Quiz_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One step multivariate model\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training a RNN forecasting model\n",
    "- get data in the required shape for the keras API\n",
    "- implement a RNN model in keras to predict the next step ahead (time *t+1*) in the time series. This model uses recent values of temperature, as well as load, as the model input.\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 and temperature data.\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 IPython.display import Image\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "%matplotlib inline\n",
    "\n",
    "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\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 the data from csv into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>temp</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",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "      <td>32.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load  temp\n",
       "2012-01-01 00:00:00 2,698.00 32.00\n",
       "2012-01-01 01:00:00 2,558.00 32.67\n",
       "2012-01-01 02:00:00 2,444.00 30.00\n",
       "2012-01-01 03:00:00 2,402.00 31.00\n",
       "2012-01-01 04:00:00 2,403.00 32.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = 'data/'\n",
    "energy = load_data(data_dir)\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": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'\n",
    "\n",
    "energy.plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and temperature in first week of July 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy['2014-07-01':'2014-07-07'].plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n",
    "\n",
    "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* 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": [],
   "source": [
    "T = 6\n",
    "HORIZON = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation - training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training dataset with load and temp features\n",
    "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]\n",
    "\n",
    "# Fit a scaler for the y values\n",
    "y_scaler = MinMaxScaler()\n",
    "y_scaler.fit(train[['load']])\n",
    "\n",
    "# Also scale the input features data (load and temp values)\n",
    "X_scaler = MinMaxScaler()\n",
    "train[['load', 'temp']] = X_scaler.fit_transform(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the TimeSeriesTensor convenience class to:\n",
    "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "2. Discard any samples with missing values\n",
    "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n",
    "\n",
    "The class takes the following parameters:\n",
    "\n",
    "- **dataset**: original time series\n",
    "- **H**: the forecast horizon\n",
    "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n",
    "- **freq**: time series frequency\n",
    "- **drop_incomplete**: (Boolean) whether to drop incomplete samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>tensor</th>\n",
       "      <th>target</th>\n",
       "      <th colspan=\"12\" halign=\"left\">X</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th>y</th>\n",
       "      <th colspan=\"6\" halign=\"left\">load</th>\n",
       "      <th colspan=\"6\" halign=\"left\">temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time step</th>\n",
       "      <th>t+1</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.18</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>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.23</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>0.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.29</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>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.35</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>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.37</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>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tensor              target    X                                               \\\n",
       "feature                  y load                          temp                  \n",
       "time step              t+1  t-5  t-4  t-3  t-2  t-1    t  t-5  t-4  t-3  t-2   \n",
       "2012-01-01 05:00:00   0.18 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43 0.40 0.41   \n",
       "2012-01-01 06:00:00   0.23 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40 0.41 0.42   \n",
       "2012-01-01 07:00:00   0.29 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41 0.42 0.41   \n",
       "2012-01-01 08:00:00   0.35 0.13 0.13 0.15 0.18 0.23 0.29 0.41 0.42 0.41 0.40   \n",
       "2012-01-01 09:00:00   0.37 0.13 0.15 0.18 0.23 0.29 0.35 0.42 0.41 0.40 0.39   \n",
       "\n",
       "tensor                         \n",
       "feature                        \n",
       "time step            t-1    t  \n",
       "2012-01-01 05:00:00 0.42 0.41  \n",
       "2012-01-01 06:00:00 0.41 0.40  \n",
       "2012-01-01 07:00:00 0.40 0.39  \n",
       "2012-01-01 08:00:00 0.39 0.39  \n",
       "2012-01-01 09:00:00 0.39 0.43  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n",
    "train_inputs = TimeSeriesTensor(dataset=train,\n",
    "                            target='load',\n",
    "                            H=HORIZON,\n",
    "                            tensor_structure=tensor_structure,\n",
    "                            freq='H',\n",
    "                            drop_incomplete=True)\n",
    "train_inputs.dataframe.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_inputs['X']\n",
    "y_train = train_inputs['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23370, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18],\n",
       "       [0.23],\n",
       "       [0.29]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23370, 6, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.22, 0.42],\n",
       "        [0.18, 0.43],\n",
       "        [0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41]],\n",
       "\n",
       "       [[0.18, 0.43],\n",
       "        [0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41],\n",
       "        [0.18, 0.4 ]],\n",
       "\n",
       "       [[0.14, 0.4 ],\n",
       "        [0.13, 0.41],\n",
       "        [0.13, 0.42],\n",
       "        [0.15, 0.41],\n",
       "        [0.18, 0.4 ],\n",
       "        [0.23, 0.39]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preparation - validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "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', 'temp']]\n",
    "valid[['load', 'temp']] = X_scaler.transform(valid)\n",
    "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n",
    "y_valid = valid_inputs['target']\n",
    "X_valid = valid_inputs['X']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463, 6, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz: Implement multivariate RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fill in your code below and replace the question mark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implement your RNN model with the data prepared above and the following requirements:\n",
    "1. Use 2 features: past load and temperature\n",
    "2. Stack 2 GRU layers\n",
    "3. 6 hidden units in the first GRU layer\n",
    "4. 4 hidden units in the second GRU layer\n",
    "5. 5 epochs\n",
    "6. Batch size 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIRST_LAYER_LATENT_DIM = ? # number of units in the 1st RNN layer\n",
    "SECOND_LAYER_LATENT_DIM = ? #number of units in the 2nd RNN layer\n",
    "BATCH_SIZE = ? # number of samples per mini-batch\n",
    "EPOCHS = ? # maximum number of times the training algorithm will cycle through all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in your code to replace the question mark\n",
    "# Hint: there is a parameter you need to add when stacking multiple RNN layers\n",
    "model = Sequential()\n",
    "?\n",
    "?\n",
    "model.add(Dense(HORIZON))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/one_step_RNN_multivariate_mutilayer.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you done, run the rest of the notebook to check if your model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)\n",
    "\n",
    "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],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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', 'temp']]\n",
    "test[['load', 'temp']] = X_scaler.transform(test)\n",
    "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n",
    "X_test = test_inputs['X']\n",
    "y_test = test_inputs['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)\n",
    "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
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 },
 "nbformat": 4,
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}