Alpha_RNNs_weather.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alpha_RNNs_weather\n",
    "# Author: Matthew Dixon\n",
    "# Version: 1.1 (27.2.2020)\n",
    "# License: MIT\n",
    "# Email: matthew.dixon@iit.edu\n",
    "# Notes: tested on Mac OS X with Python 3.6 and Tensorflow 1.3.0\n",
    "# Citation: Please cite the following reference if this notebook is used for research purposes:\n",
    "# M.F Dixon, Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks, preprint, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "colab_type": "code",
    "id": "UhZJI0-gk3ID",
    "outputId": "bd2cf1a1-4a20-4590-a3cc-572ac503cb2e"
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy import random\n",
    "import os# Generate switching data set\n",
    "import random\n",
    "\n",
    "# Imports for alpha_rnns \n",
    "from IPython import display\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from keras import optimizers\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Layer, LSTM, GRU, SimpleRNN\n",
    "from keras import optimizers\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.regularizers import l1,l2\n",
    "from keras import regularizers\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from keras.layers import Layer,RNN\n",
    "from keras import backend as K\n",
    "from keras import layers\n",
    "import keras.layers\n",
    "from keras.legacy import interfaces\n",
    "from keras import *\n",
    "from alphaRNN import *\n",
    "from alphatRNN import *\n",
    "import tensorflow as tf\n",
    "\n",
    "from numpy.random import seed\n",
    "from tensorflow import set_random_seed\n",
    "\n",
    "# To plot figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_itHHAgdk3La"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=50, min_delta=1e-7, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TktVZa5N63kv"
   },
   "source": [
    "# Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "colab_type": "code",
    "id": "xJaRcRSd63lJ",
    "outputId": "dca72eb7-ad77-46a1-e5e2-bfac5687bba4"
   },
   "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 Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>996.57</td>\n",
       "      <td>-8.41</td>\n",
       "      <td>265.01</td>\n",
       "      <td>-9.28</td>\n",
       "      <td>93.4</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1309.80</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>136.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.51</td>\n",
       "      <td>264.91</td>\n",
       "      <td>-9.31</td>\n",
       "      <td>93.9</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1310.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.63</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.31</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>94.2</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1309.19</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>265.15</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>94.1</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1309.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>214.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
       "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
       "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
       "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
       "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
       "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
       "\n",
       "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
       "0          3.33          3.11          0.22       1.94             3.12   \n",
       "1          3.23          3.02          0.21       1.89             3.03   \n",
       "2          3.21          3.01          0.20       1.88             3.02   \n",
       "3          3.26          3.07          0.19       1.92             3.08   \n",
       "4          3.27          3.08          0.19       1.92             3.09   \n",
       "\n",
       "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0       1307.75      1.03           1.75     152.3  \n",
       "1       1309.80      0.72           1.50     136.1  \n",
       "2       1310.24      0.19           0.63     171.6  \n",
       "3       1309.19      0.34           0.50     198.0  \n",
       "4       1309.00      0.32           0.63     214.3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_path = tf.keras.utils.get_file(\n",
    "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
    "    fname='jena_climate_2009_2016.csv.zip',\n",
    "    extract=True)\n",
    "csv_path, _ = os.path.splitext(zip_path)\n",
    "df = pd.read_csv(csv_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "colab_type": "code",
    "id": "0SExtqbO63lP",
    "outputId": "0717bbe3-c19c-41a0-91ef-6cae07bea5c7"
   },
   "outputs": [],
   "source": [
    "use_features = ['p (mbar)', 'T (degC)', 'Tpot (K)', 'Tdew (degC)',\n",
    "       'rh (%)', 'VPmax (mbar)', 'VPact (mbar)', 'VPdef (mbar)', 'sh (g/kg)',\n",
    "       'H2OC (mmol/mol)', 'rho (g/m**3)', 'wv (m/s)', 'max. wv (m/s)',\n",
    "       'wd (deg)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "7t_o7356k3Jj",
    "outputId": "4e973c80-0be4-4a14-a2e5-2acc1ec01956"
   },
   "outputs": [],
   "source": [
    "target = 'T (degC)'\n",
    "n_steps = 20 # number of lags to include in the model\n",
    "train_weight = 0.8\n",
    "\n",
    "df_new = df[:30000]\n",
    "df_new[use_features].astype('float32').dtypes\n",
    "split = int(len(df_new)*train_weight)\n",
    "\n",
    "df_train = df_new[use_features].iloc[:split] \n",
    "df_train[use_features].astype('float32').dtypes\n",
    "mu = df_train[use_features].mean()\n",
    "sigma = df_train[use_features].std()\n",
    "std_df_train = (df_train[use_features] - mu)/sigma\n",
    "df_train = std_df_train \n",
    "df_test = df_new[use_features].loc[split:]\n",
    "std_df_test = (df_test[use_features].loc[split:] - mu)/sigma\n",
    "df_test = std_df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented Dickey-Fuller test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GyYRT2tyk3JK",
    "outputId": "0dcac866-3950-410e-d1db-12bb28a6e377"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p (mbar) -3.8139805982886608 0.0027674707965053644 9983 {'1%': -3.431005212086098, '5%': -2.861829564712288, '10%': -2.5669241301591046}\n",
      "T (degC) -4.760996819437093 6.454147170146432e-05 9961 {'1%': -3.4310066595695945, '5%': -2.861830204343065, '10%': -2.5669244706354584}\n",
      "Tpot (K) -4.404673004589229 0.0002912309335644808 9961 {'1%': -3.4310066595695945, '5%': -2.861830204343065, '10%': -2.5669244706354584}\n",
      "Tdew (degC) -4.170958682611065 0.0007375862034841664 9966 {'1%': -3.431006330034765, '5%': -2.861830058724424, '10%': -2.566924393122448}\n",
      "rh (%) -11.234314284361963 1.8737483678258257e-20 9984 {'1%': -3.431005146443003, '5%': -2.8618295357051493, '10%': -2.5669241147185655}\n",
      "VPmax (mbar) -5.346549344218809 4.3822434707943234e-06 9962 {'1%': -3.4310065936361585, '5%': -2.861830175207641, '10%': -2.5669244551266304}\n",
      "VPact (mbar) -4.194144540593466 0.0006741005691156316 9983 {'1%': -3.431005212086098, '5%': -2.861829564712288, '10%': -2.5669241301591046}\n",
      "VPdef (mbar) -11.283382891699855 1.4377325113255772e-20 9971 {'1%': -3.431006000830514, '5%': -2.8618299132518468, '10%': -2.566924315687191}\n",
      "sh (g/kg) -4.178627224450505 0.0007159921021797893 9983 {'1%': -3.431005212086098, '5%': -2.861829564712288, '10%': -2.5669241301591046}\n",
      "H2OC (mmol/mol) -4.186406858908635 0.0006946930542226405 9983 {'1%': -3.431005212086098, '5%': -2.861829564712288, '10%': -2.5669241301591046}\n",
      "rho (g/m**3) -3.924804017981249 0.0018596998785514738 9966 {'1%': -3.431006330034765, '5%': -2.861830058724424, '10%': -2.566924393122448}\n",
      "wv (m/s) -9.371749619926387 7.345701134261768e-16 9979 {'1%': -3.4310054747900742, '5%': -2.861829680798988, '10%': -2.566924191952213}\n",
      "max. wv (m/s) -8.384588507505438 2.4578836820082934e-13 9971 {'1%': -3.431006000830514, '5%': -2.8618299132518468, '10%': -2.566924315687191}\n",
      "wd (deg) -6.901578634093209 1.2780128169026451e-09 9961 {'1%': -3.4310066595695945, '5%': -2.861830204343065, '10%': -2.5669244706354584}\n"
     ]
    }
   ],
   "source": [
    "for feature in use_features:\n",
    "    adf, p, usedlag, nobs, cvs,aic=sm.tsa.stattools.adfuller(df_new[feature][:10000])\n",
    "    print(feature, adf,p, nobs, cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 812
    },
    "colab_type": "code",
    "id": "QGGlCZuzk3Jv",
    "outputId": "46472e0c-8c16-4677-b837-cdfd4609b8b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$\\\\tau_j$')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "for feature in use_features:\n",
    "    pacf=sm.tsa.stattools.pacf(df_new[feature], nlags=30)\n",
    "    plt.plot(pacf, label=feature)\n",
    "plt.plot([2.58/np.sqrt(len(df_new))]*30, label='Upper 99% confidence interval', color='black', linestyle='--')\n",
    "plt.plot([-2.58/np.sqrt(len(df_new))]*30, label='Lower 99% confidence interval', color='black', linestyle='--')\n",
    "\n",
    "plt.legend(loc=5,prop={'size': 20})\n",
    "plt.xlabel('lag order (j)')\n",
    "plt.ylabel('$\\\\tau_j$')\n",
    "#n_steps=np.where(np.array(np.abs(pacf)>2.58/np.sqrt(len(df[use_features])))==False)[0][0] -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DgEov0A_63la"
   },
   "source": [
    "### Data structure manipulation for RNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K2qmrJyek3KU"
   },
   "outputs": [],
   "source": [
    "def get_lagged_features(value, n_steps,n_steps_ahead):\n",
    "    \"\"\"\n",
    "    value: feature value to be lagged\n",
    "    n_steps: number of lags, i.e. sequence length\n",
    "    n_steps_ahead: forecasting horizon\n",
    "    \"\"\"\n",
    "    lag_list = []\n",
    "    for lag in range(n_steps+n_steps_ahead-1, n_steps_ahead-1, -1):\n",
    "        lag_list.append(value.shift(lag))\n",
    "    return pd.concat(lag_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jdJwr-OGk3Ka"
   },
   "outputs": [],
   "source": [
    "n_steps_ahead=10\n",
    "\n",
    "x_train_list = []\n",
    "for use_feature in use_features:\n",
    "    x_train_reg = get_lagged_features(df_train, n_steps, n_steps_ahead).dropna()\n",
    "    x_train_list.append(x_train_reg)\n",
    "x_train_reg = pd.concat(x_train_list, axis=1)\n",
    "\n",
    "col_ords = []\n",
    "for i in range(n_steps):\n",
    "    for j in range(len(use_features)):\n",
    "        col_ords.append(i + j * n_steps)\n",
    "\n",
    "x_train_reg = x_train_reg.iloc[:, col_ords]\n",
    "y_train_reg = df_train.loc[x_train_reg.index, [target]].values\n",
    "x_train_reg = np.reshape(x_train_reg.values, (x_train_reg.shape[0], np.int(x_train_reg.shape[1] / len(use_features)), len(use_features)))\n",
    "y_train_reg = np.reshape(y_train_reg, (y_train_reg.shape[0], 1, 1))\n",
    "\n",
    "x_test_list = []\n",
    "for use_feature in use_features:\n",
    "    x_test_reg = get_lagged_features(df_test, n_steps,n_steps_ahead).dropna()\n",
    "    x_test_list.append(x_test_reg)\n",
    "x_test_reg = pd.concat(x_test_list, axis=1)\n",
    "\n",
    "x_test_reg = x_test_reg.iloc[:, col_ords]\n",
    "y_test_reg = df_test.loc[x_test_reg.index, [target]].values\n",
    "x_test_reg = np.reshape(x_test_reg.values, (x_test_reg.shape[0], np.int(x_test_reg.shape[1]/len(use_features)), len(use_features)))\n",
    "\n",
    "y_test_reg = np.reshape(y_test_reg, (y_test_reg.shape[0], 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Y-fJgvet63lm",
    "outputId": "2f0765c3-c022-4fc8-dffe-1d413fabdb90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23971, 20, 14) (23971, 1, 1) (5971, 20, 14) (5971, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_reg.shape,y_train_reg.shape,x_test_reg.shape,y_test_reg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zg1cfHxv63lr"
   },
   "outputs": [],
   "source": [
    "train_batch_size = y_train_reg.shape[0]\n",
    "test_batch_size = y_test_reg.shape[0]\n",
    "time_size = y_train_reg.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HkEY52YEKAcm",
    "outputId": "a5215673-86d8-474f-a5bd-3c513f8369f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23971\n"
     ]
    }
   ],
   "source": [
    "print(train_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SQZp0fs1k3LR"
   },
   "outputs": [],
   "source": [
    "x_train_reg = pd.concat(x_train_list, axis=1)\n",
    "x_train_reg = x_train_reg.iloc[:, col_ords]\n",
    "y_train_reg = df_train.loc[x_train_reg.index, [target]].values\n",
    "x_train_reg = np.reshape(x_train_reg.values, (x_train_reg.shape[0], np.int(x_train_reg.shape[1] / len(use_features)), len(use_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KL0njDSjk3LU"
   },
   "outputs": [],
   "source": [
    "x_test_reg = pd.concat(x_test_list, axis=1)\n",
    "x_test_reg = x_test_reg.iloc[:, col_ords]\n",
    "y_test_reg = df_test.loc[x_test_reg.index, [target]].values\n",
    "x_test_reg = np.reshape(x_test_reg.values, (x_test_reg.shape[0], np.int(x_test_reg.shape[1]/len(use_features)), len(use_features)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Wrappers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p56y00jbStPa"
   },
   "outputs": [],
   "source": [
    "def AlphatRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphatRNN(n_units, activation='tanh', recurrent_activation='sigmoid', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def AlphaRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphaRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))\n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def SimpleRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(SimpleRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True, stateful=False))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def GRU_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(GRU(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def LSTM_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True)) \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w1CDDB2-k0WK"
   },
   "outputs": [],
   "source": [
    "max_epoches=2000\n",
    "batch_size=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0h2IX1uck3L3",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "params = {'rnn': {'model':'', 'function':SimpleRNN_, 'l1_reg':0.0, 'H':10, 'color': 'blue', 'label':'RNN'}, \n",
    "          'alpharnn': {'model':'', 'function':AlphaRNN_,'l1_reg':0.001, 'H':5,'color': 'green', 'label': '$\\\\alpha$-RNN' }, \n",
    "          'alphatrnn': {'model':'', 'function':AlphatRNN_,'l1_reg':0.0, 'H':5, 'color': 'cyan', 'label': '$\\\\alpha_t$-RNN'},\n",
    "          'gru': {'model':'', 'function':GRU_,'l1_reg':0.001, 'H':10, 'color': 'orange', 'label': 'GRU'},\n",
    "          'lstm': {'model':'', 'function':LSTM_,'l1_reg':0.0, 'H':5, 'color':'red', 'label': 'LSTM'}\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zg0r69h1spFn"
   },
   "source": [
    "# Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val=False # Warning: Changing this to True will take several hours to run\n",
    "if cross_val:\n",
    "    n_units = [5,10,20]\n",
    "    l1_reg = [0, 0.001, 0.01, 0.1]\n",
    "    tscv = TimeSeriesSplit(n_splits = 5)\n",
    "    param_grid = dict(n_units=n_units,l1_reg=l1_reg)\n",
    "\n",
    "    for key in params.keys(): # params[key]['function']\n",
    "        model = KerasRegressor(build_fn=params[key]['function'], epochs=max_epoches, batch_size=batch_size, verbose=2)\n",
    "        grid = GridSearchCV(estimator=model,param_grid=param_grid, cv=tscv, n_jobs=1, verbose=2)\n",
    "        grid_result = grid.fit(x_train_reg,y_train_reg,callbacks=[es])\n",
    "        print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "        means = grid_result.cv_results_['mean_test_score']\n",
    "        stds = grid_result.cv_results_['std_test_score']\n",
    "        params_ = grid_result.cv_results_['params']\n",
    "        for mean, stdev, param_ in zip(means, stds, params_):\n",
    "            print(\"%f (%f) with %r\" % (mean, stdev, param_))\n",
    "\n",
    "    params[key]['H'] = grid_result.best_params_['n_units']\n",
    "    params[key]['l1_reg']= grid_result.best_params_['l1_reg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train cross-validated model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "23971/23971 [==============================] - 1s 51us/step - loss: 1.6501\n",
      "Epoch 2/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.9138\n",
      "Epoch 3/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.5909\n",
      "Epoch 4/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.4535\n",
      "Epoch 5/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.3816\n",
      "Epoch 6/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.3316\n",
      "Epoch 7/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.2927\n",
      "Epoch 8/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.2613\n",
      "Epoch 9/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.2355\n",
      "Epoch 10/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.2144\n",
      "Epoch 11/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1971\n",
      "Epoch 12/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.1829\n",
      "Epoch 13/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.1710\n",
      "Epoch 14/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.1610\n",
      "Epoch 15/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1524\n",
      "Epoch 16/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.1448\n",
      "Epoch 17/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.1379\n",
      "Epoch 18/2000\n",
      "23971/23971 [==============================] - 0s 15us/step - loss: 0.1317\n",
      "Epoch 19/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1258\n",
      "Epoch 20/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1204\n",
      "Epoch 21/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1152\n",
      "Epoch 22/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.1103\n",
      "Epoch 23/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1056\n",
      "Epoch 24/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.1011\n",
      "Epoch 25/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0968\n",
      "Epoch 26/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0926\n",
      "Epoch 27/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0887\n",
      "Epoch 28/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0849\n",
      "Epoch 29/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0813\n",
      "Epoch 30/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0778\n",
      "Epoch 31/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0746\n",
      "Epoch 32/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0715\n",
      "Epoch 33/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0686\n",
      "Epoch 34/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0660\n",
      "Epoch 35/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0635\n",
      "Epoch 36/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0612\n",
      "Epoch 37/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0591\n",
      "Epoch 38/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0571\n",
      "Epoch 39/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0554\n",
      "Epoch 40/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0538\n",
      "Epoch 41/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0523\n",
      "Epoch 42/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0510\n",
      "Epoch 43/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0498\n",
      "Epoch 44/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0487\n",
      "Epoch 45/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0477\n",
      "Epoch 46/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0467\n",
      "Epoch 47/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0459\n",
      "Epoch 48/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0451\n",
      "Epoch 49/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0444\n",
      "Epoch 50/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0437\n",
      "Epoch 51/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0431\n",
      "Epoch 52/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0426\n",
      "Epoch 53/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0420\n",
      "Epoch 54/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0415\n",
      "Epoch 55/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0410\n",
      "Epoch 56/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0406\n",
      "Epoch 57/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0401\n",
      "Epoch 58/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0397\n",
      "Epoch 59/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0394\n",
      "Epoch 60/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0390\n",
      "Epoch 61/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0386\n",
      "Epoch 62/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0383\n",
      "Epoch 63/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0380\n",
      "Epoch 64/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0377\n",
      "Epoch 65/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0374\n",
      "Epoch 66/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0371\n",
      "Epoch 67/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0368\n",
      "Epoch 68/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0366\n",
      "Epoch 69/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0363\n",
      "Epoch 70/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0361\n",
      "Epoch 71/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0359\n",
      "Epoch 72/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0356\n",
      "Epoch 73/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0354\n",
      "Epoch 74/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0352\n",
      "Epoch 75/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0350\n",
      "Epoch 76/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0348\n",
      "Epoch 77/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0346\n",
      "Epoch 78/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0345\n",
      "Epoch 79/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0343\n",
      "Epoch 80/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0342\n",
      "Epoch 81/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0340\n",
      "Epoch 82/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0339\n",
      "Epoch 83/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0336\n",
      "Epoch 84/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0336\n",
      "Epoch 85/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0334\n",
      "Epoch 86/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0333\n",
      "Epoch 87/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0331\n",
      "Epoch 88/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0331\n",
      "Epoch 89/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0328\n",
      "Epoch 90/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0329\n",
      "Epoch 91/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0326\n",
      "Epoch 92/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0327\n",
      "Epoch 93/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0323\n",
      "Epoch 94/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0325\n",
      "Epoch 95/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0321\n",
      "Epoch 96/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0324\n",
      "Epoch 97/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0318\n",
      "Epoch 98/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0323\n",
      "Epoch 99/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0316\n",
      "Epoch 100/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0322\n",
      "Epoch 101/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0314\n",
      "Epoch 102/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0321\n",
      "Epoch 103/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0312\n",
      "Epoch 104/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0320\n",
      "Epoch 105/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0309\n",
      "Epoch 106/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0320\n",
      "Epoch 107/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0307\n",
      "Epoch 108/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0320\n",
      "Epoch 109/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0306\n",
      "Epoch 110/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0319\n",
      "Epoch 111/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0304\n",
      "Epoch 112/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0319\n",
      "Epoch 113/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0302\n",
      "Epoch 114/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0319\n",
      "Epoch 115/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0301\n",
      "Epoch 116/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0318\n",
      "Epoch 117/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0299\n",
      "Epoch 118/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0317\n",
      "Epoch 119/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0298\n",
      "Epoch 120/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0316\n",
      "Epoch 121/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0297\n",
      "Epoch 122/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0315\n",
      "Epoch 123/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0296\n",
      "Epoch 124/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0313\n",
      "Epoch 125/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0296\n",
      "Epoch 126/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0311\n",
      "Epoch 127/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0295\n",
      "Epoch 128/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0310\n",
      "Epoch 129/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0295\n",
      "Epoch 130/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0308\n",
      "Epoch 131/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0295\n",
      "Epoch 132/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0306\n",
      "Epoch 133/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0294\n",
      "Epoch 134/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0304\n",
      "Epoch 135/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0294\n",
      "Epoch 136/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0302\n",
      "Epoch 137/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0294\n",
      "Epoch 138/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0300\n",
      "Epoch 139/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0294\n",
      "Epoch 140/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0299\n",
      "Epoch 141/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0294\n",
      "Epoch 142/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0297\n",
      "Epoch 143/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0294\n",
      "Epoch 144/2000\n",
      "23971/23971 [==============================] - 0s 16us/step - loss: 0.0296\n",
      "Epoch 145/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0294\n",
      "Epoch 146/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0295\n",
      "Epoch 147/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0293\n",
      "Epoch 148/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0294\n",
      "Epoch 149/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0293\n",
      "Epoch 150/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0293\n",
      "Epoch 151/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0292\n",
      "Epoch 152/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0293\n",
      "Epoch 153/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0292\n",
      "Epoch 154/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0292\n",
      "Epoch 155/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0291\n",
      "Epoch 156/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0291\n",
      "Epoch 157/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0291\n",
      "Epoch 158/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0291\n",
      "Epoch 159/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0290\n",
      "Epoch 160/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0290\n",
      "Epoch 161/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0290\n",
      "Epoch 162/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0289\n",
      "Epoch 163/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0289\n",
      "Epoch 164/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0289\n",
      "Epoch 165/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0289\n",
      "Epoch 166/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0288\n",
      "Epoch 167/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0288\n",
      "Epoch 168/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0288\n",
      "Epoch 169/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0287\n",
      "Epoch 170/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0287\n",
      "Epoch 171/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0287\n",
      "Epoch 172/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0287\n",
      "Epoch 173/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0286\n",
      "Epoch 174/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0286\n",
      "Epoch 175/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0286\n",
      "Epoch 176/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0286\n",
      "Epoch 177/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0285\n",
      "Epoch 178/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0285\n",
      "Epoch 179/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0285\n",
      "Epoch 180/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0285\n",
      "Epoch 181/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0284\n",
      "Epoch 182/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0284\n",
      "Epoch 183/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0284\n",
      "Epoch 184/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0284\n",
      "Epoch 185/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0283\n",
      "Epoch 186/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0283\n",
      "Epoch 187/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0283\n",
      "Epoch 188/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0283\n",
      "Epoch 189/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0283\n",
      "Epoch 190/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0282\n",
      "Epoch 191/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0282\n",
      "Epoch 192/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0282\n",
      "Epoch 193/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0282\n",
      "Epoch 194/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0281\n",
      "Epoch 195/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0281\n",
      "Epoch 196/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0281\n",
      "Epoch 197/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0281\n",
      "Epoch 198/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0281\n",
      "Epoch 199/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0280\n",
      "Epoch 200/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0280\n",
      "Epoch 201/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0280\n",
      "Epoch 202/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0280\n",
      "Epoch 203/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0279\n",
      "Epoch 204/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0279\n",
      "Epoch 205/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0279\n",
      "Epoch 206/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0279\n",
      "Epoch 207/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0279\n",
      "Epoch 208/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0278\n",
      "Epoch 209/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0278\n",
      "Epoch 210/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0278\n",
      "Epoch 211/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0278\n",
      "Epoch 212/2000\n",
      "23971/23971 [==============================] - 0s 17us/step - loss: 0.0278\n",
      "Epoch 213/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0277\n",
      "Epoch 214/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0277\n",
      "Epoch 215/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0277\n",
      "Epoch 216/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0277\n",
      "Epoch 217/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0277\n",
      "Epoch 218/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0276\n",
      "Epoch 219/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0276\n",
      "Epoch 220/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0276\n",
      "Epoch 221/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0276\n",
      "Epoch 222/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0276\n",
      "Epoch 223/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0276\n",
      "Epoch 224/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0275\n",
      "Epoch 225/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0275\n",
      "Epoch 226/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0275\n",
      "Epoch 227/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0275\n",
      "Epoch 228/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0275\n",
      "Epoch 229/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0274\n",
      "Epoch 230/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0274\n",
      "Epoch 231/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0274\n",
      "Epoch 232/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0274\n",
      "Epoch 233/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0274\n",
      "Epoch 234/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0274\n",
      "Epoch 235/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0273\n",
      "Epoch 236/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0273\n",
      "Epoch 237/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0273\n",
      "Epoch 238/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0273\n",
      "Epoch 239/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0273\n",
      "Epoch 240/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0273\n",
      "Epoch 241/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0272\n",
      "Epoch 242/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0272\n",
      "Epoch 243/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0272\n",
      "Epoch 244/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0272\n",
      "Epoch 245/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0272\n",
      "Epoch 246/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0272\n",
      "Epoch 247/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0271\n",
      "Epoch 248/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0271\n",
      "Epoch 249/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0271\n",
      "Epoch 250/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0271\n",
      "Epoch 251/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0271\n",
      "Epoch 252/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0271\n",
      "Epoch 253/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0270\n",
      "Epoch 254/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0270\n",
      "Epoch 255/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0270\n",
      "Epoch 256/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0270\n",
      "Epoch 257/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0270\n",
      "Epoch 258/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0270\n",
      "Epoch 259/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0270\n",
      "Epoch 260/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0269\n",
      "Epoch 261/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0269\n",
      "Epoch 262/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0269\n",
      "Epoch 263/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0269\n",
      "Epoch 264/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0269\n",
      "Epoch 265/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0269\n",
      "Epoch 266/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0269\n",
      "Epoch 267/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0268\n",
      "Epoch 268/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0268\n",
      "Epoch 269/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0268\n",
      "Epoch 270/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0268\n",
      "Epoch 271/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0268\n",
      "Epoch 272/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0268\n",
      "Epoch 273/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0268\n",
      "Epoch 274/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0267\n",
      "Epoch 275/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0267\n",
      "Epoch 276/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0267\n",
      "Epoch 277/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0267\n",
      "Epoch 278/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0267\n",
      "Epoch 279/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0267\n",
      "Epoch 280/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0267\n",
      "Epoch 281/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0267\n",
      "Epoch 282/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0266\n",
      "Epoch 283/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0266A: 0s - loss: 0.0\n",
      "Epoch 284/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0266\n",
      "Epoch 285/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0266\n",
      "Epoch 286/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0266\n",
      "Epoch 287/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0266\n",
      "Epoch 288/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0266\n",
      "Epoch 289/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0266\n",
      "Epoch 290/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0265\n",
      "Epoch 291/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0265\n",
      "Epoch 292/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0265\n",
      "Epoch 293/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0265\n",
      "Epoch 294/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0265\n",
      "Epoch 295/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0265\n",
      "Epoch 296/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0265\n",
      "Epoch 297/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0265\n",
      "Epoch 298/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0265\n",
      "Epoch 299/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0264\n",
      "Epoch 300/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0264\n",
      "Epoch 301/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 302/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 303/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 304/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 305/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 306/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 307/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0264\n",
      "Epoch 308/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0263\n",
      "Epoch 309/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0263\n",
      "Epoch 310/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0263\n",
      "Epoch 311/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0263\n",
      "Epoch 312/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0263\n",
      "Epoch 313/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0263\n",
      "Epoch 314/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0263\n",
      "Epoch 315/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0263\n",
      "Epoch 316/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0263\n",
      "Epoch 317/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0262\n",
      "Epoch 318/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0262\n",
      "Epoch 319/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0262\n",
      "Epoch 320/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0262\n",
      "Epoch 321/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0262\n",
      "Epoch 322/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0262\n",
      "Epoch 323/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0262\n",
      "Epoch 324/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0262\n",
      "Epoch 325/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0262\n",
      "Epoch 326/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0262\n",
      "Epoch 327/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0261\n",
      "Epoch 328/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0261\n",
      "Epoch 329/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0261\n",
      "Epoch 330/2000\n",
      "23971/23971 [==============================] - 0s 12us/step - loss: 0.0261\n",
      "Epoch 331/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0261\n",
      "Epoch 332/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0261\n",
      "Epoch 333/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0261\n",
      "Epoch 334/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0261\n",
      "Epoch 335/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0261\n",
      "Epoch 336/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0261\n",
      "Epoch 337/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0260\n",
      "Epoch 338/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0260\n",
      "Epoch 339/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0260\n",
      "Epoch 340/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0260\n",
      "Epoch 341/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0260\n",
      "Epoch 342/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0260\n",
      "Epoch 343/2000\n",
      "23971/23971 [==============================] - 0s 14us/step - loss: 0.0260\n",
      "Epoch 344/2000\n",
      "23971/23971 [==============================] - 0s 13us/step - loss: 0.0260\n",
      "Epoch 345/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0260\n",
      "Epoch 346/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0260\n",
      "Epoch 347/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 348/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 349/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 350/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 351/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0259\n",
      "Epoch 352/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0259\n",
      "Epoch 353/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 354/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 355/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 356/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0259\n",
      "Epoch 357/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0259\n",
      "Epoch 358/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0258\n",
      "Epoch 359/2000\n",
      "23971/23971 [==============================] - 0s 11us/step - loss: 0.0258\n",
      "Epoch 360/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0258\n",
      "Epoch 361/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 362/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 363/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 364/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 365/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0258\n",
      "Epoch 366/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 367/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0258\n",
      "Epoch 368/2000\n",
      "23971/23971 [==============================] - 0s 10us/step - loss: 0.0258\n",
      "Epoch 369/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0257\n",
      "Epoch 370/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 371/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 372/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 373/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 374/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 375/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 376/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 377/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 378/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 379/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0257\n",
      "Epoch 380/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 381/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 382/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0256\n",
      "Epoch 383/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 384/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 385/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0256\n",
      "Epoch 386/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 387/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 388/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 389/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 390/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 391/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0256\n",
      "Epoch 392/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 393/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 394/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 395/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0255\n",
      "Epoch 396/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 397/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 398/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 399/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 400/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0255\n",
      "Epoch 401/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 402/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 403/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 404/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0255\n",
      "Epoch 405/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 406/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 407/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 408/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 409/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 410/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 411/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 412/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 413/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 414/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 415/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0254\n",
      "Epoch 416/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0254\n",
      "Epoch 417/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0253\n",
      "Epoch 418/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 419/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 420/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 421/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253A: 0s - loss: 0.024\n",
      "Epoch 422/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 423/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 424/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0253\n",
      "Epoch 425/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0253A: 0s - loss: 0.02\n",
      "Epoch 426/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 427/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 428/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 429/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 430/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0253\n",
      "Epoch 431/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 432/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 433/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 434/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 435/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0252A: 0s - loss: 0.02\n",
      "Epoch 436/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0252\n",
      "Epoch 437/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 438/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 439/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 440/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 441/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 442/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0252\n",
      "Epoch 443/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0252\n",
      "Epoch 444/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0252\n",
      "Epoch 445/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 446/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 447/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 448/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 449/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 450/2000\n",
      "23971/23971 [==============================] - 0s 9us/step - loss: 0.0251\n",
      "Epoch 451/2000\n",
      "23971/23971 [==============================] - 0s 8us/step - loss: 0.0251\n",
      "Epoch 452/2000\n",
      "13000/23971 [===============>..............] - ETA: 0s - loss: 0.0222"
     ]
    }
   ],
   "source": [
    "for key in params.keys():\n",
    "    set_random_seed(0)\n",
    "    model=params[key]['function'](params[key]['H'],params[key]['l1_reg'])\n",
    "    model.fit(x_train_reg,y_train_reg,epochs=max_epoches, batch_size=batch_size,callbacks=[es],shuffle=False)\n",
    "    params[key]['model']=model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in params.keys():\n",
    "  model=params[key]['model']\n",
    "  model.summary()\n",
    "    \n",
    "  params[key]['MSE_train']= mean_squared_error(df_train[use_feature][n_steps+n_steps_ahead-1:],model.predict(x_train_reg, verbose=1))\n",
    "  params[key]['predict'] = model.predict(x_test_reg, verbose=1) \n",
    "  params[key]['MSE_test']= mean_squared_error(df_test[use_feature][n_steps+n_steps_ahead-1:],params[key]['predict'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,7))\n",
    "upper = 2000\n",
    "plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], df_test[use_feature][n_steps+n_steps_ahead-1:upper], color=\"black\", label=\"Observed\")\n",
    "\n",
    "for key in params.keys():\n",
    "  plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], params[key]['predict'][:upper-(n_steps+n_steps_ahead-1), 0], color=params[key]['color'], label=params[key]['label'])\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Y', fontsize=20)\n",
    "plt.title('Observed vs Model (Testing)', fontsize=16)\n",
    "\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot prediction error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,7))\n",
    "\n",
    "for key in params.keys():\n",
    "  plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], df_test[use_feature][n_steps+n_steps_ahead-1:upper]-params[key]['predict'][:upper-(n_steps+n_steps_ahead-1), 0], color=params[key]['color'], label=params[key]['label'] + \" (\" +  str(round(params[key]['MSE_test'],3)) +\")\")\n",
    "\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Training)', fontsize=16)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Y-$\\hat{Y}$', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Alpha_RNNs_weather_multi-variate.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}