Hyrbid_Kringing_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0d3450d-d04e-4361-bcdf-f9a3157aa280",
   "metadata": {},
   "source": [
    "# Hybrid-Kringing independent variable analysis (ML preprocessing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279c34dc-66ba-4de1-9be4-45be0eb7ba64",
   "metadata": {},
   "source": [
    "**Justification:** Wong et al., 2021 (10.1016/j.jclepro.2021.128411) found that using Hybrid-Kringing to trim the number of independent variables helps boost NO2 predicting XGBoost model R^2 score.\n",
    "\n",
    "Here we explore this idea, and see if we find simular results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "acd10e65-7462-483c-a455-01bc81c8cbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "import pykrige\n",
    "import pandas as pd \n",
    "import scipy\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c5643a5c-7363-4c55-b874-3bfcc1429cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'lat', 'long', 'State_Name', 'Date_Local', 'mean_no2',\n",
      "       'year', 'month', 'dayofmonth', 'dayofyear', 'counter', 'weekend',\n",
      "       'station_id', 'sp', 'swvl1', 't2m', 'tp', 'u10', 'v10', 'blh', 'u100',\n",
      "       'v100', 'p_roads_1000', 's_roads_1700', 's_roads_3000', 'tropomi',\n",
      "       'pod_den_1100', 'Z_r', 'Z'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>Date_Local</th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofmonth</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>...</th>\n",
       "      <th>blh</th>\n",
       "      <th>u100</th>\n",
       "      <th>v100</th>\n",
       "      <th>p_roads_1000</th>\n",
       "      <th>s_roads_1700</th>\n",
       "      <th>s_roads_3000</th>\n",
       "      <th>tropomi</th>\n",
       "      <th>pod_den_1100</th>\n",
       "      <th>Z_r</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>4.314286</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>4.957143</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>5.891667</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>7.254167</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        lat    long State_Name Date_Local   mean_no2  year  \\\n",
       "0           0  33.553056 -86.815    Alabama   1/1/2019   4.314286  2019   \n",
       "1           1  33.553056 -86.815    Alabama   1/9/2019   4.957143  2019   \n",
       "2           2  33.553056 -86.815    Alabama  1/10/2019   5.891667  2019   \n",
       "3           3  33.553056 -86.815    Alabama  1/11/2019  14.500000  2019   \n",
       "4           4  33.553056 -86.815    Alabama  1/12/2019   7.254167  2019   \n",
       "\n",
       "   month  dayofmonth  dayofyear  ...         blh      u100      v100  \\\n",
       "0      1           1          1  ...  464.630521 -0.414841  1.947801   \n",
       "1      1           9          9  ...  464.630521 -0.414841  1.947801   \n",
       "2      1          10         10  ...  464.630521 -0.414841  1.947801   \n",
       "3      1          11         11  ...  464.630521 -0.414841  1.947801   \n",
       "4      1          12         12  ...  464.630521 -0.414841  1.947801   \n",
       "\n",
       "   p_roads_1000  s_roads_1700  s_roads_3000       tropomi  pod_den_1100  Z_r  \\\n",
       "0           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "1           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "2           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "3           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "4           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "\n",
       "     Z  \n",
       "0  178  \n",
       "1  178  \n",
       "2  178  \n",
       "3  178  \n",
       "4  178  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bring in data\n",
    "# import data, remove non-necessary columns\n",
    "DATA_LOCATION = r'C:\\Users\\xavie\\Documents\\DATASETS\\master_no2_daily_nov30.csv'\n",
    "in_data = pd.read_csv(DATA_LOCATION)\n",
    "print(in_data.columns)\n",
    "in_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ad285ed2-73a3-4beb-a197-9687d9e13f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'station_id', 'month', 'Unnamed: 0', 'lat', 'long', 'mean_no2',\n",
      "       'year', 'dayofmonth', 'dayofyear', 'counter', 'weekend', 'sp', 'swvl1',\n",
      "       't2m', 'tp', 'u10', 'v10', 'blh', 'u100', 'v100', 'p_roads_1000',\n",
      "       's_roads_1700', 's_roads_3000', 'tropomi', 'pod_den_1100', 'Z_r', 'Z'],\n",
      "      dtype='object')\n"
     ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>month</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>mean_no2</th>\n",
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       "      <th>s_roads_1700</th>\n",
       "      <th>s_roads_3000</th>\n",
       "      <th>tropomi</th>\n",
       "      <th>pod_den_1100</th>\n",
       "      <th>Z_r</th>\n",
       "      <th>Z</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>10.170781</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>99859.52138</td>\n",
       "      <td>0.426193</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>...</td>\n",
       "      <td>608.579581</td>\n",
       "      <td>0.351088</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>3.565690e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>9.005671</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>99332.41407</td>\n",
       "      <td>0.426193</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>...</td>\n",
       "      <td>587.417161</td>\n",
       "      <td>0.726758</td>\n",
       "      <td>1.944154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>3.475220e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>9.835197</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>99325.53875</td>\n",
       "      <td>0.426193</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.002818</td>\n",
       "      <td>...</td>\n",
       "      <td>640.471893</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.646560e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>9.251750</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>99273.97391</td>\n",
       "      <td>0.426193</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.003752</td>\n",
       "      <td>...</td>\n",
       "      <td>617.004901</td>\n",
       "      <td>0.723391</td>\n",
       "      <td>1.006420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.82894</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>2.270010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
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      ],
      "text/plain": [
       "   station_id  month        lat    long   mean_no2    year           sp  \\\n",
       "0           1      1  33.553056 -86.815  11.176059  2019.0  99724.87984   \n",
       "1           1      2  33.553056 -86.815  10.170781  2019.0  99859.52138   \n",
       "2           1      3  33.553056 -86.815   9.005671  2019.0  99332.41407   \n",
       "3           1      4  33.553056 -86.815   9.835197  2019.0  99325.53875   \n",
       "4           1      5  33.553056 -86.815   9.251750  2019.0  99273.97391   \n",
       "\n",
       "      swvl1         t2m        tp  ...         blh      u100      v100  \\\n",
       "0  0.426193  280.264083  0.006967  ...  464.630521 -0.414841  1.947801   \n",
       "1  0.426193  280.264083  0.003241  ...  608.579581  0.351088 -0.346133   \n",
       "2  0.426193  280.264083  0.004815  ...  587.417161  0.726758  1.944154   \n",
       "3  0.426193  280.264083  0.002818  ...  640.471893  0.906035  2.034346   \n",
       "4  0.426193  280.264083  0.003752  ...  617.004901  0.723391  1.006420   \n",
       "\n",
       "   p_roads_1000  s_roads_1700  s_roads_3000       tropomi  pod_den_1100  Z_r  \\\n",
       "0           0.0       3.82894      8.555133  4.437010e+15   2642.464651  0.0   \n",
       "1           0.0       3.82894      8.555133  3.565690e+15   2642.464651  0.0   \n",
       "2           0.0       3.82894      8.555133  3.475220e+15   2642.464651  0.0   \n",
       "3           0.0       3.82894      8.555133  3.646560e+15   2642.464651  0.0   \n",
       "4           0.0       3.82894      8.555133  2.270010e+15   2642.464651  0.0   \n",
       "\n",
       "       Z  \n",
       "0  178.0  \n",
       "1  178.0  \n",
       "2  178.0  \n",
       "3  178.0  \n",
       "4  178.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to monthly averaged and make a January dataframe\n",
    "monthly = in_data.groupby(['station_id', 'month']).mean().reset_index()\n",
    "january = monthly\n",
    "\n",
    "#january = monthly.loc[monthly['month'] == 1]\n",
    "lat_lon = january[['lat', 'long']].to_numpy()\n",
    "\n",
    "january.sort_values(['station_id', 'month'], axis=0, inplace=True)\n",
    "january.reset_index(inplace=True)\n",
    "print(january.columns)\n",
    "january.drop(['index', 'Unnamed: 0', 'counter', 'dayofyear', 'weekend', 'dayofmonth'], axis=1, inplace=True)\n",
    "\n",
    "\n",
    "january.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7cdbf494-bf19-43e5-bd60-9e6b8dbff42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "january.to_csv(r'C:\\Users\\xavie\\Documents\\DATASETS\\master_monthly_no2_jan4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6511a73f-c827-4457-aa52-1bb0d502d0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_no2</th>\n",
       "      <th>sp</th>\n",
       "      <th>swvl1</th>\n",
       "      <th>t2m</th>\n",
       "      <th>tp</th>\n",
       "      <th>u10</th>\n",
       "      <th>v10</th>\n",
       "      <th>blh</th>\n",
       "      <th>u100</th>\n",
       "      <th>v100</th>\n",
       "      <th>p_roads_1000</th>\n",
       "      <th>s_roads_1700</th>\n",
       "      <th>s_roads_3000</th>\n",
       "      <th>tropomi</th>\n",
       "      <th>pod_den_1100</th>\n",
       "      <th>Z_r</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.176059</td>\n",
       "      <td>99724.87984</td>\n",
       "      <td>0.426193</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>0.489328</td>\n",
       "      <td>-0.052422</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.828940</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.312605</td>\n",
       "      <td>99646.95963</td>\n",
       "      <td>0.450334</td>\n",
       "      <td>280.307223</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>0.500518</td>\n",
       "      <td>-0.064837</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>-0.414841</td>\n",
       "      <td>1.947801</td>\n",
       "      <td>5.95545</td>\n",
       "      <td>2.959208</td>\n",
       "      <td>11.084034</td>\n",
       "      <td>4.844770e+15</td>\n",
       "      <td>2630.140482</td>\n",
       "      <td>0.860871</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.821502</td>\n",
       "      <td>97269.24728</td>\n",
       "      <td>0.144823</td>\n",
       "      <td>284.142995</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>-0.595943</td>\n",
       "      <td>-0.771905</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>0.406358</td>\n",
       "      <td>0.662893</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.210007</td>\n",
       "      <td>27.894930</td>\n",
       "      <td>1.030000e+16</td>\n",
       "      <td>6362.811560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.353431</td>\n",
       "      <td>97010.27716</td>\n",
       "      <td>0.151290</td>\n",
       "      <td>284.001643</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>-0.656265</td>\n",
       "      <td>-0.836423</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>0.406358</td>\n",
       "      <td>0.662893</td>\n",
       "      <td>14.72194</td>\n",
       "      <td>6.752895</td>\n",
       "      <td>34.580018</td>\n",
       "      <td>8.786030e+15</td>\n",
       "      <td>7065.979555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>333.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.139339</td>\n",
       "      <td>98177.93446</td>\n",
       "      <td>0.128340</td>\n",
       "      <td>285.059027</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>-0.611155</td>\n",
       "      <td>-0.783943</td>\n",
       "      <td>494.862550</td>\n",
       "      <td>0.879382</td>\n",
       "      <td>0.716942</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.304251</td>\n",
       "      <td>10.782592</td>\n",
       "      <td>3.269870e+15</td>\n",
       "      <td>55.396922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_no2           sp     swvl1         t2m        tp       u10       v10  \\\n",
       "0  11.176059  99724.87984  0.426193  280.264083  0.006967  0.489328 -0.052422   \n",
       "1  12.312605  99646.95963  0.450334  280.307223  0.006384  0.500518 -0.064837   \n",
       "2  21.821502  97269.24728  0.144823  284.142995  0.002700 -0.595943 -0.771905   \n",
       "3  23.353431  97010.27716  0.151290  284.001643  0.002956 -0.656265 -0.836423   \n",
       "4  10.139339  98177.93446  0.128340  285.059027  0.001775 -0.611155 -0.783943   \n",
       "\n",
       "          blh      u100      v100  p_roads_1000  s_roads_1700  s_roads_3000  \\\n",
       "0  464.630521 -0.414841  1.947801       0.00000      3.828940      8.555133   \n",
       "1  464.630521 -0.414841  1.947801       5.95545      2.959208     11.084034   \n",
       "2  421.091444  0.406358  0.662893       0.00000     10.210007     27.894930   \n",
       "3  421.091444  0.406358  0.662893      14.72194      6.752895     34.580018   \n",
       "4  494.862550  0.879382  0.716942       0.00000      5.304251     10.782592   \n",
       "\n",
       "        tropomi  pod_den_1100       Z_r      Z  \n",
       "0  4.437010e+15   2642.464651  0.000000  178.0  \n",
       "1  4.844770e+15   2630.140482  0.860871  189.0  \n",
       "2  1.030000e+16   6362.811560  0.000000  334.0  \n",
       "3  8.786030e+15   7065.979555  0.000000  333.0  \n",
       "4  3.269870e+15     55.396922  0.000000  262.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep_cols = ['mean_no2', 'sp', 'swvl1', 't2m', 'tp', 'u10', 'v10', 'blh', 'u100', 'v100', 'p_roads_1000', 's_roads_1700', 's_roads_3000', 'tropomi', 'pod_den_1100', 'Z_r', 'Z']\n",
    "in_data = january\n",
    "in_data = in_data[keep_cols]\n",
    "\n",
    "\n",
    "# standardize column headers\n",
    "for col in list(in_data.columns):\n",
    "        if in_data[col].dtypes == object:\n",
    "            in_data[col].replace(' ', '_', regex=True, inplace=True)\n",
    "        if ' ' in str(col)[:-1]:\n",
    "            new = str(col).replace(' ', '_')\n",
    "            if new[-1] == '_':\n",
    "                new = new[:-1]\n",
    "            in_data.rename(columns={str(col): new}, inplace=True)\n",
    "        \n",
    "            \n",
    "in_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c38b23-cabf-4ec9-9f0d-af405d60f172",
   "metadata": {},
   "source": [
    "Chen et al., 2020 as well as Wong et al., 2021 both used Spearman correlation and expected effect directions to trim variables (p < 0.3 removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ca0fb88-19ef-4a5f-b2dd-2e467670bc24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: mean_no2\n",
      "Correlation w/ NO2 = 0.9999999999999999, p score = 0.0\n",
      "\n",
      "\n",
      "Variable: sp\n",
      "Correlation w/ NO2 = 0.18882685883143055, p score = 7.701760156339729e-05\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: swvl1\n",
      "Correlation w/ NO2 = 0.023465156265842047, p score = 0.6263051968495408\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: t2m\n",
      "Correlation w/ NO2 = 0.21439141048484903, p score = 6.771759284181397e-06\n",
      "\n",
      "\n",
      "Variable: tp\n",
      "Correlation w/ NO2 = 0.1490184461855597, p score = 0.0018753914953458454\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: u10\n",
      "Correlation w/ NO2 = -0.13721640392750606, p score = 0.004228708747749465\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: v10\n",
      "Correlation w/ NO2 = -0.10839480684678732, p score = 0.024090702469024627\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: blh\n",
      "Correlation w/ NO2 = 0.1260015075094438, p score = 0.008669702545827645\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: u100\n",
      "Correlation w/ NO2 = 0.008353481033043434, p score = 0.8623957906098056\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: v100\n",
      "Correlation w/ NO2 = -0.005270619711651638, p score = 0.9129183724880933\n",
      "Consider removing\n",
      "\n",
      "\n",
      "Variable: p_roads_1000\n",
      "Correlation w/ NO2 = 0.5375831814238632, p score = 8.512128360885135e-34\n",
      "\n",
      "\n",
      "Variable: s_roads_1700\n",
      "Correlation w/ NO2 = 0.5033987049639799, p score = 3.379139061763528e-29\n",
      "\n",
      "\n",
      "Variable: s_roads_3000\n",
      "Correlation w/ NO2 = 0.6000026390356099, p score = 1.0860565042002536e-43\n",
      "\n",
      "\n",
      "Variable: tropomi\n",
      "Correlation w/ NO2 = 0.8000631911652033, p score = 1.0905923329303232e-97\n",
      "\n",
      "\n",
      "Variable: pod_den_1100\n",
      "Correlation w/ NO2 = 0.669030821034331, p score = 1.6332800730501932e-57\n",
      "\n",
      "\n",
      "Variable: Z_r\n",
      "Correlation w/ NO2 = -0.2566151886300327, p score = 6.117744640170189e-08\n",
      "\n",
      "\n",
      "Variable: Z\n",
      "Correlation w/ NO2 = -0.21559464056791527, p score = 5.993806222242541e-06\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calculate Spearman coefficient between each independent variable and NO2\n",
    "mean_no2 = in_data[['mean_no2']].to_numpy()\n",
    "\n",
    "\n",
    "for var in list(in_data.columns):\n",
    "    var_numpy = in_data[[var]].to_numpy()\n",
    "    corr, p =  scipy.stats.spearmanr(mean_no2, var_numpy, nan_policy='omit', alternative='two-sided')\n",
    "    print('Variable: %s' % var)\n",
    "    print('Correlation w/ NO2 = %s, p score = %s' % (corr, p))\n",
    "    \n",
    "    if abs(corr) < 0.2:\n",
    "        print('Consider removing')\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf53648f-e35e-4f3f-89ae-9bd1e530e42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop variables when necessary\n",
    "drop_cols = ['u10', 'v10', 'swvl1', 'u100', 'v100']\n",
    "new_data = in_data.drop(drop_cols, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c99714-c2c3-49ac-a7e1-6de842dfb04a",
   "metadata": {},
   "source": [
    "Now we make a traditional LUR model and use it to extract Variance Inflation Factors (VIF) and potentially remove variables with high (> 3) VIF relative to their correlation values.\n",
    "\n",
    "Read more: https://www.investopedia.com/terms/v/variance-inflation-factor.asp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ecfec7dd-bea8-4ba6-9174-762c637bd473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sp', 't2m', 'tp', 'blh', 'p_roads_1000', 's_roads_1700', 's_roads_3000', 'tropomi', 'pod_den_1100', 'Z_r', 'Z']\n",
      "Linear regression score: 0.7006346774624043\n",
      "Variable: sp\n",
      "Regression coef = -0.0008234897608308758\n",
      "\n",
      "Variable: t2m\n",
      "Regression coef = 0.0032190593105780258\n",
      "\n",
      "Variable: tp\n",
      "Regression coef = 6.297349318769218e-07\n",
      "\n",
      "Variable: blh\n",
      "Regression coef = -0.00021664527285197806\n",
      "\n",
      "Variable: p_roads_1000\n",
      "Regression coef = 0.6261201201463322\n",
      "\n",
      "Variable: s_roads_1700\n",
      "Regression coef = -0.002127076008242656\n",
      "\n",
      "Variable: s_roads_3000\n",
      "Regression coef = 0.06902221585530703\n",
      "\n",
      "Variable: tropomi\n",
      "Regression coef = 9.905390251692652e-16\n",
      "\n",
      "Variable: pod_den_1100\n",
      "Regression coef = 0.00012577603551721265\n",
      "\n",
      "Variable: Z_r\n",
      "Regression coef = -0.004493029112496458\n",
      "\n",
      "Variable: Z\n",
      "Regression coef = -0.008505108468922142\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sp</th>\n",
       "      <th>t2m</th>\n",
       "      <th>tp</th>\n",
       "      <th>blh</th>\n",
       "      <th>p_roads_1000</th>\n",
       "      <th>s_roads_1700</th>\n",
       "      <th>s_roads_3000</th>\n",
       "      <th>tropomi</th>\n",
       "      <th>pod_den_1100</th>\n",
       "      <th>Z_r</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99724.87984</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.828940</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99646.95963</td>\n",
       "      <td>280.307223</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>5.95545</td>\n",
       "      <td>2.959208</td>\n",
       "      <td>11.084034</td>\n",
       "      <td>4.844770e+15</td>\n",
       "      <td>2630.140482</td>\n",
       "      <td>0.860871</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97269.24728</td>\n",
       "      <td>284.142995</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.210007</td>\n",
       "      <td>27.894930</td>\n",
       "      <td>1.030000e+16</td>\n",
       "      <td>6362.811560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97010.27716</td>\n",
       "      <td>284.001643</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>14.72194</td>\n",
       "      <td>6.752895</td>\n",
       "      <td>34.580018</td>\n",
       "      <td>8.786030e+15</td>\n",
       "      <td>7065.979555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>333.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98177.93446</td>\n",
       "      <td>285.059027</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>494.862550</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.304251</td>\n",
       "      <td>10.782592</td>\n",
       "      <td>3.269870e+15</td>\n",
       "      <td>55.396922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sp         t2m        tp         blh  p_roads_1000  s_roads_1700  \\\n",
       "0  99724.87984  280.264083  0.006967  464.630521       0.00000      3.828940   \n",
       "1  99646.95963  280.307223  0.006384  464.630521       5.95545      2.959208   \n",
       "2  97269.24728  284.142995  0.002700  421.091444       0.00000     10.210007   \n",
       "3  97010.27716  284.001643  0.002956  421.091444      14.72194      6.752895   \n",
       "4  98177.93446  285.059027  0.001775  494.862550       0.00000      5.304251   \n",
       "\n",
       "   s_roads_3000       tropomi  pod_den_1100       Z_r      Z  \n",
       "0      8.555133  4.437010e+15   2642.464651  0.000000  178.0  \n",
       "1     11.084034  4.844770e+15   2630.140482  0.860871  189.0  \n",
       "2     27.894930  1.030000e+16   6362.811560  0.000000  334.0  \n",
       "3     34.580018  8.786030e+15   7065.979555  0.000000  333.0  \n",
       "4     10.782592  3.269870e+15     55.396922  0.000000  262.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# split data into dependent and independent data frames\n",
    "Y = new_data[['mean_no2']].to_numpy()\n",
    "not_no2 = [i for i in list(new_data.columns) if i not in ['mean_no2']]\n",
    "print(not_no2)\n",
    "X = new_data[not_no2]\n",
    "X = X.reset_index()\n",
    "X = X.drop('index', axis=1)\n",
    "\n",
    "# create a linear regression model (we could expirmenet with Lasso or Ridge regression)\n",
    "l_model = LinearRegression().fit(X, Y)\n",
    "print('Linear regression score: %s' % l_model.score(X, Y))\n",
    "\n",
    "for i, var in enumerate(list(X.columns)):\n",
    "    print('Variable: %s' % var) \n",
    "    coef = l_model.coef_[0][i]\n",
    "    print('Regression coef = %s\\n' % coef)\n",
    "    \n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d596fc8-34c5-4f58-a95a-bc95dcb5a22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1, 35, 'R2 = 0.7')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4537336de3d4b308b39acf44d8cc4f6",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make prediction plot with the linear model\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib widget\n",
    "prediction = l_model.predict(X)\n",
    "\n",
    "plt.title('Linear regression only')\n",
    "plt.scatter(prediction, Y)\n",
    "plt.plot(np.arange(0, 60, 0.1), np.arange(0, 60, 0.1), c='red')\n",
    "plt.xlim(0, np.amax(prediction))\n",
    "plt.ylim(0, np.amax(Y))\n",
    "plt.xlabel('Predicted NO2 concentration')\n",
    "plt.ylabel('Actual daily NO2 concentration')\n",
    "plt.text(1, 35, 'R2 = %s' % round(r2_score(Y, prediction), 2), fontsize='large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c107930-830a-401c-9554-2165cced56ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>variable</th>\n",
       "      <th>VIF</th>\n",
       "      <th>consider_dropping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp</td>\n",
       "      <td>7.855112</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t2m</td>\n",
       "      <td>1.200827</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tp</td>\n",
       "      <td>1.321986</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blh</td>\n",
       "      <td>1.322960</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p_roads_1000</td>\n",
       "      <td>1.193123</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>s_roads_1700</td>\n",
       "      <td>5.769611</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>s_roads_3000</td>\n",
       "      <td>1.841103</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tropomi</td>\n",
       "      <td>3.698138</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pod_den_1100</td>\n",
       "      <td>1.803657</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Z_r</td>\n",
       "      <td>1.102600</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Z</td>\n",
       "      <td>5.588753</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable       VIF consider_dropping\n",
       "0             sp  7.855112               Yes\n",
       "1            t2m  1.200827                No\n",
       "2             tp  1.321986                No\n",
       "3            blh  1.322960                No\n",
       "4   p_roads_1000  1.193123                No\n",
       "5   s_roads_1700  5.769611               Yes\n",
       "6   s_roads_3000  1.841103                No\n",
       "7        tropomi  3.698138               Yes\n",
       "8   pod_den_1100  1.803657                No\n",
       "9            Z_r  1.102600                No\n",
       "10             Z  5.588753               Yes"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explore VIF scores by making a dataframe\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['variable'] = X.columns\n",
    "  \n",
    "# calculating VIF for each feature\n",
    "vif_data['VIF'] = [variance_inflation_factor(X.values, i)\n",
    "                          for i in range(len(X.columns))]\n",
    "vif_data['consider_dropping'] = 'Yes'\n",
    "vif_data.loc[vif_data['VIF'] < 3, 'consider_dropping'] = 'No'\n",
    "  \n",
    "vif_data.head(n=X.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4dec68-b644-4b16-895e-c3d9fdfb59b6",
   "metadata": {},
   "source": [
    "Next we use Regression Kringing to improve our initial LUR model. The regression kringing prediction can be used as an XGBoost input.\n",
    "\n",
    "See example: https://geostat-framework.readthedocs.io/projects/pykrige/en/latest/examples/07_regression_kriging2d.html#sphx-glr-examples-07-regression-kriging2d-py "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa4107e-a17c-42b6-831a-769c8649bdc9",
   "metadata": {},
   "source": [
    "The pykrige documentation is weak. This is pulled from the source code .fit(p, x, y) docstring:\n",
    "\n",
    "    Fit the regression method and also Krige the residual.\n",
    "        Parameters\n",
    "        ----------\n",
    "        p: ndarray\n",
    "            (Ns, d) array of predictor variables (Ns samples, d dimensions)\n",
    "            for regression\n",
    "        x: ndarray\n",
    "            ndarray of (x, y) points. Needs to be a (Ns, 2) array\n",
    "            corresponding to the lon/lat, for example 2d regression kriging.\n",
    "            array of Points, (x, y, z) pairs of shape (N, 3) for 3d kriging\n",
    "        y: ndarray\n",
    "            array of targets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aed9656c-ee2e-4f68-b730-d86129b858f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(432, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>sp</th>\n",
       "      <th>t2m</th>\n",
       "      <th>tp</th>\n",
       "      <th>blh</th>\n",
       "      <th>p_roads_1000</th>\n",
       "      <th>s_roads_1700</th>\n",
       "      <th>s_roads_3000</th>\n",
       "      <th>tropomi</th>\n",
       "      <th>pod_den_1100</th>\n",
       "      <th>Z_r</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99724.87984</td>\n",
       "      <td>280.264083</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.828940</td>\n",
       "      <td>8.555133</td>\n",
       "      <td>4.437010e+15</td>\n",
       "      <td>2642.464651</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99646.95963</td>\n",
       "      <td>280.307223</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>464.630521</td>\n",
       "      <td>5.95545</td>\n",
       "      <td>2.959208</td>\n",
       "      <td>11.084034</td>\n",
       "      <td>4.844770e+15</td>\n",
       "      <td>2630.140482</td>\n",
       "      <td>0.860871</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97269.24728</td>\n",
       "      <td>284.142995</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.210007</td>\n",
       "      <td>27.894930</td>\n",
       "      <td>1.030000e+16</td>\n",
       "      <td>6362.811560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97010.27716</td>\n",
       "      <td>284.001643</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>421.091444</td>\n",
       "      <td>14.72194</td>\n",
       "      <td>6.752895</td>\n",
       "      <td>34.580018</td>\n",
       "      <td>8.786030e+15</td>\n",
       "      <td>7065.979555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98177.93446</td>\n",
       "      <td>285.059027</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>494.862550</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.304251</td>\n",
       "      <td>10.782592</td>\n",
       "      <td>3.269870e+15</td>\n",
       "      <td>55.396922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>99269.96331</td>\n",
       "      <td>285.127867</td>\n",
       "      <td>0.005105</td>\n",
       "      <td>664.830978</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.161562</td>\n",
       "      <td>7.197573</td>\n",
       "      <td>1.896360e+15</td>\n",
       "      <td>8.066816</td>\n",
       "      <td>1.421720</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>94701.89088</td>\n",
       "      <td>283.370150</td>\n",
       "      <td>0.006687</td>\n",
       "      <td>514.463807</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.183910e+15</td>\n",
       "      <td>120.683022</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98349.81728</td>\n",
       "      <td>285.023230</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>593.736151</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.547216</td>\n",
       "      <td>4.945486</td>\n",
       "      <td>1.865570e+15</td>\n",
       "      <td>8.859233</td>\n",
       "      <td>1.885530</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>97643.95183</td>\n",
       "      <td>284.212753</td>\n",
       "      <td>0.005730</td>\n",
       "      <td>453.603265</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.302154</td>\n",
       "      <td>8.485324</td>\n",
       "      <td>2.862630e+15</td>\n",
       "      <td>312.233189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>98730.25125</td>\n",
       "      <td>285.751100</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>694.765643</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.185840e+15</td>\n",
       "      <td>14.178909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sp         t2m        tp         blh  p_roads_1000  s_roads_1700  \\\n",
       "0   99724.87984  280.264083  0.006967  464.630521       0.00000      3.828940   \n",
       "1   99646.95963  280.307223  0.006384  464.630521       5.95545      2.959208   \n",
       "2   97269.24728  284.142995  0.002700  421.091444       0.00000     10.210007   \n",
       "3   97010.27716  284.001643  0.002956  421.091444      14.72194      6.752895   \n",
       "4   98177.93446  285.059027  0.001775  494.862550       0.00000      5.304251   \n",
       "..          ...         ...       ...         ...           ...           ...   \n",
       "95  99269.96331  285.127867  0.005105  664.830978       0.00000      4.161562   \n",
       "96  94701.89088  283.370150  0.006687  514.463807       0.00000      0.000000   \n",
       "97  98349.81728  285.023230  0.005701  593.736151       0.00000      3.547216   \n",
       "98  97643.95183  284.212753  0.005730  453.603265       0.00000      2.302154   \n",
       "99  98730.25125  285.751100  0.006558  694.765643       0.00000      0.000000   \n",
       "\n",
       "    s_roads_3000       tropomi  pod_den_1100       Z_r      Z  \n",
       "0       8.555133  4.437010e+15   2642.464651  0.000000  178.0  \n",
       "1      11.084034  4.844770e+15   2630.140482  0.860871  189.0  \n",
       "2      27.894930  1.030000e+16   6362.811560  0.000000  334.0  \n",
       "3      34.580018  8.786030e+15   7065.979555  0.000000  333.0  \n",
       "4      10.782592  3.269870e+15     55.396922  0.000000  262.0  \n",
       "..           ...           ...           ...       ...    ...  \n",
       "95      7.197573  1.896360e+15      8.066816  1.421720  276.0  \n",
       "96      0.000000  2.183910e+15    120.683022  0.000000  513.0  \n",
       "97      4.945486  1.865570e+15      8.859233  1.885530  275.0  \n",
       "98      8.485324  2.862630e+15    312.233189  0.000000  153.0  \n",
       "99      0.000000  2.185840e+15     14.178909  0.000000  119.0  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_X = X.loc[lambda X: X.index!= 1]\n",
    "print(out_X.shape)\n",
    "X.head(n=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6343c33a-9185-4472-9a3c-fde539271956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index = 0\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700632233934052\n",
      "Prediction: [[7.75487194]]\n",
      "Actual: [11.17605874]\n",
      "\n",
      "\n",
      "Index = 1\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700638203377429\n",
      "Prediction: [[12.15426585]]\n",
      "Actual: [12.3126051]\n",
      "\n",
      "\n",
      "Index = 2\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006087119169938\n",
      "Prediction: [[18.98357779]]\n",
      "Actual: [21.82150206]\n",
      "\n",
      "\n",
      "Index = 3\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006326378868837\n",
      "Prediction: [[27.68493793]]\n",
      "Actual: [23.353431]\n",
      "\n",
      "\n",
      "Index = 4\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006303768115205\n",
      "Prediction: [[9.87003604]]\n",
      "Actual: [10.13933877]\n",
      "\n",
      "\n",
      "Index = 5\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005990521724592\n",
      "Prediction: [[19.93637586]]\n",
      "Actual: [22.18131655]\n",
      "\n",
      "\n",
      "Index = 6\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005107253374692\n",
      "Prediction: [[21.95139646]]\n",
      "Actual: [30.62543035]\n",
      "\n",
      "\n",
      "Index = 7\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005977461179583\n",
      "Prediction: [[17.18026247]]\n",
      "Actual: [20.43632519]\n",
      "\n",
      "\n",
      "Index = 8\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006047839679668\n",
      "Prediction: [[10.90533564]]\n",
      "Actual: [13.57898993]\n",
      "\n",
      "\n",
      "Index = 9\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700620944753535\n",
      "Prediction: [[11.70930845]]\n",
      "Actual: [13.15624114]\n",
      "\n",
      "\n",
      "Index = 10\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006328740122822\n",
      "Prediction: [[6.8860766]]\n",
      "Actual: [6.27437097]\n",
      "\n",
      "\n",
      "Index = 11\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700632972601208\n",
      "Prediction: [[6.89245877]]\n",
      "Actual: [8.6902291]\n",
      "\n",
      "\n",
      "Index = 12\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006255445831986\n",
      "Prediction: [[7.56968077]]\n",
      "Actual: [11.88387094]\n",
      "\n",
      "\n",
      "Index = 13\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006215818384383\n",
      "Prediction: [[15.30043436]]\n",
      "Actual: [12.94781968]\n",
      "\n",
      "\n",
      "Index = 14\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006250623620726\n",
      "Prediction: [[19.95733414]]\n",
      "Actual: [17.99845716]\n",
      "\n",
      "\n",
      "Index = 15\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316030962594\n",
      "Prediction: [[21.55364627]]\n",
      "Actual: [20.62050877]\n",
      "\n",
      "\n",
      "Index = 16\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006250405925207\n",
      "Prediction: [[14.41594256]]\n",
      "Actual: [18.99947847]\n",
      "\n",
      "\n",
      "Index = 17\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006108095874805\n",
      "Prediction: [[10.49513777]]\n",
      "Actual: [16.41917565]\n",
      "\n",
      "\n",
      "Index = 18\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006347096838831\n",
      "Prediction: [[7.8624825]]\n",
      "Actual: [8.64852732]\n",
      "\n",
      "\n",
      "Index = 19\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336128128603\n",
      "Prediction: [[9.31783222]]\n",
      "Actual: [10.17820619]\n",
      "\n",
      "\n",
      "Index = 20\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352973637835\n",
      "Prediction: [[5.36825493]]\n",
      "Actual: [6.89344319]\n",
      "\n",
      "\n",
      "Index = 21\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006346300219919\n",
      "Prediction: [[10.60595778]]\n",
      "Actual: [11.38883077]\n",
      "\n",
      "\n",
      "Index = 22\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006333412189236\n",
      "Prediction: [[7.35372157]]\n",
      "Actual: [9.47699858]\n",
      "\n",
      "\n",
      "Index = 23\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006127338261963\n",
      "Prediction: [[9.68941943]]\n",
      "Actual: [15.17982581]\n",
      "\n",
      "\n",
      "Index = 24\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006062960747801\n",
      "Prediction: [[11.48824517]]\n",
      "Actual: [15.9694249]\n",
      "\n",
      "\n",
      "Index = 25\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006262486548969\n",
      "Prediction: [[8.23342969]]\n",
      "Actual: [11.094389]\n",
      "\n",
      "\n",
      "Index = 26\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700594397861837\n",
      "Prediction: [[15.96663486]]\n",
      "Actual: [20.40015939]\n",
      "\n",
      "\n",
      "Index = 27\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006327824846775\n",
      "Prediction: [[6.9314089]]\n",
      "Actual: [8.07757719]\n",
      "\n",
      "\n",
      "Index = 28\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006311517641202\n",
      "Prediction: [[13.86231171]]\n",
      "Actual: [13.98983946]\n",
      "\n",
      "\n",
      "Index = 29\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.70062610267504\n",
      "Prediction: [[5.22470926]]\n",
      "Actual: [3.52781513]\n",
      "\n",
      "\n",
      "Index = 30\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700591033457431\n",
      "Prediction: [[6.96562594]]\n",
      "Actual: [14.76297335]\n",
      "\n",
      "\n",
      "Index = 31\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006101941900115\n",
      "Prediction: [[10.57793379]]\n",
      "Actual: [7.57872645]\n",
      "\n",
      "\n",
      "Index = 32\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006291565230827\n",
      "Prediction: [[11.07543282]]\n",
      "Actual: [16.11022561]\n",
      "\n",
      "\n",
      "Index = 33\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006304677755901\n",
      "Prediction: [[10.94888742]]\n",
      "Actual: [15.2480967]\n",
      "\n",
      "\n",
      "Index = 34\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006283769829964\n",
      "Prediction: [[14.56517654]]\n",
      "Actual: [18.81129035]\n",
      "\n",
      "\n",
      "Index = 35\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700633732137345\n",
      "Prediction: [[7.78464366]]\n",
      "Actual: [10.41458629]\n",
      "\n",
      "\n",
      "Index = 36\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006305254513672\n",
      "Prediction: [[9.77861987]]\n",
      "Actual: [11.26834752]\n",
      "\n",
      "\n",
      "Index = 37\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006015915797674\n",
      "Prediction: [[17.38138531]]\n",
      "Actual: [13.81202003]\n",
      "\n",
      "\n",
      "Index = 38\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004333768563802\n",
      "Prediction: [[18.10545159]]\n",
      "Actual: [8.78854345]\n",
      "\n",
      "\n",
      "Index = 39\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004844747962642\n",
      "Prediction: [[23.50436596]]\n",
      "Actual: [16.20779565]\n",
      "\n",
      "\n",
      "Index = 40\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005668006440868\n",
      "Prediction: [[25.90047357]]\n",
      "Actual: [23.00592894]\n",
      "\n",
      "\n",
      "Index = 41\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006332925390792\n",
      "Prediction: [[13.42321462]]\n",
      "Actual: [15.29502026]\n",
      "\n",
      "\n",
      "Index = 42\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700466309599878\n",
      "Prediction: [[26.6532875]]\n",
      "Actual: [22.17810976]\n",
      "\n",
      "\n",
      "Index = 43\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005928410416615\n",
      "Prediction: [[26.08896702]]\n",
      "Actual: [24.06037771]\n",
      "\n",
      "\n",
      "Index = 44\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352993924074\n",
      "Prediction: [[21.42576365]]\n",
      "Actual: [22.74283365]\n",
      "\n",
      "\n",
      "Index = 45\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005773447947329\n",
      "Prediction: [[21.41623428]]\n",
      "Actual: [19.20895428]\n",
      "\n",
      "\n",
      "Index = 46\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005124331321579\n",
      "Prediction: [[26.83229018]]\n",
      "Actual: [23.35912774]\n",
      "\n",
      "\n",
      "Index = 47\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006167163620316\n",
      "Prediction: [[28.48232277]]\n",
      "Actual: [33.35293247]\n",
      "\n",
      "\n",
      "Index = 48\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005907060808447\n",
      "Prediction: [[20.30249663]]\n",
      "Actual: [18.31555045]\n",
      "\n",
      "\n",
      "Index = 49\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006343054581107\n",
      "Prediction: [[6.49301903]]\n",
      "Actual: [9.28870968]\n",
      "\n",
      "\n",
      "Index = 50\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006173293140451\n",
      "Prediction: [[5.49815476]]\n",
      "Actual: [13.00510936]\n",
      "\n",
      "\n",
      "Index = 51\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006323922122146\n",
      "Prediction: [[7.34174249]]\n",
      "Actual: [8.41298874]\n",
      "\n",
      "\n",
      "Index = 52\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006302395942354\n",
      "Prediction: [[12.83244175]]\n",
      "Actual: [11.28306691]\n",
      "\n",
      "\n",
      "Index = 53\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006345041483766\n",
      "Prediction: [[9.45081108]]\n",
      "Actual: [7.85643984]\n",
      "\n",
      "\n",
      "Index = 54\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006262996520094\n",
      "Prediction: [[8.12784141]]\n",
      "Actual: [5.55867232]\n",
      "\n",
      "\n",
      "Index = 55\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700634478978182\n",
      "Prediction: [[7.56702607]]\n",
      "Actual: [7.2650939]\n",
      "\n",
      "\n",
      "Index = 56\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006139910693016\n",
      "Prediction: [[22.25382194]]\n",
      "Actual: [20.98653471]\n",
      "\n",
      "\n",
      "Index = 57\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006348783634291\n",
      "Prediction: [[20.68865943]]\n",
      "Actual: [23.45459168]\n",
      "\n",
      "\n",
      "Index = 58\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006133594780504\n",
      "Prediction: [[19.70286947]]\n",
      "Actual: [17.92582884]\n",
      "\n",
      "\n",
      "Index = 59\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006328604927843\n",
      "Prediction: [[11.20744458]]\n",
      "Actual: [9.3633609]\n",
      "\n",
      "\n",
      "Index = 60\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006348300199021\n",
      "Prediction: [[7.39698091]]\n",
      "Actual: [7.23784587]\n",
      "\n",
      "\n",
      "Index = 61\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700635202029554\n",
      "Prediction: [[11.6833367]]\n",
      "Actual: [10.54825624]\n",
      "\n",
      "\n",
      "Index = 62\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005051102717843\n",
      "Prediction: [[9.8991183]]\n",
      "Actual: [2.93449394]\n",
      "\n",
      "\n",
      "Index = 63\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006192027605076\n",
      "Prediction: [[10.1479553]]\n",
      "Actual: [12.83135329]\n",
      "\n",
      "\n",
      "Index = 64\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700635068975788\n",
      "Prediction: [[16.27689009]]\n",
      "Actual: [18.19048142]\n",
      "\n",
      "\n",
      "Index = 65\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006342341349863\n",
      "Prediction: [[14.04326575]]\n",
      "Actual: [16.69518477]\n",
      "\n",
      "\n",
      "Index = 66\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006254939132799\n",
      "Prediction: [[12.03270854]]\n",
      "Actual: [9.41288747]\n",
      "\n",
      "\n",
      "Index = 67\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350784399552\n",
      "Prediction: [[8.66732423]]\n",
      "Actual: [7.26617794]\n",
      "\n",
      "\n",
      "Index = 68\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700613063179681\n",
      "Prediction: [[15.71288758]]\n",
      "Actual: [13.52704935]\n",
      "\n",
      "\n",
      "Index = 69\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350439501852\n",
      "Prediction: [[5.41148702]]\n",
      "Actual: [4.06923029]\n",
      "\n",
      "\n",
      "Index = 70\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700615946923774\n",
      "Prediction: [[7.80312092]]\n",
      "Actual: [2.80855716]\n",
      "\n",
      "\n",
      "Index = 71\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336537631004\n",
      "Prediction: [[14.72153338]]\n",
      "Actual: [14.2300725]\n",
      "\n",
      "\n",
      "Index = 72\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005975336804608\n",
      "Prediction: [[9.24742594]]\n",
      "Actual: [17.18931067]\n",
      "\n",
      "\n",
      "Index = 73\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005830241039978\n",
      "Prediction: [[20.80606514]]\n",
      "Actual: [27.82366664]\n",
      "\n",
      "\n",
      "Index = 74\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005322114407418\n",
      "Prediction: [[19.3006031]]\n",
      "Actual: [31.66099534]\n",
      "\n",
      "\n",
      "Index = 75\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006250263431864\n",
      "Prediction: [[10.36585014]]\n",
      "Actual: [13.7503726]\n",
      "\n",
      "\n",
      "Index = 76\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006228111404331\n",
      "Prediction: [[19.41507454]]\n",
      "Actual: [17.89002942]\n",
      "\n",
      "\n",
      "Index = 77\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005994411216588\n",
      "Prediction: [[9.16213029]]\n",
      "Actual: [4.15938668]\n",
      "\n",
      "\n",
      "Index = 78\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006351654080434\n",
      "Prediction: [[16.55258821]]\n",
      "Actual: [18.20768955]\n",
      "\n",
      "\n",
      "Index = 79\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006293320809917\n",
      "Prediction: [[14.67947266]]\n",
      "Actual: [16.83996026]\n",
      "\n",
      "\n",
      "Index = 80\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006269815111871\n",
      "Prediction: [[10.70397482]]\n",
      "Actual: [14.36590565]\n",
      "\n",
      "\n",
      "Index = 81\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006113093009215\n",
      "Prediction: [[10.24337749]]\n",
      "Actual: [4.18545948]\n",
      "\n",
      "\n",
      "Index = 82\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006291261149901\n",
      "Prediction: [[11.06510785]]\n",
      "Actual: [8.10800124]\n",
      "\n",
      "\n",
      "Index = 83\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006254755066916\n",
      "Prediction: [[12.11654264]]\n",
      "Actual: [8.73693252]\n",
      "\n",
      "\n",
      "Index = 84\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004502398364099\n",
      "Prediction: [[19.64845205]]\n",
      "Actual: [11.44778671]\n",
      "\n",
      "\n",
      "Index = 85\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006276881833388\n",
      "Prediction: [[14.21647125]]\n",
      "Actual: [17.73641768]\n",
      "\n",
      "\n",
      "Index = 86\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006312899986825\n",
      "Prediction: [[11.56413637]]\n",
      "Actual: [12.95944703]\n",
      "\n",
      "\n",
      "Index = 87\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004689866414658\n",
      "Prediction: [[22.13701014]]\n",
      "Actual: [16.03590694]\n",
      "\n",
      "\n",
      "Index = 88\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006346170526851\n",
      "Prediction: [[13.68256123]]\n",
      "Actual: [14.43743697]\n",
      "\n",
      "\n",
      "Index = 89\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006358338285974\n",
      "Prediction: [[6.27288159]]\n",
      "Actual: [6.42144927]\n",
      "\n",
      "\n",
      "Index = 90\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316366181919\n",
      "Prediction: [[3.23431085]]\n",
      "Actual: [2.76304316]\n",
      "\n",
      "\n",
      "Index = 91\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700632379041572\n",
      "Prediction: [[7.12562791]]\n",
      "Actual: [5.52681719]\n",
      "\n",
      "\n",
      "Index = 92\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006326329591825\n",
      "Prediction: [[15.32384046]]\n",
      "Actual: [13.93879655]\n",
      "\n",
      "\n",
      "Index = 93\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005264513594822\n",
      "Prediction: [[7.61074568]]\n",
      "Actual: [2.89333465]\n",
      "\n",
      "\n",
      "Index = 94\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006340866482615\n",
      "Prediction: [[5.4921124]]\n",
      "Actual: [6.05544435]\n",
      "\n",
      "\n",
      "Index = 95\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006132008345629\n",
      "Prediction: [[2.01943369]]\n",
      "Actual: [0.28256548]\n",
      "\n",
      "\n",
      "Index = 96\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006238002925163\n",
      "Prediction: [[3.00097803]]\n",
      "Actual: [0.805379]\n",
      "\n",
      "\n",
      "Index = 97\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006229349394109\n",
      "Prediction: [[1.92091626]]\n",
      "Actual: [1.65822347]\n",
      "\n",
      "\n",
      "Index = 98\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006135173231568\n",
      "Prediction: [[4.82798748]]\n",
      "Actual: [2.75738781]\n",
      "\n",
      "\n",
      "Index = 99\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006077219946906\n",
      "Prediction: [[3.03462569]]\n",
      "Actual: [0.94373206]\n",
      "\n",
      "\n",
      "Index = 100\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006283859066165\n",
      "Prediction: [[4.93474877]]\n",
      "Actual: [5.02719871]\n",
      "\n",
      "\n",
      "Index = 101\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004786385911834\n",
      "Prediction: [[10.33021581]]\n",
      "Actual: [2.95031057]\n",
      "\n",
      "\n",
      "Index = 102\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338282639595\n",
      "Prediction: [[16.49155152]]\n",
      "Actual: [15.5759531]\n",
      "\n",
      "\n",
      "Index = 103\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005927180998717\n",
      "Prediction: [[22.78572778]]\n",
      "Actual: [18.18518423]\n",
      "\n",
      "\n",
      "Index = 104\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350511535948\n",
      "Prediction: [[10.8612437]]\n",
      "Actual: [11.6212419]\n",
      "\n",
      "\n",
      "Index = 105\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006340321436093\n",
      "Prediction: [[5.36424187]]\n",
      "Actual: [6.65170781]\n",
      "\n",
      "\n",
      "Index = 106\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006330418175901\n",
      "Prediction: [[8.75693051]]\n",
      "Actual: [10.56647968]\n",
      "\n",
      "\n",
      "Index = 107\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006313048772892\n",
      "Prediction: [[6.15659626]]\n",
      "Actual: [8.70972506]\n",
      "\n",
      "\n",
      "Index = 108\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006334137254773\n",
      "Prediction: [[11.99453397]]\n",
      "Actual: [12.46130303]\n",
      "\n",
      "\n",
      "Index = 109\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006263936785624\n",
      "Prediction: [[9.76063112]]\n",
      "Actual: [9.86115006]\n",
      "\n",
      "\n",
      "Index = 110\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006180060769693\n",
      "Prediction: [[6.69640443]]\n",
      "Actual: [5.79382884]\n",
      "\n",
      "\n",
      "Index = 111\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006280699187215\n",
      "Prediction: [[9.20629783]]\n",
      "Actual: [6.17824813]\n",
      "\n",
      "\n",
      "Index = 112\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7003572867069239\n",
      "Prediction: [[15.50255804]]\n",
      "Actual: [28.14673655]\n",
      "\n",
      "\n",
      "Index = 113\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700618901715914\n",
      "Prediction: [[10.12592052]]\n",
      "Actual: [4.00507087]\n",
      "\n",
      "\n",
      "Index = 114\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005966734600739\n",
      "Prediction: [[9.7368051]]\n",
      "Actual: [3.04636516]\n",
      "\n",
      "\n",
      "Index = 115\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7001999151936413\n",
      "Prediction: [[18.17860145]]\n",
      "Actual: [28.40867658]\n",
      "\n",
      "\n",
      "Index = 116\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700378624295241\n",
      "Prediction: [[17.63690677]]\n",
      "Actual: [28.87297226]\n",
      "\n",
      "\n",
      "Index = 117\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7000980202044611\n",
      "Prediction: [[21.49323495]]\n",
      "Actual: [33.57115297]\n",
      "\n",
      "\n",
      "Index = 118\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7000494517672653\n",
      "Prediction: [[21.78017522]]\n",
      "Actual: [36.11052313]\n",
      "\n",
      "\n",
      "Index = 119\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005927205172497\n",
      "Prediction: [[9.07928984]]\n",
      "Actual: [5.97349765]\n",
      "\n",
      "\n",
      "Index = 120\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006000243340624\n",
      "Prediction: [[7.64770057]]\n",
      "Actual: [1.36397574]\n",
      "\n",
      "\n",
      "Index = 121\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700634720431203\n",
      "Prediction: [[10.26751925]]\n",
      "Actual: [7.89841654]\n",
      "\n",
      "\n",
      "Index = 122\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350691695795\n",
      "Prediction: [[8.69240187]]\n",
      "Actual: [9.65584435]\n",
      "\n",
      "\n",
      "Index = 123\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006051083320091\n",
      "Prediction: [[7.42490592]]\n",
      "Actual: [0.9515689]\n",
      "\n",
      "\n",
      "Index = 124\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006324882585138\n",
      "Prediction: [[5.13653436]]\n",
      "Actual: [5.15325633]\n",
      "\n",
      "\n",
      "Index = 125\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006305106417884\n",
      "Prediction: [[11.62427112]]\n",
      "Actual: [8.88938755]\n",
      "\n",
      "\n",
      "Index = 126\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006294806885398\n",
      "Prediction: [[12.12710395]]\n",
      "Actual: [14.81230853]\n",
      "\n",
      "\n",
      "Index = 127\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006278453727393\n",
      "Prediction: [[6.50415481]]\n",
      "Actual: [10.74565242]\n",
      "\n",
      "\n",
      "Index = 128\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006319366098555\n",
      "Prediction: [[13.09359106]]\n",
      "Actual: [13.96372826]\n",
      "\n",
      "\n",
      "Index = 129\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.70063429199023\n",
      "Prediction: [[16.48022039]]\n",
      "Actual: [14.08003223]\n",
      "\n",
      "\n",
      "Index = 130\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006305417101493\n",
      "Prediction: [[14.36016403]]\n",
      "Actual: [16.45778026]\n",
      "\n",
      "\n",
      "Index = 131\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006366403349953\n",
      "Prediction: [[15.08751368]]\n",
      "Actual: [14.06997839]\n",
      "\n",
      "\n",
      "Index = 132\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006286428369148\n",
      "Prediction: [[12.82641453]]\n",
      "Actual: [14.83036652]\n",
      "\n",
      "\n",
      "Index = 133\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006345580555471\n",
      "Prediction: [[15.18586339]]\n",
      "Actual: [14.52131565]\n",
      "\n",
      "\n",
      "Index = 134\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006132912255159\n",
      "Prediction: [[10.19186019]]\n",
      "Actual: [14.16545703]\n",
      "\n",
      "\n",
      "Index = 135\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700628233375592\n",
      "Prediction: [[5.91490563]]\n",
      "Actual: [8.45697297]\n",
      "\n",
      "\n",
      "Index = 136\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006328857984209\n",
      "Prediction: [[9.10773169]]\n",
      "Actual: [8.62373655]\n",
      "\n",
      "\n",
      "Index = 137\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006252265911755\n",
      "Prediction: [[9.59514216]]\n",
      "Actual: [12.69577637]\n",
      "\n",
      "\n",
      "Index = 138\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006333618684383\n",
      "Prediction: [[11.6042136]]\n",
      "Actual: [11.79276648]\n",
      "\n",
      "\n",
      "Index = 139\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006349255784009\n",
      "Prediction: [[7.19487392]]\n",
      "Actual: [5.75870272]\n",
      "\n",
      "\n",
      "Index = 140\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006301125402192\n",
      "Prediction: [[13.1853247]]\n",
      "Actual: [14.55124714]\n",
      "\n",
      "\n",
      "Index = 141\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006010073718274\n",
      "Prediction: [[14.17270103]]\n",
      "Actual: [9.62595616]\n",
      "\n",
      "\n",
      "Index = 142\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006333773963047\n",
      "Prediction: [[5.71518754]]\n",
      "Actual: [5.36382948]\n",
      "\n",
      "\n",
      "Index = 143\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006059663129911\n",
      "Prediction: [[6.14860672]]\n",
      "Actual: [1.158333]\n",
      "\n",
      "\n",
      "Index = 144\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006339817865275\n",
      "Prediction: [[5.78596863]]\n",
      "Actual: [5.4099207]\n",
      "\n",
      "\n",
      "Index = 145\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316666052862\n",
      "Prediction: [[10.09209145]]\n",
      "Actual: [8.60742453]\n",
      "\n",
      "\n",
      "Index = 146\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006330261791516\n",
      "Prediction: [[5.08940581]]\n",
      "Actual: [3.22747383]\n",
      "\n",
      "\n",
      "Index = 147\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006323182983281\n",
      "Prediction: [[10.20304377]]\n",
      "Actual: [11.5089576]\n",
      "\n",
      "\n",
      "Index = 148\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006201919848829\n",
      "Prediction: [[10.88003392]]\n",
      "Actual: [15.08619085]\n",
      "\n",
      "\n",
      "Index = 149\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006294455484836\n",
      "Prediction: [[14.25332042]]\n",
      "Actual: [16.7109639]\n",
      "\n",
      "\n",
      "Index = 150\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006003451026592\n",
      "Prediction: [[10.62674057]]\n",
      "Actual: [15.99395171]\n",
      "\n",
      "\n",
      "Index = 151\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005769563371738\n",
      "Prediction: [[13.49465163]]\n",
      "Actual: [20.34805897]\n",
      "\n",
      "\n",
      "Index = 152\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005873063571876\n",
      "Prediction: [[12.26951567]]\n",
      "Actual: [16.59760687]\n",
      "\n",
      "\n",
      "Index = 153\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316104563992\n",
      "Prediction: [[4.75201034]]\n",
      "Actual: [2.175263]\n",
      "\n",
      "\n",
      "Index = 154\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350485283439\n",
      "Prediction: [[11.43812859]]\n",
      "Actual: [10.31244123]\n",
      "\n",
      "\n",
      "Index = 155\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006227451274096\n",
      "Prediction: [[10.45770823]]\n",
      "Actual: [14.03479218]\n",
      "\n",
      "\n",
      "Index = 156\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006364724825036\n",
      "Prediction: [[9.39888204]]\n",
      "Actual: [7.59753814]\n",
      "\n",
      "\n",
      "Index = 157\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006314888275196\n",
      "Prediction: [[13.21185001]]\n",
      "Actual: [14.53021038]\n",
      "\n",
      "\n",
      "Index = 158\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006292990485925\n",
      "Prediction: [[7.98795335]]\n",
      "Actual: [10.68887584]\n",
      "\n",
      "\n",
      "Index = 159\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006359920587584\n",
      "Prediction: [[5.96272044]]\n",
      "Actual: [7.83562953]\n",
      "\n",
      "\n",
      "Index = 160\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338877012019\n",
      "Prediction: [[6.97163428]]\n",
      "Actual: [7.90761762]\n",
      "\n",
      "\n",
      "Index = 161\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006351282667287\n",
      "Prediction: [[4.17742962]]\n",
      "Actual: [3.02444116]\n",
      "\n",
      "\n",
      "Index = 162\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006326972932451\n",
      "Prediction: [[9.40084471]]\n",
      "Actual: [9.81716874]\n",
      "\n",
      "\n",
      "Index = 163\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006363107248551\n",
      "Prediction: [[3.4272073]]\n",
      "Actual: [3.30180539]\n",
      "\n",
      "\n",
      "Index = 164\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006335667456598\n",
      "Prediction: [[6.66601723]]\n",
      "Actual: [1.22137765]\n",
      "\n",
      "\n",
      "Index = 165\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700630431883059\n",
      "Prediction: [[7.87050018]]\n",
      "Actual: [12.691894]\n",
      "\n",
      "\n",
      "Index = 166\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006346093768572\n",
      "Prediction: [[4.84804396]]\n",
      "Actual: [4.95420048]\n",
      "\n",
      "\n",
      "Index = 167\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006265215990986\n",
      "Prediction: [[8.97898517]]\n",
      "Actual: [4.28267003]\n",
      "\n",
      "\n",
      "Index = 168\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006287078439621\n",
      "Prediction: [[6.96659189]]\n",
      "Actual: [3.90721003]\n",
      "\n",
      "\n",
      "Index = 169\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006266705537496\n",
      "Prediction: [[6.71577626]]\n",
      "Actual: [6.38591633]\n",
      "\n",
      "\n",
      "Index = 170\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700630960444305\n",
      "Prediction: [[11.1124429]]\n",
      "Actual: [9.11745932]\n",
      "\n",
      "\n",
      "Index = 171\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006294075017077\n",
      "Prediction: [[11.19675413]]\n",
      "Actual: [13.93567387]\n",
      "\n",
      "\n",
      "Index = 172\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006366387707461\n",
      "Prediction: [[5.07320666]]\n",
      "Actual: [4.35058648]\n",
      "\n",
      "\n",
      "Index = 173\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006325045710153\n",
      "Prediction: [[10.57053375]]\n",
      "Actual: [8.94389045]\n",
      "\n",
      "\n",
      "Index = 174\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006162559661853\n",
      "Prediction: [[6.55130195]]\n",
      "Actual: [11.77228806]\n",
      "\n",
      "\n",
      "Index = 175\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006306882259947\n",
      "Prediction: [[11.89244235]]\n",
      "Actual: [14.00560364]\n",
      "\n",
      "\n",
      "Index = 176\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338915942953\n",
      "Prediction: [[4.34553254]]\n",
      "Actual: [2.36943435]\n",
      "\n",
      "\n",
      "Index = 177\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700636123733184\n",
      "Prediction: [[5.31006866]]\n",
      "Actual: [5.05598581]\n",
      "\n",
      "\n",
      "Index = 178\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006331055485071\n",
      "Prediction: [[5.93009692]]\n",
      "Actual: [7.59911417]\n",
      "\n",
      "\n",
      "Index = 179\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006355510510488\n",
      "Prediction: [[4.98542302]]\n",
      "Actual: [3.41848142]\n",
      "\n",
      "\n",
      "Index = 180\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006284075756357\n",
      "Prediction: [[13.63496489]]\n",
      "Actual: [11.43617677]\n",
      "\n",
      "\n",
      "Index = 181\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006268940413789\n",
      "Prediction: [[7.42060181]]\n",
      "Actual: [11.63422896]\n",
      "\n",
      "\n",
      "Index = 182\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006360901534099\n",
      "Prediction: [[4.81584576]]\n",
      "Actual: [3.80541677]\n",
      "\n",
      "\n",
      "Index = 183\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006290591463791\n",
      "Prediction: [[12.39123425]]\n",
      "Actual: [10.13281755]\n",
      "\n",
      "\n",
      "Index = 184\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006369801700194\n",
      "Prediction: [[4.89689674]]\n",
      "Actual: [5.1131609]\n",
      "\n",
      "\n",
      "Index = 185\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006295484082683\n",
      "Prediction: [[13.52376203]]\n",
      "Actual: [15.6288867]\n",
      "\n",
      "\n",
      "Index = 186\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006299517724395\n",
      "Prediction: [[10.50651179]]\n",
      "Actual: [12.35132074]\n",
      "\n",
      "\n",
      "Index = 187\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005861300842504\n",
      "Prediction: [[10.82614717]]\n",
      "Actual: [18.40087829]\n",
      "\n",
      "\n",
      "Index = 188\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336429811229\n",
      "Prediction: [[7.80437128]]\n",
      "Actual: [9.23667074]\n",
      "\n",
      "\n",
      "Index = 189\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005786225365086\n",
      "Prediction: [[20.06283011]]\n",
      "Actual: [14.96436746]\n",
      "\n",
      "\n",
      "Index = 190\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006223468678681\n",
      "Prediction: [[8.22038519]]\n",
      "Actual: [6.20079542]\n",
      "\n",
      "\n",
      "Index = 191\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700634344164574\n",
      "Prediction: [[4.60635279]]\n",
      "Actual: [6.83135384]\n",
      "\n",
      "\n",
      "Index = 192\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316260668857\n",
      "Prediction: [[10.44406532]]\n",
      "Actual: [12.52288483]\n",
      "\n",
      "\n",
      "Index = 193\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006273869523612\n",
      "Prediction: [[3.55673081]]\n",
      "Actual: [3.24737861]\n",
      "\n",
      "\n",
      "Index = 194\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006337980335203\n",
      "Prediction: [[10.79753133]]\n",
      "Actual: [10.8902778]\n",
      "\n",
      "\n",
      "Index = 195\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006301845824598\n",
      "Prediction: [[7.43156034]]\n",
      "Actual: [5.97772655]\n",
      "\n",
      "\n",
      "Index = 196\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7001806798339876\n",
      "Prediction: [[24.51520539]]\n",
      "Actual: [14.38911823]\n",
      "\n",
      "\n",
      "Index = 197\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006374292359089\n",
      "Prediction: [[16.15861114]]\n",
      "Actual: [15.34240024]\n",
      "\n",
      "\n",
      "Index = 198\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006339438524867\n",
      "Prediction: [[16.48357174]]\n",
      "Actual: [17.15777323]\n",
      "\n",
      "\n",
      "Index = 199\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700633784647191\n",
      "Prediction: [[12.7788475]]\n",
      "Actual: [12.32219826]\n",
      "\n",
      "\n",
      "Index = 200\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006324122801845\n",
      "Prediction: [[7.0237388]]\n",
      "Actual: [8.9124791]\n",
      "\n",
      "\n",
      "Index = 201\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700634127072855\n",
      "Prediction: [[4.11027996]]\n",
      "Actual: [1.40524945]\n",
      "\n",
      "\n",
      "Index = 202\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006362970640251\n",
      "Prediction: [[7.40651943]]\n",
      "Actual: [7.07322004]\n",
      "\n",
      "\n",
      "Index = 203\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006335499706329\n",
      "Prediction: [[14.00261814]]\n",
      "Actual: [15.863895]\n",
      "\n",
      "\n",
      "Index = 204\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316416952154\n",
      "Prediction: [[11.67757013]]\n",
      "Actual: [14.87721671]\n",
      "\n",
      "\n",
      "Index = 205\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006328795828856\n",
      "Prediction: [[10.5070225]]\n",
      "Actual: [11.13934781]\n",
      "\n",
      "\n",
      "Index = 206\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336048292172\n",
      "Prediction: [[14.8678511]]\n",
      "Actual: [16.21366058]\n",
      "\n",
      "\n",
      "Index = 207\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006317269256919\n",
      "Prediction: [[14.44835506]]\n",
      "Actual: [14.54686681]\n",
      "\n",
      "\n",
      "Index = 208\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006302874000117\n",
      "Prediction: [[14.64408899]]\n",
      "Actual: [13.81689265]\n",
      "\n",
      "\n",
      "Index = 209\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006137737818933\n",
      "Prediction: [[4.86588943]]\n",
      "Actual: [8.59100435]\n",
      "\n",
      "\n",
      "Index = 210\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352740285546\n",
      "Prediction: [[9.25948873]]\n",
      "Actual: [9.78865174]\n",
      "\n",
      "\n",
      "Index = 211\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006324369387167\n",
      "Prediction: [[8.49467098]]\n",
      "Actual: [6.15811335]\n",
      "\n",
      "\n",
      "Index = 212\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006382980281237\n",
      "Prediction: [[11.52991458]]\n",
      "Actual: [9.38875858]\n",
      "\n",
      "\n",
      "Index = 213\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006247459906221\n",
      "Prediction: [[18.11837679]]\n",
      "Actual: [15.9988039]\n",
      "\n",
      "\n",
      "Index = 214\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006322771002841\n",
      "Prediction: [[7.08329539]]\n",
      "Actual: [7.16984887]\n",
      "\n",
      "\n",
      "Index = 215\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006364837244559\n",
      "Prediction: [[6.68969502]]\n",
      "Actual: [5.54949179]\n",
      "\n",
      "\n",
      "Index = 216\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005884481396288\n",
      "Prediction: [[18.00020128]]\n",
      "Actual: [13.25298935]\n",
      "\n",
      "\n",
      "Index = 217\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006394678864005\n",
      "Prediction: [[9.72392021]]\n",
      "Actual: [11.41109679]\n",
      "\n",
      "\n",
      "Index = 218\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006370971543545\n",
      "Prediction: [[4.11647565]]\n",
      "Actual: [2.81147748]\n",
      "\n",
      "\n",
      "Index = 219\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006293885799824\n",
      "Prediction: [[12.12253609]]\n",
      "Actual: [10.20379103]\n",
      "\n",
      "\n",
      "Index = 220\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006124149749339\n",
      "Prediction: [[15.79953099]]\n",
      "Actual: [11.40021681]\n",
      "\n",
      "\n",
      "Index = 221\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006366180853354\n",
      "Prediction: [[12.74811455]]\n",
      "Actual: [12.52133645]\n",
      "\n",
      "\n",
      "Index = 222\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006193471349529\n",
      "Prediction: [[2.32415928]]\n",
      "Actual: [0.72446239]\n",
      "\n",
      "\n",
      "Index = 223\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006286928832104\n",
      "Prediction: [[4.6174887]]\n",
      "Actual: [3.28827639]\n",
      "\n",
      "\n",
      "Index = 224\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006257967055382\n",
      "Prediction: [[1.72997867]]\n",
      "Actual: [1.00390974]\n",
      "\n",
      "\n",
      "Index = 225\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006276101384972\n",
      "Prediction: [[2.79345593]]\n",
      "Actual: [1.0147876]\n",
      "\n",
      "\n",
      "Index = 226\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006335075335886\n",
      "Prediction: [[1.05678345]]\n",
      "Actual: [1.82644537]\n",
      "\n",
      "\n",
      "Index = 227\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006171347476671\n",
      "Prediction: [[3.07466809]]\n",
      "Actual: [0.94393626]\n",
      "\n",
      "\n",
      "Index = 228\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006351820342791\n",
      "Prediction: [[15.34067818]]\n",
      "Actual: [13.77849655]\n",
      "\n",
      "\n",
      "Index = 229\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005740593537224\n",
      "Prediction: [[14.40342461]]\n",
      "Actual: [21.57952797]\n",
      "\n",
      "\n",
      "Index = 230\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006245642619919\n",
      "Prediction: [[19.63949318]]\n",
      "Actual: [21.21233161]\n",
      "\n",
      "\n",
      "Index = 231\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005443999270542\n",
      "Prediction: [[18.84418579]]\n",
      "Actual: [28.06796277]\n",
      "\n",
      "\n",
      "Index = 232\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006052518973945\n",
      "Prediction: [[20.82531861]]\n",
      "Actual: [24.37257735]\n",
      "\n",
      "\n",
      "Index = 233\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005959811736364\n",
      "Prediction: [[17.29976847]]\n",
      "Actual: [20.98518261]\n",
      "\n",
      "\n",
      "Index = 234\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700636792740468\n",
      "Prediction: [[5.12023519]]\n",
      "Actual: [4.23873939]\n",
      "\n",
      "\n",
      "Index = 235\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006472826824708\n",
      "Prediction: [[20.09675995]]\n",
      "Actual: [20.44532819]\n",
      "\n",
      "\n",
      "Index = 236\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006222627217062\n",
      "Prediction: [[13.58008036]]\n",
      "Actual: [16.17822577]\n",
      "\n",
      "\n",
      "Index = 237\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006372871835791\n",
      "Prediction: [[11.61284935]]\n",
      "Actual: [8.1050691]\n",
      "\n",
      "\n",
      "Index = 238\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005737527109419\n",
      "Prediction: [[14.08334885]]\n",
      "Actual: [20.61741552]\n",
      "\n",
      "\n",
      "Index = 239\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005940018052212\n",
      "Prediction: [[14.26965066]]\n",
      "Actual: [19.68386374]\n",
      "\n",
      "\n",
      "Index = 240\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336302569645\n",
      "Prediction: [[21.29846541]]\n",
      "Actual: [21.13862197]\n",
      "\n",
      "\n",
      "Index = 241\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006159300718713\n",
      "Prediction: [[9.06658135]]\n",
      "Actual: [13.58743838]\n",
      "\n",
      "\n",
      "Index = 242\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006261132409164\n",
      "Prediction: [[5.72225483]]\n",
      "Actual: [4.89452329]\n",
      "\n",
      "\n",
      "Index = 243\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006314601159198\n",
      "Prediction: [[22.27509618]]\n",
      "Actual: [21.53400532]\n",
      "\n",
      "\n",
      "Index = 244\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006287792173947\n",
      "Prediction: [[10.32117438]]\n",
      "Actual: [11.982911]\n",
      "\n",
      "\n",
      "Index = 245\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005707739380911\n",
      "Prediction: [[10.764575]]\n",
      "Actual: [16.29279519]\n",
      "\n",
      "\n",
      "Index = 246\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005977607207565\n",
      "Prediction: [[9.42842805]]\n",
      "Actual: [13.35995677]\n",
      "\n",
      "\n",
      "Index = 247\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006304549283502\n",
      "Prediction: [[9.47557151]]\n",
      "Actual: [10.47508313]\n",
      "\n",
      "\n",
      "Index = 248\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006313399709916\n",
      "Prediction: [[6.56765572]]\n",
      "Actual: [7.21501126]\n",
      "\n",
      "\n",
      "Index = 249\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006347300924314\n",
      "Prediction: [[6.08293334]]\n",
      "Actual: [6.35434997]\n",
      "\n",
      "\n",
      "Index = 250\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350052429677\n",
      "Prediction: [[7.46142657]]\n",
      "Actual: [7.47763932]\n",
      "\n",
      "\n",
      "Index = 251\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006093796538804\n",
      "Prediction: [[7.89871694]]\n",
      "Actual: [13.47428232]\n",
      "\n",
      "\n",
      "Index = 252\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006356194589866\n",
      "Prediction: [[9.72690924]]\n",
      "Actual: [7.88233139]\n",
      "\n",
      "\n",
      "Index = 253\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006032817797637\n",
      "Prediction: [[4.34616468]]\n",
      "Actual: [0.95259465]\n",
      "\n",
      "\n",
      "Index = 254\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006346406919908\n",
      "Prediction: [[6.12074464]]\n",
      "Actual: [5.75612335]\n",
      "\n",
      "\n",
      "Index = 255\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7003807790132306\n",
      "Prediction: [[27.28868053]]\n",
      "Actual: [20.78955279]\n",
      "\n",
      "\n",
      "Index = 256\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7001228222747686\n",
      "Prediction: [[24.82787615]]\n",
      "Actual: [16.48833952]\n",
      "\n",
      "\n",
      "Index = 257\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006343973945112\n",
      "Prediction: [[10.03354144]]\n",
      "Actual: [11.58768826]\n",
      "\n",
      "\n",
      "Index = 258\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006329293822651\n",
      "Prediction: [[8.37185112]]\n",
      "Actual: [9.95670252]\n",
      "\n",
      "\n",
      "Index = 259\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700632097882779\n",
      "Prediction: [[10.13799665]]\n",
      "Actual: [10.40696626]\n",
      "\n",
      "\n",
      "Index = 260\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006237099593107\n",
      "Prediction: [[21.55745752]]\n",
      "Actual: [18.36517406]\n",
      "\n",
      "\n",
      "Index = 261\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006327009417884\n",
      "Prediction: [[22.10598288]]\n",
      "Actual: [19.89354877]\n",
      "\n",
      "\n",
      "Index = 262\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006330173337211\n",
      "Prediction: [[7.55199634]]\n",
      "Actual: [4.68198371]\n",
      "\n",
      "\n",
      "Index = 263\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006281821782842\n",
      "Prediction: [[5.90241243]]\n",
      "Actual: [8.28327578]\n",
      "\n",
      "\n",
      "Index = 264\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006275092834979\n",
      "Prediction: [[8.36539787]]\n",
      "Actual: [6.758409]\n",
      "\n",
      "\n",
      "Index = 265\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006315019462028\n",
      "Prediction: [[13.67911956]]\n",
      "Actual: [12.21783974]\n",
      "\n",
      "\n",
      "Index = 266\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006325784887925\n",
      "Prediction: [[6.83809442]]\n",
      "Actual: [6.81077883]\n",
      "\n",
      "\n",
      "Index = 267\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006316169837568\n",
      "Prediction: [[9.78731609]]\n",
      "Actual: [10.73143126]\n",
      "\n",
      "\n",
      "Index = 268\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700629174387245\n",
      "Prediction: [[1.61221714]]\n",
      "Actual: [2.44134461]\n",
      "\n",
      "\n",
      "Index = 269\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005719553380708\n",
      "Prediction: [[8.36506989]]\n",
      "Actual: [5.78999616]\n",
      "\n",
      "\n",
      "Index = 270\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006268643885091\n",
      "Prediction: [[6.32176767]]\n",
      "Actual: [6.08844081]\n",
      "\n",
      "\n",
      "Index = 271\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700631615568331\n",
      "Prediction: [[2.09077291]]\n",
      "Actual: [2.0340281]\n",
      "\n",
      "\n",
      "Index = 272\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700629563282642\n",
      "Prediction: [[2.10589523]]\n",
      "Actual: [2.26712437]\n",
      "\n",
      "\n",
      "Index = 273\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700628644521837\n",
      "Prediction: [[2.62918582]]\n",
      "Actual: [2.45591384]\n",
      "\n",
      "\n",
      "Index = 274\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006250341443132\n",
      "Prediction: [[1.7906962]]\n",
      "Actual: [1.77962048]\n",
      "\n",
      "\n",
      "Index = 275\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006262452200316\n",
      "Prediction: [[1.90795787]]\n",
      "Actual: [2.08953535]\n",
      "\n",
      "\n",
      "Index = 276\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006302777834765\n",
      "Prediction: [[0.97388625]]\n",
      "Actual: [2.50347155]\n",
      "\n",
      "\n",
      "Index = 277\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006313806843009\n",
      "Prediction: [[9.66269058]]\n",
      "Actual: [10.02375365]\n",
      "\n",
      "\n",
      "Index = 278\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005558667136316\n",
      "Prediction: [[14.41356672]]\n",
      "Actual: [10.27000226]\n",
      "\n",
      "\n",
      "Index = 279\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006095519812354\n",
      "Prediction: [[12.34975234]]\n",
      "Actual: [8.46860771]\n",
      "\n",
      "\n",
      "Index = 280\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006222227163259\n",
      "Prediction: [[12.32885918]]\n",
      "Actual: [9.59746603]\n",
      "\n",
      "\n",
      "Index = 281\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006329088135816\n",
      "Prediction: [[8.97596169]]\n",
      "Actual: [10.29994903]\n",
      "\n",
      "\n",
      "Index = 282\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006020609769229\n",
      "Prediction: [[13.24821971]]\n",
      "Actual: [10.04896316]\n",
      "\n",
      "\n",
      "Index = 283\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006303955770754\n",
      "Prediction: [[12.90396145]]\n",
      "Actual: [15.36515148]\n",
      "\n",
      "\n",
      "Index = 284\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006378758840597\n",
      "Prediction: [[12.36767023]]\n",
      "Actual: [10.36880513]\n",
      "\n",
      "\n",
      "Index = 285\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006306238043429\n",
      "Prediction: [[6.39542513]]\n",
      "Actual: [7.94551285]\n",
      "\n",
      "\n",
      "Index = 286\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700630693257607\n",
      "Prediction: [[7.93333456]]\n",
      "Actual: [6.58911074]\n",
      "\n",
      "\n",
      "Index = 287\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006322845254787\n",
      "Prediction: [[5.94336755]]\n",
      "Actual: [9.4183179]\n",
      "\n",
      "\n",
      "Index = 288\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006292533794951\n",
      "Prediction: [[6.46529669]]\n",
      "Actual: [9.20150904]\n",
      "\n",
      "\n",
      "Index = 289\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352486252748\n",
      "Prediction: [[9.36903809]]\n",
      "Actual: [10.49799568]\n",
      "\n",
      "\n",
      "Index = 290\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006163156947278\n",
      "Prediction: [[7.29486815]]\n",
      "Actual: [1.43400629]\n",
      "\n",
      "\n",
      "Index = 291\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006358999051596\n",
      "Prediction: [[5.54044322]]\n",
      "Actual: [7.118455]\n",
      "\n",
      "\n",
      "Index = 292\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338670873543\n",
      "Prediction: [[12.23564601]]\n",
      "Actual: [10.51181264]\n",
      "\n",
      "\n",
      "Index = 293\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350524145719\n",
      "Prediction: [[7.71494358]]\n",
      "Actual: [8.44752631]\n",
      "\n",
      "\n",
      "Index = 294\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350379040498\n",
      "Prediction: [[7.20725965]]\n",
      "Actual: [5.72510516]\n",
      "\n",
      "\n",
      "Index = 295\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006319384728636\n",
      "Prediction: [[12.22155865]]\n",
      "Actual: [10.14214274]\n",
      "\n",
      "\n",
      "Index = 296\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006350165098216\n",
      "Prediction: [[7.51793192]]\n",
      "Actual: [5.43065312]\n",
      "\n",
      "\n",
      "Index = 297\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006355219595868\n",
      "Prediction: [[12.27402168]]\n",
      "Actual: [10.07063626]\n",
      "\n",
      "\n",
      "Index = 298\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006339071060177\n",
      "Prediction: [[6.09850779]]\n",
      "Actual: [5.64596168]\n",
      "\n",
      "\n",
      "Index = 299\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006291515598144\n",
      "Prediction: [[6.00449949]]\n",
      "Actual: [3.50057374]\n",
      "\n",
      "\n",
      "Index = 300\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006298474269822\n",
      "Prediction: [[12.6209608]]\n",
      "Actual: [9.51656519]\n",
      "\n",
      "\n",
      "Index = 301\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006360313578361\n",
      "Prediction: [[8.85273457]]\n",
      "Actual: [8.75712141]\n",
      "\n",
      "\n",
      "Index = 302\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700632388653787\n",
      "Prediction: [[13.3182831]]\n",
      "Actual: [13.99278713]\n",
      "\n",
      "\n",
      "Index = 303\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006102264627008\n",
      "Prediction: [[14.92992944]]\n",
      "Actual: [18.30346026]\n",
      "\n",
      "\n",
      "Index = 304\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006243826107239\n",
      "Prediction: [[4.00143471]]\n",
      "Actual: [1.47678442]\n",
      "\n",
      "\n",
      "Index = 305\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006357902232179\n",
      "Prediction: [[6.98847798]]\n",
      "Actual: [8.23680287]\n",
      "\n",
      "\n",
      "Index = 306\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006051590795256\n",
      "Prediction: [[8.05443416]]\n",
      "Actual: [4.12505283]\n",
      "\n",
      "\n",
      "Index = 307\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006245127692208\n",
      "Prediction: [[11.18862052]]\n",
      "Actual: [13.90407225]\n",
      "\n",
      "\n",
      "Index = 308\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005824218463945\n",
      "Prediction: [[15.17966961]]\n",
      "Actual: [20.00000342]\n",
      "\n",
      "\n",
      "Index = 309\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006357496150921\n",
      "Prediction: [[8.40017848]]\n",
      "Actual: [9.49803455]\n",
      "\n",
      "\n",
      "Index = 310\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006288663468958\n",
      "Prediction: [[6.22092412]]\n",
      "Actual: [3.833333]\n",
      "\n",
      "\n",
      "Index = 311\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006302020379815\n",
      "Prediction: [[4.2188299]]\n",
      "Actual: [1.70221526]\n",
      "\n",
      "\n",
      "Index = 312\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006339586750522\n",
      "Prediction: [[7.15896683]]\n",
      "Actual: [9.2463451]\n",
      "\n",
      "\n",
      "Index = 313\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006305968595551\n",
      "Prediction: [[5.37822675]]\n",
      "Actual: [5.2498015]\n",
      "\n",
      "\n",
      "Index = 314\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006301839156177\n",
      "Prediction: [[2.21929157]]\n",
      "Actual: [0.82649532]\n",
      "\n",
      "\n",
      "Index = 315\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006217646280344\n",
      "Prediction: [[7.82905613]]\n",
      "Actual: [5.70369016]\n",
      "\n",
      "\n",
      "Index = 316\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.70063436421095\n",
      "Prediction: [[4.94096181]]\n",
      "Actual: [8.02977439]\n",
      "\n",
      "\n",
      "Index = 317\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700633376440279\n",
      "Prediction: [[3.08472332]]\n",
      "Actual: [1.85453152]\n",
      "\n",
      "\n",
      "Index = 318\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700624505241124\n",
      "Prediction: [[2.99481278]]\n",
      "Actual: [0.76274194]\n",
      "\n",
      "\n",
      "Index = 319\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006288465984853\n",
      "Prediction: [[9.36636277]]\n",
      "Actual: [6.47331693]\n",
      "\n",
      "\n",
      "Index = 320\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006314819778233\n",
      "Prediction: [[12.93455782]]\n",
      "Actual: [13.64616983]\n",
      "\n",
      "\n",
      "Index = 321\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006313458392245\n",
      "Prediction: [[9.79387986]]\n",
      "Actual: [11.72259862]\n",
      "\n",
      "\n",
      "Index = 322\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006364461328847\n",
      "Prediction: [[4.66754919]]\n",
      "Actual: [4.94728997]\n",
      "\n",
      "\n",
      "Index = 323\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006340931768286\n",
      "Prediction: [[6.70127444]]\n",
      "Actual: [8.55246377]\n",
      "\n",
      "\n",
      "Index = 324\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006360015218234\n",
      "Prediction: [[6.78200372]]\n",
      "Actual: [6.20679732]\n",
      "\n",
      "\n",
      "Index = 325\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006191854404498\n",
      "Prediction: [[13.61383155]]\n",
      "Actual: [10.12786117]\n",
      "\n",
      "\n",
      "Index = 326\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006344044340582\n",
      "Prediction: [[10.15635319]]\n",
      "Actual: [7.70043945]\n",
      "\n",
      "\n",
      "Index = 327\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006336472410373\n",
      "Prediction: [[6.09220245]]\n",
      "Actual: [3.56885629]\n",
      "\n",
      "\n",
      "Index = 328\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700623835026933\n",
      "Prediction: [[14.84844895]]\n",
      "Actual: [12.16726583]\n",
      "\n",
      "\n",
      "Index = 329\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006385527547931\n",
      "Prediction: [[12.20507014]]\n",
      "Actual: [10.80984097]\n",
      "\n",
      "\n",
      "Index = 330\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006272505168333\n",
      "Prediction: [[7.5393629]]\n",
      "Actual: [9.33684997]\n",
      "\n",
      "\n",
      "Index = 331\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.70063258880669\n",
      "Prediction: [[14.83273608]]\n",
      "Actual: [13.07676923]\n",
      "\n",
      "\n",
      "Index = 332\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006342173994122\n",
      "Prediction: [[5.57571949]]\n",
      "Actual: [8.54285087]\n",
      "\n",
      "\n",
      "Index = 333\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700631965520452\n",
      "Prediction: [[7.26527964]]\n",
      "Actual: [6.22284457]\n",
      "\n",
      "\n",
      "Index = 334\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352559676953\n",
      "Prediction: [[5.85498228]]\n",
      "Actual: [4.8819931]\n",
      "\n",
      "\n",
      "Index = 335\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006169552418691\n",
      "Prediction: [[17.81286493]]\n",
      "Actual: [13.7144065]\n",
      "\n",
      "\n",
      "Index = 336\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006299125345206\n",
      "Prediction: [[18.71401356]]\n",
      "Actual: [19.33137148]\n",
      "\n",
      "\n",
      "Index = 337\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006242568792418\n",
      "Prediction: [[17.0184263]]\n",
      "Actual: [13.80740124]\n",
      "\n",
      "\n",
      "Index = 338\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006347920247646\n",
      "Prediction: [[6.39407303]]\n",
      "Actual: [4.29576945]\n",
      "\n",
      "\n",
      "Index = 339\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006354066518679\n",
      "Prediction: [[4.50425154]]\n",
      "Actual: [3.99676435]\n",
      "\n",
      "\n",
      "Index = 340\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006325987397868\n",
      "Prediction: [[9.74867138]]\n",
      "Actual: [11.6793361]\n",
      "\n",
      "\n",
      "Index = 341\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006212574460919\n",
      "Prediction: [[8.42824867]]\n",
      "Actual: [13.07027337]\n",
      "\n",
      "\n",
      "Index = 342\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006357414380938\n",
      "Prediction: [[7.70089084]]\n",
      "Actual: [7.59155442]\n",
      "\n",
      "\n",
      "Index = 343\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006332609957606\n",
      "Prediction: [[15.50470852]]\n",
      "Actual: [14.98163932]\n",
      "\n",
      "\n",
      "Index = 344\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006354255191842\n",
      "Prediction: [[10.32780705]]\n",
      "Actual: [9.52535923]\n",
      "\n",
      "\n",
      "Index = 345\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006317437330958\n",
      "Prediction: [[10.14019523]]\n",
      "Actual: [12.09966439]\n",
      "\n",
      "\n",
      "Index = 346\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006217721448267\n",
      "Prediction: [[8.42733325]]\n",
      "Actual: [13.38457477]\n",
      "\n",
      "\n",
      "Index = 347\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700627427453022\n",
      "Prediction: [[13.09276081]]\n",
      "Actual: [16.06008635]\n",
      "\n",
      "\n",
      "Index = 348\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006335106575006\n",
      "Prediction: [[12.08999369]]\n",
      "Actual: [13.24504094]\n",
      "\n",
      "\n",
      "Index = 349\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006351047747408\n",
      "Prediction: [[9.55634766]]\n",
      "Actual: [10.35003797]\n",
      "\n",
      "\n",
      "Index = 350\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006349182602898\n",
      "Prediction: [[7.90818834]]\n",
      "Actual: [5.8190019]\n",
      "\n",
      "\n",
      "Index = 351\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006210887939022\n",
      "Prediction: [[13.86152532]]\n",
      "Actual: [18.31556155]\n",
      "\n",
      "\n",
      "Index = 352\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006339520156419\n",
      "Prediction: [[18.44813038]]\n",
      "Actual: [18.37219133]\n",
      "\n",
      "\n",
      "Index = 353\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006340334160324\n",
      "Prediction: [[4.47351436]]\n",
      "Actual: [3.58525542]\n",
      "\n",
      "\n",
      "Index = 354\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352841292469\n",
      "Prediction: [[5.39542929]]\n",
      "Actual: [4.75747732]\n",
      "\n",
      "\n",
      "Index = 355\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006308451361003\n",
      "Prediction: [[11.44827597]]\n",
      "Actual: [8.41431542]\n",
      "\n",
      "\n",
      "Index = 356\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006354613603438\n",
      "Prediction: [[6.26338658]]\n",
      "Actual: [4.02828168]\n",
      "\n",
      "\n",
      "Index = 357\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006358771238732\n",
      "Prediction: [[7.67075019]]\n",
      "Actual: [6.22142716]\n",
      "\n",
      "\n",
      "Index = 358\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006332862503457\n",
      "Prediction: [[5.47858869]]\n",
      "Actual: [4.66399086]\n",
      "\n",
      "\n",
      "Index = 359\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700628390772019\n",
      "Prediction: [[6.54507512]]\n",
      "Actual: [3.34623406]\n",
      "\n",
      "\n",
      "Index = 360\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006323516553565\n",
      "Prediction: [[4.96914774]]\n",
      "Actual: [4.43777932]\n",
      "\n",
      "\n",
      "Index = 361\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006321864775191\n",
      "Prediction: [[7.60943373]]\n",
      "Actual: [3.7936501]\n",
      "\n",
      "\n",
      "Index = 362\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006211899492341\n",
      "Prediction: [[6.67093908]]\n",
      "Actual: [2.63593132]\n",
      "\n",
      "\n",
      "Index = 363\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006343844676128\n",
      "Prediction: [[7.36343511]]\n",
      "Actual: [10.4509001]\n",
      "\n",
      "\n",
      "Index = 364\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700633356307027\n",
      "Prediction: [[10.93007168]]\n",
      "Actual: [12.10397623]\n",
      "\n",
      "\n",
      "Index = 365\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006346728692607\n",
      "Prediction: [[7.11642229]]\n",
      "Actual: [9.74257726]\n",
      "\n",
      "\n",
      "Index = 366\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006358926909009\n",
      "Prediction: [[7.51907355]]\n",
      "Actual: [8.27219227]\n",
      "\n",
      "\n",
      "Index = 367\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006270179582548\n",
      "Prediction: [[7.80626618]]\n",
      "Actual: [7.09339797]\n",
      "\n",
      "\n",
      "Index = 368\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006266158205328\n",
      "Prediction: [[11.77226729]]\n",
      "Actual: [14.40959242]\n",
      "\n",
      "\n",
      "Index = 369\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004376461543562\n",
      "Prediction: [[7.37951803]]\n",
      "Actual: [18.94352545]\n",
      "\n",
      "\n",
      "Index = 370\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005727065394656\n",
      "Prediction: [[9.62177735]]\n",
      "Actual: [3.44525822]\n",
      "\n",
      "\n",
      "Index = 371\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006300931302765\n",
      "Prediction: [[8.74180253]]\n",
      "Actual: [10.77904765]\n",
      "\n",
      "\n",
      "Index = 372\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006251257038318\n",
      "Prediction: [[8.90822237]]\n",
      "Actual: [4.60766716]\n",
      "\n",
      "\n",
      "Index = 373\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006186686528456\n",
      "Prediction: [[25.20126529]]\n",
      "Actual: [21.86262274]\n",
      "\n",
      "\n",
      "Index = 374\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006190616267374\n",
      "Prediction: [[5.6751391]]\n",
      "Actual: [11.2683152]\n",
      "\n",
      "\n",
      "Index = 375\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700634746906729\n",
      "Prediction: [[7.27199302]]\n",
      "Actual: [7.53510219]\n",
      "\n",
      "\n",
      "Index = 376\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006341478924196\n",
      "Prediction: [[8.86637891]]\n",
      "Actual: [7.49370767]\n",
      "\n",
      "\n",
      "Index = 377\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004229774563131\n",
      "Prediction: [[24.72193787]]\n",
      "Actual: [15.0774539]\n",
      "\n",
      "\n",
      "Index = 378\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005423640929798\n",
      "Prediction: [[30.37794066]]\n",
      "Actual: [23.57026223]\n",
      "\n",
      "\n",
      "Index = 379\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005837339068678\n",
      "Prediction: [[32.7726553]]\n",
      "Actual: [28.53692035]\n",
      "\n",
      "\n",
      "Index = 380\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700631529925819\n",
      "Prediction: [[7.96192173]]\n",
      "Actual: [8.29903577]\n",
      "\n",
      "\n",
      "Index = 381\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700578333255191\n",
      "Prediction: [[32.17378022]]\n",
      "Actual: [27.40233755]\n",
      "\n",
      "\n",
      "Index = 382\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338684068056\n",
      "Prediction: [[6.72035599]]\n",
      "Actual: [6.71668048]\n",
      "\n",
      "\n",
      "Index = 383\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006268790357766\n",
      "Prediction: [[5.08859125]]\n",
      "Actual: [8.7481918]\n",
      "\n",
      "\n",
      "Index = 384\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006351402873265\n",
      "Prediction: [[3.36811177]]\n",
      "Actual: [4.45613874]\n",
      "\n",
      "\n",
      "Index = 385\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006348493265551\n",
      "Prediction: [[5.27295895]]\n",
      "Actual: [7.33721371]\n",
      "\n",
      "\n",
      "Index = 386\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006259496067335\n",
      "Prediction: [[2.63472583]]\n",
      "Actual: [1.68743981]\n",
      "\n",
      "\n",
      "Index = 387\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.70062442039343\n",
      "Prediction: [[4.44199107]]\n",
      "Actual: [2.42822581]\n",
      "\n",
      "\n",
      "Index = 388\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005081786499983\n",
      "Prediction: [[13.53368321]]\n",
      "Actual: [20.51981648]\n",
      "\n",
      "\n",
      "Index = 389\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006333280341819\n",
      "Prediction: [[13.58531973]]\n",
      "Actual: [13.33573513]\n",
      "\n",
      "\n",
      "Index = 390\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006352105171727\n",
      "Prediction: [[8.86915852]]\n",
      "Actual: [5.5319221]\n",
      "\n",
      "\n",
      "Index = 391\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7004713262860054\n",
      "Prediction: [[16.53192085]]\n",
      "Actual: [27.59862671]\n",
      "\n",
      "\n",
      "Index = 392\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7002479713313473\n",
      "Prediction: [[6.95683875]]\n",
      "Actual: [22.24864426]\n",
      "\n",
      "\n",
      "Index = 393\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006334618303085\n",
      "Prediction: [[7.13307998]]\n",
      "Actual: [7.75600745]\n",
      "\n",
      "\n",
      "Index = 394\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005951567419212\n",
      "Prediction: [[6.59082217]]\n",
      "Actual: [10.98386248]\n",
      "\n",
      "\n",
      "Index = 395\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006338608797591\n",
      "Prediction: [[15.14299173]]\n",
      "Actual: [13.22495148]\n",
      "\n",
      "\n",
      "Index = 396\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006363394308903\n",
      "Prediction: [[7.77521923]]\n",
      "Actual: [6.55978577]\n",
      "\n",
      "\n",
      "Index = 397\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006285539860785\n",
      "Prediction: [[14.70646592]]\n",
      "Actual: [17.21358861]\n",
      "\n",
      "\n",
      "Index = 398\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006296010520852\n",
      "Prediction: [[12.22823676]]\n",
      "Actual: [7.33816413]\n",
      "\n",
      "\n",
      "Index = 399\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006301872483354\n",
      "Prediction: [[9.0401293]]\n",
      "Actual: [10.99742596]\n",
      "\n",
      "\n",
      "Index = 400\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006355961486084\n",
      "Prediction: [[6.02438125]]\n",
      "Actual: [6.47559371]\n",
      "\n",
      "\n",
      "Index = 401\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006362533524211\n",
      "Prediction: [[5.71962271]]\n",
      "Actual: [6.22801542]\n",
      "\n",
      "\n",
      "Index = 402\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700629506988748\n",
      "Prediction: [[8.56042829]]\n",
      "Actual: [10.6687381]\n",
      "\n",
      "\n",
      "Index = 403\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006361387910243\n",
      "Prediction: [[6.57428072]]\n",
      "Actual: [4.29838394]\n",
      "\n",
      "\n",
      "Index = 404\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006358915538085\n",
      "Prediction: [[9.56092677]]\n",
      "Actual: [9.71242826]\n",
      "\n",
      "\n",
      "Index = 405\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006165082216531\n",
      "Prediction: [[18.3841173]]\n",
      "Actual: [14.15782823]\n",
      "\n",
      "\n",
      "Index = 406\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006354264837071\n",
      "Prediction: [[23.36771611]]\n",
      "Actual: [23.52359365]\n",
      "\n",
      "\n",
      "Index = 407\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006304388580997\n",
      "Prediction: [[15.48204015]]\n",
      "Actual: [16.26664219]\n",
      "\n",
      "\n",
      "Index = 408\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006167591705115\n",
      "Prediction: [[16.29270282]]\n",
      "Actual: [19.81033081]\n",
      "\n",
      "\n",
      "Index = 409\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006345891097383\n",
      "Prediction: [[12.84262885]]\n",
      "Actual: [11.09880827]\n",
      "\n",
      "\n",
      "Index = 410\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700635197373652\n",
      "Prediction: [[13.15404012]]\n",
      "Actual: [14.38245629]\n",
      "\n",
      "\n",
      "Index = 411\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006291840784733\n",
      "Prediction: [[4.01140015]]\n",
      "Actual: [2.18788703]\n",
      "\n",
      "\n",
      "Index = 412\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006307509903182\n",
      "Prediction: [[3.52060217]]\n",
      "Actual: [8.55431445]\n",
      "\n",
      "\n",
      "Index = 413\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006354778220096\n",
      "Prediction: [[5.08103696]]\n",
      "Actual: [6.33470976]\n",
      "\n",
      "\n",
      "Index = 414\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006310710588068\n",
      "Prediction: [[5.98740544]]\n",
      "Actual: [2.57737832]\n",
      "\n",
      "\n",
      "Index = 415\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006248960480018\n",
      "Prediction: [[5.96400761]]\n",
      "Actual: [0.81905461]\n",
      "\n",
      "\n",
      "Index = 416\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7005823629987902\n",
      "Prediction: [[1.40017581]]\n",
      "Actual: [-0.18398248]\n",
      "\n",
      "\n",
      "Index = 417\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006226228241048\n",
      "Prediction: [[1.84631282]]\n",
      "Actual: [1.09472532]\n",
      "\n",
      "\n",
      "Index = 418\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006217102629635\n",
      "Prediction: [[2.62118814]]\n",
      "Actual: [0.33887089]\n",
      "\n",
      "\n",
      "Index = 419\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006340235322398\n",
      "Prediction: [[6.53672498]]\n",
      "Actual: [4.25866541]\n",
      "\n",
      "\n",
      "Index = 420\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006323106860699\n",
      "Prediction: [[6.97927713]]\n",
      "Actual: [4.50598887]\n",
      "\n",
      "\n",
      "Index = 421\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006110281160876\n",
      "Prediction: [[8.44219441]]\n",
      "Actual: [4.198701]\n",
      "\n",
      "\n",
      "Index = 422\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006342882261587\n",
      "Prediction: [[6.24984204]]\n",
      "Actual: [7.07733635]\n",
      "\n",
      "\n",
      "Index = 423\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006270573238425\n",
      "Prediction: [[3.74142627]]\n",
      "Actual: [3.68773143]\n",
      "\n",
      "\n",
      "Index = 424\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006231469892954\n",
      "Prediction: [[4.16623123]]\n",
      "Actual: [2.234756]\n",
      "\n",
      "\n",
      "Index = 425\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.700602320218653\n",
      "Prediction: [[6.43365726]]\n",
      "Actual: [0.83691735]\n",
      "\n",
      "\n",
      "Index = 426\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006292561678422\n",
      "Prediction: [[5.05480951]]\n",
      "Actual: [3.27088962]\n",
      "\n",
      "\n",
      "Index = 427\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006281313270724\n",
      "Prediction: [[5.29311057]]\n",
      "Actual: [2.56871839]\n",
      "\n",
      "\n",
      "Index = 428\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006269147362203\n",
      "Prediction: [[4.03356068]]\n",
      "Actual: [2.85881352]\n",
      "\n",
      "\n",
      "Index = 429\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006334257148349\n",
      "Prediction: [[3.06392215]]\n",
      "Actual: [3.78240424]\n",
      "\n",
      "\n",
      "Index = 430\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006232315332581\n",
      "Prediction: [[6.25583115]]\n",
      "Actual: [3.2140363]\n",
      "\n",
      "\n",
      "Index = 431\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006175008371536\n",
      "Prediction: [[6.02917456]]\n",
      "Actual: [3.0014929]\n",
      "\n",
      "\n",
      "Index = 432\n",
      "Finished learning regression model\n",
      "Finished kriging residuals\n",
      "Regression Score:  0.7006289953067284\n",
      "Prediction: [[5.81416288]]\n",
      "Actual: [3.05084855]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build on LUR model with Regression Kringing with the \"leave one out method\"\n",
    "from pykrige.rk import RegressionKriging\n",
    "\n",
    "m_rk = RegressionKriging(regression_model=l_model, n_closest_points=20)\n",
    "\n",
    "actuals = []\n",
    "predicts = []\n",
    "# implement leave one out method\n",
    "for i in list(range(0, X.shape[0])):\n",
    "    print('Index = %s' % i)\n",
    "    #out_X = np.expand_dims(X.iloc[i], axis=1).T\n",
    "    out_X = X.iloc[lambda X: X.index == i]\n",
    "    out_Y = Y[i]\n",
    "    out_latlon = np.expand_dims(lat_lon[i], axis=1).T\n",
    "    \n",
    "    \n",
    "    # OKAY FIX THIS ANNOYING ISSUE WHERE IT DROPS FOR INDEX 0 but for whatever reason can't for any other index\n",
    "    in_X = X.iloc[lambda X: X.index != i]\n",
    "    in_Y = np.delete(Y, i, axis=0)\n",
    "    in_latlon = np.delete(lat_lon, i, axis=0)\n",
    "    \n",
    "    m_rk.fit(in_X, in_latlon, in_Y)\n",
    "    print(\"Regression Score: \", m_rk.regression_model.score(X, Y))\n",
    "    \n",
    "    out_x_df = pd.DataFrame(out_X)\n",
    "\n",
    "    kring_prediction = m_rk.predict(out_X, out_latlon)\n",
    "    print('Prediction: %s' % kring_prediction)\n",
    "    print('Actual: %s' % out_Y)\n",
    "    actuals.append(out_Y[0])\n",
    "    predicts.append(kring_prediction[0][0])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fee4fa-cb00-467d-823a-2c5379cabc13",
   "metadata": {},
   "source": [
    "### Well then, looks like we will have to Kring by each month as multiple values on the same lat/long messes with the math.\n",
    "\n",
    "This is entirely do-able. We can split the monthly dataframe into individual month data frames and add them to a list. Then We can build a regression model for each month using 450 data points, and then make a Kringed prediction, which can be joined to the input monthly dataframe. Then all month dataframes can be effectively stacked. \n",
    "\n",
    "Let's test in the Jupyter Notebook using only one month. The full pipeline is better suited for a bactch of functions. \n",
    "\n",
    "IMPORTANT: The NO2 studies use \"leave one out kriging\". This is important because basically a raster is made with all points except one, and the Kriging predicted value of the left out point is then joined to the table as NO2_krig. This is very iterative, and will be especially so for our prediction. We will need to iterate over each month, and within that iterate over every station_id so each can be left out once. \n",
    "\n",
    "For our NO2 predictions we can make a Kriging raster with each band = a month for the whole Continental USA. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc3ef10-4234-4139-b729-4afe74ad5c07",
   "metadata": {},
   "source": [
    "We may be better off dropping by station ID before going into the whole Kriging pipeline. We can definetly make this thing work!\n",
    "\n",
    "### We need to be 100% certain that this model and the ArcGIS Kriging raster are made via the same math!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a3322ce7-69f6-4419-908c-c683d8d33465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1, 35, 'R2 = 0.71')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e678cd7eb774ea4bcaf3661ea6852d3",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "ps = np.array(predicts)\n",
    "As = np.array(actuals)\n",
    "\n",
    "plt.title('Leave one out Kriging interpolation only')\n",
    "plt.scatter(ps, As)\n",
    "plt.plot(np.arange(0, 60, 0.1), np.arange(0, 60, 0.1), c='red')\n",
    "plt.xlim(0, np.amax(ps))\n",
    "plt.ylim(0, np.amax(As))\n",
    "plt.xlabel('Predicted NO2 concentration')\n",
    "plt.ylabel('Actual daily NO2 concentration')\n",
    "plt.text(1, 35, 'R2 = %s' % round(r2_score(As, ps), 2), fontsize='large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c495f74c-4125-4af9-b2ea-2d208cce72dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}