sentiment-analysis-with-lstm-cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a7bdd93ed8478235c2a290608f69054dd90df29d"
   },
   "source": [
    "![](https://diginomica.com/wp-content/uploads/2015/10/american-airlines1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1b69186dc6ab9470dcbbef3451d55824b888f8af"
   },
   "source": [
    "# 1. Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "e8f35d2c74c370d4b01dc540862a67254031e8b5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['database.sqlite', 'Tweets.csv']\n"
     ]
    }
   ],
   "source": [
    "# System\n",
    "import os\n",
    "\n",
    "# Time\n",
    "import time\n",
    "import datetime\n",
    "\n",
    "# Numerical\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Tools\n",
    "import itertools\n",
    "from collections import Counter\n",
    "\n",
    "# NLP\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize \n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "# from pywsd.utils import lemmatize_sentence\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import class_weight as cw\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# Model Selection\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, BaggingClassifier, ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Evaluation Metrics\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import f1_score, accuracy_score,confusion_matrix,classification_report\n",
    "\n",
    "# Deep Learing Preprocessing - Keras\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing import sequence\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# Deep Learning Model - Keras\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "\n",
    "# Deep Learning Model - Keras - CNN\n",
    "from keras.layers import Conv1D, Conv2D, Convolution1D, MaxPooling1D, SeparableConv1D, SpatialDropout1D, \\\n",
    "    GlobalAvgPool1D, GlobalMaxPool1D, GlobalMaxPooling1D \n",
    "from keras.layers.pooling import _GlobalPooling1D\n",
    "from keras.layers import MaxPooling2D, GlobalMaxPooling2D, GlobalAveragePooling2D\n",
    "\n",
    "from keras.layers import MaxPooling3D, GlobalMaxPooling3D, GlobalAveragePooling3D\n",
    "\n",
    "\n",
    "\n",
    "# Deep Learning Model - Keras - RNN\n",
    "from keras.layers import Embedding, LSTM, Bidirectional\n",
    "\n",
    "# Deep Learning Model - Keras - General\n",
    "from keras.layers import Input, Add, concatenate, Dense, Activation, BatchNormalization, Dropout, Flatten\n",
    "from keras.layers import LeakyReLU, PReLU, Lambda, Multiply\n",
    "\n",
    "\n",
    "\n",
    "# Deep Learning Parameters - Keras\n",
    "from keras.optimizers import RMSprop, Adam\n",
    "\n",
    "# Deep Learning Callbacs - Keras\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard, ReduceLROnPlateau\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "bcf244fb2623377e6c472520181f55b570f22225"
   },
   "outputs": [],
   "source": [
    "# print date and time for given type of representation\n",
    "def date_time(x):\n",
    "    if x==1:\n",
    "        return 'Timestamp: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now())\n",
    "    if x==2:    \n",
    "        return 'Timestamp: {:%Y-%b-%d %H:%M:%S}'.format(datetime.datetime.now())\n",
    "    if x==3:  \n",
    "        return 'Date now: %s' % datetime.datetime.now()\n",
    "    if x==4:  \n",
    "        return 'Date today: %s' % datetime.date.today() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f9941e463091c0974df63b98ddf42ef039d07044"
   },
   "source": [
    "# 2. Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "24a1fce867bde9c19af86e6ad3fdcc9368db4907"
   },
   "outputs": [],
   "source": [
    "input_directory = r\"../input/\"\n",
    "output_directory = r\"../output/\"\n",
    "\n",
    "if not os.path.exists(output_directory):\n",
    "    os.mkdir(output_directory)\n",
    "    \n",
    "figure_directory = \"../output/figures\"\n",
    "if not os.path.exists(figure_directory):\n",
    "    os.mkdir(figure_directory)\n",
    "    \n",
    "    \n",
    "file_name_pred_batch = figure_directory+r\"/result\"\n",
    "file_name_pred_sample = figure_directory+r\"/sample\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../input/Tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "82c38ef0a849c0e1b35dfaa6609497033690ecb3"
   },
   "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>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id             ...                           user_timezone\n",
       "0  570306133677760513             ...              Eastern Time (US & Canada)\n",
       "1  570301130888122368             ...              Pacific Time (US & Canada)\n",
       "2  570301083672813571             ...              Central Time (US & Canada)\n",
       "3  570301031407624196             ...              Pacific Time (US & Canada)\n",
       "4  570300817074462722             ...              Pacific Time (US & Canada)\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "5cf2744b1ea01acdb6e9100bba9c3e2a41833de0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 15 columns):\n",
      "tweet_id                        14640 non-null int64\n",
      "airline_sentiment               14640 non-null object\n",
      "airline_sentiment_confidence    14640 non-null float64\n",
      "negativereason                  9178 non-null object\n",
      "negativereason_confidence       10522 non-null float64\n",
      "airline                         14640 non-null object\n",
      "airline_sentiment_gold          40 non-null object\n",
      "name                            14640 non-null object\n",
      "negativereason_gold             32 non-null object\n",
      "retweet_count                   14640 non-null int64\n",
      "text                            14640 non-null object\n",
      "tweet_coord                     1019 non-null object\n",
      "tweet_created                   14640 non-null object\n",
      "tweet_location                  9907 non-null object\n",
      "user_timezone                   9820 non-null object\n",
      "dtypes: float64(2), int64(2), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "cd9d8e8cccbe7be826ff80ebba85c94059b19d81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'airline_sentiment', 'airline_sentiment_confidence',\n",
       "       'negativereason', 'negativereason_confidence', 'airline',\n",
       "       'airline_sentiment_gold', 'name', 'negativereason_gold',\n",
       "       'retweet_count', 'text', 'tweet_coord', 'tweet_created',\n",
       "       'tweet_location', 'user_timezone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "13bfcbe5c9e172fad039501b7c6604dc81abe010"
   },
   "source": [
    "# 3. Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "0ffeb6d063fb09a80db926657faddf0c719333b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(20, 5)\n",
    "\n",
    "ticksize = 14\n",
    "titlesize = ticksize + 8\n",
    "labelsize = ticksize + 5\n",
    "\n",
    "params = {'figure.figsize' : figsize,\n",
    "          'axes.labelsize' : labelsize,\n",
    "          'axes.titlesize' : titlesize,\n",
    "          'xtick.labelsize': ticksize,\n",
    "          'ytick.labelsize': ticksize}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.subplot(121)\n",
    "col = \"airline\"\n",
    "xlabel = \"Airlines\"\n",
    "ylabel = \"Count\"\n",
    "\n",
    "sns.countplot(x=df[col])\n",
    "plt.title(\"Airlines Review Count\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(xlabel)\n",
    "plt.ylabel(ylabel)\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "col = \"airline_sentiment\"\n",
    "xlabel = \"Sentiment\"\n",
    "ylabel = \"Count\"\n",
    "sns.countplot(df[col])\n",
    "plt.title(\"Review Sentiment Count\")\n",
    "plt.xlabel(xlabel)\n",
    "plt.ylabel(ylabel)\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d9ccaeb7b9230429515785e84ae9b36d15e1ac70"
   },
   "source": [
    "Note:\n",
    "1. Most of the review present here are negative.\n",
    "2. Highest number of tweets are about \"United Airlines\", \"US Airlines\" and \"American Airlines\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "6415fba24b67ddc166a43327d1327bc8c75f7f2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(20, 5)\n",
    "\n",
    "ticksize = 14\n",
    "titlesize = ticksize + 8\n",
    "labelsize = ticksize + 5\n",
    "\n",
    "xlabel = \"Negative Reason\"\n",
    "ylabel = \"Count\"\n",
    "\n",
    "title = \"Negative Reason Per Airlines\"\n",
    "\n",
    "\n",
    "params = {'figure.figsize' : figsize,\n",
    "          'axes.labelsize' : labelsize,\n",
    "          'axes.titlesize' : titlesize,\n",
    "          'xtick.labelsize': ticksize,\n",
    "          'ytick.labelsize': ticksize}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "col1 = \"negativereason\"\n",
    "col2 = \"airline\"\n",
    "sns.countplot(x=df[col1], hue=df[col2])\n",
    "plt.title(title)\n",
    "plt.xlabel(xlabel)\n",
    "plt.ylabel(ylabel)\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0ee91b5d86edbe0bd0e18faf300fcfd53c3e4ad4"
   },
   "source": [
    "Note:\n",
    "1. Most of the negative tweets are about \"Customer Service Issue\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "120e55e73a76526029cb3ffd648980ce11d8c8d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(20, 5)\n",
    "\n",
    "ticksize = 14\n",
    "titlesize = ticksize + 8\n",
    "labelsize = ticksize + 5\n",
    "\n",
    "xlabel = \"Airlines\"\n",
    "ylabel = \"Count\"\n",
    "\n",
    "\n",
    "params = {'figure.figsize' : figsize,\n",
    "          'axes.labelsize' : labelsize,\n",
    "          'axes.titlesize' : titlesize,\n",
    "          'xtick.labelsize': ticksize,\n",
    "          'ytick.labelsize': ticksize}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "col1 = \"airline\"\n",
    "col2 = \"airline_sentiment\"\n",
    "sns.countplot(x=df[col1], hue=df[col2])\n",
    "plt.xlabel(xlabel)\n",
    "plt.ylabel(ylabel)\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "1176c686fb26135f757d5fc98d264fffecee47aa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df[\"negativereason_confidence\"].fillna(-1)\n",
    "\n",
    "figsize=(18, 5)\n",
    "\n",
    "ticksize = 12\n",
    "titlesize = ticksize + 8\n",
    "labelsize = ticksize + 5\n",
    "\n",
    "params = {'figure.figsize' : figsize,\n",
    "          'axes.labelsize' : labelsize,\n",
    "          'axes.titlesize' : titlesize,\n",
    "          'xtick.labelsize': ticksize,\n",
    "          'ytick.labelsize': ticksize}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "sns.distplot(x)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f0d7b703a2c86d3a4652e52f62957078b078fb0f"
   },
   "source": [
    "Note:\n",
    "1. Most of negative confidence values are centered around 0.6-0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "dbbb6aef2c299642ab26cadaa3778c804bc8c4cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(18, 30)\n",
    "\n",
    "ticksize = 12\n",
    "titlesize = ticksize + 8\n",
    "labelsize = ticksize + 5\n",
    "\n",
    "xlabel = \"Airlines\"\n",
    "ylabel = \"Count\"\n",
    "\n",
    "\n",
    "params = {'figure.figsize' : figsize,\n",
    "          'axes.labelsize' : labelsize,\n",
    "          'axes.titlesize' : titlesize,\n",
    "          'xtick.labelsize': ticksize,\n",
    "          'ytick.labelsize': ticksize}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "col = \"user_timezone\"\n",
    "col2 = \"airline_sentiment\"\n",
    "sns.countplot(y=df[col], hue=df[col2])\n",
    "plt.xticks(rotation=90)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "498ac53969f7c96c9b4ada819fa65d80b95dacd0"
   },
   "source": [
    "## 4. Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "94b58472944a5843de64f63e1624b757abb06bc1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.70533111 0.69006457 0.71360133]\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "X = df\n",
    "\n",
    "X[\"text\"] = X[\"text\"].apply(lambda x: BeautifulSoup(x, \"lxml\").get_text())\n",
    "X[\"text\"] = X[\"text\"].apply(lambda x: x.lower())\n",
    "X[\"text\"] = X[\"text\"].apply(lambda x: re.sub(\"[^a-zA-Z]\", \" \", x))\n",
    "X[\"text\"] = X[\"text\"].apply(lambda x: re.sub(\"\\s+\", \" \", x))\n",
    "\n",
    "X = X[X[\"airline_sentiment_confidence\"]>0.5]\n",
    "\n",
    "y = X[\"airline_sentiment\"]\n",
    "X = X[\"text\"]\n",
    "\n",
    "stopwords = stopwords.words('english')\n",
    "# vectorizer = TfidfVectorizer()\n",
    "vectorizer = CountVectorizer(stop_words=stopwords)\n",
    "\n",
    "X = vectorizer.fit_transform(X)\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=5, n_jobs=-1, class_weight='balanced', random_state=0)\n",
    "# model = SVC()\n",
    "\n",
    "print(cross_val_score(model, X, y, cv=3))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "63bd6eb6b6e61629f6584772897655d98fa4253e"
   },
   "source": [
    "## 5. Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "727e5f0c3130d2df7403d96c743c8d4f4c3b7c4d"
   },
   "source": [
    "### 5.1 Output Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "c8703accc04a96fda2e561b6e66e474a23dde81a"
   },
   "outputs": [],
   "source": [
    "main_model_dir = output_directory + r\"models/\"\n",
    "main_log_dir = output_directory + r\"logs/\"\n",
    "\n",
    "try:\n",
    "    os.mkdir(main_model_dir)\n",
    "except:\n",
    "    print(\"Could not create main model directory\")\n",
    "    \n",
    "try:\n",
    "    os.mkdir(main_log_dir)\n",
    "except:\n",
    "    print(\"Could not create main log directory\")\n",
    "\n",
    "\n",
    "\n",
    "model_dir = main_model_dir + time.strftime('%Y-%m-%d %H-%M-%S') + \"/\"\n",
    "log_dir = main_log_dir + time.strftime('%Y-%m-%d %H-%M-%S')\n",
    "\n",
    "\n",
    "try:\n",
    "    os.mkdir(model_dir)\n",
    "except:\n",
    "    print(\"Could not create model directory\")\n",
    "    \n",
    "try:\n",
    "    os.mkdir(log_dir)\n",
    "except:\n",
    "    print(\"Could not create log directory\")\n",
    "    \n",
    "model_file = model_dir + \"{epoch:02d}-val_acc-{val_acc:.2f}-val_loss-{val_loss:.2f}.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "0eeca633f28358dbfafd3db7fd22518fc77a276a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Settting Callbacks\n",
      "Set Callbacks at  Timestamp: 2019-03-16 15:18:56\n"
     ]
    }
   ],
   "source": [
    "print(\"Settting Callbacks\")\n",
    "\n",
    "checkpoint = ModelCheckpoint(\n",
    "    model_file, \n",
    "    monitor='val_acc', \n",
    "    save_best_only=True)\n",
    "\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=3,\n",
    "    verbose=1,\n",
    "    restore_best_weights=True)\n",
    "\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    factor=0.5,\n",
    "    patience=1,\n",
    "    verbose=1)\n",
    "\n",
    "\n",
    "callbacks = [checkpoint, reduce_lr, early_stopping]\n",
    "\n",
    "# callbacks = [early_stopping]\n",
    "\n",
    "print(\"Set Callbacks at \", date_time(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "42410b8507438ce15c28b5e9778908515dfaeb51"
   },
   "source": [
    "### 5.2. Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "c97ce5c809f58080f13ab1da76fbeff96817a321"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.text\n",
    "Y = df.airline_sentiment\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "Y = label_encoder.fit_transform(Y)\n",
    "\n",
    "Y = to_categorical(Y)\n",
    "\n",
    "# Y = Y.reshape(-1, 1)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "972b31926122af0747be40fa2b5d3bf14dcbe16a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12466, 176)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)\n",
    "\n",
    "max_words = len(set(\" \".join(X_train).split()))\n",
    "max_len = X_train.apply(lambda x: len(x)).max()\n",
    "\n",
    "# max_words = 1000\n",
    "# max_len = 150\n",
    "max_words, max_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "6f82546a785e1eba0e6df397078497dffc94e902"
   },
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=max_words)\n",
    "\n",
    "tokenizer.fit_on_texts(X_train)\n",
    "\n",
    "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
    "X_train_seq = sequence.pad_sequences(X_train_seq, maxlen=max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "15756f1979d321902f18964c00c515fb31342613"
   },
   "outputs": [],
   "source": [
    "# Calculate Class Weights\n",
    "def get_weight(y):\n",
    "    class_weight_current =  cw.compute_class_weight('balanced', np.unique(y), y)\n",
    "    return class_weight_current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "f65f33959fa6abe41bfea8c660414a757ac048bf"
   },
   "outputs": [],
   "source": [
    "class_weight = get_weight(Y_train.flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "19a3f4532b9398234b6f78e758688d7903d6b66b"
   },
   "source": [
    "### 5.3 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "ffc3ae3a4fb844cd20a9cb33750de42b2c71392d"
   },
   "outputs": [],
   "source": [
    "def get_rnn_model(num_class=2):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Embedding(max_words, 100, input_length=max_len))\n",
    "    model.add(LSTM(256))\n",
    "    \n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    \n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    if num_class>2:\n",
    "        model.add(Dense(num_class, activation='softmax'))\n",
    "    else:\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    model.summary()\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "def get_cnn_model(num_class=2):   \n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Embedding(max_words, 100, input_length=max_len))\n",
    "    \n",
    "    model.add(Conv1D(1024, 3, padding='valid', activation='relu', strides=1))\n",
    "    model.add(GlobalMaxPooling1D())\n",
    "    \n",
    "    \n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    model.add(Dense(2048, activation='relu'))\n",
    "    \n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.5))\n",
    "    \n",
    "    if num_class>2:\n",
    "        model.add(Dense(num_class, activation='softmax'))\n",
    "    else:\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "a5472569b1c7c8f13fe14c9dcac3a7957db1300e"
   },
   "outputs": [],
   "source": [
    "def plot_performance(history=None, figure_directory=None, ylim_pad=[0, 0]):\n",
    "    xlabel = 'Epoch'\n",
    "    legends = ['Training', 'Validation']\n",
    "\n",
    "    plt.figure(figsize=(20, 5))\n",
    "\n",
    "    y1 = history.history['acc']\n",
    "    y2 = history.history['val_acc']\n",
    "\n",
    "    min_y = min(min(y1), min(y2))-ylim_pad[0]\n",
    "    max_y = max(max(y1), max(y2))+ylim_pad[0]\n",
    "\n",
    "\n",
    "    plt.subplot(121)\n",
    "\n",
    "    plt.plot(y1)\n",
    "    plt.plot(y2)\n",
    "\n",
    "    plt.title('Model Accuracy\\n'+date_time(1), fontsize=17)\n",
    "    plt.xlabel(xlabel, fontsize=15)\n",
    "    plt.ylabel('Accuracy', fontsize=15)\n",
    "    plt.ylim(min_y, max_y)\n",
    "    plt.legend(legends, loc='upper left')\n",
    "    plt.grid()\n",
    "\n",
    "    y1 = history.history['loss']\n",
    "    y2 = history.history['val_loss']\n",
    "\n",
    "    min_y = min(min(y1), min(y2))-ylim_pad[1]\n",
    "    max_y = max(max(y1), max(y2))+ylim_pad[1]\n",
    "\n",
    "\n",
    "    plt.subplot(122)\n",
    "\n",
    "    plt.plot(y1)\n",
    "    plt.plot(y2)\n",
    "\n",
    "    plt.title('Model Loss\\n'+date_time(1), fontsize=17)\n",
    "    plt.xlabel(xlabel, fontsize=15)\n",
    "    plt.ylabel('Loss', fontsize=15)\n",
    "    plt.ylim(min_y, max_y)\n",
    "    plt.legend(legends, loc='upper left')\n",
    "    plt.grid()\n",
    "    if figure_directory:\n",
    "        plt.savefig(figure_directory+\"/history\")\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "b424eb9a6e314ef397df89dcfeffc4c9c15839ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 176, 100)          1246600   \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 256)               365568    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 1539      \n",
      "=================================================================\n",
      "Total params: 1,748,363\n",
      "Trainable params: 1,746,827\n",
      "Non-trainable params: 1,536\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "num_class = 3\n",
    "model1 = get_rnn_model(num_class=num_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "72903b1167548759e416d9b8ba643fdf90305e06"
   },
   "outputs": [],
   "source": [
    "loss = 'categorical_crossentropy'\n",
    "# loss = 'binary_crossentropy'\n",
    "metrics = ['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2bd1166ccac602ca7d908e9f9b17c8ffb2a1f92c"
   },
   "source": [
    "## 10.3. Model Trainning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a1ce715c032b60131950b0946776b41040ec97f4"
   },
   "source": [
    "### 10.3.1. RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "3499506821ecfcfa499a1195742796152939cc94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting...\n",
      "\n",
      "Timestamp: 2019-03-16 15:18:57\n",
      "\n",
      "\n",
      "Compliling Model ...\n",
      "\n",
      "Trainning Model ...\n",
      "\n",
      "Train on 9955 samples, validate on 2489 samples\n",
      "Epoch 1/100\n",
      "9955/9955 [==============================] - 29s 3ms/step - loss: 1.4276 - acc: 0.5213 - val_loss: 0.9035 - val_acc: 0.6689\n",
      "Epoch 2/100\n",
      "9955/9955 [==============================] - 27s 3ms/step - loss: 0.8321 - acc: 0.6702 - val_loss: 0.6435 - val_acc: 0.7405\n",
      "Epoch 3/100\n",
      "9955/9955 [==============================] - 27s 3ms/step - loss: 0.5275 - acc: 0.7827 - val_loss: 0.6807 - val_acc: 0.7589\n",
      "\n",
      "Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 4/100\n",
      "9955/9955 [==============================] - 27s 3ms/step - loss: 0.3445 - acc: 0.8693 - val_loss: 0.8868 - val_acc: 0.7087\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 5/100\n",
      "9955/9955 [==============================] - 27s 3ms/step - loss: 0.2687 - acc: 0.8983 - val_loss: 0.6964 - val_acc: 0.7702\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Restoring model weights from the end of the best epoch\n",
      "Epoch 00005: early stopping\n",
      "\n",
      "Elapsed Time: 00:02:18\n",
      "Completed Model Trainning Timestamp: 2019-03-16 15:21:15\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting...\\n\")\n",
    "\n",
    "start_time = time.time()\n",
    "print(date_time(1))\n",
    "\n",
    "print(\"\\n\\nCompliling Model ...\\n\")\n",
    "learning_rate = 0.001\n",
    "optimizer = Adam(learning_rate)\n",
    "# optimizer = Adam()\n",
    "\n",
    "model1.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "verbose = 1\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "validation_split = 0.2\n",
    "\n",
    "print(\"Trainning Model ...\\n\")\n",
    "\n",
    "history1 = model1.fit(\n",
    "    X_train_seq,\n",
    "    Y_train,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    verbose=verbose,\n",
    "    callbacks=callbacks,\n",
    "    validation_split=validation_split,\n",
    "    class_weight =class_weight\n",
    "    )\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "elapsed_time = time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time))\n",
    "\n",
    "print(\"\\nElapsed Time: \" + elapsed_time)\n",
    "print(\"Completed Model Trainning\", date_time(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "174b2e76795372f6e7d09d6fa48579f782609b78"
   },
   "source": [
    "#### 10.3.1.2  Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "5e6865d7574bb08bfc6e20ce51c8c0a6ab39ef4c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(history=history1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "29bb4e66b2f030e88d88ca12d48209a93991647c"
   },
   "source": [
    "### 10.3.1. RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "106063a66e2031658fd6649c2bc68ff54a2d6e9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 176, 100)          1246600   \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 174, 1024)         308224    \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2048)              2099200   \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3)                 6147      \n",
      "=================================================================\n",
      "Total params: 3,672,459\n",
      "Trainable params: 3,666,315\n",
      "Non-trainable params: 6,144\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "num_class = 3\n",
    "model2 = get_cnn_model(num_class=num_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "f15fae20f343530b2e83e9c890fe674fc0d3ea1a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting...\n",
      "\n",
      "Timestamp: 2019-03-16 15:21:16\n",
      "\n",
      "\n",
      "Compliling Model ...\n",
      "\n",
      "Trainning Model ...\n",
      "\n",
      "Train on 9955 samples, validate on 2489 samples\n",
      "Epoch 1/100\n",
      "9955/9955 [==============================] - 5s 500us/step - loss: 1.4042 - acc: 0.5215 - val_loss: 0.6624 - val_acc: 0.7368\n",
      "Epoch 2/100\n",
      "9955/9955 [==============================] - 2s 168us/step - loss: 0.8706 - acc: 0.6809 - val_loss: 0.6180 - val_acc: 0.7429\n",
      "Epoch 3/100\n",
      "9955/9955 [==============================] - 2s 168us/step - loss: 0.6106 - acc: 0.7645 - val_loss: 0.5908 - val_acc: 0.7553\n",
      "Epoch 4/100\n",
      "9955/9955 [==============================] - 2s 169us/step - loss: 0.4498 - acc: 0.8310 - val_loss: 0.5640 - val_acc: 0.7867\n",
      "Epoch 5/100\n",
      "9955/9955 [==============================] - 2s 168us/step - loss: 0.3546 - acc: 0.8670 - val_loss: 0.5955 - val_acc: 0.7758\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 6/100\n",
      "9955/9955 [==============================] - 2s 167us/step - loss: 0.2523 - acc: 0.9056 - val_loss: 0.6477 - val_acc: 0.7746\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 7/100\n",
      "9955/9955 [==============================] - 2s 167us/step - loss: 0.1873 - acc: 0.9306 - val_loss: 0.6793 - val_acc: 0.7750\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Restoring model weights from the end of the best epoch\n",
      "Epoch 00007: early stopping\n",
      "\n",
      "Elapsed Time: 00:00:16\n",
      "Completed Model Trainning Timestamp: 2019-03-16 15:21:33\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting...\\n\")\n",
    "\n",
    "start_time = time.time()\n",
    "print(date_time(1))\n",
    "\n",
    "print(\"\\n\\nCompliling Model ...\\n\")\n",
    "learning_rate = 0.001\n",
    "optimizer = Adam(learning_rate)\n",
    "# optimizer = Adam()\n",
    "\n",
    "model2.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "verbose = 1\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "validation_split = 0.2\n",
    "\n",
    "print(\"Trainning Model ...\\n\")\n",
    "\n",
    "history2 = model2.fit(\n",
    "    X_train_seq,\n",
    "    Y_train,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    verbose=verbose,\n",
    "    callbacks=callbacks,\n",
    "    validation_split=validation_split,\n",
    "    class_weight =class_weight\n",
    "    )\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "elapsed_time = time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time))\n",
    "\n",
    "print(\"\\nElapsed Time: \" + elapsed_time)\n",
    "print(\"Completed Model Trainning\", date_time(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "357b590e09d3e623822c3e68b280ef42db952075"
   },
   "source": [
    "#### 10.3.1.2 Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "50d81ffc4f78b45a59b7f08ec404d008a3c63f94"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(history=history2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b64920b6ac5a1bb4e60f1d937245007292dd1681"
   },
   "source": [
    "## 10.5 Inference/ Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "a6e1d8e3639c5e739a67c484b00faf148fa6edd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2196/2196 [==============================] - 7s 3ms/step\n",
      "2196/2196 [==============================] - 0s 74us/step\n"
     ]
    }
   ],
   "source": [
    "test_X_seq = tokenizer.texts_to_sequences(X_test)\n",
    "test_X_seq = sequence.pad_sequences(test_X_seq, maxlen=max_len)\n",
    "accuracy1 = model1.evaluate(test_X_seq, Y_test)\n",
    "accuracy2 = model2.evaluate(test_X_seq, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "08c78c65d45aaa855de2d16533dc5ddeb1a52c9b"
   },
   "source": [
    "### 10.5.1 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "fd3d9e8a332ab50eb69d3995fa0847c642ca942d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance of RNN (Test Accuracy):\n",
      "Accuracy: 74.27%\n",
      "Loss: 0.684\n",
      "\n",
      "\n",
      "Model Performance of RNN (Test Accuracy):\n",
      "v: 79.64%\n",
      "Loss: 0.563\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Performance of RNN (Test Accuracy):\")\n",
    "print('Accuracy: {:0.2f}%\\nLoss: {:0.3f}\\n'.format(accuracy1[1]*100, accuracy1[0]))\n",
    "\n",
    "print(\"\\nModel Performance of RNN (Test Accuracy):\")\n",
    "print('v: {:0.2f}%\\nLoss: {:0.3f}\\n'.format(accuracy2[1]*100, accuracy2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "68d1b5720cecae415e852c1f35563da08cfea2b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2196/2196 [==============================] - 8s 4ms/step\n",
      "2196/2196 [==============================] - 0s 147us/step\n"
     ]
    }
   ],
   "source": [
    "ypreds1 = model1.predict_classes(test_X_seq, verbose=1)\n",
    "ypreds2 = model2.predict_classes(test_X_seq, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "3feb7baf3e5658dce6bdcf929af34676b5f23fd7"
   },
   "outputs": [],
   "source": [
    "def plot_model_performace(result):\n",
    "    sns.set_style(\"ticks\")\n",
    "    figsize=(22, 6)\n",
    "\n",
    "    ticksize = 12\n",
    "    titlesize = ticksize + 8\n",
    "    labelsize = ticksize + 5\n",
    "\n",
    "    xlabel = \"Model\"\n",
    "    ylabel = \"Score\"\n",
    "\n",
    "    title = \"Model Performance\"\n",
    "\n",
    "    params = {'figure.figsize' : figsize,\n",
    "              'axes.labelsize' : labelsize,\n",
    "              'axes.titlesize' : titlesize,\n",
    "              'xtick.labelsize': ticksize,\n",
    "              'ytick.labelsize': ticksize}\n",
    "\n",
    "    plt.rcParams.update(params)\n",
    "\n",
    "    col1 = \"model\"\n",
    "    col2 = \"score\"\n",
    "    sns.barplot(x=col1, y=col2, data=result)\n",
    "    plt.title(title.title())\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.grid()\n",
    "    plt.plot()\n",
    "    plt.show()\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "dc19b344d18b16e4f15d4b21eb86bf6acde8c2f0"
   },
   "outputs": [],
   "source": [
    "# print(classification_report(Y_test, ypreds1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "47e7e60a9e06662083a4971f84589129d307be40"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_confusion_matrix' 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-35-084bd3742821>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mypreds1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"RNN\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plot_confusion_matrix' is not defined"
     ]
    }
   ],
   "source": [
    "plot_confusion_matrix(Y_test, ypreds1, title=\"RNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "0fc73dddd076f5c15ff41a9dff2abf47d7126589"
   },
   "outputs": [],
   "source": [
    "# print(classification_report(Y_test, ypreds2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "73e7f87fdfb70dd3eddabd607a9eefb96a086831"
   },
   "source": [
    "#### 10.5.1.2 Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "675a75e41d24b34491a9b9e310b30803b90e9188"
   },
   "outputs": [],
   "source": [
    "# plot_confusion_matrix(Y_test, ypreds2, title=\"CNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "6679f27d3d8d66fcc4c99d3183821051f823f688"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "result = pd.DataFrame({'model': 'RNN', 'score': accuracy1[1]*100}, index=[-1])\n",
    "row2 = pd.DataFrame({'model': 'CNN', 'score': accuracy2[1]*100}, index=[-1])\n",
    "result = pd.concat([row2, result.ix[:]]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "fa8ce5f06a1b2884dd5b1c113dcd041c86aa26bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  model      score\n",
      "0   CNN  79.644809\n",
      "1   RNN  74.271403\n"
     ]
    }
   ],
   "source": [
    "plot_model_performace(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "abb7d608d7ed5bcd412125d31d176a022d599444"
   },
   "source": [
    "# Reference:\n",
    "1. [Text Preprocessing and Machine Learning Modeling](https://www.kaggle.com/futurist/text-preprocessing-and-machine-learning-modeling)\n",
    "2. [keras mlp cnn test for text classification](https://www.kaggle.com/jacklinggu/keras-mlp-cnn-test-for-text-classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "4508c011e7f7449b027acc000decbf6561e69ca0"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "a44c65a66fdfbf03738c03f4eaeb90c2166a45d2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}