dl_timeseries_forecasting_methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yjiPjeJErPaG"
   },
   "source": [
    "# Deep learning based time series Forecast\n",
    "Employing different deep learning architectures for time series forecasting\n",
    "\n",
    "Univariate input and single step forecast\n",
    "\n",
    "### <a id='1234'>Contents</a>\n",
    "- <a href='#0'>Libraries</a>\n",
    "- <a href='#1'>1. Data</a>\n",
    "    - <a href='#1.1'>1.1. Data overview</a>\n",
    "- <a href='#2'>2. Data Manipulation</a>\n",
    "- <a href='#3'>3. Exploratory Data Analysis</a>\n",
    "- <a href='#4'>4. Data preprocessing</a>\n",
    "- <a href='#5'>5. Model Building</a>\n",
    "    - <a href='#5.1'>5.1. MLP</a>\n",
    "    - <a href='#5.2'>5.2. CNN</a>\n",
    "    - <a href='#5.3'>5.3. Vanilla LSTM</a>\n",
    "    - <a href='#5.4'>5.4. Stacked LSTM</a>\n",
    "    - <a href='#5.5'>5.5. Bidirectional LSTM</a>\n",
    "    - <a href='#5.6'>5.6. CNN-LSTM</a>\n",
    "    - <a href='#5.7'>5.7. Conv-LSTM</a>\n",
    "- <a href='#6'>6. Model Performances</a>\n",
    "    - <a href='#6.1'>6.1. Model performance metrics</a>\n",
    "    - <a href='#6.2'>6.2. Compare model metrics</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pQg37smprPaH"
   },
   "source": [
    "# <a id='0'>Libraries</a>       \n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 662,
     "status": "ok",
     "timestamp": 1556273433246,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "K5x8hnOKrPaI",
    "outputId": "629b41a5-7200-483a-bc01-0148328aec00"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import re\n",
    "import nltk\n",
    "import platform\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import json\n",
    "import random\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from numpy import array\n",
    "from scipy import signal\n",
    "from datetime import datetime\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from json.decoder import JSONDecodeError\n",
    "from decimal import Decimal, ROUND_DOWN\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "from keras.models import model_from_json\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Bidirectional\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import ConvLSTM2D\n",
    "from keras.layers.convolutional import Conv1D, MaxPooling1D\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 618,
     "status": "ok",
     "timestamp": 1556273435582,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "s1mIJvmRrPaN",
    "outputId": "b7dbaae0-a317-4305-f97e-1670059e9693"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version 3.7.3\n",
      "pandas version 0.24.2\n",
      "sklearn version 0.20.3\n",
      "numpy version 1.16.2\n",
      "matplotlib version 3.0.3\n",
      "matplotlib version 3.4\n"
     ]
    }
   ],
   "source": [
    "print('python version', platform.python_version())\n",
    "print('pandas version', pd.__version__)\n",
    "print('sklearn version', sklearn.__version__)\n",
    "print('numpy version', np.__version__)\n",
    "print('matplotlib version', matplotlib.__version__)\n",
    "print('matplotlib version', nltk.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YelLyU-0rPaQ"
   },
   "source": [
    "# <a id='1'>1.Data</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ThSTrXzNrPaR"
   },
   "outputs": [],
   "source": [
    "data_dir = 'data/'\n",
    "data_dir = data_dir + 'call/WC_2018/row/'\n",
    "call_file = data_dir + 'call_wc2018_IN.csv' # columns: date, minute, direction, count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4BsSf8erPaW"
   },
   "outputs": [],
   "source": [
    "# Taking only India data\n",
    "df_call = pd.read_csv(call_file, sep='\\t', encoding='utf-8', names=['date', 'minute', 'direction', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4aZb3F5VrPaY"
   },
   "source": [
    "## <a id='1.1'>1.1. Data overview</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 574,
     "status": "ok",
     "timestamp": 1556273441459,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "mpIZ_ImErPaZ",
    "outputId": "a628e881-4d8c-47f6-b411-b63c4c38d431"
   },
   "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>date</th>\n",
       "      <th>minute</th>\n",
       "      <th>direction</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20180601</td>\n",
       "      <td>25463520</td>\n",
       "      <td>outgoing</td>\n",
       "      <td>699396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20180601</td>\n",
       "      <td>25463520</td>\n",
       "      <td>incoming</td>\n",
       "      <td>479642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20180601</td>\n",
       "      <td>25463521</td>\n",
       "      <td>incoming</td>\n",
       "      <td>439945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20180601</td>\n",
       "      <td>25463521</td>\n",
       "      <td>outgoing</td>\n",
       "      <td>651144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20180601</td>\n",
       "      <td>25463522</td>\n",
       "      <td>incoming</td>\n",
       "      <td>432172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date    minute direction   count\n",
       "0  20180601  25463520  outgoing  699396\n",
       "1  20180601  25463520  incoming  479642\n",
       "2  20180601  25463521  incoming  439945\n",
       "3  20180601  25463521  outgoing  651144\n",
       "4  20180601  25463522  incoming  432172"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_call.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 648,
     "status": "ok",
     "timestamp": 1556273443003,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "MgkElvIgrPad",
    "outputId": "8f2180f9-5cb7-4a36-e1a5-c7c9f421f250"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  175700\n",
      "Columns  :  4\n",
      "\n",
      "Features : \n",
      " ['date', 'minute', 'direction', 'count']\n",
      "\n",
      "Missing values :   47\n",
      "\n",
      "Unique values :  \n",
      " date             61\n",
      "minute        87826\n",
      "direction         3\n",
      "count        163028\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (\"Rows     : \" ,df_call.shape[0])\n",
    "print (\"Columns  : \" ,df_call.shape[1])\n",
    "print (\"\\nFeatures : \\n\" ,df_call.columns.tolist())\n",
    "print (\"\\nMissing values :  \", df_call.isnull().sum().values.sum())\n",
    "print (\"\\nUnique values :  \\n\",df_call.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 667,
     "status": "ok",
     "timestamp": 1556273447041,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "QbQ2jp0MrPag",
    "outputId": "d190d90d-bce9-4a13-c9ec-b4cbf95ff180"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['outgoing', 'incoming', nan, 'o5tgoing'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_call['direction'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 459,
     "status": "ok",
     "timestamp": 1556273447735,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "3sWzApWHrPaj",
    "outputId": "3e652f06-ff33-4317-f013-23897ab3eb6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outgoing    87826\n",
       "incoming    87826\n",
       "o5tgoing        1\n",
       "Name: direction, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_call['direction'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oh8Wz37SrPam"
   },
   "source": [
    "# <a id='2'>2. Data Manipulation</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R37fPAjnrPam"
   },
   "outputs": [],
   "source": [
    "# order is important!\n",
    "df_call['time'] = df_call['minute'].apply(lambda x: datetime.utcfromtimestamp(x * 60).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "df_call['time'] = pd.to_datetime(df_call['time'], format='%Y-%m-%d %H:%M:%S', errors='coerce')\n",
    "df_call.drop('date', axis=1, inplace=True)\n",
    "df_call.drop('minute', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1aKpz_iNrPao"
   },
   "outputs": [],
   "source": [
    "df_call[\"direction\"] = df_call[\"direction\"].replace({'o5tgoing':\"outgoing\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 603,
     "status": "ok",
     "timestamp": 1556273453665,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "tWsVSFajrPaq",
    "outputId": "39cbdac4-ede0-4173-e6b2-6401a6978055"
   },
   "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>direction</th>\n",
       "      <th>count</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>outgoing</td>\n",
       "      <td>699396</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>incoming</td>\n",
       "      <td>479642</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>incoming</td>\n",
       "      <td>439945</td>\n",
       "      <td>2018-06-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>outgoing</td>\n",
       "      <td>651144</td>\n",
       "      <td>2018-06-01 00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>incoming</td>\n",
       "      <td>432172</td>\n",
       "      <td>2018-06-01 00:02:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  direction   count                time\n",
       "0  outgoing  699396 2018-06-01 00:00:00\n",
       "1  incoming  479642 2018-06-01 00:00:00\n",
       "2  incoming  439945 2018-06-01 00:01:00\n",
       "3  outgoing  651144 2018-06-01 00:01:00\n",
       "4  incoming  432172 2018-06-01 00:02:00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_call.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aWSFaJREIGdL"
   },
   "outputs": [],
   "source": [
    "df_call.loc[df_call.direction == 'outgoing'][['time', 'count']].to_csv(data_dir + 'wc_outgoing_calls.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6tBRxOxbrPat"
   },
   "source": [
    "# <a id='3'>3. Exploratory Data Analysis</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6292,
     "status": "ok",
     "timestamp": 1556272778651,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "Zohl8bNsrPau",
    "outputId": "bd75f94c-0deb-4b46-bead-25d878270a07"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "plt.plot(df_call.loc[df_call.direction == 'incoming'][['time']], df_call.loc[df_call.direction == 'incoming'][['count']], '-', label='Call', markersize=1.0)\n",
    "plt.xlabel(\"Time\", color='blue')\n",
    "plt.ylabel(\"#Calls\", color='blue')\n",
    "plt.xticks(rotation=20)\n",
    "matplotlib.rc('axes.formatter', useoffset=False)\n",
    "plt.title(\"Incoming calls\", color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "smeV_PDKrPa2"
   },
   "source": [
    "# <a id='4'>4. Data preprocessing</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l5qDADCmrPa3"
   },
   "outputs": [],
   "source": [
    "df_call_train = df_call[df_call['time'] < '2018-07-01']\n",
    "df_call_val = df_call[df_call['time'] >= '2018-07-01']\n",
    "df_call_val = df_call_val[df_call_val['time'] < '2018-07-11']\n",
    "df_call_test = df_call[df_call['time'] >= '2018-07-11']\n",
    "df_call_test = df_call_test[df_call_test['time'] < '2018-07-21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6283,
     "status": "ok",
     "timestamp": 1556272778654,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "RGag5es4rPa5",
    "outputId": "baf9622e-d4db-468c-ab3f-821ec06ebe49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86403\n",
      "28811\n",
      "28805\n"
     ]
    }
   ],
   "source": [
    "print(df_call_train.shape[0])\n",
    "print(df_call_val.shape[0])\n",
    "print(df_call_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2jJEFA4srPa9"
   },
   "outputs": [],
   "source": [
    "df_train = df_call_train.loc[df_call_train.direction == 'incoming']\n",
    "df_val = df_call_val.loc[df_call_val.direction == 'incoming']\n",
    "df_test = df_call_test.loc[df_call_test.direction == 'incoming']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m63gzyNLrPa_"
   },
   "outputs": [],
   "source": [
    "call_seq_train = list(df_train['count'])\n",
    "call_seq_val = list(df_val['count'])\n",
    "call_seq_test = list(df_test['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6273,
     "status": "ok",
     "timestamp": 1556272778658,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "FnNODs_5rPbB",
    "outputId": "b1d6dc0d-33a8-46a7-ca99-02298a0e3337"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train size:  43187\n",
      "val size:  14399\n",
      "test size:  14400\n"
     ]
    }
   ],
   "source": [
    "print('train size: ', len(call_seq_train))\n",
    "print('val size: ', len(call_seq_val))\n",
    "print('test size: ', len(call_seq_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0327O9CdrPbM"
   },
   "source": [
    "### DL Based Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zr9L8v1crPbM"
   },
   "outputs": [],
   "source": [
    "# Transfrom univariate time series to supervised problem\n",
    "def split_univariate_sequence(sequence, n_stpes):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(sequence)):\n",
    "        # find the end of this pattern\n",
    "        end_ix = i + n_stpes\n",
    "        # check if it's beyond the sequence\n",
    "        if end_ix > len(sequence) - 1:\n",
    "            break\n",
    "        # get input and output parts\n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w72F4LXyrPbP"
   },
   "source": [
    "# <a id='5'>5. Model Building</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TxNZVflsrPbP"
   },
   "outputs": [],
   "source": [
    "algorithms = ['mlp', 'cnn', 'vanilla_lstm', 'stacked_lstm', 'bidirectional_lstm', 'cnn_lstm', 'conv_lstm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FbTzgFw_NsVZ"
   },
   "outputs": [],
   "source": [
    "model_json_dir = 'wc_call_model_jsons'\n",
    "model_weights_dir = 'wc_call_model_weights'\n",
    "model_hisotries_dir = 'wc_call_model_histories'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4APauTNvrPbV"
   },
   "outputs": [],
   "source": [
    "# dictionary for storing model json file names for different algorigthms\n",
    "model_json_files_dict = {}\n",
    "# dictionary for storing model weight file names for different algorigthms\n",
    "model_weight_files_dict = {}\n",
    "# dictionary for storing model weight file names for different algorigthms\n",
    "model_history_files_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7vsiHYaJrPbW"
   },
   "outputs": [],
   "source": [
    "for algo in algorithms:\n",
    "    model_json_files_dict[algo] = os.path.join(model_json_dir, 'in_call_%s.json' %algo)\n",
    "    model_weight_files_dict[algo] = os.path.join(model_weights_dir, 'in_call_%s.h5' %algo)\n",
    "    model_history_files_dict[algo] = os.path.join(model_hisotries_dir, 'in_call_%s.json' %algo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0MVylQbrrPbb"
   },
   "outputs": [],
   "source": [
    "n_features = 1\n",
    "\n",
    "n_steps = 30 # 30 time steps as input\n",
    "n_sub_seq = 3 # dividing time steps into 3\n",
    "n_sub_steps = 10 # 10 steps in each sub sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cAG8rpkFrPbd"
   },
   "outputs": [],
   "source": [
    "# Preprocessing for DL based analysis\n",
    "X_train, y_train = split_univariate_sequence(call_seq_train, n_steps)\n",
    "X_val, y_val = split_univariate_sequence(call_seq_val, n_steps)\n",
    "X_test, y_test = split_univariate_sequence(call_seq_test, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6232,
     "status": "ok",
     "timestamp": 1556272778669,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "QfRQnin9nKM7",
    "outputId": "e35b7962-9d8c-48b7-9350-4ac588916b65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43157, 30) (43157,)\n",
      "(14369, 30) (14369,)\n",
      "(14370, 30) (14370,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)\n",
    "print(X_val.shape, y_val.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-VcmHS4WrPbg"
   },
   "outputs": [],
   "source": [
    "# Initializing hyper parameters\n",
    "activation='relu'\n",
    "optimizer='adam'\n",
    "loss='mse'\n",
    "epochs=10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jyOTAAhOrPbh"
   },
   "outputs": [],
   "source": [
    "class Models(object):\n",
    "    # keep funciton name format \"algo_model\"(where 'algo' as in 'algorithm' array) for dynamic calling purpose\n",
    "    def mlp_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(100, activation=activation, input_dim=input_dim))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "    \n",
    "    def cnn_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(Conv1D(filters=64, kernel_size=2, activation=activation, input_shape=input_dim))\n",
    "        model.add(MaxPooling1D(pool_size=2))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(100, activation=activation))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "    \n",
    "    def vanilla_lstm_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(100, activation=activation, input_shape=input_dim))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "    \n",
    "    def stacked_lstm_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(100, activation=activation, return_sequences=True, input_shape=input_dim))\n",
    "        model.add(LSTM(50, activation=activation))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "        \n",
    "    def bidirectional_lstm_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(Bidirectional(LSTM(100, activation=activation), input_shape=input_dim))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "        \n",
    "    def cnn_lstm_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(TimeDistributed(Conv1D(filters=64, kernel_size=2, activation=activation), input_shape=input_dim))\n",
    "        model.add(TimeDistributed(MaxPooling1D(pool_size=2)))\n",
    "        model.add(TimeDistributed(Flatten()))\n",
    "        model.add(LSTM(100, activation=activation))\n",
    "        model.add(Dense(1))\n",
    "        return model\n",
    "        \n",
    "    def conv_lstm_model(self, input_dim):\n",
    "        model = Sequential()\n",
    "        model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation=activation, input_shape=input_dim))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(1))\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "an_dSGaqrPbj"
   },
   "outputs": [],
   "source": [
    "def train_model(x_train, y_train, x_val, y_val, input_dim, algo, model_json, model_weights, model_history, re_train=False):\n",
    "    if (os.path.isfile(model_json)):\n",
    "        with open(model_json, \"r\") as json_file:\n",
    "            model = model_from_json(json_file.read())\n",
    "    else:\n",
    "        models = Models()\n",
    "        model_func = getattr(models, '%s_model' %algo)\n",
    "        model = model_func(input_dim)\n",
    "        with open(model_json, \"w\") as json_file:\n",
    "            json_file.write(model.to_json())\n",
    "\n",
    "    if ((re_train == True) or (not os.path.isfile(model_weights))):\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy', 'mse', 'mae', 'mape', 'cosine'])\n",
    "        mc = ModelCheckpoint(model_weights, monitor='val_acc', mode='max', verbose=1, save_best_only=True)\n",
    "        history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=epochs, verbose=0, callbacks=[es, mc])\n",
    "        model.save_weights(model_weights)\n",
    "        with open(model_history, 'w') as f:\n",
    "            json.dump(hist.history, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2FoOEJz9rPbl"
   },
   "source": [
    "## <a id='5.1'>5.1. MLP</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UXCz4p_lrPbm"
   },
   "outputs": [],
   "source": [
    "algo = 'mlp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "01lyJ_vrrPbp"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val\n",
    "y_val_temp = y_val\n",
    "input_shape = n_steps\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6210,
     "status": "ok",
     "timestamp": 1556272778676,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "JErX870vrPbq",
    "outputId": "72b4f698-3168-498b-ac61-a946aa29db9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q_kZFO6zrPbz"
   },
   "source": [
    "## <a id='5.2'>5.2. CNN</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lauSvPgArPbz"
   },
   "outputs": [],
   "source": [
    "algo = 'cnn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AL9p4bABrPb3"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n",
    "y_val_temp = y_val\n",
    "input_shape = (n_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-i555WZqrPb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.00000, saving model to wc_call_model_histories/in_call_cnn.json\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.00000 to 0.00007, saving model to wc_call_model_histories/in_call_cnn.json\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.00007\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.00007\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.00007\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.00007 to 0.00014, saving model to wc_call_model_histories/in_call_cnn.json\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.00014\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.00014 to 0.00021, saving model to wc_call_model_histories/in_call_cnn.json\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00086: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00092: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00097: val_acc did not improve from 0.00021\n",
      "\n",
      "Epoch 00098: val_acc improved from 0.00021 to 0.00028, saving model to wc_call_model_histories/in_call_cnn.json\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-8041bb31f459>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_json\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_history\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-30-fca47a2990a5>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(x_train, y_train, x_val, y_val, input_dim, algo, model_json, model_weights, model_history, re_train)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mse'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mae'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cosine'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mmc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'val_acc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'max'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_best_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_history\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DAfctZ6zrPb-"
   },
   "source": [
    "## <a id='5.3'>5.3. Vanilla LSTM</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j0GSdNm5rPb_"
   },
   "outputs": [],
   "source": [
    "algo = 'vanilla_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H-tHIJNsrPcB"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n",
    "y_val_temp = y_val\n",
    "input_shape = (n_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cuZ4bXe3rPcC"
   },
   "outputs": [],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LQQm2arNrPcH"
   },
   "source": [
    "## <a id='5.4'>5.4. Stacked LSTM</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eBqc4uZPrPcI"
   },
   "outputs": [],
   "source": [
    "algo = 'stacked_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pfR2xdIBrPcK"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n",
    "y_val_temp = y_val\n",
    "input_shape = (n_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oelYOdGhrPcL"
   },
   "outputs": [],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RQU-HwnrrPcT"
   },
   "source": [
    "## <a id='5.5'>5.5. Bidirectional LSTM</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wy6qyPsBrPcT"
   },
   "outputs": [],
   "source": [
    "algo = 'bidirectional_lstm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vET6mDuprPcV"
   },
   "source": [
    "### Incoming calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "onGJLs9xrPcW"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n",
    "y_val_temp = y_val\n",
    "input_shape = (n_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cDsEI2jgrPcX"
   },
   "outputs": [],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e4ef2A_NrPcb"
   },
   "source": [
    "## <a id='5.6'>5.6. CNN-LSTM</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bOMZ9DYErPcb"
   },
   "outputs": [],
   "source": [
    "algo = 'cnn_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WCCf1qmQrPce"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], n_sub_seq, n_sub_steps, n_features))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], n_sub_seq, n_sub_steps, n_features))\n",
    "y_val_temp = y_val\n",
    "input_shape = (None, n_sub_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TnRmaV_wrPcf"
   },
   "outputs": [],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RtaSokgerPck"
   },
   "source": [
    "## <a id='5.7'>5.7. Conv-LSTM</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mcLDfEq7rPck"
   },
   "outputs": [],
   "source": [
    "algo = 'conv_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aM4hzEyErPcm"
   },
   "outputs": [],
   "source": [
    "X_train_temp = X_train.reshape((X_train.shape[0], n_sub_seq, 1, n_sub_steps, n_features))\n",
    "y_train_temp = y_train\n",
    "X_val_temp = X_val.reshape((X_val.shape[0], n_sub_seq, 1, n_sub_steps, n_features))\n",
    "y_val_temp = y_val\n",
    "input_shape = (n_sub_seq, 1, n_sub_steps, n_features)\n",
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]\n",
    "model_history = model_history_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X0XRBaJurPco"
   },
   "outputs": [],
   "source": [
    "train_model(X_train_temp, y_train_temp, X_val_temp, y_val_temp, input_shape, algo, model_json, model_history, model_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TmeExCQerPct"
   },
   "source": [
    "# <a id='6'>6. Model Performances</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MS9wCdqKrPct"
   },
   "outputs": [],
   "source": [
    "def get_mlp_prediction(model, x_input, input_dim):\n",
    "    x_input = x_input.reshape((1, input_dim))\n",
    "    yhat = model.predict(x_input, verbose=0)\n",
    "    return yhat\n",
    "\n",
    "def get_cnn_prediction(model, x_input, input_dim, n_features):\n",
    "    x_input = x_input.reshape((1, input_dim, n_features))\n",
    "    yhat = model.predict(x_input, verbose=0)\n",
    "    return yhat\n",
    "\n",
    "def get_lstm_prediction(model, x_input, input_dim, n_features):\n",
    "    x_input = x_input.reshape((1, input_dim, n_features))\n",
    "    yhat = model.predict(x_input, verbose=0)\n",
    "    return yhat\n",
    "\n",
    "def get_cnn_lstm_prediction(model, x_input, n_sub_seq, n_sub_steps, n_features):\n",
    "    x_input = x_input.reshape((1, n_sub_seq, n_sub_steps, n_features))\n",
    "    yhat = model.predict(x_input, verbose=0)\n",
    "    return yhat\n",
    "\n",
    "def get_convlstm_prediction(model, x_input, n_sub_seq, n_sub_steps, n_features):\n",
    "    x_input = x_input.reshape((1, n_sub_seq, 1, n_sub_steps, n_features))\n",
    "    yhat = model.predict(x_input, verbose=0)\n",
    "    return yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pTNOQ1rGrPcu"
   },
   "outputs": [],
   "source": [
    "# dictionaries for predictions key: algorithm value: dataframe(ytrue, yhat)\n",
    "pred_dict = {} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FjTgXhDJeCKO"
   },
   "outputs": [],
   "source": [
    "result_dir = 'wc_call_results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lrQ_cgOLrPcv"
   },
   "outputs": [],
   "source": [
    "for algo in algorithms:\n",
    "    pred_dict[algo] = pd.DataFrame(columns=('ytrue', 'yhat'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ongh0pcLrPcy"
   },
   "source": [
    "### MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ex1nZJU5rPcy"
   },
   "outputs": [],
   "source": [
    "algo = 'mlp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EYiidWPeXRPp"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8GXCn3AaXWDs"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GRGd-8CtckQg"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L3dB5EO_eLIz"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_mlp_prediction(model, X[i], input_shape)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "If2WFqBLrPc8"
   },
   "source": [
    "### CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XQP9SyCgYNPM"
   },
   "outputs": [],
   "source": [
    "algo = 'cnn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CpZ5AsN6ZCy1"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PudNcjreejcq"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A_xeD589gYRO"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cDtUCWoNnM_Q"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_cnn_prediction(model, X[i], input_shape, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Da6RnQJYrPdA"
   },
   "source": [
    "### Vanilla LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tb4jIrs8Yajw"
   },
   "outputs": [],
   "source": [
    "algo = 'vanilla_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7gXLREe-ZMjJ"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uab9gZ1gel2Z"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vum4R4h_gb-8"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LLrv_3qyqdxo"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_lstm_prediction(model, X[i], input_shape, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lcmlzjpCrPdC"
   },
   "source": [
    "### Stacked LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OUC7LxbzYeYU"
   },
   "outputs": [],
   "source": [
    "algo = 'stacked_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kTwc0xlKZPnp"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NmhBMmYieoyj"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bmO3ogZUgnph"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lh3K7MdTqvTV"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_lstm_prediction(model, X[i], input_shape, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gI3QtvEmrPdI"
   },
   "source": [
    "### Bidirectional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wo-qC8LzYgAi"
   },
   "outputs": [],
   "source": [
    "algo = 'bidirectional_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "31Pz5jr4ZT0v"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vYoxAAwuesDD"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LC6HHqK4gx3Q"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wJ-DPUq-q0ad"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_lstm_prediction(model, X[i], input_shape, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KHMLBNDorPdL"
   },
   "source": [
    "### CNN-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3HJlf1LhYhRY"
   },
   "outputs": [],
   "source": [
    "algo = 'cnn_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_7bMPulhZXWo"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "assXrpoHexcv"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pkLhmfWvgzNO"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S3l2xuB0q29Y"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_cnn_lstm_prediction(model, X[i], n_sub_seq, n_sub_steps, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZKB0SlsZrPdN"
   },
   "source": [
    "### Conv-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xnEZgdcaYisu"
   },
   "outputs": [],
   "source": [
    "algo = 'conv_lstm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pvbCLwT7ZbqU"
   },
   "outputs": [],
   "source": [
    "model_json = model_json_files_dict[algo]\n",
    "model_weights = model_weight_files_dict[algo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N1Debr2rezOG"
   },
   "outputs": [],
   "source": [
    "with open(model_json, \"r\") as json_file:\n",
    "  model = model_from_json(json_file.read())\n",
    "model.load_weights(model_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rUxNAgWGg0gC"
   },
   "outputs": [],
   "source": [
    "X = X_test\n",
    "y = y_test\n",
    "input_shape = n_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RlbmHcviq4z_"
   },
   "outputs": [],
   "source": [
    "result_file_name = '%s_incall_results.csv' %algo\n",
    "result_file = os.path.join(result_dir, result_file_name)\n",
    "if (os.path.isfile(result_file)):\n",
    "  pred_dict[algo] = pd.read_csv(result_file, sep='\\t', header=0, names=['ytrue', 'yhat'])\n",
    "else:\n",
    "  for i in range(len(X)):\n",
    "    ytrue = y[i]\n",
    "    yhat = get_convlstm_prediction(model, X[i], n_sub_seq, n_sub_steps, n_features)\n",
    "    yhat = round(yhat[0][0])\n",
    "    pred_dict[algo].loc[i] = [ytrue, yhat]\n",
    "  pred_dict[algo].to_csv(result_file, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eulhenf6rPdP"
   },
   "source": [
    "## <a id='6.1'>6.1. Model performance metrics</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C_eOXfdbrPdP"
   },
   "outputs": [],
   "source": [
    "def get_MAE(y_true, y_pred):\n",
    "    return round(mean_absolute_error(y_true, y_pred))\n",
    "\n",
    "def get_MSE(y_true, y_pred):\n",
    "    return round(mean_squared_error(y_true, y_pred), 2)\n",
    "\n",
    "def get_RMSE(y_true, y_pred):\n",
    "    return round(np.sqrt(mean_squared_error(y_true, y_pred)), 2)\n",
    "\n",
    "def get_MAPE(y_true, y_pred):\n",
    "    ##return round(np.mean(np.abs((y_true - y_pred) / y_true)) * 100)\n",
    "    return float(Decimal(str(np.mean(np.abs((y_true - y_pred) / y_true)) * 100)).quantize(Decimal('0.001'), rounding=ROUND_DOWN))\n",
    "  \n",
    "def get_CosineProximity(y_true, y_pred):\n",
    "    return float(Decimal(str(cosine_similarity([y_true], [y_pred])[0][0])).quantize(Decimal('0.00001'), rounding=ROUND_DOWN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Pcch4ozrPdQ"
   },
   "outputs": [],
   "source": [
    "model_perf_df = pd.DataFrame(columns=('Algorithm', 'MAE', 'MSE', 'RMSE', 'MAPE', 'Cosine_Proximity'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m0pWB_dEId1E"
   },
   "outputs": [],
   "source": [
    "for algo in algorithms:\n",
    "  row = list()\n",
    "  row.append(algo)\n",
    "  y_true = pred_dict[algo]['ytrue'].values\n",
    "  y_pred = pred_dict[algo]['yhat'].values\n",
    "  row.append(get_MAE(y_true, y_pred))\n",
    "  row.append(get_MSE(y_true, y_pred))\n",
    "  row.append(get_RMSE(y_true, y_pred))\n",
    "  row.append(get_MAPE(y_true, y_pred))\n",
    "  row.append(get_CosineProximity(y_true, y_pred))\n",
    "  model_perf_df.loc[len(model_perf_df)] = row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8sCNKEncrPdR"
   },
   "source": [
    "## <a id='6.2'>6.2. Compare model metrics</a>\n",
    "<div style=\"text-align: right\"><a href='#1234'>Table of Contents</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12176,
     "status": "ok",
     "timestamp": 1556272784966,
     "user": {
      "displayName": "Harilal Poyil",
      "photoUrl": "https://lh3.googleusercontent.com/-fe6A1ToLKoA/AAAAAAAAAAI/AAAAAAAAAAc/vrhwSHy_8mY/s64/photo.jpg",
      "userId": "03888189367678641493"
     },
     "user_tz": -120
    },
    "id": "P2-Y2OiiMb80",
    "outputId": "bd337ee8-c248-476d-f4e1-0f9a36c11926"
   },
   "outputs": [],
   "source": [
    "model_perf_df"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "wc_incoming_call_forecast_v1.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}