mayrajeo/LiDAR predictions.ipynb

## LiDAR predictions.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Predictions for volume"
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:43.637114Z",
          "end_time": "2020-10-29T13:19:46.502623Z"
        }
      },
      "cell_type": "code",
      "source": "from enveco.las import *\nfrom enveco.image import *\nfrom enveco.tabular import *",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:46.504264Z",
          "end_time": "2020-10-29T13:19:46.507920Z"
        }
      },
      "cell_type": "code",
      "source": "from fastai.data.all import *\nfrom fastai.tabular.all import *\nfrom fastai.callback.progress import ShowGraphCallback",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# ANN"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:46.510390Z",
          "end_time": "2020-10-29T13:19:46.556260Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preprocessor = EnvecoPreprocessor('../enveco_data/enveco/AV.leaf.on.train.csv', \n                                  '../enveco_data/enveco/AV.leaf.on.val.csv',\n                                  '../enveco_data/enveco/AV.leaf.on.test.csv')\n",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Our training set contains a clear outlier for pretty much all variables (v more than 300m³ larger compared to second-largest), so drop it."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:46.558853Z",
          "end_time": "2020-10-29T13:19:46.595466Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preprocessor.train_df[preprocessor.train_df.v > 600]",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "    sampleplotid          x           y  measurementdate     g     d     h  \\\n75        903136  357090.53  6897053.68             2013  67.5  32.5  28.1   \n\n         v    v_ma    v_ku   v_lp  myear  \\\n75  867.35  519.86  331.11  16.38   2013   \n\n                                                                                                           filename_1  \\\n75  /wrk/project_ogiir-csc/mml/laserkeilaus/2008_17_automaattinen/2013/20130113_BLOM_ahtari_block3_kesa/1/N4142A2.laz   \n\n      a_date_1 a_window_1  filename_2  a_date_2  a_window_2  v_lp_proc  \\\n75  2013-06-28          C         NaN       NaN         NaN          2   \n\n    is_valid  \n75         0  ",
            "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>sampleplotid</th>\n      <th>x</th>\n      <th>y</th>\n      <th>measurementdate</th>\n      <th>g</th>\n      <th>d</th>\n      <th>h</th>\n      <th>v</th>\n      <th>v_ma</th>\n      <th>v_ku</th>\n      <th>v_lp</th>\n      <th>myear</th>\n      <th>filename_1</th>\n      <th>a_date_1</th>\n      <th>a_window_1</th>\n      <th>filename_2</th>\n      <th>a_date_2</th>\n      <th>a_window_2</th>\n      <th>v_lp_proc</th>\n      <th>is_valid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>75</th>\n      <td>903136</td>\n      <td>357090.53</td>\n      <td>6897053.68</td>\n      <td>2013</td>\n      <td>67.5</td>\n      <td>32.5</td>\n      <td>28.1</td>\n      <td>867.35</td>\n      <td>519.86</td>\n      <td>331.11</td>\n      <td>16.38</td>\n      <td>2013</td>\n      <td>/wrk/project_ogiir-csc/mml/laserkeilaus/2008_17_automaattinen/2013/20130113_BLOM_ahtari_block3_kesa/1/N4142A2.laz</td>\n      <td>2013-06-28</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:46.597996Z",
          "end_time": "2020-10-29T13:19:46.604034Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preprocessor.train_val_df = preprocessor.train_val_df[preprocessor.train_val_df.v < 600].copy()",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Preprocess dataframes and extract lidar features from them. Specify which features are needed:\n\n* height_features:\n    * hmax: maximum height of the point cloud\n    * hmean: mean height for vegetation pixels\n    * hstd: standard deviation for vegetation pixels\n    * cv: hstd / hmean\n* point_features:\n    * vege: proportion of vegetation points\n    * ground: proportion of ground points\n    * veg_ground_ratio: vege/ground\n* intensity_features:\n    * imax: maximum intensity for vegetation points\n    * imean: mean intensity for vegetation points\n    * imed: median intensity for vegetation points\n* height_quantiles:\n    * H where percentages of vegetation points (0%, 5%,...95%, 100%) were accumulated (e.g. h00, h05...h95, h100)\n* point_proportions\n    * Proportion of vegetation points having H greater or equal to corresponding percentile of H. H was divided to 10 equal distance fractions\n "
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:19:46.606638Z",
          "end_time": "2020-10-29T13:21:48.249060Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trainval_tb, test_tb = preprocessor.preprocess_lidar(target_col='v', path='../enveco_data/enveco/AV_las/', min_h=1.5,\n                                                     height_features=True,\n                                                     point_features=True, \n                                                     intensity_features=True, \n                                                     height_quantiles=True,\n                                                     point_proportions=True)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Adding height based features\nAdding point distribution based features\nAdding intensity based features\nAdding height quantiles\nAdding point proportions\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Put to dataloaders"
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:48.251128Z",
          "end_time": "2020-10-29T13:21:48.262618Z"
        }
      },
      "cell_type": "code",
      "source": "dls = trainval_tb.dataloaders(bs=64, y_block=RegressionBlock())",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:48.265805Z",
          "end_time": "2020-10-29T13:21:48.283343Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trainval_tb.train.ys.describe()",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "                 v\ncount  1043.000000\nmean    145.020355\nstd      83.184227\nmin       2.700000\n25%      80.864998\n50%     132.059998\n75%     191.239998\nmax     512.200012",
            "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>v</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1043.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>145.020355</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>83.184227</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2.700000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>80.864998</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>132.059998</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>191.239998</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>512.200012</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:48.285832Z",
          "end_time": "2020-10-29T13:21:48.335837Z"
        }
      },
      "cell_type": "code",
      "source": "dls.show_batch()",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>hmax</th>\n      <th>hmean</th>\n      <th>hstd</th>\n      <th>cv</th>\n      <th>veg</th>\n      <th>ground</th>\n      <th>veg_ground_ratio</th>\n      <th>imax</th>\n      <th>imean</th>\n      <th>imedian</th>\n      <th>h00</th>\n      <th>h05</th>\n      <th>h10</th>\n      <th>h15</th>\n      <th>h20</th>\n      <th>h25</th>\n      <th>h30</th>\n      <th>h35</th>\n      <th>h40</th>\n      <th>h45</th>\n      <th>h50</th>\n      <th>h55</th>\n      <th>h60</th>\n      <th>h65</th>\n      <th>h70</th>\n      <th>h75</th>\n      <th>h80</th>\n      <th>h85</th>\n      <th>h90</th>\n      <th>h95</th>\n      <th>h100</th>\n      <th>p00</th>\n      <th>p10</th>\n      <th>p20</th>\n      <th>p30</th>\n      <th>p40</th>\n      <th>p50</th>\n      <th>p60</th>\n      <th>p70</th>\n      <th>p80</th>\n      <th>p90</th>\n      <th>v</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.600000</td>\n      <td>9.800871</td>\n      <td>2.968472</td>\n      <td>0.302878</td>\n      <td>0.607330</td>\n      <td>0.392670</td>\n      <td>1.546667</td>\n      <td>9.0</td>\n      <td>3.931035</td>\n      <td>4.0</td>\n      <td>2.061</td>\n      <td>3.99875</td>\n      <td>5.5125</td>\n      <td>5.942250</td>\n      <td>6.8960</td>\n      <td>8.31450</td>\n      <td>8.707000</td>\n      <td>9.406250</td>\n      <td>9.9140</td>\n      <td>10.366000</td>\n      <td>10.545000</td>\n      <td>10.745250</td>\n      <td>10.9540</td>\n      <td>11.103250</td>\n      <td>11.5500</td>\n      <td>11.84175</td>\n      <td>12.215000</td>\n      <td>12.691750</td>\n      <td>13.170000</td>\n      <td>13.35075</td>\n      <td>15.600000</td>\n      <td>1.0</td>\n      <td>0.965517</td>\n      <td>0.922414</td>\n      <td>0.818965</td>\n      <td>0.758621</td>\n      <td>0.620690</td>\n      <td>0.353448</td>\n      <td>0.163793</td>\n      <td>0.034483</td>\n      <td>0.008621</td>\n      <td>171.710007</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>22.504000</td>\n      <td>16.520193</td>\n      <td>5.065229</td>\n      <td>0.306608</td>\n      <td>0.488789</td>\n      <td>0.511211</td>\n      <td>0.956140</td>\n      <td>8.0</td>\n      <td>3.899083</td>\n      <td>4.0</td>\n      <td>1.698</td>\n      <td>3.21780</td>\n      <td>10.0580</td>\n      <td>12.240400</td>\n      <td>14.4384</td>\n      <td>14.89400</td>\n      <td>16.231600</td>\n      <td>16.870599</td>\n      <td>17.1490</td>\n      <td>17.844200</td>\n      <td>17.991000</td>\n      <td>18.423599</td>\n      <td>18.8062</td>\n      <td>19.122601</td>\n      <td>19.6872</td>\n      <td>19.78100</td>\n      <td>20.080401</td>\n      <td>20.386999</td>\n      <td>20.721201</td>\n      <td>21.44660</td>\n      <td>22.504000</td>\n      <td>1.0</td>\n      <td>0.926606</td>\n      <td>0.908257</td>\n      <td>0.899083</td>\n      <td>0.889908</td>\n      <td>0.834862</td>\n      <td>0.743119</td>\n      <td>0.541284</td>\n      <td>0.165138</td>\n      <td>0.009174</td>\n      <td>280.529999</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>12.978000</td>\n      <td>9.149214</td>\n      <td>1.896714</td>\n      <td>0.207309</td>\n      <td>0.417840</td>\n      <td>0.582160</td>\n      <td>0.717742</td>\n      <td>7.0</td>\n      <td>3.269663</td>\n      <td>3.0</td>\n      <td>3.766</td>\n      <td>6.44880</td>\n      <td>6.8458</td>\n      <td>7.221800</td>\n      <td>7.5488</td>\n      <td>7.99700</td>\n      <td>8.111000</td>\n      <td>8.198200</td>\n      <td>8.5344</td>\n      <td>8.725800</td>\n      <td>9.127000</td>\n      <td>9.403800</td>\n      <td>9.9820</td>\n      <td>10.126000</td>\n      <td>10.4624</td>\n      <td>10.68500</td>\n      <td>10.828000</td>\n      <td>11.074800</td>\n      <td>11.425000</td>\n      <td>11.83540</td>\n      <td>12.978000</td>\n      <td>1.0</td>\n      <td>0.977528</td>\n      <td>0.966292</td>\n      <td>0.898876</td>\n      <td>0.775281</td>\n      <td>0.528090</td>\n      <td>0.426966</td>\n      <td>0.202247</td>\n      <td>0.044944</td>\n      <td>0.011236</td>\n      <td>109.220001</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10.261000</td>\n      <td>6.856414</td>\n      <td>1.746087</td>\n      <td>0.254665</td>\n      <td>0.285714</td>\n      <td>0.714286</td>\n      <td>0.400000</td>\n      <td>8.0</td>\n      <td>3.431035</td>\n      <td>4.0</td>\n      <td>1.717</td>\n      <td>3.86705</td>\n      <td>4.5723</td>\n      <td>5.140450</td>\n      <td>5.4460</td>\n      <td>5.72300</td>\n      <td>6.343500</td>\n      <td>6.676650</td>\n      <td>6.7048</td>\n      <td>6.792750</td>\n      <td>6.869500</td>\n      <td>7.033000</td>\n      <td>7.3136</td>\n      <td>7.534900</td>\n      <td>7.6973</td>\n      <td>7.97050</td>\n      <td>8.371400</td>\n      <td>8.687850</td>\n      <td>8.982700</td>\n      <td>9.28355</td>\n      <td>10.261000</td>\n      <td>1.0</td>\n      <td>0.982759</td>\n      <td>0.948276</td>\n      <td>0.896552</td>\n      <td>0.793103</td>\n      <td>0.672414</td>\n      <td>0.396552</td>\n      <td>0.206897</td>\n      <td>0.051724</td>\n      <td>0.017241</td>\n      <td>52.209999</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17.847000</td>\n      <td>12.561500</td>\n      <td>2.165856</td>\n      <td>0.172420</td>\n      <td>0.213483</td>\n      <td>0.786517</td>\n      <td>0.271429</td>\n      <td>5.0</td>\n      <td>2.578947</td>\n      <td>3.0</td>\n      <td>8.505</td>\n      <td>9.68695</td>\n      <td>10.0323</td>\n      <td>10.351499</td>\n      <td>10.7874</td>\n      <td>10.98625</td>\n      <td>11.426100</td>\n      <td>11.532650</td>\n      <td>11.6252</td>\n      <td>12.016850</td>\n      <td>12.214000</td>\n      <td>12.817250</td>\n      <td>13.1302</td>\n      <td>13.254600</td>\n      <td>13.7521</td>\n      <td>14.03700</td>\n      <td>14.230200</td>\n      <td>14.697350</td>\n      <td>15.129800</td>\n      <td>15.97400</td>\n      <td>17.847000</td>\n      <td>1.0</td>\n      <td>0.947368</td>\n      <td>0.815789</td>\n      <td>0.605263</td>\n      <td>0.473684</td>\n      <td>0.315789</td>\n      <td>0.157895</td>\n      <td>0.052632</td>\n      <td>0.052632</td>\n      <td>0.026316</td>\n      <td>75.209999</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>10.182999</td>\n      <td>5.963647</td>\n      <td>1.636837</td>\n      <td>0.274469</td>\n      <td>0.441365</td>\n      <td>0.558635</td>\n      <td>0.790076</td>\n      <td>9.0</td>\n      <td>4.492754</td>\n      <td>4.0</td>\n      <td>1.546</td>\n      <td>3.29970</td>\n      <td>3.8524</td>\n      <td>4.183700</td>\n      <td>4.4150</td>\n      <td>4.82850</td>\n      <td>5.144400</td>\n      <td>5.366400</td>\n      <td>5.5944</td>\n      <td>5.776500</td>\n      <td>6.006000</td>\n      <td>6.204800</td>\n      <td>6.4898</td>\n      <td>6.654800</td>\n      <td>6.7788</td>\n      <td>7.11900</td>\n      <td>7.459200</td>\n      <td>7.629000</td>\n      <td>7.916200</td>\n      <td>8.69030</td>\n      <td>10.182999</td>\n      <td>1.0</td>\n      <td>0.985507</td>\n      <td>0.937198</td>\n      <td>0.792271</td>\n      <td>0.637681</td>\n      <td>0.420290</td>\n      <td>0.227053</td>\n      <td>0.077295</td>\n      <td>0.019324</td>\n      <td>0.004831</td>\n      <td>74.040001</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>13.942000</td>\n      <td>6.779027</td>\n      <td>3.498746</td>\n      <td>0.516113</td>\n      <td>0.634286</td>\n      <td>0.365714</td>\n      <td>1.734375</td>\n      <td>10.0</td>\n      <td>5.270270</td>\n      <td>5.0</td>\n      <td>1.643</td>\n      <td>2.41800</td>\n      <td>2.8600</td>\n      <td>3.070000</td>\n      <td>3.3260</td>\n      <td>3.44600</td>\n      <td>3.687000</td>\n      <td>4.116500</td>\n      <td>4.9850</td>\n      <td>5.713000</td>\n      <td>6.575000</td>\n      <td>6.795500</td>\n      <td>7.4840</td>\n      <td>8.624000</td>\n      <td>9.1860</td>\n      <td>9.63850</td>\n      <td>10.048000</td>\n      <td>10.887000</td>\n      <td>11.983000</td>\n      <td>13.06500</td>\n      <td>13.942000</td>\n      <td>1.0</td>\n      <td>0.864865</td>\n      <td>0.621622</td>\n      <td>0.549550</td>\n      <td>0.423423</td>\n      <td>0.360360</td>\n      <td>0.225225</td>\n      <td>0.126126</td>\n      <td>0.072072</td>\n      <td>0.009009</td>\n      <td>81.430000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>17.152000</td>\n      <td>8.959800</td>\n      <td>3.850629</td>\n      <td>0.429767</td>\n      <td>0.490196</td>\n      <td>0.509804</td>\n      <td>0.961538</td>\n      <td>11.0</td>\n      <td>5.020000</td>\n      <td>5.0</td>\n      <td>2.402</td>\n      <td>3.35140</td>\n      <td>3.6083</td>\n      <td>3.883150</td>\n      <td>5.0410</td>\n      <td>5.57425</td>\n      <td>6.600400</td>\n      <td>6.922000</td>\n      <td>7.5940</td>\n      <td>8.173350</td>\n      <td>8.997000</td>\n      <td>10.432100</td>\n      <td>10.8232</td>\n      <td>11.159300</td>\n      <td>11.3984</td>\n      <td>11.65950</td>\n      <td>12.923000</td>\n      <td>13.355250</td>\n      <td>13.728900</td>\n      <td>14.94135</td>\n      <td>17.152000</td>\n      <td>1.0</td>\n      <td>0.830000</td>\n      <td>0.740000</td>\n      <td>0.620000</td>\n      <td>0.500000</td>\n      <td>0.430000</td>\n      <td>0.210000</td>\n      <td>0.090000</td>\n      <td>0.020000</td>\n      <td>0.010000</td>\n      <td>123.110001</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>29.886999</td>\n      <td>18.134550</td>\n      <td>6.754955</td>\n      <td>0.372491</td>\n      <td>0.762626</td>\n      <td>0.237374</td>\n      <td>3.212766</td>\n      <td>9.0</td>\n      <td>4.066225</td>\n      <td>4.0</td>\n      <td>2.054</td>\n      <td>6.24700</td>\n      <td>7.9150</td>\n      <td>8.866000</td>\n      <td>11.7730</td>\n      <td>13.21450</td>\n      <td>14.383001</td>\n      <td>15.547500</td>\n      <td>16.6810</td>\n      <td>18.641001</td>\n      <td>20.042999</td>\n      <td>20.984501</td>\n      <td>21.7190</td>\n      <td>22.449499</td>\n      <td>22.8320</td>\n      <td>23.32650</td>\n      <td>24.459000</td>\n      <td>25.253000</td>\n      <td>25.907000</td>\n      <td>26.63850</td>\n      <td>29.886999</td>\n      <td>1.0</td>\n      <td>0.966887</td>\n      <td>0.880795</td>\n      <td>0.807947</td>\n      <td>0.695364</td>\n      <td>0.576159</td>\n      <td>0.470199</td>\n      <td>0.225166</td>\n      <td>0.046358</td>\n      <td>0.006623</td>\n      <td>388.489990</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>18.273001</td>\n      <td>11.179065</td>\n      <td>4.593397</td>\n      <td>0.410893</td>\n      <td>0.623656</td>\n      <td>0.376344</td>\n      <td>1.657143</td>\n      <td>9.0</td>\n      <td>3.943965</td>\n      <td>4.0</td>\n      <td>1.527</td>\n      <td>3.16300</td>\n      <td>4.1045</td>\n      <td>5.197150</td>\n      <td>6.0334</td>\n      <td>7.64700</td>\n      <td>8.823100</td>\n      <td>9.383450</td>\n      <td>10.7618</td>\n      <td>11.286750</td>\n      <td>11.623000</td>\n      <td>12.443000</td>\n      <td>13.5368</td>\n      <td>14.126500</td>\n      <td>14.5831</td>\n      <td>14.95950</td>\n      <td>15.635600</td>\n      <td>16.180349</td>\n      <td>16.640600</td>\n      <td>17.24065</td>\n      <td>18.273001</td>\n      <td>1.0</td>\n      <td>0.939655</td>\n      <td>0.849138</td>\n      <td>0.767241</td>\n      <td>0.689655</td>\n      <td>0.590517</td>\n      <td>0.443966</td>\n      <td>0.306034</td>\n      <td>0.125000</td>\n      <td>0.004310</td>\n      <td>127.139999</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Create `Learner` object. Specify y_range to better control the training, set 1000 m³ as the maximum value for volume.\n\nLoss function is Mean Squared Error, monitor also Root Mean Squared Error, mean-normalized RMSE, Mean Absolute Error, R2Score, bias and mean-scaled bias."
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:48.337224Z",
          "end_time": "2020-10-29T13:21:48.369873Z"
        }
      },
      "cell_type": "code",
      "source": "learn = tabular_learner(dls, metrics=[rmse, rrmse, bias, bias_pct, mae, R2Score()],  y_range=(0,1000))\nlearn.summary()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "TabularModel (Input shape: ['64 x 0', '64 x 41'])\n================================================================\nLayer (type)         Output Shape         Param #    Trainable \n================================================================\nBatchNorm1d          64 x 41              82         True      \n________________________________________________________________\nBatchNorm1d          64 x 41              82         True      \n________________________________________________________________\nLinear               64 x 200             8,200      True      \n________________________________________________________________\nReLU                 64 x 100             0          False     \n________________________________________________________________\nBatchNorm1d          64 x 200             400        True      \n________________________________________________________________\nLinear               64 x 100             20,000     True      \n________________________________________________________________\nReLU                 64 x 100             0          False     \n________________________________________________________________\nLinear               64 x 1               101        True      \n________________________________________________________________\nSigmoidRange         64 x 1               0          False     \n________________________________________________________________\n\nTotal params: 28,865\nTotal trainable params: 28,865\nTotal non-trainable params: 0\n\nOptimizer used: <function Adam at 0x7fcdf8839f70>\nLoss function: FlattenedLoss of MSELoss()\n\nCallbacks:\n  - TrainEvalCallback\n  - Recorder\n  - ProgressCallback"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:48.371762Z",
          "end_time": "2020-10-29T13:21:50.036198Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "learn.lr_find()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "SuggestedLRs(lr_min=0.33113112449646, lr_steep=0.0020892962347716093)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "1e-2 seems good learning rate."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:50.037970Z",
          "end_time": "2020-10-29T13:21:58.363085Z"
        }
      },
      "cell_type": "code",
      "source": "learn.fit_one_cycle(20, max_lr=1e-2, cbs=ShowGraphCallback())",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>112227.609375</td>\n      <td>108314.203125</td>\n      <td>329.111206</td>\n      <td>218.288925</td>\n      <td>319.359222</td>\n      <td>211.820740</td>\n      <td>319.695404</td>\n      <td>-10.901612</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>90528.062500</td>\n      <td>53770.359375</td>\n      <td>231.884354</td>\n      <td>153.801453</td>\n      <td>211.167999</td>\n      <td>140.060974</td>\n      <td>214.669250</td>\n      <td>-4.908310</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>61993.457031</td>\n      <td>12128.046875</td>\n      <td>110.127411</td>\n      <td>73.043983</td>\n      <td>64.136932</td>\n      <td>42.539970</td>\n      <td>91.151154</td>\n      <td>-0.332635</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>40082.105469</td>\n      <td>2482.590576</td>\n      <td>49.825600</td>\n      <td>33.047722</td>\n      <td>16.315359</td>\n      <td>10.821455</td>\n      <td>36.528969</td>\n      <td>0.727212</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>26890.009766</td>\n      <td>1811.698975</td>\n      <td>42.564056</td>\n      <td>28.231373</td>\n      <td>-6.956649</td>\n      <td>-4.614122</td>\n      <td>29.329323</td>\n      <td>0.800930</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>18666.628906</td>\n      <td>1474.109985</td>\n      <td>38.394142</td>\n      <td>25.465605</td>\n      <td>2.495167</td>\n      <td>1.654964</td>\n      <td>26.861332</td>\n      <td>0.838024</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>13333.678711</td>\n      <td>1563.555542</td>\n      <td>39.541821</td>\n      <td>26.226822</td>\n      <td>-2.507159</td>\n      <td>-1.662918</td>\n      <td>27.118664</td>\n      <td>0.828196</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>9768.866211</td>\n      <td>1600.109497</td>\n      <td>40.001369</td>\n      <td>26.531624</td>\n      <td>9.959161</td>\n      <td>6.605592</td>\n      <td>27.635195</td>\n      <td>0.824179</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>7286.332520</td>\n      <td>1267.779907</td>\n      <td>35.605896</td>\n      <td>23.616249</td>\n      <td>4.931350</td>\n      <td>3.270807</td>\n      <td>25.060030</td>\n      <td>0.860696</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>5521.758789</td>\n      <td>1332.007935</td>\n      <td>36.496685</td>\n      <td>24.207081</td>\n      <td>-0.967523</td>\n      <td>-0.641727</td>\n      <td>25.065601</td>\n      <td>0.853638</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4286.783203</td>\n      <td>1564.606445</td>\n      <td>39.555103</td>\n      <td>26.235632</td>\n      <td>-4.097027</td>\n      <td>-2.717427</td>\n      <td>26.538107</td>\n      <td>0.828080</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3393.985107</td>\n      <td>1272.895874</td>\n      <td>35.677666</td>\n      <td>23.663851</td>\n      <td>2.378954</td>\n      <td>1.577884</td>\n      <td>25.177155</td>\n      <td>0.860134</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2764.527588</td>\n      <td>1230.930786</td>\n      <td>35.084621</td>\n      <td>23.270504</td>\n      <td>3.011802</td>\n      <td>1.997632</td>\n      <td>24.730862</td>\n      <td>0.864745</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2293.671143</td>\n      <td>1238.460083</td>\n      <td>35.191761</td>\n      <td>23.341566</td>\n      <td>2.423253</td>\n      <td>1.607266</td>\n      <td>24.670784</td>\n      <td>0.863917</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>1953.827026</td>\n      <td>1288.185303</td>\n      <td>35.891300</td>\n      <td>23.805548</td>\n      <td>-0.305492</td>\n      <td>-0.202623</td>\n      <td>25.110077</td>\n      <td>0.858454</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1709.624878</td>\n      <td>1304.693359</td>\n      <td>36.120544</td>\n      <td>23.957600</td>\n      <td>-0.490740</td>\n      <td>-0.325492</td>\n      <td>25.065413</td>\n      <td>0.856640</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1492.221680</td>\n      <td>1242.058472</td>\n      <td>35.242851</td>\n      <td>23.375454</td>\n      <td>2.607059</td>\n      <td>1.729178</td>\n      <td>24.938089</td>\n      <td>0.863522</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1337.267578</td>\n      <td>1249.959106</td>\n      <td>35.354759</td>\n      <td>23.449678</td>\n      <td>2.250042</td>\n      <td>1.492381</td>\n      <td>24.899082</td>\n      <td>0.862654</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1263.877075</td>\n      <td>1261.272339</td>\n      <td>35.514397</td>\n      <td>23.555561</td>\n      <td>3.281155</td>\n      <td>2.176285</td>\n      <td>25.053846</td>\n      <td>0.861411</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1228.017578</td>\n      <td>1269.839600</td>\n      <td>35.634808</td>\n      <td>23.635426</td>\n      <td>3.035729</td>\n      <td>2.013502</td>\n      <td>25.172073</td>\n      <td>0.860469</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "See validation results"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.365432Z",
          "end_time": "2020-10-29T13:21:58.418472Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "learn.validate()",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "(#7) [1269.839599609375,35.63480758666992,23.635425567626953,3.03572940826416,2.0135018825531006,25.172073364257812,0.8604694740895673]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.420769Z",
          "end_time": "2020-10-29T13:21:58.470971Z"
        }
      },
      "cell_type": "code",
      "source": "reg_interp = RegressionInterpretation.from_learner(learn, ds_idx=1)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.472642Z",
          "end_time": "2020-10-29T13:21:58.808008Z"
        }
      },
      "cell_type": "code",
      "source": "reg_interp.plot_results()\nplt.show()",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Evaluate test set."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.809479Z",
          "end_time": "2020-10-29T13:21:58.820399Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_dls = test_tb.dataloaders(y_block=RegressionBlock(), shuffle_train=False, drop_last=False)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.822103Z",
          "end_time": "2020-10-29T13:21:58.865381Z"
        }
      },
      "cell_type": "code",
      "source": "test_interp = RegressionInterpretation.from_learner(learn, dl=test_dls)",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:58.866932Z",
          "end_time": "2020-10-29T13:21:59.161949Z"
        }
      },
      "cell_type": "code",
      "source": "test_interp.plot_results()\nplt.show()",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Ensemble of ANNs"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Fit several models at once"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:59.163529Z",
          "end_time": "2020-10-29T13:21:59.244067Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ensemble = ANNEnsemble(dls, \n                       y_range=(0,1000), \n                       metrics=[rmse, rrmse, bias, bias_pct, mae, R2Score()], n_models=10)",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:21:59.245822Z",
          "end_time": "2020-10-29T13:22:50.291167Z"
        },
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "ensemble.fit_one_cycle(20, 1e-2)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>175319.078125</td>\n      <td>140464.234375</td>\n      <td>374.785583</td>\n      <td>248.583267</td>\n      <td>363.555511</td>\n      <td>241.134720</td>\n      <td>363.555481</td>\n      <td>-14.434272</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>142824.921875</td>\n      <td>82998.812500</td>\n      <td>288.095154</td>\n      <td>191.084274</td>\n      <td>272.876434</td>\n      <td>180.990189</td>\n      <td>274.847504</td>\n      <td>-8.119945</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>99305.390625</td>\n      <td>18713.753906</td>\n      <td>136.798218</td>\n      <td>90.733871</td>\n      <td>109.197914</td>\n      <td>72.427475</td>\n      <td>121.542389</td>\n      <td>-1.056275</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>64290.941406</td>\n      <td>4291.291016</td>\n      <td>65.507950</td>\n      <td>43.449326</td>\n      <td>36.973663</td>\n      <td>24.523445</td>\n      <td>47.009464</td>\n      <td>0.528471</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>42900.664062</td>\n      <td>1969.754761</td>\n      <td>44.381920</td>\n      <td>29.437103</td>\n      <td>3.600900</td>\n      <td>2.388362</td>\n      <td>31.307716</td>\n      <td>0.783562</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>29613.234375</td>\n      <td>1723.156860</td>\n      <td>41.510929</td>\n      <td>27.532869</td>\n      <td>6.463117</td>\n      <td>4.286778</td>\n      <td>29.223495</td>\n      <td>0.810659</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>21003.990234</td>\n      <td>1416.647095</td>\n      <td>37.638374</td>\n      <td>24.964327</td>\n      <td>-0.828781</td>\n      <td>-0.549704</td>\n      <td>26.675381</td>\n      <td>0.844338</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>15165.536133</td>\n      <td>1280.235229</td>\n      <td>35.780376</td>\n      <td>23.731977</td>\n      <td>-0.772423</td>\n      <td>-0.512323</td>\n      <td>25.164555</td>\n      <td>0.859327</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>11129.493164</td>\n      <td>1314.261475</td>\n      <td>36.252743</td>\n      <td>24.045282</td>\n      <td>6.911073</td>\n      <td>4.583893</td>\n      <td>25.529631</td>\n      <td>0.855588</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>8269.918945</td>\n      <td>1464.495728</td>\n      <td>38.268730</td>\n      <td>25.382423</td>\n      <td>-5.373031</td>\n      <td>-3.563759</td>\n      <td>25.981661</td>\n      <td>0.839081</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>6284.540039</td>\n      <td>1435.053223</td>\n      <td>37.882095</td>\n      <td>25.125980</td>\n      <td>-3.777121</td>\n      <td>-2.505243</td>\n      <td>25.640343</td>\n      <td>0.842316</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>4877.111816</td>\n      <td>1574.088013</td>\n      <td>39.674778</td>\n      <td>26.315010</td>\n      <td>-3.087186</td>\n      <td>-2.047632</td>\n      <td>26.972296</td>\n      <td>0.827039</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>3865.533203</td>\n      <td>1521.330933</td>\n      <td>39.004242</td>\n      <td>25.870264</td>\n      <td>-4.139447</td>\n      <td>-2.745563</td>\n      <td>26.450272</td>\n      <td>0.832836</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>3109.151123</td>\n      <td>1192.876587</td>\n      <td>34.538044</td>\n      <td>22.907976</td>\n      <td>4.422950</td>\n      <td>2.933601</td>\n      <td>23.720108</td>\n      <td>0.868926</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2543.399414</td>\n      <td>1192.060913</td>\n      <td>34.526237</td>\n      <td>22.900146</td>\n      <td>1.782570</td>\n      <td>1.182321</td>\n      <td>23.654848</td>\n      <td>0.869016</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>2123.486084</td>\n      <td>1154.796265</td>\n      <td>33.982292</td>\n      <td>22.539366</td>\n      <td>2.497856</td>\n      <td>1.656748</td>\n      <td>23.335621</td>\n      <td>0.873110</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1832.513794</td>\n      <td>1169.451294</td>\n      <td>34.197243</td>\n      <td>22.681934</td>\n      <td>0.684941</td>\n      <td>0.454300</td>\n      <td>23.271189</td>\n      <td>0.871500</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1599.430176</td>\n      <td>1162.628540</td>\n      <td>34.097340</td>\n      <td>22.615673</td>\n      <td>1.056111</td>\n      <td>0.700485</td>\n      <td>23.179865</td>\n      <td>0.872250</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1470.042969</td>\n      <td>1147.510376</td>\n      <td>33.874924</td>\n      <td>22.468151</td>\n      <td>1.328684</td>\n      <td>0.881274</td>\n      <td>22.924376</td>\n      <td>0.873911</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1375.833130</td>\n      <td>1149.415405</td>\n      <td>33.903030</td>\n      <td>22.486794</td>\n      <td>1.328993</td>\n      <td>0.881478</td>\n      <td>22.899502</td>\n      <td>0.873702</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>133098.312500</td>\n      <td>117176.562500</td>\n      <td>342.310638</td>\n      <td>227.043671</td>\n      <td>333.377472</td>\n      <td>221.118576</td>\n      <td>334.053009</td>\n      <td>-11.875412</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>104196.484375</td>\n      <td>58007.898438</td>\n      <td>240.848297</td>\n      <td>159.746964</td>\n      <td>223.513611</td>\n      <td>148.249420</td>\n      <td>227.080765</td>\n      <td>-5.373933</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>69834.031250</td>\n      <td>12903.067383</td>\n      <td>113.591667</td>\n      <td>75.341713</td>\n      <td>70.211807</td>\n      <td>46.569241</td>\n      <td>98.339821</td>\n      <td>-0.417795</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>44933.617188</td>\n      <td>2298.564697</td>\n      <td>47.943348</td>\n      <td>31.799286</td>\n      <td>17.219446</td>\n      <td>11.421106</td>\n      <td>35.195374</td>\n      <td>0.747433</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>30191.207031</td>\n      <td>1628.629761</td>\n      <td>40.356285</td>\n      <td>26.767031</td>\n      <td>5.930201</td>\n      <td>3.933312</td>\n      <td>27.552370</td>\n      <td>0.821045</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>20923.791016</td>\n      <td>1404.587280</td>\n      <td>37.477821</td>\n      <td>24.857838</td>\n      <td>0.671574</td>\n      <td>0.445433</td>\n      <td>25.279108</td>\n      <td>0.845663</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>14908.266602</td>\n      <td>1313.922241</td>\n      <td>36.248066</td>\n      <td>24.042181</td>\n      <td>5.269733</td>\n      <td>3.495245</td>\n      <td>24.923840</td>\n      <td>0.855626</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>10838.396484</td>\n      <td>1319.509277</td>\n      <td>36.325050</td>\n      <td>24.093243</td>\n      <td>6.538149</td>\n      <td>4.336545</td>\n      <td>24.622288</td>\n      <td>0.855012</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>8039.920410</td>\n      <td>1284.389771</td>\n      <td>35.838383</td>\n      <td>23.770451</td>\n      <td>1.775299</td>\n      <td>1.177499</td>\n      <td>24.350061</td>\n      <td>0.858871</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6081.623047</td>\n      <td>1361.403931</td>\n      <td>36.897209</td>\n      <td>24.472736</td>\n      <td>1.181163</td>\n      <td>0.783427</td>\n      <td>24.783381</td>\n      <td>0.850408</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4703.149414</td>\n      <td>1209.252441</td>\n      <td>34.774307</td>\n      <td>23.064684</td>\n      <td>2.432495</td>\n      <td>1.613396</td>\n      <td>22.928177</td>\n      <td>0.867127</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3710.877441</td>\n      <td>1204.548828</td>\n      <td>34.706612</td>\n      <td>23.019783</td>\n      <td>3.980463</td>\n      <td>2.640113</td>\n      <td>23.083050</td>\n      <td>0.867644</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2989.975830</td>\n      <td>1195.955078</td>\n      <td>34.582581</td>\n      <td>22.937517</td>\n      <td>3.780951</td>\n      <td>2.507784</td>\n      <td>23.444336</td>\n      <td>0.868588</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2466.153076</td>\n      <td>1359.208008</td>\n      <td>36.867439</td>\n      <td>24.452990</td>\n      <td>-2.529458</td>\n      <td>-1.677708</td>\n      <td>24.364632</td>\n      <td>0.850650</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2125.556396</td>\n      <td>1170.886230</td>\n      <td>34.218216</td>\n      <td>22.695845</td>\n      <td>2.264648</td>\n      <td>1.502069</td>\n      <td>23.176176</td>\n      <td>0.871343</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1819.946899</td>\n      <td>1188.546265</td>\n      <td>34.475300</td>\n      <td>22.866362</td>\n      <td>2.677624</td>\n      <td>1.775982</td>\n      <td>23.231749</td>\n      <td>0.869402</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1623.388062</td>\n      <td>1206.538086</td>\n      <td>34.735256</td>\n      <td>23.038782</td>\n      <td>0.336544</td>\n      <td>0.223219</td>\n      <td>23.123438</td>\n      <td>0.867425</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1476.119141</td>\n      <td>1188.765747</td>\n      <td>34.478481</td>\n      <td>22.868471</td>\n      <td>1.546053</td>\n      <td>1.025447</td>\n      <td>23.142899</td>\n      <td>0.869378</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1337.164062</td>\n      <td>1190.969116</td>\n      <td>34.510422</td>\n      <td>22.889656</td>\n      <td>1.583605</td>\n      <td>1.050354</td>\n      <td>23.391611</td>\n      <td>0.869136</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1239.385010</td>\n      <td>1180.743896</td>\n      <td>34.361958</td>\n      <td>22.791185</td>\n      <td>1.199972</td>\n      <td>0.795903</td>\n      <td>23.179106</td>\n      <td>0.870259</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>141375.750000</td>\n      <td>129555.601562</td>\n      <td>359.938324</td>\n      <td>238.735550</td>\n      <td>352.346069</td>\n      <td>233.699844</td>\n      <td>352.346069</td>\n      <td>-13.235625</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>115416.867188</td>\n      <td>72888.156250</td>\n      <td>269.978058</td>\n      <td>179.067795</td>\n      <td>256.087280</td>\n      <td>169.854492</td>\n      <td>258.450806</td>\n      <td>-7.008982</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>79867.703125</td>\n      <td>17434.343750</td>\n      <td>132.039169</td>\n      <td>87.577347</td>\n      <td>93.310181</td>\n      <td>61.889648</td>\n      <td>114.936287</td>\n      <td>-0.915693</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>51961.648438</td>\n      <td>2807.457520</td>\n      <td>52.985447</td>\n      <td>35.143547</td>\n      <td>25.135754</td>\n      <td>16.671740</td>\n      <td>38.460377</td>\n      <td>0.691515</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>34722.851562</td>\n      <td>1932.932129</td>\n      <td>43.965126</td>\n      <td>29.160658</td>\n      <td>10.693248</td>\n      <td>7.092489</td>\n      <td>29.634115</td>\n      <td>0.787609</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>24051.939453</td>\n      <td>1379.826172</td>\n      <td>37.146011</td>\n      <td>24.637758</td>\n      <td>0.319696</td>\n      <td>0.212044</td>\n      <td>25.956142</td>\n      <td>0.848384</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>17051.308594</td>\n      <td>1317.404907</td>\n      <td>36.296070</td>\n      <td>24.074020</td>\n      <td>3.524177</td>\n      <td>2.337474</td>\n      <td>25.116268</td>\n      <td>0.855243</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>12330.143555</td>\n      <td>1218.064941</td>\n      <td>34.900791</td>\n      <td>23.148575</td>\n      <td>1.811031</td>\n      <td>1.201199</td>\n      <td>24.061544</td>\n      <td>0.866158</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>9076.009766</td>\n      <td>1269.430664</td>\n      <td>35.629070</td>\n      <td>23.631620</td>\n      <td>-0.076278</td>\n      <td>-0.050593</td>\n      <td>24.681417</td>\n      <td>0.860514</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6805.425781</td>\n      <td>1201.213989</td>\n      <td>34.658535</td>\n      <td>22.987894</td>\n      <td>7.711295</td>\n      <td>5.114655</td>\n      <td>23.841183</td>\n      <td>0.868010</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>5230.263672</td>\n      <td>1183.067871</td>\n      <td>34.395752</td>\n      <td>22.813601</td>\n      <td>5.046560</td>\n      <td>3.347222</td>\n      <td>23.063515</td>\n      <td>0.870004</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>4101.845215</td>\n      <td>1330.857056</td>\n      <td>36.480915</td>\n      <td>24.196621</td>\n      <td>-2.552647</td>\n      <td>-1.693089</td>\n      <td>24.814255</td>\n      <td>0.853765</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>3303.471924</td>\n      <td>1091.921265</td>\n      <td>33.044231</td>\n      <td>21.917179</td>\n      <td>3.937118</td>\n      <td>2.611364</td>\n      <td>22.953115</td>\n      <td>0.880019</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2677.371338</td>\n      <td>1079.766479</td>\n      <td>32.859802</td>\n      <td>21.794853</td>\n      <td>4.145465</td>\n      <td>2.749554</td>\n      <td>22.766014</td>\n      <td>0.881355</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2249.106689</td>\n      <td>1072.233154</td>\n      <td>32.744972</td>\n      <td>21.718689</td>\n      <td>2.239102</td>\n      <td>1.485124</td>\n      <td>22.460926</td>\n      <td>0.882183</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1900.797852</td>\n      <td>1062.361084</td>\n      <td>32.593880</td>\n      <td>21.618475</td>\n      <td>3.210927</td>\n      <td>2.129705</td>\n      <td>22.409363</td>\n      <td>0.883267</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1645.140503</td>\n      <td>1079.247314</td>\n      <td>32.851898</td>\n      <td>21.789610</td>\n      <td>2.178191</td>\n      <td>1.444725</td>\n      <td>22.680155</td>\n      <td>0.881412</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1471.260010</td>\n      <td>1087.188354</td>\n      <td>32.972538</td>\n      <td>21.869629</td>\n      <td>2.831337</td>\n      <td>1.877935</td>\n      <td>22.912342</td>\n      <td>0.880539</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1323.462891</td>\n      <td>1097.814697</td>\n      <td>33.133286</td>\n      <td>21.976246</td>\n      <td>2.972268</td>\n      <td>1.971410</td>\n      <td>23.088051</td>\n      <td>0.879372</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1226.172241</td>\n      <td>1098.143188</td>\n      <td>33.138245</td>\n      <td>21.979536</td>\n      <td>1.803175</td>\n      <td>1.195988</td>\n      <td>22.966330</td>\n      <td>0.879336</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>106271.265625</td>\n      <td>97142.843750</td>\n      <td>311.677460</td>\n      <td>206.725662</td>\n      <td>298.318359</td>\n      <td>197.865005</td>\n      <td>299.790283</td>\n      <td>-9.674098</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>84217.851562</td>\n      <td>48661.503906</td>\n      <td>220.593521</td>\n      <td>146.312607</td>\n      <td>202.802200</td>\n      <td>134.512207</td>\n      <td>206.605591</td>\n      <td>-4.346947</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>57263.191406</td>\n      <td>12226.349609</td>\n      <td>110.572823</td>\n      <td>73.339409</td>\n      <td>65.196144</td>\n      <td>43.242512</td>\n      <td>92.030823</td>\n      <td>-0.343437</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>37156.535156</td>\n      <td>2872.764404</td>\n      <td>53.598171</td>\n      <td>35.549946</td>\n      <td>17.267920</td>\n      <td>11.453258</td>\n      <td>40.609726</td>\n      <td>0.684339</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>24971.765625</td>\n      <td>1664.930176</td>\n      <td>40.803555</td>\n      <td>27.063688</td>\n      <td>-2.873166</td>\n      <td>-1.905679</td>\n      <td>28.675901</td>\n      <td>0.817057</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>17380.115234</td>\n      <td>1903.343628</td>\n      <td>43.627327</td>\n      <td>28.936607</td>\n      <td>-5.533227</td>\n      <td>-3.670012</td>\n      <td>29.419699</td>\n      <td>0.790860</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>12452.569336</td>\n      <td>1433.782959</td>\n      <td>37.865326</td>\n      <td>25.114859</td>\n      <td>0.862212</td>\n      <td>0.571877</td>\n      <td>26.494257</td>\n      <td>0.842455</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>9109.332031</td>\n      <td>1383.822876</td>\n      <td>37.199768</td>\n      <td>24.673414</td>\n      <td>1.204364</td>\n      <td>0.798816</td>\n      <td>26.024076</td>\n      <td>0.847945</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>6793.254883</td>\n      <td>1339.014038</td>\n      <td>36.592541</td>\n      <td>24.270660</td>\n      <td>0.457503</td>\n      <td>0.303447</td>\n      <td>25.349112</td>\n      <td>0.852869</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>5215.125000</td>\n      <td>1459.914307</td>\n      <td>38.208824</td>\n      <td>25.342688</td>\n      <td>-1.280090</td>\n      <td>-0.849043</td>\n      <td>26.078066</td>\n      <td>0.839584</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4064.181396</td>\n      <td>1306.157837</td>\n      <td>36.140800</td>\n      <td>23.971035</td>\n      <td>1.156982</td>\n      <td>0.767389</td>\n      <td>24.845509</td>\n      <td>0.856479</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3231.685547</td>\n      <td>1425.160278</td>\n      <td>37.751293</td>\n      <td>25.039223</td>\n      <td>8.881402</td>\n      <td>5.890749</td>\n      <td>25.592674</td>\n      <td>0.843403</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2638.236816</td>\n      <td>1600.262695</td>\n      <td>40.003284</td>\n      <td>26.532898</td>\n      <td>-2.713707</td>\n      <td>-1.799915</td>\n      <td>26.522198</td>\n      <td>0.824162</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2193.939453</td>\n      <td>1290.436157</td>\n      <td>35.922638</td>\n      <td>23.826334</td>\n      <td>5.948390</td>\n      <td>3.945377</td>\n      <td>24.910717</td>\n      <td>0.858206</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>1863.555176</td>\n      <td>1323.835815</td>\n      <td>36.384556</td>\n      <td>24.132710</td>\n      <td>0.496706</td>\n      <td>0.329449</td>\n      <td>24.939167</td>\n      <td>0.854536</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1636.081299</td>\n      <td>1309.527588</td>\n      <td>36.187393</td>\n      <td>24.001938</td>\n      <td>5.594775</td>\n      <td>3.710835</td>\n      <td>24.880533</td>\n      <td>0.856109</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1453.704712</td>\n      <td>1329.690674</td>\n      <td>36.464924</td>\n      <td>24.186016</td>\n      <td>2.538552</td>\n      <td>1.683740</td>\n      <td>25.043585</td>\n      <td>0.853893</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1314.127075</td>\n      <td>1271.322388</td>\n      <td>35.655609</td>\n      <td>23.649223</td>\n      <td>3.748964</td>\n      <td>2.486567</td>\n      <td>24.519785</td>\n      <td>0.860307</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1215.253784</td>\n      <td>1264.070679</td>\n      <td>35.553768</td>\n      <td>23.581675</td>\n      <td>3.356078</td>\n      <td>2.225979</td>\n      <td>24.572231</td>\n      <td>0.861103</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1137.194824</td>\n      <td>1297.868774</td>\n      <td>36.025948</td>\n      <td>23.894857</td>\n      <td>5.308153</td>\n      <td>3.520728</td>\n      <td>25.120367</td>\n      <td>0.857390</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>130128.578125</td>\n      <td>119210.148438</td>\n      <td>345.268250</td>\n      <td>229.005356</td>\n      <td>336.222504</td>\n      <td>223.005600</td>\n      <td>336.490448</td>\n      <td>-12.098863</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>104936.976562</td>\n      <td>63252.804688</td>\n      <td>251.501114</td>\n      <td>166.812622</td>\n      <td>233.480103</td>\n      <td>154.859863</td>\n      <td>236.398544</td>\n      <td>-5.950245</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>71859.367188</td>\n      <td>15439.763672</td>\n      <td>124.256844</td>\n      <td>82.415581</td>\n      <td>76.925255</td>\n      <td>51.022057</td>\n      <td>104.448738</td>\n      <td>-0.696528</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>46609.992188</td>\n      <td>2180.361816</td>\n      <td>46.694344</td>\n      <td>30.970863</td>\n      <td>19.234167</td>\n      <td>12.757407</td>\n      <td>34.912800</td>\n      <td>0.760421</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>31194.521484</td>\n      <td>1383.984741</td>\n      <td>37.201946</td>\n      <td>24.674858</td>\n      <td>-1.459118</td>\n      <td>-0.967786</td>\n      <td>25.297169</td>\n      <td>0.847927</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>21632.162109</td>\n      <td>1473.188477</td>\n      <td>38.382133</td>\n      <td>25.457638</td>\n      <td>5.070861</td>\n      <td>3.363340</td>\n      <td>26.027117</td>\n      <td>0.838125</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>15391.738281</td>\n      <td>1282.271362</td>\n      <td>35.808819</td>\n      <td>23.750841</td>\n      <td>-1.268633</td>\n      <td>-0.841443</td>\n      <td>24.584162</td>\n      <td>0.859103</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>11178.728516</td>\n      <td>1238.084351</td>\n      <td>35.186424</td>\n      <td>23.338028</td>\n      <td>0.900664</td>\n      <td>0.597382</td>\n      <td>24.217743</td>\n      <td>0.863959</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>8274.648438</td>\n      <td>1238.504272</td>\n      <td>35.192390</td>\n      <td>23.341986</td>\n      <td>4.543677</td>\n      <td>3.013675</td>\n      <td>23.555212</td>\n      <td>0.863913</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6205.563965</td>\n      <td>1254.010864</td>\n      <td>35.412018</td>\n      <td>23.487656</td>\n      <td>2.023888</td>\n      <td>1.342380</td>\n      <td>24.264130</td>\n      <td>0.862209</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4744.019531</td>\n      <td>1186.551758</td>\n      <td>34.446362</td>\n      <td>22.847168</td>\n      <td>1.609663</td>\n      <td>1.067638</td>\n      <td>23.634781</td>\n      <td>0.869621</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3747.200195</td>\n      <td>1236.935669</td>\n      <td>35.170097</td>\n      <td>23.327198</td>\n      <td>5.875631</td>\n      <td>3.897118</td>\n      <td>24.521296</td>\n      <td>0.864085</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2988.208008</td>\n      <td>1185.286377</td>\n      <td>34.427990</td>\n      <td>22.834982</td>\n      <td>2.024685</td>\n      <td>1.342909</td>\n      <td>23.335861</td>\n      <td>0.869760</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2469.722412</td>\n      <td>1277.730835</td>\n      <td>35.745361</td>\n      <td>23.708752</td>\n      <td>7.653448</td>\n      <td>5.076287</td>\n      <td>24.190668</td>\n      <td>0.859602</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2062.306396</td>\n      <td>1197.480957</td>\n      <td>34.604641</td>\n      <td>22.952150</td>\n      <td>1.164770</td>\n      <td>0.772555</td>\n      <td>23.855200</td>\n      <td>0.868420</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1844.043701</td>\n      <td>1201.222534</td>\n      <td>34.658657</td>\n      <td>22.987976</td>\n      <td>-0.639608</td>\n      <td>-0.424232</td>\n      <td>23.825466</td>\n      <td>0.868009</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1616.628906</td>\n      <td>1182.009155</td>\n      <td>34.380360</td>\n      <td>22.803391</td>\n      <td>1.548803</td>\n      <td>1.027271</td>\n      <td>23.618368</td>\n      <td>0.870120</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1482.566406</td>\n      <td>1171.824829</td>\n      <td>34.231926</td>\n      <td>22.704939</td>\n      <td>2.369771</td>\n      <td>1.571793</td>\n      <td>23.552372</td>\n      <td>0.871239</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1377.695801</td>\n      <td>1159.712280</td>\n      <td>34.054550</td>\n      <td>22.587292</td>\n      <td>1.235844</td>\n      <td>0.819696</td>\n      <td>23.335608</td>\n      <td>0.872570</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1255.770142</td>\n      <td>1175.071289</td>\n      <td>34.279312</td>\n      <td>22.736368</td>\n      <td>1.123025</td>\n      <td>0.744866</td>\n      <td>23.509232</td>\n      <td>0.870883</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>141116.718750</td>\n      <td>121027.703125</td>\n      <td>347.890350</td>\n      <td>230.744522</td>\n      <td>336.040619</td>\n      <td>222.884979</td>\n      <td>336.420258</td>\n      <td>-12.298576</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>114635.125000</td>\n      <td>67866.984375</td>\n      <td>260.512939</td>\n      <td>172.789886</td>\n      <td>243.347366</td>\n      <td>161.404510</td>\n      <td>246.537201</td>\n      <td>-6.457254</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>79870.484375</td>\n      <td>17291.027344</td>\n      <td>131.495361</td>\n      <td>87.216660</td>\n      <td>94.363449</td>\n      <td>62.588249</td>\n      <td>110.832024</td>\n      <td>-0.899946</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>52122.226562</td>\n      <td>3355.890625</td>\n      <td>57.930050</td>\n      <td>38.423145</td>\n      <td>23.007977</td>\n      <td>15.260453</td>\n      <td>43.653858</td>\n      <td>0.631253</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>34952.210938</td>\n      <td>1969.419312</td>\n      <td>44.378139</td>\n      <td>29.434597</td>\n      <td>-0.559890</td>\n      <td>-0.371357</td>\n      <td>30.448544</td>\n      <td>0.783599</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>24259.654297</td>\n      <td>1948.509766</td>\n      <td>44.141926</td>\n      <td>29.277924</td>\n      <td>4.733279</td>\n      <td>3.139432</td>\n      <td>29.618906</td>\n      <td>0.785897</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>17314.845703</td>\n      <td>1512.372803</td>\n      <td>38.889236</td>\n      <td>25.793985</td>\n      <td>-0.451609</td>\n      <td>-0.299538</td>\n      <td>26.050968</td>\n      <td>0.833820</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>12544.083008</td>\n      <td>1396.437256</td>\n      <td>37.368935</td>\n      <td>24.785618</td>\n      <td>1.328604</td>\n      <td>0.881220</td>\n      <td>24.993876</td>\n      <td>0.846559</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>9223.499023</td>\n      <td>1352.198242</td>\n      <td>36.772243</td>\n      <td>24.389851</td>\n      <td>4.917050</td>\n      <td>3.261322</td>\n      <td>24.933077</td>\n      <td>0.851420</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6966.457031</td>\n      <td>1478.521484</td>\n      <td>38.451546</td>\n      <td>25.503677</td>\n      <td>-1.353267</td>\n      <td>-0.897579</td>\n      <td>25.411858</td>\n      <td>0.837539</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>5327.846680</td>\n      <td>1293.617188</td>\n      <td>35.966888</td>\n      <td>23.855684</td>\n      <td>1.244378</td>\n      <td>0.825356</td>\n      <td>24.429447</td>\n      <td>0.857857</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>4117.534668</td>\n      <td>1304.677856</td>\n      <td>36.120323</td>\n      <td>23.957453</td>\n      <td>4.798084</td>\n      <td>3.182415</td>\n      <td>24.651442</td>\n      <td>0.856641</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>3275.732910</td>\n      <td>1441.746582</td>\n      <td>37.970337</td>\n      <td>25.184505</td>\n      <td>-2.248118</td>\n      <td>-1.491105</td>\n      <td>25.560556</td>\n      <td>0.841580</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2663.387695</td>\n      <td>1328.245850</td>\n      <td>36.445107</td>\n      <td>24.172871</td>\n      <td>1.932502</td>\n      <td>1.281766</td>\n      <td>24.944567</td>\n      <td>0.854052</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2209.243164</td>\n      <td>1294.081909</td>\n      <td>35.973347</td>\n      <td>23.859968</td>\n      <td>2.816211</td>\n      <td>1.867903</td>\n      <td>24.783018</td>\n      <td>0.857806</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1877.201782</td>\n      <td>1345.854248</td>\n      <td>36.685886</td>\n      <td>24.332573</td>\n      <td>0.482564</td>\n      <td>0.320069</td>\n      <td>25.111732</td>\n      <td>0.852117</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1636.791138</td>\n      <td>1387.490845</td>\n      <td>37.249039</td>\n      <td>24.706095</td>\n      <td>-2.319402</td>\n      <td>-1.538385</td>\n      <td>25.155542</td>\n      <td>0.847542</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1476.813721</td>\n      <td>1295.832031</td>\n      <td>35.997665</td>\n      <td>23.876099</td>\n      <td>1.982048</td>\n      <td>1.314629</td>\n      <td>24.709347</td>\n      <td>0.857613</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1345.297241</td>\n      <td>1304.329224</td>\n      <td>36.115501</td>\n      <td>23.954254</td>\n      <td>0.322499</td>\n      <td>0.213903</td>\n      <td>24.697123</td>\n      <td>0.856680</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1252.320312</td>\n      <td>1289.962524</td>\n      <td>35.916046</td>\n      <td>23.821962</td>\n      <td>1.055681</td>\n      <td>0.700199</td>\n      <td>24.674992</td>\n      <td>0.858258</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>134322.718750</td>\n      <td>122650.882812</td>\n      <td>350.215485</td>\n      <td>232.286713</td>\n      <td>336.381592</td>\n      <td>223.111130</td>\n      <td>337.669464</td>\n      <td>-12.476932</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>105346.429688</td>\n      <td>57762.394531</td>\n      <td>240.338089</td>\n      <td>159.408539</td>\n      <td>218.210663</td>\n      <td>144.732132</td>\n      <td>224.108337</td>\n      <td>-5.346957</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>70683.898438</td>\n      <td>11433.081055</td>\n      <td>106.925583</td>\n      <td>70.920311</td>\n      <td>70.641518</td>\n      <td>46.854256</td>\n      <td>88.610512</td>\n      <td>-0.256272</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>45397.554688</td>\n      <td>2411.201416</td>\n      <td>49.103989</td>\n      <td>32.569103</td>\n      <td>16.320274</td>\n      <td>10.824715</td>\n      <td>36.019604</td>\n      <td>0.735056</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>30454.587891</td>\n      <td>1718.550781</td>\n      <td>41.455406</td>\n      <td>27.496042</td>\n      <td>-7.311614</td>\n      <td>-4.849559</td>\n      <td>30.061960</td>\n      <td>0.811165</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>21168.449219</td>\n      <td>1552.035156</td>\n      <td>39.395878</td>\n      <td>26.130024</td>\n      <td>-3.714750</td>\n      <td>-2.463874</td>\n      <td>28.255737</td>\n      <td>0.829462</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>15123.258789</td>\n      <td>1382.181030</td>\n      <td>37.177696</td>\n      <td>24.658773</td>\n      <td>1.390921</td>\n      <td>0.922554</td>\n      <td>26.553497</td>\n      <td>0.848125</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>11019.667969</td>\n      <td>1672.653076</td>\n      <td>40.898083</td>\n      <td>27.126387</td>\n      <td>-5.506504</td>\n      <td>-3.652287</td>\n      <td>28.097622</td>\n      <td>0.816208</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>8173.991699</td>\n      <td>2133.085205</td>\n      <td>46.185337</td>\n      <td>30.633253</td>\n      <td>-10.695145</td>\n      <td>-7.093746</td>\n      <td>30.357447</td>\n      <td>0.765616</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6208.517578</td>\n      <td>1250.442871</td>\n      <td>35.361603</td>\n      <td>23.454218</td>\n      <td>-0.464269</td>\n      <td>-0.307935</td>\n      <td>25.156586</td>\n      <td>0.862601</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4768.657227</td>\n      <td>1201.422119</td>\n      <td>34.661537</td>\n      <td>22.989887</td>\n      <td>2.984342</td>\n      <td>1.979418</td>\n      <td>24.274160</td>\n      <td>0.867987</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3742.409668</td>\n      <td>1220.848877</td>\n      <td>34.940651</td>\n      <td>23.175013</td>\n      <td>0.901730</td>\n      <td>0.598088</td>\n      <td>24.931351</td>\n      <td>0.865853</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>3000.784180</td>\n      <td>1259.810547</td>\n      <td>35.493813</td>\n      <td>23.541908</td>\n      <td>1.087367</td>\n      <td>0.721216</td>\n      <td>25.145596</td>\n      <td>0.861571</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2483.928955</td>\n      <td>1177.149048</td>\n      <td>34.309605</td>\n      <td>22.756460</td>\n      <td>2.616594</td>\n      <td>1.735503</td>\n      <td>24.164852</td>\n      <td>0.870654</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2108.940430</td>\n      <td>1227.659546</td>\n      <td>35.037971</td>\n      <td>23.239563</td>\n      <td>-0.968603</td>\n      <td>-0.642443</td>\n      <td>24.393946</td>\n      <td>0.865104</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1844.806030</td>\n      <td>1202.566895</td>\n      <td>34.678047</td>\n      <td>23.000835</td>\n      <td>3.834375</td>\n      <td>2.543218</td>\n      <td>24.573231</td>\n      <td>0.867861</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1613.420776</td>\n      <td>1208.127441</td>\n      <td>34.758125</td>\n      <td>23.053949</td>\n      <td>2.551632</td>\n      <td>1.692416</td>\n      <td>24.611511</td>\n      <td>0.867250</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1478.109253</td>\n      <td>1206.213745</td>\n      <td>34.730587</td>\n      <td>23.035686</td>\n      <td>3.993995</td>\n      <td>2.649089</td>\n      <td>24.636131</td>\n      <td>0.867461</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1379.408325</td>\n      <td>1196.406372</td>\n      <td>34.589108</td>\n      <td>22.941845</td>\n      <td>2.394050</td>\n      <td>1.587897</td>\n      <td>24.291719</td>\n      <td>0.868538</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1268.295044</td>\n      <td>1195.068970</td>\n      <td>34.569771</td>\n      <td>22.929020</td>\n      <td>0.458882</td>\n      <td>0.304362</td>\n      <td>24.102901</td>\n      <td>0.868685</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>131084.390625</td>\n      <td>118241.148438</td>\n      <td>343.862061</td>\n      <td>228.072693</td>\n      <td>333.927734</td>\n      <td>221.483566</td>\n      <td>334.516876</td>\n      <td>-11.992388</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>103335.648438</td>\n      <td>58603.519531</td>\n      <td>242.081650</td>\n      <td>160.565002</td>\n      <td>221.018494</td>\n      <td>146.594482</td>\n      <td>225.451065</td>\n      <td>-5.439380</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>69821.929688</td>\n      <td>13899.295898</td>\n      <td>117.895279</td>\n      <td>78.196159</td>\n      <td>69.544373</td>\n      <td>46.126556</td>\n      <td>98.200882</td>\n      <td>-0.527261</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>45110.511719</td>\n      <td>2796.496826</td>\n      <td>52.881912</td>\n      <td>35.074879</td>\n      <td>21.678522</td>\n      <td>14.378670</td>\n      <td>37.222382</td>\n      <td>0.692720</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>30207.058594</td>\n      <td>2072.765137</td>\n      <td>45.527626</td>\n      <td>30.197016</td>\n      <td>7.862994</td>\n      <td>5.215272</td>\n      <td>30.904789</td>\n      <td>0.772244</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>20981.351562</td>\n      <td>1574.670288</td>\n      <td>39.682114</td>\n      <td>26.319874</td>\n      <td>0.588167</td>\n      <td>0.390112</td>\n      <td>27.601233</td>\n      <td>0.826975</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>14936.177734</td>\n      <td>1583.072754</td>\n      <td>39.787849</td>\n      <td>26.390003</td>\n      <td>-2.716465</td>\n      <td>-1.801744</td>\n      <td>27.367821</td>\n      <td>0.826051</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>10905.676758</td>\n      <td>1730.918579</td>\n      <td>41.604313</td>\n      <td>27.594809</td>\n      <td>-2.588917</td>\n      <td>-1.717146</td>\n      <td>28.089319</td>\n      <td>0.809806</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>8142.661621</td>\n      <td>1287.750610</td>\n      <td>35.885242</td>\n      <td>23.801531</td>\n      <td>1.369623</td>\n      <td>0.908427</td>\n      <td>24.804073</td>\n      <td>0.858501</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>6139.552246</td>\n      <td>1318.625122</td>\n      <td>36.312881</td>\n      <td>24.085171</td>\n      <td>4.667078</td>\n      <td>3.095523</td>\n      <td>24.901033</td>\n      <td>0.855109</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4760.103027</td>\n      <td>1323.711304</td>\n      <td>36.382847</td>\n      <td>24.131577</td>\n      <td>1.197664</td>\n      <td>0.794372</td>\n      <td>25.154791</td>\n      <td>0.854550</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3775.309570</td>\n      <td>1339.620850</td>\n      <td>36.600830</td>\n      <td>24.276157</td>\n      <td>-2.887700</td>\n      <td>-1.915319</td>\n      <td>24.818237</td>\n      <td>0.852802</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>3026.769287</td>\n      <td>1409.519897</td>\n      <td>37.543571</td>\n      <td>24.901447</td>\n      <td>9.381452</td>\n      <td>6.222416</td>\n      <td>25.286320</td>\n      <td>0.845121</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2488.994873</td>\n      <td>1251.130615</td>\n      <td>35.371326</td>\n      <td>23.460667</td>\n      <td>3.189936</td>\n      <td>2.115782</td>\n      <td>24.472359</td>\n      <td>0.862525</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2101.284180</td>\n      <td>1315.781128</td>\n      <td>36.273697</td>\n      <td>24.059181</td>\n      <td>0.655240</td>\n      <td>0.434600</td>\n      <td>25.000122</td>\n      <td>0.855421</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1798.602051</td>\n      <td>1234.352417</td>\n      <td>35.133350</td>\n      <td>23.302824</td>\n      <td>1.099096</td>\n      <td>0.728995</td>\n      <td>24.111151</td>\n      <td>0.864369</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1590.110840</td>\n      <td>1228.575195</td>\n      <td>35.051041</td>\n      <td>23.248232</td>\n      <td>6.388068</td>\n      <td>4.237001</td>\n      <td>23.742729</td>\n      <td>0.865004</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1424.815674</td>\n      <td>1229.325317</td>\n      <td>35.061737</td>\n      <td>23.255325</td>\n      <td>3.258190</td>\n      <td>2.161053</td>\n      <td>24.373093</td>\n      <td>0.864921</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1318.849121</td>\n      <td>1253.414917</td>\n      <td>35.403599</td>\n      <td>23.482073</td>\n      <td>3.293314</td>\n      <td>2.184349</td>\n      <td>24.812412</td>\n      <td>0.862274</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1248.801880</td>\n      <td>1223.093628</td>\n      <td>34.972755</td>\n      <td>23.196308</td>\n      <td>2.659699</td>\n      <td>1.764093</td>\n      <td>24.102491</td>\n      <td>0.865606</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>114081.968750</td>\n      <td>108798.820312</td>\n      <td>329.846649</td>\n      <td>218.776703</td>\n      <td>320.587158</td>\n      <td>212.635178</td>\n      <td>320.587158</td>\n      <td>-10.954861</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>90155.398438</td>\n      <td>51399.835938</td>\n      <td>226.715332</td>\n      <td>150.373016</td>\n      <td>209.411545</td>\n      <td>138.895966</td>\n      <td>212.833542</td>\n      <td>-4.647836</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>60054.781250</td>\n      <td>8967.546875</td>\n      <td>94.697136</td>\n      <td>62.809574</td>\n      <td>60.389782</td>\n      <td>40.054604</td>\n      <td>77.728302</td>\n      <td>0.014642</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>38527.050781</td>\n      <td>2105.111328</td>\n      <td>45.881496</td>\n      <td>30.431726</td>\n      <td>10.270074</td>\n      <td>6.811811</td>\n      <td>32.768925</td>\n      <td>0.768689</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>25834.156250</td>\n      <td>1554.749878</td>\n      <td>39.430317</td>\n      <td>26.152864</td>\n      <td>1.320405</td>\n      <td>0.875783</td>\n      <td>28.214802</td>\n      <td>0.829163</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>17997.974609</td>\n      <td>1550.075684</td>\n      <td>39.370998</td>\n      <td>26.113522</td>\n      <td>-4.793452</td>\n      <td>-3.179343</td>\n      <td>27.373127</td>\n      <td>0.829677</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>12840.649414</td>\n      <td>1374.352417</td>\n      <td>37.072258</td>\n      <td>24.588840</td>\n      <td>6.291566</td>\n      <td>4.172994</td>\n      <td>26.124458</td>\n      <td>0.848986</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>9356.641602</td>\n      <td>1833.765015</td>\n      <td>42.822483</td>\n      <td>28.402782</td>\n      <td>-6.606081</td>\n      <td>-4.381602</td>\n      <td>28.886433</td>\n      <td>0.798505</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>7044.287598</td>\n      <td>1340.460449</td>\n      <td>36.612297</td>\n      <td>24.283764</td>\n      <td>-0.742092</td>\n      <td>-0.492206</td>\n      <td>25.563742</td>\n      <td>0.852710</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>5410.837891</td>\n      <td>1237.699585</td>\n      <td>35.180954</td>\n      <td>23.334398</td>\n      <td>3.891933</td>\n      <td>2.581394</td>\n      <td>24.517736</td>\n      <td>0.864001</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4235.800781</td>\n      <td>1379.396362</td>\n      <td>37.140228</td>\n      <td>24.633923</td>\n      <td>-3.303948</td>\n      <td>-2.191403</td>\n      <td>25.581049</td>\n      <td>0.848431</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3368.518311</td>\n      <td>1353.933716</td>\n      <td>36.795841</td>\n      <td>24.405502</td>\n      <td>-3.892380</td>\n      <td>-2.581691</td>\n      <td>25.139622</td>\n      <td>0.851229</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2745.918457</td>\n      <td>1261.029175</td>\n      <td>35.510971</td>\n      <td>23.553289</td>\n      <td>-3.134284</td>\n      <td>-2.078870</td>\n      <td>24.786974</td>\n      <td>0.861438</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2293.837402</td>\n      <td>1178.642334</td>\n      <td>34.331360</td>\n      <td>22.770891</td>\n      <td>1.570067</td>\n      <td>1.041375</td>\n      <td>24.041740</td>\n      <td>0.870490</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>1944.067139</td>\n      <td>1196.009644</td>\n      <td>34.583374</td>\n      <td>22.938044</td>\n      <td>3.332299</td>\n      <td>2.210207</td>\n      <td>24.063728</td>\n      <td>0.868582</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1693.958862</td>\n      <td>1198.921387</td>\n      <td>34.625443</td>\n      <td>22.965946</td>\n      <td>1.677949</td>\n      <td>1.112930</td>\n      <td>24.151894</td>\n      <td>0.868262</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1497.104492</td>\n      <td>1199.557373</td>\n      <td>34.634628</td>\n      <td>22.972038</td>\n      <td>3.530943</td>\n      <td>2.341962</td>\n      <td>24.155964</td>\n      <td>0.868192</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1348.651489</td>\n      <td>1193.310181</td>\n      <td>34.544319</td>\n      <td>22.912140</td>\n      <td>4.167167</td>\n      <td>2.763948</td>\n      <td>24.117529</td>\n      <td>0.868879</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1254.931030</td>\n      <td>1197.361328</td>\n      <td>34.602909</td>\n      <td>22.951000</td>\n      <td>3.624503</td>\n      <td>2.404017</td>\n      <td>24.172926</td>\n      <td>0.868433</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1201.295410</td>\n      <td>1200.904419</td>\n      <td>34.654068</td>\n      <td>22.984932</td>\n      <td>4.751587</td>\n      <td>3.151575</td>\n      <td>24.129200</td>\n      <td>0.868044</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>125725.218750</td>\n      <td>110288.835938</td>\n      <td>332.097656</td>\n      <td>220.269730</td>\n      <td>316.332458</td>\n      <td>209.813187</td>\n      <td>318.648621</td>\n      <td>-11.118585</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>97391.757812</td>\n      <td>52840.640625</td>\n      <td>229.870926</td>\n      <td>152.466003</td>\n      <td>211.179901</td>\n      <td>140.068863</td>\n      <td>215.924286</td>\n      <td>-4.806152</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>64570.605469</td>\n      <td>11049.356445</td>\n      <td>105.115921</td>\n      <td>69.720024</td>\n      <td>58.690784</td>\n      <td>38.927715</td>\n      <td>86.854088</td>\n      <td>-0.214108</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>41586.792969</td>\n      <td>2420.880371</td>\n      <td>49.202438</td>\n      <td>32.634399</td>\n      <td>20.525789</td>\n      <td>13.614098</td>\n      <td>34.807434</td>\n      <td>0.733993</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>27879.863281</td>\n      <td>1484.753784</td>\n      <td>38.532501</td>\n      <td>25.557371</td>\n      <td>-0.180213</td>\n      <td>-0.119529</td>\n      <td>27.003893</td>\n      <td>0.836855</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>19364.583984</td>\n      <td>1363.865967</td>\n      <td>36.930557</td>\n      <td>24.494856</td>\n      <td>-1.474041</td>\n      <td>-0.977684</td>\n      <td>26.058308</td>\n      <td>0.850138</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>13852.056641</td>\n      <td>1347.372681</td>\n      <td>36.706573</td>\n      <td>24.346294</td>\n      <td>-1.261631</td>\n      <td>-0.836800</td>\n      <td>25.452198</td>\n      <td>0.851950</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>10109.955078</td>\n      <td>1249.109253</td>\n      <td>35.342743</td>\n      <td>23.441708</td>\n      <td>1.525133</td>\n      <td>1.011572</td>\n      <td>24.865753</td>\n      <td>0.862747</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>8</td>\n      <td>7542.180664</td>\n      <td>1156.481079</td>\n      <td>34.007072</td>\n      <td>22.555801</td>\n      <td>3.249897</td>\n      <td>2.155553</td>\n      <td>23.910646</td>\n      <td>0.872925</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>9</td>\n      <td>5728.272949</td>\n      <td>1486.383789</td>\n      <td>38.553650</td>\n      <td>25.571400</td>\n      <td>-4.687499</td>\n      <td>-3.109068</td>\n      <td>25.693811</td>\n      <td>0.836676</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>10</td>\n      <td>4435.251465</td>\n      <td>1331.510254</td>\n      <td>36.489864</td>\n      <td>24.202557</td>\n      <td>-2.526094</td>\n      <td>-1.675477</td>\n      <td>24.881470</td>\n      <td>0.853693</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>11</td>\n      <td>3484.203125</td>\n      <td>1191.774536</td>\n      <td>34.522087</td>\n      <td>22.897394</td>\n      <td>2.166012</td>\n      <td>1.436646</td>\n      <td>23.888336</td>\n      <td>0.869047</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>12</td>\n      <td>2842.901855</td>\n      <td>1170.888428</td>\n      <td>34.218246</td>\n      <td>22.695866</td>\n      <td>1.323025</td>\n      <td>0.877520</td>\n      <td>23.558907</td>\n      <td>0.871342</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>13</td>\n      <td>2377.100830</td>\n      <td>1186.099365</td>\n      <td>34.439793</td>\n      <td>22.842812</td>\n      <td>3.799953</td>\n      <td>2.520387</td>\n      <td>23.647274</td>\n      <td>0.869671</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>14</td>\n      <td>2018.661743</td>\n      <td>1276.718628</td>\n      <td>35.731201</td>\n      <td>23.699362</td>\n      <td>-0.931727</td>\n      <td>-0.617985</td>\n      <td>24.341351</td>\n      <td>0.859714</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>15</td>\n      <td>1739.926514</td>\n      <td>1138.059692</td>\n      <td>33.735142</td>\n      <td>22.375437</td>\n      <td>3.403055</td>\n      <td>2.257137</td>\n      <td>23.405127</td>\n      <td>0.874950</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>16</td>\n      <td>1533.517822</td>\n      <td>1150.168701</td>\n      <td>33.914139</td>\n      <td>22.494162</td>\n      <td>2.643854</td>\n      <td>1.753583</td>\n      <td>23.536983</td>\n      <td>0.873619</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>17</td>\n      <td>1379.454346</td>\n      <td>1175.644409</td>\n      <td>34.287670</td>\n      <td>22.741913</td>\n      <td>1.333483</td>\n      <td>0.884456</td>\n      <td>23.833673</td>\n      <td>0.870820</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>18</td>\n      <td>1299.137207</td>\n      <td>1183.184326</td>\n      <td>34.397446</td>\n      <td>22.814722</td>\n      <td>0.800277</td>\n      <td>0.530798</td>\n      <td>23.783741</td>\n      <td>0.869991</td>\n      <td>00:00</td>\n    </tr>\n    <tr>\n      <td>19</td>\n      <td>1210.314941</td>\n      <td>1167.064941</td>\n      <td>34.162334</td>\n      <td>22.658781</td>\n      <td>1.462212</td>\n      <td>0.969838</td>\n      <td>23.729740</td>\n      <td>0.871762</td>\n      <td>00:00</td>\n    </tr>\n  </tbody>\n</table>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:50.293988Z",
          "end_time": "2020-10-29T13:22:51.068206Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "res = ensemble.validate()",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:51.070750Z",
          "end_time": "2020-10-29T13:22:51.093655Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "res",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "   model_identifier      _rmse     _rrmse     _bias  _bias_pct        mae  \\\n0          ensemble  33.256371  22.057884  2.115138   1.402903  22.668173   \n1                 0  33.903030  22.486794  1.328993   0.881478  22.899504   \n2                 1  34.361958  22.791185  1.199972   0.795903  23.179106   \n3                 2  33.138245  21.979536  1.803175   1.195988  22.966330   \n4                 3  36.025948  23.894857  5.308153   3.520728  25.120367   \n5                 4  34.279312  22.736368  1.123025   0.744866  23.509230   \n6                 5  35.916046  23.821962  1.055681   0.700199  24.674992   \n7                 6  34.569771  22.929020  0.458882   0.304362  24.102900   \n8                 7  34.972755  23.196308  2.659699   1.764093  24.102493   \n9                 8  34.654068  22.984932  4.751587   3.151575  24.129196   \n10                9  34.162334  22.658781  1.462212   0.969838  23.729738   \n\n    r2_score  \n0   0.878474  \n1   0.873702  \n2   0.870259  \n3   0.879336  \n4   0.857390  \n5   0.870883  \n6   0.858258  \n7   0.868685  \n8   0.865606  \n9   0.868044  \n10  0.871762  ",
            "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>model_identifier</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ensemble</td>\n      <td>33.256371</td>\n      <td>22.057884</td>\n      <td>2.115138</td>\n      <td>1.402903</td>\n      <td>22.668173</td>\n      <td>0.878474</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>33.903030</td>\n      <td>22.486794</td>\n      <td>1.328993</td>\n      <td>0.881478</td>\n      <td>22.899504</td>\n      <td>0.873702</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>34.361958</td>\n      <td>22.791185</td>\n      <td>1.199972</td>\n      <td>0.795903</td>\n      <td>23.179106</td>\n      <td>0.870259</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>33.138245</td>\n      <td>21.979536</td>\n      <td>1.803175</td>\n      <td>1.195988</td>\n      <td>22.966330</td>\n      <td>0.879336</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>36.025948</td>\n      <td>23.894857</td>\n      <td>5.308153</td>\n      <td>3.520728</td>\n      <td>25.120367</td>\n      <td>0.857390</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4</td>\n      <td>34.279312</td>\n      <td>22.736368</td>\n      <td>1.123025</td>\n      <td>0.744866</td>\n      <td>23.509230</td>\n      <td>0.870883</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>5</td>\n      <td>35.916046</td>\n      <td>23.821962</td>\n      <td>1.055681</td>\n      <td>0.700199</td>\n      <td>24.674992</td>\n      <td>0.858258</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>6</td>\n      <td>34.569771</td>\n      <td>22.929020</td>\n      <td>0.458882</td>\n      <td>0.304362</td>\n      <td>24.102900</td>\n      <td>0.868685</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>7</td>\n      <td>34.972755</td>\n      <td>23.196308</td>\n      <td>2.659699</td>\n      <td>1.764093</td>\n      <td>24.102493</td>\n      <td>0.865606</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8</td>\n      <td>34.654068</td>\n      <td>22.984932</td>\n      <td>4.751587</td>\n      <td>3.151575</td>\n      <td>24.129196</td>\n      <td>0.868044</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>9</td>\n      <td>34.162334</td>\n      <td>22.658781</td>\n      <td>1.462212</td>\n      <td>0.969838</td>\n      <td>23.729738</td>\n      <td>0.871762</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:51.105524Z",
          "end_time": "2020-10-29T13:22:51.883471Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ens_int = RegressionInterpretation.from_ensemble(ensemble)",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:51.891153Z",
          "end_time": "2020-10-29T13:22:52.921752Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ens_int.plot_results()\nplt.show()",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:52.925611Z",
          "end_time": "2020-10-29T13:22:54.128954Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_res = ensemble.validate(dl=test_dls[0])\ntest_res",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "   model_identifier      _rmse     _rrmse     _bias  _bias_pct        mae  \\\n0          ensemble  32.933350  21.664732 -2.690828  -1.770123  21.510918   \n1                 0  33.291924  21.900616 -3.783731  -2.489073  22.575481   \n2                 1  33.721363  22.183115 -4.314877  -2.838480  23.327198   \n3                 2  32.721478  21.525356 -2.679696  -1.762800  21.930616   \n4                 3  34.592617  22.756260 -1.461997  -0.961754  23.852276   \n5                 4  34.707684  22.831953 -3.335921  -2.194488  22.497400   \n6                 5  36.365723  23.922670 -2.305969  -1.516949  23.980967   \n7                 6  33.585270  22.093590 -4.137344  -2.721693  22.503216   \n8                 7  32.593872  21.441412 -1.492864  -0.982059  21.689709   \n9                 8  34.956860  22.995871 -0.274745  -0.180737  22.703474   \n10                9  35.444599  23.316723 -3.121132  -2.053192  23.045393   \n\n    r2_score  \n0   0.884745  \n1   0.882221  \n2   0.879163  \n3   0.886223  \n4   0.872838  \n5   0.871991  \n6   0.859469  \n7   0.880137  \n8   0.887109  \n9   0.870146  \n10  0.866498  ",
            "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>model_identifier</th>\n      <th>_rmse</th>\n      <th>_rrmse</th>\n      <th>_bias</th>\n      <th>_bias_pct</th>\n      <th>mae</th>\n      <th>r2_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ensemble</td>\n      <td>32.933350</td>\n      <td>21.664732</td>\n      <td>-2.690828</td>\n      <td>-1.770123</td>\n      <td>21.510918</td>\n      <td>0.884745</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>33.291924</td>\n      <td>21.900616</td>\n      <td>-3.783731</td>\n      <td>-2.489073</td>\n      <td>22.575481</td>\n      <td>0.882221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>33.721363</td>\n      <td>22.183115</td>\n      <td>-4.314877</td>\n      <td>-2.838480</td>\n      <td>23.327198</td>\n      <td>0.879163</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>32.721478</td>\n      <td>21.525356</td>\n      <td>-2.679696</td>\n      <td>-1.762800</td>\n      <td>21.930616</td>\n      <td>0.886223</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>34.592617</td>\n      <td>22.756260</td>\n      <td>-1.461997</td>\n      <td>-0.961754</td>\n      <td>23.852276</td>\n      <td>0.872838</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4</td>\n      <td>34.707684</td>\n      <td>22.831953</td>\n      <td>-3.335921</td>\n      <td>-2.194488</td>\n      <td>22.497400</td>\n      <td>0.871991</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>5</td>\n      <td>36.365723</td>\n      <td>23.922670</td>\n      <td>-2.305969</td>\n      <td>-1.516949</td>\n      <td>23.980967</td>\n      <td>0.859469</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>6</td>\n      <td>33.585270</td>\n      <td>22.093590</td>\n      <td>-4.137344</td>\n      <td>-2.721693</td>\n      <td>22.503216</td>\n      <td>0.880137</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>7</td>\n      <td>32.593872</td>\n      <td>21.441412</td>\n      <td>-1.492864</td>\n      <td>-0.982059</td>\n      <td>21.689709</td>\n      <td>0.887109</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8</td>\n      <td>34.956860</td>\n      <td>22.995871</td>\n      <td>-0.274745</td>\n      <td>-0.180737</td>\n      <td>22.703474</td>\n      <td>0.870146</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>9</td>\n      <td>35.444599</td>\n      <td>23.316723</td>\n      <td>-3.121132</td>\n      <td>-2.053192</td>\n      <td>23.045393</td>\n      <td>0.866498</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:54.133045Z",
          "end_time": "2020-10-29T13:22:55.088248Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_ens_interp = RegressionInterpretation.from_ensemble(ensemble, dl=test_dls[0])",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "text/html": ""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:55.092811Z",
          "end_time": "2020-10-29T13:22:55.587103Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_ens_interp.plot_results()\nplt.show()",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Comparison: Random forest"
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:55.588986Z",
          "end_time": "2020-10-29T13:22:55.802759Z"
        }
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestRegressor",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Below values for max_features and min_samples_leaf should generally work well."
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:55.804120Z",
          "end_time": "2020-10-29T13:22:55.808331Z"
        }
      },
      "cell_type": "code",
      "source": "rf = RandomForestRegressor(n_estimators=500, max_features=0.5, min_samples_leaf=4, oob_score=True)",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:55.811722Z",
          "end_time": "2020-10-29T13:22:59.818933Z"
        }
      },
      "cell_type": "code",
      "source": "rf.fit(trainval_tb.train.xs, trainval_tb.train.ys.values.ravel())",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "RandomForestRegressor(max_features=0.5, min_samples_leaf=4, n_estimators=500,\n                      oob_score=True)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:59.820569Z",
          "end_time": "2020-10-29T13:22:59.879639Z"
        }
      },
      "cell_type": "code",
      "source": "rf_preds = rf.predict(trainval_tb.valid.xs)",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Validation results"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:22:59.882392Z",
          "end_time": "2020-10-29T13:23:00.271870Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_sklearn_regression(rf, trainval_tb.valid.xs, trainval_tb.valid.ys)\nplt.show()",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Test"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:23:00.273718Z",
          "end_time": "2020-10-29T13:23:00.697798Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_sklearn_regression(rf, test_tb.train.xs, test_tb.train.ys)\nplt.show()",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 504x432 with 2 Axes>",
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## SHAP for RF"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Todo comments and info."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-10-29T13:23:00.699639Z",
          "end_time": "2020-10-29T13:23:01.251571Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import shap\nshap.initjs()",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return 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r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\nn===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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          "end_time": "2020-10-29T13:23:05.596478Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "explainer = shap.TreeExplainer(rf)\nshap_values = explainer.shap_values(test_tb.train.xs)",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Get most influential features"
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          "end_time": "2020-10-29T13:23:06.210433Z"
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      "cell_type": "code",
      "source": "shap.summary_plot(shap_values, test_tb.train.xs)",
      "execution_count": 36,
      "outputs": [
        {
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          "data": {
            "text/plain": "<Figure size 576x684 with 2 Axes>",
            "image/png": 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