model_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NLTK\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "#other\n",
    "import re\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Keras\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation\n",
    "from keras.layers.embeddings import Embedding\n",
    "\n",
    "#plot\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "py.init_notebook_mode(connected=True)\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/ashish/test/yelp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id        date               review_id  stars  \\\n",
       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "\n",
       "                                                text    type  \\\n",
       "0  My wife took me here on my birthday for breakf...  review   \n",
       "1  I have no idea why some people give bad review...  review   \n",
       "2  love the gyro plate. Rice is so good and I als...  review   \n",
       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
       "\n",
       "                  user_id  cool  useful  funny  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
       "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df.drop(['review_id','user_id','business_id','date','useful','funny','cool','type'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   stars                                               text\n",
       "0      5  My wife took me here on my birthday for breakf...\n",
       "1      5  I have no idea why some people give bad review...\n",
       "2      4  love the gyro plate. Rice is so good and I als...\n",
       "3      5  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...\n",
       "4      5  General Manager Scott Petello is a good egg!!!..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   stars                                               text\n",
       "0      5  My wife took me here on my birthday for breakf...\n",
       "1      5  I have no idea why some people give bad review...\n",
       "2      4  love the gyro plate. Rice is so good and I als...\n",
       "3      5  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...\n",
       "4      5  General Manager Scott Petello is a good egg!!!..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.777500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.214636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stars\n",
       "count  10000.000000\n",
       "mean       3.777500\n",
       "std        1.214636\n",
       "min        1.000000\n",
       "25%        3.000000\n",
       "50%        4.000000\n",
       "75%        5.000000\n",
       "max        5.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df['stars'].map(lambda x : 1 if int(x) > 3 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2       1\n",
      "3       1\n",
      "4       1\n",
      "5       1\n",
      "6       1\n",
      "7       1\n",
      "8       1\n",
      "9       1\n",
      "10      1\n",
      "11      1\n",
      "12      1\n",
      "13      1\n",
      "14      1\n",
      "15      0\n",
      "16      0\n",
      "17      1\n",
      "18      0\n",
      "19      1\n",
      "20      0\n",
      "21      1\n",
      "22      1\n",
      "23      0\n",
      "24      1\n",
      "25      1\n",
      "26      1\n",
      "27      1\n",
      "28      1\n",
      "29      1\n",
      "       ..\n",
      "9970    1\n",
      "9971    1\n",
      "9972    1\n",
      "9973    1\n",
      "9974    0\n",
      "9975    1\n",
      "9976    0\n",
      "9977    1\n",
      "9978    1\n",
      "9979    1\n",
      "9980    1\n",
      "9981    1\n",
      "9982    1\n",
      "9983    0\n",
      "9984    0\n",
      "9985    1\n",
      "9986    1\n",
      "9987    0\n",
      "9988    1\n",
      "9989    1\n",
      "9990    1\n",
      "9991    1\n",
      "9992    1\n",
      "9993    1\n",
      "9994    1\n",
      "9995    0\n",
      "9996    1\n",
      "9997    1\n",
      "9998    0\n",
      "9999    1\n",
      "Name: stars, Length: 10000, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    \n",
    "    ## Remove puncuation\n",
    "    text = text.translate(string.punctuation)\n",
    "    \n",
    "    ## Convert words to lower case and split them\n",
    "    text = text.lower().split()\n",
    "    \n",
    "    ## Remove stop words\n",
    "    stops = set(stopwords.words(\"english\"))\n",
    "    text = [w for w in text if not w in stops and len(w) >= 3]\n",
    "    \n",
    "    text = \" \".join(text)\n",
    "\n",
    "    # Clean the text\n",
    "    text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", text)\n",
    "    text = re.sub(r\"what's\", \"what is \", text)\n",
    "    text = re.sub(r\"\\'s\", \" \", text)\n",
    "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
    "    text = re.sub(r\"n't\", \" not \", text)\n",
    "    text = re.sub(r\"i'm\", \"i am \", text)\n",
    "    text = re.sub(r\"\\'re\", \" are \", text)\n",
    "    text = re.sub(r\"\\'d\", \" would \", text)\n",
    "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
    "    text = re.sub(r\",\", \" \", text)\n",
    "    text = re.sub(r\"\\.\", \" \", text)\n",
    "    text = re.sub(r\"!\", \" ! \", text)\n",
    "    text = re.sub(r\"\\/\", \" \", text)\n",
    "    text = re.sub(r\"\\^\", \" ^ \", text)\n",
    "    text = re.sub(r\"\\+\", \" + \", text)\n",
    "    text = re.sub(r\"\\-\", \" - \", text)\n",
    "    text = re.sub(r\"\\=\", \" = \", text)\n",
    "    text = re.sub(r\"'\", \" \", text)\n",
    "    text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", text)\n",
    "    text = re.sub(r\":\", \" : \", text)\n",
    "    text = re.sub(r\" e g \", \" eg \", text)\n",
    "    text = re.sub(r\" b g \", \" bg \", text)\n",
    "    text = re.sub(r\" u s \", \" american \", text)\n",
    "    text = re.sub(r\"\\0s\", \"0\", text)\n",
    "    text = re.sub(r\" 9 11 \", \"911\", text)\n",
    "    text = re.sub(r\"e - mail\", \"email\", text)\n",
    "    text = re.sub(r\"j k\", \"jk\", text)\n",
    "    text = re.sub(r\"\\s{2,}\", \" \", text)\n",
    "    \n",
    "    text = text.split()\n",
    "    stemmer = SnowballStemmer('english')\n",
    "    stemmed_words = [stemmer.stem(word) for word in text]\n",
    "    text = \" \".join(stemmed_words)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text']=df['text'].map(lambda x:clean_text(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    }\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>stars</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>wife took birthday breakfast excel weather per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>idea peopl give bad review place goe show you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>love gyro plate rice good also dig candi select</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>rosi dakota love chaparr dog park ! ! ! conven...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>general manag scott petello good egg ! ! ! det...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>quiessenc is simpli put beauti full window ear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>drop drive here ate back next day more food go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>luckili travel far make connect flight this th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>definit come happi hour ! price amaz sake bomb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>nobuo show uniqu talent everyth menu care craf...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stars                                               text\n",
       "0      5  wife took birthday breakfast excel weather per...\n",
       "1      5  idea peopl give bad review place goe show you ...\n",
       "2      4    love gyro plate rice good also dig candi select\n",
       "3      5  rosi dakota love chaparr dog park ! ! ! conven...\n",
       "4      5  general manag scott petello good egg ! ! ! det...\n",
       "5      4  quiessenc is simpli put beauti full window ear...\n",
       "6      5  drop drive here ate back next day more food go...\n",
       "7      4  luckili travel far make connect flight this th...\n",
       "8      4  definit come happi hour ! price amaz sake bomb...\n",
       "9      5  nobuo show uniqu talent everyth menu care craf..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary_size = 20000\n",
    "tokenizer = Tokenizer(num_words= vocabulary_size)\n",
    "tokenizer.fit_on_texts(df['text'])\n",
    "\n",
    "sequences = tokenizer.texts_to_sequences(df['text'])\n",
    "data = pad_sequences(sequences, maxlen=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 50)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lstm model\n",
    "model_lstm = Sequential()\n",
    "model_lstm.add(Embedding(20000, 100, input_length=50))\n",
    "model_lstm.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n",
    "model_lstm.add(Dense(1, activation='sigmoid'))\n",
    "model_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 3000 samples\n",
      "Epoch 1/3\n",
      "7000/7000 [==============================] - 27s 4ms/step - loss: 0.5298 - acc: 0.7443 - val_loss: 0.4478 - val_acc: 0.7947\n",
      "Epoch 2/3\n",
      "7000/7000 [==============================] - 25s 4ms/step - loss: 0.3323 - acc: 0.8654 - val_loss: 0.4569 - val_acc: 0.7933\n",
      "Epoch 3/3\n",
      "7000/7000 [==============================] - 26s 4ms/step - loss: 0.2260 - acc: 0.9164 - val_loss: 0.5437 - val_acc: 0.7810\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb722add748>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lstm.fit(data, np.array(labels), validation_split=0.3, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    " def create_conv_model():\n",
    "    model_conv = Sequential()\n",
    "    model_conv.add(Embedding(vocabulary_size, 100, input_length=50))\n",
    "    model_conv.add(Dropout(0.2))\n",
    "    model_conv.add(Conv1D(64, 5, activation='relu'))\n",
    "    model_conv.add(MaxPooling1D(pool_size=4))\n",
    "    model_conv.add(LSTM(100))\n",
    "    model_conv.add(Dense(1, activation='sigmoid'))\n",
    "    model_conv.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 3000 samples\n",
      "Epoch 1/3\n",
      "7000/7000 [==============================] - 17s 2ms/step - loss: 0.5321 - acc: 0.7317 - val_loss: 0.4440 - val_acc: 0.7870\n",
      "Epoch 2/3\n",
      "7000/7000 [==============================] - 16s 2ms/step - loss: 0.3273 - acc: 0.8674 - val_loss: 0.4623 - val_acc: 0.7763\n",
      "Epoch 3/3\n",
      "7000/7000 [==============================] - 15s 2ms/step - loss: 0.1896 - acc: 0.9297 - val_loss: 0.5983 - val_acc: 0.7753\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb710fe3c18>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_conv = create_conv_model()\n",
    "model_conv.fit(data, np.array(labels), validation_split=0.3, epochs = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_save = pd.DataFrame(data)\n",
    "df_label = pd.DataFrame(np.array(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat([df_save, df_label], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('train_dense_word_vectors.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "embeddings_index = dict()\n",
    "f = open('/home/ashish/test/glove.6B/glove.6B.100d.txt')\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "print('Loaded %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a weight matrix for words in training docs\n",
    "embedding_matrix = np.zeros((vocabulary_size, 100))\n",
    "for word, index in tokenizer.word_index.items():\n",
    "    if index > vocabulary_size - 1:\n",
    "        break\n",
    "    else:\n",
    "        embedding_vector = embeddings_index.get(word)\n",
    "        if embedding_vector is not None:\n",
    "            embedding_matrix[index] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_glove = Sequential()\n",
    "model_glove.add(Embedding(vocabulary_size, 100, input_length=50, weights=[embedding_matrix], trainable=False))\n",
    "model_glove.add(Dropout(0.2))\n",
    "model_glove.add(Conv1D(64, 5, activation='relu'))\n",
    "model_glove.add(MaxPooling1D(pool_size=4))\n",
    "model_glove.add(LSTM(100))\n",
    "model_glove.add(Dense(1, activation='sigmoid'))\n",
    "model_glove.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6000 samples, validate on 4000 samples\n",
      "Epoch 1/3\n",
      "6000/6000 [==============================] - 7s 1ms/step - loss: 0.5994 - acc: 0.6923 - val_loss: 0.5661 - val_acc: 0.7033\n",
      "Epoch 2/3\n",
      "6000/6000 [==============================] - 5s 908us/step - loss: 0.5457 - acc: 0.7143 - val_loss: 0.5510 - val_acc: 0.7202\n",
      "Epoch 3/3\n",
      "6000/6000 [==============================] - 6s 958us/step - loss: 0.5050 - acc: 0.7492 - val_loss: 0.5325 - val_acc: 0.7378\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb710098588>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_glove.fit(data, np.array(labels), validation_split=0.4, epochs = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_embds = model_lstm.layers[0].get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_embds = model_conv.layers[0].get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_emds = model_glove.layers[0].get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = []\n",
    "for word, i in tokenizer.word_index.items():\n",
    "    word_list.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_words(data, start, stop, step):\n",
    "    trace = go.Scatter(\n",
    "        x = data[start:stop:step,0], \n",
    "        y = data[start:stop:step, 1],\n",
    "        mode = 'markers',\n",
    "        text= word_list[start:stop:step]\n",
    "    )\n",
    "    layout = dict(title= 't-SNE 1 vs t-SNE 2',\n",
    "                  yaxis = dict(title='t-SNE 2'),\n",
    "                  xaxis = dict(title='t-SNE 1'),\n",
    "                  hovermode= 'closest')\n",
    "    fig = dict(data = [trace], layout= layout)\n",
    "    py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_words = 2000\n",
    "lstm_tsne_embds = TSNE(n_components=2).fit_transform(lstm_embds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "mode": "markers",
         "text": [
          "i",
          "place",
          "good",
          "food",
          "like",
          "great",
          "get",
          "time",
          "one",
          "would",
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          "it",
          "servic",
          "the",
          "love",
          "friend",
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       "                        {\"hovermode\": \"closest\", \"title\": {\"text\": \"t-SNE 1 vs t-SNE 2\"}, \"xaxis\": {\"title\": {\"text\": \"t-SNE 1\"}}, \"yaxis\": {\"title\": {\"text\": \"t-SNE 2\"}}},\n",
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   "source": [
    "plot_words(lstm_tsne_embds, 0, number_of_words, 1) "
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   "execution_count": 40,
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   "source": [
    "conv_tsne_embds = TSNE(n_components=2).fit_transform(conv_embds)"
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   "execution_count": 41,
   "metadata": {},
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "plot_words(conv_tsne_embds, 0, number_of_words, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_tsne_embds = TSNE(n_components=2).fit_transform(glove_emds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'glove_tsne_embds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-34abdea05005>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_words\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglove_tsne_embds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'glove_tsne_embds' is not defined"
     ]
    }
   ],
   "source": [
    "plot_words(glove_tsne_embds, 0, number_of_words, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/ashish/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tokenized'] = df.apply(lambda row : nltk.word_tokenize(row['text']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_w2v = Word2Vec(df['tokenized'], size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = model_w2v[model_w2v.wv.vocab]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsvd = TruncatedSVD(n_components=5, n_iter=10)\n",
    "result = tsvd.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsvd_word_list = []\n",
    "words = list(model_w2v.wv.vocab)\n",
    "for i, word in enumerate(words):\n",
    "    tsvd_word_list.append(word)\n",
    "\n",
    "trace = go.Scatter(\n",
    "    x = result[0:number_of_words, 0], \n",
    "    y = result[0:number_of_words, 1],\n",
    "    mode = 'markers',\n",
    "    text= tsvd_word_list[0:number_of_words]\n",
    ")\n",
    "\n",
    "layout = dict(title= 'SVD 1 vs SVD 2',\n",
    "              yaxis = dict(title='SVD 2'),\n",
    "              xaxis = dict(title='SVD 1'),\n",
    "              hovermode= 'closest')\n",
    "\n",
    "fg = dict(data = [trace], layout= layout)\n",
    "py.iplot(fig)"
   ]
  }
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