h2o_automl_example_with_multivariate_time_series.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "H2O_AutoML_example_with_multivariate_time_series.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "VfLAY-w7MUWz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# H2O AutoML example with multivariate time series\n",
        "- this notebook was designed to run on Google's Colaboratory platform (https://colab.research.google.com)\n",
        "- the feature matrix consists of lags of the target signal and lags of other features\n",
        "- H2O's AutoML is used to forecast the target signal \n",
        "- unlike an LSTM model, this type of model has no memory and is unaware of any temporal structure\n",
        "- for info on the dataset used and the methodology see: https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/ "
      ]
    },
    {
      "metadata": {
        "id": "Cl_jiK7kMUW7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Install Java\n",
        "\n",
        "- necessary for H2O\n",
        "- version 8\n",
        "- note the exclamation '!' is a cell magic that allows us to execute commands (in this case Ubuntu linux)\n",
        "\n",
        "\n",
        "-note: Colab won't persist installations or your working directory"
      ]
    },
    {
      "metadata": {
        "id": "A-N_2BB9MUXB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!apt-get install openjdk-8-jdk -qq > /dev/null"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "euPA__3QMUXW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## install h2o library"
      ]
    },
    {
      "metadata": {
        "id": "PlxqeAdEMUXd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "769fe1a9-e8bb-4ef0-b308-e888288177d1"
      },
      "cell_type": "code",
      "source": [
        "!pip install h2o -qq"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K    100% |████████████████████████████████| 120.9MB 176kB/s \n",
            "\u001b[?25h  Building wheel for h2o (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Zj8rvaeMMUXu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Import Dependencies"
      ]
    },
    {
      "metadata": {
        "id": "3FmfPfFzMUXy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import h2o\n",
        "from h2o.automl import H2OAutoML\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fn2Jxoh2MUYD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Read pollution.csv into pandas df\n",
        "- set date as index\n",
        "- display first 5 rows\n",
        "- the Air Pollution data is discussed here: https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/\n",
        "- this is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China.\n",
        "- we can use this data and frame a forecasting problem where, given the weather conditions and pollution for prior hours, we forecast the pollution at the next hour."
      ]
    },
    {
      "metadata": {
        "id": "uSpgxus0MUYe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "outputId": "e51a1d23-81e9-402f-eb68-c824f8f6a8c1"
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/sagarmk/Forecasting-on-Air-pollution-with-RNN-LSTM/master/pollution.csv',index_col=0)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>pollution</th>\n",
              "      <th>dew</th>\n",
              "      <th>temp</th>\n",
              "      <th>press</th>\n",
              "      <th>wnd_dir</th>\n",
              "      <th>wnd_spd</th>\n",
              "      <th>snow</th>\n",
              "      <th>rain</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-02 00:00:00</th>\n",
              "      <td>129.0</td>\n",
              "      <td>-16</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>1.79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 01:00:00</th>\n",
              "      <td>148.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 02:00:00</th>\n",
              "      <td>159.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 03:00:00</th>\n",
              "      <td>181.0</td>\n",
              "      <td>-7</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>1022.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>5.36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 04:00:00</th>\n",
              "      <td>138.0</td>\n",
              "      <td>-7</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>1022.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>6.25</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain\n",
              "date                                                                          \n",
              "2010-01-02 00:00:00      129.0  -16  -4.0  1020.0      SE     1.79     0     0\n",
              "2010-01-02 01:00:00      148.0  -15  -4.0  1020.0      SE     2.68     0     0\n",
              "2010-01-02 02:00:00      159.0  -11  -5.0  1021.0      SE     3.57     0     0\n",
              "2010-01-02 03:00:00      181.0   -7  -5.0  1022.0      SE     5.36     1     0\n",
              "2010-01-02 04:00:00      138.0   -7  -5.0  1022.0      SE     6.25     2     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "sYh5wJcnMUZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7f83a9bd-86a3-45ee-9290-4d16910091e7"
      },
      "cell_type": "code",
      "source": [
        "print('number of rows = ', df.shape[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of rows =  43800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FgVoMUe3MUZh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Plot the data\n",
        "- for this step, we'll use the same code used here: https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/"
      ]
    },
    {
      "metadata": {
        "id": "lsvQT0sbMUZn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "62e6f76d-3285-4512-dfdf-bbf621881088"
      },
      "cell_type": "code",
      "source": [
        "# specify columns to plot\n",
        "values = df.values\n",
        "groups = [0, 1, 2, 3, 5, 6, 7]\n",
        "i = 1\n",
        "# plot each column\n",
        "plt.figure()\n",
        "for group in groups:\n",
        "    plt.subplot(len(groups), 1, i)\n",
        "    plt.plot(values[:, group])\n",
        "    plt.title(df.columns[group], y=0.5, loc='right')\n",
        "    i += 1\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Pk5JVxHv/F0qgX+Mqwzh4uDmRkKZZ+uYXlXL+mmZ2v+1oHB2CvDkamUzn1k0I8nenhW9j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            "text/plain": [
              "<Figure size 432x288 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8u2uYHESMUZ5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Inorder to model the time-series with conventional supervised machine learning, the data must be preprocessed \n",
        "- in this case, we will just predict the target signal ('pollution') one step ahead\n",
        "- the features and the target will be lagged by num_lags and aggregated by mean with windows of num_lags"
      ]
    },
    {
      "metadata": {
        "id": "FC7S8rA8MUZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "fa67aeb7-7e64-43cc-89c0-7cc13aefa08f"
      },
      "cell_type": "code",
      "source": [
        "df2 = df.copy()\n",
        "num_lags = 3 # number of lags and window lenghts for mean aggregation\n",
        "delay = 1 # predict target one step ahead\n",
        "for column in df2:\n",
        "    for lag in range(1,num_lags+1):\n",
        "        df2[column + '_lag' + str(lag)] = df2[column].shift(lag*-1-(delay-1))\n",
        "        if column != 'wnd_dir':\n",
        "            df2[column + '_avg_window_length' + str(lag+1)] = df2[column].shift(-1-(delay-1)).rolling(window=lag+1,center=False).mean().shift(1-(lag+1))\n",
        "\n",
        "df2.dropna(inplace=True) \n",
        "\n",
        "mask = (df2.columns.str.contains('pollution') | df2.columns.str.contains('lag') | df2.columns.str.contains('window'))\n",
        "df_processed = df2[df2.columns[mask]]\n",
        "\n",
        "# the columns in the processed dataframe\n",
        "df_processed.columns"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['pollution', 'pollution_lag1', 'pollution_avg_window_length2',\n",
              "       'pollution_lag2', 'pollution_avg_window_length3', 'pollution_lag3',\n",
              "       'pollution_avg_window_length4', 'dew_lag1', 'dew_avg_window_length2',\n",
              "       'dew_lag2', 'dew_avg_window_length3', 'dew_lag3',\n",
              "       'dew_avg_window_length4', 'temp_lag1', 'temp_avg_window_length2',\n",
              "       'temp_lag2', 'temp_avg_window_length3', 'temp_lag3',\n",
              "       'temp_avg_window_length4', 'press_lag1', 'press_avg_window_length2',\n",
              "       'press_lag2', 'press_avg_window_length3', 'press_lag3',\n",
              "       'press_avg_window_length4', 'wnd_dir_lag1', 'wnd_dir_lag2',\n",
              "       'wnd_dir_lag3', 'wnd_spd_lag1', 'wnd_spd_avg_window_length2',\n",
              "       'wnd_spd_lag2', 'wnd_spd_avg_window_length3', 'wnd_spd_lag3',\n",
              "       'wnd_spd_avg_window_length4', 'snow_lag1', 'snow_avg_window_length2',\n",
              "       'snow_lag2', 'snow_avg_window_length3', 'snow_lag3',\n",
              "       'snow_avg_window_length4', 'rain_lag1', 'rain_avg_window_length2',\n",
              "       'rain_lag2', 'rain_avg_window_length3', 'rain_lag3',\n",
              "       'rain_avg_window_length4'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "tJqprPCRMUaN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Take a look at the pollution lags and windows"
      ]
    },
    {
      "metadata": {
        "id": "8FM0yE5VMUaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "be4b5b7d-30d8-42cd-d999-412f12462a5a"
      },
      "cell_type": "code",
      "source": [
        "mask = df_processed.columns.str.contains('pollution')\n",
        "df_processed[df_processed.columns[mask]].head(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>pollution</th>\n",
              "      <th>pollution_lag1</th>\n",
              "      <th>pollution_avg_window_length2</th>\n",
              "      <th>pollution_lag2</th>\n",
              "      <th>pollution_avg_window_length3</th>\n",
              "      <th>pollution_lag3</th>\n",
              "      <th>pollution_avg_window_length4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-02 00:00:00</th>\n",
              "      <td>129.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>153.5</td>\n",
              "      <td>159.0</td>\n",
              "      <td>162.666667</td>\n",
              "      <td>181.0</td>\n",
              "      <td>156.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 01:00:00</th>\n",
              "      <td>148.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>159.333333</td>\n",
              "      <td>138.0</td>\n",
              "      <td>146.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 02:00:00</th>\n",
              "      <td>159.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>159.5</td>\n",
              "      <td>138.0</td>\n",
              "      <td>142.666667</td>\n",
              "      <td>109.0</td>\n",
              "      <td>133.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 03:00:00</th>\n",
              "      <td>181.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>123.5</td>\n",
              "      <td>109.0</td>\n",
              "      <td>117.333333</td>\n",
              "      <td>105.0</td>\n",
              "      <td>119.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 04:00:00</th>\n",
              "      <td>138.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>112.666667</td>\n",
              "      <td>124.0</td>\n",
              "      <td>114.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 05:00:00</th>\n",
              "      <td>109.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>114.5</td>\n",
              "      <td>124.0</td>\n",
              "      <td>116.333333</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 06:00:00</th>\n",
              "      <td>105.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>125.333333</td>\n",
              "      <td>132.0</td>\n",
              "      <td>129.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 07:00:00</th>\n",
              "      <td>124.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>130.666667</td>\n",
              "      <td>140.0</td>\n",
              "      <td>136.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 08:00:00</th>\n",
              "      <td>120.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>141.333333</td>\n",
              "      <td>152.0</td>\n",
              "      <td>143.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 09:00:00</th>\n",
              "      <td>132.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>146.666667</td>\n",
              "      <td>148.0</td>\n",
              "      <td>151.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     pollution  pollution_lag1  pollution_avg_window_length2  \\\n",
              "date                                                                           \n",
              "2010-01-02 00:00:00      129.0           148.0                         153.5   \n",
              "2010-01-02 01:00:00      148.0           159.0                         170.0   \n",
              "2010-01-02 02:00:00      159.0           181.0                         159.5   \n",
              "2010-01-02 03:00:00      181.0           138.0                         123.5   \n",
              "2010-01-02 04:00:00      138.0           109.0                         107.0   \n",
              "2010-01-02 05:00:00      109.0           105.0                         114.5   \n",
              "2010-01-02 06:00:00      105.0           124.0                         122.0   \n",
              "2010-01-02 07:00:00      124.0           120.0                         126.0   \n",
              "2010-01-02 08:00:00      120.0           132.0                         136.0   \n",
              "2010-01-02 09:00:00      132.0           140.0                         146.0   \n",
              "\n",
              "                     pollution_lag2  pollution_avg_window_length3  \\\n",
              "date                                                                \n",
              "2010-01-02 00:00:00           159.0                    162.666667   \n",
              "2010-01-02 01:00:00           181.0                    159.333333   \n",
              "2010-01-02 02:00:00           138.0                    142.666667   \n",
              "2010-01-02 03:00:00           109.0                    117.333333   \n",
              "2010-01-02 04:00:00           105.0                    112.666667   \n",
              "2010-01-02 05:00:00           124.0                    116.333333   \n",
              "2010-01-02 06:00:00           120.0                    125.333333   \n",
              "2010-01-02 07:00:00           132.0                    130.666667   \n",
              "2010-01-02 08:00:00           140.0                    141.333333   \n",
              "2010-01-02 09:00:00           152.0                    146.666667   \n",
              "\n",
              "                     pollution_lag3  pollution_avg_window_length4  \n",
              "date                                                               \n",
              "2010-01-02 00:00:00           181.0                        156.50  \n",
              "2010-01-02 01:00:00           138.0                        146.75  \n",
              "2010-01-02 02:00:00           109.0                        133.25  \n",
              "2010-01-02 03:00:00           105.0                        119.00  \n",
              "2010-01-02 04:00:00           124.0                        114.50  \n",
              "2010-01-02 05:00:00           120.0                        120.25  \n",
              "2010-01-02 06:00:00           132.0                        129.00  \n",
              "2010-01-02 07:00:00           140.0                        136.00  \n",
              "2010-01-02 08:00:00           152.0                        143.00  \n",
              "2010-01-02 09:00:00           148.0                        151.00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "65OtaTShMUan",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## perform train/test split\n",
        "- split the data sequentially mainting chronological order\n",
        "- 80% train/ 20% test"
      ]
    },
    {
      "metadata": {
        "id": "Hoif9kDoMUar",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_processed.reset_index(drop=True,inplace=True)\n",
        "df_train = df_processed.loc[:int(df_processed.shape[0]*0.8),:]\n",
        "df_test = df_processed.loc[int(df_processed.shape[0]*0.8):,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kfgsPFhGMUa3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## plot the train/test split"
      ]
    },
    {
      "metadata": {
        "id": "1VXWoCoPMUa7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "a7e9d6ac-d999-4b72-a9c5-d8911611be76"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(df_train.index,df_train['pollution'])\n",
        "plt.plot(df_test.index,df_test['pollution'])\n",
        "plt.ylabel('pollution',fontsize=18)\n",
        "plt.legend(['train','test'])\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DVwFXAgOBN4BTtNYy6m4XklyhRwhhhqmHBLXWu4Fbo5SWpCMX18TjtFlVnJ3kl9Syu6Ca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\n4or6gI4hlzyUUo8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AAve/IV6beZaLKNlT6OfE87q2JWfDdeiCzV9hxvDBge8Szxo92LJj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\nf7QqTeNh2q3yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zavHejIbMUbP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Initialize h2o session"
      ]
    },
    {
      "metadata": {
        "id": "uYCYQufxMUbT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "f21fcb3e-4a3c-4af2-aeba-ebc106a661c5"
      },
      "cell_type": "code",
      "source": [
        "h2o.init(nthreads=-1)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"10.0.2\" 2018-07-17; OpenJDK Runtime Environment (build 10.0.2+13-Ubuntu-1ubuntu0.18.04.4); OpenJDK 64-Bit Server VM (build 10.0.2+13-Ubuntu-1ubuntu0.18.04.4, mixed mode)\n",
            "  Starting server from /usr/local/lib/python3.6/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpun2oxa6l\n",
            "  JVM stdout: /tmp/tmpun2oxa6l/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpun2oxa6l/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
              "<td>02 secs</td></tr>\n",
              "<tr><td>H2O cluster timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O data parsing timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O cluster version:</td>\n",
              "<td>3.22.1.4</td></tr>\n",
              "<tr><td>H2O cluster version age:</td>\n",
              "<td>8 days </td></tr>\n",
              "<tr><td>H2O cluster name:</td>\n",
              "<td>H2O_from_python_unknownUser_o5ce22</td></tr>\n",
              "<tr><td>H2O cluster total nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O cluster free memory:</td>\n",
              "<td>3 Gb</td></tr>\n",
              "<tr><td>H2O cluster total cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O cluster allowed cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O cluster status:</td>\n",
              "<td>accepting new members, healthy</td></tr>\n",
              "<tr><td>H2O connection url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O connection proxy:</td>\n",
              "<td>None</td></tr>\n",
              "<tr><td>H2O internal security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>H2O API Extensions:</td>\n",
              "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
              "<tr><td>Python version:</td>\n",
              "<td>3.6.7 final</td></tr></table></div>"
            ],
            "text/plain": [
              "--------------------------  ---------------------------------------------------\n",
              "H2O cluster uptime:         02 secs\n",
              "H2O cluster timezone:       Etc/UTC\n",
              "H2O data parsing timezone:  UTC\n",
              "H2O cluster version:        3.22.1.4\n",
              "H2O cluster version age:    8 days\n",
              "H2O cluster name:           H2O_from_python_unknownUser_o5ce22\n",
              "H2O cluster total nodes:    1\n",
              "H2O cluster free memory:    3 Gb\n",
              "H2O cluster total cores:    2\n",
              "H2O cluster allowed cores:  2\n",
              "H2O cluster status:         accepting new members, healthy\n",
              "H2O connection url:         http://127.0.0.1:54321\n",
              "H2O connection proxy:\n",
              "H2O internal security:      False\n",
              "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
              "Python version:             3.6.7 final\n",
              "--------------------------  ---------------------------------------------------"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "d2W1QAS8MUbu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## accessing H2OFlow UI\n",
        "- by default H2OFlow UI will open on localhost port 54321\n",
        "- accessing this UI is optional\n",
        "- on google colab, if you want to access H2OFlow, you will need to tunnel in to the localhost\n",
        "   - we'll use a Nodejs package to accomplish this\n",
        "- for more information on H2OFlow: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/flow.html"
      ]
    },
    {
      "metadata": {
        "id": "RBRIiFUqMUb0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "89f893b8-8017-491d-ff49-4fc6e29e0ff6"
      },
      "cell_type": "code",
      "source": [
        "!npm install -g localtunnel -qq > /dev/null"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nSS248OOMUcI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "get_ipython().system_raw('lt --port 54321 >> url.txt 2>&1 &')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gj4I5BvBMUcY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### open the follwoing link to H2OFlow if desired "
      ]
    },
    {
      "metadata": {
        "id": "DFQ5OluwMUcb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "746ed1b3-5783-466c-f008-a616ebb09dd8"
      },
      "cell_type": "code",
      "source": [
        "!cat url.txt"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "your url is: https://purple-otter-30.localtunnel.me\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jBypQSxPMUcm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Convert pandas dataframes to h2o frames"
      ]
    },
    {
      "metadata": {
        "id": "yhJrmvz8MUcp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "cb2d9de0-43b2-47db-d3c8-db78b3644ffc"
      },
      "cell_type": "code",
      "source": [
        "hf_train = h2o.H2OFrame(df_train)\n",
        "hf_test = h2o.H2OFrame(df_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GBn2ILZXMUc3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "bb5e9021-7110-4a18-bb80-907913968193"
      },
      "cell_type": "code",
      "source": [
        "hf_train.describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows:35037\n",
            "Cols:46\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>       </th><th>pollution        </th><th>pollution_lag1   </th><th>pollution_avg_window_length2  </th><th>pollution_lag2   </th><th>pollution_avg_window_length3  </th><th>pollution_lag3   </th><th>pollution_avg_window_length4  </th><th>dew_lag1          </th><th>dew_avg_window_length2  </th><th>dew_lag2          </th><th>dew_avg_window_length3  </th><th>dew_lag3          </th><th>dew_avg_window_length4  </th><th>temp_lag1         </th><th>temp_avg_window_length2  </th><th>temp_lag2         </th><th>temp_avg_window_length3  </th><th>temp_lag3         </th><th>temp_avg_window_length4  </th><th>press_lag1        </th><th>press_avg_window_length2  </th><th>press_lag2        </th><th>press_avg_window_length3  </th><th>press_lag3        </th><th>press_avg_window_length4  </th><th>wnd_dir_lag1  </th><th>wnd_dir_lag2  </th><th>wnd_dir_lag3  </th><th>wnd_spd_lag1      </th><th>wnd_spd_avg_window_length2  </th><th>wnd_spd_lag2      </th><th>wnd_spd_avg_window_length3  </th><th>wnd_spd_lag3      </th><th>wnd_spd_avg_window_length4  </th><th>snow_lag1          </th><th>snow_avg_window_length2  </th><th>snow_lag2          </th><th>snow_avg_window_length3  </th><th>snow_lag3          </th><th>snow_avg_window_length4  </th><th>rain_lag1          </th><th>rain_avg_window_length2  </th><th>rain_lag2          </th><th>rain_avg_window_length3  </th><th>rain_lag3          </th><th>rain_avg_window_length4  </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>type   </td><td>int              </td><td>int              </td><td>real                          </td><td>int              </td><td>real                          </td><td>int              </td><td>real                          </td><td>int               </td><td>real                    </td><td>int               </td><td>real                    </td><td>int               </td><td>real                    </td><td>int               </td><td>real                     </td><td>int               </td><td>real                     </td><td>int               </td><td>real                     </td><td>real              </td><td>real                      </td><td>real              </td><td>real                      </td><td>real              </td><td>real                      </td><td>enum          </td><td>enum          </td><td>enum          </td><td>real              </td><td>real                        </td><td>real              </td><td>real                        </td><td>real              </td><td>real                        </td><td>int                </td><td>real                     </td><td>int                </td><td>real                     </td><td>int                </td><td>real                     </td><td>int                </td><td>real                     </td><td>int                </td><td>real                     </td><td>int                </td><td>real                     </td></tr>\n",
              "<tr><td>mins   </td><td>0.0              </td><td>0.0              </td><td>0.0                           </td><td>0.0              </td><td>0.0                           </td><td>0.0              </td><td>0.0                           </td><td>-33.0             </td><td>-32.5                   </td><td>-33.0             </td><td>-32.333333333333336     </td><td>-33.0             </td><td>-31.5                   </td><td>-19.0             </td><td>-18.5                    </td><td>-19.0             </td><td>-18.666666666666668      </td><td>-19.0             </td><td>-18.5                    </td><td>991.0             </td><td>991.0                     </td><td>991.0             </td><td>991.3333333333334         </td><td>991.0             </td><td>991.75                    </td><td>              </td><td>              </td><td>              </td><td>0.45              </td><td>0.6699999999998757          </td><td>0.45              </td><td>0.5966666666663781          </td><td>0.45              </td><td>0.6699999999987535          </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>mean   </td><td>93.36549932928058</td><td>93.36247395610361</td><td>93.36064731569459             </td><td>93.35882067528613</td><td>93.35874456526892             </td><td>93.35493906441785</td><td>93.35667294574304             </td><td>1.7743813682678187</td><td>1.774295744498676       </td><td>1.7742101207295118</td><td>1.774181579473128       </td><td>1.773953249422043 </td><td>1.7740317378771024      </td><td>12.154693609612686</td><td>12.154836315894626       </td><td>12.154979022176542</td><td>12.154998049680815       </td><td>12.15532151725317 </td><td>12.15516454034306        </td><td>1016.3997773781998</td><td>1016.3996917544328        </td><td>1016.3996061306625</td><td>1016.3995966169106        </td><td>1016.3994063418685</td><td>1016.3994919656368        </td><td>              </td><td>              </td><td>              </td><td>24.957655050375337</td><td>24.959479407483837          </td><td>24.96130376459178 </td><td>24.961363320680015          </td><td>24.965131147073134</td><td>24.96329080686061           </td><td>0.05748209036161772</td><td>0.05748209036161772      </td><td>0.05748209036161772</td><td>0.057482090361617694     </td><td>0.05748209036161772</td><td>0.0574749550475212       </td><td>0.20989239946342433</td><td>0.2098923994634244       </td><td>0.20989239946342433</td><td>0.20989239946342442      </td><td>0.20989239946342433</td><td>0.20989239946342433      </td></tr>\n",
              "<tr><td>maxs   </td><td>994.0            </td><td>994.0            </td><td>983.0                         </td><td>994.0            </td><td>865.3333333333334             </td><td>994.0            </td><td>855.0                         </td><td>28.0              </td><td>28.0                    </td><td>28.0              </td><td>28.0                    </td><td>28.0              </td><td>28.0                    </td><td>41.0              </td><td>41.0                     </td><td>41.0              </td><td>41.0                     </td><td>41.0              </td><td>40.75                    </td><td>1046.0            </td><td>1046.0                    </td><td>1046.0            </td><td>1046.0                    </td><td>1046.0            </td><td>1046.0                    </td><td>              </td><td>              </td><td>              </td><td>585.6             </td><td>583.5899999999997           </td><td>585.6             </td><td>581.58                      </td><td>585.6             </td><td>579.5699999999994           </td><td>27.0               </td><td>26.5                     </td><td>27.0               </td><td>26.0                     </td><td>27.0               </td><td>25.5                     </td><td>36.0               </td><td>35.5                     </td><td>36.0               </td><td>35.0                     </td><td>36.0               </td><td>34.5                     </td></tr>\n",
              "<tr><td>sigma  </td><td>91.91025864899358</td><td>91.91083022692456</td><td>90.816767204728               </td><td>91.91120230473103</td><td>89.97156220224014             </td><td>91.91130184370424</td><td>89.18544765954863             </td><td>14.494245647416877</td><td>14.478716729023043      </td><td>14.494479282813261</td><td>14.465956798069655      </td><td>14.494785419767135</td><td>14.453505854826703      </td><td>12.30718345069174 </td><td>12.284682250601287       </td><td>12.306924751796219</td><td>12.260385243735554       </td><td>12.306614297205664</td><td>12.23209869901723        </td><td>10.386615878686598</td><td>10.380773178783398        </td><td>10.386605980920418</td><td>10.374856748136967        </td><td>10.386584812040583</td><td>10.368012708638854        </td><td>              </td><td>              </td><td>              </td><td>51.31115782014467 </td><td>50.661545347005465          </td><td>51.314118858612034</td><td>50.15115419961667           </td><td>51.3175242597527  </td><td>49.67473886864784           </td><td>0.7738710029485413 </td><td>0.7544674396038666       </td><td>0.7738710029485413 </td><td>0.739272499343031        </td><td>0.7738710029485413 </td><td>0.7251850039981366       </td><td>1.4899906873477353 </td><td>1.4530405905633517       </td><td>1.4899906873477353 </td><td>1.4253262927082684       </td><td>1.4899906873477353 </td><td>1.4005950908345153       </td></tr>\n",
              "<tr><td>zeros  </td><td>1946             </td><td>1946             </td><td>1766                          </td><td>1946             </td><td>1680                          </td><td>1946             </td><td>1616                          </td><td>636               </td><td>344                     </td><td>636               </td><td>240                     </td><td>636               </td><td>178                     </td><td>882               </td><td>518                      </td><td>882               </td><td>376                      </td><td>882               </td><td>285                      </td><td>0                 </td><td>0                         </td><td>0                 </td><td>0                         </td><td>0                 </td><td>0                         </td><td>              </td><td>              </td><td>              </td><td>0                 </td><td>0                           </td><td>0                 </td><td>0                           </td><td>0                 </td><td>0                           </td><td>34706              </td><td>34649                    </td><td>34706              </td><td>34595                    </td><td>34706              </td><td>34544                    </td><td>33521              </td><td>33077                    </td><td>33521              </td><td>32712                    </td><td>33521              </td><td>32389                    </td></tr>\n",
              "<tr><td>missing</td><td>0                </td><td>0                </td><td>0                             </td><td>0                </td><td>0                             </td><td>0                </td><td>0                             </td><td>0                 </td><td>0                       </td><td>0                 </td><td>0                       </td><td>0                 </td><td>0                       </td><td>0                 </td><td>0                        </td><td>0                 </td><td>0                        </td><td>0                 </td><td>0                        </td><td>0                 </td><td>0                         </td><td>0                 </td><td>0                         </td><td>0                 </td><td>0                         </td><td>0             </td><td>0             </td><td>0             </td><td>0                 </td><td>0                           </td><td>0                 </td><td>0                           </td><td>0                 </td><td>0                           </td><td>0                  </td><td>0                        </td><td>0                  </td><td>0                        </td><td>0                  </td><td>0                        </td><td>0                  </td><td>0                        </td><td>0                  </td><td>0                        </td><td>0                  </td><td>0                        </td></tr>\n",
              "<tr><td>0      </td><td>129.0            </td><td>148.0            </td><td>153.5                         </td><td>159.0            </td><td>162.66666666666666            </td><td>181.0            </td><td>156.5                         </td><td>-15.0             </td><td>-13.0                   </td><td>-11.0             </td><td>-11.0                   </td><td>-7.0              </td><td>-10.0                   </td><td>-4.0              </td><td>-4.5                     </td><td>-5.0              </td><td>-4.666666666666667       </td><td>-5.0              </td><td>-4.75                    </td><td>1020.0            </td><td>1020.5                    </td><td>1021.0            </td><td>1021.0                    </td><td>1022.0            </td><td>1021.25                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>2.68              </td><td>3.125                       </td><td>3.57              </td><td>3.87                        </td><td>5.36              </td><td>4.465                       </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.3333333333333333       </td><td>1.0                </td><td>0.75                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>1      </td><td>148.0            </td><td>159.0            </td><td>170.0                         </td><td>181.0            </td><td>159.33333333333334            </td><td>138.0            </td><td>146.75                        </td><td>-11.0             </td><td>-9.0                    </td><td>-7.0              </td><td>-8.333333333333334      </td><td>-7.0              </td><td>-8.0                    </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.25                    </td><td>1021.0            </td><td>1021.5                    </td><td>1022.0            </td><td>1021.6666666666666        </td><td>1022.0            </td><td>1021.75                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>3.57              </td><td>4.465                       </td><td>5.36              </td><td>5.06                        </td><td>6.25              </td><td>5.58                        </td><td>0.0                </td><td>0.5                      </td><td>1.0                </td><td>1.0                      </td><td>2.0                </td><td>1.5                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>2      </td><td>159.0            </td><td>181.0            </td><td>159.5                         </td><td>138.0            </td><td>142.66666666666666            </td><td>109.0            </td><td>133.25                        </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.0                    </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.333333333333333       </td><td>-6.0              </td><td>-5.5                     </td><td>1022.0            </td><td>1022.0                    </td><td>1022.0            </td><td>1022.0                    </td><td>1022.0            </td><td>1022.25                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>5.36              </td><td>5.805                       </td><td>6.25              </td><td>6.25                        </td><td>7.14              </td><td>6.92                        </td><td>1.0                </td><td>1.5                      </td><td>2.0                </td><td>2.0                      </td><td>3.0                </td><td>2.5                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>3      </td><td>181.0            </td><td>138.0            </td><td>123.5                         </td><td>109.0            </td><td>117.33333333333331            </td><td>105.0            </td><td>119.0                         </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.0                    </td><td>-5.0              </td><td>-5.5                     </td><td>-6.0              </td><td>-5.666666666666667       </td><td>-6.0              </td><td>-5.5                     </td><td>1022.0            </td><td>1022.0                    </td><td>1022.0            </td><td>1022.3333333333334        </td><td>1023.0            </td><td>1022.75                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>6.25              </td><td>6.695                       </td><td>7.14              </td><td>7.44                        </td><td>8.93              </td><td>8.26                        </td><td>2.0                </td><td>2.5                      </td><td>3.0                </td><td>3.0                      </td><td>4.0                </td><td>2.25                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>4      </td><td>138.0            </td><td>109.0            </td><td>107.0                         </td><td>105.0            </td><td>112.66666666666669            </td><td>124.0            </td><td>114.5                         </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.25                   </td><td>-6.0              </td><td>-6.0                     </td><td>-6.0              </td><td>-5.666666666666667       </td><td>-5.0              </td><td>-5.75                    </td><td>1022.0            </td><td>1022.5                    </td><td>1023.0            </td><td>1023.0                    </td><td>1024.0            </td><td>1023.25                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>7.14              </td><td>8.035                       </td><td>8.93              </td><td>8.93                        </td><td>10.72             </td><td>9.825                       </td><td>3.0                </td><td>3.5                      </td><td>4.0                </td><td>2.3333333333333335       </td><td>0.0                </td><td>1.75                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>5      </td><td>109.0            </td><td>105.0            </td><td>114.5                         </td><td>124.0            </td><td>116.33333333333331            </td><td>120.0            </td><td>120.25                        </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.333333333333333      </td><td>-8.0              </td><td>-7.25                   </td><td>-6.0              </td><td>-5.5                     </td><td>-5.0              </td><td>-5.666666666666667       </td><td>-6.0              </td><td>-5.5                     </td><td>1023.0            </td><td>1023.5                    </td><td>1024.0            </td><td>1023.6666666666666        </td><td>1024.0            </td><td>1024.0                    </td><td>SE            </td><td>SE            </td><td>SE            </td><td>8.93              </td><td>9.825                       </td><td>10.72             </td><td>10.72                       </td><td>12.51             </td><td>11.614999999999998          </td><td>4.0                </td><td>2.0                      </td><td>0.0                </td><td>1.3333333333333333       </td><td>0.0                </td><td>1.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>6      </td><td>105.0            </td><td>124.0            </td><td>122.0                         </td><td>120.0            </td><td>125.33333333333331            </td><td>132.0            </td><td>129.0                         </td><td>-7.0              </td><td>-7.5                    </td><td>-8.0              </td><td>-7.333333333333333      </td><td>-7.0              </td><td>-7.25                   </td><td>-5.0              </td><td>-5.5                     </td><td>-6.0              </td><td>-5.333333333333333       </td><td>-5.0              </td><td>-5.25                    </td><td>1024.0            </td><td>1024.0                    </td><td>1024.0            </td><td>1024.3333333333333        </td><td>1025.0            </td><td>1024.75                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>10.72             </td><td>11.614999999999998          </td><td>12.51             </td><td>12.509999999999998          </td><td>14.3              </td><td>13.739999999999998          </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.25                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>7      </td><td>124.0            </td><td>120.0            </td><td>126.0                         </td><td>132.0            </td><td>130.66666666666666            </td><td>140.0            </td><td>136.0                         </td><td>-8.0              </td><td>-7.5                    </td><td>-7.0              </td><td>-7.333333333333333      </td><td>-7.0              </td><td>-7.5                    </td><td>-6.0              </td><td>-5.5                     </td><td>-5.0              </td><td>-5.333333333333333       </td><td>-5.0              </td><td>-5.25                    </td><td>1024.0            </td><td>1024.5                    </td><td>1025.0            </td><td>1025.0                    </td><td>1026.0            </td><td>1025.25                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>12.51             </td><td>13.405                      </td><td>14.3              </td><td>14.746666666666664          </td><td>17.43             </td><td>16.2                        </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.3333333333333333       </td><td>1.0                </td><td>0.25                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>8      </td><td>120.0            </td><td>132.0            </td><td>136.0                         </td><td>140.0            </td><td>141.33333333333334            </td><td>152.0            </td><td>143.0                         </td><td>-7.0              </td><td>-7.0                    </td><td>-7.0              </td><td>-7.333333333333333      </td><td>-8.0              </td><td>-7.5                    </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.0                     </td><td>1025.0            </td><td>1025.5                    </td><td>1026.0            </td><td>1025.6666666666667        </td><td>1026.0            </td><td>1025.75                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>14.3              </td><td>15.865000000000002          </td><td>17.43             </td><td>17.43                       </td><td>20.56             </td><td>18.994999999999997          </td><td>0.0                </td><td>0.5                      </td><td>1.0                </td><td>0.3333333333333333       </td><td>0.0                </td><td>0.25                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "<tr><td>9      </td><td>132.0            </td><td>140.0            </td><td>146.0                         </td><td>152.0            </td><td>146.66666666666666            </td><td>148.0            </td><td>151.0                         </td><td>-7.0              </td><td>-7.5                    </td><td>-8.0              </td><td>-7.666666666666667      </td><td>-8.0              </td><td>-7.75                   </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.0                     </td><td>-5.0              </td><td>-5.0                     </td><td>1026.0            </td><td>1026.0                    </td><td>1026.0            </td><td>1026.0                    </td><td>1026.0            </td><td>1025.75                   </td><td>SE            </td><td>SE            </td><td>SE            </td><td>17.43             </td><td>18.995000000000005          </td><td>20.56             </td><td>20.56                       </td><td>23.69             </td><td>22.3475                     </td><td>1.0                </td><td>0.5                      </td><td>0.0                </td><td>0.3333333333333333       </td><td>0.0                </td><td>0.25                     </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td><td>0.0                </td><td>0.0                      </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "C356YVomMUdI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Select response and features \n",
        "- y: target signal\n",
        "- X: feature matrix with lagged values"
      ]
    },
    {
      "metadata": {
        "id": "xfOkvlg_MUdL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = 'pollution'\n",
        "X = hf_train.columns\n",
        "X.remove(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6j_tr5G3MUdV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "5192cb8d-204a-4848-8f09-d30c1aa3f20f"
      },
      "cell_type": "code",
      "source": [
        "X"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pollution_lag1',\n",
              " 'pollution_avg_window_length2',\n",
              " 'pollution_lag2',\n",
              " 'pollution_avg_window_length3',\n",
              " 'pollution_lag3',\n",
              " 'pollution_avg_window_length4',\n",
              " 'dew_lag1',\n",
              " 'dew_avg_window_length2',\n",
              " 'dew_lag2',\n",
              " 'dew_avg_window_length3',\n",
              " 'dew_lag3',\n",
              " 'dew_avg_window_length4',\n",
              " 'temp_lag1',\n",
              " 'temp_avg_window_length2',\n",
              " 'temp_lag2',\n",
              " 'temp_avg_window_length3',\n",
              " 'temp_lag3',\n",
              " 'temp_avg_window_length4',\n",
              " 'press_lag1',\n",
              " 'press_avg_window_length2',\n",
              " 'press_lag2',\n",
              " 'press_avg_window_length3',\n",
              " 'press_lag3',\n",
              " 'press_avg_window_length4',\n",
              " 'wnd_dir_lag1',\n",
              " 'wnd_dir_lag2',\n",
              " 'wnd_dir_lag3',\n",
              " 'wnd_spd_lag1',\n",
              " 'wnd_spd_avg_window_length2',\n",
              " 'wnd_spd_lag2',\n",
              " 'wnd_spd_avg_window_length3',\n",
              " 'wnd_spd_lag3',\n",
              " 'wnd_spd_avg_window_length4',\n",
              " 'snow_lag1',\n",
              " 'snow_avg_window_length2',\n",
              " 'snow_lag2',\n",
              " 'snow_avg_window_length3',\n",
              " 'snow_lag3',\n",
              " 'snow_avg_window_length4',\n",
              " 'rain_lag1',\n",
              " 'rain_avg_window_length2',\n",
              " 'rain_lag2',\n",
              " 'rain_avg_window_length3',\n",
              " 'rain_lag3',\n",
              " 'rain_avg_window_length4']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "9SADhCIGMUdq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Run AutoML"
      ]
    },
    {
      "metadata": {
        "id": "03TbZOX4MUdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a53fcb4c-2cac-400e-dc55-cd196042fb1c"
      },
      "cell_type": "code",
      "source": [
        "aml = H2OAutoML(max_runtime_secs = 600,\n",
        "                seed = 42)\n",
        "aml.train(x = X, \n",
        "          y = y,\n",
        "          training_frame = hf_train,\n",
        "          leaderboard_frame = hf_test)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Iw42hzH-MUd9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Check the leaderboard\n",
        "- models are ranked by peformance on the test data since we specified a leadboard_frame"
      ]
    },
    {
      "metadata": {
        "id": "qPN2euhmMUeE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "20b5ba7c-62ec-406f-9634-109644e4cda6"
      },
      "cell_type": "code",
      "source": [
        "lb = aml.leaderboard\n",
        "\n",
        "lb"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">    mse</th><th style=\"text-align: right;\">    mae</th><th style=\"text-align: right;\">     rmsle</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>StackedEnsemble_AllModels_AutoML_20190224_160001   </td><td style=\"text-align: right;\">                 560.13 </td><td style=\"text-align: right;\">23.6671</td><td style=\"text-align: right;\">560.13 </td><td style=\"text-align: right;\">12.6952</td><td style=\"text-align: right;\">  0.454746</td></tr>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190224_160001</td><td style=\"text-align: right;\">                 560.13 </td><td style=\"text-align: right;\">23.6671</td><td style=\"text-align: right;\">560.13 </td><td style=\"text-align: right;\">12.6952</td><td style=\"text-align: right;\">  0.454746</td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_20190224_160001                   </td><td style=\"text-align: right;\">                 574.076</td><td style=\"text-align: right;\">23.9599</td><td style=\"text-align: right;\">574.076</td><td style=\"text-align: right;\">12.5184</td><td style=\"text-align: right;\">  0.448085</td></tr>\n",
              "<tr><td>XRT_1_AutoML_20190224_160001                       </td><td style=\"text-align: right;\">                 576.801</td><td style=\"text-align: right;\">24.0167</td><td style=\"text-align: right;\">576.801</td><td style=\"text-align: right;\">12.7962</td><td style=\"text-align: right;\">  0.44649 </td></tr>\n",
              "<tr><td>DRF_1_AutoML_20190224_160001                       </td><td style=\"text-align: right;\">                 579.176</td><td style=\"text-align: right;\">24.0661</td><td style=\"text-align: right;\">579.176</td><td style=\"text-align: right;\">12.8207</td><td style=\"text-align: right;\">  0.448355</td></tr>\n",
              "<tr><td>GLM_grid_1_AutoML_20190224_160001_model_1          </td><td style=\"text-align: right;\">                 598.125</td><td style=\"text-align: right;\">24.4566</td><td style=\"text-align: right;\">598.125</td><td style=\"text-align: right;\">13.147 </td><td style=\"text-align: right;\">nan       </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "yNPsptmuMUeS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Select the top model"
      ]
    },
    {
      "metadata": {
        "id": "FNLIsKqUMUeX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "leader_model = aml.leader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-jw_3eISMUei",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Make predictions on the test data"
      ]
    },
    {
      "metadata": {
        "id": "QgfmCaRUMUeo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7f0d23ad-8e55-43bc-e343-e90d6137ca37"
      },
      "cell_type": "code",
      "source": [
        "hf_test_predict = leader_model.predict(hf_test)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AvfwUpRLMUe9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "53e7bc79-aa1e-4b12-d8e5-93c2fc14ad6d"
      },
      "cell_type": "code",
      "source": [
        "hf_test_predict.head(5)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">  22.9331</td></tr>\n",
              "<tr><td style=\"text-align: right;\">  21.2417</td></tr>\n",
              "<tr><td style=\"text-align: right;\">  24.6804</td></tr>\n",
              "<tr><td style=\"text-align: right;\">  27.7829</td></tr>\n",
              "<tr><td style=\"text-align: right;\">  57.0336</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "q6jgTI53MUfJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "f26ce2a1-5b61-4901-d599-703d758dc1fe"
      },
      "cell_type": "code",
      "source": [
        "df_results = pd.DataFrame()\n",
        "df_results['ground_truth'] = df_test['pollution'].reset_index(drop=True)\n",
        "df_results['predictions'] = h2o.as_list(hf_test_predict,use_pandas=True)\n",
        "df_results.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ground_truth</th>\n",
              "      <th>predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>22.933093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23.0</td>\n",
              "      <td>21.241658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20.0</td>\n",
              "      <td>24.680386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.0</td>\n",
              "      <td>27.782896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.0</td>\n",
              "      <td>57.033641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ground_truth  predictions\n",
              "0          18.0    22.933093\n",
              "1          23.0    21.241658\n",
              "2          20.0    24.680386\n",
              "3          23.0    27.782896\n",
              "4          24.0    57.033641"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "3XKjc2MqMUfV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get R2 for this prediction"
      ]
    },
    {
      "metadata": {
        "id": "NsfHsn7DMUfY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6dc21091-63ef-4531-dd62-7d42251017ef"
      },
      "cell_type": "code",
      "source": [
        "slope, intercept, r_value, p_value, std_err = stats.linregress(x=df_results['predictions'],y=df_results['ground_truth'])\n",
        "print('R2 = ',r_value*r_value)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 =  0.9369043171226586\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bdbBikC6MUfk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "ad59d57a-aceb-4d82-b88c-2674a862dd98"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(x=df_results['predictions'],y=df_results['ground_truth'],s=1)\n",
        "plt.xlabel('predictions',fontsize=18)\n",
        "plt.ylabel('ground_truth',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AucBvQosuw4ygAvgVcCUwEdirtT6utW4DnsPsjSEIA4ZoJhtnCY1EivINKfYxd2oFq2vr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\nWSsHCjMza+VAYWZmrf4PO1/6na3JeooAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CyozxwmmMUfw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "3a489b34-9c83-47d9-e83b-4a682ba5b145"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(df_results['ground_truth'])\n",
        "plt.plot(df_results['predictions'])\n",
        "plt.ylabel('pollution',fontsize=18)\n",
        "plt.legend(['ground_truth','prediction'])\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KFYGJO6HGKvG359fhF189LY9rsNxj7WOz/aYz5i6zu4gOFxQSWcpq5Qcie6Gq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\nNBqNRpOQ/weu3+iQSTwdvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ONYXks5AMUf9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Let's now try to predict 5 steps ahead\n",
        "- this time, we'll put everything into one notebook cell"
      ]
    },
    {
      "metadata": {
        "id": "akKJnov_MUgA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "1461ba2b-fde6-482d-ffd8-b0899e8bb8d6"
      },
      "cell_type": "code",
      "source": [
        "df2 = df.copy()\n",
        "num_lags = 5 # number of lags and window lenghts for mean aggregation\n",
        "delay = 5 # predict target five steps ahead\n",
        "for column in df2:\n",
        "    for lag in range(1,num_lags+1):\n",
        "        df2[column + '_lag' + str(lag)] = df2[column].shift(lag*-1-(delay-1))\n",
        "        if column != 'wnd_dir':\n",
        "            df2[column + '_avg_window_length' + str(lag+1)] = df2[column].shift(-1-(delay-1)).rolling(window=lag+1,center=False).mean().shift(1-(lag+1))\n",
        "\n",
        "df2.dropna(inplace=True) \n",
        "\n",
        "mask = (df2.columns.str.contains('pollution') | df2.columns.str.contains('lag') | df2.columns.str.contains('window'))\n",
        "df_processed = df2[df2.columns[mask]]\n",
        "\n",
        "df_processed.reset_index(drop=True,inplace=True)\n",
        "df_train = df_processed.loc[:int(df_processed.shape[0]*0.8),:]\n",
        "df_test = df_processed.loc[int(df_processed.shape[0]*0.8):,:]\n",
        "\n",
        "hf_train = h2o.H2OFrame(df_train)\n",
        "hf_test = h2o.H2OFrame(df_test)\n",
        "\n",
        "y = 'pollution'\n",
        "x = hf_train.columns\n",
        "x.remove(y)\n",
        "\n",
        "aml = H2OAutoML(max_runtime_secs = 600,\n",
        "                seed = 42)\n",
        "aml.train(x = x, \n",
        "          y = y,\n",
        "          training_frame = hf_train,\n",
        "          leaderboard_frame = hf_test)\n",
        "\n",
        "leader_model = aml.leader\n",
        "\n",
        "hf_test_predict = leader_model.predict(hf_test)\n",
        "\n",
        "df_results = pd.DataFrame()\n",
        "df_results['ground_truth'] = df_test['pollution'].reset_index(drop=True)\n",
        "df_results['predictions'] = h2o.as_list(hf_test_predict,use_pandas=True)\n",
        "\n",
        "slope, intercept, r_value, p_value, std_err = stats.linregress(x=df_results['predictions'],y=df_results['ground_truth'])\n",
        "print('R2 = ',r_value*r_value)\n",
        "\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "AutoML progress: |████████████████████████████████████████████████████████| 100%\n",
            "drf prediction progress: |████████████████████████████████████████████████| 100%\n",
            "R2 =  0.6740027842197897\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VXNX-A5QMUgn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "94b0ce64-2014-4060-98de-f1d3567ee743"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(x=df_results['predictions'],y=df_results['ground_truth'],s=1)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tya2jvXOW7U+2DNZJD7lXnPqovcvP2h2H2HRP5FmpToMoGFUdxXliDcyjofiz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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hLedAZCYMUg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "39b55cb6-1f7c-448b-eefb-20b79628485b"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(df_results['ground_truth'])\n",
        "plt.plot(df_results['predictions'])\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RTZKE3fMjX5/R1UhbIgM5rF7VsAcj52NcaCz6+u1jXwIABPntxfF+SdZnnFlf\n0hxqGcjNhcbp78slVvZmp2drUtST71BgyXaUrfw7hqLKIqTMRsFO6SXexegn7pVMvK/2iWvTfxsH\nLh9nELNMfkf1khDq2WmV5gf4OsH7wk7DsLyyG++XoNaNMXNVNTD3kO5zoqod30purpsRxZHqyq3H\nqVR/8I87cyfYKUSs3plUaH93SDGHDc9chyC/HWFWTQWS8lg6eDK7Z+Q+94tmDapc1b7NskJ/vXx3\n79IQ6hmel+9VaZFwA+MD1U7vrauT2S/sCTnkuMj5HbMj5UU4HkS6Vdu0FzlAbKJgaX5xkGjGdift\n/QsAkl95DjFWm6NH+f/poexST7jyFLNF+Y2vbm3Dj3OSqsQbJSHUnaSAZ/9vBiYvmkw0ddsNbAmX\ns3oRmuvKPd87W4qhLctMAkzF8LiQ6l3+ncTj2b/wGF9rBy440TmMU725sQMH/d5EjrGpjpwyegzl\nvjGtBvRqeSoAQByptS2X2gTjmbVHsPd4H0KGnZvkUH63EHS9R+l4JnMZoHPMTv91aqgbbH22nZic\nTz09OBD6g9UQUO+fmmVdyGQyUOWTwYaNKJvVhchgVW6vOxJFKJJARZn77t4T2JvTOmiZaMFM3/rd\nNgDAE/de4/ocq9/IaVy8SANkV79+JmRMtXGiawRYof2ecJMsu7Wj2VTiCPc2u+kEFu2ELLE42T0N\nCGhKjjRlV0EHSkJTv/dXhK1Q/RH45+8F/NaeIzKg6wD9kQH8+7r/h9b6F13fO3OF1txxZIskWpy2\nR+QQO+HiVeyY81t4J1qu5HOP+XJrt77jm6vxrz/Jznc5TS4U9Ykl0zNC+xObmSWa4+o3z603B/Q9\ns1afdVUG0C+rOyjtPmI0KRZOUzePFmR/y9RgxdV3w9fYifW7OyxPywcTUqhLsoRNbXpXs0T3HN3n\nwNyD8DW1IzDvAOxaMb0biwxs69qFhOwtcrCn32ZzaY9vb6ozyLKM4ZDqvtXVp2ove08fwHe2/Bhj\ncTWIw819tEEarvBQ918+vw+fenBd7lKM5jBdgizL8M0+pAS35OJ6FsdFScKvXzygi3AsZXYe6bVM\nZgdA13+amDnEIu/s7zIdSxhNlhKwQ3w+/ZGdYhDq+RCQLq9pWcyo4Bj7M10oNfNO53b8eONjumOy\nmJwSpbaqS/m0cnHbTW3ZslTHZHA6Yv1CWtmYh0PWO52nhG2ZWG9ZhlT+bxtP4p6H304f79Yk+vrl\nnt+ifbQTW7tUm6+dxu2fcxDB5RvSuxrlgy0HldSiIyH7XdqtkGXZEPGauzf11GgH/NNPInjGlswv\nwsXTbmhWA+ihlkFs2t+Fh54ww8QvAAAgAElEQVTZ5Xi5ia6pD4/F8NO/7MXXHiXMkJMYV6RIX5Be\nKdNCqeGzf4ZZuzffJ7sGdp/7hVzOeJStKGwumAkp1LtC+r0O2eo+g2ugdxhOxIaOzPY0tCL1cN3a\nrlOa+uotztvJaQeZuGS9z6mvuQVs5Qi8dvRCyp2th3pw/2+35uXaiRxsj1Z+3lqUnf0mAGuB7CXi\nNJ4QlbgC1vq5jWdCFlGhetSGqq0imw+J74WhfUmD6OCoGkMi50HrdTvD1mdVtYguBeCfI+SiWq5x\ntXLE8/yDAK5Ilv8ugK0AngLAAegEcIcgCFGe528HcA+UNb9HBUF4PC+1NrR58AwngVAM1UhdKNVn\nYlT/fnKNAPjNZxrNGFNry0xltNd0k7fdMwVssiOn9IFT420BFwCYQBS+WQKA7Dfk/uOG7ShbvgnS\nWA2AGzydu2FvJ+ZNr8HMxsqs65EpboSetsTi2bVYv9983IsHmBatUM/HtMftQvYPn9mJsrMzPz9f\nOGrqPM9fDeBMQRAuAXATgJ8AeADAI4IgXAHgKIC7eJ6vBHAfgOsArALwBZ7n3dkdcoqxQRnAR9Ys\nxsLWphMjKUHj2qWN0eagMPdeGTLe2NluOKZg3OCjvlaR/Nq8GdrBIdNOtGFvp+7zNN/cjK6TLaZp\n+PiT6QDsp/5ensDJAWWmyVYOI55wP5PoG4rg8ZcO4r8ey25G2T8cwX88sgGb93U6F/ZAz2AYX/zZ\n29h3vE8fmGOR4Ir0mEfr9X7dTn1b+732etnkf3F+xR3qZPraFCfrrUIecWN+eQvAPyb/HgRQCUVo\nv5A89iIUQX4RgK2CIAwJghAGsAHAZTmtbQawVYMoW76J+J3Xhb2+oQg+9f11eGnTSVflV7e/DAAI\ncW5TyZIf9omAMvX/4TOqHV3bgV1NFwmLj395U79JLgvOoSb27PGw0XdKO5dlGaxBqo9HTT2FpbDw\n0GDaX7d2eztGXSoXkVhuzDVv7e7AQGgU//1ba5t4Jry2tQ2DozH88vn90DaIjyX3K9I6VazSoOgQ\n2lU387Woy+GBzLe+c5z5Mob/21XE6bs84CjUBUEQBUFIuV98EsDLACoFQUjNgXoATAfQDEArvVLH\ni0OyIZkcZDhMXe5gMkTcKAyt6I143JvQQTCc6FQXE/WaundIA4F2j9RM8l88uVpA1GPe8d7BMGEb\nv/y8Bb95+SBe3KDXtiOxBF7ceBLDYy4Xea02B89Q+/rzuqP4t/9ZrzcpWCDKEriprYDPXV4iO8rP\nW4uyc9ZldK5V10gNzsYIjXIfOXguF/FtVq2esFlncsZt1lfy3c2zeYL1II+4jsbgef4foAj1GwBo\nnUkt8xc6XbOurgI+H+dUzET5KYIhWkNTk7uIrfoG93bJisoAGoIVnu9hpLEheR6hdSqrgpbXNR6v\nqa5IHwtGXXQSRtJdw7iw19RUDY5jgeS7UF9fmdFvrKurRGW5/fPR0jccRd+wXkj5fVzG7WukJaT2\nr/V7FHPDXe9Vo1eefPkA/vrWcbR0j+L+uy9xvF5ZZYBYt+ouNVjNqe6kPh8WZSx2OG9d2zsIzDsA\nqbEdTU0fdKzra5tbEBcl3HzpfN3xioogMAQw/rindk6VDSUkMOUjkMP6PlJZqSyIyjLQ2KAEkJVH\nZ6KuVn3PGuor0ZR87zhjoisCU6aYB4S6OvU99PlYTR2U94BhGEyZUmE6zy1+P7n/sSyTls/m71XB\nXV1jeI+N0emsiPqGCnCsd9nnBrcLpTcC+E8ANwmCMMTz/CjP8+VJM8tMAB3Jf82a02YCsJ3fDVgk\nzHciFLbXqnp73Xl79Pe7z+MRCsUxGlOFj3IPO8j37+tTXn5SoNHoaMTyusbjIyPh9LHhqPMGGIHF\nO9Hbqy7KbTukn0X09o4gnlC1m/7+MewRerDjcC/++dZlJhOJFX19owgFlW710onXEBfjeO+im12d\nm0IUJRft644/rjkETNEf0157Z7Iddgg9ru45OhomlhvS5Btxuo5MSB8xNEi+rpaOQWUizFQOuarr\nw39W3CsvWNyoOz425qUfq6TKvn1iC8rO2oBE11zd+aGkS6ssA72nleOyJGNoSH3P+/pGwSV/v5ts\niv2D5ndUKzcSCXNfkWUZw8OZe8PF4yKxXSRJSitjdu02NGT9HgMA09SCz77wdfz3ZV/LuI52g7Gb\nhdIpAH4A4BZBEFKO3K8DuC35920AVgPYDOACnudreZ6vgmJPz+12MTlm9WZn10EtuZw0kUS+lwn8\nkTbV99W44EqCm6K3dxtzefzfW8fR2a9PjvSL5/Zh84FudNsFmdjw8onX8FrrG95PzOE6kpP73dH2\nIdvvjYxGrBQKd5U+2DKALkLAWiFT7XgxFZHMcK1jLQAArkG/0Kr/DapDr9WbE2pykaveMSme2+hP\n99SwDfYFHNMHOwQfARiI5m/XKzcLpf8EoBHAn3mef4Pn+TcAfBvAnTzPrwdQD+B3Sa39XgBroAj9\n+wVB8PbG5AIPz9JLQA4DZfrFlI8gcMY76A25XxQ0Xyl7WjXBOtsPe7Tdw+xho2wPZv+yh6MJbNrf\nlf9dlBgGXWPdGIlln0TKKvVCinMWKVpsTaW7VAx+H/n5RcQQ2CrnaNIf/DGb3Y70v+XY4En8SXjO\ns6eHl+USUlF1odJ6fcFKpGvPiFW3ON/foa758COxSsshG/+yinwucnSZo/lFEIRHATxK+Op6Qtln\nATybg3oVCG+NzzBAYOFusBWj+OvRv+HuFXfaFHa4s82D5xpPQY442PuzXCglCmZNJx1NjCnBMZIv\nbXp56lUB7+zvRv9wBO++ZF4GdyXDBMfgn79P/QzgW5sfAgA8cs2DWV3b6f0KJLMGVgTdLS9NrSUv\n+j3f+xSCy0YQ2X2F7fnBZe+ArTLrOplkxfzRjp8DAM5uWo6l9Ytdn+cl0ySpn6aFOmNxXLZ35/WC\nU3Qn0VMqyzQT1jMZ/fGyszYQS5nfrMIK+ZLI0pg5Xhqb0b94Gc6XGbXfE6ojQ5IlBBbsI31rrI3m\nNO+ac9xB2/6fAz9E2XlAZOtN6ffyWNJU0X56DAlRMu/oAu/Nwlb3mYPHcpn7xbNPsT1WQmZMVGZO\nTlsPkgQ64LbdyIVEj/mKPGnqRFXdQlNnAEWksemBQylKThOQeQWs0N5Hf54ULQMbdLctJGkgk2QZ\nw2MxsFYJRLV9VjasF4w3l8aJiqsc6l4FkCeJZX//BBNB2crXdcckuHcl5Fi1LpkkSBQdNHVAfXdT\nQU+pqr2zvxt3/+AN7zfVXrt8RMlLQ4gG1g5YHaNdGQeShBMRjNTbmzvSZgKXjzYTV083uPPNz8x+\nvP9EPw63qTbcbH8Dk4rNN/SXYbkH5Re+CjS2aO7BZLVeQHryZbpZFSmwLzvd2Ng+CVFCz4C6DkKW\nLeoxSZawbkc78btCULJC3R3uG7tnIARNKuh0V7KajrMV5NXv1MvL1fSDIUS6kjTLpthZ5utoovRk\ny0zs1oiiDK5Wb4u3GgjXJrf3s5q2M5VDYCq9LfwEz9wAXzPZpqqdEX17y4/wRtvbxHJOvN7yBsSg\n/bKOnNYo3UkekTDA6IKHMhVgns7TFx4cieBw26DOpKbNoPjQn3bhe39QB7dsNXWrtuqGstk6O+Ow\nmnGU0c8iPIs3ovzUhTCRT9OUkSLeNpkx3vK7v9+eTF5mU3tDNVq6Ryy/yzclKNQzDO1zoKMvROzM\nVr62gaUZJKiSycYCRjbfg2PsNxtwgqkcRmD+AcdygcXbEZcT6BkIoX84omQsNFC2fBPKliveq+Go\n6G5vVpu2N2qte/sOWZS0p3dEv9Bq3JwbUAZR4/HXj5MjkAGylvvUG5pI3zKym+xgdAh/PPQX8xdJ\noZfNe//k1rX43h926PKTt/dau+uSBiYrlN+r/81arVVXNq1cyOgaUxSGcLDDU4RwUK4BAJQnmgzX\nJEO8sukRMZ52PzO+hemgP9ufoVlGdQw+yi8TUqjnLmGONzcAYzC7HZlGshIjPQnVZDXml0zao6qG\nYIcldHyurhfDTDu2H+6Ff+FulJ+3Nr3xCKmuX/2/J/HTNW94ro+uGqYFuMzYcVifnoEJmIXRwSm/\nR9nyTQjXq+sYfz35V8trkgbQQ77V6b+V/P1mnj70F7xNyALqn3cAgJSVVOfqFAGqzeXu9ymvNlM+\nguCKt3QDlxcdYCg2jPIL14CbqsyqTvWO4mh7yp1Wf6FOKIMv40uga0yfSVW9t/XNpWgZgpJitOZk\nNZDJdA3dp2zMVhalHYuTpi/GWmmT+FGhXjg8NTZTgPHWyhpIELYayTdUQ95P08hz69X0BkbhxFYN\ngK0k531ObVzga1BcQFOmJVOtfFH4Zx+BEHjFVX2sMM6IUi/I1q6dePHYatIpRIxumwwjweoFj1Y5\nu9cBZKGe4OxdLxOiZOme6WtqR9kFr5q0WS9uowwrgWtoh/a3+Tnlev45h8CWhRCYtz/93cGBg66v\nvT85SwrMU84ZDcX1LugWBLkg8W875Gh5WkGxXb+SyX+ry7dyll6FDieTHG40x3Kyj20WlKxQt/Iy\nyPh6TFLLSA4E6WeYw+cnw2Gzam19NAZ+rs5dwrAXNpxU72W4TXCZdeY/UZT0Qie1UGb47f657oWF\nHVav828P/BGrW/6OcMJdtKBRKwws2pPUjM0ER8m78xhxEwWpZSQUw90/eMN2h3uG0c+2Xtp0Enf/\n4A1TgJhdVwss3Itoxan059RMjuGUdZvUgC3LMgYbN7qu/3BMP9CTPJ5ITK9UgsvL49PQXOluj122\nLAQ1XbVyH+dFcndTHNaDpNP2m/5hrcdMcsCpHMYT+/5gfT4A0aeuaTCB7HP1eKHkhLqnlXYPmjrD\nMHhnfzfYcsVWmaksd7IvJmxcWdgqde9OL52URFfMfTRtUx15oclo9klp8oBZW+mPDGBz53aX9nbG\n9nPbiHMELQDIhB0UfFPb9NeWveXf8LoXa2uPIphHHVJbaJsllTBu91HjfpwOdfPpI3/LzllnUm5e\n334KXoiK+jUUhgUYfzT5t7XATbVTMFELAKiUGtVdySyQY2VpG7rdwrX2CVgNsrky0X739+So1+09\nu4nHlZtLsN5GPv9MTD/1IsxuGACbjh5FWTIX1ImBDkXr4bylGrBDTvqpm79Q/qfVpgOMeeMMLxyN\nOW+7lmJaXTmgU1iS2fhsnoNxs93vbfkfjCVCaCivx6La+RZnKZBeZ503Qx6mt26VgZDoPl8QoKl3\nFsFoKbzoK52RU0QNcfOBbsgzGDCsuzacUTbTUAcGvibn/W5VP3V9re1+phQth1wmQpbVmBDHiFJN\ngXhCAuMHYnFrM5tXtMnmlB3EnEkOSzm5fyaUnKbuDW+aOlermjmGEn3Y2bMXccZj4iCHZ03SPEhb\ndnHZquoeyMQPfs8p1UYdjYsYSyha5FjchVAkLDr9bJe6J61bP+t8LFBtilgvohKRTX+Qi7lwHfTi\nX76hbx3xuI9lIEf1My9ZltF+esw0WLb3juI3r+i3YnM7+Elp0ZY6QT3xL28ew9OvHzafJDNKbh2N\nUwLZo1FzdIrVgqzpiJtqm6+fRDtLdr4AIKKwJhctJSfU5YSHyYeHlz7BRkx7Db520L22K0ssYieW\nOZYTRXOdSJ2MyyaiwysyEJY09t3kKPNqyzqcGCIvMEYZVat5QLP/6EDUxVqH8acxwKEBNduzlIep\nrVs/da/DhNvy5HB8bxfVRRlbFOI41tTvN+ztwn89tlnnEgkAh1rJsQeJ7tnKPQzmlJQ/uDRWo8m5\nYxiYALy0qQWvbyOZgRiwVUNgWFmjqds/6172COEo4ddn+bow5e7zEMmQsdf/fHY3zIKSE+qMnB+L\n0hjMyZo6+txNxeW4H5FtN0DstV+MkyFDdL1dXuGEugwZu2Nr1QO+BJiyUbx0cg1+uP0R8jma6UVn\nn2rrFfqPurij/Yvc2Z99oi/LOzsIETeBXmFSZkgHBUKWgc6+MTz87B7NOeSyDCvj7XZCVmtd5izy\n/bSL/Sn2JnesMqZjJiHJsjp1NAh1tkyZtcqiTzW/eJCmrMbHPzNHBBs7POM+lULW87viOr+UnlD3\nFHvkQVOvZZpNx9x3WI3PqsM5bgNDAn7vCfa5Rm+LZClkWUZMVo3qvqmtAGNfT73XgIqbVAtGwWls\nsxOt7qa2pIVSK1IlOy38q63qRkK7y3xcSsA/52B6gd3yurKMXz2/H7s0i6N2feWPwv8RjqrlIwly\nyuFDrYOmHChxJgK2rksn7EVJRFtMSHvPAMBzR19GKK5djCXXj/FHTTZ1VUjbvHOsKnhT58VJ5kir\nmYpfWYyuKveb5IC31AjZSWWnJGT5ZmIulNrCeHiA7hvfx5g7ilucVv21uHVp9HHeNXW/xle5DJWI\nwOVMw/CZqz1tGTmZYqByH0jDDsOwGI3Zn2s0rxh/6cCgO63L1+B9Y+XvbPmx53OMaE05e4a3WKZD\n0CIBiHrcM9eO/uEIGMLmP4sWsjD6DrWWr0VwcR8SXaqtfUPHZmwNr4F/gVrutdY30F496LzoG61I\nz3hSA1OMCYFhJPtt5jSDCqP4eaK12/usTA4SzvHwupBNV15s8u7vlQ9KTlNnwLp3Z8rWgmE4/5Nn\n3OlY0OmWbv2gUx3HSUBaMYNd6rosceGozH6BmKtW7bHaSE4WDHrC9q563YxgOJJZiKkn/+AcWrO0\nk5HRBDmgywhpMPdqYdM9J0I6BwAIlJuFatinmF8kvyoMO5Nh/sY6HB5xEYsgmz2UYozST7f1bYF/\n/l5wDQQPGo1QT83oSLNpp7c7wY0Z0sPIniO8JVnCi8fXgKkYNtXNGRp85B27zi67n2qxU9z7Abt5\nvxrK6i3q5P7t3NTtbYf3x/dbB0EY0bqxeVlsXN+2FZFE5qv5gSWqry/LsI5tGWH0gtAoWJob7Pef\njIkxxEV3Gw979TvnZOf9V71l81Q4OnaItD7sjeRPWXNsPZggedDtr7Re3DcvaZpJIArfNLMb75GB\nY/qz5VRkqF7E9EZ64WtqR2DhHpgx37NPzsRkKOtkgNuUu9paCP3HsPrkWpSdqQRq+WcTvHUsyMRb\nLJeUoPmFRafLBUyrPB0konERRnuCKUeJ1cusE+rWr2pCTmB9l3kHQFlWbJz6Y0rPaRvJzE7uJZ2t\nXNOdlYOW1lSztXsnApy7XYasqK+2DjuPxkV8cf3XIYWqwHrYeziWcGfSmYoljmW03cBtrvNT4ZMA\nFikffDEwvljGi+FrW613kRySbRZDM9x4pW2kAz/Z+SvthSz7l+2aEmMu1y2eMJcjKG1/Ep7TXee1\nU2tNZdwyIp3GQ38aQ1AzmWU4DznraZqAXMOgf9TbyOyGwdEsxBohyyIJO+3ZSjgY+8+T73dnE84k\nXW+u2EBIbGWPMcLUuuRnf/QmAICtsLbFarU4hku2g8v30C85p3HVauqHx5w3PEnVKXVa2Yr1KFvx\nNkRZnW30DoZxuM3JHTQ7G9KQxz7O+OLY1r0Lu3v36o5zdT0a7xd937d3HdU8BJepX1K81a5PfdA2\n6i7qmESbtB9stQe/dAMivG1akmtKTqgzcrZd28O9DJ8tp90uzS92PrkJC03dKJx9nLN5APCmqRcb\no3ZnF1HqZheoo4PHHctY4SaaNRMFWxhTBSPjU+zhItTUAj/60y70RNzuReu+Alo3Rq23UEJ0J5h+\ns/9pvHLSrBXvH9hPrImdaVRrP0+dF5O9b3ruEz1M0Szwz9SYk3z2KR6MJCTyekahKDmhLoPRpaXN\nFaKPYNJxexvNy2L3wlsJ2gQ7hj8fepF86QynevkI4MkXEVGvQWo9f7S/X5REZecdB6Ki+SXlXPYZ\nWZYxFB3BUFQJrgrFzbbrXE2+tYNZ90AonWLXsnwG3f7nz2lnEuoFdBt/ZMCJPsU11OhWats2usXI\nZPAR6QzDoe3d+nWCipjZ/TgbAovdZUFNUWSTeukJdUbObvssK0KVBNueAY4l+4679Ze2ErTDZcex\ntXeb7tiA2AVREnU+sV9Y+RnX40woWlxtwguHB/QBS1pB/vWN38Fv9/8RABBymb2RRFWFOzu/BBlf\n2/AtfG3DtwAAMck8QMgZrpRJXASsZjcq7SwssMg5ejktADOVKh4W9J3o6VdMoG09RjOYTeU0sQ9q\nmgC1fLU8jWiufmL/04br5FYAcB5NMcWeBbtaKOV5/kwAzwP4sSAIP+N5fjaAp6AsHXYCuEMQhCjP\n87cDuAeK2+2jgiA8nqd6WyJ73OUkG4yeE43ldeSCOpu6XfY59/XeH9uIl0+W61zhFtXOdwxukmQZ\nr7e8gUHuZAFTDjHIpf4iQ/HDfvTFAxicPoSt3Tvx8eUfdl8bwkvvNj7ATVDLqdApNDfwruuTYmTW\nOgR96sAkQrWpc/X2QVGAGjU5Jo6YFvVlF15hbNUAnt3/Kj6w/AaIWbtwMKlKGephY34hbO+ovyKT\nTFM8vvGanjnXOGrqPM9XAvgpAK3h7AEAjwiCcAWAowDuSpa7D8B1AFYB+ALP8xY+ftkRtwvSkIHW\nHnfZ1LIlHDdqaQxWNC4n1Mn91N4L+04f9JyxUBQlPH/8FccXKJdkuguUFeuH/ob7Ht+i20wZ8DYo\nGukvc5cL3s09jnb0ZVQH2aefaWwOed1sJFk3jvxsiXXX2It9jZ1Y1/069rZ2oM8m/7u7qpD7vNtn\npOZ+8f5MvaQmyAdGT7VC48b8EgVwMwBttMAqAC8k/34RiiC/CMBWQRCGBEEIA9gA4LLcVVUlHLVp\nNInDn/7uJr9I9via9CvsDAPMqppuKpdoX+jqel49Uk6NdpiDIhz68w93/MzTPcYjI+IgQtEEjDrb\nmpN/d3V+pusQybMdS6wfew5feuu+rKfhQ1IvekK9eGzvU1ldJwVR2BDSPRw+1e8+B5EFcjo9c3YC\nloPZLOb0/AqZ646Elz1g84Gj+UUQhASABM/rppOVgiCkVq96AEwH0AxAuwVP6rgldXUV8Pm85zAp\nC1pXmwGHYk3QmpqqUXHa3AmlUXXC0thQZXl+WZk7zxW7+zsJklNZuHqNOzQD2v7RfXjj1AZXp+0d\n2o+rz7jQdLypqdrxXM6n6kFNTdVgQuS1iXAigm7JOe+4E08feRZH+pzXcwCA5Rjr3yAziAbNi/2k\nIKBjwy04HXAfbOOEtk7+gPP7zg3Mh3+6D0gAve3l8CdTurMsC8hAba29dwvHeZcpucTNbwTc9bdM\nyEXwkdW46DheDgx4d1cCgAgpC14SJQtdRpfNmr7ToxCj9jc/3WdtGgqFs8vB3Ns7gsZG60GjpODi\nuqCmn2950vWpe9qOoXfhiGkA3HzU2ac8nhCRUh57e0fQH7H2h+847W3nIhItg+4HYUmU0dVNTpcL\nmUFkxDzgczXm7KMtgTdd39OK1C5YDCOjt1ft8xHCAn300PkILlUdAcS6E0gklMgfThP1LUnKu90/\nYJ8PRiKkry4kYyF377G2XbxiNyBk6v0yyvN8KgpjJhTTTAcUbR2G40WgSA+VAc5tWmFbxHbnlwnk\nZlhsylauRdlybykVUgxLfdjfJyBm2KrtIYs0wlqijPu9bxkme+eyGMH90gp/vBZ7Tx8i14WTXEe3\n5hK2Wj9okGzqUqiGcKaiHGm340upS8VeiHSCZH7hIg0Fu3+mmvrrAG4D8Pvk/1cD2AzgMZ7nawEk\noNjT78lFJb0gV/egfKXbII3cwiC7HYk2Dq3Jvg7FNigWiGx/5s93Z+aYNeRXMy4ORAZtB2kuB0Ld\nK09ufwWwyKKw/3RuNgb3hrP3y8fftRR/6tavh5Ae7xCjZN0cilvMRpKcrnC/eU0+IAYfecjnni1u\nvF/O43n+DQAfB/Dvyb/vB3Anz/PrAdQD+F1ycfReAGugCP37BUFwr9aUAO4EqrlT+8JNua8MJe/8\n7sAzDiUKPcDKiPitTT5/Ofq3AtYliYuFUmOJmYlzTaN2taQaAdpDbRjPHI+Z1ynEoP1AlEvcLJRu\nh+LtYuR6QtlnATybfbWcGO+eqt4otgtWvlGSa+Vvt6JiMRQdhl1ftHuqkb2Xoewsdwu7bqmpDKAv\nHgDjMSthXjF414RF0jqavqWWNizC0dB+3bFGcQlGWMVOP97NlCOyeZ2ikJRcROl4hywCSluol6JA\nBwAw5hQGbpHDufd8KKsJe04zm29kSe8JMppw7gsrFjQSLqT+KYrjW6hbIUuFec+pUM8xTj60pIUi\nYyY7ysRgMDpsu1NSodc3hLjX7JcFQNQbAxr8ZlOjKTEey9rOXo3J7SYKke0m40ZemKDSZOJqthU+\nc+rWUje/lCpePFN0551YluOaZEA0+0yGmXB0jOSdo+//5GynmgyS49z7xRKXKbizZYIK9fGLUxi0\ncScYheIJdWmM5E5GSRNVBuH6hLuoYC2kFA7hLTdB7J2TdbWyIdE9G8VLUK1nFmfeVrG5Yprtaf3R\nzNIwjDeyi2y2ZkIK9WLnK7Yjk+fkRVOXxdw+MimUn6i28UK2dsz3NH8E8VYe18z1nvFivPZTpmIE\nPq4wr35NpX32y6m+2aZjZb6gKWWGVgAeHCuuy2KuyFc2xwkp1MnabuGoD9jlKctzAqKE9VZuKW6Z\n9R4PN1frW5Mwv2AkqllzNsoGdpb7exYQacQic6ZLbjyXxy/vvAuzGr0Pfm+329u4Zw/cnGm1sqKy\nunDmi3MWTrX9nrWI62iJ6X3qR9nixJ7kEyrUNQRZ5y3FcsV0xpxCdUrAWlDUBL2bMxgPSY8qg85C\n/eyGs1xdS44FwQRUb4na+AJX55EWdsftukAWdsxUMiqWYVwFEi1g9Plk2oa7TGW05uKgWId457yM\n65cpEQw7F8oRN8+71vb7wy3DLrOYjtP+lQVSnlyzJ6RQz5ctikQ5YxbSdt3Lz2YSpOu+w7LGRNkE\nygNlEIecw5KZQBRsmepi5kYwx46tyGuUQHjbdQhvuy5n12M8bkWmQ7cRj3PbfHHVbYidUFMvkzSx\n79x9cfrvWy+dl3ndHPcYie0AABspSURBVJBGaxC3yA7qR1ne7mtkSpkyw5likdtvVHa3AcW4VRqy\ngGrqGrLJm+0VtgCdyYtLYwLOftFBPwe23DlZECOzkDz6S4t9M8CRYtZy9UgkHyD5iLZwOeE9iyVb\nlYVWqtHy3ex2xzAMxF7VhBWV9TnJ3z37FlSVq79h0awpuOys3G69liJxehYS7Yuxyv8J03cXVNxQ\nMCGZ2lpyGsgDDNt8jDhezg3oF1D7hseX/30ucLOfbiZMTKFeQE2dNO3O+u6SYYd1DyaCAONseqoo\n84EJOGuo9f5p+moZdkGPHjmHeJ5xEIqfWlwQn+xEd/G8RnKxjnNB0wWoLPPjPz50Dr7/6UsAAM01\ntVlf144bz5tvOlbly9/iuBS1mgWQ+8cZVSuI35Wz+myjJFfg8UpFYppzIeQv7/oEFeqFuxdL2Hc0\nW/FlFIpeNPV5/jM93y/eTraVcyyDKRrvhGgijtgx1R5P2pW9ub4CfkPGqETn/IyEnjQ6xfI7hs3d\nQxYHMsutI0dVQZKLQSsWVwbN5fPq0VSrXPvq2ZdnfV0r/uvO81FRZp5VRePehElk72X457M+ll1l\nLJqv0k9OFc0x+vfu7IWEKNNxip0GLmoW7r3uWuaWCSnUM1pg6J0HX8y7JwRJWGX9KEyaufvHkInN\nXk6Q3coYBigLqi/PqdPDEAdULeOLt15lcUX1DZVlADKbkdCTRr1pqVwss1zxsSPneSqf6JqLRM8s\nxI6sTB/LxUSkrtq8yB3gApgaJ2yBmCVLp83C/Ok1xL46FhIR9+nNUokea+8lOVyNc5rORLzVxb6r\nloue5ONM8j/ScS2kthuvGGe8Vnjd6cwtE1KoZ7RvYc8SVITmej7Px5AWJs33174UUzgHrUI2aupm\nEt1zIA6bXSdZN8ZdAGWyfoFXJgQZLajUv6RVlaxi005SV22eSjOGPaSjBy7GHTfyrmzORsR+xZ5s\nPWVXiR0/E+ywXvDI8ex2irJCGpuC+MkzIWuiLlmHV2Vp9FbH61r5hmc0IIYrbb+vkBUFhmQ+9BFm\nn4kO5+Aqd1vTWZUhv7PzKxaRF/8NbXJhs7eBmQQbtfdM8+Uo57nE2O3/q7ZDvvLCT2ihLifca63X\nnr0A4YoW54IGAqyzwIkeXon4SdUsUsXadx6jFjJSfsxUxhepR+zo2abjnEtNPcJoNTEGYqt+845E\nzyycO0XvgndWMpFSvG0J4q08gn4O1Ybp8RUrZqCWURf35Gg5rlgx3dYmtTx4CfG4NFqHyO4rET+u\nmnzee/l8fP1j55vKiqdnmcLHIzvt3eUyRewze2o4DaaVnGKn/upHzkV196XEMhxnpa3qSRDubyR+\n0l6797HKgMcyrM4jByDn/Jfj9kFCrrHUt8hfVPumEAe103H9/joN5dnFGwDAe5fa9xcO2SkJTETp\nAxLcbepONXUNKe8Xt9GQ51dfjfdeMR8yIVG908AwO7DIdKzCp7c1rzpnJn7+xSvTnx13vDFoPDJr\n7gQXnzkN3/7UxabjXEZ2ABmspO+wUqgGYxH95s0fWXkNACDRuQALfOegpjKAr17w77rzbrpoDmYy\nGj/4RDCpgVrPnmYEFuCM8HtNx//x6oWQoxWQRtQZybXnz8KCGblLXZDo8j47I41QTtp0Sgvn59Th\n/IVkUwY5p4kZMQcLwqEx9XnIcb3pwkfYw/OjN5jD9Y1wdd0Z1+e0TFaogj79YDLbvwQAMCzmPn2t\n08CcSGRnWOWiijlR9JnTC8siZzIhUZdGDWnzi0uvkRX1Z4JhGFSECS84IyN6yKwZpuBYTheaf2nl\nrbhx5k26MoPoQFlAHRycX1799xxhQbKKq0ZFuXnACWSwUTegLIrqkFjMalK1cHG4DmU+dVZy70et\np7sMGET2XYqooJax+81lbCXK5FrEWvSCo7U75SOvnptrH5p42xJP5aWxmnRbvetiVbg6uQAumakO\nTGWcfnYXPXQ+InuusB4YDDnHM3HdNLL7mHV+FB9BU79wqbPHBlfjwqdcIitJIZA3ifAb+vNZMxRv\nHa25K3byDOf7usDJPTkayW7Daka2URAlFt/4xAVgNBHcdKFUw5ERwVP5lA2xZvQMxNsWI3F6Rvo7\nKVQNadhsA090z0aib7oyumv6QhVXg6qA3iQhG17Kq+ZZDxIAIPmVDZNliUG8bTGmjZjNE7W+RpT7\nzItDwYyEOoN3XTTPUAkOzQ0VKGeV2Y4cI7uMabWJlO+4JMuQQzWQhlSvkhtn3Wh59yBbBgaA2D0P\nsWOqGahxiir84q180oXSpTY76NIbQrZvr1my3sQlixzuvX0lls2rw7suUpUAu0FLjgWxbI4aDu9n\n9dqnNNwIOWJtA48zen/2z9x0MRJdDtq6F3lgKFsZ1NePx5WoKs/e/CLHA4i3kAVwBOS4CaOycXaj\nYsacU7ZYc2GlTPTgBbb3n19tdt/U4jSBFnvcpcnQIsc076hk09cYoKbC0C+opq4yKCyGOFILcUB5\nkaRQFb58/r9alk/ZoRmGRaJzIeSIqhkn2sgr+vFTSxA/djZYhgHDqo1fw9Wb8z+H9F4cF888Vye8\nLEkEkOhcCJ9k1tQZhkWAC5iCcBoryIs5kf16U42SiU+hqbYMdVV67XHJnClgGQZnl1+J+KlFiLeQ\np9/VmgHsirIPKscIAmDlXNVtstpfg3k1sxHZexnCO67W2XBljY/+srmqnTTRNR/SQLN5RmHgkuob\nAMBSeGiJHlDXDKIHL0Cid6apzIWN+nWFgFSDhTOn4EsfOlcXKFRfptZVq2BFD69EZNfVOqFvNCkA\nwCNfuNJ0LEUfe0L3eXpDBeKtyxDZdynkWBCRPZebzEiyZpZKih7+4NWq2ZDx6xOLcYx+JtDMOJte\n3BDZeQ38Mfeuh2K/OjsIb7kJ4S03YtYU5dhZFWp//vItyjNftdT6mYe3XYdPLDEHWmmxcrCI7L8E\n0SPnYFqtd+8q3eKxqBfqMc1aUWqPUm3/jyXc2d69MiGFujTUhNjBi1EZWoQVgavxb+fcjXk1c8CI\namfV2sqDSc0p3fwazTo11Y3sugqRXRoXPotRl2M508o8O2b2gxb7p0EanUIU7iktM2XrlDTmnUTP\nLMSOnYVyTtGctYuIidPTsbBhNqIHL9TXFYA8ph9Y4i3q4hgDsytkaqAKMGVIdCwCRLKmFuD8CG+9\nAeEtN+LDlyvmlmvPm4l3XzIXddVBfOoW84v2tfO+gi+f/3lld59UArJkk0kal8lFs6bgA6sW4puf\nUDWwgN++S57XtBLhLTdicZMy2xIHyT7o4R3XQBqtR2XST1saaUh728ial+/gMX2kItNJznXOMiwS\nvTMRb1uCyFbV/CYR7l/GqW0Z3no9qsr9KA9aT80Dsn5Q9/mUNpBDNYjsuhpypArx1qWI7L4C4S03\nIrxFb/6LGxZCo0fOwfUXqHb9mno1Cjl2Yhk4g7fJgunWaxjRQ+qzIS3ca3nfFfNx353npwdP3awM\n80zlZdHYJup7xTH+ZHsvRnOV8r7Mm0aOa0j0zEZVsNzkdirH9H1aa0KLn1IGvUC4GfLYFEgDzZjV\nQL5+ZM/lloFvup2mDOYXaawmndo63qL0q/gJ9X3+33fyk20yk0QlRWfRzCk42j6Ef7n1TJyh0fbm\nDN6CY/EdiHfOh29qG/wzFa+SlC0z9dBZrW0waeNKmR+iwnlKkqukJhSOqqOpNFYNptFsIGisIrwU\nMofoAbLXR7yNBxMMIXZcmWoeaR1FeVI2pLxoUsJI7G9G/FQIYt8MyNFy1FUF0guLidPT4WvsNHk3\nAMCNF87G2lOL4J91FGXRaYhFOUSF8+BrOgWuvht1cJ9V8X2XL9TZPv0+DrddtRC3XUV2g6su12uC\n2petIuhHaPu1gMSCu5rFzRfPNZQ1a+pad9Glc+tw183LwM+pxVd/uQli/zRwtb3p72Mnl0HsnZV+\nfl/44Dn47ye3AQCkoUbEW5eovvi+OGacXYmDCfU+4RFru4b2hdTUGADg96mDUd9QDPG2xYpLpMzh\ngqX2mQohs7pORV43YSBHK8GxDERJX8f3XnwGVkfeSn9WZjxqfT59+c34yb7DEIfrIPbOQXN9BVIm\n7kRfM1ZerXS+Rf6VOBrfkT4vvPNqQLPIKvZPR+yYDGl0CrimdvhnHFdMZgmlT956r2L+iJ84C/ET\nZ+Gz71U9wlZwN2D1kY3wzzqCePtCsNUDiLfxmD2tCp39Y6ZfOxaOp9s79WsTIvnZxFt5fPfTZtNM\nZNc1KL9wdboMND4PiY5F+O9/+BDqqypx9943AQDTq6ZinyFbspzwKYPqqUXwTWvVfScONun6XhhD\nOoEqJ/yI7r80+QuUB7xq2WK8A+VZnQy8BeAW4m/Khpxr6jzP/5jn+U08z2/ked7eCJYhX/7wOfjO\nZy/TCXQAEKMBxFvPAOJlSHUFWWbSnSY1tU90qKaClK1z1bkzMbOxEtJQky5/x6JZU9KjtDRWi0Mt\ng2AYpAVyePu1KA94s0fK4WpE914JOZwcDJKao3bDimXzUwtvLBIdi5I+00xSoC6A38cifvzs5KYL\nZlvgP12zGHOZlQhvuw5+sRadfSFlhnP0XIS33ICOTmU66MYh49bL5uOmi5w9Mr5/+Tfw4BXfTAvm\nsxcqZoH5zerv8vlYQPQDMmdrp4638kicnoHwlhvTAx3DMGAYBpevmJ72JJDGCNpVUqDPmVqFBTNq\ncOmKlIsgg0TXAsjRSuXfWC0WzKhBZM/liB1boXNLdSJ65Jx0GoX/vOM8BP2qIJZlING5EGK/ct/3\nXWmf/VJMusDJIovI/osR9HP4rzv16zLnLVEE7/Xnm5/1zRfPR3jLTUj0zjAtRgNAY3kDwltuQuzQ\nRQCAM+bVo/zUpZBCVVhRdmX6OYyeWIhEnzKbCW+5USfQ03XtmwE5WonEqSUIb7kJ0kAzpJF6fPja\nxaay86ar3mmSrLilRnZdDbF3DuLHzwYSAcs+EE2os+lAcsA81DKUPqa3ZbOorylDNG72bot3KAON\nONRoyhk1dUqNzhPouvMVk6EcCyKy/xLIMhDZa474jbfyCG+/BrHD5yGy+4r08US3wREjnjJ5amYI\nmt+Vrw1qcirUeZ6/CsBiQRAuAfBJAA/n8vop/D4OZxHChrUdfsU85YWSw5WYVqdMbz96I4+lc2oh\nDTUp9t4tN6YFwMdu5PGtT11kuua85mrE25Yg1rIU8ZalCEcTYMBAPD1LmQaLflx6prekTBcvVzTF\nqnJ/UvCxCG+/FtEDyv1/8vnLbQXeuy+Zh8+9Ty+AjEIAAK5dOQuQfFh1zgwEA1rtj8WJTmXhKpfe\nJlWBSlT6VVPC595/Fr77Lxdj1tQqzJmqvODL5tXhjhuW4N8/YL/mkOiaj/hxfV6QpXPMEahyuFq3\nnsDPaMJHb1iCM+fX4z8/ppiL/t+dqt18xcIGfPxdS9PpERpqyiBHqiD2zYAXpIFmSAPKc184Uz+w\naL1mAOhs8yRiw0rbxNt4yGO18PsYzDeYRD77vjPx7X++CB9YpcyOtF4UKbNC/MQKiN3zTNfX2pJT\ntvb+jhpE912O3l5VyMxvrkH82DlJ8467nsFA6c/XnW+e+Wk161feaTV9b0fPgOoWmDJd/dPVS9I2\n6cieKxA9cFHSJKQcm1IZTAdlRfYqm5o88O6PIbzjGsUUaPGT7r19Je5+zzJUlfvxwMduUExeY1MU\nM1tSMKcGVUDpmylzpRytVExi267XbSYe2XcJ7rpZNU2m+sBlZzUjvPUGRA9chMAJq4jt7Mi1+eVa\nAM8BgCAIB3mer+N5vkYQhIIkcD5/6VT86kur4PexGAyF8JW/dEHsnYUltyjCYFpdBb7ykZXo6g/h\na4++Q7zGY1+5GnFRwoNP74QkyYrWKfnSL8v7rlyAKVWqZn7zxXNRX+MtleldN5+Bcxc3YcXCBgT9\nHL708w3oT7bQf915vm63mCfuvQbxhIR/e3i9zvbZMEXvrWIUAgBw8fJmnL2oEeVBH7r7Q/jrW8fT\n312yPDkQ5TERl49j0wPq1StnorY6iLMW1OvcP634l/csx69e2I97b18JSZIxGo5j+Xzy5iTyWC0S\nXXPha25Bg7wA16ychWtWks1LH71+CRpry7FiYQNOdo1g9tQqnDG3DgdbFJPcx260DoX/xscvwL4T\nfbji7Bm45+G3AQCff7/ZJKM1fcxrdo6liB07C1xdD8SkVxaX9Hm//vzZeG1bG775iQvAMAymN1h4\n0Bge4ZVn64OXtP7RN1yo1/Rbe9TUy6vOnYm/72hPf16xsAF7CK6Rt1+/BJXlPjz6wgF8864LMXuq\nfoGxviaI/uGobWj/Jcun4eZL5ll+/9EbeAitg/jku1XBWFcdRGSbsmg6Z2oVWnv0/aiizIfovssA\nLp5ey2muq8TXP3IpZMhoSxxIl33w06ppdMlsVVk4Z8lUnLOoERzHYLugmlYuXDYNB9qUv2dPrUJb\nzyge+OSF8PtY/L9fvQNIHH52z5X48kbF3AOJw+UrpuOiZdOw70Rf+t3l59Th8+8/G4/97QC+8cm8\nGDJyLtSbAWzXfO5NHitYVv6UbTPA+pFoN08JASUp1RP3XoO393TiiZf1O6ywLIMgy+HrH1N9sB/6\n3GX4j0c24Hy+Kd2Bn7j3GsiynFGIt49jdXbWe/7xbDz6wgH8863LTC9I6jf94ov6UX1mYyUe+OSF\nuO/xLbb3Smk50+or8PhXr8bgaAzrdp5K27KdvE1yhfE3O3HRsmm4aJm7bHcAEG89A/HWMxBYae/C\nmNIda6uCOGeR8uJ/7n1n4rVtp/Cui+Yg4Lc+f25zNeYmhbSPY5EQJcuAFn52LYS2QVcD/ufecx4e\n+eve9OfULO3D1y3Gh65dROxj2kVGBsoazMzGSnz19pWm8gzD4PGvXk28jtbuPaupCl/9yLn4/tM7\n07+RBMsyuHhZMy46Yxrxmj/87GWmd+MX/3EVPvOQYrv+/qcvSSc0A8gRrkE/hwc/Y47MnT+9Bic6\nh/HV21fi2TePYZ1mEAKAD13D45m1R3THUsFsO4TT6WONtdZZH/8tOYvsHggpAhv6dAv//oEVuuf6\nnbsvRk2FHxVlPswevgEnQgKm+BQFxO9jce5i/WL6uYub8MgX8qOlAwCTyzS2PM8/CuAlQRCeT35+\nG8BdgiAcJpVPJETZl2EwjROyLOPhP+3C4jm1uPlSsv9qNC7iR09vx3uuWIjlC3KT9yHFxj0dWL+r\nHaIk431XLcK6HW04Y149rj7Puy+sHf+37iiicREfvsFFsiUC0biIh/6wHf9wpdIGL288gZ7+ED5+\nS+6TTNnx5MsHUF9Thlsud7f7EgD87H93Yc07aqTikjm1uOdD/7+9ew+RqgzjOP5VU/OyrFpmKqWS\n8YRdDEEUVkutSFNT1DDUSi2UsLRMK0pLu1AYdiEqDNfCNCIkQ0lTtJt5wzIUzZ4wL0VrulIrarLu\npf54z+S4s7uuNLvunPl9/tp55+zunN/Oec67533nvD24ol1q73jjjgK+23OYqaNvTMsdF3/wI6ze\nfIDHxp59PT3h0NGT5K/YxaTh13NZm9QpqxUdO1FM08aNKCkrJ6d59WM03+74ne0/HaGoyV5aNWjP\n1OHnf6fHktJyikvKKr009PayHazefID8WbexZstBcls24c6+V/2X9/J5Q9O6xmlJaRnzl27njrzO\n3NC1+jtqlpaVc+LvElrlNE1570I47ouOF5O/Yjc39+hIz25nLo2WlJUybtk0ul80kKdGnvtePQCf\nfr2Xk6dKGTmgK2MWzKPhqTZ8PGNylSfz/QXHWLRyNxOHXkuXDlXfhTQNqnwTp7uozwEOufuC6PE+\noLu7V/rJg8LCaqYanEPbtjkUFp57IYhso1xSKZPKKZdUmZJJ27Y5VRb1dM9+WQuMAjCzHkBBVQVd\nRETSL61F3d03Ad+b2SbCzJcp6fz5IiJSvbR/+Mjdn0z3zxQRkZrJyNsEiIhI5VTURURiREVdRCRG\nVNRFRGJERV1EJEbS+uEjERG5sNRTFxGJERV1EZEYUVEXEYkRFXURkRhRURcRiREVdRGRGEn7Db3q\ngpm9BvQmLGQzzd23XeCXVOvMbB7Ql/A3ewnYBnwANAIOAfe4e7GZjQUeAcqBd90938waA+8DnYAy\nYIK770v9LZnHzJoBu4DngfUoE6L9fRwoBZ4BdpLluZhZS2Ax0BpoCswF/gDeIdSRne7+YLTtTOCu\nqH2uu68ys1zgQyAXOAGMcfc/63xHaiDjeup1tbh1fWJm/YHron0eCLwOPAe85e59gb3ARDNrQTiI\nbwX6AY+aWRtgDFDk7n2AFwknhbiYBSQOrqzPxMwuAZ4F+gBDgGEoF4DxgLt7f8KaD28QjqNp7p4H\n5JrZIDPrAtzNmfxeNbNGhJPfV1EunwBPXIB9qJGMK+pUWNwaaG1mqasux8s3hJ4DQBHQgnAgroja\nVhIOzl7ANnc/5u6ngI1AHiGz5dG266K2jGdm1wDdgM+ipn5keSaEfV7n7sfd/ZC7T0K5ABwFEmtW\ntiZ0BLok/ZefyKU/sNrdT7t7IXCQ8B5LziWxbb2UiUX9csKC1gmJxa1jy93L3P1k9PB+YBXQwt2L\no7YjQHtSs0lpd/dy4B8zq34hzMwwH5ie9FiZQGeguZmtMLMNZnYLygV3/wi40sz2EjpJM4C/kjap\ncS5JbfVSJhb1iv7/KsIZwsyGEYr6QxWeqiqD823PGGZ2L7DZ3fdXsUnWZRJpQOiRjiBccniPs/ct\nK3Mxs3HAr+7eFRgALKmwyfnsf73OJBOLegFn98w7EAZ/Ys3MbgeeBga5+zHgRDRICNCRkEvFbFLa\no4GwBu5+uq5eey0ZDAwzsy3AA8BslAnAYWCTu5e6+y/AceC4ciEPWAPg7juAZsClSc/XOJektnop\nE4t61i1uHY28vwIMSRpxXweMjL4eCXwObAV6mlmraLQ/D9hAyCxxTX4o8GVdvfba4u6j3b2nu/cG\nFhJmv2R1JpG1wAAzaxgNmrZEuUAYIO4FYGadCCe7PWbWJ3p+BCGXL4DBZtbEzDoQCviPnJ1LIsN6\nKSPv0mhmLwM3EaZiTYnOvLFlZpOAOcDPSc33EYrZxYTBnAnuXmJmo4CZhOlYb7r70mj0fiFwNVAM\njHf33+pwF2qVmc0BDhB6YovJ8kzMbDLhMh3AC4Tpr1mdS3TiWgS0I0wLnk2Y0riA0Lnd6u7To20f\nBsYScpnl7uuj719CuLRVBIyL/mOudzKyqIuISOUy8fKLiIhUQUVdRCRGVNRFRGJERV1EJEZU1EVE\nYkRFXUQkRlTURURiREVdRCRG/gWTMdazdeD9zQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "haAz1k1zMUhh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Things to Try:\n",
        "- compare with univarate model\n",
        "- compare with LSTM"
      ]
    },
    {
      "metadata": {
        "id": "ahNGT3aYMUhv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Close h2o session"
      ]
    },
    {
      "metadata": {
        "id": "qUWMb6leMUh8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "ce614f70-4e24-474b-802c-0b6082ac3fef"
      },
      "cell_type": "code",
      "source": [
        "h2o.cluster().shutdown()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "H2O session _sid_80c8 closed.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}