0      hopes product\n",
      "9 -10.488481       0         zero stars\n",
      "Top 10 featues for positive class \n",
      "        Coeff  Labels Negative features\n",
      "0 -10.474219       1               not\n",
      "1 -10.475047       1             great\n",
      "2 -10.476671       1               tea\n",
      "3 -10.477038       1              good\n",
      "4 -10.479447       1              like\n",
      "5 -10.479973       1              love\n",
      "6 -10.480086       1              best\n",
      "7 -10.480207       1           product\n",
      "8 -10.480700       1             taste\n",
      "9 -10.480751       1            coffee\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from tqdm import tqdm\n",
    "\n",
    "tfidf_auc_train = []\n",
    "tfidf_auc_cv = []\n",
    "auc_test = []\n",
    "\n",
    "for alpha_value in [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]:\n",
    "    nb_tfidf = MultinomialNB(alpha=alpha_value)\n",
    "    nb_tfidf.fit(tfidf_dict['train_tf_idf'],Y_train)\n",
    "    \n",
    "    train_proba = nb_tfidf.score(tfidf_dict['train_tf_idf'], Y_train)\n",
    "    tfidf_auc_train.append(train_proba)\n",
    "    cv_proba = nb_tfidf.score(tfidf_dict['cv_tf_idf'], Y_val)\n",
    "    tfidf_auc_cv.append(cv_proba)\n",
    "\n",
    "f = most_informative_feature_for_binary_classification(tf_idf_vect, nb_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 28611.0,
     "status": "ok",
     "timestamp": 1.550769681942E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "BjThuoWRsxv5",
    "outputId": "ee4e6656-d23c-4e25-d85e-42857b4b0c90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.1: 0.95265, 1: 0.90616666666666668, 100: 0.8858166666666667, 1000: 0.8858166666666667, 10000: 0.8858166666666667, 1e-05: 0.9609833333333333, 0.0001: 0.96074999999999999, 10: 0.88568333333333338, 100000: 0.8858166666666667, 0.01: 0.95938333333333337, 0.001: 0.96035000000000004}\n",
      "{0.1: 0.91864999999999997, 1: 0.88395000000000001, 100: 0.86280000000000001, 1000: 0.86280000000000001, 10000: 0.86280000000000001, 1e-05: 0.90300000000000002, 0.0001: 0.90549999999999997, 10: 0.86270000000000002, 100000: 0.86280000000000001, 0.01: 0.91479999999999995, 0.001: 0.90995000000000004}\n"
     ]
    }
   ],
   "source": [
    "alpha_list = [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]\n",
    "train_alpha_dict = dict(zip(alpha_list, tfidf_auc_train))\n",
    "val_alpha_dict = dict(zip(alpha_list, tfidf_auc_cv))\n",
    "print(train_alpha_dict)\n",
    "print(val_alpha_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 28259.0,
     "status": "ok",
     "timestamp": 1.550769681945E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "ufnRSwa8tZRw",
    "outputId": "4e567bbf-5f33-4081-de5d-dc8eecc0c672"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_best_alpha = max(val_alpha_dict, key=val_alpha_dict.get)\n",
    "tfidf_best_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27903.0,
     "status": "ok",
     "timestamp": 1.550769681946E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "pP76s0SvtVzJ",
    "outputId": "d9a7bfb8-c710-4049-e057-ad6960329466"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ARUh4ULg27N2gA/EHnC4FAACg0CEkAQCgCOneuLt8XD5a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\ngwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac76d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "plt.figure(figsize=(13, 10))\n",
    "# max_idx = auc_train.index(max(tfidf_auc_train))\n",
    "neighbors_settings = [ 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]\n",
    "plt.plot(neighbors_settings, tfidf_auc_train, label=\"training accuracy\", color='green')\n",
    "plt.plot(neighbors_settings, tfidf_auc_cv, label=\"validation accuracy\", color='yellow')\n",
    "# plt.plot(neighbors_settings, auc_test, label=\"test accuracy\", color='red')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.xscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 28313.0,
     "status": "ok",
     "timestamp": 1.550769682822E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "UX9J5kYytgWB",
    "outputId": "a7aefbf7-0091-4782-94e1-6f3c15eb6990"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00777046,  0.99222954],\n",
       "       [ 0.61995316,  0.38004684],\n",
       "       [ 0.00291888,  0.99708112],\n",
       "       ..., \n",
       "       [ 0.13856742,  0.86143258],\n",
       "       [ 0.00127726,  0.99872274],\n",
       "       [ 0.52000291,  0.47999709]])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb = MultinomialNB(alpha=tfidf_best_alpha)\n",
    "nb.fit(tfidf_dict['train_tf_idf'],Y_train)\n",
    "tfidf_test_predict = nb.predict_proba(tfidf_dict['test_tf_idf'])\n",
    "tfidf_train_predict = nb.predict_proba(tfidf_dict['train_tf_idf'])\n",
    "tfidf_train_conf = nb.predict(tfidf_dict['train_tf_idf'])\n",
    "tfidf_test_conf = nb.predict(tfidf_dict['test_tf_idf'])\n",
    "tfidf_test_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27901.0,
     "status": "ok",
     "timestamp": 1.550769682824E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "UuHS0tY4vRKw",
    "outputId": "96edfa00-e56f-417e-cece-52a1562bba85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "60000\n"
     ]
    }
   ],
   "source": [
    "print(len(tfidf_auc_train))\n",
    "print(len(tfidf_train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27511.0,
     "status": "ok",
     "timestamp": 1.550769682826E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "P7gMK0j3wGPh",
    "outputId": "858e4f18-40c8-4a1f-a75e-0c8cff077bf2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.959417124485\n",
      "0.985620736644\n"
     ]
    }
   ],
   "source": [
    "tfidf_fpr_train, tfidf_tpr_train, tfidf_thresholds_train = roc_curve(Y_train, tfidf_train_predict[:, 1])\n",
    "tfidf_fpr_test, tfidf_tpr_test, tfidf_thresholds_test = roc_curve(Y_test, tfidf_test_predict[:, 1])\n",
    "tfidf_test_auc = auc(tfidf_fpr_test, tfidf_tpr_test)\n",
    "tfidf_train_auc = auc(tfidf_fpr_train, tfidf_tpr_train)\n",
    "print(tfidf_test_auc)\n",
    "print(tfidf_train_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 27057.0,
     "status": "ok",
     "timestamp": 1.550769682829E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "7qv61a1auHtU",
    "outputId": "273b97e6-f81b-44cd-9899-e085b55f746d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zs2c5+sfvFikpKfbgR1RUFL1796ZJkybk5OTwySefMHPmTI4fP84HH3xgPz4q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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc64e4a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "plt.figure(figsize=(13, 10))\n",
    "plt.plot([0,1], [0,1], color='black', lw=2, linestyle='--')\n",
    "plt.plot(tfidf_fpr_test,tfidf_tpr_test,label=\"Test, auc=\"+str(tfidf_test_auc), color = 'red')\n",
    "plt.plot(tfidf_fpr_train,tfidf_tpr_train,label=\"Train, auc=\"+str(tfidf_train_auc), color = 'green')\n",
    "\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 26601.0,
     "status": "ok",
     "timestamp": 1.55076968283E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "Na_ErfjPtkfo",
    "outputId": "fc4bebca-5501-400d-d46a-fcbadae96d20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1251  1425]\n",
      " [  168 17156]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.47      0.61      2676\n",
      "          1       0.92      0.99      0.96     17324\n",
      "\n",
      "avg / total       0.92      0.92      0.91     20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "tfidf_train_conf_matrix = confusion_matrix(Y_train, tfidf_train_conf)\n",
    "tfidf_test_conf_matrix = confusion_matrix(Y_test, tfidf_test_conf)\n",
    "class_report = classification_report(Y_test, tfidf_test_conf)\n",
    "print(tfidf_test_conf_matrix)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 26375.0,
     "status": "ok",
     "timestamp": 1.5507696835E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "Hu0UuWiEtnT3",
    "outputId": "e2aff2ef-80a1-4b30-a04f-aab5549c3b02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0xc4134cf8>, <matplotlib.text.Text at 0xeed97b70>]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KEkREJL9odkNsGrgoIiIiKaklQURE8ou6G2JTkiAiIvlF3Q2xKUkQEZH8opaE2JQkiIhI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\nVslBgpmZmVVykGBmZmaVHCSYmZlZpf8BFWBV9Z77yx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4e1d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(tfidf_train_conf_matrix, annot=True, ax = ax, fmt='g')\n",
    "ax.set_ylabel('Predicted labels')\n",
    "ax.set_xlabel('True labels')\n",
    "ax.set_title('Train Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(['negative', 'positive']) \n",
    "ax.yaxis.set_ticklabels(['negative', 'positive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0xb252c2e8>, <matplotlib.text.Text at 0xa72987b8>]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MrOmoqyt+qzFVkyQA5wPrkNwNa0ZO+TPAPpUIyMzMmiAnCZlVU3fDr4F9IuK/knJXw/of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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab876a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "sns.heatmap(tfidf_test_conf_matrix, annot=True, ax = ax, fmt='g')\n",
    "ax.set_ylabel('Predicted labels')\n",
    "ax.set_xlabel('True labels')\n",
    "ax.set_title('Test Confusion Matrix') \n",
    "ax.xaxis.set_ticklabels(['negative', 'positive']) \n",
    "ax.yaxis.set_ticklabels(['negative', 'positive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119.0
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 25912.0,
     "status": "ok",
     "timestamp": 1.550769683502E12,
     "user": {
      "displayName": "Rajendra Choudhary",
      "photoUrl": "https://lh3.googleusercontent.com/-H2n68bMjaqM/AAAAAAAAAAI/AAAAAAAAADs/35wbnA9B3WM/s64/photo.jpg",
      "userId": "11987022453046813676"
     },
     "user_tz": -330.0
    },
    "id": "ZZUpP79Atp6O",
    "outputId": "1b013bdd-9920-4256-c30b-a99a2bcc5610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-------+----------------+------------------+----------------+\n",
      "| Vectorizer | alpha |     Train      | Cross validation |      Test      |\n",
      "+------------+-------+----------------+------------------+----------------+\n",
      "|    BoW     |  0.1  | 0.973071057077 |      0.9166      | 0.927312069814 |\n",
      "|   Tf-idf   |  0.1  | 0.985620736644 |     0.91865      | 0.959417124485 |\n",
      "+------------+-------+----------------+------------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "    \n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Vectorizer\", \"alpha\", \"Train\", \"Cross validation\", \"Test\"]\n",
    "\n",
    "x.add_row([\"BoW\", bow_best_alpha, bow_train_auc, max(bow_auc_cv), bow_test_auc])\n",
    "x.add_row([\"Tf-idf\", tfidf_best_alpha, tfidf_train_auc, max(tfidf_auc_cv), tfidf_test_auc])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "Steps taken to increase accuracy: <br>\n",
    "i. Summary and Text columns are appended in single column <br>\n",
    "ii. length of words is taken from appended column and stacked with sparse matrix <br>\n",
    "\n",
    "Observations: <br>\n",
    "i. Accuracy increased around 2% for each vectorizer. <br>"
   ]
  }
 ],
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