_2_Check_SimpleModels_YearSplit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import ensemble\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn import neighbors\n",
    "\n",
    "from lightgbm import LGBMRegressor, Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42\n",
    "random.seed(RANDOM_SEED)\n",
    "np.random.seed(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predict(y_true, y_pred):\n",
    "    slope, intercept,r_value, p_value, std_err = stats.linregress(y_true, y_pred)\n",
    "    line = slope*y_true+intercept\n",
    "    plt.scatter(y_true, y_pred)\n",
    "    plt.plot(y_true, line, color = 'red')\n",
    "    plt.plot([y_true.min(), y_true.max()],\n",
    "            [y_true.min(), y_true.max()], color='black', lw=0.5)\n",
    "    plt.xlabel('observed')\n",
    "    plt.ylabel('predicted')\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(y_true, y_pred):\n",
    "    return np.mean((y_true - y_pred)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RMSE(y_true, y_pred):\n",
    "    return np.sqrt(MSE(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def R2(y_true, y_pred):\n",
    "    return  1 - np.sum((y_true - y_pred)**2) / np.sum((y_true - np.mean(y_true))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./csv/data_norm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>9_ARVI2</th>\n",
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       "      <th>11_ARVI2</th>\n",
       "      <th>0_CIrededge</th>\n",
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       "      <th>2_CIrededge</th>\n",
       "      <th>3_CIrededge</th>\n",
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       "      <th>...</th>\n",
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       "      <th>10_S2_B08_MEAN</th>\n",
       "      <th>10_S2_B08_MAX</th>\n",
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       "      <th>10_S2_B8A_MEAN</th>\n",
       "      <th>10_S2_B8A_MAX</th>\n",
       "      <th>10_S2_B09_MIN</th>\n",
       "      <th>10_S2_B09_MEAN</th>\n",
       "      <th>10_S2_B09_MAX</th>\n",
       "      <th>10_S2_B10_MIN</th>\n",
       "      <th>10_S2_B10_MEAN</th>\n",
       "      <th>10_S2_B10_MAX</th>\n",
       "      <th>10_S2_B11_MIN</th>\n",
       "      <th>10_S2_B11_MEAN</th>\n",
       "      <th>10_S2_B11_MAX</th>\n",
       "      <th>10_S2_B12_MIN</th>\n",
       "      <th>10_S2_B12_MEAN</th>\n",
       "      <th>10_S2_B12_MAX</th>\n",
       "      <th>10_NDVI_MIN</th>\n",
       "      <th>10_NDVI_MEAN</th>\n",
       "      <th>10_NDVI_MAX</th>\n",
       "      <th>10_ATSAVI_MIN</th>\n",
       "      <th>10_ATSAVI_MEAN</th>\n",
       "      <th>10_ATSAVI_MAX</th>\n",
       "      <th>10_ARVI2_MIN</th>\n",
       "      <th>10_ARVI2_MEAN</th>\n",
       "      <th>10_ARVI2_MAX</th>\n",
       "      <th>10_GARI_MIN</th>\n",
       "      <th>10_GARI_MEAN</th>\n",
       "      <th>10_GARI_MAX</th>\n",
       "      <th>10_CIrededge_MIN</th>\n",
       "      <th>10_CIrededge_MEAN</th>\n",
       "      <th>10_CIrededge_MAX</th>\n",
       "      <th>10_CVI_MIN</th>\n",
       "      <th>10_CVI_MEAN</th>\n",
       "      <th>10_CVI_MAX</th>\n",
       "      <th>10_LCI_MIN</th>\n",
       "      <th>10_LCI_MEAN</th>\n",
       "      <th>10_LCI_MAX</th>\n",
       "      <th>10_MVI_MIN</th>\n",
       "      <th>10_MVI_MEAN</th>\n",
       "      <th>10_MVI_MAX</th>\n",
       "      <th>10_mARI_MIN</th>\n",
       "      <th>10_mARI_MEAN</th>\n",
       "      <th>10_mARI_MAX</th>\n",
       "      <th>10_NDRE_MIN</th>\n",
       "      <th>10_NDRE_MEAN</th>\n",
       "      <th>10_NDRE_MAX</th>\n",
       "      <th>10_CLIM_aet_MIN</th>\n",
       "      <th>10_CLIM_aet_MEAN</th>\n",
       "      <th>10_CLIM_aet_MAX</th>\n",
       "      <th>10_CLIM_pet_MIN</th>\n",
       "      <th>10_CLIM_pet_MEAN</th>\n",
       "      <th>10_CLIM_pet_MAX</th>\n",
       "      <th>10_CLIM_pr_MIN</th>\n",
       "      <th>10_CLIM_pr_MEAN</th>\n",
       "      <th>10_CLIM_pr_MAX</th>\n",
       "      <th>10_CLIM_soil_MIN</th>\n",
       "      <th>10_CLIM_soil_MEAN</th>\n",
       "      <th>10_CLIM_soil_MAX</th>\n",
       "      <th>10_CLIM_srad_MIN</th>\n",
       "      <th>10_CLIM_srad_MEAN</th>\n",
       "      <th>10_CLIM_srad_MAX</th>\n",
       "      <th>10_CLIM_tmmn_MIN</th>\n",
       "      <th>10_CLIM_tmmn_MEAN</th>\n",
       "      <th>10_CLIM_tmmn_MAX</th>\n",
       "      <th>10_CLIM_tmmx_MIN</th>\n",
       "      <th>10_CLIM_tmmx_MEAN</th>\n",
       "      <th>10_CLIM_tmmx_MAX</th>\n",
       "      <th>10_CLIM_vap_MIN</th>\n",
       "      <th>10_CLIM_vap_MEAN</th>\n",
       "      <th>10_CLIM_vap_MAX</th>\n",
       "      <th>10_CLIM_vpd_MIN</th>\n",
       "      <th>10_CLIM_vpd_MEAN</th>\n",
       "      <th>10_CLIM_vpd_MAX</th>\n",
       "      <th>10_CLIM_vs_MIN</th>\n",
       "      <th>10_CLIM_vs_MEAN</th>\n",
       "      <th>10_CLIM_vs_MAX</th>\n",
       "      <th>11_S2_B01_MIN</th>\n",
       "      <th>11_S2_B01_MEAN</th>\n",
       "      <th>11_S2_B01_MAX</th>\n",
       "      <th>11_S2_B02_MIN</th>\n",
       "      <th>11_S2_B02_MEAN</th>\n",
       "      <th>11_S2_B02_MAX</th>\n",
       "      <th>11_S2_B03_MIN</th>\n",
       "      <th>11_S2_B03_MEAN</th>\n",
       "      <th>11_S2_B03_MAX</th>\n",
       "      <th>11_S2_B04_MIN</th>\n",
       "      <th>11_S2_B04_MEAN</th>\n",
       "      <th>11_S2_B04_MAX</th>\n",
       "      <th>11_S2_B05_MIN</th>\n",
       "      <th>11_S2_B05_MEAN</th>\n",
       "      <th>11_S2_B05_MAX</th>\n",
       "      <th>11_S2_B06_MIN</th>\n",
       "      <th>11_S2_B06_MEAN</th>\n",
       "      <th>11_S2_B06_MAX</th>\n",
       "      <th>11_S2_B07_MIN</th>\n",
       "      <th>11_S2_B07_MEAN</th>\n",
       "      <th>11_S2_B07_MAX</th>\n",
       "      <th>11_S2_B08_MIN</th>\n",
       "      <th>11_S2_B08_MEAN</th>\n",
       "      <th>11_S2_B08_MAX</th>\n",
       "      <th>11_S2_B8A_MIN</th>\n",
       "      <th>11_S2_B8A_MEAN</th>\n",
       "      <th>11_S2_B8A_MAX</th>\n",
       "      <th>11_S2_B09_MIN</th>\n",
       "      <th>11_S2_B09_MEAN</th>\n",
       "      <th>11_S2_B09_MAX</th>\n",
       "      <th>11_S2_B10_MIN</th>\n",
       "      <th>11_S2_B10_MEAN</th>\n",
       "      <th>11_S2_B10_MAX</th>\n",
       "      <th>11_S2_B11_MIN</th>\n",
       "      <th>11_S2_B11_MEAN</th>\n",
       "      <th>11_S2_B11_MAX</th>\n",
       "      <th>11_S2_B12_MIN</th>\n",
       "      <th>11_S2_B12_MEAN</th>\n",
       "      <th>11_S2_B12_MAX</th>\n",
       "      <th>11_NDVI_MIN</th>\n",
       "      <th>11_NDVI_MEAN</th>\n",
       "      <th>11_NDVI_MAX</th>\n",
       "      <th>11_ATSAVI_MIN</th>\n",
       "      <th>11_ATSAVI_MEAN</th>\n",
       "      <th>11_ATSAVI_MAX</th>\n",
       "      <th>11_ARVI2_MIN</th>\n",
       "      <th>11_ARVI2_MEAN</th>\n",
       "      <th>11_ARVI2_MAX</th>\n",
       "      <th>11_GARI_MIN</th>\n",
       "      <th>11_GARI_MEAN</th>\n",
       "      <th>11_GARI_MAX</th>\n",
       "      <th>11_CIrededge_MIN</th>\n",
       "      <th>11_CIrededge_MEAN</th>\n",
       "      <th>11_CIrededge_MAX</th>\n",
       "      <th>11_CVI_MIN</th>\n",
       "      <th>11_CVI_MEAN</th>\n",
       "      <th>11_CVI_MAX</th>\n",
       "      <th>11_LCI_MIN</th>\n",
       "      <th>11_LCI_MEAN</th>\n",
       "      <th>11_LCI_MAX</th>\n",
       "      <th>11_MVI_MIN</th>\n",
       "      <th>11_MVI_MEAN</th>\n",
       "      <th>11_MVI_MAX</th>\n",
       "      <th>11_mARI_MIN</th>\n",
       "      <th>11_mARI_MEAN</th>\n",
       "      <th>11_mARI_MAX</th>\n",
       "      <th>11_NDRE_MIN</th>\n",
       "      <th>11_NDRE_MEAN</th>\n",
       "      <th>11_NDRE_MAX</th>\n",
       "      <th>11_CLIM_aet_MIN</th>\n",
       "      <th>11_CLIM_aet_MEAN</th>\n",
       "      <th>11_CLIM_aet_MAX</th>\n",
       "      <th>11_CLIM_pet_MIN</th>\n",
       "      <th>11_CLIM_pet_MEAN</th>\n",
       "      <th>11_CLIM_pet_MAX</th>\n",
       "      <th>11_CLIM_pr_MIN</th>\n",
       "      <th>11_CLIM_pr_MEAN</th>\n",
       "      <th>11_CLIM_pr_MAX</th>\n",
       "      <th>11_CLIM_soil_MIN</th>\n",
       "      <th>11_CLIM_soil_MEAN</th>\n",
       "      <th>11_CLIM_soil_MAX</th>\n",
       "      <th>11_CLIM_srad_MIN</th>\n",
       "      <th>11_CLIM_srad_MEAN</th>\n",
       "      <th>11_CLIM_srad_MAX</th>\n",
       "      <th>11_CLIM_tmmn_MIN</th>\n",
       "      <th>11_CLIM_tmmn_MEAN</th>\n",
       "      <th>11_CLIM_tmmn_MAX</th>\n",
       "      <th>11_CLIM_tmmx_MIN</th>\n",
       "      <th>11_CLIM_tmmx_MEAN</th>\n",
       "      <th>11_CLIM_tmmx_MAX</th>\n",
       "      <th>11_CLIM_vap_MIN</th>\n",
       "      <th>11_CLIM_vap_MEAN</th>\n",
       "      <th>11_CLIM_vap_MAX</th>\n",
       "      <th>11_CLIM_vpd_MIN</th>\n",
       "      <th>11_CLIM_vpd_MEAN</th>\n",
       "      <th>11_CLIM_vpd_MAX</th>\n",
       "      <th>11_CLIM_vs_MIN</th>\n",
       "      <th>11_CLIM_vs_MEAN</th>\n",
       "      <th>11_CLIM_vs_MAX</th>\n",
       "      <th>BDOD</th>\n",
       "      <th>CEC</th>\n",
       "      <th>CFVO</th>\n",
       "      <th>CLAY</th>\n",
       "      <th>NITR</th>\n",
       "      <th>OCD</th>\n",
       "      <th>OCS</th>\n",
       "      <th>PHH2O</th>\n",
       "      <th>SAND</th>\n",
       "      <th>SILT</th>\n",
       "      <th>SOC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.843609</td>\n",
       "      <td>-0.502552</td>\n",
       "      <td>-0.580374</td>\n",
       "      <td>-0.608198</td>\n",
       "      <td>-0.729947</td>\n",
       "      <td>-0.200413</td>\n",
       "      <td>-0.064783</td>\n",
       "      <td>-0.195807</td>\n",
       "      <td>0.142577</td>\n",
       "      <td>0.578572</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-0.962752</td>\n",
       "      <td>-0.963574</td>\n",
       "      <td>-0.616715</td>\n",
       "      <td>-0.211439</td>\n",
       "      <td>-0.014773</td>\n",
       "      <td>-0.155385</td>\n",
       "      <td>-0.364569</td>\n",
       "      <td>0.062101</td>\n",
       "      <td>0.082685</td>\n",
       "      <td>0.317995</td>\n",
       "      <td>0.316501</td>\n",
       "      <td>1.461759</td>\n",
       "      <td>1.123764</td>\n",
       "      <td>-0.379976</td>\n",
       "      <td>-0.504366</td>\n",
       "      <td>-0.595569</td>\n",
       "      <td>0.043007</td>\n",
       "      <td>0.029942</td>\n",
       "      <td>0.321365</td>\n",
       "      <td>-0.380483</td>\n",
       "      <td>-0.650926</td>\n",
       "      <td>-0.566078</td>\n",
       "      <td>-0.310209</td>\n",
       "      <td>-0.481912</td>\n",
       "      <td>0.551840</td>\n",
       "      <td>0.189091</td>\n",
       "      <td>-0.207686</td>\n",
       "      <td>-0.139011</td>\n",
       "      <td>-0.077359</td>\n",
       "      <td>0.317078</td>\n",
       "      <td>0.570320</td>\n",
       "      <td>0.852382</td>\n",
       "      <td>0.153528</td>\n",
       "      <td>-0.518089</td>\n",
       "      <td>-0.492637</td>\n",
       "      <td>-0.110053</td>\n",
       "      <td>-0.318126</td>\n",
       "      <td>0.245222</td>\n",
       "      <td>0.784835</td>\n",
       "      <td>0.784576</td>\n",
       "      <td>0.768891</td>\n",
       "      <td>-0.160791</td>\n",
       "      <td>0.214768</td>\n",
       "      <td>0.285876</td>\n",
       "      <td>0.636191</td>\n",
       "      <td>0.290214</td>\n",
       "      <td>-0.887611</td>\n",
       "      <td>-0.958666</td>\n",
       "      <td>-0.588464</td>\n",
       "      <td>-0.819447</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>1.860522</td>\n",
       "      <td>0.887858</td>\n",
       "      <td>1.122930</td>\n",
       "      <td>-0.425862</td>\n",
       "      <td>-0.192457</td>\n",
       "      <td>-0.194379</td>\n",
       "      <td>0.091802</td>\n",
       "      <td>-0.301555</td>\n",
       "      <td>-1.160025</td>\n",
       "      <td>-1.203018</td>\n",
       "      <td>-1.317833</td>\n",
       "      <td>-1.132195</td>\n",
       "      <td>0.066944</td>\n",
       "      <td>0.658617</td>\n",
       "      <td>-0.359580</td>\n",
       "      <td>-0.131790</td>\n",
       "      <td>-0.440826</td>\n",
       "      <td>-0.489766</td>\n",
       "      <td>-0.698997</td>\n",
       "      <td>-0.681647</td>\n",
       "      <td>-1.196644</td>\n",
       "      <td>-0.980236</td>\n",
       "      <td>-0.737320</td>\n",
       "      <td>-1.709061</td>\n",
       "      <td>-0.627569</td>\n",
       "      <td>-0.430451</td>\n",
       "      <td>-0.775060</td>\n",
       "      <td>-1.864160</td>\n",
       "      <td>-1.428217</td>\n",
       "      <td>0.085820</td>\n",
       "      <td>0.252073</td>\n",
       "      <td>0.093481</td>\n",
       "      <td>-0.020614</td>\n",
       "      <td>-0.371609</td>\n",
       "      <td>0.091979</td>\n",
       "      <td>0.437716</td>\n",
       "      <td>-0.691393</td>\n",
       "      <td>0.535145</td>\n",
       "      <td>0.341487</td>\n",
       "      <td>-0.477662</td>\n",
       "      <td>-1.009045</td>\n",
       "      <td>-1.128480</td>\n",
       "      <td>-0.758110</td>\n",
       "      <td>-0.529240</td>\n",
       "      <td>-0.474062</td>\n",
       "      <td>-0.650922</td>\n",
       "      <td>-0.823768</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.255730</td>\n",
       "      <td>-0.068810</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.942831</td>\n",
       "      <td>-0.362740</td>\n",
       "      <td>-1.272626</td>\n",
       "      <td>-1.245667</td>\n",
       "      <td>-0.542212</td>\n",
       "      <td>-0.283003</td>\n",
       "      <td>-0.271566</td>\n",
       "      <td>-0.376652</td>\n",
       "      <td>-0.660562</td>\n",
       "      <td>-0.361698</td>\n",
       "      <td>-0.081608</td>\n",
       "      <td>-0.402265</td>\n",
       "      <td>-0.240614</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>-0.391988</td>\n",
       "      <td>-1.205350</td>\n",
       "      <td>-1.213146</td>\n",
       "      <td>-0.573909</td>\n",
       "      <td>-0.357488</td>\n",
       "      <td>-0.473959</td>\n",
       "      <td>-0.471344</td>\n",
       "      <td>-0.748373</td>\n",
       "      <td>-0.514515</td>\n",
       "      <td>-0.526826</td>\n",
       "      <td>-1.410201</td>\n",
       "      <td>-0.607952</td>\n",
       "      <td>0.283026</td>\n",
       "      <td>-0.111511</td>\n",
       "      <td>-1.466957</td>\n",
       "      <td>-1.323709</td>\n",
       "      <td>-0.507268</td>\n",
       "      <td>-0.624264</td>\n",
       "      <td>-1.107309</td>\n",
       "      <td>-1.158500</td>\n",
       "      <td>-1.909706</td>\n",
       "      <td>-3.194740</td>\n",
       "      <td>-3.165916</td>\n",
       "      <td>-3.475287</td>\n",
       "      <td>-3.491975</td>\n",
       "      <td>-2.905376</td>\n",
       "      <td>-0.745247</td>\n",
       "      <td>-3.051246</td>\n",
       "      <td>-2.191308</td>\n",
       "      <td>MH2O0YH</td>\n",
       "      <td>3.686</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.515054</td>\n",
       "      <td>0.317976</td>\n",
       "      <td>-0.462308</td>\n",
       "      <td>-0.859399</td>\n",
       "      <td>-1.236450</td>\n",
       "      <td>-0.958027</td>\n",
       "      <td>-0.077404</td>\n",
       "      <td>-0.364856</td>\n",
       "      <td>0.757765</td>\n",
       "      <td>0.226884</td>\n",
       "      <td>-0.200695</td>\n",
       "      <td>-0.840913</td>\n",
       "      <td>1.292991</td>\n",
       "      <td>1.400487</td>\n",
       "      <td>0.799544</td>\n",
       "      <td>0.136473</td>\n",
       "      <td>-0.442062</td>\n",
       "      <td>0.428740</td>\n",
       "      <td>1.234173</td>\n",
       "      <td>0.767606</td>\n",
       "      <td>1.905559</td>\n",
       "      <td>0.870257</td>\n",
       "      <td>1.344179</td>\n",
       "      <td>-0.636711</td>\n",
       "      <td>1.124863</td>\n",
       "      <td>0.884128</td>\n",
       "      <td>0.082262</td>\n",
       "      <td>-0.548773</td>\n",
       "      <td>-0.985461</td>\n",
       "      <td>-0.202105</td>\n",
       "      <td>0.624678</td>\n",
       "      <td>0.185260</td>\n",
       "      <td>1.379820</td>\n",
       "      <td>0.504115</td>\n",
       "      <td>0.766191</td>\n",
       "      <td>-0.955136</td>\n",
       "      <td>1.269524</td>\n",
       "      <td>1.416410</td>\n",
       "      <td>0.725818</td>\n",
       "      <td>-0.008259</td>\n",
       "      <td>-0.528954</td>\n",
       "      <td>0.314705</td>\n",
       "      <td>1.233426</td>\n",
       "      <td>0.664737</td>\n",
       "      <td>2.083726</td>\n",
       "      <td>0.830103</td>\n",
       "      <td>1.374792</td>\n",
       "      <td>-0.657703</td>\n",
       "      <td>0.478826</td>\n",
       "      <td>1.066705</td>\n",
       "      <td>0.335411</td>\n",
       "      <td>-0.075713</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.302434</td>\n",
       "      <td>-0.522049</td>\n",
       "      <td>-0.419616</td>\n",
       "      <td>0.916948</td>\n",
       "      <td>0.195212</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>-2.320056</td>\n",
       "      <td>0.261553</td>\n",
       "      <td>0.926073</td>\n",
       "      <td>0.094181</td>\n",
       "      <td>-0.194492</td>\n",
       "      <td>-0.061164</td>\n",
       "      <td>-0.108568</td>\n",
       "      <td>-1.014196</td>\n",
       "      <td>-0.155869</td>\n",
       "      <td>0.444152</td>\n",
       "      <td>-0.179432</td>\n",
       "      <td>-0.114577</td>\n",
       "      <td>-3.174143</td>\n",
       "      <td>0.566180</td>\n",
       "      <td>0.532213</td>\n",
       "      <td>-0.046594</td>\n",
       "      <td>-0.427194</td>\n",
       "      <td>-1.061319</td>\n",
       "      <td>-0.839901</td>\n",
       "      <td>0.041138</td>\n",
       "      <td>-0.211576</td>\n",
       "      <td>0.947780</td>\n",
       "      <td>0.397005</td>\n",
       "      <td>-0.265066</td>\n",
       "      <td>-0.656252</td>\n",
       "      <td>1.469573</td>\n",
       "      <td>1.342553</td>\n",
       "      <td>0.550036</td>\n",
       "      <td>-0.377277</td>\n",
       "      <td>-0.886310</td>\n",
       "      <td>0.305398</td>\n",
       "      <td>2.070964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080771</td>\n",
       "      <td>-0.690610</td>\n",
       "      <td>0.525548</td>\n",
       "      <td>1.139355</td>\n",
       "      <td>0.783403</td>\n",
       "      <td>-0.669586</td>\n",
       "      <td>-0.716184</td>\n",
       "      <td>-0.217924</td>\n",
       "      <td>1.213116</td>\n",
       "      <td>1.125613</td>\n",
       "      <td>0.774321</td>\n",
       "      <td>-0.793594</td>\n",
       "      <td>-1.720165</td>\n",
       "      <td>-2.330020</td>\n",
       "      <td>-0.317915</td>\n",
       "      <td>0.208280</td>\n",
       "      <td>-0.840646</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-0.991360</td>\n",
       "      <td>-1.455867</td>\n",
       "      <td>-0.491663</td>\n",
       "      <td>-0.741524</td>\n",
       "      <td>-0.871773</td>\n",
       "      <td>1.547266</td>\n",
       "      <td>1.557476</td>\n",
       "      <td>-1.009668</td>\n",
       "      <td>-1.357175</td>\n",
       "      <td>-1.300540</td>\n",
       "      <td>-1.089771</td>\n",
       "      <td>-1.558757</td>\n",
       "      <td>-1.212007</td>\n",
       "      <td>-1.464488</td>\n",
       "      <td>-1.619841</td>\n",
       "      <td>-1.930468</td>\n",
       "      <td>-1.659264</td>\n",
       "      <td>-0.123225</td>\n",
       "      <td>0.031016</td>\n",
       "      <td>-0.186888</td>\n",
       "      <td>0.434913</td>\n",
       "      <td>-0.680764</td>\n",
       "      <td>-2.740271</td>\n",
       "      <td>-0.671927</td>\n",
       "      <td>-0.892480</td>\n",
       "      <td>-0.602108</td>\n",
       "      <td>0.217660</td>\n",
       "      <td>-0.251914</td>\n",
       "      <td>-0.426710</td>\n",
       "      <td>-0.257869</td>\n",
       "      <td>-0.206779</td>\n",
       "      <td>-0.311901</td>\n",
       "      <td>-0.094010</td>\n",
       "      <td>-0.069168</td>\n",
       "      <td>-0.232145</td>\n",
       "      <td>-0.791355</td>\n",
       "      <td>-0.753642</td>\n",
       "      <td>-0.555031</td>\n",
       "      <td>0.442300</td>\n",
       "      <td>-0.896690</td>\n",
       "      <td>-0.957555</td>\n",
       "      <td>0.481002</td>\n",
       "      <td>-0.682391</td>\n",
       "      <td>-1.088980</td>\n",
       "      <td>-0.166239</td>\n",
       "      <td>-0.919482</td>\n",
       "      <td>-1.370331</td>\n",
       "      <td>0.508426</td>\n",
       "      <td>-0.530793</td>\n",
       "      <td>-1.014765</td>\n",
       "      <td>0.767030</td>\n",
       "      <td>0.819259</td>\n",
       "      <td>-0.098007</td>\n",
       "      <td>-1.824843</td>\n",
       "      <td>0.605231</td>\n",
       "      <td>0.099029</td>\n",
       "      <td>-1.081922</td>\n",
       "      <td>-0.794136</td>\n",
       "      <td>-0.190248</td>\n",
       "      <td>-1.034583</td>\n",
       "      <td>-0.723428</td>\n",
       "      <td>-0.221589</td>\n",
       "      <td>0.285403</td>\n",
       "      <td>-0.310094</td>\n",
       "      <td>-0.826749</td>\n",
       "      <td>1.248792</td>\n",
       "      <td>1.129115</td>\n",
       "      <td>0.768454</td>\n",
       "      <td>0.775156</td>\n",
       "      <td>0.382208</td>\n",
       "      <td>0.268202</td>\n",
       "      <td>0.456800</td>\n",
       "      <td>0.446906</td>\n",
       "      <td>0.092773</td>\n",
       "      <td>1.237586</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>0.670012</td>\n",
       "      <td>0.040279</td>\n",
       "      <td>0.085420</td>\n",
       "      <td>-0.293362</td>\n",
       "      <td>0.775687</td>\n",
       "      <td>-0.014909</td>\n",
       "      <td>-0.712793</td>\n",
       "      <td>0.559628</td>\n",
       "      <td>1.197459</td>\n",
       "      <td>0.748653</td>\n",
       "      <td>-0.610374</td>\n",
       "      <td>-0.745048</td>\n",
       "      <td>-0.240887</td>\n",
       "      <td>1.248325</td>\n",
       "      <td>1.122374</td>\n",
       "      <td>0.761580</td>\n",
       "      <td>-0.793594</td>\n",
       "      <td>-1.567910</td>\n",
       "      <td>-2.165793</td>\n",
       "      <td>-0.317915</td>\n",
       "      <td>-0.031771</td>\n",
       "      <td>-0.840646</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-0.984596</td>\n",
       "      <td>-1.492533</td>\n",
       "      <td>-0.465907</td>\n",
       "      <td>-0.759424</td>\n",
       "      <td>-0.933871</td>\n",
       "      <td>1.563318</td>\n",
       "      <td>1.405856</td>\n",
       "      <td>-1.009668</td>\n",
       "      <td>-1.347161</td>\n",
       "      <td>-1.338903</td>\n",
       "      <td>-1.100409</td>\n",
       "      <td>-1.558757</td>\n",
       "      <td>-1.293366</td>\n",
       "      <td>-1.464488</td>\n",
       "      <td>-1.619841</td>\n",
       "      <td>-1.857682</td>\n",
       "      <td>-1.659935</td>\n",
       "      <td>-0.120787</td>\n",
       "      <td>-0.240157</td>\n",
       "      <td>-0.187720</td>\n",
       "      <td>0.437490</td>\n",
       "      <td>-0.582620</td>\n",
       "      <td>-2.740271</td>\n",
       "      <td>-0.658805</td>\n",
       "      <td>-0.938453</td>\n",
       "      <td>-0.619868</td>\n",
       "      <td>0.241609</td>\n",
       "      <td>-0.303368</td>\n",
       "      <td>-0.452409</td>\n",
       "      <td>-0.229518</td>\n",
       "      <td>-0.277393</td>\n",
       "      <td>-0.343035</td>\n",
       "      <td>-0.065569</td>\n",
       "      <td>-0.138157</td>\n",
       "      <td>-0.263812</td>\n",
       "      <td>-0.739712</td>\n",
       "      <td>-0.817868</td>\n",
       "      <td>-0.576857</td>\n",
       "      <td>0.473988</td>\n",
       "      <td>-0.911843</td>\n",
       "      <td>-0.989290</td>\n",
       "      <td>0.510847</td>\n",
       "      <td>-0.713369</td>\n",
       "      <td>-1.136373</td>\n",
       "      <td>-0.126717</td>\n",
       "      <td>-1.063016</td>\n",
       "      <td>-1.424827</td>\n",
       "      <td>0.538062</td>\n",
       "      <td>-0.583252</td>\n",
       "      <td>-1.070419</td>\n",
       "      <td>0.802676</td>\n",
       "      <td>0.816303</td>\n",
       "      <td>-0.117590</td>\n",
       "      <td>-1.823957</td>\n",
       "      <td>0.553704</td>\n",
       "      <td>-0.111418</td>\n",
       "      <td>-1.018406</td>\n",
       "      <td>-0.917643</td>\n",
       "      <td>-0.220830</td>\n",
       "      <td>-0.992126</td>\n",
       "      <td>-0.839155</td>\n",
       "      <td>-0.250517</td>\n",
       "      <td>0.319174</td>\n",
       "      <td>-0.310784</td>\n",
       "      <td>-0.872434</td>\n",
       "      <td>1.284733</td>\n",
       "      <td>1.174384</td>\n",
       "      <td>0.768235</td>\n",
       "      <td>0.815518</td>\n",
       "      <td>0.430973</td>\n",
       "      <td>0.264862</td>\n",
       "      <td>0.486075</td>\n",
       "      <td>0.458104</td>\n",
       "      <td>0.091760</td>\n",
       "      <td>1.281382</td>\n",
       "      <td>1.094643</td>\n",
       "      <td>0.669503</td>\n",
       "      <td>0.084966</td>\n",
       "      <td>0.068497</td>\n",
       "      <td>-0.303762</td>\n",
       "      <td>0.815095</td>\n",
       "      <td>-0.046043</td>\n",
       "      <td>-0.751096</td>\n",
       "      <td>0.594050</td>\n",
       "      <td>1.312825</td>\n",
       "      <td>0.740316</td>\n",
       "      <td>-0.551622</td>\n",
       "      <td>-0.743943</td>\n",
       "      <td>-0.244146</td>\n",
       "      <td>1.285086</td>\n",
       "      <td>1.167835</td>\n",
       "      <td>0.761324</td>\n",
       "      <td>-0.794764</td>\n",
       "      <td>-1.787905</td>\n",
       "      <td>-2.724800</td>\n",
       "      <td>-0.317915</td>\n",
       "      <td>-0.206252</td>\n",
       "      <td>-0.840646</td>\n",
       "      <td>-1.087328</td>\n",
       "      <td>-1.142664</td>\n",
       "      <td>-1.505781</td>\n",
       "      <td>-0.513397</td>\n",
       "      <td>-0.788589</td>\n",
       "      <td>-0.946498</td>\n",
       "      <td>1.564100</td>\n",
       "      <td>1.188240</td>\n",
       "      <td>-1.009668</td>\n",
       "      <td>-1.345515</td>\n",
       "      <td>-1.351284</td>\n",
       "      <td>-1.130922</td>\n",
       "      <td>-1.558757</td>\n",
       "      <td>-1.351560</td>\n",
       "      <td>-1.464488</td>\n",
       "      <td>-1.619841</td>\n",
       "      <td>-1.815776</td>\n",
       "      <td>-1.728070</td>\n",
       "      <td>-0.120765</td>\n",
       "      <td>-0.423267</td>\n",
       "      <td>-0.187720</td>\n",
       "      <td>0.439842</td>\n",
       "      <td>-0.588770</td>\n",
       "      <td>-2.740271</td>\n",
       "      <td>-0.011836</td>\n",
       "      <td>1.412126</td>\n",
       "      <td>-0.587811</td>\n",
       "      <td>0.612194</td>\n",
       "      <td>1.612335</td>\n",
       "      <td>0.316188</td>\n",
       "      <td>-0.304458</td>\n",
       "      <td>1.138153</td>\n",
       "      <td>0.361270</td>\n",
       "      <td>-0.973884</td>\n",
       "      <td>0.750047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.885775</td>\n",
       "      <td>-1.044369</td>\n",
       "      <td>-1.090406</td>\n",
       "      <td>-1.162903</td>\n",
       "      <td>-0.722985</td>\n",
       "      <td>0.886156</td>\n",
       "      <td>1.005692</td>\n",
       "      <td>1.163796</td>\n",
       "      <td>1.162127</td>\n",
       "      <td>0.360649</td>\n",
       "      <td>-0.439680</td>\n",
       "      <td>-1.325770</td>\n",
       "      <td>-1.200362</td>\n",
       "      <td>-0.633209</td>\n",
       "      <td>-0.827486</td>\n",
       "      <td>-1.006266</td>\n",
       "      <td>-1.037709</td>\n",
       "      <td>-0.549127</td>\n",
       "      <td>0.673852</td>\n",
       "      <td>0.970708</td>\n",
       "      <td>1.280458</td>\n",
       "      <td>1.030002</td>\n",
       "      <td>1.196650</td>\n",
       "      <td>2.545416</td>\n",
       "      <td>-1.125338</td>\n",
       "      <td>-1.131114</td>\n",
       "      <td>-0.867043</td>\n",
       "      <td>-1.108447</td>\n",
       "      <td>-1.363433</td>\n",
       "      <td>-1.328652</td>\n",
       "      <td>-1.149181</td>\n",
       "      <td>-0.537289</td>\n",
       "      <td>-0.083163</td>\n",
       "      <td>0.144217</td>\n",
       "      <td>-0.119489</td>\n",
       "      <td>1.080583</td>\n",
       "      <td>-0.664637</td>\n",
       "      <td>-1.479532</td>\n",
       "      <td>-1.401947</td>\n",
       "      <td>1.951703</td>\n",
       "      <td>0.566706</td>\n",
       "      <td>0.743357</td>\n",
       "      <td>1.764914</td>\n",
       "      <td>1.665993</td>\n",
       "      <td>0.410561</td>\n",
       "      <td>0.184012</td>\n",
       "      <td>-0.284403</td>\n",
       "      <td>0.319540</td>\n",
       "      <td>1.780471</td>\n",
       "      <td>2.172334</td>\n",
       "      <td>1.269631</td>\n",
       "      <td>1.939712</td>\n",
       "      <td>-0.262331</td>\n",
       "      <td>-0.482854</td>\n",
       "      <td>-0.718342</td>\n",
       "      <td>-0.530413</td>\n",
       "      <td>-0.580809</td>\n",
       "      <td>-1.321773</td>\n",
       "      <td>-1.524730</td>\n",
       "      <td>-1.603977</td>\n",
       "      <td>-1.518324</td>\n",
       "      <td>-0.078158</td>\n",
       "      <td>1.947040</td>\n",
       "      <td>-0.722131</td>\n",
       "      <td>-0.428790</td>\n",
       "      <td>5.427914</td>\n",
       "      <td>6.344659</td>\n",
       "      <td>6.617395</td>\n",
       "      <td>6.560273</td>\n",
       "      <td>6.491878</td>\n",
       "      <td>4.611237</td>\n",
       "      <td>3.921587</td>\n",
       "      <td>3.425656</td>\n",
       "      <td>3.694054</td>\n",
       "      <td>1.284989</td>\n",
       "      <td>-0.422690</td>\n",
       "      <td>5.228712</td>\n",
       "      <td>6.002206</td>\n",
       "      <td>-0.366538</td>\n",
       "      <td>-0.667327</td>\n",
       "      <td>-1.168407</td>\n",
       "      <td>-1.080123</td>\n",
       "      <td>-1.410391</td>\n",
       "      <td>-1.268875</td>\n",
       "      <td>-1.166422</td>\n",
       "      <td>-0.877409</td>\n",
       "      <td>-0.606561</td>\n",
       "      <td>0.742709</td>\n",
       "      <td>-0.626287</td>\n",
       "      <td>-1.897650</td>\n",
       "      <td>-1.560805</td>\n",
       "      <td>-0.438589</td>\n",
       "      <td>-0.767023</td>\n",
       "      <td>-1.086790</td>\n",
       "      <td>-1.159104</td>\n",
       "      <td>-1.453395</td>\n",
       "      <td>-1.063364</td>\n",
       "      <td>-0.888066</td>\n",
       "      <td>-0.625458</td>\n",
       "      <td>-0.977707</td>\n",
       "      <td>0.154776</td>\n",
       "      <td>-0.391373</td>\n",
       "      <td>-1.974944</td>\n",
       "      <td>-1.679033</td>\n",
       "      <td>-0.798914</td>\n",
       "      <td>-0.904605</td>\n",
       "      <td>-1.104658</td>\n",
       "      <td>-1.144280</td>\n",
       "      <td>-1.052129</td>\n",
       "      <td>-0.773712</td>\n",
       "      <td>-0.594230</td>\n",
       "      <td>-0.455976</td>\n",
       "      <td>-0.696226</td>\n",
       "      <td>0.528746</td>\n",
       "      <td>-0.362740</td>\n",
       "      <td>-1.582096</td>\n",
       "      <td>-1.410695</td>\n",
       "      <td>-0.796765</td>\n",
       "      <td>-0.998340</td>\n",
       "      <td>-1.200853</td>\n",
       "      <td>-1.168975</td>\n",
       "      <td>-1.168999</td>\n",
       "      <td>-1.033085</td>\n",
       "      <td>-0.865528</td>\n",
       "      <td>-0.552917</td>\n",
       "      <td>-0.576562</td>\n",
       "      <td>0.299271</td>\n",
       "      <td>-0.391988</td>\n",
       "      <td>-1.642958</td>\n",
       "      <td>-1.444056</td>\n",
       "      <td>-0.612766</td>\n",
       "      <td>-0.765120</td>\n",
       "      <td>-1.044888</td>\n",
       "      <td>-0.979987</td>\n",
       "      <td>-0.889702</td>\n",
       "      <td>-0.463911</td>\n",
       "      <td>-0.543556</td>\n",
       "      <td>0.107080</td>\n",
       "      <td>-0.333126</td>\n",
       "      <td>0.571030</td>\n",
       "      <td>0.086945</td>\n",
       "      <td>-1.475083</td>\n",
       "      <td>-1.347065</td>\n",
       "      <td>-0.539467</td>\n",
       "      <td>-0.695827</td>\n",
       "      <td>-1.173613</td>\n",
       "      <td>-1.232267</td>\n",
       "      <td>-1.922098</td>\n",
       "      <td>-2.937892</td>\n",
       "      <td>-2.922116</td>\n",
       "      <td>-2.191129</td>\n",
       "      <td>-3.074027</td>\n",
       "      <td>-3.034781</td>\n",
       "      <td>-0.745247</td>\n",
       "      <td>-3.084725</td>\n",
       "      <td>-2.312300</td>\n",
       "      <td>O9TURWL</td>\n",
       "      <td>5.657</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.522505</td>\n",
       "      <td>1.554115</td>\n",
       "      <td>1.458176</td>\n",
       "      <td>-1.698882</td>\n",
       "      <td>-0.121968</td>\n",
       "      <td>-3.352056</td>\n",
       "      <td>1.058261</td>\n",
       "      <td>1.233039</td>\n",
       "      <td>1.316011</td>\n",
       "      <td>1.235888</td>\n",
       "      <td>1.300542</td>\n",
       "      <td>0.849564</td>\n",
       "      <td>1.085381</td>\n",
       "      <td>1.383533</td>\n",
       "      <td>1.851676</td>\n",
       "      <td>0.247451</td>\n",
       "      <td>0.796091</td>\n",
       "      <td>-3.110536</td>\n",
       "      <td>2.680444</td>\n",
       "      <td>2.125552</td>\n",
       "      <td>1.870451</td>\n",
       "      <td>1.444073</td>\n",
       "      <td>1.858885</td>\n",
       "      <td>-0.997293</td>\n",
       "      <td>1.752869</td>\n",
       "      <td>1.650332</td>\n",
       "      <td>1.968221</td>\n",
       "      <td>-0.280812</td>\n",
       "      <td>0.782116</td>\n",
       "      <td>-3.102298</td>\n",
       "      <td>2.130336</td>\n",
       "      <td>1.811028</td>\n",
       "      <td>2.012402</td>\n",
       "      <td>1.628797</td>\n",
       "      <td>1.925828</td>\n",
       "      <td>-0.980790</td>\n",
       "      <td>1.005661</td>\n",
       "      <td>1.393687</td>\n",
       "      <td>2.278394</td>\n",
       "      <td>0.097394</td>\n",
       "      <td>0.730954</td>\n",
       "      <td>-2.279200</td>\n",
       "      <td>3.405180</td>\n",
       "      <td>2.461852</td>\n",
       "      <td>2.033135</td>\n",
       "      <td>1.627764</td>\n",
       "      <td>2.044501</td>\n",
       "      <td>-0.943202</td>\n",
       "      <td>0.589097</td>\n",
       "      <td>1.462961</td>\n",
       "      <td>1.013108</td>\n",
       "      <td>0.515433</td>\n",
       "      <td>0.330449</td>\n",
       "      <td>-3.983501</td>\n",
       "      <td>0.371852</td>\n",
       "      <td>-0.086780</td>\n",
       "      <td>1.054873</td>\n",
       "      <td>0.637326</td>\n",
       "      <td>1.029468</td>\n",
       "      <td>-2.304553</td>\n",
       "      <td>0.496260</td>\n",
       "      <td>1.740899</td>\n",
       "      <td>1.193353</td>\n",
       "      <td>2.210330</td>\n",
       "      <td>0.279182</td>\n",
       "      <td>0.717332</td>\n",
       "      <td>0.308115</td>\n",
       "      <td>-0.285698</td>\n",
       "      <td>0.768800</td>\n",
       "      <td>0.467869</td>\n",
       "      <td>0.965846</td>\n",
       "      <td>-3.225185</td>\n",
       "      <td>1.417541</td>\n",
       "      <td>1.514026</td>\n",
       "      <td>1.418577</td>\n",
       "      <td>-1.736225</td>\n",
       "      <td>-0.560923</td>\n",
       "      <td>-3.131921</td>\n",
       "      <td>1.195729</td>\n",
       "      <td>1.431894</td>\n",
       "      <td>1.178176</td>\n",
       "      <td>1.161156</td>\n",
       "      <td>1.306170</td>\n",
       "      <td>1.392300</td>\n",
       "      <td>1.737601</td>\n",
       "      <td>2.050365</td>\n",
       "      <td>3.067236</td>\n",
       "      <td>0.392356</td>\n",
       "      <td>0.699379</td>\n",
       "      <td>-1.756324</td>\n",
       "      <td>3.809511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498732</td>\n",
       "      <td>0.353775</td>\n",
       "      <td>-0.361248</td>\n",
       "      <td>2.158398</td>\n",
       "      <td>1.970399</td>\n",
       "      <td>-0.325077</td>\n",
       "      <td>1.756181</td>\n",
       "      <td>1.283123</td>\n",
       "      <td>-1.722897</td>\n",
       "      <td>1.381113</td>\n",
       "      <td>1.147400</td>\n",
       "      <td>-0.812587</td>\n",
       "      <td>-1.763704</td>\n",
       "      <td>-2.290043</td>\n",
       "      <td>-0.185172</td>\n",
       "      <td>0.242685</td>\n",
       "      <td>-0.302014</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-1.010884</td>\n",
       "      <td>-1.455867</td>\n",
       "      <td>-0.462458</td>\n",
       "      <td>-0.730915</td>\n",
       "      <td>-0.871773</td>\n",
       "      <td>1.931800</td>\n",
       "      <td>2.124629</td>\n",
       "      <td>-0.227202</td>\n",
       "      <td>-1.575325</td>\n",
       "      <td>-1.474457</td>\n",
       "      <td>-1.368936</td>\n",
       "      <td>-0.846084</td>\n",
       "      <td>-0.550994</td>\n",
       "      <td>-0.671140</td>\n",
       "      <td>-1.666344</td>\n",
       "      <td>-1.839944</td>\n",
       "      <td>-1.633969</td>\n",
       "      <td>0.309984</td>\n",
       "      <td>0.509961</td>\n",
       "      <td>0.272808</td>\n",
       "      <td>-1.436278</td>\n",
       "      <td>-2.127031</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>-1.413379</td>\n",
       "      <td>1.023037</td>\n",
       "      <td>2.292591</td>\n",
       "      <td>-1.706632</td>\n",
       "      <td>0.516192</td>\n",
       "      <td>2.759915</td>\n",
       "      <td>-1.384959</td>\n",
       "      <td>0.134485</td>\n",
       "      <td>2.929816</td>\n",
       "      <td>-1.008420</td>\n",
       "      <td>0.112525</td>\n",
       "      <td>2.913873</td>\n",
       "      <td>-1.132502</td>\n",
       "      <td>0.193839</td>\n",
       "      <td>2.918270</td>\n",
       "      <td>0.107795</td>\n",
       "      <td>0.056672</td>\n",
       "      <td>3.014851</td>\n",
       "      <td>0.047813</td>\n",
       "      <td>0.093300</td>\n",
       "      <td>2.983195</td>\n",
       "      <td>-0.222324</td>\n",
       "      <td>0.177121</td>\n",
       "      <td>2.582614</td>\n",
       "      <td>-0.016800</td>\n",
       "      <td>0.206686</td>\n",
       "      <td>2.916521</td>\n",
       "      <td>1.378121</td>\n",
       "      <td>1.410584</td>\n",
       "      <td>0.597262</td>\n",
       "      <td>0.224200</td>\n",
       "      <td>1.454431</td>\n",
       "      <td>1.409683</td>\n",
       "      <td>-1.116262</td>\n",
       "      <td>-0.850193</td>\n",
       "      <td>2.363674</td>\n",
       "      <td>-1.229320</td>\n",
       "      <td>-0.428791</td>\n",
       "      <td>2.675879</td>\n",
       "      <td>-1.126564</td>\n",
       "      <td>0.686449</td>\n",
       "      <td>0.330533</td>\n",
       "      <td>-1.674900</td>\n",
       "      <td>1.412788</td>\n",
       "      <td>1.135231</td>\n",
       "      <td>-1.642898</td>\n",
       "      <td>1.408510</td>\n",
       "      <td>1.001577</td>\n",
       "      <td>-2.321053</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.242575</td>\n",
       "      <td>-1.467004</td>\n",
       "      <td>1.613509</td>\n",
       "      <td>1.102364</td>\n",
       "      <td>0.792456</td>\n",
       "      <td>1.518242</td>\n",
       "      <td>1.002185</td>\n",
       "      <td>-0.899086</td>\n",
       "      <td>0.618614</td>\n",
       "      <td>0.340965</td>\n",
       "      <td>-0.328456</td>\n",
       "      <td>2.202174</td>\n",
       "      <td>1.912520</td>\n",
       "      <td>-0.271115</td>\n",
       "      <td>1.813520</td>\n",
       "      <td>1.270940</td>\n",
       "      <td>-1.647056</td>\n",
       "      <td>1.436873</td>\n",
       "      <td>1.134945</td>\n",
       "      <td>-0.812587</td>\n",
       "      <td>-1.728046</td>\n",
       "      <td>-2.363762</td>\n",
       "      <td>-0.185172</td>\n",
       "      <td>0.055486</td>\n",
       "      <td>-0.302014</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-1.056203</td>\n",
       "      <td>-1.535257</td>\n",
       "      <td>-0.436008</td>\n",
       "      <td>-0.748485</td>\n",
       "      <td>-0.933871</td>\n",
       "      <td>1.950787</td>\n",
       "      <td>2.047945</td>\n",
       "      <td>-0.227202</td>\n",
       "      <td>-1.566985</td>\n",
       "      <td>-1.495223</td>\n",
       "      <td>-1.381126</td>\n",
       "      <td>-0.846084</td>\n",
       "      <td>-0.610036</td>\n",
       "      <td>-0.671140</td>\n",
       "      <td>-1.666344</td>\n",
       "      <td>-1.772554</td>\n",
       "      <td>-1.634636</td>\n",
       "      <td>0.314369</td>\n",
       "      <td>0.324218</td>\n",
       "      <td>0.272453</td>\n",
       "      <td>-1.433983</td>\n",
       "      <td>-2.021150</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>-1.399666</td>\n",
       "      <td>0.814370</td>\n",
       "      <td>2.191401</td>\n",
       "      <td>-1.690946</td>\n",
       "      <td>0.321585</td>\n",
       "      <td>2.635316</td>\n",
       "      <td>-1.347524</td>\n",
       "      <td>-0.069598</td>\n",
       "      <td>2.796461</td>\n",
       "      <td>-0.991685</td>\n",
       "      <td>-0.050848</td>\n",
       "      <td>2.792126</td>\n",
       "      <td>-1.075652</td>\n",
       "      <td>0.031439</td>\n",
       "      <td>2.791453</td>\n",
       "      <td>0.144277</td>\n",
       "      <td>-0.025717</td>\n",
       "      <td>2.898216</td>\n",
       "      <td>0.082667</td>\n",
       "      <td>-0.002745</td>\n",
       "      <td>2.898406</td>\n",
       "      <td>-0.182136</td>\n",
       "      <td>0.179245</td>\n",
       "      <td>2.515100</td>\n",
       "      <td>0.019603</td>\n",
       "      <td>0.138394</td>\n",
       "      <td>2.839547</td>\n",
       "      <td>1.409895</td>\n",
       "      <td>1.416122</td>\n",
       "      <td>0.569082</td>\n",
       "      <td>0.226005</td>\n",
       "      <td>1.451829</td>\n",
       "      <td>1.242940</td>\n",
       "      <td>-1.052112</td>\n",
       "      <td>-0.970296</td>\n",
       "      <td>2.295457</td>\n",
       "      <td>-1.185594</td>\n",
       "      <td>-0.569851</td>\n",
       "      <td>2.601639</td>\n",
       "      <td>-1.110490</td>\n",
       "      <td>0.792091</td>\n",
       "      <td>0.386929</td>\n",
       "      <td>-1.671320</td>\n",
       "      <td>1.485780</td>\n",
       "      <td>1.135726</td>\n",
       "      <td>-1.640104</td>\n",
       "      <td>1.496963</td>\n",
       "      <td>1.003073</td>\n",
       "      <td>-2.251472</td>\n",
       "      <td>0.400323</td>\n",
       "      <td>0.241749</td>\n",
       "      <td>-1.467740</td>\n",
       "      <td>1.680906</td>\n",
       "      <td>1.102345</td>\n",
       "      <td>0.831550</td>\n",
       "      <td>1.555777</td>\n",
       "      <td>0.992347</td>\n",
       "      <td>-0.881753</td>\n",
       "      <td>0.729896</td>\n",
       "      <td>0.319480</td>\n",
       "      <td>-0.310198</td>\n",
       "      <td>2.284126</td>\n",
       "      <td>1.904374</td>\n",
       "      <td>-0.215256</td>\n",
       "      <td>1.927657</td>\n",
       "      <td>1.271151</td>\n",
       "      <td>-1.643965</td>\n",
       "      <td>1.508983</td>\n",
       "      <td>1.135381</td>\n",
       "      <td>-0.813817</td>\n",
       "      <td>-1.980706</td>\n",
       "      <td>-2.950848</td>\n",
       "      <td>-0.185172</td>\n",
       "      <td>-0.109585</td>\n",
       "      <td>-0.302014</td>\n",
       "      <td>-1.087328</td>\n",
       "      <td>-1.232721</td>\n",
       "      <td>-1.548715</td>\n",
       "      <td>-0.479848</td>\n",
       "      <td>-0.777142</td>\n",
       "      <td>-0.946498</td>\n",
       "      <td>1.951690</td>\n",
       "      <td>1.843961</td>\n",
       "      <td>-0.227202</td>\n",
       "      <td>-1.565255</td>\n",
       "      <td>-1.499182</td>\n",
       "      <td>-1.415773</td>\n",
       "      <td>-0.846084</td>\n",
       "      <td>-0.641752</td>\n",
       "      <td>-0.671140</td>\n",
       "      <td>-1.666344</td>\n",
       "      <td>-1.728779</td>\n",
       "      <td>-1.702120</td>\n",
       "      <td>0.314398</td>\n",
       "      <td>0.207686</td>\n",
       "      <td>0.272453</td>\n",
       "      <td>-1.434137</td>\n",
       "      <td>-2.122053</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>0.365556</td>\n",
       "      <td>2.165604</td>\n",
       "      <td>-0.625979</td>\n",
       "      <td>0.586907</td>\n",
       "      <td>0.987846</td>\n",
       "      <td>-0.096934</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>1.998725</td>\n",
       "      <td>-0.949998</td>\n",
       "      <td>0.552503</td>\n",
       "      <td>0.027779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.506280</td>\n",
       "      <td>-0.064720</td>\n",
       "      <td>0.050852</td>\n",
       "      <td>0.652771</td>\n",
       "      <td>0.467481</td>\n",
       "      <td>-1.137781</td>\n",
       "      <td>-1.027181</td>\n",
       "      <td>-1.116117</td>\n",
       "      <td>-0.974412</td>\n",
       "      <td>-0.102437</td>\n",
       "      <td>-0.330652</td>\n",
       "      <td>1.015516</td>\n",
       "      <td>0.982034</td>\n",
       "      <td>-0.325317</td>\n",
       "      <td>0.034980</td>\n",
       "      <td>0.094382</td>\n",
       "      <td>0.805999</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.477848</td>\n",
       "      <td>-0.579808</td>\n",
       "      <td>-0.711306</td>\n",
       "      <td>-0.356045</td>\n",
       "      <td>1.029730</td>\n",
       "      <td>2.900829</td>\n",
       "      <td>1.462188</td>\n",
       "      <td>1.509555</td>\n",
       "      <td>-0.572166</td>\n",
       "      <td>-0.298164</td>\n",
       "      <td>-0.188298</td>\n",
       "      <td>0.959799</td>\n",
       "      <td>0.431103</td>\n",
       "      <td>-1.153453</td>\n",
       "      <td>-1.260397</td>\n",
       "      <td>-1.177570</td>\n",
       "      <td>-1.187979</td>\n",
       "      <td>0.404746</td>\n",
       "      <td>-0.587026</td>\n",
       "      <td>1.037903</td>\n",
       "      <td>1.557088</td>\n",
       "      <td>-0.025616</td>\n",
       "      <td>0.127219</td>\n",
       "      <td>0.167745</td>\n",
       "      <td>1.158449</td>\n",
       "      <td>0.644839</td>\n",
       "      <td>-1.193471</td>\n",
       "      <td>-1.314996</td>\n",
       "      <td>-1.128726</td>\n",
       "      <td>-1.101808</td>\n",
       "      <td>-0.192264</td>\n",
       "      <td>0.939002</td>\n",
       "      <td>1.396085</td>\n",
       "      <td>1.741510</td>\n",
       "      <td>-0.047648</td>\n",
       "      <td>-0.003398</td>\n",
       "      <td>0.038644</td>\n",
       "      <td>1.022152</td>\n",
       "      <td>0.507379</td>\n",
       "      <td>-1.408230</td>\n",
       "      <td>-1.594139</td>\n",
       "      <td>-1.564628</td>\n",
       "      <td>-1.554348</td>\n",
       "      <td>-0.362114</td>\n",
       "      <td>2.639180</td>\n",
       "      <td>1.515664</td>\n",
       "      <td>2.307646</td>\n",
       "      <td>-0.243212</td>\n",
       "      <td>-0.104452</td>\n",
       "      <td>0.014692</td>\n",
       "      <td>0.462684</td>\n",
       "      <td>0.441186</td>\n",
       "      <td>-0.645229</td>\n",
       "      <td>-0.749179</td>\n",
       "      <td>-0.691994</td>\n",
       "      <td>-0.638782</td>\n",
       "      <td>0.412057</td>\n",
       "      <td>1.051820</td>\n",
       "      <td>1.095446</td>\n",
       "      <td>1.236724</td>\n",
       "      <td>-0.577674</td>\n",
       "      <td>-0.578546</td>\n",
       "      <td>-0.620165</td>\n",
       "      <td>-0.976172</td>\n",
       "      <td>-0.989278</td>\n",
       "      <td>2.043972</td>\n",
       "      <td>3.188548</td>\n",
       "      <td>2.996967</td>\n",
       "      <td>3.079458</td>\n",
       "      <td>1.605140</td>\n",
       "      <td>-0.576696</td>\n",
       "      <td>-0.798772</td>\n",
       "      <td>-1.185140</td>\n",
       "      <td>-0.430820</td>\n",
       "      <td>-0.429768</td>\n",
       "      <td>-0.350474</td>\n",
       "      <td>-0.729008</td>\n",
       "      <td>-0.639717</td>\n",
       "      <td>0.275783</td>\n",
       "      <td>0.621853</td>\n",
       "      <td>0.408904</td>\n",
       "      <td>0.513190</td>\n",
       "      <td>0.508014</td>\n",
       "      <td>-0.218796</td>\n",
       "      <td>-0.930035</td>\n",
       "      <td>-1.057510</td>\n",
       "      <td>-0.724725</td>\n",
       "      <td>-0.595094</td>\n",
       "      <td>-0.447649</td>\n",
       "      <td>-0.632745</td>\n",
       "      <td>-0.270432</td>\n",
       "      <td>-0.073988</td>\n",
       "      <td>-0.112445</td>\n",
       "      <td>-0.096895</td>\n",
       "      <td>-0.085977</td>\n",
       "      <td>0.962317</td>\n",
       "      <td>-0.243496</td>\n",
       "      <td>-0.634817</td>\n",
       "      <td>-0.685827</td>\n",
       "      <td>-0.704843</td>\n",
       "      <td>-0.547440</td>\n",
       "      <td>-0.516652</td>\n",
       "      <td>-0.438011</td>\n",
       "      <td>-0.407831</td>\n",
       "      <td>-0.754939</td>\n",
       "      <td>-0.897607</td>\n",
       "      <td>-1.067804</td>\n",
       "      <td>-0.662844</td>\n",
       "      <td>0.245659</td>\n",
       "      <td>-0.475385</td>\n",
       "      <td>-0.474132</td>\n",
       "      <td>-0.475514</td>\n",
       "      <td>-0.457338</td>\n",
       "      <td>-0.170944</td>\n",
       "      <td>0.313647</td>\n",
       "      <td>-0.099644</td>\n",
       "      <td>0.508221</td>\n",
       "      <td>0.510200</td>\n",
       "      <td>0.462311</td>\n",
       "      <td>0.675554</td>\n",
       "      <td>0.392646</td>\n",
       "      <td>1.173219</td>\n",
       "      <td>0.285400</td>\n",
       "      <td>0.327004</td>\n",
       "      <td>0.037931</td>\n",
       "      <td>1.494892</td>\n",
       "      <td>1.285916</td>\n",
       "      <td>1.263081</td>\n",
       "      <td>0.397512</td>\n",
       "      <td>1.076839</td>\n",
       "      <td>2.038535</td>\n",
       "      <td>1.947937</td>\n",
       "      <td>1.641830</td>\n",
       "      <td>2.079419</td>\n",
       "      <td>1.038880</td>\n",
       "      <td>-0.496849</td>\n",
       "      <td>0.664894</td>\n",
       "      <td>0.456400</td>\n",
       "      <td>35AFSDD</td>\n",
       "      <td>3.082</td>\n",
       "      <td>2019</td>\n",
       "      <td>-1.180534</td>\n",
       "      <td>-1.149292</td>\n",
       "      <td>-1.644908</td>\n",
       "      <td>-1.847743</td>\n",
       "      <td>-2.625956</td>\n",
       "      <td>-1.053528</td>\n",
       "      <td>1.938507</td>\n",
       "      <td>1.024284</td>\n",
       "      <td>0.720854</td>\n",
       "      <td>-0.058400</td>\n",
       "      <td>0.391185</td>\n",
       "      <td>0.200670</td>\n",
       "      <td>-0.394029</td>\n",
       "      <td>0.146354</td>\n",
       "      <td>0.099412</td>\n",
       "      <td>-0.617239</td>\n",
       "      <td>-1.038906</td>\n",
       "      <td>-0.200005</td>\n",
       "      <td>2.588992</td>\n",
       "      <td>1.261226</td>\n",
       "      <td>1.212138</td>\n",
       "      <td>0.562686</td>\n",
       "      <td>0.375785</td>\n",
       "      <td>-0.444089</td>\n",
       "      <td>-0.719311</td>\n",
       "      <td>-0.047815</td>\n",
       "      <td>-0.422490</td>\n",
       "      <td>-1.036370</td>\n",
       "      <td>-1.587129</td>\n",
       "      <td>-0.455282</td>\n",
       "      <td>2.311518</td>\n",
       "      <td>1.259300</td>\n",
       "      <td>1.362909</td>\n",
       "      <td>0.541319</td>\n",
       "      <td>0.698691</td>\n",
       "      <td>-0.040104</td>\n",
       "      <td>-0.465577</td>\n",
       "      <td>0.009222</td>\n",
       "      <td>-0.043006</td>\n",
       "      <td>-0.646635</td>\n",
       "      <td>-1.009774</td>\n",
       "      <td>-0.311084</td>\n",
       "      <td>3.241278</td>\n",
       "      <td>1.244366</td>\n",
       "      <td>1.166712</td>\n",
       "      <td>0.459615</td>\n",
       "      <td>0.285931</td>\n",
       "      <td>-0.496459</td>\n",
       "      <td>0.406344</td>\n",
       "      <td>1.774530</td>\n",
       "      <td>0.931916</td>\n",
       "      <td>0.263373</td>\n",
       "      <td>0.436298</td>\n",
       "      <td>0.884134</td>\n",
       "      <td>1.026278</td>\n",
       "      <td>0.439648</td>\n",
       "      <td>1.535413</td>\n",
       "      <td>0.682508</td>\n",
       "      <td>1.470018</td>\n",
       "      <td>-1.020598</td>\n",
       "      <td>0.119992</td>\n",
       "      <td>1.777294</td>\n",
       "      <td>0.784974</td>\n",
       "      <td>0.082761</td>\n",
       "      <td>0.214848</td>\n",
       "      <td>0.873456</td>\n",
       "      <td>0.826379</td>\n",
       "      <td>0.160287</td>\n",
       "      <td>1.190216</td>\n",
       "      <td>0.363583</td>\n",
       "      <td>1.258747</td>\n",
       "      <td>0.521045</td>\n",
       "      <td>-1.225831</td>\n",
       "      <td>-1.434553</td>\n",
       "      <td>-1.560772</td>\n",
       "      <td>-1.745795</td>\n",
       "      <td>-2.519196</td>\n",
       "      <td>-1.194217</td>\n",
       "      <td>2.404703</td>\n",
       "      <td>1.080354</td>\n",
       "      <td>0.410779</td>\n",
       "      <td>-0.348859</td>\n",
       "      <td>0.070180</td>\n",
       "      <td>-0.050112</td>\n",
       "      <td>-0.736752</td>\n",
       "      <td>-0.262068</td>\n",
       "      <td>-0.442865</td>\n",
       "      <td>-0.942270</td>\n",
       "      <td>-1.476519</td>\n",
       "      <td>-0.646780</td>\n",
       "      <td>2.181855</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.014367</td>\n",
       "      <td>1.964974</td>\n",
       "      <td>-0.340733</td>\n",
       "      <td>-0.027683</td>\n",
       "      <td>0.641815</td>\n",
       "      <td>0.156454</td>\n",
       "      <td>0.944188</td>\n",
       "      <td>0.356076</td>\n",
       "      <td>0.434961</td>\n",
       "      <td>0.368659</td>\n",
       "      <td>1.048250</td>\n",
       "      <td>-0.826832</td>\n",
       "      <td>-1.960023</td>\n",
       "      <td>-2.581304</td>\n",
       "      <td>-0.067179</td>\n",
       "      <td>0.414710</td>\n",
       "      <td>-0.319389</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-1.088977</td>\n",
       "      <td>-1.509670</td>\n",
       "      <td>-0.491663</td>\n",
       "      <td>-0.749827</td>\n",
       "      <td>-0.890723</td>\n",
       "      <td>1.901442</td>\n",
       "      <td>1.991100</td>\n",
       "      <td>-0.827699</td>\n",
       "      <td>-1.466250</td>\n",
       "      <td>-1.381287</td>\n",
       "      <td>-1.257270</td>\n",
       "      <td>-0.925270</td>\n",
       "      <td>-0.629921</td>\n",
       "      <td>-0.869477</td>\n",
       "      <td>-1.630175</td>\n",
       "      <td>-1.814508</td>\n",
       "      <td>-1.633969</td>\n",
       "      <td>0.396626</td>\n",
       "      <td>0.450093</td>\n",
       "      <td>0.157884</td>\n",
       "      <td>-0.687802</td>\n",
       "      <td>-1.478705</td>\n",
       "      <td>-1.902616</td>\n",
       "      <td>-1.056384</td>\n",
       "      <td>-0.879437</td>\n",
       "      <td>-0.580023</td>\n",
       "      <td>-0.097004</td>\n",
       "      <td>-0.526506</td>\n",
       "      <td>-0.504856</td>\n",
       "      <td>-0.068587</td>\n",
       "      <td>-0.348684</td>\n",
       "      <td>-0.484028</td>\n",
       "      <td>-0.769878</td>\n",
       "      <td>0.411841</td>\n",
       "      <td>-0.077915</td>\n",
       "      <td>-0.460391</td>\n",
       "      <td>0.206524</td>\n",
       "      <td>-0.252602</td>\n",
       "      <td>-0.073719</td>\n",
       "      <td>-0.900381</td>\n",
       "      <td>0.450574</td>\n",
       "      <td>-0.237405</td>\n",
       "      <td>-0.744995</td>\n",
       "      <td>1.605057</td>\n",
       "      <td>-0.168316</td>\n",
       "      <td>-0.774590</td>\n",
       "      <td>1.515804</td>\n",
       "      <td>-0.186943</td>\n",
       "      <td>-0.582511</td>\n",
       "      <td>1.684066</td>\n",
       "      <td>0.987906</td>\n",
       "      <td>0.740764</td>\n",
       "      <td>-0.628297</td>\n",
       "      <td>0.907214</td>\n",
       "      <td>1.350447</td>\n",
       "      <td>0.332034</td>\n",
       "      <td>0.010540</td>\n",
       "      <td>0.732293</td>\n",
       "      <td>0.330440</td>\n",
       "      <td>-0.677565</td>\n",
       "      <td>0.968536</td>\n",
       "      <td>0.800664</td>\n",
       "      <td>-1.131812</td>\n",
       "      <td>-0.923393</td>\n",
       "      <td>1.489705</td>\n",
       "      <td>0.491373</td>\n",
       "      <td>0.417249</td>\n",
       "      <td>1.038050</td>\n",
       "      <td>-0.031130</td>\n",
       "      <td>0.105980</td>\n",
       "      <td>1.115350</td>\n",
       "      <td>1.179177</td>\n",
       "      <td>1.359856</td>\n",
       "      <td>0.399989</td>\n",
       "      <td>0.401089</td>\n",
       "      <td>0.332120</td>\n",
       "      <td>0.983655</td>\n",
       "      <td>1.355869</td>\n",
       "      <td>1.065996</td>\n",
       "      <td>0.029554</td>\n",
       "      <td>-1.043190</td>\n",
       "      <td>-0.964810</td>\n",
       "      <td>1.966623</td>\n",
       "      <td>-0.307911</td>\n",
       "      <td>0.034633</td>\n",
       "      <td>0.609823</td>\n",
       "      <td>0.203078</td>\n",
       "      <td>0.944021</td>\n",
       "      <td>0.337236</td>\n",
       "      <td>0.480939</td>\n",
       "      <td>0.407987</td>\n",
       "      <td>1.035719</td>\n",
       "      <td>-0.826832</td>\n",
       "      <td>-1.940097</td>\n",
       "      <td>-2.660715</td>\n",
       "      <td>-0.067179</td>\n",
       "      <td>0.225638</td>\n",
       "      <td>-0.319389</td>\n",
       "      <td>-0.875240</td>\n",
       "      <td>-1.143377</td>\n",
       "      <td>-1.588663</td>\n",
       "      <td>-0.455941</td>\n",
       "      <td>-0.766577</td>\n",
       "      <td>-0.952227</td>\n",
       "      <td>1.920198</td>\n",
       "      <td>1.916674</td>\n",
       "      <td>-0.827699</td>\n",
       "      <td>-1.457073</td>\n",
       "      <td>-1.417063</td>\n",
       "      <td>-1.268839</td>\n",
       "      <td>-0.925270</td>\n",
       "      <td>-0.698780</td>\n",
       "      <td>-0.869477</td>\n",
       "      <td>-1.630175</td>\n",
       "      <td>-1.751272</td>\n",
       "      <td>-1.634636</td>\n",
       "      <td>0.401400</td>\n",
       "      <td>0.246373</td>\n",
       "      <td>0.157410</td>\n",
       "      <td>-0.685394</td>\n",
       "      <td>-1.371491</td>\n",
       "      <td>-1.902616</td>\n",
       "      <td>-1.042955</td>\n",
       "      <td>-0.902486</td>\n",
       "      <td>-0.598420</td>\n",
       "      <td>-0.074406</td>\n",
       "      <td>-0.518607</td>\n",
       "      <td>-0.528130</td>\n",
       "      <td>-0.041761</td>\n",
       "      <td>-0.262142</td>\n",
       "      <td>-0.467328</td>\n",
       "      <td>-0.750090</td>\n",
       "      <td>0.364090</td>\n",
       "      <td>-0.113998</td>\n",
       "      <td>-0.413800</td>\n",
       "      <td>0.274243</td>\n",
       "      <td>-0.283570</td>\n",
       "      <td>-0.034635</td>\n",
       "      <td>-0.743199</td>\n",
       "      <td>0.388744</td>\n",
       "      <td>-0.199253</td>\n",
       "      <td>-0.609807</td>\n",
       "      <td>1.532924</td>\n",
       "      <td>-0.128770</td>\n",
       "      <td>-0.606464</td>\n",
       "      <td>1.451803</td>\n",
       "      <td>-0.148348</td>\n",
       "      <td>-0.475080</td>\n",
       "      <td>1.613776</td>\n",
       "      <td>1.022153</td>\n",
       "      <td>0.854519</td>\n",
       "      <td>-0.641322</td>\n",
       "      <td>0.909326</td>\n",
       "      <td>1.417937</td>\n",
       "      <td>0.129357</td>\n",
       "      <td>0.053866</td>\n",
       "      <td>0.715876</td>\n",
       "      <td>0.292185</td>\n",
       "      <td>-0.637437</td>\n",
       "      <td>0.900042</td>\n",
       "      <td>0.755749</td>\n",
       "      <td>-1.115804</td>\n",
       "      <td>-0.805220</td>\n",
       "      <td>1.487399</td>\n",
       "      <td>0.518930</td>\n",
       "      <td>0.422806</td>\n",
       "      <td>1.038356</td>\n",
       "      <td>-0.003295</td>\n",
       "      <td>0.168928</td>\n",
       "      <td>1.117598</td>\n",
       "      <td>1.197971</td>\n",
       "      <td>1.445396</td>\n",
       "      <td>0.399359</td>\n",
       "      <td>0.431112</td>\n",
       "      <td>0.334705</td>\n",
       "      <td>0.983501</td>\n",
       "      <td>1.390774</td>\n",
       "      <td>1.157775</td>\n",
       "      <td>0.019295</td>\n",
       "      <td>-1.027757</td>\n",
       "      <td>-0.881680</td>\n",
       "      <td>1.971084</td>\n",
       "      <td>-0.289279</td>\n",
       "      <td>0.075120</td>\n",
       "      <td>0.601463</td>\n",
       "      <td>0.254894</td>\n",
       "      <td>0.940978</td>\n",
       "      <td>0.335303</td>\n",
       "      <td>0.508777</td>\n",
       "      <td>0.413180</td>\n",
       "      <td>1.035971</td>\n",
       "      <td>-0.828107</td>\n",
       "      <td>-2.212500</td>\n",
       "      <td>-3.289921</td>\n",
       "      <td>-0.067179</td>\n",
       "      <td>0.042524</td>\n",
       "      <td>-0.319389</td>\n",
       "      <td>-1.087328</td>\n",
       "      <td>-1.335644</td>\n",
       "      <td>-1.602383</td>\n",
       "      <td>-0.513397</td>\n",
       "      <td>-0.795300</td>\n",
       "      <td>-0.964776</td>\n",
       "      <td>1.921091</td>\n",
       "      <td>1.707527</td>\n",
       "      <td>-0.827699</td>\n",
       "      <td>-1.455385</td>\n",
       "      <td>-1.427783</td>\n",
       "      <td>-1.301833</td>\n",
       "      <td>-0.925270</td>\n",
       "      <td>-0.740795</td>\n",
       "      <td>-0.869477</td>\n",
       "      <td>-1.630175</td>\n",
       "      <td>-1.707188</td>\n",
       "      <td>-1.702120</td>\n",
       "      <td>0.401431</td>\n",
       "      <td>0.112087</td>\n",
       "      <td>0.157410</td>\n",
       "      <td>-0.684545</td>\n",
       "      <td>-1.508740</td>\n",
       "      <td>-1.902616</td>\n",
       "      <td>0.365556</td>\n",
       "      <td>1.557960</td>\n",
       "      <td>0.061045</td>\n",
       "      <td>1.421385</td>\n",
       "      <td>0.779088</td>\n",
       "      <td>-0.534358</td>\n",
       "      <td>-0.218642</td>\n",
       "      <td>1.568439</td>\n",
       "      <td>-0.385144</td>\n",
       "      <td>-0.858249</td>\n",
       "      <td>0.085561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.457086</td>\n",
       "      <td>0.099466</td>\n",
       "      <td>0.172048</td>\n",
       "      <td>0.416339</td>\n",
       "      <td>0.352612</td>\n",
       "      <td>-0.648014</td>\n",
       "      <td>-0.499663</td>\n",
       "      <td>-0.262070</td>\n",
       "      <td>-0.366009</td>\n",
       "      <td>0.803305</td>\n",
       "      <td>0.650603</td>\n",
       "      <td>0.740502</td>\n",
       "      <td>0.609093</td>\n",
       "      <td>-0.341811</td>\n",
       "      <td>0.295615</td>\n",
       "      <td>0.394559</td>\n",
       "      <td>1.188656</td>\n",
       "      <td>1.390533</td>\n",
       "      <td>0.033380</td>\n",
       "      <td>-0.041826</td>\n",
       "      <td>0.059556</td>\n",
       "      <td>0.103097</td>\n",
       "      <td>2.036161</td>\n",
       "      <td>1.337011</td>\n",
       "      <td>1.819421</td>\n",
       "      <td>1.568723</td>\n",
       "      <td>-0.464512</td>\n",
       "      <td>-0.485809</td>\n",
       "      <td>-0.574414</td>\n",
       "      <td>0.513770</td>\n",
       "      <td>0.210061</td>\n",
       "      <td>-1.395878</td>\n",
       "      <td>-1.461439</td>\n",
       "      <td>-1.545062</td>\n",
       "      <td>-1.490310</td>\n",
       "      <td>0.619424</td>\n",
       "      <td>1.120431</td>\n",
       "      <td>0.452033</td>\n",
       "      <td>1.142201</td>\n",
       "      <td>-0.077359</td>\n",
       "      <td>-0.125925</td>\n",
       "      <td>-0.248956</td>\n",
       "      <td>0.829711</td>\n",
       "      <td>0.474646</td>\n",
       "      <td>-1.415382</td>\n",
       "      <td>-1.520585</td>\n",
       "      <td>-1.641979</td>\n",
       "      <td>-1.427110</td>\n",
       "      <td>0.451812</td>\n",
       "      <td>0.969835</td>\n",
       "      <td>1.026160</td>\n",
       "      <td>1.516745</td>\n",
       "      <td>-0.047648</td>\n",
       "      <td>-0.059207</td>\n",
       "      <td>-0.030173</td>\n",
       "      <td>0.873873</td>\n",
       "      <td>0.698580</td>\n",
       "      <td>-1.015416</td>\n",
       "      <td>-1.296454</td>\n",
       "      <td>-1.455661</td>\n",
       "      <td>-1.362698</td>\n",
       "      <td>0.387959</td>\n",
       "      <td>1.774005</td>\n",
       "      <td>1.504871</td>\n",
       "      <td>2.191750</td>\n",
       "      <td>0.113104</td>\n",
       "      <td>-0.068700</td>\n",
       "      <td>-0.061591</td>\n",
       "      <td>-0.405960</td>\n",
       "      <td>-0.423577</td>\n",
       "      <td>-0.015007</td>\n",
       "      <td>-0.119476</td>\n",
       "      <td>0.031402</td>\n",
       "      <td>-0.060481</td>\n",
       "      <td>-0.729861</td>\n",
       "      <td>-0.619291</td>\n",
       "      <td>-0.777667</td>\n",
       "      <td>-0.817204</td>\n",
       "      <td>2.909978</td>\n",
       "      <td>1.442104</td>\n",
       "      <td>0.863313</td>\n",
       "      <td>0.508003</td>\n",
       "      <td>0.318722</td>\n",
       "      <td>-0.426362</td>\n",
       "      <td>-0.304092</td>\n",
       "      <td>-0.333637</td>\n",
       "      <td>-0.581732</td>\n",
       "      <td>1.129535</td>\n",
       "      <td>-0.378332</td>\n",
       "      <td>-0.926451</td>\n",
       "      <td>-0.203010</td>\n",
       "      <td>0.097474</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>0.028512</td>\n",
       "      <td>0.327961</td>\n",
       "      <td>0.329838</td>\n",
       "      <td>-0.133838</td>\n",
       "      <td>-0.225175</td>\n",
       "      <td>-0.176273</td>\n",
       "      <td>-0.191343</td>\n",
       "      <td>1.340646</td>\n",
       "      <td>1.161823</td>\n",
       "      <td>0.399309</td>\n",
       "      <td>0.585547</td>\n",
       "      <td>-0.620862</td>\n",
       "      <td>-0.565459</td>\n",
       "      <td>-0.450951</td>\n",
       "      <td>-0.793736</td>\n",
       "      <td>-0.384612</td>\n",
       "      <td>1.088096</td>\n",
       "      <td>1.196156</td>\n",
       "      <td>1.572133</td>\n",
       "      <td>1.297772</td>\n",
       "      <td>1.303328</td>\n",
       "      <td>-0.243496</td>\n",
       "      <td>-0.610285</td>\n",
       "      <td>-0.817431</td>\n",
       "      <td>-0.446755</td>\n",
       "      <td>-0.469465</td>\n",
       "      <td>-0.387301</td>\n",
       "      <td>-0.488698</td>\n",
       "      <td>-0.372151</td>\n",
       "      <td>0.120261</td>\n",
       "      <td>0.156977</td>\n",
       "      <td>0.063041</td>\n",
       "      <td>0.332150</td>\n",
       "      <td>0.289524</td>\n",
       "      <td>-0.225196</td>\n",
       "      <td>-0.636833</td>\n",
       "      <td>-0.781257</td>\n",
       "      <td>-0.546709</td>\n",
       "      <td>-0.398942</td>\n",
       "      <td>-0.250403</td>\n",
       "      <td>-0.415449</td>\n",
       "      <td>-0.327460</td>\n",
       "      <td>-0.097038</td>\n",
       "      <td>-0.359530</td>\n",
       "      <td>-0.153892</td>\n",
       "      <td>-0.095073</td>\n",
       "      <td>0.531757</td>\n",
       "      <td>0.037331</td>\n",
       "      <td>-0.713209</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>0.025470</td>\n",
       "      <td>-0.054513</td>\n",
       "      <td>-0.082350</td>\n",
       "      <td>0.139329</td>\n",
       "      <td>0.090412</td>\n",
       "      <td>-0.305201</td>\n",
       "      <td>-0.379065</td>\n",
       "      <td>-0.387843</td>\n",
       "      <td>-0.368561</td>\n",
       "      <td>0.640313</td>\n",
       "      <td>0.434646</td>\n",
       "      <td>0.192667</td>\n",
       "      <td>0.361622</td>\n",
       "      <td>PM05EG9</td>\n",
       "      <td>2.707</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.515463</td>\n",
       "      <td>-0.941315</td>\n",
       "      <td>-1.594315</td>\n",
       "      <td>-2.007443</td>\n",
       "      <td>-2.315807</td>\n",
       "      <td>0.551391</td>\n",
       "      <td>-0.760086</td>\n",
       "      <td>-0.456865</td>\n",
       "      <td>1.500215</td>\n",
       "      <td>0.572887</td>\n",
       "      <td>0.420081</td>\n",
       "      <td>-0.457434</td>\n",
       "      <td>0.571096</td>\n",
       "      <td>0.535765</td>\n",
       "      <td>0.415525</td>\n",
       "      <td>0.070034</td>\n",
       "      <td>-0.594470</td>\n",
       "      <td>-0.172820</td>\n",
       "      <td>0.549204</td>\n",
       "      <td>0.905622</td>\n",
       "      <td>1.628923</td>\n",
       "      <td>0.560279</td>\n",
       "      <td>0.915752</td>\n",
       "      <td>0.413990</td>\n",
       "      <td>0.196888</td>\n",
       "      <td>0.179166</td>\n",
       "      <td>-0.012856</td>\n",
       "      <td>-0.404618</td>\n",
       "      <td>-1.012305</td>\n",
       "      <td>0.025999</td>\n",
       "      <td>-0.047455</td>\n",
       "      <td>0.428459</td>\n",
       "      <td>1.794452</td>\n",
       "      <td>0.626350</td>\n",
       "      <td>0.823608</td>\n",
       "      <td>0.059055</td>\n",
       "      <td>0.421532</td>\n",
       "      <td>0.393815</td>\n",
       "      <td>0.283721</td>\n",
       "      <td>-0.069920</td>\n",
       "      <td>-0.658241</td>\n",
       "      <td>-0.286077</td>\n",
       "      <td>0.447462</td>\n",
       "      <td>0.819652</td>\n",
       "      <td>1.697838</td>\n",
       "      <td>0.456853</td>\n",
       "      <td>0.868007</td>\n",
       "      <td>0.301782</td>\n",
       "      <td>0.901650</td>\n",
       "      <td>2.640949</td>\n",
       "      <td>1.063103</td>\n",
       "      <td>0.821719</td>\n",
       "      <td>1.167392</td>\n",
       "      <td>-0.873168</td>\n",
       "      <td>-2.740689</td>\n",
       "      <td>1.433133</td>\n",
       "      <td>1.549962</td>\n",
       "      <td>0.450456</td>\n",
       "      <td>0.746231</td>\n",
       "      <td>0.556360</td>\n",
       "      <td>0.740457</td>\n",
       "      <td>2.899414</td>\n",
       "      <td>1.212147</td>\n",
       "      <td>1.019609</td>\n",
       "      <td>1.445013</td>\n",
       "      <td>-1.044908</td>\n",
       "      <td>0.343141</td>\n",
       "      <td>1.670332</td>\n",
       "      <td>1.367121</td>\n",
       "      <td>0.270105</td>\n",
       "      <td>0.373257</td>\n",
       "      <td>0.821848</td>\n",
       "      <td>-0.503120</td>\n",
       "      <td>-1.068087</td>\n",
       "      <td>-1.763260</td>\n",
       "      <td>-2.146317</td>\n",
       "      <td>-2.544407</td>\n",
       "      <td>0.603674</td>\n",
       "      <td>-0.674551</td>\n",
       "      <td>-0.504041</td>\n",
       "      <td>1.575019</td>\n",
       "      <td>0.524003</td>\n",
       "      <td>0.335321</td>\n",
       "      <td>-0.496410</td>\n",
       "      <td>0.101182</td>\n",
       "      <td>0.116278</td>\n",
       "      <td>0.069029</td>\n",
       "      <td>-0.357092</td>\n",
       "      <td>-1.003016</td>\n",
       "      <td>-0.031874</td>\n",
       "      <td>1.944964</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.219766</td>\n",
       "      <td>0.141488</td>\n",
       "      <td>0.354253</td>\n",
       "      <td>0.414334</td>\n",
       "      <td>0.448450</td>\n",
       "      <td>-0.434905</td>\n",
       "      <td>1.456478</td>\n",
       "      <td>0.827790</td>\n",
       "      <td>1.013241</td>\n",
       "      <td>0.559491</td>\n",
       "      <td>-0.002550</td>\n",
       "      <td>-0.774601</td>\n",
       "      <td>-1.590341</td>\n",
       "      <td>-2.221511</td>\n",
       "      <td>-0.494905</td>\n",
       "      <td>0.030272</td>\n",
       "      <td>-0.753770</td>\n",
       "      <td>-0.931507</td>\n",
       "      <td>-0.942552</td>\n",
       "      <td>-1.434346</td>\n",
       "      <td>-0.472193</td>\n",
       "      <td>-0.718922</td>\n",
       "      <td>-0.843349</td>\n",
       "      <td>1.527027</td>\n",
       "      <td>1.609787</td>\n",
       "      <td>-0.955078</td>\n",
       "      <td>-1.411713</td>\n",
       "      <td>-1.312963</td>\n",
       "      <td>-1.089771</td>\n",
       "      <td>-1.796315</td>\n",
       "      <td>-1.419189</td>\n",
       "      <td>-1.662825</td>\n",
       "      <td>-1.594006</td>\n",
       "      <td>-1.931964</td>\n",
       "      <td>-1.621322</td>\n",
       "      <td>-0.469792</td>\n",
       "      <td>-0.218435</td>\n",
       "      <td>-0.301813</td>\n",
       "      <td>0.434913</td>\n",
       "      <td>-0.930121</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>-0.163895</td>\n",
       "      <td>0.217007</td>\n",
       "      <td>0.612561</td>\n",
       "      <td>0.096635</td>\n",
       "      <td>-0.011176</td>\n",
       "      <td>0.010329</td>\n",
       "      <td>0.611109</td>\n",
       "      <td>-0.084256</td>\n",
       "      <td>-0.259055</td>\n",
       "      <td>0.197541</td>\n",
       "      <td>0.487866</td>\n",
       "      <td>-0.107904</td>\n",
       "      <td>0.262636</td>\n",
       "      <td>0.431537</td>\n",
       "      <td>-0.079786</td>\n",
       "      <td>-0.322653</td>\n",
       "      <td>-0.737950</td>\n",
       "      <td>-0.210876</td>\n",
       "      <td>-0.445421</td>\n",
       "      <td>-0.810192</td>\n",
       "      <td>-0.294106</td>\n",
       "      <td>-0.533906</td>\n",
       "      <td>-0.711029</td>\n",
       "      <td>0.098778</td>\n",
       "      <td>-0.484695</td>\n",
       "      <td>-0.721300</td>\n",
       "      <td>-0.147159</td>\n",
       "      <td>1.090982</td>\n",
       "      <td>1.410584</td>\n",
       "      <td>0.176958</td>\n",
       "      <td>0.224200</td>\n",
       "      <td>1.454431</td>\n",
       "      <td>0.273783</td>\n",
       "      <td>-0.120383</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>0.579530</td>\n",
       "      <td>-0.349398</td>\n",
       "      <td>0.811613</td>\n",
       "      <td>0.431155</td>\n",
       "      <td>-1.071173</td>\n",
       "      <td>-0.925800</td>\n",
       "      <td>0.322772</td>\n",
       "      <td>1.055379</td>\n",
       "      <td>0.591863</td>\n",
       "      <td>-0.005032</td>\n",
       "      <td>0.739183</td>\n",
       "      <td>0.224490</td>\n",
       "      <td>0.499365</td>\n",
       "      <td>-0.847564</td>\n",
       "      <td>1.128845</td>\n",
       "      <td>1.245041</td>\n",
       "      <td>1.012661</td>\n",
       "      <td>0.428101</td>\n",
       "      <td>-0.119186</td>\n",
       "      <td>0.345085</td>\n",
       "      <td>1.780581</td>\n",
       "      <td>0.892163</td>\n",
       "      <td>-1.286426</td>\n",
       "      <td>-1.022649</td>\n",
       "      <td>0.126772</td>\n",
       "      <td>0.388084</td>\n",
       "      <td>0.454805</td>\n",
       "      <td>0.420226</td>\n",
       "      <td>-0.379270</td>\n",
       "      <td>1.345961</td>\n",
       "      <td>0.812338</td>\n",
       "      <td>1.051216</td>\n",
       "      <td>0.575812</td>\n",
       "      <td>-0.015885</td>\n",
       "      <td>-0.774601</td>\n",
       "      <td>-1.378527</td>\n",
       "      <td>-1.612644</td>\n",
       "      <td>-0.494905</td>\n",
       "      <td>-0.248461</td>\n",
       "      <td>-0.753770</td>\n",
       "      <td>-0.931507</td>\n",
       "      <td>-0.906763</td>\n",
       "      <td>-1.385721</td>\n",
       "      <td>-0.436008</td>\n",
       "      <td>-0.737545</td>\n",
       "      <td>-0.906338</td>\n",
       "      <td>1.542925</td>\n",
       "      <td>1.467211</td>\n",
       "      <td>-0.955078</td>\n",
       "      <td>-1.402117</td>\n",
       "      <td>-1.355652</td>\n",
       "      <td>-1.100409</td>\n",
       "      <td>-1.796315</td>\n",
       "      <td>-1.524101</td>\n",
       "      <td>-1.662825</td>\n",
       "      <td>-1.594006</td>\n",
       "      <td>-1.851504</td>\n",
       "      <td>-1.621987</td>\n",
       "      <td>-0.468911</td>\n",
       "      <td>-0.551536</td>\n",
       "      <td>-0.302763</td>\n",
       "      <td>0.437490</td>\n",
       "      <td>-0.861045</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>-0.151179</td>\n",
       "      <td>0.087024</td>\n",
       "      <td>0.559792</td>\n",
       "      <td>0.120065</td>\n",
       "      <td>-0.090206</td>\n",
       "      <td>-0.028933</td>\n",
       "      <td>0.632457</td>\n",
       "      <td>-0.123612</td>\n",
       "      <td>-0.291856</td>\n",
       "      <td>0.229714</td>\n",
       "      <td>0.384374</td>\n",
       "      <td>-0.143129</td>\n",
       "      <td>0.298192</td>\n",
       "      <td>0.343325</td>\n",
       "      <td>-0.115977</td>\n",
       "      <td>-0.280002</td>\n",
       "      <td>-0.695622</td>\n",
       "      <td>-0.258569</td>\n",
       "      <td>-0.404865</td>\n",
       "      <td>-0.803645</td>\n",
       "      <td>-0.348799</td>\n",
       "      <td>-0.490017</td>\n",
       "      <td>-0.676751</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>-0.442263</td>\n",
       "      <td>-0.678768</td>\n",
       "      <td>-0.207518</td>\n",
       "      <td>1.124575</td>\n",
       "      <td>1.411137</td>\n",
       "      <td>0.153975</td>\n",
       "      <td>0.226005</td>\n",
       "      <td>1.434883</td>\n",
       "      <td>0.069163</td>\n",
       "      <td>-0.074638</td>\n",
       "      <td>0.353761</td>\n",
       "      <td>0.537604</td>\n",
       "      <td>-0.311409</td>\n",
       "      <td>0.664137</td>\n",
       "      <td>0.392019</td>\n",
       "      <td>-1.054405</td>\n",
       "      <td>-0.803929</td>\n",
       "      <td>0.298613</td>\n",
       "      <td>1.089179</td>\n",
       "      <td>0.635097</td>\n",
       "      <td>-0.006759</td>\n",
       "      <td>0.778986</td>\n",
       "      <td>0.291108</td>\n",
       "      <td>0.497550</td>\n",
       "      <td>-0.799363</td>\n",
       "      <td>1.144255</td>\n",
       "      <td>1.245463</td>\n",
       "      <td>1.052754</td>\n",
       "      <td>0.473221</td>\n",
       "      <td>-0.120588</td>\n",
       "      <td>0.387505</td>\n",
       "      <td>1.739021</td>\n",
       "      <td>0.882277</td>\n",
       "      <td>-1.274198</td>\n",
       "      <td>-0.902753</td>\n",
       "      <td>0.101869</td>\n",
       "      <td>0.419384</td>\n",
       "      <td>0.526726</td>\n",
       "      <td>0.411835</td>\n",
       "      <td>-0.322488</td>\n",
       "      <td>1.274332</td>\n",
       "      <td>0.811495</td>\n",
       "      <td>1.085684</td>\n",
       "      <td>0.619272</td>\n",
       "      <td>-0.017584</td>\n",
       "      <td>-0.775710</td>\n",
       "      <td>-1.575608</td>\n",
       "      <td>-2.093194</td>\n",
       "      <td>-0.494905</td>\n",
       "      <td>-0.453606</td>\n",
       "      <td>-0.753770</td>\n",
       "      <td>-1.164123</td>\n",
       "      <td>-1.052607</td>\n",
       "      <td>-1.398445</td>\n",
       "      <td>-0.491031</td>\n",
       "      <td>-0.767273</td>\n",
       "      <td>-0.919080</td>\n",
       "      <td>1.543701</td>\n",
       "      <td>1.264811</td>\n",
       "      <td>-0.955078</td>\n",
       "      <td>-1.400450</td>\n",
       "      <td>-1.376784</td>\n",
       "      <td>-1.130922</td>\n",
       "      <td>-1.796315</td>\n",
       "      <td>-1.590914</td>\n",
       "      <td>-1.662825</td>\n",
       "      <td>-1.594006</td>\n",
       "      <td>-1.807521</td>\n",
       "      <td>-1.689144</td>\n",
       "      <td>-0.468895</td>\n",
       "      <td>-0.767423</td>\n",
       "      <td>-0.302763</td>\n",
       "      <td>0.439842</td>\n",
       "      <td>-0.939235</td>\n",
       "      <td>-2.321444</td>\n",
       "      <td>0.742948</td>\n",
       "      <td>0.172532</td>\n",
       "      <td>-1.313004</td>\n",
       "      <td>-0.196996</td>\n",
       "      <td>1.565944</td>\n",
       "      <td>0.656406</td>\n",
       "      <td>0.382067</td>\n",
       "      <td>0.277581</td>\n",
       "      <td>0.845431</td>\n",
       "      <td>-0.788868</td>\n",
       "      <td>0.822273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.721496</td>\n",
       "      <td>1.741335</td>\n",
       "      <td>2.126326</td>\n",
       "      <td>2.435101</td>\n",
       "      <td>2.040847</td>\n",
       "      <td>0.947782</td>\n",
       "      <td>0.730353</td>\n",
       "      <td>0.255758</td>\n",
       "      <td>0.674929</td>\n",
       "      <td>-0.871979</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>2.293414</td>\n",
       "      <td>2.842792</td>\n",
       "      <td>0.834779</td>\n",
       "      <td>0.741064</td>\n",
       "      <td>0.826632</td>\n",
       "      <td>1.571312</td>\n",
       "      <td>1.716222</td>\n",
       "      <td>0.257402</td>\n",
       "      <td>-0.173385</td>\n",
       "      <td>-0.207795</td>\n",
       "      <td>-0.116774</td>\n",
       "      <td>0.150944</td>\n",
       "      <td>0.484020</td>\n",
       "      <td>1.473773</td>\n",
       "      <td>1.866757</td>\n",
       "      <td>0.850733</td>\n",
       "      <td>1.403428</td>\n",
       "      <td>1.893370</td>\n",
       "      <td>2.638968</td>\n",
       "      <td>2.321506</td>\n",
       "      <td>1.043523</td>\n",
       "      <td>0.665044</td>\n",
       "      <td>0.620376</td>\n",
       "      <td>0.656058</td>\n",
       "      <td>1.080583</td>\n",
       "      <td>0.033868</td>\n",
       "      <td>2.199797</td>\n",
       "      <td>2.995482</td>\n",
       "      <td>0.307017</td>\n",
       "      <td>0.116672</td>\n",
       "      <td>0.270154</td>\n",
       "      <td>0.787201</td>\n",
       "      <td>1.020546</td>\n",
       "      <td>0.113875</td>\n",
       "      <td>-0.043534</td>\n",
       "      <td>0.054501</td>\n",
       "      <td>-0.027941</td>\n",
       "      <td>-0.297584</td>\n",
       "      <td>-0.170998</td>\n",
       "      <td>1.541413</td>\n",
       "      <td>1.943798</td>\n",
       "      <td>0.117716</td>\n",
       "      <td>-0.056671</td>\n",
       "      <td>-0.129575</td>\n",
       "      <td>-0.255662</td>\n",
       "      <td>-0.160641</td>\n",
       "      <td>0.023943</td>\n",
       "      <td>0.218191</td>\n",
       "      <td>0.116796</td>\n",
       "      <td>0.242557</td>\n",
       "      <td>-0.865734</td>\n",
       "      <td>-0.388933</td>\n",
       "      <td>0.051383</td>\n",
       "      <td>-0.081101</td>\n",
       "      <td>0.032259</td>\n",
       "      <td>-0.115453</td>\n",
       "      <td>-0.087018</td>\n",
       "      <td>-0.281519</td>\n",
       "      <td>-0.248503</td>\n",
       "      <td>-0.236623</td>\n",
       "      <td>0.099880</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0.091610</td>\n",
       "      <td>-1.354109</td>\n",
       "      <td>-0.717592</td>\n",
       "      <td>0.058507</td>\n",
       "      <td>-0.085478</td>\n",
       "      <td>0.126113</td>\n",
       "      <td>0.241788</td>\n",
       "      <td>0.411820</td>\n",
       "      <td>0.762104</td>\n",
       "      <td>0.510137</td>\n",
       "      <td>-0.259939</td>\n",
       "      <td>-0.388675</td>\n",
       "      <td>-0.609522</td>\n",
       "      <td>-0.276156</td>\n",
       "      <td>-0.715814</td>\n",
       "      <td>-0.576696</td>\n",
       "      <td>0.806636</td>\n",
       "      <td>0.825771</td>\n",
       "      <td>0.217893</td>\n",
       "      <td>0.079780</td>\n",
       "      <td>0.158450</td>\n",
       "      <td>-0.090891</td>\n",
       "      <td>0.236312</td>\n",
       "      <td>0.181255</td>\n",
       "      <td>0.210254</td>\n",
       "      <td>0.478961</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>-0.672810</td>\n",
       "      <td>-0.477662</td>\n",
       "      <td>0.253141</td>\n",
       "      <td>0.065102</td>\n",
       "      <td>0.028287</td>\n",
       "      <td>-0.298753</td>\n",
       "      <td>0.107011</td>\n",
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       "      <td>-1.315638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1547 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0_S2_B01  0_S2_B02  0_S2_B03  0_S2_B04  0_S2_B05  0_S2_B06  0_S2_B07  \\\n",
       "0 -0.843609 -0.502552 -0.580374 -0.608198 -0.729947 -0.200413 -0.064783   \n",
       "1 -0.885775 -1.044369 -1.090406 -1.162903 -0.722985  0.886156  1.005692   \n",
       "2 -0.506280 -0.064720  0.050852  0.652771  0.467481 -1.137781 -1.027181   \n",
       "3 -0.457086  0.099466  0.172048  0.416339  0.352612 -0.648014 -0.499663   \n",
       "4  1.721496  1.741335  2.126326  2.435101  2.040847  0.947782  0.730353   \n",
       "\n",
       "   0_S2_B08  0_S2_B8A  0_S2_B09  0_S2_B10  0_S2_B11  0_S2_B12  1_S2_B01  \\\n",
       "0 -0.195807  0.142577  0.578572 -0.548708 -0.962752 -0.963574 -0.616715   \n",
       "1  1.163796  1.162127  0.360649 -0.439680 -1.325770 -1.200362 -0.633209   \n",
       "2 -1.116117 -0.974412 -0.102437 -0.330652  1.015516  0.982034 -0.325317   \n",
       "3 -0.262070 -0.366009  0.803305  0.650603  0.740502  0.609093 -0.341811   \n",
       "4  0.255758  0.674929 -0.871979 -0.548708  2.293414  2.842792  0.834779   \n",
       "\n",
       "   1_S2_B02  1_S2_B03  1_S2_B04  1_S2_B05  1_S2_B06  1_S2_B07  1_S2_B08  \\\n",
       "0 -0.211439 -0.014773 -0.155385 -0.364569  0.062101  0.082685  0.317995   \n",
       "1 -0.827486 -1.006266 -1.037709 -0.549127  0.673852  0.970708  1.280458   \n",
       "2  0.034980  0.094382  0.805999  0.985231 -0.477848 -0.579808 -0.711306   \n",
       "3  0.295615  0.394559  1.188656  1.390533  0.033380 -0.041826  0.059556   \n",
       "4  0.741064  0.826632  1.571312  1.716222  0.257402 -0.173385 -0.207795   \n",
       "\n",
       "   1_S2_B8A  1_S2_B09  1_S2_B10  1_S2_B11  1_S2_B12  2_S2_B01  2_S2_B02  \\\n",
       "0  0.316501  1.461759  1.123764 -0.379976 -0.504366 -0.595569  0.043007   \n",
       "1  1.030002  1.196650  2.545416 -1.125338 -1.131114 -0.867043 -1.108447   \n",
       "2 -0.356045  1.029730  2.900829  1.462188  1.509555 -0.572166 -0.298164   \n",
       "3  0.103097  2.036161  1.337011  1.819421  1.568723 -0.464512 -0.485809   \n",
       "4 -0.116774  0.150944  0.484020  1.473773  1.866757  0.850733  1.403428   \n",
       "\n",
       "   2_S2_B03  2_S2_B04  2_S2_B05  2_S2_B06  2_S2_B07  2_S2_B08  2_S2_B8A  \\\n",
       "0  0.029942  0.321365 -0.380483 -0.650926 -0.566078 -0.310209 -0.481912   \n",
       "1 -1.363433 -1.328652 -1.149181 -0.537289 -0.083163  0.144217 -0.119489   \n",
       "2 -0.188298  0.959799  0.431103 -1.153453 -1.260397 -1.177570 -1.187979   \n",
       "3 -0.574414  0.513770  0.210061 -1.395878 -1.461439 -1.545062 -1.490310   \n",
       "4  1.893370  2.638968  2.321506  1.043523  0.665044  0.620376  0.656058   \n",
       "\n",
       "   2_S2_B09  2_S2_B10  2_S2_B11  2_S2_B12  3_S2_B01  3_S2_B02  3_S2_B03  \\\n",
       "0  0.551840  0.189091 -0.207686 -0.139011 -0.077359  0.317078  0.570320   \n",
       "1  1.080583 -0.664637 -1.479532 -1.401947  1.951703  0.566706  0.743357   \n",
       "2  0.404746 -0.587026  1.037903  1.557088 -0.025616  0.127219  0.167745   \n",
       "3  0.619424  1.120431  0.452033  1.142201 -0.077359 -0.125925 -0.248956   \n",
       "4  1.080583  0.033868  2.199797  2.995482  0.307017  0.116672  0.270154   \n",
       "\n",
       "   3_S2_B04  3_S2_B05  3_S2_B06  3_S2_B07  3_S2_B08  3_S2_B8A  3_S2_B09  \\\n",
       "0  0.852382  0.153528 -0.518089 -0.492637 -0.110053 -0.318126  0.245222   \n",
       "1  1.764914  1.665993  0.410561  0.184012 -0.284403  0.319540  1.780471   \n",
       "2  1.158449  0.644839 -1.193471 -1.314996 -1.128726 -1.101808 -0.192264   \n",
       "3  0.829711  0.474646 -1.415382 -1.520585 -1.641979 -1.427110  0.451812   \n",
       "4  0.787201  1.020546  0.113875 -0.043534  0.054501 -0.027941 -0.297584   \n",
       "\n",
       "   3_S2_B10  3_S2_B11  3_S2_B12  4_S2_B01  4_S2_B02  4_S2_B03  4_S2_B04  \\\n",
       "0  0.784835  0.784576  0.768891 -0.160791  0.214768  0.285876  0.636191   \n",
       "1  2.172334  1.269631  1.939712 -0.262331 -0.482854 -0.718342 -0.530413   \n",
       "2  0.939002  1.396085  1.741510 -0.047648 -0.003398  0.038644  1.022152   \n",
       "3  0.969835  1.026160  1.516745 -0.047648 -0.059207 -0.030173  0.873873   \n",
       "4 -0.170998  1.541413  1.943798  0.117716 -0.056671 -0.129575 -0.255662   \n",
       "\n",
       "   4_S2_B05  4_S2_B06  4_S2_B07  4_S2_B08  4_S2_B8A  4_S2_B09  4_S2_B10  \\\n",
       "0  0.290214 -0.887611 -0.958666 -0.588464 -0.819447  0.007565  1.860522   \n",
       "1 -0.580809 -1.321773 -1.524730 -1.603977 -1.518324 -0.078158  1.947040   \n",
       "2  0.507379 -1.408230 -1.594139 -1.564628 -1.554348 -0.362114  2.639180   \n",
       "3  0.698580 -1.015416 -1.296454 -1.455661 -1.362698  0.387959  1.774005   \n",
       "4 -0.160641  0.023943  0.218191  0.116796  0.242557 -0.865734 -0.388933   \n",
       "\n",
       "   4_S2_B11  4_S2_B12  5_S2_B01  5_S2_B02  5_S2_B03  5_S2_B04  5_S2_B05  \\\n",
       "0  0.887858  1.122930 -0.425862 -0.192457 -0.194379  0.091802 -0.301555   \n",
       "1 -0.722131 -0.428790  5.427914  6.344659  6.617395  6.560273  6.491878   \n",
       "2  1.515664  2.307646 -0.243212 -0.104452  0.014692  0.462684  0.441186   \n",
       "3  1.504871  2.191750  0.113104 -0.068700 -0.061591 -0.405960 -0.423577   \n",
       "4  0.051383 -0.081101  0.032259 -0.115453 -0.087018 -0.281519 -0.248503   \n",
       "\n",
       "   5_S2_B06  5_S2_B07  5_S2_B08  5_S2_B8A  5_S2_B09  5_S2_B10  5_S2_B11  \\\n",
       "0 -1.160025 -1.203018 -1.317833 -1.132195  0.066944  0.658617 -0.359580   \n",
       "1  4.611237  3.921587  3.425656  3.694054  1.284989 -0.422690  5.228712   \n",
       "2 -0.645229 -0.749179 -0.691994 -0.638782  0.412057  1.051820  1.095446   \n",
       "3 -0.015007 -0.119476  0.031402 -0.060481 -0.729861 -0.619291 -0.777667   \n",
       "4 -0.236623  0.099880  0.007473  0.091610 -1.354109 -0.717592  0.058507   \n",
       "\n",
       "   5_S2_B12  6_S2_B01  6_S2_B02  6_S2_B03  6_S2_B04  6_S2_B05  6_S2_B06  \\\n",
       "0 -0.131790 -0.440826 -0.489766 -0.698997 -0.681647 -1.196644 -0.980236   \n",
       "1  6.002206 -0.366538 -0.667327 -1.168407 -1.080123 -1.410391 -1.268875   \n",
       "2  1.236724 -0.577674 -0.578546 -0.620165 -0.976172 -0.989278  2.043972   \n",
       "3 -0.817204  2.909978  1.442104  0.863313  0.508003  0.318722 -0.426362   \n",
       "4 -0.085478  0.126113  0.241788  0.411820  0.762104  0.510137 -0.259939   \n",
       "\n",
       "   6_S2_B07  6_S2_B08  6_S2_B8A  6_S2_B09  6_S2_B10  6_S2_B11  6_S2_B12  \\\n",
       "0 -0.737320 -1.709061 -0.627569 -0.430451 -0.775060 -1.864160 -1.428217   \n",
       "1 -1.166422 -0.877409 -0.606561  0.742709 -0.626287 -1.897650 -1.560805   \n",
       "2  3.188548  2.996967  3.079458  1.605140 -0.576696 -0.798772 -1.185140   \n",
       "3 -0.304092 -0.333637 -0.581732  1.129535 -0.378332 -0.926451 -0.203010   \n",
       "4 -0.388675 -0.609522 -0.276156 -0.715814 -0.576696  0.806636  0.825771   \n",
       "\n",
       "   7_S2_B01  7_S2_B02  7_S2_B03  7_S2_B04  7_S2_B05  7_S2_B06  7_S2_B07  \\\n",
       "0  0.085820  0.252073  0.093481 -0.020614 -0.371609  0.091979  0.437716   \n",
       "1 -0.438589 -0.767023 -1.086790 -1.159104 -1.453395 -1.063364 -0.888066   \n",
       "2 -0.430820 -0.429768 -0.350474 -0.729008 -0.639717  0.275783  0.621853   \n",
       "3  0.097474  0.024793  0.028512  0.327961  0.329838 -0.133838 -0.225175   \n",
       "4  0.217893  0.079780  0.158450 -0.090891  0.236312  0.181255  0.210254   \n",
       "\n",
       "   7_S2_B08  7_S2_B8A  7_S2_B09  7_S2_B10  7_S2_B11  7_S2_B12  8_S2_B01  \\\n",
       "0 -0.691393  0.535145  0.341487 -0.477662 -1.009045 -1.128480 -0.758110   \n",
       "1 -0.625458 -0.977707  0.154776 -0.391373 -1.974944 -1.679033 -0.798914   \n",
       "2  0.408904  0.513190  0.508014 -0.218796 -0.930035 -1.057510 -0.724725   \n",
       "3 -0.176273 -0.191343  1.340646  1.161823  0.399309  0.585547 -0.620862   \n",
       "4  0.478961  0.052150 -0.672810 -0.477662  0.253141  0.065102  0.028287   \n",
       "\n",
       "   8_S2_B02  8_S2_B03  8_S2_B04  8_S2_B05  8_S2_B06  8_S2_B07  8_S2_B08  \\\n",
       "0 -0.529240 -0.474062 -0.650922 -0.823768  0.091670  0.255730 -0.068810   \n",
       "1 -0.904605 -1.104658 -1.144280 -1.052129 -0.773712 -0.594230 -0.455976   \n",
       "2 -0.595094 -0.447649 -0.632745 -0.270432 -0.073988 -0.112445 -0.096895   \n",
       "3 -0.565459 -0.450951 -0.793736 -0.384612  1.088096  1.196156  1.572133   \n",
       "4 -0.298753  0.107011 -0.461368  0.236061  1.204304  1.164598  1.064605   \n",
       "\n",
       "   8_S2_B8A  8_S2_B09  8_S2_B10  8_S2_B11  8_S2_B12  9_S2_B01  9_S2_B02  \\\n",
       "0  0.001479  0.942831 -0.362740 -1.272626 -1.245667 -0.542212 -0.283003   \n",
       "1 -0.696226  0.528746 -0.362740 -1.582096 -1.410695 -0.796765 -0.998340   \n",
       "2 -0.085977  0.962317 -0.243496 -0.634817 -0.685827 -0.704843 -0.547440   \n",
       "3  1.297772  1.303328 -0.243496 -0.610285 -0.817431 -0.446755 -0.469465   \n",
       "4  0.980987  0.270552 -0.243496  0.116214 -0.157321  0.210841 -0.062638   \n",
       "\n",
       "   9_S2_B03  9_S2_B04  9_S2_B05  9_S2_B06  9_S2_B07  9_S2_B08  9_S2_B8A  \\\n",
       "0 -0.271566 -0.376652 -0.660562 -0.361698 -0.081608 -0.402265 -0.240614   \n",
       "1 -1.200853 -1.168975 -1.168999 -1.033085 -0.865528 -0.552917 -0.576562   \n",
       "2 -0.516652 -0.438011 -0.407831 -0.754939 -0.897607 -1.067804 -0.662844   \n",
       "3 -0.387301 -0.488698 -0.372151  0.120261  0.156977  0.063041  0.332150   \n",
       "4  0.279880 -0.184574  0.284951  0.774864  0.854686  0.678998  0.923272   \n",
       "\n",
       "   9_S2_B09  9_S2_B10  9_S2_B11  9_S2_B12  10_S2_B01  10_S2_B02  10_S2_B03  \\\n",
       "0  0.313893 -0.391988 -1.205350 -1.213146  -0.573909  -0.357488  -0.473959   \n",
       "1  0.299271 -0.391988 -1.642958 -1.444056  -0.612766  -0.765120  -1.044888   \n",
       "2  0.245659 -0.475385 -0.474132 -0.475514  -0.457338  -0.170944   0.313647   \n",
       "3  0.289524 -0.225196 -0.636833 -0.781257  -0.546709  -0.398942  -0.250403   \n",
       "4 -0.636514 -0.308592  0.359942  0.153076   0.665629   0.602866   0.705731   \n",
       "\n",
       "   10_S2_B04  10_S2_B05  10_S2_B06  10_S2_B07  10_S2_B08  10_S2_B8A  \\\n",
       "0  -0.471344  -0.748373  -0.514515  -0.526826  -1.410201  -0.607952   \n",
       "1  -0.979987  -0.889702  -0.463911  -0.543556   0.107080  -0.333126   \n",
       "2  -0.099644   0.508221   0.510200   0.462311   0.675554   0.392646   \n",
       "3  -0.415449  -0.327460  -0.097038  -0.359530  -0.153892  -0.095073   \n",
       "4   0.048478   0.824674   1.704435   1.735851   1.848918   1.708712   \n",
       "\n",
       "   10_S2_B09  10_S2_B10  10_S2_B11  10_S2_B12  11_S2_B01  11_S2_B02  \\\n",
       "0   0.283026  -0.111511  -1.466957  -1.323709  -0.507268  -0.624264   \n",
       "1   0.571030   0.086945  -1.475083  -1.347065  -0.539467  -0.695827   \n",
       "2   1.173219   0.285400   0.327004   0.037931   1.494892   1.285916   \n",
       "3   0.531757   0.037331  -0.713209  -0.667413   0.025470  -0.054513   \n",
       "4  -0.260254   0.583083   0.877586   0.432643   0.564063   0.526248   \n",
       "\n",
       "   11_S2_B03  11_S2_B04  11_S2_B05  11_S2_B06  11_S2_B07  11_S2_B08  \\\n",
       "0  -1.107309  -1.158500  -1.909706  -3.194740  -3.165916  -3.475287   \n",
       "1  -1.173613  -1.232267  -1.922098  -2.937892  -2.922116  -2.191129   \n",
       "2   1.263081   0.397512   1.076839   2.038535   1.947937   1.641830   \n",
       "3  -0.082350   0.139329   0.090412  -0.305201  -0.379065  -0.387843   \n",
       "4   0.837626   0.185433   1.091710   2.040829   2.229397   2.168765   \n",
       "\n",
       "   11_S2_B8A  11_S2_B09  11_S2_B10  11_S2_B11  11_S2_B12 Field_ID  Yield  \\\n",
       "0  -3.491975  -2.905376  -0.745247  -3.051246  -2.191308  MH2O0YH  3.686   \n",
       "1  -3.074027  -3.034781  -0.745247  -3.084725  -2.312300  O9TURWL  5.657   \n",
       "2   2.079419   1.038880  -0.496849   0.664894   0.456400  35AFSDD  3.082   \n",
       "3  -0.368561   0.640313   0.434646   0.192667   0.361622  PM05EG9  2.707   \n",
       "4   2.411538  -0.508801  -0.621048   1.561772   0.996830  V7PZBCG  2.679   \n",
       "\n",
       "   Year    0_NDVI    1_NDVI    2_NDVI    3_NDVI    4_NDVI    5_NDVI    6_NDVI  \\\n",
       "0  2019  0.515054  0.317976 -0.462308 -0.859399 -1.236450 -0.958027 -0.077404   \n",
       "1  2019  1.522505  1.554115  1.458176 -1.698882 -0.121968 -3.352056  1.058261   \n",
       "2  2019 -1.180534 -1.149292 -1.644908 -1.847743 -2.625956 -1.053528  1.938507   \n",
       "3  2019 -0.515463 -0.941315 -1.594315 -2.007443 -2.315807  0.551391 -0.760086   \n",
       "4  2019 -1.747604 -1.386647 -1.517578 -0.705863  0.383538  0.357940 -1.204846   \n",
       "\n",
       "     7_NDVI    8_NDVI    9_NDVI   10_NDVI   11_NDVI  0_ATSAVI  1_ATSAVI  \\\n",
       "0 -0.364856  0.757765  0.226884 -0.200695 -0.840913  1.292991  1.400487   \n",
       "1  1.233039  1.316011  1.235888  1.300542  0.849564  1.085381  1.383533   \n",
       "2  1.024284  0.720854 -0.058400  0.391185  0.200670 -0.394029  0.146354   \n",
       "3 -0.456865  1.500215  0.572887  0.420081 -0.457434  0.571096  0.535765   \n",
       "4  0.311518  0.962608  0.477719  0.653199  0.605846 -2.256662 -0.916743   \n",
       "\n",
       "   2_ATSAVI  3_ATSAVI  4_ATSAVI  5_ATSAVI  6_ATSAVI  7_ATSAVI  8_ATSAVI  \\\n",
       "0  0.799544  0.136473 -0.442062  0.428740  1.234173  0.767606  1.905559   \n",
       "1  1.851676  0.247451  0.796091 -3.110536  2.680444  2.125552  1.870451   \n",
       "2  0.099412 -0.617239 -1.038906 -0.200005  2.588992  1.261226  1.212138   \n",
       "3  0.415525  0.070034 -0.594470 -0.172820  0.549204  0.905622  1.628923   \n",
       "4 -0.432436 -0.959524 -0.704232 -1.148505 -1.271101 -1.006333  0.025520   \n",
       "\n",
       "   9_ATSAVI  10_ATSAVI  11_ATSAVI   0_ARVI2   1_ARVI2   2_ARVI2   3_ARVI2  \\\n",
       "0  0.870257   1.344179  -0.636711  1.124863  0.884128  0.082262 -0.548773   \n",
       "1  1.444073   1.858885  -0.997293  1.752869  1.650332  1.968221 -0.280812   \n",
       "2  0.562686   0.375785  -0.444089 -0.719311 -0.047815 -0.422490 -1.036370   \n",
       "3  0.560279   0.915752   0.413990  0.196888  0.179166 -0.012856 -0.404618   \n",
       "4 -0.762915  -1.273545  -1.888605 -2.561841 -0.962105 -0.865279 -0.872868   \n",
       "\n",
       "    4_ARVI2   5_ARVI2   6_ARVI2   7_ARVI2   8_ARVI2   9_ARVI2  10_ARVI2  \\\n",
       "0 -0.985461 -0.202105  0.624678  0.185260  1.379820  0.504115  0.766191   \n",
       "1  0.782116 -3.102298  2.130336  1.811028  2.012402  1.628797  1.925828   \n",
       "2 -1.587129 -0.455282  2.311518  1.259300  1.362909  0.541319  0.698691   \n",
       "3 -1.012305  0.025999 -0.047455  0.428459  1.794452  0.626350  0.823608   \n",
       "4 -0.328007 -0.713256 -1.424132 -0.539123  0.687718 -0.275294 -0.381152   \n",
       "\n",
       "   11_ARVI2  0_CIrededge  1_CIrededge  2_CIrededge  3_CIrededge  4_CIrededge  \\\n",
       "0 -0.955136     1.269524     1.416410     0.725818    -0.008259    -0.528954   \n",
       "1 -0.980790     1.005661     1.393687     2.278394     0.097394     0.730954   \n",
       "2 -0.040104    -0.465577     0.009222    -0.043006    -0.646635    -1.009774   \n",
       "3  0.059055     0.421532     0.393815     0.283721    -0.069920    -0.658241   \n",
       "4 -0.759143    -1.674266    -0.864778    -0.529531    -0.897774    -0.748540   \n",
       "\n",
       "   5_CIrededge  6_CIrededge  7_CIrededge  8_CIrededge  9_CIrededge  \\\n",
       "0     0.314705     1.233426     0.664737     2.083726     0.830103   \n",
       "1    -2.279200     3.405180     2.461852     2.033135     1.627764   \n",
       "2    -0.311084     3.241278     1.244366     1.166712     0.459615   \n",
       "3    -0.286077     0.447462     0.819652     1.697838     0.456853   \n",
       "4    -1.091183    -1.159544    -0.942913    -0.082050    -0.808280   \n",
       "\n",
       "   10_CIrededge  11_CIrededge    0_GARI    1_GARI    2_GARI    3_GARI  \\\n",
       "0      1.374792     -0.657703  0.478826  1.066705  0.335411 -0.075713   \n",
       "1      2.044501     -0.943202  0.589097  1.462961  1.013108  0.515433   \n",
       "2      0.285931     -0.496459  0.406344  1.774530  0.931916  0.263373   \n",
       "3      0.868007      0.301782  0.901650  2.640949  1.063103  0.821719   \n",
       "4     -1.182489     -1.571754 -0.825577  0.582810  0.797886  0.197517   \n",
       "\n",
       "     4_GARI    5_GARI    6_GARI    7_GARI    8_GARI    9_GARI   10_GARI  \\\n",
       "0  0.198000  0.302434 -0.522049 -0.419616  0.916948  0.195212  0.338892   \n",
       "1  0.330449 -3.983501  0.371852 -0.086780  1.054873  0.637326  1.029468   \n",
       "2  0.436298  0.884134  1.026278  0.439648  1.535413  0.682508  1.470018   \n",
       "3  1.167392 -0.873168 -2.740689  1.433133  1.549962  0.450456  0.746231   \n",
       "4 -0.646323 -0.931577 -0.391000 -0.667954  0.338148 -0.456456 -0.587760   \n",
       "\n",
       "    11_GARI     0_CVI     1_CVI     2_CVI     3_CVI     4_CVI     5_CVI  \\\n",
       "0 -2.320056  0.261553  0.926073  0.094181 -0.194492 -0.061164 -0.108568   \n",
       "1 -2.304553  0.496260  1.740899  1.193353  2.210330  0.279182  0.717332   \n",
       "2 -1.020598  0.119992  1.777294  0.784974  0.082761  0.214848  0.873456   \n",
       "3  0.556360  0.740457  2.899414  1.212147  1.019609  1.445013 -1.044908   \n",
       "4 -0.474138 -0.718836  0.701790  1.092272  0.138620 -0.843968 -1.347766   \n",
       "\n",
       "      6_CVI     7_CVI     8_CVI     9_CVI    10_CVI    11_CVI     0_LCI  \\\n",
       "0 -1.014196 -0.155869  0.444152 -0.179432 -0.114577 -3.174143  0.566180   \n",
       "1  0.308115 -0.285698  0.768800  0.467869  0.965846 -3.225185  1.417541   \n",
       "2  0.826379  0.160287  1.190216  0.363583  1.258747  0.521045 -1.225831   \n",
       "3  0.343141  1.670332  1.367121  0.270105  0.373257  0.821848 -0.503120   \n",
       "4 -0.614189 -0.619656  0.340744 -0.615419 -0.214994 -0.382830 -1.480480   \n",
       "\n",
       "      1_LCI     2_LCI     3_LCI     4_LCI     5_LCI     6_LCI     7_LCI  \\\n",
       "0  0.532213 -0.046594 -0.427194 -1.061319 -0.839901  0.041138 -0.211576   \n",
       "1  1.514026  1.418577 -1.736225 -0.560923 -3.131921  1.195729  1.431894   \n",
       "2 -1.434553 -1.560772 -1.745795 -2.519196 -1.194217  2.404703  1.080354   \n",
       "3 -1.068087 -1.763260 -2.146317 -2.544407  0.603674 -0.674551 -0.504041   \n",
       "4 -1.534501 -1.329238 -0.869751  0.343150  0.350806 -1.126320  0.172492   \n",
       "\n",
       "      8_LCI     9_LCI    10_LCI    11_LCI     0_MVI     1_MVI     2_MVI  \\\n",
       "0  0.947780  0.397005 -0.265066 -0.656252  1.469573  1.342553  0.550036   \n",
       "1  1.178176  1.161156  1.306170  1.392300  1.737601  2.050365  3.067236   \n",
       "2  0.410779 -0.348859  0.070180 -0.050112 -0.736752 -0.262068 -0.442865   \n",
       "3  1.575019  0.524003  0.335321 -0.496410  0.101182  0.116278  0.069029   \n",
       "4  0.689927  0.293952  0.465042  0.296277 -1.657928 -0.763113 -0.557114   \n",
       "\n",
       "      3_MVI     4_MVI     5_MVI     6_MVI  ...  9_LCI_MEAN  9_LCI_MAX  \\\n",
       "0 -0.377277 -0.886310  0.305398  2.070964  ...   -0.080771  -0.690610   \n",
       "1  0.392356  0.699379 -1.756324  3.809511  ...    0.498732   0.353775   \n",
       "2 -0.942270 -1.476519 -0.646780  2.181855  ...   -1.014367   1.964974   \n",
       "3 -0.357092 -1.003016 -0.031874  1.944964  ...   -1.219766   0.141488   \n",
       "4 -1.060350 -0.978666 -1.370038 -1.174921  ...   -0.819443  -0.086684   \n",
       "\n",
       "   9_MVI_MIN  9_MVI_MEAN  9_MVI_MAX  9_mARI_MIN  9_mARI_MEAN  9_mARI_MAX  \\\n",
       "0   0.525548    1.139355   0.783403   -0.669586    -0.716184   -0.217924   \n",
       "1  -0.361248    2.158398   1.970399   -0.325077     1.756181    1.283123   \n",
       "2  -0.340733   -0.027683   0.641815    0.156454     0.944188    0.356076   \n",
       "3   0.354253    0.414334   0.448450   -0.434905     1.456478    0.827790   \n",
       "4  -0.954947   -1.244455  -1.298390    0.089036    -0.325166   -0.589845   \n",
       "\n",
       "   9_NDRE_MIN  9_NDRE_MEAN  9_NDRE_MAX  9_CLIM_aet_MIN  9_CLIM_aet_MEAN  \\\n",
       "0    1.213116     1.125613    0.774321       -0.793594        -1.720165   \n",
       "1   -1.722897     1.381113    1.147400       -0.812587        -1.763704   \n",
       "2    0.434961     0.368659    1.048250       -0.826832        -1.960023   \n",
       "3    1.013241     0.559491   -0.002550       -0.774601        -1.590341   \n",
       "4   -0.773471    -1.327653   -1.749292        0.037352         0.015041   \n",
       "\n",
       "   9_CLIM_aet_MAX  9_CLIM_pet_MIN  9_CLIM_pet_MEAN  9_CLIM_pet_MAX  \\\n",
       "0       -2.330020       -0.317915         0.208280       -0.840646   \n",
       "1       -2.290043       -0.185172         0.242685       -0.302014   \n",
       "2       -2.581304       -0.067179         0.414710       -0.319389   \n",
       "3       -2.221511       -0.494905         0.030272       -0.753770   \n",
       "4        1.484920        1.068507         1.740049        0.636248   \n",
       "\n",
       "   9_CLIM_pr_MIN  9_CLIM_pr_MEAN  9_CLIM_pr_MAX  9_CLIM_soil_MIN  \\\n",
       "0      -0.875240       -0.991360      -1.455867        -0.491663   \n",
       "1      -0.875240       -1.010884      -1.455867        -0.462458   \n",
       "2      -0.875240       -1.088977      -1.509670        -0.491663   \n",
       "3      -0.931507       -0.942552      -1.434346        -0.472193   \n",
       "4      -0.087497       -0.389389      -0.519695         0.238455   \n",
       "\n",
       "   9_CLIM_soil_MEAN  9_CLIM_soil_MAX  9_CLIM_srad_MIN  9_CLIM_srad_MEAN  \\\n",
       "0         -0.741524        -0.871773         1.547266          1.557476   \n",
       "1         -0.730915        -0.871773         1.931800          2.124629   \n",
       "2         -0.749827        -0.890723         1.901442          1.991100   \n",
       "3         -0.718922        -0.843349         1.527027          1.609787   \n",
       "4         -0.088823        -0.206166         2.245498          1.572619   \n",
       "\n",
       "   9_CLIM_srad_MAX  9_CLIM_tmmn_MIN  9_CLIM_tmmn_MEAN  9_CLIM_tmmn_MAX  \\\n",
       "0        -1.009668        -1.357175         -1.300540        -1.089771   \n",
       "1        -0.227202        -1.575325         -1.474457        -1.368936   \n",
       "2        -0.827699        -1.466250         -1.381287        -1.257270   \n",
       "3        -0.955078        -1.411713         -1.312963        -1.089771   \n",
       "4        -0.427367         1.315157          1.351686         1.366877   \n",
       "\n",
       "   9_CLIM_tmmx_MIN  9_CLIM_tmmx_MEAN  9_CLIM_tmmx_MAX  9_CLIM_vap_MIN  \\\n",
       "0        -1.558757         -1.212007        -1.464488       -1.619841   \n",
       "1        -0.846084         -0.550994        -0.671140       -1.666344   \n",
       "2        -0.925270         -0.629921        -0.869477       -1.630175   \n",
       "3        -1.796315         -1.419189        -1.662825       -1.594006   \n",
       "4         1.133564          1.422176         2.006410       -0.007733   \n",
       "\n",
       "   9_CLIM_vap_MEAN  9_CLIM_vap_MAX  9_CLIM_vpd_MIN  9_CLIM_vpd_MEAN  \\\n",
       "0        -1.930468       -1.659264       -0.123225         0.031016   \n",
       "1        -1.839944       -1.633969        0.309984         0.509961   \n",
       "2        -1.814508       -1.633969        0.396626         0.450093   \n",
       "3        -1.931964       -1.621322       -0.469792        -0.218435   \n",
       "4         0.350586        0.674214        1.782895         2.315984   \n",
       "\n",
       "   9_CLIM_vpd_MAX  9_CLIM_vs_MIN  9_CLIM_vs_MEAN  9_CLIM_vs_MAX  \\\n",
       "0       -0.186888       0.434913       -0.680764      -2.740271   \n",
       "1        0.272808      -1.436278       -2.127031      -2.321444   \n",
       "2        0.157884      -0.687802       -1.478705      -1.902616   \n",
       "3       -0.301813       0.434913       -0.930121      -2.321444   \n",
       "4        1.613590       0.060675       -0.281794      -1.483789   \n",
       "\n",
       "   10_S2_B01_MIN  10_S2_B01_MEAN  10_S2_B01_MAX  10_S2_B02_MIN  \\\n",
       "0      -0.671927       -0.892480      -0.602108       0.217660   \n",
       "1      -1.413379        1.023037       2.292591      -1.706632   \n",
       "2      -1.056384       -0.879437      -0.580023      -0.097004   \n",
       "3      -0.163895        0.217007       0.612561       0.096635   \n",
       "4       1.813309        0.755230      -0.289764       1.028525   \n",
       "\n",
       "   10_S2_B02_MEAN  10_S2_B02_MAX  10_S2_B03_MIN  10_S2_B03_MEAN  \\\n",
       "0       -0.251914      -0.426710      -0.257869       -0.206779   \n",
       "1        0.516192       2.759915      -1.384959        0.134485   \n",
       "2       -0.526506      -0.504856      -0.068587       -0.348684   \n",
       "3       -0.011176       0.010329       0.611109       -0.084256   \n",
       "4        0.566596      -0.079394       0.662731        0.915308   \n",
       "\n",
       "   10_S2_B03_MAX  10_S2_B04_MIN  10_S2_B04_MEAN  10_S2_B04_MAX  10_S2_B05_MIN  \\\n",
       "0      -0.311901      -0.094010       -0.069168      -0.232145      -0.791355   \n",
       "1       2.929816      -1.008420        0.112525       2.913873      -1.132502   \n",
       "2      -0.484028      -0.769878        0.411841      -0.077915      -0.460391   \n",
       "3      -0.259055       0.197541        0.487866      -0.107904       0.262636   \n",
       "4       0.124455       0.177663        1.068806       0.723228       0.476490   \n",
       "\n",
       "   10_S2_B05_MEAN  10_S2_B05_MAX  10_S2_B06_MIN  10_S2_B06_MEAN  \\\n",
       "0       -0.753642      -0.555031       0.442300       -0.896690   \n",
       "1        0.193839       2.918270       0.107795        0.056672   \n",
       "2        0.206524      -0.252602      -0.073719       -0.900381   \n",
       "3        0.431537      -0.079786      -0.322653       -0.737950   \n",
       "4        1.286366       0.508099       0.940167        0.706859   \n",
       "\n",
       "   10_S2_B06_MAX  10_S2_B07_MIN  10_S2_B07_MEAN  10_S2_B07_MAX  10_S2_B08_MIN  \\\n",
       "0      -0.957555       0.481002       -0.682391      -1.088980      -0.166239   \n",
       "1       3.014851       0.047813        0.093300       2.983195      -0.222324   \n",
       "2       0.450574      -0.237405       -0.744995       1.605057      -0.168316   \n",
       "3      -0.210876      -0.445421       -0.810192      -0.294106      -0.533906   \n",
       "4      -0.123794       0.523892        0.589684      -0.301219       0.573250   \n",
       "\n",
       "   10_S2_B08_MEAN  10_S2_B08_MAX  10_S2_B8A_MIN  10_S2_B8A_MEAN  \\\n",
       "0       -0.919482      -1.370331       0.508426       -0.530793   \n",
       "1        0.177121       2.582614      -0.016800        0.206686   \n",
       "2       -0.774590       1.515804      -0.186943       -0.582511   \n",
       "3       -0.711029       0.098778      -0.484695       -0.721300   \n",
       "4        0.426582      -0.355604       0.586100        0.547766   \n",
       "\n",
       "   10_S2_B8A_MAX  10_S2_B09_MIN  10_S2_B09_MEAN  10_S2_B09_MAX  10_S2_B10_MIN  \\\n",
       "0      -1.014765       0.767030        0.819259      -0.098007      -1.824843   \n",
       "1       2.916521       1.378121        1.410584       0.597262       0.224200   \n",
       "2       1.684066       0.987906        0.740764      -0.628297       0.907214   \n",
       "3      -0.147159       1.090982        1.410584       0.176958       0.224200   \n",
       "4      -0.171599       0.170663       -0.677371      -0.109791      -0.458814   \n",
       "\n",
       "   10_S2_B10_MEAN  10_S2_B10_MAX  10_S2_B11_MIN  10_S2_B11_MEAN  \\\n",
       "0        0.605231       0.099029      -1.081922       -0.794136   \n",
       "1        1.454431       1.409683      -1.116262       -0.850193   \n",
       "2        1.350447       0.332034       0.010540        0.732293   \n",
       "3        1.454431       0.273783      -0.120383        0.482559   \n",
       "4       -0.694565      -0.658238       0.757450        1.400210   \n",
       "\n",
       "   10_S2_B11_MAX  10_S2_B12_MIN  10_S2_B12_MEAN  10_S2_B12_MAX  10_NDVI_MIN  \\\n",
       "0      -0.190248      -1.034583       -0.723428      -0.221589     0.285403   \n",
       "1       2.363674      -1.229320       -0.428791       2.675879    -1.126564   \n",
       "2       0.330440      -0.677565        0.968536       0.800664    -1.131812   \n",
       "3       0.579530      -0.349398        0.811613       0.431155    -1.071173   \n",
       "4       1.392823       0.645925        1.683478       2.081736    -0.979200   \n",
       "\n",
       "   10_NDVI_MEAN  10_NDVI_MAX  10_ATSAVI_MIN  10_ATSAVI_MEAN  10_ATSAVI_MAX  \\\n",
       "0     -0.310094    -0.826749       1.248792        1.129115       0.768454   \n",
       "1      0.686449     0.330533      -1.674900        1.412788       1.135231   \n",
       "2     -0.923393     1.489705       0.491373        0.417249       1.038050   \n",
       "3     -0.925800     0.322772       1.055379        0.591863      -0.005032   \n",
       "4     -0.631400    -0.064498      -0.709572       -1.292207      -1.793832   \n",
       "\n",
       "   10_ARVI2_MIN  10_ARVI2_MEAN  10_ARVI2_MAX  10_GARI_MIN  10_GARI_MEAN  \\\n",
       "0      0.775156       0.382208      0.268202     0.456800      0.446906   \n",
       "1     -1.642898       1.408510      1.001577    -2.321053      0.298662   \n",
       "2     -0.031130       0.105980      1.115350     1.179177      1.359856   \n",
       "3      0.739183       0.224490      0.499365    -0.847564      1.128845   \n",
       "4     -0.911665      -1.046661     -0.860214     0.224893     -0.317467   \n",
       "\n",
       "   10_GARI_MAX  10_CIrededge_MIN  10_CIrededge_MEAN  10_CIrededge_MAX  \\\n",
       "0     0.092773          1.237586           1.046875          0.670012   \n",
       "1     0.242575         -1.467004           1.613509          1.102364   \n",
       "2     0.399989          0.401089           0.332120          0.983655   \n",
       "3     1.245041          1.012661           0.428101         -0.119186   \n",
       "4    -0.077092         -0.715350          -1.217719         -1.488957   \n",
       "\n",
       "   10_CVI_MIN  10_CVI_MEAN  10_CVI_MAX  10_LCI_MIN  10_LCI_MEAN  10_LCI_MAX  \\\n",
       "0    0.040279     0.085420   -0.293362    0.775687    -0.014909   -0.712793   \n",
       "1    0.792456     1.518242    1.002185   -0.899086     0.618614    0.340965   \n",
       "2    1.355869     1.065996    0.029554   -1.043190    -0.964810    1.966623   \n",
       "3    0.345085     1.780581    0.892163   -1.286426    -1.022649    0.126772   \n",
       "4   -0.057397    -0.348568    0.027866   -1.035789    -0.695484   -0.103447   \n",
       "\n",
       "   10_MVI_MIN  10_MVI_MEAN  10_MVI_MAX  10_mARI_MIN  10_mARI_MEAN  \\\n",
       "0    0.559628     1.197459    0.748653    -0.610374     -0.745048   \n",
       "1   -0.328456     2.202174    1.912520    -0.271115      1.813520   \n",
       "2   -0.307911     0.034633    0.609823     0.203078      0.944021   \n",
       "3    0.388084     0.454805    0.420226    -0.379270      1.345961   \n",
       "4   -0.923017    -1.216140   -1.292577     0.136688     -0.370151   \n",
       "\n",
       "   10_mARI_MAX  10_NDRE_MIN  10_NDRE_MEAN  10_NDRE_MAX  10_CLIM_aet_MIN  \\\n",
       "0    -0.240887     1.248325      1.122374     0.761580        -0.793594   \n",
       "1     1.270940    -1.647056      1.436873     1.134945        -0.812587   \n",
       "2     0.337236     0.480939      0.407987     1.035719        -0.826832   \n",
       "3     0.812338     1.051216      0.575812    -0.015885        -0.774601   \n",
       "4    -0.615479    -0.710770     -1.285729    -1.763963         0.037352   \n",
       "\n",
       "   10_CLIM_aet_MEAN  10_CLIM_aet_MAX  10_CLIM_pet_MIN  10_CLIM_pet_MEAN  \\\n",
       "0         -1.567910        -2.165793        -0.317915         -0.031771   \n",
       "1         -1.728046        -2.363762        -0.185172          0.055486   \n",
       "2         -1.940097        -2.660715        -0.067179          0.225638   \n",
       "3         -1.378527        -1.612644        -0.494905         -0.248461   \n",
       "4          0.274743         1.484989         1.068507          1.543225   \n",
       "\n",
       "   10_CLIM_pet_MAX  10_CLIM_pr_MIN  10_CLIM_pr_MEAN  10_CLIM_pr_MAX  \\\n",
       "0        -0.840646       -0.875240        -0.984596       -1.492533   \n",
       "1        -0.302014       -0.875240        -1.056203       -1.535257   \n",
       "2        -0.319389       -0.875240        -1.143377       -1.588663   \n",
       "3        -0.753770       -0.931507        -0.906763       -1.385721   \n",
       "4         0.636248       -0.087497         0.030355        0.013506   \n",
       "\n",
       "   10_CLIM_soil_MIN  10_CLIM_soil_MEAN  10_CLIM_soil_MAX  10_CLIM_srad_MIN  \\\n",
       "0         -0.465907          -0.759424         -0.933871          1.563318   \n",
       "1         -0.436008          -0.748485         -0.933871          1.950787   \n",
       "2         -0.455941          -0.766577         -0.952227          1.920198   \n",
       "3         -0.436008          -0.737545         -0.906338          1.542925   \n",
       "4          0.361275           0.290363          1.112786          2.266880   \n",
       "\n",
       "   10_CLIM_srad_MEAN  10_CLIM_srad_MAX  10_CLIM_tmmn_MIN  10_CLIM_tmmn_MEAN  \\\n",
       "0           1.405856         -1.009668         -1.347161          -1.338903   \n",
       "1           2.047945         -0.227202         -1.566985          -1.495223   \n",
       "2           1.916674         -0.827699         -1.457073          -1.417063   \n",
       "3           1.467211         -0.955078         -1.402117          -1.355652   \n",
       "4           1.421552         -0.427367          1.125859           1.273868   \n",
       "\n",
       "   10_CLIM_tmmn_MAX  10_CLIM_tmmx_MIN  10_CLIM_tmmx_MEAN  10_CLIM_tmmx_MAX  \\\n",
       "0         -1.100409         -1.558757          -1.293366         -1.464488   \n",
       "1         -1.381126         -0.846084          -0.610036         -0.671140   \n",
       "2         -1.268839         -0.925270          -0.698780         -0.869477   \n",
       "3         -1.100409         -1.796315          -1.524101         -1.662825   \n",
       "4          1.369904          1.133564           1.280213          2.006410   \n",
       "\n",
       "   10_CLIM_vap_MIN  10_CLIM_vap_MEAN  10_CLIM_vap_MAX  10_CLIM_vpd_MIN  \\\n",
       "0        -1.619841         -1.857682        -1.659935        -0.120787   \n",
       "1        -1.666344         -1.772554        -1.634636         0.314369   \n",
       "2        -1.630175         -1.751272        -1.634636         0.401400   \n",
       "3        -1.594006         -1.851504        -1.621987        -0.468911   \n",
       "4        -0.007733          0.483342         0.673870         1.445772   \n",
       "\n",
       "   10_CLIM_vpd_MEAN  10_CLIM_vpd_MAX  10_CLIM_vs_MIN  10_CLIM_vs_MEAN  \\\n",
       "0         -0.240157        -0.187720        0.437490        -0.582620   \n",
       "1          0.324218         0.272453       -1.433983        -2.021150   \n",
       "2          0.246373         0.157410       -0.685394        -1.371491   \n",
       "3         -0.551536        -0.302763        0.437490        -0.861045   \n",
       "4          2.075726         1.614625        0.063195        -0.257791   \n",
       "\n",
       "   10_CLIM_vs_MAX  11_S2_B01_MIN  11_S2_B01_MEAN  11_S2_B01_MAX  \\\n",
       "0       -2.740271      -0.658805       -0.938453      -0.619868   \n",
       "1       -2.321444      -1.399666        0.814370       2.191401   \n",
       "2       -1.902616      -1.042955       -0.902486      -0.598420   \n",
       "3       -2.321444      -0.151179        0.087024       0.559792   \n",
       "4       -1.483789       1.824450        0.831155      -0.316527   \n",
       "\n",
       "   11_S2_B02_MIN  11_S2_B02_MEAN  11_S2_B02_MAX  11_S2_B03_MIN  \\\n",
       "0       0.241609       -0.303368      -0.452409      -0.229518   \n",
       "1      -1.690946        0.321585       2.635316      -1.347524   \n",
       "2      -0.074406       -0.518607      -0.528130      -0.041761   \n",
       "3       0.120065       -0.090206      -0.028933       0.632457   \n",
       "4       1.055957        0.642021      -0.115872       0.683663   \n",
       "\n",
       "   11_S2_B03_MEAN  11_S2_B03_MAX  11_S2_B04_MIN  11_S2_B04_MEAN  \\\n",
       "0       -0.277393      -0.343035      -0.065569       -0.138157   \n",
       "1       -0.069598       2.796461      -0.991685       -0.050848   \n",
       "2       -0.262142      -0.467328      -0.750090        0.364090   \n",
       "3       -0.123612      -0.291856       0.229714        0.384374   \n",
       "4        0.970647       0.079561       0.209581        0.993333   \n",
       "\n",
       "   11_S2_B04_MAX  11_S2_B05_MIN  11_S2_B05_MEAN  11_S2_B05_MAX  11_S2_B06_MIN  \\\n",
       "0      -0.263812      -0.739712       -0.817868      -0.576857       0.473988   \n",
       "1       2.792126      -1.075652        0.031439       2.791453       0.144277   \n",
       "2      -0.113998      -0.413800        0.274243      -0.283570      -0.034635   \n",
       "3      -0.143129       0.298192        0.343325      -0.115977      -0.280002   \n",
       "4       0.664205       0.508782        1.321145       0.454136       0.964721   \n",
       "\n",
       "   11_S2_B06_MEAN  11_S2_B06_MAX  11_S2_B07_MIN  11_S2_B07_MEAN  \\\n",
       "0       -0.911843      -0.989290       0.510847       -0.713369   \n",
       "1       -0.025717       2.898216       0.082667       -0.002745   \n",
       "2       -0.743199       0.388744      -0.199253       -0.609807   \n",
       "3       -0.695622      -0.258569      -0.404865       -0.803645   \n",
       "4        0.936872       0.327095       0.553242        0.825066   \n",
       "\n",
       "   11_S2_B07_MAX  11_S2_B08_MIN  11_S2_B08_MEAN  11_S2_B08_MAX  11_S2_B8A_MIN  \\\n",
       "0      -1.136373      -0.126717       -1.063016      -1.424827       0.538062   \n",
       "1       2.898406      -0.182136        0.179245       2.515100       0.019603   \n",
       "2       1.532924      -0.128770       -0.606464       1.451803      -0.148348   \n",
       "3      -0.348799      -0.490017       -0.676751       0.039444      -0.442263   \n",
       "4       0.417633       0.603987        0.678471       0.562142       0.614736   \n",
       "\n",
       "   11_S2_B8A_MEAN  11_S2_B8A_MAX  11_S2_B09_MIN  11_S2_B09_MEAN  \\\n",
       "0       -0.583252      -1.070419       0.802676        0.816303   \n",
       "1        0.138394       2.839547       1.409895        1.416122   \n",
       "2       -0.475080       1.613776       1.022153        0.854519   \n",
       "3       -0.678768      -0.207518       1.124575        1.411137   \n",
       "4        0.770249       0.493915       0.210090       -0.678258   \n",
       "\n",
       "   11_S2_B09_MAX  11_S2_B10_MIN  11_S2_B10_MEAN  11_S2_B10_MAX  11_S2_B11_MIN  \\\n",
       "0      -0.117590      -1.823957        0.553704      -0.111418      -1.018406   \n",
       "1       0.569082       0.226005        1.451829       1.242940      -1.052112   \n",
       "2      -0.641322       0.909326        1.417937       0.129357       0.053866   \n",
       "3       0.153975       0.226005        1.434883       0.069163      -0.074638   \n",
       "4      -0.129228      -0.457315       -0.479987      -0.322095       0.786972   \n",
       "\n",
       "   11_S2_B11_MEAN  11_S2_B11_MAX  11_S2_B12_MIN  11_S2_B12_MEAN  \\\n",
       "0       -0.917643      -0.220830      -0.992126       -0.839155   \n",
       "1       -0.970296       2.295457      -1.185594       -0.569851   \n",
       "2        0.715876       0.292185      -0.637437        0.900042   \n",
       "3        0.353761       0.537604      -0.311409        0.664137   \n",
       "4        1.400876       1.338913       0.677423        1.611668   \n",
       "\n",
       "   11_S2_B12_MAX  11_NDVI_MIN  11_NDVI_MEAN  11_NDVI_MAX  11_ATSAVI_MIN  \\\n",
       "0      -0.250517     0.319174     -0.310784    -0.872434       1.284733   \n",
       "1       2.601639    -1.110490      0.792091     0.386929      -1.671320   \n",
       "2       0.755749    -1.115804     -0.805220     1.487399       0.518930   \n",
       "3       0.392019    -1.054405     -0.803929     0.298613       1.089179   \n",
       "4       2.016787    -0.961279     -0.503696    -0.095909      -0.695307   \n",
       "\n",
       "   11_ATSAVI_MEAN  11_ATSAVI_MAX  11_ARVI2_MIN  11_ARVI2_MEAN  11_ARVI2_MAX  \\\n",
       "0        1.174384       0.768235      0.815518       0.430973      0.264862   \n",
       "1        1.485780       1.135726     -1.640104       1.496963      1.003073   \n",
       "2        0.422806       1.038356     -0.003295       0.168928      1.117598   \n",
       "3        0.635097      -0.006759      0.778986       0.291108      0.497550   \n",
       "4       -1.319555      -1.799046     -0.897510      -1.005664     -0.870997   \n",
       "\n",
       "   11_GARI_MIN  11_GARI_MEAN  11_GARI_MAX  11_CIrededge_MIN  \\\n",
       "0     0.486075      0.458104     0.091760          1.281382   \n",
       "1    -2.251472      0.400323     0.241749         -1.467740   \n",
       "2     1.197971      1.445396     0.399359          0.431112   \n",
       "3    -0.799363      1.144255     1.245463          1.052754   \n",
       "4     0.257533     -0.366122    -0.078316         -0.703709   \n",
       "\n",
       "   11_CIrededge_MEAN  11_CIrededge_MAX  11_CVI_MIN  11_CVI_MEAN  11_CVI_MAX  \\\n",
       "0           1.094643          0.669503    0.084966     0.068497   -0.303762   \n",
       "1           1.680906          1.102345    0.831550     1.555777    0.992347   \n",
       "2           0.334705          0.983501    1.390774     1.157775    0.019295   \n",
       "3           0.473221         -0.120588    0.387505     1.739021    0.882277   \n",
       "4          -1.238421         -1.491910   -0.011984    -0.356425    0.017606   \n",
       "\n",
       "   11_LCI_MIN  11_LCI_MEAN  11_LCI_MAX  11_MVI_MIN  11_MVI_MEAN  11_MVI_MAX  \\\n",
       "0    0.815095    -0.046043   -0.751096    0.594050     1.312825    0.740316   \n",
       "1   -0.881753     0.729896    0.319480   -0.310198     2.284126    1.904374   \n",
       "2   -1.027757    -0.881680    1.971084   -0.289279     0.075120    0.601463   \n",
       "3   -1.274198    -0.902753    0.101869    0.419384     0.526726    0.411835   \n",
       "4   -1.020258    -0.585077   -0.132025   -0.915581    -1.222891   -1.301249   \n",
       "\n",
       "   11_mARI_MIN  11_mARI_MEAN  11_mARI_MAX  11_NDRE_MIN  11_NDRE_MEAN  \\\n",
       "0    -0.551622     -0.743943    -0.244146     1.285086      1.167835   \n",
       "1    -0.215256      1.927657     1.271151    -1.643965      1.508983   \n",
       "2     0.254894      0.940978     0.335303     0.508777      0.413180   \n",
       "3    -0.322488      1.274332     0.811495     1.085684      0.619272   \n",
       "4     0.189069     -0.385705    -0.619598    -0.696790     -1.312068   \n",
       "\n",
       "   11_NDRE_MAX  11_CLIM_aet_MIN  11_CLIM_aet_MEAN  11_CLIM_aet_MAX  \\\n",
       "0     0.761324        -0.794764         -1.787905        -2.724800   \n",
       "1     1.135381        -0.813817         -1.980706        -2.950848   \n",
       "2     1.035971        -0.828107         -2.212500        -3.289921   \n",
       "3    -0.017584        -0.775710         -1.575608        -2.093194   \n",
       "4    -1.768906         0.038821          0.336515         1.443799   \n",
       "\n",
       "   11_CLIM_pet_MIN  11_CLIM_pet_MEAN  11_CLIM_pet_MAX  11_CLIM_pr_MIN  \\\n",
       "0        -0.317915         -0.206252        -0.840646       -1.087328   \n",
       "1        -0.185172         -0.109585        -0.302014       -1.087328   \n",
       "2        -0.067179          0.042524        -0.319389       -1.087328   \n",
       "3        -0.494905         -0.453606        -0.753770       -1.164123   \n",
       "4         1.068507          1.474050         0.636248       -0.012199   \n",
       "\n",
       "   11_CLIM_pr_MEAN  11_CLIM_pr_MAX  11_CLIM_soil_MIN  11_CLIM_soil_MEAN  \\\n",
       "0        -1.142664       -1.505781         -0.513397          -0.788589   \n",
       "1        -1.232721       -1.548715         -0.479848          -0.777142   \n",
       "2        -1.335644       -1.602383         -0.513397          -0.795300   \n",
       "3        -1.052607       -1.398445         -0.491031          -0.767273   \n",
       "4         0.031299        0.007651          0.481888           0.602876   \n",
       "\n",
       "   11_CLIM_soil_MAX  11_CLIM_srad_MIN  11_CLIM_srad_MEAN  11_CLIM_srad_MAX  \\\n",
       "0         -0.946498          1.564100           1.188240         -1.009668   \n",
       "1         -0.946498          1.951690           1.843961         -0.227202   \n",
       "2         -0.964776          1.921091           1.707527         -0.827699   \n",
       "3         -0.919080          1.543701           1.264811         -0.955078   \n",
       "4          1.091525          2.267881           1.310753         -0.427367   \n",
       "\n",
       "   11_CLIM_tmmn_MIN  11_CLIM_tmmn_MEAN  11_CLIM_tmmn_MAX  11_CLIM_tmmx_MIN  \\\n",
       "0         -1.345515          -1.351284         -1.130922         -1.558757   \n",
       "1         -1.565255          -1.499182         -1.415773         -0.846084   \n",
       "2         -1.455385          -1.427783         -1.301833         -0.925270   \n",
       "3         -1.400450          -1.376784         -1.130922         -1.796315   \n",
       "4          1.126553           1.239479          1.375766          1.133564   \n",
       "\n",
       "   11_CLIM_tmmx_MEAN  11_CLIM_tmmx_MAX  11_CLIM_vap_MIN  11_CLIM_vap_MEAN  \\\n",
       "0          -1.351560         -1.464488        -1.619841         -1.815776   \n",
       "1          -0.641752         -0.671140        -1.666344         -1.728779   \n",
       "2          -0.740795         -0.869477        -1.630175         -1.707188   \n",
       "3          -1.590914         -1.662825        -1.594006         -1.807521   \n",
       "4           1.248318          2.006410        -0.007733          0.568715   \n",
       "\n",
       "   11_CLIM_vap_MAX  11_CLIM_vpd_MIN  11_CLIM_vpd_MEAN  11_CLIM_vpd_MAX  \\\n",
       "0        -1.728070        -0.120765         -0.423267        -0.187720   \n",
       "1        -1.702120         0.314398          0.207686         0.272453   \n",
       "2        -1.702120         0.401431          0.112087         0.157410   \n",
       "3        -1.689144        -0.468895         -0.767423        -0.302763   \n",
       "4         0.665889         1.445821          1.985824         1.614625   \n",
       "\n",
       "   11_CLIM_vs_MIN  11_CLIM_vs_MEAN  11_CLIM_vs_MAX      BDOD       CEC  \\\n",
       "0        0.439842        -0.588770       -2.740271 -0.011836  1.412126   \n",
       "1       -1.434137        -2.122053       -2.321444  0.365556  2.165604   \n",
       "2       -0.684545        -1.508740       -1.902616  0.365556  1.557960   \n",
       "3        0.439842        -0.939235       -2.321444  0.742948  0.172532   \n",
       "4        0.065046        -0.238305       -1.483789  1.120340 -0.775392   \n",
       "\n",
       "       CFVO      CLAY      NITR       OCD       OCS     PHH2O      SAND  \\\n",
       "0 -0.587811  0.612194  1.612335  0.316188 -0.304458  1.138153  0.361270   \n",
       "1 -0.625979  0.586907  0.987846 -0.096934 -0.218642  1.998725 -0.949998   \n",
       "2  0.061045  1.421385  0.779088 -0.534358 -0.218642  1.568439 -0.385144   \n",
       "3 -1.313004 -0.196996  1.565944  0.656406  0.382067  0.277581  0.845431   \n",
       "4  0.595398  0.005301 -1.763473 -0.194139 -0.647720 -0.152705  0.724391   \n",
       "\n",
       "       SILT       SOC  \n",
       "0 -0.973884  0.750047  \n",
       "1  0.552503  0.027779  \n",
       "2 -0.858249  0.085561  \n",
       "3 -0.788868  0.822273  \n",
       "4 -0.835122 -1.315638  \n",
       "\n",
       "[5 rows x 1547 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset mean 3.216 dataset RMSE 1.722'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'dataset mean {:.3f} dataset RMSE {:.3f}'.format(data.Yield.mean(), RMSE(data.Yield.mean(), data.Yield))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['const', 'monthly', 'min', 'mean', 'max', 'corr_features'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dict of features groups\n",
    "with open(\"./csv/features_groups.json\", \"r\") as read_file:\n",
    "    features_dict = json.load(read_file)\n",
    "features_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_stat_features(features, month):\n",
    "    stat_features = [feature for feature in features if int(feature.split('_')[0])==month]\n",
    "    return(stat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_features = find_stat_features(features_dict['min']+features_dict['max']+features_dict['mean'], 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_list = features_dict['const']+features_dict['monthly'] + stat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "554"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(f_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[f_list] = data[f_list].fillna(data[f_list].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['train_ids', 'test_ids', 'year_split'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dict of ids for splitting \n",
    "with open(\"./csv/split.json\", \"r\") as read_file:\n",
    "    split = json.load(read_file)\n",
    "split.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_valid(df, features, train_idx, valid_idx, test_idx):    \n",
    "    \n",
    "    #train/valid split\n",
    "    train = df.loc[df.Field_ID.isin(train_idx)]\n",
    "    valid = df.loc[df.Field_ID.isin(valid_idx)]\n",
    "    test = df.loc[df.Field_ID.isin(test_idx)].sort_values(by=['Field_ID'])\n",
    "    \n",
    "    xTrain, yTrain = train[features].values, train['Yield'].values\n",
    "    xValid, yValid = valid[features].values, valid['Yield'].values\n",
    "    xTest = test[features].values\n",
    "    \n",
    "    return (xTrain, yTrain, xValid, yValid, xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test basic \n",
    "dict_classifiers = {\n",
    "    \n",
    "    'ridge': linear_model.Ridge(alpha=1.0),\n",
    "    'linear': linear_model.LinearRegression(),\n",
    "    'dt': tree.DecisionTreeRegressor(max_depth = 10, random_state = RANDOM_SEED),\n",
    "    'linsvr': svm.LinearSVR(),\n",
    "    'svr': svm.SVR(),\n",
    "    'rf': RandomForestRegressor(n_estimators = 50, max_depth = 5, random_state = RANDOM_SEED),\n",
    "    'knn': neighbors.KNeighborsRegressor(n_neighbors=5),\n",
    "    'gbr': ensemble.GradientBoostingRegressor(n_estimators = 50, max_depth = 5, random_state = RANDOM_SEED)   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/8] ridge: RMSE 1.316/8.141, R2 0.412/-26.731\n",
      "[2/8] linear: RMSE 1.267/39.305, R2 0.455/-948.756\n",
      "[3/8] dt: RMSE 1.000/2.828, R2 0.650/-1.845\n",
      "[4/8] linsvr: RMSE 1.433/10.569, R2 0.304/-38.503\n",
      "[5/8] svr: RMSE 1.355/1.660, R2 0.379/0.051\n",
      "[6/8] rf: RMSE 1.329/1.673, R2 0.402/0.038\n",
      "[7/8] knn: RMSE 1.358/1.767, R2 0.377/-0.075\n",
      "[8/8] gbr: RMSE 0.918/1.894, R2 0.712/-0.249\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "for i, (k, model) in enumerate(dict_classifiers.items()):\n",
    "    \n",
    "    train_rmse_list = []\n",
    "    valid_rmse_list = []\n",
    "    \n",
    "    train_r2_list = []\n",
    "    valid_r2_list = []\n",
    "    \n",
    "    for f, (train_index, valid_index) in split['year_split'].items():\n",
    "        \n",
    "        xTrain_norm, yTrain, xValid_norm, yValid, _ = make_train_valid(data, f_list, train_index, valid_index, split['test_ids'])\n",
    "        \n",
    "        model.fit(xTrain_norm, yTrain)\n",
    "    \n",
    "        yPred_Train = model.predict(xTrain_norm)\n",
    "        yPred_Valid = model.predict(xValid_norm)\n",
    "    \n",
    "        train_rmse_list.append(RMSE(yTrain, yPred_Train))\n",
    "        valid_rmse_list.append(RMSE(yValid, yPred_Valid))\n",
    "        \n",
    "        train_r2_list.append(R2(yTrain, yPred_Train))\n",
    "        valid_r2_list.append(R2(yValid, yPred_Valid))\n",
    "                                                \n",
    "    rmse_train = np.mean(train_rmse_list)\n",
    "    rmse_valid = np.mean(valid_rmse_list)\n",
    "    \n",
    "    r2_train = np.mean(train_r2_list)\n",
    "    r2_valid = np.mean(valid_r2_list)\n",
    "\n",
    "    print('[{}/{}] {}: RMSE {:0.3f}/{:0.3f}, R2 {:0.3f}/{:0.3f}'.format(i+1, len(dict_classifiers),\n",
    "                                                                          k, rmse_train, rmse_valid, r2_train, r2_valid))\n",
    "    \n",
    "    ax1.scatter(rmse_train, rmse_valid)\n",
    "    # ax1.axhline(RMSE(box.Yield.mean(), box.Yield))\n",
    "    ax1.annotate(k, (rmse_train, rmse_valid))\n",
    "    ax1.set_xlabel('train RMSE')\n",
    "    ax1.set_ylabel('valid RMSE')\n",
    "    \n",
    "    ax2.scatter(r2_train, r2_valid)\n",
    "    ax2.annotate(k, (r2_train, r2_valid))\n",
    "    ax2.set_xlim([0, 1])\n",
    "    ax2.set_ylim([0, 1])\n",
    "    ax2.set_xlabel('train R2')\n",
    "    ax2.set_ylabel('valid R2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVR / RF are  the best models so far"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train RMSE 1.35 / valid RMSE 1.66\n",
      "train CORR 0.38 / valid CORR 0.05\n"
     ]
    }
   ],
   "source": [
    "valid_predict_list = []\n",
    "test_predict_list = []\n",
    "\n",
    "train_rmse_list = []\n",
    "valid_rmse_list = []\n",
    "\n",
    "train_r2_list = []\n",
    "valid_r2_list = []\n",
    "\n",
    "svr = svm.SVR()\n",
    "\n",
    "for f, (train_index, valid_index) in split['year_split'].items():\n",
    "\n",
    "    xTrain_norm, yTrain, xValid_norm, yValid, xTest_norm = make_train_valid(data, f_list, train_index, valid_index, split['test_ids'])\n",
    "\n",
    "    svr.fit(xTrain_norm, yTrain)\n",
    "\n",
    "    yPred_Train = svr.predict(xTrain_norm)\n",
    "    yPred_Valid = svr.predict(xValid_norm)\n",
    "    \n",
    "    valid_predict_list.append([yValid, yPred_Valid])\n",
    "    test_predict_list.append(svr.predict(xTest_norm))\n",
    "\n",
    "    train_rmse_list.append(RMSE(yTrain, yPred_Train))\n",
    "    valid_rmse_list.append(RMSE(yValid, yPred_Valid))\n",
    "\n",
    "    train_r2_list.append(R2(yTrain, yPred_Train))\n",
    "    valid_r2_list.append(R2(yValid, yPred_Valid))\n",
    "    \n",
    "rmse_train = np.mean(train_rmse_list)\n",
    "rmse_valid = np.mean(valid_rmse_list)\n",
    "\n",
    "r2_train = np.mean(train_r2_list)\n",
    "r2_valid = np.mean(valid_r2_list)\n",
    "    \n",
    "print('train RMSE {:0.2f} / valid RMSE {:0.2f}'.format(rmse_train, rmse_valid))\n",
    "print('train CORR {:0.2f} / valid CORR {:0.2f}'.format(r2_train, r2_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, len(valid_predict_list), figsize = (20, 4))\n",
    "for f, (gt, pred) in enumerate(valid_predict_list):\n",
    "    ax[f].set_title('fold {}, RMSE {:.3f}, CORR {:.3f}'.format(f+1, valid_rmse_list[f], valid_r2_list[f]))\n",
    "    ax[f].scatter(gt, pred)\n",
    "    ax[f].set_xlim(0, 15)\n",
    "    ax[f].set_ylim(0, 15)\n",
    "    ax[f].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = data.loc[data.Field_ID.isin(split['test_ids'])].sort_values(by=['Field_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Yield'] = np.mean(test_predict_list, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[['Field_ID', 'Yield']].to_csv('./predicts/Submission_SVR_const_monthly_stat.csv', index = False)\n",
    "# result score - 1.808593963770017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train RMSE 1.33 / valid RMSE 1.68\n",
      "train CORR 0.40 / valid CORR 0.03\n"
     ]
    }
   ],
   "source": [
    "valid_predict_list = []\n",
    "test_predict_list = []\n",
    "\n",
    "train_rmse_list = []\n",
    "valid_rmse_list = []\n",
    "\n",
    "train_r2_list = []\n",
    "valid_r2_list = []\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators = 100, max_depth = 5, random_state = RANDOM_SEED)\n",
    "\n",
    "for f, (train_index, valid_index) in split['year_split'].items():\n",
    "\n",
    "    xTrain_norm, yTrain, xValid_norm, yValid, xTest_norm = make_train_valid(data, f_list, train_index, valid_index, split['test_ids'])\n",
    "\n",
    "    rf.fit(xTrain_norm, yTrain)\n",
    "\n",
    "    yPred_Train = rf.predict(xTrain_norm)\n",
    "    yPred_Valid = rf.predict(xValid_norm)\n",
    "    \n",
    "    valid_predict_list.append([yValid, yPred_Valid])\n",
    "    test_predict_list.append(rf.predict(xTest_norm))\n",
    "\n",
    "    train_rmse_list.append(RMSE(yTrain, yPred_Train))\n",
    "    valid_rmse_list.append(RMSE(yValid, yPred_Valid))\n",
    "\n",
    "    train_r2_list.append(R2(yTrain, yPred_Train))\n",
    "    valid_r2_list.append(R2(yValid, yPred_Valid))\n",
    "    \n",
    "rmse_train = np.mean(train_rmse_list)\n",
    "rmse_valid = np.mean(valid_rmse_list)\n",
    "\n",
    "r2_train = np.mean(train_r2_list)\n",
    "r2_valid = np.mean(valid_r2_list)\n",
    "    \n",
    "print('train RMSE {:0.2f} / valid RMSE {:0.2f}'.format(rmse_train, rmse_valid))\n",
    "print('train CORR {:0.2f} / valid CORR {:0.2f}'.format(r2_train, r2_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, len(valid_predict_list), figsize = (20, 4))\n",
    "for f, (gt, pred) in enumerate(valid_predict_list):\n",
    "    ax[f].set_title('fold {}, RMSE {:.3f}, CORR {:.3f}'.format(f+1, valid_rmse_list[f], valid_r2_list[f]))\n",
    "    ax[f].scatter(gt, pred)\n",
    "    ax[f].set_xlim(0, 15)\n",
    "    ax[f].set_ylim(0, 15)\n",
    "    ax[f].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Yield'] = np.mean(test_predict_list, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[['Field_ID', 'Yield']].to_csv('./predicts/Submission_RF_monthly_stat.csv', index = False)\n",
    "# result score - 1.793441802530261"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train RMSE 0.15 / valid RMSE 1.73\n",
      "train CORR 0.99 / valid CORR -0.03\n"
     ]
    }
   ],
   "source": [
    "valid_predict_list = []\n",
    "test_predict_list = []\n",
    "\n",
    "train_rmse_list = []\n",
    "valid_rmse_list = []\n",
    "\n",
    "train_r2_list = []\n",
    "valid_r2_list = []\n",
    "\n",
    "feat_impot_list = []\n",
    "\n",
    "lgbmr= LGBMRegressor(n_estimators=1000, max_depth = 5, random_state = RANDOM_SEED, silent=True)\n",
    "\n",
    "for f, (train_index, valid_index) in split['year_split'].items():\n",
    "\n",
    "    xTrain_norm, yTrain, xValid_norm, yValid, xTest_norm = make_train_valid(data, f_list, train_index, valid_index, split['test_ids'])\n",
    "\n",
    "    lgbmr.fit(xTrain_norm, yTrain, verbose=False, eval_metric=['mae', 'rmse'])\n",
    "\n",
    "    yPred_Train = lgbmr.predict(xTrain_norm)\n",
    "    yPred_Valid = lgbmr.predict(xValid_norm)\n",
    "    \n",
    "    valid_predict_list.append([yValid, yPred_Valid])\n",
    "    test_predict_list.append(lgbmr.predict(xTest_norm))\n",
    "\n",
    "    train_rmse_list.append(RMSE(yTrain, yPred_Train))\n",
    "    valid_rmse_list.append(RMSE(yValid, yPred_Valid))\n",
    "\n",
    "    train_r2_list.append(R2(yTrain, yPred_Train))\n",
    "    valid_r2_list.append(R2(yValid, yPred_Valid))\n",
    "    \n",
    "    feat_impot_list.append(lgbmr.feature_importances_.astype(float))\n",
    "    \n",
    "rmse_train = np.mean(train_rmse_list)\n",
    "rmse_valid = np.mean(valid_rmse_list)\n",
    "\n",
    "r2_train = np.mean(train_r2_list)\n",
    "r2_valid = np.mean(valid_r2_list)\n",
    "    \n",
    "print('train RMSE {:0.2f} / valid RMSE {:0.2f}'.format(rmse_train, rmse_valid))\n",
    "print('train CORR {:0.2f} / valid CORR {:0.2f}'.format(r2_train, r2_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, len(valid_predict_list), figsize = (20, 4))\n",
    "for f, (gt, pred) in enumerate(valid_predict_list):\n",
    "    ax[f].set_title('fold {}, RMSE {:.2f}, CORR {:.2f}'.format(f+1, valid_rmse_list[f], valid_r2_list[f]))\n",
    "    ax[f].scatter(gt, pred)\n",
    "    ax[f].set_xlim(0, 15)\n",
    "    ax[f].set_ylim(0, 15)\n",
    "    ax[f].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impot_df = pd.DataFrame({\n",
    "    'features':f_list,\n",
    "    'importarnce': np.mean(np.array(feat_impot_list), axis=0)\n",
    "})\n",
    "feat_impot_df = feat_impot_df.sort_values(by=['importarnce'], ascending = False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,2))\n",
    "plt.bar(feat_impot_df.features, feat_impot_df.importarnce)\n",
    "# plt.ylim(0,0.4)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Yield'] = np.mean(test_predict_list, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[['Field_ID', 'Yield']].to_csv('./predicts/Submission_LGBMR_monthly_stat.csv', index = False)\n",
    "# result score - 1.790077871357966"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor, Pool, cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train RMSE 1.31 / valid RMSE 1.61\n",
      "train CORR 0.40 / valid CORR 0.11\n"
     ]
    }
   ],
   "source": [
    "valid_predict_list = []\n",
    "test_predict_list = []\n",
    "\n",
    "train_rmse_list = []\n",
    "valid_rmse_list = []\n",
    "\n",
    "train_r2_list = []\n",
    "valid_r2_list = []\n",
    "\n",
    "feat_impot_list = []\n",
    "\n",
    "cb = CatBoostRegressor(loss_function='RMSE', n_estimators=1000, max_depth=5,  random_state = RANDOM_SEED, silent = False)\n",
    "\n",
    "for f, (train_index, valid_index) in split['year_split'].items():\n",
    "\n",
    "    xTrain_norm, yTrain, xValid_norm, yValid, xTest_norm = make_train_valid(data, f_list, train_index, valid_index, split['test_ids'])\n",
    "\n",
    "    cb.fit(xTrain_norm, yTrain, eval_set = (xValid_norm, yValid),  verbose=False)\n",
    "\n",
    "    yPred_Train = cb.predict(xTrain_norm)\n",
    "    yPred_Valid = cb.predict(xValid_norm)\n",
    "    \n",
    "    valid_predict_list.append([yValid, yPred_Valid])\n",
    "    test_predict_list.append(cb.predict(xTest_norm))\n",
    "\n",
    "    train_rmse_list.append(RMSE(yTrain, yPred_Train))\n",
    "    valid_rmse_list.append(RMSE(yValid, yPred_Valid))\n",
    "\n",
    "    train_r2_list.append(R2(yTrain, yPred_Train))\n",
    "    valid_r2_list.append(R2(yValid, yPred_Valid))\n",
    "    \n",
    "    feat_impot_list.append(cb.feature_importances_.astype(float))\n",
    "    \n",
    "rmse_train = np.mean(train_rmse_list)\n",
    "rmse_valid = np.mean(valid_rmse_list)\n",
    "\n",
    "r2_train = np.mean(train_r2_list)\n",
    "r2_valid = np.mean(valid_r2_list)\n",
    "    \n",
    "print('train RMSE {:0.2f} / valid RMSE {:0.2f}'.format(rmse_train, rmse_valid))\n",
    "print('train CORR {:0.2f} / valid CORR {:0.2f}'.format(r2_train, r2_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S5ShqJUbStD3Z5vVXdNnZJ0zLrKjHVLfsX5ziolbdYrP6jMXTaqpKukfApFqf7L63x/TZoS3TtmR/8bylkqY36pPsqh21MtLoVp1af9+BiPuz1QhpaTSLqlivKZZwMy+tvSeSSOvMPuhuzWZRWJKzRcXdfu8vd2jDoy9whEaT2vm6tetwduRTJnv8NHrqtc8ObdF19zxVquM6u4lJWviKvoZ3QzRpak+A4OEfwyOjqvSYKr2m8cDcJcG9JpI2D8Pi6kprt0l0l7jl6PJbtsbuQRM1h8/OfeO6eP39uuLWrVO78ge3goVXhtI2PDKqi9ffr5gT5CXWasJOTLppW/OqK30Hzepp+HevNcdAUrVy4vOnJptrDACa1WvW1LpKGppdTwKiHDXQV3eZirudvXzyqd2HsyN/Otr4qR7LHvflIjjBZ/XQrqSzk6M96s3LIXl/t3/5+Y6GvjQODvTprjXe2Sui9t4an3Qa6KtozkGzIr/4Ndo8BKLELS8jo+NTzcCky/WkU+wx8e3cTTc4fh6Njk80/Lv3mLTnpf0tzzFQOyfmNFQTADSir9Ibm32dWFdhPQlpembXaOx6RnWPFJatfKiepj28UbvSa5oze5Z2jc7cQIly6GjjJ3gse5zqXj3Vf2n6ZCd4VqDlxx4+dehKT8SZeRr5zlnptUSTc+8aHdf9l7098rZmz8DD5M4Iauekx8GtYKwMNcbJa/4GNbOlutNn6gJQHuG5CcMTNlfn9skqg8g/pKnexqUTPvd9Jmlu0qCfH7b38Zafq9JjU2c/Zo4vhGVyqBfyr6/SO9X0CQdHq6csvfr3TpZUf3LuWisnzZ6B57KzT9D2RzZNu67Sa7rs7BOSlI+CqXWWiTRUGz6dPqtWt4s7qrfRBlrNnNhVqFP3okvN7u1RpdITe2pe5FNfpXdqXrd6On22wKoszlSI8to7xvxkzai+J6++fZvOX9B662zuwQfOfsy8hQhjkgNMqZ5nZ3Cgb1rT59Kbt2h4ZFROmprzpNbjqyo9pnn9FZn/nF86b6lOOOrQRIe+1Fs5WbVsUFedu0SDA31Tz1+tuZZVywZ19Ly+aY+7+vdOJhhLKmo5SnPvr2rzcvUZi9VX6U3teYsu7qxfjW6pbjYngE56iaZPV2kkR7LMIPIPyL/q3sxp7Rk+EnFiHKCKPX4g6cApR8MrBFENGqeZZ/+pzgUU3s155ilLva3stQIurpawZjvZA30V3bVmRcOPQzGFl6NmJmLur/RofNLFTkYePtNXtxvoq2hs/0TdQ3F7Iw4LDYp73aLmFWt2SzVbvJBn4xMudmMKOqteXknT5ydMKssMIv+A/Kt+b5J2J35M3FlYOZQTtdD4wdQXqqiVg7gvqU7eClCzx43GHfrSzEoVkKZap2Ovdax0vWOpgyvgf/fNWzv4G9XXa9JEaA2i0mOSaUZTJrw3YFyDrNrAjTrU4Kpzl2hg12N6+MIVsa9bcF4xjk1HUTnR+MmLJGeO5TApFFmP5fdEEUVWXccZDk1FEdZrpknnIs+ILHEoJ+rraONndm9PxLS6yFqtSVPb1aDh2HPkWdxW0lqNh0a2rB552MHqq0zMWP7rnUEveL8Nj77gHXpp8XPixKkeSRXcuy6qASPFN7uq/0ad3l7ymsZxTbRVywan9v6r9VrT6EHRGW2frjGvv0ImobD6Kr36r7+/VJfc8IAm6P50TI+kfWP79Ynr79clS2q/7pPO6fG175z6mQ1kaFRHGz+9PaYjmeQ0l+L27GlXg6bWF0Kg6Ab6Krrq3Dck2tMl6kwx4fdJuGlTfUxU1lZ6TFe/92QN7HpM/+nCFVPXN9vsqnfWGho4QLxKr6kSOu133C78yBYngUCRjY5P6JL1D8g1uiUJLZmUtDPhvDwDoTkoo6YqOG3tnXyvQqyOH+q1+ozFWn3jA9MOH0D7JF2BjDsmtJ0NGr4QoszS3NOl1mPiDqWq7nHTKvbeA5rX22O66twlUw3U6jwzHHKRL+ztgzJIcrgjslPrzxM+/H54ZFSX3rxFUu0NeNXHVtcT1yyd1MjmYfKuoDra+JmYdLri1q2FavpUD5W44d4nddfPd7R9vL7QlsGwef0VOSftGh2PPQY06jnrnUGLAAC6U7vfv+y9B7Sm+l4JrrTT9OmcegfbVU/dDgBZ2jUav2dQ1Ml4ak3lURVuGI1NTCZuGKH7dLTxMzYxmXh3Nsk7JGE8p2s/8/or2vy5t0vy3jT3PbmrI+MGtwxG2blvXH2VXn3xvKVTb9jlxx6uK27dGvnaz+uv6LKzT+DNDaBpNIeB1kSttEtMttqM8J7OJm/Pqv0RL2RfpefApGcBc2b3at/YREca2UObh/Xcs7v1oTW30TgHEKvWGbvipuyodybZZhtG6E65PqvXeW9aMG1ui6RzA/VVenTVuSdJat/pk3fuG9fCNbdpcKBPe1/e39Cpn5s1ONA34wvWaWvvnPG6hN+w1ccMbR7Wc9vuk0msXAAAkBNx6yk0feKZpP7Zvdo7NjF1iFy46ROcNH//5PR5lC489RhtePQFOTc247kH+mdr639u/xlGq1vbP3L8pJx6Gjo8A0B51Ds6I+57cr3TuzfbMEJ3ynXjZ8OjL0w7c1RUk6Oq10wXvHmBrly1ZNr11Q/Ooc3D2v5w7dPkNaORiaoHB/ok7W5q4sa4N3wjb9hVywa1cddjenztigZHBwAA7dLIxi14nKS9YxM67XWH64lfj2p4ZHTGutXo+ISuu+epGXOXOGlqw6IWzHzuuHWruDnTmsXWdgBhvWY69bXz9MSvRxNnTbPzLTbbMEJ3ynXjZ3hkVKetvXNqYY9bqK86d0ndD8hVywY19OzDGhzo1dMjo1NbiTqlevrzjRs3Slv2Jn5MvTc8b1gAALrb6jMW65PX38/ZvJpQb37FuAlrq+tX0u4Ztw30V2acHUdS05OnxmFrO4CqBYf364m1pzf12GbnW+QEHeWS68aPFP3B2uzWloG+iu5as0KSt/fQ3rHOfLCaNO0NlHTLXnBvpzi8YQEA6H40fdqjehhYWHUdcviR6XuDV3pNe17aPzUvYnU99OBKT+p75yTdeJf2nkYA8uepHfumphFp5j3e7FlhpQPfrWf39iTaoQLdqaONn9m9PTqkvzL1YWpW+9R0VcEP1rQmEU17a0r1bFojoRnXq8eRB2uOatZEPV8SnFEHAIDuVZ3nBekLzvETtYFs1bJB/e9fPqhec5pwTr1mmtVjGh2fnPY8o+MTsetsraxPVtcHpf0zaqtq5TTNALpPp9/jwe/WGzdu1ApypbA62vjp7bGpM2FJMz/Makm7UVNrr5u+Sq8OrvTEnoEsavLA6pmxkmyVCTZromqo9FpDpw7ljDoAAHSnuDN6oTXBrebLjz08ct1saPOwdu4b14TrleQdFjY63ti+V60cWl9dd6t14g3mAQLKh/d4c9g7srZMD/WK2ltl78v7Z+w1I6U/Z03cXjcDfRVdfo7XdImbTyhcc3ChStqECd6PhRQAgHJiUufW9FV66879GLdudvXt23T+gmSNnoG+il7eP5n6ofX1TrzBPEBAOfEebwx7R9aX+Rw/4Q/jqL2A2jFnTdJDpGo1eNKshQUSAIDyiZuDBvWZSVedu6TpjWdxZ/UK66v0Tm0U7PSGOk7iAZQT7/HGsHdkfU03fsxsgaSvS3qVpElJ1zjnvtxqQZ2cs6Zew4WGDJB/7coiAGhUM3lE06d5zrW2rhZ3Vq95/RX1z57V9g1/SXASDzSDdaPut/L4+VmX0FXYO7K+Vvb42S/pEufcfWZ2iKRNZnaHc+7hVoui4QKgAW3LIgBoUMN5NJjwTJ9FZbLMxo46q1dw3sY84CQeaBLrRh3Ua6ZD+2bFzg/bzJ6dGx59IY3SSoO9I+vrafaBzrlnnHP3+f/fLekRSXwKAegosghAXjSTR6vPWBzb+siuJdIZ8/orOvrwPg30JTuTaVizj6tatWxQg/P6NDjQJ5PXhMvjqYxXLRvUXWveqsfXvlN3rXlr7upD/rBu1D59ld4ZP3/h90+ueWKeSec02GADgj1VGrP6jMWRfxv2jjzAXAq7GJvZQkk/knSic+7F0G0XSbpIkubPn3/K+vXrWx6vWXv27NHcuXMzGz8PNZR9/DzUUITxV65cuck5tzylklJDFnVPDWUfPw81ZD1+GjXkNYuk+DyKyqKnR0b1671j0x7fY6Z5/RXtfmm/xiamn148TUf2Sc9FfL8In8E0bT1mGpzXp1kTL2vu3LkaGR3Xc7te0tjEpGb39ujIww6W5H35mZicWYnJWmoaVRXhfcD42dfQ7Vl0xBHzT/ncl/4+mwJ9cVnUyfFfefhhkqRtz+6OzN1qNoWzqppDDz/zYmReVe+3fceoXEyyhn//2b09WvyqQ1L4zZLL+r3Y6vhRnyONfEZk/funUUOtLGq58WNmcyX9k6TPO+durnXfxYsXu23btrU0Xis2btyoFStWZDZ+Hmoo+/h5qKEI45tZ7lZwyKLuqqHs4+ehhqzHT6OGPGaRlDyPgllU7wyfnx3aomvvfnLGc1R6pPHJ6rjevDfVwwoG+ioyk3buG5+6LeySJfv1hS0Hjvx//Svn6I6LV0yNed09T2nCOfWa6YI3L9CVq5ZoaPOwLr9l67SzsIYbRT3+FcGvTtX7BE+1nnQZaNcZUIvwPmD87Gvo9iw65rXHuZ7fz3YKoHAWddqlSyf1/51/tqT4vH3fqcfoylVLYp8j7iRF1T0Jw9k5r7+id570at20aVgfOf7lqd8/6uyEnZD1e7Hs46dRQ60saundZWYVSTdJ+ka9L1oA0C5kEYC8aDaP6s1vWP2yEdWIadRnh7boG3c/OaNR8+/fPP1LzZWrlkQ+f9K5GNNs1jD/I9AY1o1mmtdf0WVnnxDZnDnysNlTP8fNr1Nv3p16c3LF5djyYw/Xc9vuk0nM44W2aeWsXibpHyQ94pz7b+mVBADJkUUA8qLdeRTXiGnleTZu3KgnLlzR8nNGoVkDZKPM60YmaaC/ol2j4woedVXptWkTt4ebMwO7Hpu6bytniGom91YtG9TGXY/p8bUrGnoc0IhW9vg5TdIfSNpiZvf71/2pc+67LVcFAMmRRQDygjwCkActZ1H1kMxPXn9/W+f7qqev0jtt75xaes3086vOklR7j8Oo5szGjQcaP5whCkXUdOPHOfdjFf+EEwByjiwCkBfkEYA8aCWLwvPLXH37tsgmSNJTlPdVevXGYw7TXT/fkbiGwYG+aQ2buBrCgvW0ssfh6jMWRx4Oxhmi0M2ym0ELAAAAAJApk8XOLxPXBHnPKYO6adPwtOtN0m++7nA98evRGXvahCeLnz3LNDo+88xZgwN9umvNW2dcH64hSqOnTI9Tb64eoBvR+AEAAACAkjq40qNta98ZeVutJsjyYw9P3BwJz1EWdwasqL1qwjUc1lfR3rH9Gp9wdR/bLOYoQ9HQ+AEAAAAARIprgrTSHAk3c2b39tQ8hXl4rDTPGgiUAY0fAAAAAEBHBZs5Gzdu1IoGGjfskQM0pifrAgAAAAAAANAeNH4AAAAAAAAKisYPAAAAAABAQdH4AQAAAAAAKCgaPwAAAAAAAAVF4wcAAAAAAKCgaPwAAAAAAAAUFI0fAAAAAACAgqLxAwAAAAAAUFA0fgAAAAAAAAqKxg8AAAAAAEBB0fgBAAAAAAAoKBo/AAAAAAAABUXjBwAAAAAAoKBo/AAAAAAAABQUjR8AAAAAAICCaqnxY2Znmtk2M/uZma1JqygAaARZBCAPyCIAeUEeAQhquvFjZr2SviLpHZLeIOkCM3tDWoUBQBJkEYA8IIsA5AV5BCCslT1+3iTpZ865XzjnxiR9U9K70ykLABIjiwDkAVkEIC/IIwDTzGrhsYOSngr8vF3Sm8N3MrOLJF3k//iymT3UwpitOkLSrzIcPw81lH38PNRQhPGPTaOQlJBF3VlD2cfPQw1Zj59GDWRR67JeDso+fh5qKPv4adSQpyySEuQRWcT4Oayh7OOnUUNsFrXS+LGI69yMK5y7RtI1kmRm9zrnlrcwZkuyHj8PNZR9/DzUUPbx24As6sIayj5+HmrIevy81JCirsuiPNRQ9vHzUEPZx89LDSmrm0dkEePnrYayj9/uGlo51Gu7pAWBn4+W9HRr5QBAw8giAHlAFgHIC/IIwDStNH7+TdLrzew1ZjZb0vmSbkmnLABIjCwCkAdkEYC8II8ATNP0oV7Ouf1m9lFJt0vqlfRV59zWOg+7ptnxUpL1+FL2NZR9fCn7Gso+fqrIoqZlXUPZx5eyryHr8aV81JCKLs0iKfsayj6+lH0NZR9fykcNqWkij/Lw+2ddQ9nHl7KvoezjS22swZybcfg5AAAAAAAACqCVQ70AAAAAAACQYzR+AAAAAAAACqojjR8zO9PMtpnZz8xsTSfGDI2/wMw2mNkjZrbVzD7e6Rr8OnrNbLOZfSej8QfM7EYze9R/Ld7S4fE/6b/+D5nZdWZ2cJvH+6qZPW9mDwWuO9zM7jCzx/x/52VQw9X+3+BBM/uWmQ10cvzAbZ8yM2dmR7Rr/DzKMo/IoqnxS5VF/piZ5hFZlD9kEVlEFk1dRxZliCwii8iiqes6lkVxNQRuSz2P2t74MbNeSV+R9A5Jb5B0gZm9od3jhuyXdIlz7jcknSrp/8+gBkn6uKRHMhi36suSvu+cO17SyZ2sxcwGJX1M0nLn3InyJpo7v83DrpN0Zui6NZJ+6Jx7vaQf+j93uoY7JJ3onDtJ0k8lXdrh8WVmCySdLunJNo6dOznII7LIU7YskrLPo6jxyaKMkEVTyCKySCKLMkMWTSGLyCKps1kUV0Pb8qgTe/y8SdLPnHO/cM6NSfqmpHd3YNwpzrlnnHP3+f/fLe/NNNjJGszsaEnvlPQ/OzluYPxDJf22pH+QJOfcmHNupMNlzJLUZ2azJPVLerqdgznnfiRpR+jqd0v6mv//r0la1ekanHM/cM7t93+8W9LRnRzf90VJfyKpbLO7Z5pHZFE5s0jKPo/Iotwhi8giiSyqXkcWZYcsIosksqh6XceyKK4GX1vyqBONn0FJTwV+3q4Ov6GDzGyhpGWS7unw0F+S9wec7PC4Va+V9IKk/+Xvyvg/zWxOpwZ3zg1L+kt5nctnJO1yzv2gU+MHHOmce8av6RlJr8yghqA/lPS9Tg5oZudIGnbOPdDJcXMiN3lEFpU+i6R85RFZ1FlkEVlEFkUjizqLLCKLyKJoHc8iqb151InGj0Vcl0k33czmSrpJ0ieccy92cNx3SXreObepU2NGmCXpjZL+xjm3TNJetf8wpyn+MZrvlvQaSUdJmmNm7+vU+HlkZp+Rt4vrNzo4Zr+kz0j6XKfGzJlc5BFZRBblCVmUCbKILCKLQsiiTJBFZBFZFJJFFvnjtjWPOtH42S5pQeDno9WB3cfCzKwiL1C+4Zy7ucPDnybpHDN7Qt4ulG81s2s7XMN2Sdudc9Uu+o3yQqZTflfS4865F5xz45JulvSbHRy/6jkze7Uk+f8+n0ENMrMPSHqXpAudc538gH2dvGB/wF8ej5Z0n5m9qoM1ZCnzPCKLyKKAzPOILMoMWUQWkUUBZFFmyCKyiCwKyDCLpDbnUScaP/8m6fVm9hozmy1vsqhbOjDuFDMzecdNPuKc+2+dHFuSnHOXOueOds4tlPf73+mc62gn1Tn3rKSnzGyxf9XbJD3cwRKelHSqmfX7f4+3KZtJ1G6R9AH//x+Q9O1OF2BmZ0r6tKRznHP7Ojm2c26Lc+6VzrmF/vK4XdIb/eWjDDLNI7KILArJNI/IokyRRWQRWeQjizJFFpFFZJEvyyySOpBHzrm2XySdJW9m7J9L+kwnxgyN/1vydlt8UNL9/uWsTtfh17JC0ncyGnuppHv912FI0rwOj3+FpEclPSTpHyUd1ObxrpN3rOq4/8b5sKRXyJsl/jH/38MzqOFn8o6nri6Lf9vJ8UO3PyHpiCyWx6wuWeYRWTQ1dqmyyB8z0zwii/J3IYumaiGLyCKyKMMLWTRVC1lEFnUsi+JqCN2eah6Z/6QAAAAAAAAomE4c6gUAAAAAAIAM0PgBAAAAAAAoKBo/AAAAAAAABUXjBwAAAAAAoKBo/AAAAAAAABQUjR8AAAAAAICCovEDAAAAAABQUP8XqM946QGznc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, len(valid_predict_list), figsize = (20, 4))\n",
    "for f, (gt, pred) in enumerate(valid_predict_list):\n",
    "    ax[f].set_title('fold {}, RMSE {:.2f}, CORR {:.2f}'.format(f+1, valid_rmse_list[f], valid_r2_list[f]))\n",
    "    ax[f].scatter(gt, pred)\n",
    "    ax[f].set_xlim(0, 15)\n",
    "    ax[f].set_ylim(0, 15)\n",
    "    ax[f].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impot_df = pd.DataFrame({\n",
    "    'features':f_list,\n",
    "    'importarnce': np.mean(np.array(feat_impot_list), axis=0)\n",
    "})\n",
    "feat_impot_df = feat_impot_df.sort_values(by=['importarnce'], ascending = False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,2))\n",
    "plt.bar(feat_impot_df.features[:100], feat_impot_df.importarnce[:100])\n",
    "# plt.ylim(0,0.4)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Yield'] = np.mean(test_predict_list, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[['Field_ID', 'Yield']].to_csv('./predicts/Submission_CB_monthly_stat.csv', index = False)\n",
    "# result score - 1.766331761828724"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml] *",
   "language": "python",
   "name": "conda-env-ml-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}