1_CNN_dilated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dilated CNN model\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training a Convolutional Neural Network (CNN) forecasting model\n",
    "- get data in the required shape for the keras API\n",
    "- implement a CNN model in keras to predict the next step ahead (time *t+1*) in the time series\n",
    "- enable early stopping to reduce the likelihood of model overfitting\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from collections import UserDict\n",
    "from glob import glob\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "from common.utils import load_data, mape\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data into Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('data/')[['load']]\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create train, validation and test sets\n",
    "\n",
    "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n",
    "\n",
    "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7dce58dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[(energy.index >=valid_start_dt) & (energy.index < test_start_dt)][['load']] \\\n",
    "          .rename(columns={'load':'validation'}), how='outer') \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'validation', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - training set\n",
    "\n",
    "For this example, we will set *T=10*. This means that the input for each sample is a vector of the prevous 10 hours of the energy load. The choice of *T=10* was arbitrary but should be selected through experimentation.\n",
    "\n",
    "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/one_step_forecast_T10.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 10\n",
    "HORIZON = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data preparation for the training set will involve the following steps:\n",
    "\n",
    "1. Filter the original dataset to include only that time period reserved for the training set\n",
    "2. Scale the time series such that the values fall within the interval (0, 1)\n",
    "3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "4. Discard any samples with missing values\n",
    "5. Transform this Pandas dataframe into a numpy array of shape (samples, features) for input into Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Filter the original dataset to include only that time period reserved for the training set\n",
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = energy.copy()[energy.index < valid_start_dt][['load']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Scale the time series such that the values fall within the interval (0, 1)\n",
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2012-01-01 00:00:00  0.22\n",
       "2012-01-01 01:00:00  0.18\n",
       "2012-01-01 02:00:00  0.14\n",
       "2012-01-01 03:00:00  0.13\n",
       "2012-01-01 04:00:00  0.13\n",
       "2012-01-01 05:00:00  0.15\n",
       "2012-01-01 06:00:00  0.18\n",
       "2012-01-01 07:00:00  0.23\n",
       "2012-01-01 08:00:00  0.29\n",
       "2012-01-01 09:00:00  0.35"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7dcb842e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7dcb840f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "First, we create the target (*y_t+1*) variable. If we use the convention that the dataframe is indexed on time *t*, we need to shift the *load* variable forward one hour in time. Using the freq parameter we can tell Pandas that the frequency of the time series is hourly. This ensures the shift does not jump over any missing periods in the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>y_t+1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  y_t+1\n",
       "2012-01-01 00:00:00  0.22   0.18\n",
       "2012-01-01 01:00:00  0.18   0.14\n",
       "2012-01-01 02:00:00  0.14   0.13\n",
       "2012-01-01 03:00:00  0.13   0.13\n",
       "2012-01-01 04:00:00  0.13   0.15\n",
       "2012-01-01 05:00:00  0.15   0.18\n",
       "2012-01-01 06:00:00  0.18   0.23\n",
       "2012-01-01 07:00:00  0.23   0.29\n",
       "2012-01-01 08:00:00  0.29   0.35\n",
       "2012-01-01 09:00:00  0.35   0.37"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted = train.copy()\n",
    "train_shifted['y_t+1'] = train_shifted['load'].shift(-1, freq='H')\n",
    "train_shifted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to shift the load variable back 6 times to create the input sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-9</th>\n",
       "      <th>load_t-8</th>\n",
       "      <th>load_t-7</th>\n",
       "      <th>load_t-6</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t-0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-9  load_t-8  load_t-7  \\\n",
       "2012-01-01 00:00:00           0.22   0.18       nan       nan       nan   \n",
       "2012-01-01 01:00:00           0.18   0.14       nan       nan       nan   \n",
       "2012-01-01 02:00:00           0.14   0.13       nan       nan       nan   \n",
       "2012-01-01 03:00:00           0.13   0.13       nan       nan       nan   \n",
       "2012-01-01 04:00:00           0.13   0.15       nan       nan       nan   \n",
       "2012-01-01 05:00:00           0.15   0.18       nan       nan       nan   \n",
       "2012-01-01 06:00:00           0.18   0.23       nan       nan       nan   \n",
       "2012-01-01 07:00:00           0.23   0.29       nan       nan      0.22   \n",
       "2012-01-01 08:00:00           0.29   0.35       nan      0.22      0.18   \n",
       "2012-01-01 09:00:00           0.35   0.37      0.22      0.18      0.14   \n",
       "\n",
       "                     load_t-6  load_t-5  load_t-4  load_t-3  load_t-2  \\\n",
       "2012-01-01 00:00:00       nan       nan       nan       nan       nan   \n",
       "2012-01-01 01:00:00       nan       nan       nan       nan       nan   \n",
       "2012-01-01 02:00:00       nan       nan       nan       nan      0.22   \n",
       "2012-01-01 03:00:00       nan       nan       nan      0.22      0.18   \n",
       "2012-01-01 04:00:00       nan       nan      0.22      0.18      0.14   \n",
       "2012-01-01 05:00:00       nan      0.22      0.18      0.14      0.13   \n",
       "2012-01-01 06:00:00      0.22      0.18      0.14      0.13      0.13   \n",
       "2012-01-01 07:00:00      0.18      0.14      0.13      0.13      0.15   \n",
       "2012-01-01 08:00:00      0.14      0.13      0.13      0.15      0.18   \n",
       "2012-01-01 09:00:00      0.13      0.13      0.15      0.18      0.23   \n",
       "\n",
       "                     load_t-1  load_t-0  \n",
       "2012-01-01 00:00:00       nan      0.22  \n",
       "2012-01-01 01:00:00      0.22      0.18  \n",
       "2012-01-01 02:00:00      0.18      0.14  \n",
       "2012-01-01 03:00:00      0.14      0.13  \n",
       "2012-01-01 04:00:00      0.13      0.13  \n",
       "2012-01-01 05:00:00      0.13      0.15  \n",
       "2012-01-01 06:00:00      0.15      0.18  \n",
       "2012-01-01 07:00:00      0.18      0.23  \n",
       "2012-01-01 08:00:00      0.23      0.29  \n",
       "2012-01-01 09:00:00      0.29      0.35  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for t in range(1, T+1):\n",
    "    train_shifted['load_t-'+str(T-t)] = train_shifted['load'].shift(T-t, freq='H')\n",
    "train_shifted = train_shifted.rename(columns={'load':'load_original'})\n",
    "train_shifted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Discard any samples with missing values\n",
    "Notice how we have missing values for the input sequences for the first 5 samples. We will discard these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-9</th>\n",
       "      <th>load_t-8</th>\n",
       "      <th>load_t-7</th>\n",
       "      <th>load_t-6</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t-0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 10:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 11:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 12:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 13:00:00</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-9  load_t-8  load_t-7  \\\n",
       "2012-01-01 09:00:00           0.35   0.37      0.22      0.18      0.14   \n",
       "2012-01-01 10:00:00           0.37   0.37      0.18      0.14      0.13   \n",
       "2012-01-01 11:00:00           0.37   0.37      0.14      0.13      0.13   \n",
       "2012-01-01 12:00:00           0.37   0.36      0.13      0.13      0.15   \n",
       "2012-01-01 13:00:00           0.36   0.35      0.13      0.15      0.18   \n",
       "\n",
       "                     load_t-6  load_t-5  load_t-4  load_t-3  load_t-2  \\\n",
       "2012-01-01 09:00:00      0.13      0.13      0.15      0.18      0.23   \n",
       "2012-01-01 10:00:00      0.13      0.15      0.18      0.23      0.29   \n",
       "2012-01-01 11:00:00      0.15      0.18      0.23      0.29      0.35   \n",
       "2012-01-01 12:00:00      0.18      0.23      0.29      0.35      0.37   \n",
       "2012-01-01 13:00:00      0.23      0.29      0.35      0.37      0.37   \n",
       "\n",
       "                     load_t-1  load_t-0  \n",
       "2012-01-01 09:00:00      0.29      0.35  \n",
       "2012-01-01 10:00:00      0.35      0.37  \n",
       "2012-01-01 11:00:00      0.37      0.37  \n",
       "2012-01-01 12:00:00      0.37      0.37  \n",
       "2012-01-01 13:00:00      0.37      0.36  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted = train_shifted.dropna(how='any')\n",
    "train_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Transform into a numpy arrays of shapes (samples, time steps, features) and (samples,1) for input into Keras\n",
    "Now convert the target variable into a numpy array. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_shifted[['y_t+1']].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a vector for target variable of shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23366, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable for the first 3 samples looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.37],\n",
       "       [0.37],\n",
       "       [0.37]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now convert the inputs into a numpy array with shape `(samples, time steps, features)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()\n",
    "X_train = X_train[... , np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tensor for the input features now has the shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23366, 10, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the first 3 samples looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.22],\n",
       "        [0.18],\n",
       "        [0.14],\n",
       "        [0.13],\n",
       "        [0.13],\n",
       "        [0.15],\n",
       "        [0.18],\n",
       "        [0.23],\n",
       "        [0.29],\n",
       "        [0.35]],\n",
       "\n",
       "       [[0.18],\n",
       "        [0.14],\n",
       "        [0.13],\n",
       "        [0.13],\n",
       "        [0.15],\n",
       "        [0.18],\n",
       "        [0.23],\n",
       "        [0.29],\n",
       "        [0.35],\n",
       "        [0.37]],\n",
       "\n",
       "       [[0.14],\n",
       "        [0.13],\n",
       "        [0.13],\n",
       "        [0.15],\n",
       "        [0.18],\n",
       "        [0.23],\n",
       "        [0.29],\n",
       "        [0.35],\n",
       "        [0.37],\n",
       "        [0.37]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-9</th>\n",
       "      <th>load_t-8</th>\n",
       "      <th>load_t-7</th>\n",
       "      <th>load_t-6</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t-0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 10:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 11:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-9  load_t-8  load_t-7  \\\n",
       "2012-01-01 09:00:00           0.35   0.37      0.22      0.18      0.14   \n",
       "2012-01-01 10:00:00           0.37   0.37      0.18      0.14      0.13   \n",
       "2012-01-01 11:00:00           0.37   0.37      0.14      0.13      0.13   \n",
       "\n",
       "                     load_t-6  load_t-5  load_t-4  load_t-3  load_t-2  \\\n",
       "2012-01-01 09:00:00      0.13      0.13      0.15      0.18      0.23   \n",
       "2012-01-01 10:00:00      0.13      0.15      0.18      0.23      0.29   \n",
       "2012-01-01 11:00:00      0.15      0.18      0.23      0.29      0.35   \n",
       "\n",
       "                     load_t-1  load_t-0  \n",
       "2012-01-01 09:00:00      0.29      0.35  \n",
       "2012-01-01 10:00:00      0.35      0.37  \n",
       "2012-01-01 11:00:00      0.37      0.37  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - validation set\n",
    "Now we follow a similar process for the validation set. We keep *T* hours from the training set in order to construct initial features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-31 15:00:00</th>\n",
       "      <td>3,813.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 16:00:00</th>\n",
       "      <td>3,859.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 17:00:00</th>\n",
       "      <td>3,936.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 18:00:00</th>\n",
       "      <td>3,957.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 19:00:00</th>\n",
       "      <td>3,969.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2014-08-31 15:00:00 3,813.00\n",
       "2014-08-31 16:00:00 3,859.00\n",
       "2014-08-31 17:00:00 3,936.00\n",
       "2014-08-31 18:00:00 3,957.00\n",
       "2014-08-31 19:00:00 3,969.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n",
    "valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the series using the transformer fitted on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-31 15:00:00</th>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 16:00:00</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 17:00:00</th>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 18:00:00</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 19:00:00</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-08-31 15:00:00  0.57\n",
       "2014-08-31 16:00:00  0.58\n",
       "2014-08-31 17:00:00  0.60\n",
       "2014-08-31 18:00:00  0.61\n",
       "2014-08-31 19:00:00  0.61"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid['load'] = scaler.transform(valid)\n",
    "valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare validation inputs in the same way as the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_shifted = valid.copy()\n",
    "valid_shifted['y+1'] = valid_shifted['load'].shift(-1, freq='H')\n",
    "for t in range(1, T+1):\n",
    "    valid_shifted['load_t-'+str(T-t)] = valid_shifted['load'].shift(T-t, freq='H')\n",
    "valid_shifted = valid_shifted.dropna(how='any')\n",
    "y_valid = valid_shifted['y+1'].as_matrix()\n",
    "X_valid = valid_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()\n",
    "X_valid = X_valid[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463, 10, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement the Convolutional Neural Network\n",
    "We implement the convolutional neural network with 3 layers, 5 neurons in each layer, a kernel size of 3 in each layer, and dilation rates of 1, 2 and 4 for each successive layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/cnn_dilated.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Conv1D, Dense, Flatten\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "LATENT_DIM = 5\n",
    "KERNEL_SIZE = 2\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /data/anaconda/envs/dnntutorial/lib/python3.6/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": [
    "model = Sequential()\n",
    "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=1, input_shape=(T, 1)))\n",
    "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=2))\n",
    "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=4))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(HORIZON, activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 10, 5)             15        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 10, 5)             55        \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 10, 5)             55        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 176\n",
      "Trainable params: 176\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Adam optimizer and mean squared error as the loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Early stopping trick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/early_stopping.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_val = ModelCheckpoint('model_{epoch:02d}.h5', save_best_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /data/anaconda/envs/dnntutorial/lib/python3.6/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",
      "Train on 23366 samples, validate on 1463 samples\n",
      "Epoch 1/10\n",
      "23366/23366 [==============================] - 1s 54us/step - loss: 0.0101 - val_loss: 0.0030\n",
      "Epoch 2/10\n",
      "23366/23366 [==============================] - 1s 42us/step - loss: 0.0019 - val_loss: 0.0011\n",
      "Epoch 3/10\n",
      "23366/23366 [==============================] - 1s 41us/step - loss: 9.6631e-04 - val_loss: 6.5879e-04\n",
      "Epoch 4/10\n",
      "23366/23366 [==============================] - 1s 42us/step - loss: 7.3052e-04 - val_loss: 6.1729e-04\n",
      "Epoch 5/10\n",
      "23366/23366 [==============================] - 1s 42us/step - loss: 6.4154e-04 - val_loss: 4.3739e-04\n",
      "Epoch 6/10\n",
      "23366/23366 [==============================] - 1s 41us/step - loss: 5.6116e-04 - val_loss: 4.0309e-04\n",
      "Epoch 7/10\n",
      "23366/23366 [==============================] - 1s 41us/step - loss: 5.2003e-04 - val_loss: 3.5128e-04\n",
      "Epoch 8/10\n",
      "23366/23366 [==============================] - 1s 41us/step - loss: 4.8347e-04 - val_loss: 4.6161e-04\n",
      "Epoch 9/10\n",
      "23366/23366 [==============================] - 1s 42us/step - loss: 4.5197e-04 - val_loss: 5.7465e-04\n",
      "Epoch 10/10\n",
      "23366/23366 [==============================] - 1s 41us/step - loss: 4.3400e-04 - val_loss: 3.0176e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,\n",
    "          y_train,\n",
    "          batch_size=BATCH_SIZE,\n",
    "          epochs=EPOCHS,\n",
    "          validation_data=(X_valid, y_valid),\n",
    "          callbacks=[earlystop, best_val],\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the model with the smallest mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_epoch = np.argmin(np.array(history.history['val_loss']))+1\n",
    "model.load_weights(\"model_{:02d}.h5\".format(best_epoch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot training and validation losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7c00df978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n",
    "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n",
    "plt.xlabel('epoch', fontsize=12)\n",
    "plt.ylabel('loss', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model\n",
    "Create the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>2,514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>2,434.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>2,390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>2,382.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>2,419.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2014-11-01 00:00:00 2,514.00\n",
       "2014-11-01 01:00:00 2,434.00\n",
       "2014-11-01 02:00:00 2,390.00\n",
       "2014-11-01 03:00:00 2,382.00\n",
       "2014-11-01 04:00:00 2,419.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "test = energy.copy()[test_start_dt:][['load']]\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.16\n",
       "2014-11-01 01:00:00  0.14\n",
       "2014-11-01 02:00:00  0.13\n",
       "2014-11-01 03:00:00  0.12\n",
       "2014-11-01 04:00:00  0.14"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create test set features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_shifted = test.copy()\n",
    "test_shifted['y_t+1'] = test_shifted['load'].shift(-1, freq='H')\n",
    "for t in range(1, T+1):\n",
    "    test_shifted['load_t-'+str(T-t)] = test_shifted['load'].shift(T-t, freq='H')\n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "y_test = test_shifted['y_t+1'].as_matrix()\n",
    "X_test = test_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()\n",
    "X_test = X_test[... , np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44],\n",
       "       [0.46],\n",
       "       [0.46],\n",
       "       ...,\n",
       "       [0.51],\n",
       "       [0.45],\n",
       "       [0.41]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 09:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,411.42</td>\n",
       "      <td>3,436.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 10:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,462.32</td>\n",
       "      <td>3,464.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 11:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,468.06</td>\n",
       "      <td>3,439.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 12:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,388.74</td>\n",
       "      <td>3,407.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 13:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,386.48</td>\n",
       "      <td>3,389.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 09:00:00  t+1    3,411.42 3,436.00\n",
       "1 2014-11-01 10:00:00  t+1    3,462.32 3,464.00\n",
       "2 2014-11-01 11:00:00  t+1    3,468.06 3,439.00\n",
       "3 2014-11-01 12:00:00  t+1    3,388.74 3,407.00\n",
       "4 2014-11-01 13:00:00  t+1    3,386.48 3,389.00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
    "eval_df['timestamp'] = test_shifted.index\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.transpose(y_test).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean absolute percentage error over all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01260933761669605"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7b0723be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean up model files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in glob('model_*.h5'):\n",
    "    os.remove(m)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dnntutorial",
   "language": "python",
   "name": "dnntutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}