EnglishData_classification_with_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilingual Text Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "from keras.layers import GRU,Dropout,BatchNormalization\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/dataset.csv', sep='\\t', header=None).applymap(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List all languages that are exist in the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['english', 'albanian', 'arabic', 'bulgarian', 'chinese',\n",
       "       'croatian', 'czech', 'danish', 'dutch', 'estonian', 'finnish',\n",
       "       'french', 'german', 'greek', 'hebrew', 'hungarian', 'icelandic',\n",
       "       'italian', 'japanese', 'korean', 'lithuanian', 'norwegian',\n",
       "       'polish', 'portuguese', 'romanian', 'russian', 'serbian',\n",
       "       'slovenian', 'spanish', 'swedish', 'turkish', 'ukrainian'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "languages = dataset[dataset.columns[0]].unique()\n",
    "languages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List all labels (categories)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['advertising', 'agriculture', 'animation', 'arts_and_crafts',\n",
       "       'entertainment', 'astrology', 'vehicles', 'games',\n",
       "       'books_and_literature', 'business', 'gambling', 'jobs', 'clothing',\n",
       "       'comic_books', 'dating', 'education', 'adult', 'food', 'health',\n",
       "       'hobbies_and_interests', 'humor', 'illegal_content', 'investing',\n",
       "       'jewelry', 'logistics', 'marketing', 'movies', 'music', 'hacking',\n",
       "       'media', 'finance', 'pets', 'politics', 'religion',\n",
       "       'sci_fi_and_fantasy', 'science', 'shopping', 'society', 'sports',\n",
       "       'tech', 'teens', 'television', 'travel', 'under_construction',\n",
       "       'weather'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = dataset[dataset.columns[1]].unique()\n",
    "categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**now I'm going to create a dataframe containing the number of posts for any pair language/category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.DataFrame(np.zeros([len(languages), len(categories)], dtype=int), index=[i for i in languages],\n",
    "                     columns=[i for i in categories])\n",
    "\n",
    "for _, row in dataset.iterrows():\n",
    "    stats.loc[[row[0]], [row[1]]] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's see how data is ditributed between labels**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "sn.heatmap(stats, annot=True, cmap='Blues', fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So now we have an inside look on how this dataset distribution is. It's obvious some category or language don't have much data so it's probably needed to discard it during use in any project.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>advertising</td>\n",
       "      <td>Exclusive: U.S. government seeks Facebook help...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>advertising</td>\n",
       "      <td>Trump asks SEC to mull half-year corporate fil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>advertising</td>\n",
       "      <td>Wall St. up on trade hopes, S&amp;P equals longest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>advertising</td>\n",
       "      <td>Asian shares hit one-year low on Turkey, China...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>advertising</td>\n",
       "      <td>Asian stocks weaken as Turkey worries weigh, d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language        Label                                               Text\n",
       "0  english  advertising  Exclusive: U.S. government seeks Facebook help...\n",
       "1  english  advertising  Trump asks SEC to mull half-year corporate fil...\n",
       "2  english  advertising  Wall St. up on trade hopes, S&P equals longest...\n",
       "3  english  advertising  Asian shares hit one-year low on Turkey, China...\n",
       "4  english  advertising  Asian stocks weaken as Turkey worries weigh, d..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename Dataset Columns\n",
    "dataset.columns = [\"Language\",\"Label\",\"Text\"]\n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>Exclusive: U.S. government seeks Facebook help...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>Trump asks SEC to mull half-year corporate fil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>Wall St. up on trade hopes, S&amp;P equals longest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>Asian shares hit one-year low on Turkey, China...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>Asian stocks weaken as Turkey worries weigh, d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  Exclusive: U.S. government seeks Facebook help...\n",
       "1  english      1  Trump asks SEC to mull half-year corporate fil...\n",
       "2  english      1  Wall St. up on trade hopes, S&P equals longest...\n",
       "3  english      1  Asian shares hit one-year low on Turkey, China...\n",
       "4  english      1  Asian stocks weaken as Turkey worries weigh, d..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "dataset[\"Label\"] = label_encoder.fit_transform(dataset[\"Label\"])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we have a dataset for each language\n",
    "languagesData=[]\n",
    "loc = 0\n",
    "for i in languages:\n",
    "    name = languages[loc]+\"Data\" \n",
    "    globals()[name] = pd.DataFrame( dataset[dataset.Language == i])\n",
    "    loc += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing on English data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Convert text to lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Language  Label                                               Text\n",
      "0  english      1  exclusive: u.s. government seeks facebook help...\n",
      "1  english      1  trump asks sec to mull half-year corporate fil...\n",
      "2  english      1  wall st. up on trade hopes, s&p equals longest...\n",
      "3  english      1  asian shares hit one-year low on turkey, china...\n",
      "4  english      1  asian stocks weaken as turkey worries weigh, d...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lower_words(text):\n",
    "    text = text.str.lower()\n",
    "    return text\n",
    "\n",
    "englishData['Text'] = lower_words(englishData['Text'])\n",
    "input_str = englishData['Text']\n",
    "print(englishData.head())\n",
    "type(input_str)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Remove URL's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclusive: u.s. government seeks facebook help...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump asks sec to mull half-year corporate fil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st. up on trade hopes, s&amp;p equals longest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian shares hit one-year low on turkey, china...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stocks weaken as turkey worries weigh, d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclusive: u.s. government seeks facebook help...\n",
       "1  english      1  trump asks sec to mull half-year corporate fil...\n",
       "2  english      1  wall st. up on trade hopes, s&p equals longest...\n",
       "3  english      1  asian shares hit one-year low on turkey, china...\n",
       "4  english      1  asian stocks weaken as turkey worries weigh, d..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_url(text):\n",
    "    text = text.apply(lambda x: re.split('https:\\/\\/.*', str(x))[0])\n",
    "    return text\n",
    "\n",
    "\n",
    "englishData['Text'] = remove_url(englishData['Text'])\n",
    "englishData.head()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Remove numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclusive: u.s. government seeks facebook help...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump asks sec to mull half-year corporate fil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st. up on trade hopes, s&amp;p equals longest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian shares hit one-year low on turkey, china...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stocks weaken as turkey worries weigh, d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclusive: u.s. government seeks facebook help...\n",
       "1  english      1  trump asks sec to mull half-year corporate fil...\n",
       "2  english      1  wall st. up on trade hopes, s&p equals longest...\n",
       "3  english      1  asian shares hit one-year low on turkey, china...\n",
       "4  english      1  asian stocks weaken as turkey worries weigh, d..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_numbers(text):\n",
    "    text = text.str.replace('\\d+', '')\n",
    "    return text\n",
    "\n",
    "\n",
    "englishData['Text'] = remove_numbers(englishData['Text'])\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.  Remove punctuation:\n",
    "    * The following code removes this set of symbols [!”#$%&’()*+,-./:;<=>?@[\\]^_`{|}~]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclusive us government seeks facebook help to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump asks sec to mull halfyear corporate fili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st up on trade hopes sp equals longest bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian shares hit oneyear low on turkey china w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stocks weaken as turkey worries weigh do...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclusive us government seeks facebook help to...\n",
       "1  english      1  trump asks sec to mull halfyear corporate fili...\n",
       "2  english      1  wall st up on trade hopes sp equals longest bu...\n",
       "3  english      1  asian shares hit oneyear low on turkey china w...\n",
       "4  english      1  asian stocks weaken as turkey worries weigh do..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_punctuations(text):\n",
    "    text = text.str.replace('[^\\w\\s]','')\n",
    "    return text\n",
    "\n",
    "\n",
    "englishData['Text'] = remove_punctuations(englishData['Text'])\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Remove whitespaces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclusive us government seeks facebook help to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump asks sec to mull halfyear corporate fili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st up on trade hopes sp equals longest bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian shares hit oneyear low on turkey china w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stocks weaken as turkey worries weigh do...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclusive us government seeks facebook help to...\n",
       "1  english      1  trump asks sec to mull halfyear corporate fili...\n",
       "2  english      1  wall st up on trade hopes sp equals longest bu...\n",
       "3  english      1  asian shares hit oneyear low on turkey china w...\n",
       "4  english      1  asian stocks weaken as turkey worries weigh do..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_blank_space(col):\n",
    "    col = col.str.strip()\n",
    "    col = col.replace('\\s+', ' ', regex=True)   \n",
    "    return col\n",
    "\n",
    "\n",
    "englishData['Text'] = remove_blank_space(englishData.Text)\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Remove stopping words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclusive us government seeks facebook help to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump asks sec to mull halfyear corporate fili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st up on trade hopes sp equals longest bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian shares hit oneyear low on turkey china w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stocks weaken as turkey worries weigh do...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclusive us government seeks facebook help to...\n",
       "1  english      1  trump asks sec to mull halfyear corporate fili...\n",
       "2  english      1  wall st up on trade hopes sp equals longest bu...\n",
       "3  english      1  asian shares hit oneyear low on turkey china w...\n",
       "4  english      1  asian stocks weaken as turkey worries weigh do..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_stop_words(text, stop):    \n",
    "    text.apply(lambda x: [item for item in x if item not in stop])\n",
    "    return text\n",
    "\n",
    "stop = stopwords.words('english')\n",
    "remove_stop_words(englishData['Text'],stop)\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclus us govern seek facebook help to wiretap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump ask sec to mull halfyear corpor file vs ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st up on trade hope sp equal longest bull...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian share hit oneyear low on turkey china wo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stock weaken as turkey worri weigh dolla...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclus us govern seek facebook help to wiretap...\n",
       "1  english      1  trump ask sec to mull halfyear corpor file vs ...\n",
       "2  english      1  wall st up on trade hope sp equal longest bull...\n",
       "3  english      1  asian share hit oneyear low on turkey china wo...\n",
       "4  english      1  asian stock weaken as turkey worri weigh dolla..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def stem_sentences(sentence):\n",
    "    tokens = sentence.split()\n",
    "    stemmed_tokens = [porter_stemmer.stem(token) for token in tokens]\n",
    "    return ' '.join(stemmed_tokens)\n",
    "\n",
    "\n",
    "porter_stemmer = PorterStemmer()\n",
    "englishData['Text'] = englishData['Text'].apply(stem_sentences)\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>exclus u govern seek facebook help to wiretap ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>trump ask sec to mull halfyear corpor file v q...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>wall st up on trade hope sp equal longest bull...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian share hit oneyear low on turkey china wo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>english</td>\n",
       "      <td>1</td>\n",
       "      <td>asian stock weaken a turkey worri weigh dollar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language  Label                                               Text\n",
       "0  english      1  exclus u govern seek facebook help to wiretap ...\n",
       "1  english      1  trump ask sec to mull halfyear corpor file v q...\n",
       "2  english      1  wall st up on trade hope sp equal longest bull...\n",
       "3  english      1  asian share hit oneyear low on turkey china wo...\n",
       "4  english      1  asian stock weaken a turkey worri weigh dollar..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lemmatize_text(text):\n",
    "    lemma_tokens =  [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(text)]\n",
    "    return ' '.join(lemma_tokens)\n",
    "\n",
    "\n",
    "\n",
    "w_tokenizer = nltk.tokenize.WhitespaceTokenizer()\n",
    "lemmatizer = nltk.stem.WordNetLemmatizer()\n",
    "englishData['Text']  = englishData.Text.apply(lemmatize_text)\n",
    "englishData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ESentences = englishData.Text\n",
    "Ey = englishData.Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Esentences_train, Esentences_test, Ey_train, Ey_test = train_test_split(\n",
    "   ESentences, Ey, test_size=0.25, random_state=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=5000)\n",
    "tokenizer.fit_on_texts(Esentences_train)\n",
    "\n",
    "EX_train = tokenizer.texts_to_sequences(Esentences_train)\n",
    "EX_test = tokenizer.texts_to_sequences(Esentences_test)\n",
    "\n",
    "vocab_size = len(tokenizer.word_index) + 1  # Adding 1 because of reserved 0 index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    acc = history.history['acc']\n",
    "    val_acc = history.history['val_acc']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    x = range(1, len(acc) + 1)\n",
    "\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(x, acc, 'b', label='Training acc')\n",
    "    plt.plot(x, val_acc, 'r', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.legend()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(x, loss, 'b', label='Training loss')\n",
    "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1392   13 1392 2644 1400    2 3829    5 4516    1   44 1756 2804 2694\n",
      "  539 4431    3  203  214    6    1   70    4  196  814  474    1 2508\n",
      "   13   92  267 1392  762    1   81  224    6  566    3   20  563 3377\n",
      "  196  911  267   12    1 4906  575  814   52  367  692  637  312 3416\n",
      "    5 1264  758    6 1505   98   52  107 4274  267    1  653   51  176\n",
      "  242   22 4907  840    1 1368  163  123 1696   18   61   60  521    4\n",
      "  521    4 1572   12   18   40    8 2805 2116  777 2268   44  531 2916\n",
      "    1 2645]\n"
     ]
    }
   ],
   "source": [
    "maxlen = 100\n",
    "\n",
    "EX_train = pad_sequences(EX_train, padding='post', maxlen=maxlen)\n",
    "EX_test = pad_sequences(EX_test, padding='post', maxlen=maxlen)\n",
    "\n",
    "Ey_train = keras.utils.to_categorical(Ey_train)\n",
    "Ey_test = keras.utils.to_categorical(Ey_test)\n",
    "\n",
    "print(EX_train[0, :])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 100, 50)           3363900   \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 5000)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               500100    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 45)                4545      \n",
      "=================================================================\n",
      "Total params: 3,868,545\n",
      "Trainable params: 3,868,545\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding_dim = 50\n",
    "\n",
    "model = Sequential()\n",
    "model.add(layers.Embedding(input_dim=vocab_size, \n",
    "                           output_dim=embedding_dim, \n",
    "                           input_length=maxlen))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(100, activation='relu'))\n",
    "model.add(layers.Dense(45, activation='softmax'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3389 samples, validate on 1130 samples\n",
      "Epoch 1/20\n",
      "3389/3389 [==============================] - 12s 4ms/step - loss: 3.7374 - acc: 0.0850 - val_loss: 3.6310 - val_acc: 0.1115\n",
      "Epoch 2/20\n",
      "3389/3389 [==============================] - 11s 3ms/step - loss: 2.8939 - acc: 0.3562 - val_loss: 3.1856 - val_acc: 0.1823\n",
      "Epoch 3/20\n",
      "3389/3389 [==============================] - 11s 3ms/step - loss: 1.3359 - acc: 0.7693 - val_loss: 3.1620 - val_acc: 0.2000\n",
      "Epoch 4/20\n",
      "3389/3389 [==============================] - 11s 3ms/step - loss: 0.5862 - acc: 0.8421 - val_loss: 3.2841 - val_acc: 0.2106\n",
      "Epoch 5/20\n",
      "3389/3389 [==============================] - 10s 3ms/step - loss: 0.4440 - acc: 0.8613 - val_loss: 3.2952 - val_acc: 0.2230\n",
      "Epoch 6/20\n",
      "3389/3389 [==============================] - 11s 3ms/step - loss: 0.3779 - acc: 0.8699 - val_loss: 3.3120 - val_acc: 0.2274\n",
      "Epoch 7/20\n",
      "3389/3389 [==============================] - 15s 4ms/step - loss: 0.3443 - acc: 0.8746 - val_loss: 3.3197 - val_acc: 0.2221\n",
      "Epoch 8/20\n",
      "3389/3389 [==============================] - 14s 4ms/step - loss: 0.3101 - acc: 0.8772 - val_loss: 3.4087 - val_acc: 0.2195\n",
      "Epoch 9/20\n",
      "3389/3389 [==============================] - 14s 4ms/step - loss: 0.2998 - acc: 0.8758 - val_loss: 3.3634 - val_acc: 0.2265\n",
      "Epoch 10/20\n",
      "3389/3389 [==============================] - 15s 4ms/step - loss: 0.2934 - acc: 0.8781 - val_loss: 3.3887 - val_acc: 0.2230\n",
      "Epoch 11/20\n",
      "3389/3389 [==============================] - 15s 4ms/step - loss: 0.2819 - acc: 0.8775 - val_loss: 3.3328 - val_acc: 0.2274\n",
      "Epoch 12/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2758 - acc: 0.8746 - val_loss: 3.3405 - val_acc: 0.2292\n",
      "Epoch 13/20\n",
      "3389/3389 [==============================] - 15s 4ms/step - loss: 0.2609 - acc: 0.8802 - val_loss: 3.3589 - val_acc: 0.2257\n",
      "Epoch 14/20\n",
      "3389/3389 [==============================] - 15s 4ms/step - loss: 0.2600 - acc: 0.8740 - val_loss: 3.3607 - val_acc: 0.2239\n",
      "Epoch 15/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2518 - acc: 0.8799 - val_loss: 3.3434 - val_acc: 0.2257\n",
      "Epoch 16/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2490 - acc: 0.8823 - val_loss: 3.3909 - val_acc: 0.2177\n",
      "Epoch 17/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2463 - acc: 0.8743 - val_loss: 3.3871 - val_acc: 0.2212\n",
      "Epoch 18/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2479 - acc: 0.8772 - val_loss: 3.3340 - val_acc: 0.2363\n",
      "Epoch 19/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2435 - acc: 0.8749 - val_loss: 3.3465 - val_acc: 0.2301\n",
      "Epoch 20/20\n",
      "3389/3389 [==============================] - 16s 5ms/step - loss: 0.2393 - acc: 0.8775 - val_loss: 3.3353 - val_acc: 0.2327\n",
      "3389/3389 [==============================] - 1s 255us/step\n",
      "Training Accuracy: 0.9091\n",
      "1130/1130 [==============================] - 0s 150us/step\n",
      "Testing Accuracy:  0.2327\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(EX_train, Ey_train,\n",
    "                    epochs=20,\n",
    "                    verbose=True,\n",
    "                    validation_data=(EX_test, Ey_test))\n",
    "loss, accuracy = model.evaluate(EX_train, Ey_train, verbose=True)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(EX_test, Ey_test, verbose=True)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using pretrained word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_embedding_matrix(filepath, word_index, embedding_dim):\n",
    "    vocab_size = len(word_index) + 1  # Adding again 1 because of reserved 0 index\n",
    "    embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "\n",
    "    with open(filepath) as f:\n",
    "        for line in f:\n",
    "            word, *vector = line.split()\n",
    "            if word in word_index:\n",
    "                idx = word_index[word] \n",
    "                embedding_matrix[idx] = np.array(\n",
    "                    vector, dtype=np.float32)[:embedding_dim]\n",
    "\n",
    "    return embedding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 50\n",
    "embedding_matrix = create_embedding_matrix(\n",
    "    'glove.6B.50d.txt',\n",
    "    tokenizer.word_index, embedding_dim)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33749516929754153"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_elements = np.count_nonzero(np.count_nonzero(embedding_matrix, axis=1))\n",
    "nonzero_elements / vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 100, 50)           3363900   \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 256)               235776    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 45)                11565     \n",
      "=================================================================\n",
      "Total params: 3,611,241\n",
      "Trainable params: 3,611,241\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.Embedding(vocab_size, embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=True))\n",
    "model.add(GRU(256))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Dense(45, activation='sigmoid'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3389 samples, validate on 1130 samples\n",
      "Epoch 1/10\n",
      "3389/3389 [==============================] - 75s 22ms/step - loss: 3.7344 - acc: 0.0667 - val_loss: 3.6273 - val_acc: 0.1115\n",
      "Epoch 2/10\n",
      "3389/3389 [==============================] - 51s 15ms/step - loss: 3.5465 - acc: 0.1207 - val_loss: 3.5142 - val_acc: 0.1425\n",
      "Epoch 3/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 3.2693 - acc: 0.1750 - val_loss: 3.2355 - val_acc: 0.1673\n",
      "Epoch 4/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 2.8908 - acc: 0.2449 - val_loss: 3.0560 - val_acc: 0.2195\n",
      "Epoch 5/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 2.5415 - acc: 0.3243 - val_loss: 2.8208 - val_acc: 0.2602\n",
      "Epoch 6/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 2.2361 - acc: 0.3836 - val_loss: 2.8151 - val_acc: 0.2743\n",
      "Epoch 7/10\n",
      "3389/3389 [==============================] - 51s 15ms/step - loss: 2.0402 - acc: 0.4346 - val_loss: 2.8560 - val_acc: 0.2584\n",
      "Epoch 8/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 1.8233 - acc: 0.4842 - val_loss: 2.8471 - val_acc: 0.2637\n",
      "Epoch 9/10\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 1.6778 - acc: 0.5214 - val_loss: 2.6347 - val_acc: 0.3124\n",
      "Epoch 10/10\n",
      "3389/3389 [==============================] - 50s 15ms/step - loss: 1.3757 - acc: 0.5958 - val_loss: 2.6823 - val_acc: 0.3150\n",
      "3389/3389 [==============================] - 13s 4ms/step\n",
      "Training Accuracy: 0.6831\n",
      "1130/1130 [==============================] - 4s 4ms/step\n",
      "Testing Accuracy:  0.3150\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = model.fit(EX_train, Ey_train,\n",
    "                    epochs=10,\n",
    "                    verbose=True,\n",
    "                    validation_data=(EX_test, Ey_test)\n",
    "                   )\n",
    "loss, accuracy = model.evaluate(EX_train, Ey_train, verbose=True)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(EX_test, Ey_test, verbose=True)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3389 samples, validate on 1130 samples\n",
      "Epoch 1/50\n",
      "3389/3389 [==============================] - 48s 14ms/step - loss: 1.1984 - acc: 0.6621 - val_loss: 2.6723 - val_acc: 0.3283\n",
      "Epoch 2/50\n",
      "3389/3389 [==============================] - 50s 15ms/step - loss: 1.0382 - acc: 0.6976 - val_loss: 2.6736 - val_acc: 0.3416\n",
      "Epoch 3/50\n",
      "3389/3389 [==============================] - 62s 18ms/step - loss: 0.8808 - acc: 0.7389 - val_loss: 2.7328 - val_acc: 0.3381\n",
      "Epoch 4/50\n",
      "3389/3389 [==============================] - 64s 19ms/step - loss: 0.7775 - acc: 0.7725 - val_loss: 2.7530 - val_acc: 0.3522\n",
      "Epoch 5/50\n",
      "3389/3389 [==============================] - 65s 19ms/step - loss: 0.7099 - acc: 0.7793 - val_loss: 2.7969 - val_acc: 0.3566\n",
      "Epoch 6/50\n",
      "3389/3389 [==============================] - 63s 19ms/step - loss: 0.6235 - acc: 0.8126 - val_loss: 2.8608 - val_acc: 0.3416\n",
      "Epoch 7/50\n",
      "3389/3389 [==============================] - 65s 19ms/step - loss: 0.5261 - acc: 0.8286 - val_loss: 2.9352 - val_acc: 0.3407\n",
      "Epoch 8/50\n",
      "3389/3389 [==============================] - 62s 18ms/step - loss: 0.4547 - acc: 0.8519 - val_loss: 2.9995 - val_acc: 0.3513\n",
      "Epoch 9/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.4348 - acc: 0.8504 - val_loss: 3.0326 - val_acc: 0.3407\n",
      "Epoch 10/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.4080 - acc: 0.8578 - val_loss: 3.0664 - val_acc: 0.3602\n",
      "Epoch 11/50\n",
      "3389/3389 [==============================] - 61s 18ms/step - loss: 0.3628 - acc: 0.8708 - val_loss: 3.1017 - val_acc: 0.3540\n",
      "Epoch 12/50\n",
      "3389/3389 [==============================] - 67s 20ms/step - loss: 0.3387 - acc: 0.8702 - val_loss: 3.2560 - val_acc: 0.3442\n",
      "Epoch 13/50\n",
      "3389/3389 [==============================] - 63s 19ms/step - loss: 0.3296 - acc: 0.8699 - val_loss: 3.2169 - val_acc: 0.3442\n",
      "Epoch 14/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.2912 - acc: 0.8826 - val_loss: 3.2536 - val_acc: 0.3575\n",
      "Epoch 15/50\n",
      "3389/3389 [==============================] - 54s 16ms/step - loss: 0.2669 - acc: 0.8834 - val_loss: 3.3452 - val_acc: 0.3487\n",
      "Epoch 16/50\n",
      "3389/3389 [==============================] - 52s 15ms/step - loss: 0.2477 - acc: 0.8852 - val_loss: 3.3875 - val_acc: 0.3549\n",
      "Epoch 17/50\n",
      "3389/3389 [==============================] - 52s 15ms/step - loss: 0.2361 - acc: 0.8891 - val_loss: 3.4687 - val_acc: 0.3522\n",
      "Epoch 18/50\n",
      "3389/3389 [==============================] - 73s 21ms/step - loss: 0.2300 - acc: 0.8908 - val_loss: 3.5337 - val_acc: 0.3469\n",
      "Epoch 19/50\n",
      "3389/3389 [==============================] - 73s 21ms/step - loss: 0.2305 - acc: 0.8885 - val_loss: 3.6526 - val_acc: 0.3212\n",
      "Epoch 20/50\n",
      "3389/3389 [==============================] - 71s 21ms/step - loss: 0.3918 - acc: 0.8466 - val_loss: 3.4086 - val_acc: 0.3425\n",
      "Epoch 21/50\n",
      "3389/3389 [==============================] - 74s 22ms/step - loss: 0.2536 - acc: 0.8902 - val_loss: 3.4581 - val_acc: 0.3425\n",
      "Epoch 22/50\n",
      "3389/3389 [==============================] - 69s 20ms/step - loss: 0.2287 - acc: 0.8891 - val_loss: 3.5598 - val_acc: 0.3345\n",
      "Epoch 23/50\n",
      "3389/3389 [==============================] - 60s 18ms/step - loss: 0.2063 - acc: 0.8938 - val_loss: 3.6290 - val_acc: 0.3310\n",
      "Epoch 24/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.2162 - acc: 0.8964 - val_loss: 3.6941 - val_acc: 0.3425\n",
      "Epoch 25/50\n",
      "3389/3389 [==============================] - 57s 17ms/step - loss: 0.1991 - acc: 0.8950 - val_loss: 3.6933 - val_acc: 0.3478\n",
      "Epoch 26/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.1947 - acc: 0.8994 - val_loss: 3.7411 - val_acc: 0.3354\n",
      "Epoch 27/50\n",
      "3389/3389 [==============================] - 60s 18ms/step - loss: 0.1866 - acc: 0.8988 - val_loss: 3.8350 - val_acc: 0.3372\n",
      "Epoch 28/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.1851 - acc: 0.8997 - val_loss: 3.8278 - val_acc: 0.3496\n",
      "Epoch 29/50\n",
      "3389/3389 [==============================] - 59s 17ms/step - loss: 0.1826 - acc: 0.8997 - val_loss: 3.8925 - val_acc: 0.3496\n",
      "Epoch 30/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.1849 - acc: 0.8985 - val_loss: 3.9454 - val_acc: 0.3398\n",
      "Epoch 31/50\n",
      "3389/3389 [==============================] - 60s 18ms/step - loss: 0.1842 - acc: 0.8970 - val_loss: 3.9385 - val_acc: 0.3487\n",
      "Epoch 32/50\n",
      "3389/3389 [==============================] - 68s 20ms/step - loss: 0.2060 - acc: 0.8911 - val_loss: 3.8630 - val_acc: 0.3442\n",
      "Epoch 33/50\n",
      "3389/3389 [==============================] - 69s 20ms/step - loss: 0.2064 - acc: 0.8964 - val_loss: 3.9878 - val_acc: 0.3434\n",
      "Epoch 34/50\n",
      "3389/3389 [==============================] - 66s 20ms/step - loss: 0.2022 - acc: 0.8944 - val_loss: 3.8534 - val_acc: 0.3434\n",
      "Epoch 35/50\n",
      "3389/3389 [==============================] - 66s 19ms/step - loss: 0.1831 - acc: 0.9026 - val_loss: 3.9947 - val_acc: 0.3398\n",
      "Epoch 36/50\n",
      "3389/3389 [==============================] - 62s 18ms/step - loss: 0.1789 - acc: 0.9012 - val_loss: 4.0323 - val_acc: 0.3549\n",
      "Epoch 37/50\n",
      "3389/3389 [==============================] - 62s 18ms/step - loss: 0.1717 - acc: 0.9056 - val_loss: 4.1274 - val_acc: 0.3398\n",
      "Epoch 38/50\n",
      "3389/3389 [==============================] - 57s 17ms/step - loss: 0.1685 - acc: 0.9000 - val_loss: 4.1447 - val_acc: 0.3442\n",
      "Epoch 39/50\n",
      "3389/3389 [==============================] - 58s 17ms/step - loss: 0.1699 - acc: 0.9035 - val_loss: 4.1434 - val_acc: 0.3425\n",
      "Epoch 40/50\n",
      "3389/3389 [==============================] - 65s 19ms/step - loss: 0.1654 - acc: 0.9012 - val_loss: 4.2717 - val_acc: 0.3540\n",
      "Epoch 41/50\n",
      "3389/3389 [==============================] - 65s 19ms/step - loss: 0.1650 - acc: 0.9017 - val_loss: 4.2836 - val_acc: 0.3354\n",
      "Epoch 42/50\n",
      "3389/3389 [==============================] - 66s 20ms/step - loss: 0.1643 - acc: 0.9047 - val_loss: 4.2665 - val_acc: 0.3451\n",
      "Epoch 43/50\n",
      "3389/3389 [==============================] - 65s 19ms/step - loss: 0.1643 - acc: 0.9056 - val_loss: 4.2778 - val_acc: 0.3398\n",
      "Epoch 44/50\n",
      "3389/3389 [==============================] - 66s 20ms/step - loss: 0.2094 - acc: 0.8932 - val_loss: 4.0911 - val_acc: 0.3319\n",
      "Epoch 45/50\n",
      "3389/3389 [==============================] - 64s 19ms/step - loss: 0.2088 - acc: 0.8976 - val_loss: 4.1667 - val_acc: 0.3407\n",
      "Epoch 46/50\n",
      "3389/3389 [==============================] - 69s 20ms/step - loss: 0.1972 - acc: 0.8947 - val_loss: 4.2307 - val_acc: 0.3274\n",
      "Epoch 47/50\n",
      "3389/3389 [==============================] - 68s 20ms/step - loss: 0.1866 - acc: 0.8997 - val_loss: 4.2389 - val_acc: 0.3319\n",
      "Epoch 48/50\n",
      "3389/3389 [==============================] - 70s 21ms/step - loss: 0.1842 - acc: 0.8970 - val_loss: 4.2932 - val_acc: 0.3487\n",
      "Epoch 49/50\n",
      "3389/3389 [==============================] - 77s 23ms/step - loss: 0.1762 - acc: 0.9020 - val_loss: 4.3374 - val_acc: 0.3372\n",
      "Epoch 50/50\n",
      "3389/3389 [==============================] - 74s 22ms/step - loss: 0.1668 - acc: 0.9076 - val_loss: 4.4825 - val_acc: 0.3434\n",
      "3389/3389 [==============================] - 18s 5ms/step\n",
      "Training Accuracy: 0.9062\n",
      "1130/1130 [==============================] - 7s 6ms/step\n",
      "Testing Accuracy:  0.3434\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = model.fit(EX_train, Ey_train,\n",
    "                    epochs=50,\n",
    "                    verbose=True,\n",
    "                    validation_data=(EX_test, Ey_test)\n",
    "                   )\n",
    "loss, accuracy = model.evaluate(EX_train, Ey_train, verbose=True)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(EX_test, Ey_test, verbose=True)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n",
    "plot_history(history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}