BG prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 2. Обучение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "jupyter": {
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Импорт библиотек\n",
    "import pandas as pd\n",
    "\n",
    "import pickle\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "\n",
    "from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "import xgboost as xgb\n",
    "\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# ##################################################################################################################################################################\n",
    "#                      !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!  ATTENTION !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "# ##################################################################################################################################################################\n",
    "#\n",
    "# At the moment, only a part of the hybrid metal(III) halides dataset is presented, it will be fully available after the article is rejected.\n",
    "#\n",
    "# Your results from this notebook will be different and not very informative.\n",
    "#\n",
    "# Therefore, where important information is displayed, copies of the outputs obtained on all data are given.\n",
    "#\n",
    "# ##################################################################################################################################################################\n",
    "#                      !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!  ATTENTION !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "# ##################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REFCODE</th>\n",
       "      <th>Band gap</th>\n",
       "      <th>M</th>\n",
       "      <th>X</th>\n",
       "      <th>Bi</th>\n",
       "      <th>Sb</th>\n",
       "      <th>I</th>\n",
       "      <th>Br</th>\n",
       "      <th>Cl</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>...</th>\n",
       "      <th>t2-d3</th>\n",
       "      <th>t2-d4</th>\n",
       "      <th>d1-d2</th>\n",
       "      <th>d1-d3</th>\n",
       "      <th>d1-d4</th>\n",
       "      <th>d2-d3</th>\n",
       "      <th>d2-d4</th>\n",
       "      <th>d3-d4</th>\n",
       "      <th>delta d</th>\n",
       "      <th>sigma^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGODEI</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90.072</td>\n",
       "      <td>94.778</td>\n",
       "      <td>86.451</td>\n",
       "      <td>80.071</td>\n",
       "      <td>91.585</td>\n",
       "      <td>91.585</td>\n",
       "      <td>177.438</td>\n",
       "      <td>86.451</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>26.536168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGODIM</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>89.135</td>\n",
       "      <td>93.715</td>\n",
       "      <td>88.559</td>\n",
       "      <td>87.220</td>\n",
       "      <td>87.640</td>\n",
       "      <td>89.067</td>\n",
       "      <td>175.548</td>\n",
       "      <td>88.436</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>4.061154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGODOS</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90.864</td>\n",
       "      <td>95.538</td>\n",
       "      <td>86.936</td>\n",
       "      <td>80.996</td>\n",
       "      <td>91.075</td>\n",
       "      <td>91.075</td>\n",
       "      <td>177.386</td>\n",
       "      <td>86.936</td>\n",
       "      <td>0.000974</td>\n",
       "      <td>22.655187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGODOS01</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90.625</td>\n",
       "      <td>94.803</td>\n",
       "      <td>86.278</td>\n",
       "      <td>81.729</td>\n",
       "      <td>91.836</td>\n",
       "      <td>91.836</td>\n",
       "      <td>177.509</td>\n",
       "      <td>86.278</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>19.705712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AYENEZ</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>...</td>\n",
       "      <td>92.116</td>\n",
       "      <td>93.791</td>\n",
       "      <td>88.483</td>\n",
       "      <td>84.036</td>\n",
       "      <td>88.227</td>\n",
       "      <td>85.743</td>\n",
       "      <td>173.823</td>\n",
       "      <td>88.717</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>11.252038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AYENEZ01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>93.164</td>\n",
       "      <td>93.804</td>\n",
       "      <td>89.474</td>\n",
       "      <td>83.165</td>\n",
       "      <td>87.247</td>\n",
       "      <td>85.220</td>\n",
       "      <td>174.042</td>\n",
       "      <td>89.449</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>13.529862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AYENOJ</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>...</td>\n",
       "      <td>91.911</td>\n",
       "      <td>94.924</td>\n",
       "      <td>87.010</td>\n",
       "      <td>84.379</td>\n",
       "      <td>89.589</td>\n",
       "      <td>88.861</td>\n",
       "      <td>175.368</td>\n",
       "      <td>87.675</td>\n",
       "      <td>0.002088</td>\n",
       "      <td>10.257849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AYENOJ01</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>92.448</td>\n",
       "      <td>94.979</td>\n",
       "      <td>87.837</td>\n",
       "      <td>83.753</td>\n",
       "      <td>88.837</td>\n",
       "      <td>88.501</td>\n",
       "      <td>175.644</td>\n",
       "      <td>88.342</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>10.992420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AYEPAX</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.221</td>\n",
       "      <td>93.360</td>\n",
       "      <td>86.534</td>\n",
       "      <td>84.468</td>\n",
       "      <td>91.715</td>\n",
       "      <td>90.523</td>\n",
       "      <td>176.133</td>\n",
       "      <td>85.874</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>14.016866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AYEPAX01</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.087</td>\n",
       "      <td>93.227</td>\n",
       "      <td>87.178</td>\n",
       "      <td>84.231</td>\n",
       "      <td>91.451</td>\n",
       "      <td>89.855</td>\n",
       "      <td>176.199</td>\n",
       "      <td>86.479</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>14.269430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    REFCODE  Band gap    M    X   Bi   Sb    I   Br   Cl  Temperature  ...  \\\n",
       "0    AGODEI      2.00  0.0  0.0  1.0  0.0  1.0  0.0  0.0        150.0  ...   \n",
       "1    AGODIM      2.07  0.0  0.0  1.0  0.0  1.0  0.0  0.0        150.0  ...   \n",
       "2    AGODOS      1.97  0.0  0.0  1.0  0.0  1.0  0.0  0.0        120.0  ...   \n",
       "3  AGODOS01      1.94  0.0  0.0  1.0  0.0  1.0  0.0  0.0        297.0  ...   \n",
       "4    AYENEZ      2.00  0.0  0.0  1.0  0.0  1.0  0.0  0.0        292.0  ...   \n",
       "5  AYENEZ01      2.00  0.0  0.0  1.0  0.0  1.0  0.0  0.0        150.0  ...   \n",
       "6    AYENOJ      1.95  0.0  0.0  1.0  0.0  1.0  0.0  0.0        292.0  ...   \n",
       "7  AYENOJ01      1.95  0.0  0.0  1.0  0.0  1.0  0.0  0.0        150.0  ...   \n",
       "8    AYEPAX      1.92  0.0  0.0  1.0  0.0  1.0  0.0  0.0        286.0  ...   \n",
       "9  AYEPAX01      1.92  0.0  0.0  1.0  0.0  1.0  0.0  0.0        150.0  ...   \n",
       "\n",
       "    t2-d3   t2-d4   d1-d2   d1-d3   d1-d4   d2-d3    d2-d4   d3-d4   delta d  \\\n",
       "0  90.072  94.778  86.451  80.071  91.585  91.585  177.438  86.451  0.001670   \n",
       "1  89.135  93.715  88.559  87.220  87.640  89.067  175.548  88.436  0.002430   \n",
       "2  90.864  95.538  86.936  80.996  91.075  91.075  177.386  86.936  0.000974   \n",
       "3  90.625  94.803  86.278  81.729  91.836  91.836  177.509  86.278  0.001250   \n",
       "4  92.116  93.791  88.483  84.036  88.227  85.743  173.823  88.717  0.002171   \n",
       "5  93.164  93.804  89.474  83.165  87.247  85.220  174.042  89.449  0.002060   \n",
       "6  91.911  94.924  87.010  84.379  89.589  88.861  175.368  87.675  0.002088   \n",
       "7  92.448  94.979  87.837  83.753  88.837  88.501  175.644  88.342  0.001992   \n",
       "8  88.221  93.360  86.534  84.468  91.715  90.523  176.133  85.874  0.001954   \n",
       "9  88.087  93.227  87.178  84.231  91.451  89.855  176.199  86.479  0.001843   \n",
       "\n",
       "     sigma^2  \n",
       "0  26.536168  \n",
       "1   4.061154  \n",
       "2  22.655187  \n",
       "3  19.705712  \n",
       "4  11.252038  \n",
       "5  13.529862  \n",
       "6  10.257849  \n",
       "7  10.992420  \n",
       "8  14.016866  \n",
       "9  14.269430  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датасета:\n",
    "df = pd.read_csv('Data_for_bg_prediction.csv', sep='\\t', index_col=[0])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Избавляемя от хлоридных комплексов\n",
    "df.drop(index=df[df['Cl']>0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 79 entries, 0 to 84\n",
      "Data columns (total 37 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   REFCODE                       79 non-null     object \n",
      " 1   Band gap                      79 non-null     float64\n",
      " 2   M                             79 non-null     float64\n",
      " 3   X                             79 non-null     float64\n",
      " 4   Bi                            79 non-null     float64\n",
      " 5   Sb                            79 non-null     float64\n",
      " 6   I                             79 non-null     float64\n",
      " 7   Br                            79 non-null     float64\n",
      " 8   Cl                            79 non-null     float64\n",
      " 9   Temperature                   79 non-null     float64\n",
      " 10  min Hal...Hal                 79 non-null     float64\n",
      " 11  aver Hal...Hal                79 non-null     float64\n",
      " 12  Number of Hal...Hal contacts  79 non-null     float64\n",
      " 13  N/aver-d                      79 non-null     float64\n",
      " 14  Hal-t1                        76 non-null     float64\n",
      " 15  Hal-t2                        76 non-null     float64\n",
      " 16  Hal-d1                        76 non-null     float64\n",
      " 17  Hal-d2                        76 non-null     float64\n",
      " 18  Hal-d3                        76 non-null     float64\n",
      " 19  Hal-d4                        76 non-null     float64\n",
      " 20  t1-t2                         76 non-null     float64\n",
      " 21  t1-d1                         76 non-null     float64\n",
      " 22  t1-d2                         76 non-null     float64\n",
      " 23  t1-d3                         76 non-null     float64\n",
      " 24  t1-d4                         76 non-null     float64\n",
      " 25  t2-d1                         76 non-null     float64\n",
      " 26  t2-d2                         76 non-null     float64\n",
      " 27  t2-d3                         76 non-null     float64\n",
      " 28  t2-d4                         76 non-null     float64\n",
      " 29  d1-d2                         76 non-null     float64\n",
      " 30  d1-d3                         76 non-null     float64\n",
      " 31  d1-d4                         76 non-null     float64\n",
      " 32  d2-d3                         76 non-null     float64\n",
      " 33  d2-d4                         76 non-null     float64\n",
      " 34  d3-d4                         76 non-null     float64\n",
      " 35  delta d                       79 non-null     float64\n",
      " 36  sigma^2                       79 non-null     float64\n",
      "dtypes: float64(36), object(1)\n",
      "memory usage: 23.5+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "null_counts is deprecated. Use show_counts instead\n"
     ]
    }
   ],
   "source": [
    "df.info(verbose=True, null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Вспомогательные функции для выбора фичей:\n",
    "from my_scripts import get_descriptors_by_ent #на самом деле далее ей не пользовался, удобней сразу вбивать фичи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Что все выводилось в этом блокноте\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 1. Предварительное обучение на всех геометрических (21) дескрипторах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Возможные пространства\n",
    "descriptors_sets = [['M', 'X', 'Temperature', 'min Hal...Hal', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4','t1-t2', 't1-d1',\n",
    "                       't1-d2', 't1-d3', 't1-d4', 't2-d1', 't2-d2', 't2-d3', 't2-d4', 'd1-d2', 'd1-d3', 'd1-d4', 'd2-d3', 'd2-d4', 'd3-d4'],\n",
    "                ['M', 'X', 'Temperature', 'aver Hal...Hal', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4','t1-t2', 't1-d1',\n",
    "                       't1-d2', 't1-d3', 't1-d4', 't2-d1', 't2-d2', 't2-d3', 't2-d4', 'd1-d2', 'd1-d3', 'd1-d4', 'd2-d3', 'd2-d4', 'd3-d4'],\n",
    "                ['M', 'X', 'Temperature', 'aver Hal...Hal', 'Number of Hal...Hal contacts', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4','t1-t2', 't1-d1',\n",
    "                       't1-d2', 't1-d3', 't1-d4', 't2-d1', 't2-d2', 't2-d3', 't2-d4', 'd1-d2', 'd1-d3', 'd1-d4', 'd2-d3', 'd2-d4', 'd3-d4'],\n",
    "                ['M', 'X', 'Temperature', 'N/aver-d', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4','t1-t2', 't1-d1',\n",
    "                       't1-d2', 't1-d3', 't1-d4', 't2-d1', 't2-d2', 't2-d3', 't2-d4', 'd1-d2', 'd1-d3', 'd1-d4', 'd2-d3', 'd2-d4', 'd3-d4'],\n",
    "                ['M', 'X', 'Temperature', 'min Hal...Hal', 'Number of Hal...Hal contacts', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4','t1-t2', 't1-d1',\n",
    "                       't1-d2', 't1-d3', 't1-d4', 't2-d1', 't2-d2', 't2-d3', 't2-d4', 'd1-d2', 'd1-d3', 'd1-d4', 'd2-d3', 'd2-d4', 'd3-d4'],\n",
    "               ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from my_scripts import train_test_split_and_normolize #Функция для разбиения на трейн и тест и нормализации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In train set 64 In test set 12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(48, 9, 13, 3, 3, 0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test, scaler = train_test_split_and_normolize(df, drop=True, train_size=0.85, random_state=156, shuffle=True, stratify=df[df['Hal-t1'].notna()]['X'])\n",
    "sum(x_train['I'] == 1), sum(x_test['I'] == 1), sum(x_train['Br'] == 1), sum(x_test['Br'] == 1), sum(x_train['Br'] == 0.5), sum(x_test['Br'] == 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from my_scripts import pipline_true, find_biggest_error # Функкции для всего цикла обучения и нахождения объектов с наибольшей ошибкой\n",
    "from my_scripts import score_plots, r2_plot # Для отрисовки графиков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Поиск гиперпараметров\n",
    "\n",
    "# a = [0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 1]\n",
    "# gpr1 = {'GPR_rbf ' + str(x): GaussianProcessRegressor(kernel=RBF(), alpha=x, n_restarts_optimizer=10, normalize_y=True) for x in a}\n",
    "#          # 'GPR_mat': GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True),\n",
    "# res_gpr2 = pipline_true(x_train, x_test, y_train, y_test, descriptors_sets[3], models=gpr1, metrics=metrics)\n",
    "# res_gpr2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "metrics = {'MAE': mean_absolute_error, 'MSE': mean_squared_error, 'RMSE': mean_squared_error, 'R2': r2_score}\n",
    "models1 = {'Linear': LinearRegression(),\n",
    "          'SVR_lin': SVR(kernel=\"linear\", C=100, gamma=\"auto\"),\n",
    "          'SVR_poly': SVR(kernel=\"poly\", C=100, gamma=\"auto\"),\n",
    "          'SVR_rbf': SVR(kernel=\"rbf\", C=100, gamma=\"auto\"),\n",
    "          'GPR_rbf': GaussianProcessRegressor(kernel=RBF(), alpha=0.2, n_restarts_optimizer=10, normalize_y=True),\n",
    "          'GPR_mat': GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True),\n",
    "          'GPR_rt': GaussianProcessRegressor(kernel=RationalQuadratic(), normalize_y=True, n_restarts_optimizer=10, alpha=0.2),\n",
    "          'RF': RandomForestRegressor(n_estimators=1000, max_depth=1, n_jobs=-1),\n",
    "          'XGB': xgb.XGBRegressor(max_depth=1),\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    }
   ],
   "source": [
    "res = []\n",
    "for descriptors_set in descriptors_sets:\n",
    "    res.append(pipline_true(x_train, x_test, y_train, y_test, descriptors_set, models=models1, metrics=metrics))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Результаты кросс-валидации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_mat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.062612</td>\n",
       "      <td>0.001796</td>\n",
       "      <td>0.080794</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0.072809</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.077226</td>\n",
       "      <td>0.001231</td>\n",
       "      <td>0.067630</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.001450</td>\n",
       "      <td>0.067630</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.094692</td>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.025848</td>\n",
       "      <td>0.001144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.148254</td>\n",
       "      <td>0.184538</td>\n",
       "      <td>0.126066</td>\n",
       "      <td>0.118601</td>\n",
       "      <td>0.102204</td>\n",
       "      <td>0.094798</td>\n",
       "      <td>0.121348</td>\n",
       "      <td>0.109268</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>0.089170</td>\n",
       "      <td>0.091117</td>\n",
       "      <td>0.089911</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>0.089170</td>\n",
       "      <td>0.104862</td>\n",
       "      <td>0.103213</td>\n",
       "      <td>0.098971</td>\n",
       "      <td>0.093391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.007367</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.009399</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.008575</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.007960</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.008729</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.017032</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.055502</td>\n",
       "      <td>0.138780</td>\n",
       "      <td>0.029739</td>\n",
       "      <td>0.055039</td>\n",
       "      <td>0.019292</td>\n",
       "      <td>0.034409</td>\n",
       "      <td>0.026478</td>\n",
       "      <td>0.046414</td>\n",
       "      <td>0.016119</td>\n",
       "      <td>0.035437</td>\n",
       "      <td>0.016260</td>\n",
       "      <td>0.035812</td>\n",
       "      <td>0.016119</td>\n",
       "      <td>0.035437</td>\n",
       "      <td>0.021482</td>\n",
       "      <td>0.050994</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>0.035806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.085793</td>\n",
       "      <td>0.002635</td>\n",
       "      <td>0.096930</td>\n",
       "      <td>0.001914</td>\n",
       "      <td>0.092577</td>\n",
       "      <td>0.002214</td>\n",
       "      <td>0.089201</td>\n",
       "      <td>0.001726</td>\n",
       "      <td>0.095385</td>\n",
       "      <td>0.002234</td>\n",
       "      <td>0.093405</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.095385</td>\n",
       "      <td>0.002234</td>\n",
       "      <td>0.130498</td>\n",
       "      <td>0.001599</td>\n",
       "      <td>0.035876</td>\n",
       "      <td>0.001672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.148254</td>\n",
       "      <td>0.184538</td>\n",
       "      <td>0.126066</td>\n",
       "      <td>0.118601</td>\n",
       "      <td>0.102204</td>\n",
       "      <td>0.094798</td>\n",
       "      <td>0.121348</td>\n",
       "      <td>0.109268</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>0.089170</td>\n",
       "      <td>0.091117</td>\n",
       "      <td>0.089911</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>0.089170</td>\n",
       "      <td>0.104862</td>\n",
       "      <td>0.103213</td>\n",
       "      <td>0.098971</td>\n",
       "      <td>0.093391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.899876</td>\n",
       "      <td>0.005911</td>\n",
       "      <td>0.872264</td>\n",
       "      <td>0.004946</td>\n",
       "      <td>0.883446</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>0.891817</td>\n",
       "      <td>0.004237</td>\n",
       "      <td>0.876289</td>\n",
       "      <td>0.005474</td>\n",
       "      <td>0.881373</td>\n",
       "      <td>0.005256</td>\n",
       "      <td>0.876289</td>\n",
       "      <td>0.005474</td>\n",
       "      <td>0.768514</td>\n",
       "      <td>0.006197</td>\n",
       "      <td>0.982466</td>\n",
       "      <td>0.001564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.062612  0.001796  0.080794  0.001350  0.072809  0.001512   \n",
       "MAE on validate   0.148254  0.184538  0.126066  0.118601  0.102204  0.094798   \n",
       "MSE on train      0.007367  0.000427  0.009399  0.000357  0.008575  0.000386   \n",
       "MSE on validate   0.055502  0.138780  0.029739  0.055039  0.019292  0.034409   \n",
       "RMSE on train     0.085793  0.002635  0.096930  0.001914  0.092577  0.002214   \n",
       "RMSE on validate  0.148254  0.184538  0.126066  0.118601  0.102204  0.094798   \n",
       "R2 on train       0.899876  0.005911  0.872264  0.004946  0.883446  0.005646   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.077226  0.001231  0.067630  0.001424  0.065843  0.001450   \n",
       "MAE on validate   0.121348  0.109268  0.091060  0.089170  0.091117  0.089911   \n",
       "MSE on train      0.007960  0.000297  0.009103  0.000401  0.008729  0.000390   \n",
       "MSE on validate   0.026478  0.046414  0.016119  0.035437  0.016260  0.035812   \n",
       "RMSE on train     0.089201  0.001726  0.095385  0.002234  0.093405  0.002227   \n",
       "RMSE on validate  0.121348  0.109268  0.091060  0.089170  0.091117  0.089911   \n",
       "R2 on train       0.891817  0.004237  0.876289  0.005474  0.881373  0.005256   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.067630  0.001424  0.094692  0.001368  0.025848  0.001144  \n",
       "MAE on validate   0.091060  0.089170  0.104862  0.103213  0.098971  0.093391  \n",
       "MSE on train      0.009103  0.000401  0.017032  0.000412  0.001290  0.000109  \n",
       "MSE on validate   0.016119  0.035437  0.021482  0.050994  0.018381  0.035806  \n",
       "RMSE on train     0.095385  0.002234  0.130498  0.001599  0.035876  0.001672  \n",
       "RMSE on validate  0.091060  0.089170  0.104862  0.103213  0.098971  0.093391  \n",
       "R2 on train       0.876289  0.005474  0.768514  0.006197  0.982466  0.001564  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_mat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.065814</td>\n",
       "      <td>0.002010</td>\n",
       "      <td>0.081032</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>0.071388</td>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.077904</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.068402</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.066591</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.068402</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.094784</td>\n",
       "      <td>0.001484</td>\n",
       "      <td>0.025366</td>\n",
       "      <td>0.001146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.155607</td>\n",
       "      <td>0.192495</td>\n",
       "      <td>0.120008</td>\n",
       "      <td>0.107882</td>\n",
       "      <td>0.102547</td>\n",
       "      <td>0.096823</td>\n",
       "      <td>0.123532</td>\n",
       "      <td>0.110041</td>\n",
       "      <td>0.091626</td>\n",
       "      <td>0.089236</td>\n",
       "      <td>0.091661</td>\n",
       "      <td>0.090069</td>\n",
       "      <td>0.091626</td>\n",
       "      <td>0.089236</td>\n",
       "      <td>0.104494</td>\n",
       "      <td>0.104092</td>\n",
       "      <td>0.095173</td>\n",
       "      <td>0.092160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.007908</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.009622</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.008334</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.008110</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.009296</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.009296</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.017060</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.060689</td>\n",
       "      <td>0.167297</td>\n",
       "      <td>0.025858</td>\n",
       "      <td>0.047074</td>\n",
       "      <td>0.019744</td>\n",
       "      <td>0.034935</td>\n",
       "      <td>0.027180</td>\n",
       "      <td>0.046956</td>\n",
       "      <td>0.016234</td>\n",
       "      <td>0.035725</td>\n",
       "      <td>0.016387</td>\n",
       "      <td>0.036131</td>\n",
       "      <td>0.016234</td>\n",
       "      <td>0.035725</td>\n",
       "      <td>0.021585</td>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.017419</td>\n",
       "      <td>0.034242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.088882</td>\n",
       "      <td>0.002773</td>\n",
       "      <td>0.098072</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.091266</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>0.090033</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.096391</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.094387</td>\n",
       "      <td>0.002224</td>\n",
       "      <td>0.096391</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.130607</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.035303</td>\n",
       "      <td>0.001682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.155607</td>\n",
       "      <td>0.192495</td>\n",
       "      <td>0.120008</td>\n",
       "      <td>0.107882</td>\n",
       "      <td>0.102547</td>\n",
       "      <td>0.096823</td>\n",
       "      <td>0.123532</td>\n",
       "      <td>0.110041</td>\n",
       "      <td>0.091626</td>\n",
       "      <td>0.089236</td>\n",
       "      <td>0.091661</td>\n",
       "      <td>0.090069</td>\n",
       "      <td>0.091626</td>\n",
       "      <td>0.089236</td>\n",
       "      <td>0.104494</td>\n",
       "      <td>0.104092</td>\n",
       "      <td>0.095173</td>\n",
       "      <td>0.092160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.892531</td>\n",
       "      <td>0.006466</td>\n",
       "      <td>0.869237</td>\n",
       "      <td>0.005087</td>\n",
       "      <td>0.886729</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>0.889784</td>\n",
       "      <td>0.004723</td>\n",
       "      <td>0.873667</td>\n",
       "      <td>0.005518</td>\n",
       "      <td>0.878866</td>\n",
       "      <td>0.005306</td>\n",
       "      <td>0.873667</td>\n",
       "      <td>0.005518</td>\n",
       "      <td>0.768122</td>\n",
       "      <td>0.006127</td>\n",
       "      <td>0.983017</td>\n",
       "      <td>0.001588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.065814  0.002010  0.081032  0.001468  0.071388  0.001313   \n",
       "MAE on validate   0.155607  0.192495  0.120008  0.107882  0.102547  0.096823   \n",
       "MSE on train      0.007908  0.000466  0.009622  0.000375  0.008334  0.000351   \n",
       "MSE on validate   0.060689  0.167297  0.025858  0.047074  0.019744  0.034935   \n",
       "RMSE on train     0.088882  0.002773  0.098072  0.001995  0.091266  0.002054   \n",
       "RMSE on validate  0.155607  0.192495  0.120008  0.107882  0.102547  0.096823   \n",
       "R2 on train       0.892531  0.006466  0.869237  0.005087  0.886729  0.005207   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.077904  0.001486  0.068402  0.001451  0.066591  0.001458   \n",
       "MAE on validate   0.123532  0.110041  0.091626  0.089236  0.091661  0.090069   \n",
       "MSE on train      0.008110  0.000339  0.009296  0.000404  0.008914  0.000394   \n",
       "MSE on validate   0.027180  0.046956  0.016234  0.035725  0.016387  0.036131   \n",
       "RMSE on train     0.090033  0.001956  0.096391  0.002227  0.094387  0.002224   \n",
       "RMSE on validate  0.123532  0.110041  0.091626  0.089236  0.091661  0.090069   \n",
       "R2 on train       0.889784  0.004723  0.873667  0.005518  0.878866  0.005306   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.068402  0.001451  0.094784  0.001484  0.025366  0.001146  \n",
       "MAE on validate   0.091626  0.089236  0.104494  0.104092  0.095173  0.092160  \n",
       "MSE on train      0.009296  0.000404  0.017060  0.000387  0.001249  0.000108  \n",
       "MSE on validate   0.016234  0.035725  0.021585  0.051529  0.017419  0.034242  \n",
       "RMSE on train     0.096391  0.002227  0.130607  0.001496  0.035303  0.001682  \n",
       "RMSE on validate  0.091626  0.089236  0.104494  0.104092  0.095173  0.092160  \n",
       "R2 on train       0.873667  0.005518  0.768122  0.006127  0.983017  0.001588  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_mat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.066041</td>\n",
       "      <td>0.002004</td>\n",
       "      <td>0.081653</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.071397</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.078979</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>0.066730</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.064222</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>0.066730</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.094736</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.001144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.159381</td>\n",
       "      <td>0.195262</td>\n",
       "      <td>0.132577</td>\n",
       "      <td>0.129457</td>\n",
       "      <td>0.103183</td>\n",
       "      <td>0.099667</td>\n",
       "      <td>0.131108</td>\n",
       "      <td>0.124742</td>\n",
       "      <td>0.091804</td>\n",
       "      <td>0.091135</td>\n",
       "      <td>0.090920</td>\n",
       "      <td>0.091947</td>\n",
       "      <td>0.091804</td>\n",
       "      <td>0.091135</td>\n",
       "      <td>0.103665</td>\n",
       "      <td>0.104093</td>\n",
       "      <td>0.095209</td>\n",
       "      <td>0.092268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.007837</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.009648</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.008313</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.008904</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.008416</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.008904</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.062934</td>\n",
       "      <td>0.169825</td>\n",
       "      <td>0.034074</td>\n",
       "      <td>0.069265</td>\n",
       "      <td>0.020425</td>\n",
       "      <td>0.036661</td>\n",
       "      <td>0.032507</td>\n",
       "      <td>0.066002</td>\n",
       "      <td>0.016604</td>\n",
       "      <td>0.035870</td>\n",
       "      <td>0.016589</td>\n",
       "      <td>0.035918</td>\n",
       "      <td>0.016604</td>\n",
       "      <td>0.035870</td>\n",
       "      <td>0.021412</td>\n",
       "      <td>0.051302</td>\n",
       "      <td>0.017445</td>\n",
       "      <td>0.034335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.088484</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>0.098207</td>\n",
       "      <td>0.001863</td>\n",
       "      <td>0.091146</td>\n",
       "      <td>0.002393</td>\n",
       "      <td>0.089531</td>\n",
       "      <td>0.001989</td>\n",
       "      <td>0.094336</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.091717</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.094336</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.130630</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.035302</td>\n",
       "      <td>0.001684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.159381</td>\n",
       "      <td>0.195262</td>\n",
       "      <td>0.132577</td>\n",
       "      <td>0.129457</td>\n",
       "      <td>0.103183</td>\n",
       "      <td>0.099667</td>\n",
       "      <td>0.131108</td>\n",
       "      <td>0.124742</td>\n",
       "      <td>0.091804</td>\n",
       "      <td>0.091135</td>\n",
       "      <td>0.090920</td>\n",
       "      <td>0.091947</td>\n",
       "      <td>0.091804</td>\n",
       "      <td>0.091135</td>\n",
       "      <td>0.103665</td>\n",
       "      <td>0.104093</td>\n",
       "      <td>0.095209</td>\n",
       "      <td>0.092268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.893490</td>\n",
       "      <td>0.006436</td>\n",
       "      <td>0.868861</td>\n",
       "      <td>0.005355</td>\n",
       "      <td>0.886997</td>\n",
       "      <td>0.006198</td>\n",
       "      <td>0.891000</td>\n",
       "      <td>0.004971</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.885626</td>\n",
       "      <td>0.004852</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>0.768046</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.983018</td>\n",
       "      <td>0.001591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.066041  0.002004  0.081653  0.001721  0.071397  0.001727   \n",
       "MAE on validate   0.159381  0.195262  0.132577  0.129457  0.103183  0.099667   \n",
       "MSE on train      0.007837  0.000465  0.009648  0.000352  0.008313  0.000414   \n",
       "MSE on validate   0.062934  0.169825  0.034074  0.069265  0.020425  0.036661   \n",
       "RMSE on train     0.088484  0.002776  0.098207  0.001863  0.091146  0.002393   \n",
       "RMSE on validate  0.159381  0.195262  0.132577  0.129457  0.103183  0.099667   \n",
       "R2 on train       0.893490  0.006436  0.868861  0.005355  0.886997  0.006198   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.078979  0.001920  0.066730  0.001347  0.064222  0.001340   \n",
       "MAE on validate   0.131108  0.124742  0.091804  0.091135  0.090920  0.091947   \n",
       "MSE on train      0.008020  0.000347  0.008904  0.000381  0.008416  0.000362   \n",
       "MSE on validate   0.032507  0.066002  0.016604  0.035870  0.016589  0.035918   \n",
       "RMSE on train     0.089531  0.001989  0.094336  0.002126  0.091717  0.002082   \n",
       "RMSE on validate  0.131108  0.124742  0.091804  0.091135  0.090920  0.091947   \n",
       "R2 on train       0.891000  0.004971  0.879000  0.005180  0.885626  0.004852   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.066730  0.001347  0.094736  0.001410  0.025367  0.001144  \n",
       "MAE on validate   0.091804  0.091135  0.103665  0.104093  0.095209  0.092268  \n",
       "MSE on train      0.008904  0.000381  0.017067  0.000449  0.001249  0.000109  \n",
       "MSE on validate   0.016604  0.035870  0.021412  0.051302  0.017445  0.034335  \n",
       "RMSE on train     0.094336  0.002126  0.130630  0.001742  0.035302  0.001684  \n",
       "RMSE on validate  0.091804  0.091135  0.103665  0.104093  0.095209  0.092268  \n",
       "R2 on train       0.879000  0.005180  0.768046  0.006306  0.983018  0.001591  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.072510</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>0.082252</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.074297</td>\n",
       "      <td>0.001641</td>\n",
       "      <td>0.076997</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.068814</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>0.065712</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.068814</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>0.094849</td>\n",
       "      <td>0.001381</td>\n",
       "      <td>0.025986</td>\n",
       "      <td>0.001125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.167139</td>\n",
       "      <td>0.201941</td>\n",
       "      <td>0.122543</td>\n",
       "      <td>0.114648</td>\n",
       "      <td>0.103847</td>\n",
       "      <td>0.094764</td>\n",
       "      <td>0.125569</td>\n",
       "      <td>0.119078</td>\n",
       "      <td>0.094090</td>\n",
       "      <td>0.091472</td>\n",
       "      <td>0.092426</td>\n",
       "      <td>0.092147</td>\n",
       "      <td>0.094090</td>\n",
       "      <td>0.091472</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>0.103928</td>\n",
       "      <td>0.099215</td>\n",
       "      <td>0.093375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.008652</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.010009</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.007870</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.008652</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.017062</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.068078</td>\n",
       "      <td>0.210615</td>\n",
       "      <td>0.027956</td>\n",
       "      <td>0.057999</td>\n",
       "      <td>0.019624</td>\n",
       "      <td>0.035827</td>\n",
       "      <td>0.029726</td>\n",
       "      <td>0.059207</td>\n",
       "      <td>0.017089</td>\n",
       "      <td>0.036533</td>\n",
       "      <td>0.016901</td>\n",
       "      <td>0.036413</td>\n",
       "      <td>0.017089</td>\n",
       "      <td>0.036533</td>\n",
       "      <td>0.021406</td>\n",
       "      <td>0.051187</td>\n",
       "      <td>0.018426</td>\n",
       "      <td>0.035172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.092970</td>\n",
       "      <td>0.002960</td>\n",
       "      <td>0.100023</td>\n",
       "      <td>0.002085</td>\n",
       "      <td>0.092120</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.088703</td>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.096058</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.092991</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.096058</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.130613</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.035906</td>\n",
       "      <td>0.001663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.167139</td>\n",
       "      <td>0.201941</td>\n",
       "      <td>0.122543</td>\n",
       "      <td>0.114648</td>\n",
       "      <td>0.103847</td>\n",
       "      <td>0.094764</td>\n",
       "      <td>0.125569</td>\n",
       "      <td>0.119078</td>\n",
       "      <td>0.094090</td>\n",
       "      <td>0.091472</td>\n",
       "      <td>0.092426</td>\n",
       "      <td>0.092147</td>\n",
       "      <td>0.094090</td>\n",
       "      <td>0.091472</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>0.103928</td>\n",
       "      <td>0.099215</td>\n",
       "      <td>0.093375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.882417</td>\n",
       "      <td>0.007132</td>\n",
       "      <td>0.863972</td>\n",
       "      <td>0.005564</td>\n",
       "      <td>0.884584</td>\n",
       "      <td>0.005835</td>\n",
       "      <td>0.893031</td>\n",
       "      <td>0.003707</td>\n",
       "      <td>0.874545</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.882430</td>\n",
       "      <td>0.004887</td>\n",
       "      <td>0.874546</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.768109</td>\n",
       "      <td>0.005995</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>0.001555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.072510  0.002540  0.082252  0.001435  0.074297  0.001641   \n",
       "MAE on validate   0.167139  0.201941  0.122543  0.114648  0.103847  0.094764   \n",
       "MSE on train      0.008652  0.000520  0.010009  0.000395  0.008491  0.000395   \n",
       "MSE on validate   0.068078  0.210615  0.027956  0.057999  0.019624  0.035827   \n",
       "RMSE on train     0.092970  0.002960  0.100023  0.002085  0.092120  0.002270   \n",
       "RMSE on validate  0.167139  0.201941  0.122543  0.114648  0.103847  0.094764   \n",
       "R2 on train       0.882417  0.007132  0.863972  0.005564  0.884584  0.005835   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.076997  0.001278  0.068814  0.001445  0.065712  0.001402   \n",
       "MAE on validate   0.125569  0.119078  0.094090  0.091472  0.092426  0.092147   \n",
       "MSE on train      0.007870  0.000250  0.009232  0.000392  0.008652  0.000370   \n",
       "MSE on validate   0.029726  0.059207  0.017089  0.036533  0.016901  0.036413   \n",
       "RMSE on train     0.088703  0.001438  0.096058  0.002147  0.092991  0.002096   \n",
       "RMSE on validate  0.125569  0.119078  0.094090  0.091472  0.092426  0.092147   \n",
       "R2 on train       0.893031  0.003707  0.874545  0.005248  0.882430  0.004887   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.068814  0.001445  0.094849  0.001381  0.025986  0.001125  \n",
       "MAE on validate   0.094090  0.091472  0.103797  0.103928  0.099215  0.093375  \n",
       "MSE on train      0.009232  0.000392  0.017062  0.000405  0.001292  0.000109  \n",
       "MSE on validate   0.017089  0.036533  0.021406  0.051187  0.018426  0.035172  \n",
       "RMSE on train     0.096058  0.002147  0.130613  0.001569  0.035906  0.001663  \n",
       "RMSE on validate  0.094090  0.091472  0.103797  0.103928  0.099215  0.093375  \n",
       "R2 on train       0.874546  0.005248  0.768109  0.005995  0.982438  0.001555  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.062613</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.080434</td>\n",
       "      <td>0.001647</td>\n",
       "      <td>0.071008</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.077365</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>0.066423</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.063923</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.066423</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.094746</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.025848</td>\n",
       "      <td>0.001144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.151094</td>\n",
       "      <td>0.188095</td>\n",
       "      <td>0.127910</td>\n",
       "      <td>0.123456</td>\n",
       "      <td>0.101949</td>\n",
       "      <td>0.099675</td>\n",
       "      <td>0.127831</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>0.091646</td>\n",
       "      <td>0.090751</td>\n",
       "      <td>0.090737</td>\n",
       "      <td>0.091611</td>\n",
       "      <td>0.091646</td>\n",
       "      <td>0.090751</td>\n",
       "      <td>0.104066</td>\n",
       "      <td>0.103838</td>\n",
       "      <td>0.098971</td>\n",
       "      <td>0.093391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.007363</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.009346</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.007847</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.008793</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.008792</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.057656</td>\n",
       "      <td>0.144495</td>\n",
       "      <td>0.031364</td>\n",
       "      <td>0.061079</td>\n",
       "      <td>0.020173</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.031347</td>\n",
       "      <td>0.063073</td>\n",
       "      <td>0.016506</td>\n",
       "      <td>0.035740</td>\n",
       "      <td>0.016495</td>\n",
       "      <td>0.035776</td>\n",
       "      <td>0.016506</td>\n",
       "      <td>0.035740</td>\n",
       "      <td>0.021444</td>\n",
       "      <td>0.051229</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>0.035806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.085767</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.096653</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.091200</td>\n",
       "      <td>0.002414</td>\n",
       "      <td>0.088561</td>\n",
       "      <td>0.001966</td>\n",
       "      <td>0.093744</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.091209</td>\n",
       "      <td>0.002106</td>\n",
       "      <td>0.093744</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.130623</td>\n",
       "      <td>0.001476</td>\n",
       "      <td>0.035876</td>\n",
       "      <td>0.001672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.151094</td>\n",
       "      <td>0.188095</td>\n",
       "      <td>0.127910</td>\n",
       "      <td>0.123456</td>\n",
       "      <td>0.101949</td>\n",
       "      <td>0.099675</td>\n",
       "      <td>0.127831</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>0.091646</td>\n",
       "      <td>0.090751</td>\n",
       "      <td>0.090737</td>\n",
       "      <td>0.091611</td>\n",
       "      <td>0.091646</td>\n",
       "      <td>0.090751</td>\n",
       "      <td>0.104066</td>\n",
       "      <td>0.103838</td>\n",
       "      <td>0.098971</td>\n",
       "      <td>0.093391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.899932</td>\n",
       "      <td>0.006021</td>\n",
       "      <td>0.872984</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>0.886859</td>\n",
       "      <td>0.006311</td>\n",
       "      <td>0.893346</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>0.880511</td>\n",
       "      <td>0.005187</td>\n",
       "      <td>0.886889</td>\n",
       "      <td>0.004869</td>\n",
       "      <td>0.880511</td>\n",
       "      <td>0.005187</td>\n",
       "      <td>0.768072</td>\n",
       "      <td>0.005870</td>\n",
       "      <td>0.982466</td>\n",
       "      <td>0.001564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.062613  0.001833  0.080434  0.001647  0.071008  0.001729   \n",
       "MAE on validate   0.151094  0.188095  0.127910  0.123456  0.101949  0.099675   \n",
       "MSE on train      0.007363  0.000436  0.009346  0.000371  0.008323  0.000419   \n",
       "MSE on validate   0.057656  0.144495  0.031364  0.061079  0.020173  0.036581   \n",
       "RMSE on train     0.085767  0.002691  0.096653  0.001973  0.091200  0.002414   \n",
       "RMSE on validate  0.151094  0.188095  0.127910  0.123456  0.101949  0.099675   \n",
       "R2 on train       0.899932  0.006021  0.872984  0.005280  0.886859  0.006311   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.077365  0.001794  0.066423  0.001389  0.063923  0.001369   \n",
       "MAE on validate   0.127831  0.123466  0.091646  0.090751  0.090737  0.091611   \n",
       "MSE on train      0.007847  0.000338  0.008793  0.000382  0.008323  0.000363   \n",
       "MSE on validate   0.031347  0.063073  0.016506  0.035740  0.016495  0.035776   \n",
       "RMSE on train     0.088561  0.001966  0.093744  0.002150  0.091209  0.002106   \n",
       "RMSE on validate  0.127831  0.123466  0.091646  0.090751  0.090737  0.091611   \n",
       "R2 on train       0.893346  0.004893  0.880511  0.005187  0.886889  0.004869   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.066423  0.001389  0.094746  0.001451  0.025848  0.001144  \n",
       "MAE on validate   0.091646  0.090751  0.104066  0.103838  0.098971  0.093391  \n",
       "MSE on train      0.008792  0.000382  0.017065  0.000381  0.001290  0.000109  \n",
       "MSE on validate   0.016506  0.035740  0.021444  0.051229  0.018381  0.035806  \n",
       "RMSE on train     0.093744  0.002150  0.130623  0.001476  0.035876  0.001672  \n",
       "RMSE on validate  0.091646  0.090751  0.104066  0.103838  0.098971  0.093391  \n",
       "R2 on train       0.880511  0.005187  0.768072  0.005870  0.982466  0.001564  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for r in res:\n",
    "    r[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Объекты, на которых модели больше всего ошибаются"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>SVR_lin</th>\n",
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       "      <th>GPR_rt</th>\n",
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       "      <th>XGB</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>78  0.828 56  0.542 33  0.400 18  0.351 79  0....</td>\n",
       "      <td>78  0.253 79  0.238 52  0.181 72  0.144 28  0....</td>\n",
       "      <td>79  0.190 28  0.118 78  0.105 75  0.096 52  0....</td>\n",
       "      <td>78  0.223 79  0.179 33  0.136 28  0.134 82  0....</td>\n",
       "      <td>79  0.238 78  0.097 28  0.086 52  0.082 82  0....</td>\n",
       "      <td>79  0.241 78  0.090 28  0.087 52  0.087 42  0....</td>\n",
       "      <td>79  0.238 78  0.097 28  0.086 52  0.082 82  0....</td>\n",
       "      <td>79  0.376 83  0.116 42  0.095 52  0.078 78  0....</td>\n",
       "      <td>79  0.227 28  0.150 83  0.063 75  0.063 82  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>78  0.910 56  0.736 33  0.632 18  0.592 79  0....</td>\n",
       "      <td>78  0.503 79  0.488 52  0.426 72  0.380 28  0....</td>\n",
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       "      <td>79  0.487 78  0.311 28  0.294 52  0.287 82  0....</td>\n",
       "      <td>79  0.613 83  0.341 42  0.309 52  0.280 78  0....</td>\n",
       "      <td>79  0.476 28  0.387 83  0.252 75  0.251 82  0....</td>\n",
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      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   78  0.828 56  0.542 33  0.400 18  0.351 79  0....   \n",
       "RMSE on validate  78  0.910 56  0.736 33  0.632 18  0.592 79  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   78  0.253 79  0.238 52  0.181 72  0.144 28  0....   \n",
       "RMSE on validate  78  0.503 79  0.488 52  0.426 72  0.380 28  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.190 28  0.118 78  0.105 75  0.096 52  0....   \n",
       "RMSE on validate  79  0.436 28  0.344 78  0.324 75  0.310 52  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.223 79  0.179 33  0.136 28  0.134 82  0....   \n",
       "RMSE on validate  78  0.473 79  0.423 33  0.369 28  0.367 82  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.238 78  0.097 28  0.086 52  0.082 82  0....   \n",
       "RMSE on validate  79  0.487 78  0.311 28  0.294 52  0.287 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.241 78  0.090 28  0.087 52  0.087 42  0....   \n",
       "RMSE on validate  79  0.491 78  0.300 28  0.296 52  0.295 42  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.238 78  0.097 28  0.086 52  0.082 82  0....   \n",
       "RMSE on validate  79  0.487 78  0.311 28  0.294 52  0.287 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.376 83  0.116 42  0.095 52  0.078 78  0....   \n",
       "RMSE on validate  79  0.613 83  0.341 42  0.309 52  0.280 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.227 28  0.150 83  0.063 75  0.063 82  0....  \n",
       "RMSE on validate  79  0.476 28  0.387 83  0.252 75  0.251 82  0....  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
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       "      <th>XGB</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>78  1.156 33  0.446 56  0.411 18  0.380 79  0....</td>\n",
       "      <td>79  0.231 78  0.201 52  0.139 28  0.133 72  0....</td>\n",
       "      <td>79  0.188 28  0.121 78  0.101 75  0.096 52  0....</td>\n",
       "      <td>78  0.220 79  0.179 33  0.162 28  0.136 82  0....</td>\n",
       "      <td>79  0.240 78  0.099 28  0.089 52  0.080 42  0....</td>\n",
       "      <td>79  0.244 78  0.092 28  0.090 52  0.085 42  0....</td>\n",
       "      <td>79  0.240 78  0.099 28  0.089 52  0.080 42  0....</td>\n",
       "      <td>79  0.379 83  0.119 42  0.097 52  0.079 78  0....</td>\n",
       "      <td>79  0.220 28  0.135 82  0.072 75  0.052 42  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>78  1.075 33  0.668 56  0.641 18  0.616 79  0....</td>\n",
       "      <td>79  0.480 78  0.448 52  0.373 28  0.365 72  0....</td>\n",
       "      <td>79  0.434 28  0.349 78  0.318 75  0.309 52  0....</td>\n",
       "      <td>78  0.470 79  0.423 33  0.403 28  0.369 82  0....</td>\n",
       "      <td>79  0.490 78  0.316 28  0.298 52  0.283 42  0....</td>\n",
       "      <td>79  0.494 78  0.304 28  0.300 52  0.291 42  0....</td>\n",
       "      <td>79  0.490 78  0.316 28  0.298 52  0.283 42  0....</td>\n",
       "      <td>79  0.615 83  0.345 42  0.311 52  0.281 78  0....</td>\n",
       "      <td>79  0.469 28  0.367 82  0.268 75  0.229 42  0....</td>\n",
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      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   78  1.156 33  0.446 56  0.411 18  0.380 79  0....   \n",
       "RMSE on validate  78  1.075 33  0.668 56  0.641 18  0.616 79  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.231 78  0.201 52  0.139 28  0.133 72  0....   \n",
       "RMSE on validate  79  0.480 78  0.448 52  0.373 28  0.365 72  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.188 28  0.121 78  0.101 75  0.096 52  0....   \n",
       "RMSE on validate  79  0.434 28  0.349 78  0.318 75  0.309 52  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.220 79  0.179 33  0.162 28  0.136 82  0....   \n",
       "RMSE on validate  78  0.470 79  0.423 33  0.403 28  0.369 82  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.240 78  0.099 28  0.089 52  0.080 42  0....   \n",
       "RMSE on validate  79  0.490 78  0.316 28  0.298 52  0.283 42  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.244 78  0.092 28  0.090 52  0.085 42  0....   \n",
       "RMSE on validate  79  0.494 78  0.304 28  0.300 52  0.291 42  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.240 78  0.099 28  0.089 52  0.080 42  0....   \n",
       "RMSE on validate  79  0.490 78  0.316 28  0.298 52  0.283 42  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.379 83  0.119 42  0.097 52  0.079 78  0....   \n",
       "RMSE on validate  79  0.615 83  0.345 42  0.311 52  0.281 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.220 28  0.135 82  0.072 75  0.052 42  0....  \n",
       "RMSE on validate  79  0.469 28  0.367 82  0.268 75  0.229 42  0....  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
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       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>78  1.156 33  0.444 18  0.433 56  0.428 79  0....</td>\n",
       "      <td>78  0.426 79  0.217 56  0.176 82  0.167 52  0....</td>\n",
       "      <td>79  0.196 28  0.119 78  0.114 82  0.096 52  0....</td>\n",
       "      <td>78  0.406 82  0.185 79  0.181 47  0.171 28  0....</td>\n",
       "      <td>79  0.235 78  0.109 28  0.089 52  0.082 82  0....</td>\n",
       "      <td>79  0.236 78  0.101 28  0.091 52  0.086 42  0....</td>\n",
       "      <td>79  0.235 78  0.109 28  0.089 52  0.082 82  0....</td>\n",
       "      <td>79  0.377 83  0.118 42  0.096 52  0.077 78  0....</td>\n",
       "      <td>79  0.220 28  0.137 82  0.072 75  0.052 42  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>78  1.075 33  0.666 18  0.658 56  0.654 79  0....</td>\n",
       "      <td>78  0.652 79  0.466 56  0.419 82  0.408 52  0....</td>\n",
       "      <td>79  0.443 28  0.346 78  0.337 82  0.310 52  0....</td>\n",
       "      <td>78  0.637 82  0.431 79  0.425 47  0.414 28  0....</td>\n",
       "      <td>79  0.485 78  0.331 28  0.298 52  0.286 82  0....</td>\n",
       "      <td>79  0.486 78  0.319 28  0.302 52  0.294 42  0....</td>\n",
       "      <td>79  0.485 78  0.331 28  0.298 52  0.286 82  0....</td>\n",
       "      <td>79  0.614 83  0.344 42  0.310 52  0.279 78  0....</td>\n",
       "      <td>79  0.469 28  0.370 82  0.268 75  0.229 42  0....</td>\n",
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      ],
      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   78  1.156 33  0.444 18  0.433 56  0.428 79  0....   \n",
       "RMSE on validate  78  1.075 33  0.666 18  0.658 56  0.654 79  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   78  0.426 79  0.217 56  0.176 82  0.167 52  0....   \n",
       "RMSE on validate  78  0.652 79  0.466 56  0.419 82  0.408 52  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.196 28  0.119 78  0.114 82  0.096 52  0....   \n",
       "RMSE on validate  79  0.443 28  0.346 78  0.337 82  0.310 52  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.406 82  0.185 79  0.181 47  0.171 28  0....   \n",
       "RMSE on validate  78  0.637 82  0.431 79  0.425 47  0.414 28  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.235 78  0.109 28  0.089 52  0.082 82  0....   \n",
       "RMSE on validate  79  0.485 78  0.331 28  0.298 52  0.286 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.236 78  0.101 28  0.091 52  0.086 42  0....   \n",
       "RMSE on validate  79  0.486 78  0.319 28  0.302 52  0.294 42  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.235 78  0.109 28  0.089 52  0.082 82  0....   \n",
       "RMSE on validate  79  0.485 78  0.331 28  0.298 52  0.286 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.377 83  0.118 42  0.096 52  0.077 78  0....   \n",
       "RMSE on validate  79  0.614 83  0.344 42  0.310 52  0.279 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.220 28  0.137 82  0.072 75  0.052 42  0....  \n",
       "RMSE on validate  79  0.469 28  0.370 82  0.268 75  0.229 42  0....  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>MSE on validate</th>\n",
       "      <td>78  1.556 18  0.508 33  0.395 60  0.264 79  0....</td>\n",
       "      <td>78  0.367 79  0.220 28  0.129 33  0.115 52  0....</td>\n",
       "      <td>79  0.201 28  0.118 78  0.105 82  0.098 52  0....</td>\n",
       "      <td>78  0.362 79  0.180 82  0.174 28  0.141 75  0....</td>\n",
       "      <td>79  0.239 78  0.111 28  0.091 52  0.081 82  0....</td>\n",
       "      <td>79  0.239 78  0.103 28  0.092 52  0.086 42  0....</td>\n",
       "      <td>79  0.239 78  0.111 28  0.091 52  0.081 82  0....</td>\n",
       "      <td>79  0.376 83  0.118 42  0.095 52  0.079 78  0....</td>\n",
       "      <td>79  0.218 28  0.145 75  0.070 83  0.070 82  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>78  1.247 18  0.712 33  0.629 60  0.513 79  0....</td>\n",
       "      <td>78  0.606 79  0.469 28  0.360 33  0.340 52  0....</td>\n",
       "      <td>79  0.449 28  0.343 78  0.324 82  0.313 52  0....</td>\n",
       "      <td>78  0.602 79  0.424 82  0.417 28  0.376 75  0....</td>\n",
       "      <td>79  0.489 78  0.334 28  0.302 52  0.285 82  0....</td>\n",
       "      <td>79  0.489 78  0.321 28  0.304 52  0.294 42  0....</td>\n",
       "      <td>79  0.489 78  0.334 28  0.302 52  0.285 82  0....</td>\n",
       "      <td>79  0.613 83  0.344 42  0.309 52  0.281 78  0....</td>\n",
       "      <td>79  0.467 28  0.381 75  0.266 83  0.265 82  0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   78  1.556 18  0.508 33  0.395 60  0.264 79  0....   \n",
       "RMSE on validate  78  1.247 18  0.712 33  0.629 60  0.513 79  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   78  0.367 79  0.220 28  0.129 33  0.115 52  0....   \n",
       "RMSE on validate  78  0.606 79  0.469 28  0.360 33  0.340 52  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.201 28  0.118 78  0.105 82  0.098 52  0....   \n",
       "RMSE on validate  79  0.449 28  0.343 78  0.324 82  0.313 52  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.362 79  0.180 82  0.174 28  0.141 75  0....   \n",
       "RMSE on validate  78  0.602 79  0.424 82  0.417 28  0.376 75  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.239 78  0.111 28  0.091 52  0.081 82  0....   \n",
       "RMSE on validate  79  0.489 78  0.334 28  0.302 52  0.285 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.239 78  0.103 28  0.092 52  0.086 42  0....   \n",
       "RMSE on validate  79  0.489 78  0.321 28  0.304 52  0.294 42  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.239 78  0.111 28  0.091 52  0.081 82  0....   \n",
       "RMSE on validate  79  0.489 78  0.334 28  0.302 52  0.285 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.376 83  0.118 42  0.095 52  0.079 78  0....   \n",
       "RMSE on validate  79  0.613 83  0.344 42  0.309 52  0.281 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.218 28  0.145 75  0.070 83  0.070 82  0....  \n",
       "RMSE on validate  79  0.467 28  0.381 75  0.266 83  0.265 82  0....  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>78  0.863 56  0.542 33  0.434 18  0.383 79  0....</td>\n",
       "      <td>78  0.340 79  0.239 52  0.173 28  0.130 82  0....</td>\n",
       "      <td>79  0.194 28  0.118 78  0.115 82  0.098 75  0....</td>\n",
       "      <td>78  0.366 82  0.200 79  0.190 47  0.168 28  0....</td>\n",
       "      <td>79  0.235 78  0.106 28  0.087 52  0.084 82  0....</td>\n",
       "      <td>79  0.236 78  0.099 28  0.089 52  0.088 82  0....</td>\n",
       "      <td>79  0.235 78  0.106 28  0.087 52  0.084 82  0....</td>\n",
       "      <td>79  0.377 83  0.117 42  0.097 52  0.076 78  0....</td>\n",
       "      <td>79  0.227 28  0.150 83  0.063 75  0.063 82  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>78  0.929 56  0.736 33  0.659 18  0.619 79  0....</td>\n",
       "      <td>78  0.583 79  0.489 52  0.417 28  0.361 82  0....</td>\n",
       "      <td>79  0.440 28  0.344 78  0.339 82  0.313 75  0....</td>\n",
       "      <td>78  0.605 82  0.447 79  0.436 47  0.410 28  0....</td>\n",
       "      <td>79  0.485 78  0.326 28  0.295 52  0.289 82  0....</td>\n",
       "      <td>79  0.485 78  0.315 28  0.299 52  0.297 82  0....</td>\n",
       "      <td>79  0.485 78  0.326 28  0.295 52  0.289 82  0....</td>\n",
       "      <td>79  0.614 83  0.342 42  0.312 52  0.277 78  0....</td>\n",
       "      <td>79  0.476 28  0.387 83  0.252 75  0.251 82  0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   78  0.863 56  0.542 33  0.434 18  0.383 79  0....   \n",
       "RMSE on validate  78  0.929 56  0.736 33  0.659 18  0.619 79  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   78  0.340 79  0.239 52  0.173 28  0.130 82  0....   \n",
       "RMSE on validate  78  0.583 79  0.489 52  0.417 28  0.361 82  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.194 28  0.118 78  0.115 82  0.098 75  0....   \n",
       "RMSE on validate  79  0.440 28  0.344 78  0.339 82  0.313 75  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.366 82  0.200 79  0.190 47  0.168 28  0....   \n",
       "RMSE on validate  78  0.605 82  0.447 79  0.436 47  0.410 28  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.235 78  0.106 28  0.087 52  0.084 82  0....   \n",
       "RMSE on validate  79  0.485 78  0.326 28  0.295 52  0.289 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.236 78  0.099 28  0.089 52  0.088 82  0....   \n",
       "RMSE on validate  79  0.485 78  0.315 28  0.299 52  0.297 82  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.235 78  0.106 28  0.087 52  0.084 82  0....   \n",
       "RMSE on validate  79  0.485 78  0.326 28  0.295 52  0.289 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.377 83  0.117 42  0.097 52  0.076 78  0....   \n",
       "RMSE on validate  79  0.614 83  0.342 42  0.312 52  0.277 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.227 28  0.150 83  0.063 75  0.063 82  0....  \n",
       "RMSE on validate  79  0.476 28  0.387 83  0.252 75  0.251 82  0....  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for r in res:\n",
    "    find_biggest_error(r[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Предсказание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.062889</td>\n",
       "      <td>0.080127</td>\n",
       "      <td>0.072826</td>\n",
       "      <td>0.077270</td>\n",
       "      <td>0.067789</td>\n",
       "      <td>0.066008</td>\n",
       "      <td>0.067789</td>\n",
       "      <td>0.094708</td>\n",
       "      <td>0.025815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.133967</td>\n",
       "      <td>0.079876</td>\n",
       "      <td>0.055942</td>\n",
       "      <td>0.066526</td>\n",
       "      <td>0.051898</td>\n",
       "      <td>0.052882</td>\n",
       "      <td>0.051898</td>\n",
       "      <td>0.099165</td>\n",
       "      <td>0.065006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.007484</td>\n",
       "      <td>0.009338</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.009141</td>\n",
       "      <td>0.008766</td>\n",
       "      <td>0.009141</td>\n",
       "      <td>0.017331</td>\n",
       "      <td>0.001343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.084284</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>0.005615</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>0.022855</td>\n",
       "      <td>0.012182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.086511</td>\n",
       "      <td>0.096631</td>\n",
       "      <td>0.092827</td>\n",
       "      <td>0.089445</td>\n",
       "      <td>0.095607</td>\n",
       "      <td>0.093629</td>\n",
       "      <td>0.095607</td>\n",
       "      <td>0.131645</td>\n",
       "      <td>0.036649</td>\n",
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       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.290317</td>\n",
       "      <td>0.127861</td>\n",
       "      <td>0.074935</td>\n",
       "      <td>0.086131</td>\n",
       "      <td>0.069078</td>\n",
       "      <td>0.069856</td>\n",
       "      <td>0.069078</td>\n",
       "      <td>0.151179</td>\n",
       "      <td>0.110372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.898333</td>\n",
       "      <td>0.873157</td>\n",
       "      <td>0.882948</td>\n",
       "      <td>0.891322</td>\n",
       "      <td>0.875831</td>\n",
       "      <td>0.880917</td>\n",
       "      <td>0.875831</td>\n",
       "      <td>0.764580</td>\n",
       "      <td>0.981755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>-0.198967</td>\n",
       "      <td>0.767437</td>\n",
       "      <td>0.920122</td>\n",
       "      <td>0.894470</td>\n",
       "      <td>0.932119</td>\n",
       "      <td>0.930582</td>\n",
       "      <td>0.932119</td>\n",
       "      <td>0.674879</td>\n",
       "      <td>0.826707</td>\n",
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      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.062889  0.080127  0.072826  0.077270  0.067789  0.066008   \n",
       "MAE on test        0.133967  0.079876  0.055942  0.066526  0.051898  0.052882   \n",
       "MSE on all train   0.007484  0.009338  0.008617  0.008000  0.009141  0.008766   \n",
       "MSE on test        0.084284  0.016349  0.005615  0.007418  0.004772  0.004880   \n",
       "RMSE on all train  0.086511  0.096631  0.092827  0.089445  0.095607  0.093629   \n",
       "RMSE on test       0.290317  0.127861  0.074935  0.086131  0.069078  0.069856   \n",
       "R2 on all train    0.898333  0.873157  0.882948  0.891322  0.875831  0.880917   \n",
       "R2 on test        -0.198967  0.767437  0.920122  0.894470  0.932119  0.930582   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.067789  0.094708  0.025815  \n",
       "MAE on test        0.051898  0.099165  0.065006  \n",
       "MSE on all train   0.009141  0.017331  0.001343  \n",
       "MSE on test        0.004772  0.022855  0.012182  \n",
       "RMSE on all train  0.095607  0.131645  0.036649  \n",
       "RMSE on test       0.069078  0.151179  0.110372  \n",
       "R2 on all train    0.875831  0.764580  0.981755  \n",
       "R2 on test         0.932119  0.674879  0.826707  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>MAE on all train</th>\n",
       "      <td>0.066261</td>\n",
       "      <td>0.081013</td>\n",
       "      <td>0.071332</td>\n",
       "      <td>0.078054</td>\n",
       "      <td>0.068574</td>\n",
       "      <td>0.066767</td>\n",
       "      <td>0.068574</td>\n",
       "      <td>0.094726</td>\n",
       "      <td>0.025498</td>\n",
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       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.130230</td>\n",
       "      <td>0.091004</td>\n",
       "      <td>0.057796</td>\n",
       "      <td>0.078231</td>\n",
       "      <td>0.053428</td>\n",
       "      <td>0.054356</td>\n",
       "      <td>0.053428</td>\n",
       "      <td>0.101705</td>\n",
       "      <td>0.069409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.008036</td>\n",
       "      <td>0.009616</td>\n",
       "      <td>0.008342</td>\n",
       "      <td>0.008170</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.008952</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>0.016978</td>\n",
       "      <td>0.001295</td>\n",
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       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.072872</td>\n",
       "      <td>0.016812</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>0.008869</td>\n",
       "      <td>0.004730</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>0.004730</td>\n",
       "      <td>0.024369</td>\n",
       "      <td>0.013845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.089644</td>\n",
       "      <td>0.098062</td>\n",
       "      <td>0.091336</td>\n",
       "      <td>0.090389</td>\n",
       "      <td>0.096616</td>\n",
       "      <td>0.094616</td>\n",
       "      <td>0.096616</td>\n",
       "      <td>0.130301</td>\n",
       "      <td>0.035990</td>\n",
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       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.269949</td>\n",
       "      <td>0.129662</td>\n",
       "      <td>0.073214</td>\n",
       "      <td>0.094176</td>\n",
       "      <td>0.068778</td>\n",
       "      <td>0.069670</td>\n",
       "      <td>0.068778</td>\n",
       "      <td>0.156105</td>\n",
       "      <td>0.117665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.890837</td>\n",
       "      <td>0.869374</td>\n",
       "      <td>0.886676</td>\n",
       "      <td>0.889015</td>\n",
       "      <td>0.873197</td>\n",
       "      <td>0.878392</td>\n",
       "      <td>0.873197</td>\n",
       "      <td>0.769363</td>\n",
       "      <td>0.982405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>-0.036633</td>\n",
       "      <td>0.760842</td>\n",
       "      <td>0.923749</td>\n",
       "      <td>0.873833</td>\n",
       "      <td>0.932708</td>\n",
       "      <td>0.930952</td>\n",
       "      <td>0.932708</td>\n",
       "      <td>0.653345</td>\n",
       "      <td>0.803049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.066261  0.081013  0.071332  0.078054  0.068574  0.066767   \n",
       "MAE on test        0.130230  0.091004  0.057796  0.078231  0.053428  0.054356   \n",
       "MSE on all train   0.008036  0.009616  0.008342  0.008170  0.009335  0.008952   \n",
       "MSE on test        0.072872  0.016812  0.005360  0.008869  0.004730  0.004854   \n",
       "RMSE on all train  0.089644  0.098062  0.091336  0.090389  0.096616  0.094616   \n",
       "RMSE on test       0.269949  0.129662  0.073214  0.094176  0.068778  0.069670   \n",
       "R2 on all train    0.890837  0.869374  0.886676  0.889015  0.873197  0.878392   \n",
       "R2 on test        -0.036633  0.760842  0.923749  0.873833  0.932708  0.930952   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.068574  0.094726  0.025498  \n",
       "MAE on test        0.053428  0.101705  0.069409  \n",
       "MSE on all train   0.009335  0.016978  0.001295  \n",
       "MSE on test        0.004730  0.024369  0.013845  \n",
       "RMSE on all train  0.096616  0.130301  0.035990  \n",
       "RMSE on test       0.068778  0.156105  0.117665  \n",
       "R2 on all train    0.873197  0.769363  0.982405  \n",
       "R2 on test         0.932708  0.653345  0.803049  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.066505</td>\n",
       "      <td>0.081083</td>\n",
       "      <td>0.071475</td>\n",
       "      <td>0.079489</td>\n",
       "      <td>0.066883</td>\n",
       "      <td>0.064352</td>\n",
       "      <td>0.066883</td>\n",
       "      <td>0.094896</td>\n",
       "      <td>0.025498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.130789</td>\n",
       "      <td>0.093338</td>\n",
       "      <td>0.063213</td>\n",
       "      <td>0.080135</td>\n",
       "      <td>0.055267</td>\n",
       "      <td>0.057145</td>\n",
       "      <td>0.055267</td>\n",
       "      <td>0.099623</td>\n",
       "      <td>0.069409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.007971</td>\n",
       "      <td>0.009592</td>\n",
       "      <td>0.008372</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>0.008947</td>\n",
       "      <td>0.008458</td>\n",
       "      <td>0.008947</td>\n",
       "      <td>0.017340</td>\n",
       "      <td>0.001295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.082460</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.008963</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.005322</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.024024</td>\n",
       "      <td>0.013845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.089278</td>\n",
       "      <td>0.097938</td>\n",
       "      <td>0.091498</td>\n",
       "      <td>0.090052</td>\n",
       "      <td>0.094589</td>\n",
       "      <td>0.091966</td>\n",
       "      <td>0.094589</td>\n",
       "      <td>0.131680</td>\n",
       "      <td>0.035990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.287159</td>\n",
       "      <td>0.142512</td>\n",
       "      <td>0.080982</td>\n",
       "      <td>0.094673</td>\n",
       "      <td>0.071653</td>\n",
       "      <td>0.072952</td>\n",
       "      <td>0.071653</td>\n",
       "      <td>0.154996</td>\n",
       "      <td>0.117665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.891727</td>\n",
       "      <td>0.869702</td>\n",
       "      <td>0.886275</td>\n",
       "      <td>0.889840</td>\n",
       "      <td>0.878463</td>\n",
       "      <td>0.885109</td>\n",
       "      <td>0.878463</td>\n",
       "      <td>0.764455</td>\n",
       "      <td>0.982405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>-0.173025</td>\n",
       "      <td>0.711088</td>\n",
       "      <td>0.906710</td>\n",
       "      <td>0.872498</td>\n",
       "      <td>0.926964</td>\n",
       "      <td>0.924292</td>\n",
       "      <td>0.926964</td>\n",
       "      <td>0.658256</td>\n",
       "      <td>0.803049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.066505  0.081083  0.071475  0.079489  0.066883  0.064352   \n",
       "MAE on test        0.130789  0.093338  0.063213  0.080135  0.055267  0.057145   \n",
       "MSE on all train   0.007971  0.009592  0.008372  0.008109  0.008947  0.008458   \n",
       "MSE on test        0.082460  0.020310  0.006558  0.008963  0.005134  0.005322   \n",
       "RMSE on all train  0.089278  0.097938  0.091498  0.090052  0.094589  0.091966   \n",
       "RMSE on test       0.287159  0.142512  0.080982  0.094673  0.071653  0.072952   \n",
       "R2 on all train    0.891727  0.869702  0.886275  0.889840  0.878463  0.885109   \n",
       "R2 on test        -0.173025  0.711088  0.906710  0.872498  0.926964  0.924292   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.066883  0.094896  0.025498  \n",
       "MAE on test        0.055267  0.099623  0.069409  \n",
       "MSE on all train   0.008947  0.017340  0.001295  \n",
       "MSE on test        0.005134  0.024024  0.013845  \n",
       "RMSE on all train  0.094589  0.131680  0.035990  \n",
       "RMSE on test       0.071653  0.154996  0.117665  \n",
       "R2 on all train    0.878463  0.764455  0.982405  \n",
       "R2 on test         0.926964  0.658256  0.803049  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
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       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.073276</td>\n",
       "      <td>0.081923</td>\n",
       "      <td>0.074502</td>\n",
       "      <td>0.077180</td>\n",
       "      <td>0.069003</td>\n",
       "      <td>0.065861</td>\n",
       "      <td>0.069003</td>\n",
       "      <td>0.094186</td>\n",
       "      <td>0.026324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.127480</td>\n",
       "      <td>0.096715</td>\n",
       "      <td>0.068275</td>\n",
       "      <td>0.073452</td>\n",
       "      <td>0.057748</td>\n",
       "      <td>0.059429</td>\n",
       "      <td>0.057748</td>\n",
       "      <td>0.100779</td>\n",
       "      <td>0.063259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.008544</td>\n",
       "      <td>0.007921</td>\n",
       "      <td>0.009277</td>\n",
       "      <td>0.008694</td>\n",
       "      <td>0.009277</td>\n",
       "      <td>0.017056</td>\n",
       "      <td>0.001313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.055600</td>\n",
       "      <td>0.021006</td>\n",
       "      <td>0.007608</td>\n",
       "      <td>0.007603</td>\n",
       "      <td>0.005504</td>\n",
       "      <td>0.005678</td>\n",
       "      <td>0.005504</td>\n",
       "      <td>0.024199</td>\n",
       "      <td>0.011558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.093800</td>\n",
       "      <td>0.100067</td>\n",
       "      <td>0.092435</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.096320</td>\n",
       "      <td>0.093240</td>\n",
       "      <td>0.096320</td>\n",
       "      <td>0.130598</td>\n",
       "      <td>0.036241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.235796</td>\n",
       "      <td>0.144933</td>\n",
       "      <td>0.087223</td>\n",
       "      <td>0.087195</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>0.075351</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>0.155561</td>\n",
       "      <td>0.107506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.880480</td>\n",
       "      <td>0.863975</td>\n",
       "      <td>0.883934</td>\n",
       "      <td>0.892401</td>\n",
       "      <td>0.873973</td>\n",
       "      <td>0.881902</td>\n",
       "      <td>0.873974</td>\n",
       "      <td>0.768312</td>\n",
       "      <td>0.982158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.209077</td>\n",
       "      <td>0.701188</td>\n",
       "      <td>0.891775</td>\n",
       "      <td>0.891844</td>\n",
       "      <td>0.921698</td>\n",
       "      <td>0.919232</td>\n",
       "      <td>0.921698</td>\n",
       "      <td>0.655757</td>\n",
       "      <td>0.835591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.073276  0.081923  0.074502  0.077180  0.069003  0.065861   \n",
       "MAE on test        0.127480  0.096715  0.068275  0.073452  0.057748  0.059429   \n",
       "MSE on all train   0.008798  0.010013  0.008544  0.007921  0.009277  0.008694   \n",
       "MSE on test        0.055600  0.021006  0.007608  0.007603  0.005504  0.005678   \n",
       "RMSE on all train  0.093800  0.100067  0.092435  0.089000  0.096320  0.093240   \n",
       "RMSE on test       0.235796  0.144933  0.087223  0.087195  0.074192  0.075351   \n",
       "R2 on all train    0.880480  0.863975  0.883934  0.892401  0.873973  0.881902   \n",
       "R2 on test         0.209077  0.701188  0.891775  0.891844  0.921698  0.919232   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.069003  0.094186  0.026324  \n",
       "MAE on test        0.057748  0.100779  0.063259  \n",
       "MSE on all train   0.009277  0.017056  0.001313  \n",
       "MSE on test        0.005504  0.024199  0.011558  \n",
       "RMSE on all train  0.096320  0.130598  0.036241  \n",
       "RMSE on test       0.074192  0.155561  0.107506  \n",
       "R2 on all train    0.873974  0.768312  0.982158  \n",
       "R2 on test         0.921698  0.655757  0.835591  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
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       "      <th>XGB</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.062876</td>\n",
       "      <td>0.079567</td>\n",
       "      <td>0.070956</td>\n",
       "      <td>0.077894</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.064075</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.094590</td>\n",
       "      <td>0.025815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.133987</td>\n",
       "      <td>0.081771</td>\n",
       "      <td>0.059252</td>\n",
       "      <td>0.064123</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.055273</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.099466</td>\n",
       "      <td>0.065006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.007484</td>\n",
       "      <td>0.009242</td>\n",
       "      <td>0.008382</td>\n",
       "      <td>0.007934</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.008363</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.017132</td>\n",
       "      <td>0.001343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.084185</td>\n",
       "      <td>0.016370</td>\n",
       "      <td>0.006068</td>\n",
       "      <td>0.006766</td>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.005276</td>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.023540</td>\n",
       "      <td>0.012182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.086511</td>\n",
       "      <td>0.096138</td>\n",
       "      <td>0.091556</td>\n",
       "      <td>0.089074</td>\n",
       "      <td>0.093992</td>\n",
       "      <td>0.091451</td>\n",
       "      <td>0.093991</td>\n",
       "      <td>0.130891</td>\n",
       "      <td>0.036649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.290146</td>\n",
       "      <td>0.127944</td>\n",
       "      <td>0.077899</td>\n",
       "      <td>0.082257</td>\n",
       "      <td>0.071382</td>\n",
       "      <td>0.072638</td>\n",
       "      <td>0.071382</td>\n",
       "      <td>0.153429</td>\n",
       "      <td>0.110372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.898333</td>\n",
       "      <td>0.874449</td>\n",
       "      <td>0.886131</td>\n",
       "      <td>0.892221</td>\n",
       "      <td>0.879992</td>\n",
       "      <td>0.886391</td>\n",
       "      <td>0.879992</td>\n",
       "      <td>0.767270</td>\n",
       "      <td>0.981755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>-0.197557</td>\n",
       "      <td>0.767137</td>\n",
       "      <td>0.913677</td>\n",
       "      <td>0.903749</td>\n",
       "      <td>0.927517</td>\n",
       "      <td>0.924943</td>\n",
       "      <td>0.927517</td>\n",
       "      <td>0.665131</td>\n",
       "      <td>0.826707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.062876  0.079567  0.070956  0.077894  0.066585  0.064075   \n",
       "MAE on test        0.133987  0.081771  0.059252  0.064123  0.053080  0.055273   \n",
       "MSE on all train   0.007484  0.009242  0.008382  0.007934  0.008834  0.008363   \n",
       "MSE on test        0.084185  0.016370  0.006068  0.006766  0.005095  0.005276   \n",
       "RMSE on all train  0.086511  0.096138  0.091556  0.089074  0.093992  0.091451   \n",
       "RMSE on test       0.290146  0.127944  0.077899  0.082257  0.071382  0.072638   \n",
       "R2 on all train    0.898333  0.874449  0.886131  0.892221  0.879992  0.886391   \n",
       "R2 on test        -0.197557  0.767137  0.913677  0.903749  0.927517  0.924943   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.066585  0.094590  0.025815  \n",
       "MAE on test        0.053080  0.099466  0.065006  \n",
       "MSE on all train   0.008834  0.017132  0.001343  \n",
       "MSE on test        0.005095  0.023540  0.012182  \n",
       "RMSE on all train  0.093991  0.130891  0.036649  \n",
       "RMSE on test       0.071382  0.153429  0.110372  \n",
       "R2 on all train    0.879992  0.767270  0.981755  \n",
       "R2 on test         0.927517  0.665131  0.826707  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for r in res:\n",
    "    r[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Определение наименее важных признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianProcessRegressor(alpha=0.75, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianProcessRegressor</label><div class=\"sk-toggleable__content\"><pre>GaussianProcessRegressor(alpha=0.75, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianProcessRegressor(alpha=0.75, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Permutation explainer: 65it [00:21,  2.26it/s]                                                                         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x828 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_0 = x_train[descriptors_sets[0]]\n",
    "regressor = GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.75, n_restarts_optimizer=10, normalize_y=True)\n",
    "regressor.fit(x_0, y_train)\n",
    "explainer = shap.explainers.Permutation(regressor.predict, x_0)\n",
    "shap_values = shap.explainers.Permutation(regressor.predict, x_0)\n",
    "shap_values = explainer(x_0)\n",
    "shap.summary_plot(shap_values, x_0, show=True, max_display=25)\n",
    "#plt.savefig(\"shap21.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x828 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, x_0, plot_type=\"bar\", show=True, max_display=25)\n",
    "#plt.savefig(\"shap21_2.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 2. Обучение на 15 геометрических признаках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Наилучшее пространство признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "desc_15 = ['M', 'X', 'Temperature', 'min Hal...Hal', 'Hal-t1', 'Hal-t2', 'Hal-d1', 'Hal-d2', 'Hal-d3', 'Hal-d4', 't1-t2', 't1-d2', 't2-d2', 't2-d3', 'd1-d2', 'd1-d3', 'd2-d3', 'd2-d4', 'd3-d4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Подбор гиперпараметров\n",
    "\n",
    "# a = [0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 1]\n",
    "# gpr1 = {'GPR_rbf ' + str(x): GaussianProcessRegressor(kernel=RBF(), alpha=x, n_restarts_optimizer=10, normalize_y=True) for x in a}\n",
    "#          # 'GPR_mat': GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True),\n",
    "# res_gpr2 = pipline_true(x_train, x_test, y_train, y_test, desc_15, models=gpr1, metrics=metrics)\n",
    "# res_gpr2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    }
   ],
   "source": [
    "res2 = pipline_true(x_train, x_test, y_train, y_test, desc_15, models=models1, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "    .dataframe thead tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_mat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.070683</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>0.084556</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.075983</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>0.080485</td>\n",
       "      <td>0.002295</td>\n",
       "      <td>0.067791</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.066321</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.067791</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.094877</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.028991</td>\n",
       "      <td>0.001265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.115800</td>\n",
       "      <td>0.122375</td>\n",
       "      <td>0.122850</td>\n",
       "      <td>0.116603</td>\n",
       "      <td>0.110174</td>\n",
       "      <td>0.100246</td>\n",
       "      <td>0.125103</td>\n",
       "      <td>0.109943</td>\n",
       "      <td>0.087175</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>0.087592</td>\n",
       "      <td>0.088806</td>\n",
       "      <td>0.087175</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>0.104598</td>\n",
       "      <td>0.104146</td>\n",
       "      <td>0.097524</td>\n",
       "      <td>0.095089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.009391</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.011472</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.008932</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.009404</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.009404</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.017071</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.000122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.028151</td>\n",
       "      <td>0.060184</td>\n",
       "      <td>0.028476</td>\n",
       "      <td>0.056451</td>\n",
       "      <td>0.022030</td>\n",
       "      <td>0.038707</td>\n",
       "      <td>0.027549</td>\n",
       "      <td>0.050846</td>\n",
       "      <td>0.015247</td>\n",
       "      <td>0.035068</td>\n",
       "      <td>0.015436</td>\n",
       "      <td>0.035441</td>\n",
       "      <td>0.015247</td>\n",
       "      <td>0.035068</td>\n",
       "      <td>0.021618</td>\n",
       "      <td>0.051402</td>\n",
       "      <td>0.018412</td>\n",
       "      <td>0.038121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.096873</td>\n",
       "      <td>0.002670</td>\n",
       "      <td>0.107057</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.094486</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>0.091337</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.096946</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.095103</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.096946</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.130646</td>\n",
       "      <td>0.001651</td>\n",
       "      <td>0.039414</td>\n",
       "      <td>0.001705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.115800</td>\n",
       "      <td>0.122375</td>\n",
       "      <td>0.122850</td>\n",
       "      <td>0.116603</td>\n",
       "      <td>0.110174</td>\n",
       "      <td>0.100246</td>\n",
       "      <td>0.125103</td>\n",
       "      <td>0.109943</td>\n",
       "      <td>0.087175</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>0.087592</td>\n",
       "      <td>0.088806</td>\n",
       "      <td>0.087175</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>0.104598</td>\n",
       "      <td>0.104146</td>\n",
       "      <td>0.097524</td>\n",
       "      <td>0.095089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.872375</td>\n",
       "      <td>0.006600</td>\n",
       "      <td>0.844102</td>\n",
       "      <td>0.009109</td>\n",
       "      <td>0.878604</td>\n",
       "      <td>0.005433</td>\n",
       "      <td>0.886552</td>\n",
       "      <td>0.005415</td>\n",
       "      <td>0.872204</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>0.877017</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.872204</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>0.767985</td>\n",
       "      <td>0.006302</td>\n",
       "      <td>0.978847</td>\n",
       "      <td>0.001718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.070683  0.001814  0.084556  0.002525  0.075983  0.001666   \n",
       "MAE on validate   0.115800  0.122375  0.122850  0.116603  0.110174  0.100246   \n",
       "MSE on train      0.009391  0.000490  0.011472  0.000681  0.008932  0.000387   \n",
       "MSE on validate   0.028151  0.060184  0.028476  0.056451  0.022030  0.038707   \n",
       "RMSE on train     0.096873  0.002670  0.107057  0.003347  0.094486  0.002142   \n",
       "RMSE on validate  0.115800  0.122375  0.122850  0.116603  0.110174  0.100246   \n",
       "R2 on train       0.872375  0.006600  0.844102  0.009109  0.878604  0.005433   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.080485  0.002295  0.067791  0.001399  0.066321  0.001424   \n",
       "MAE on validate   0.125103  0.109943  0.087175  0.088141  0.087592  0.088806   \n",
       "MSE on train      0.008347  0.000386  0.009404  0.000413  0.009050  0.000402   \n",
       "MSE on validate   0.027549  0.050846  0.015247  0.035068  0.015436  0.035441   \n",
       "RMSE on train     0.091337  0.002206  0.096946  0.002268  0.095103  0.002252   \n",
       "RMSE on validate  0.125103  0.109943  0.087175  0.088141  0.087592  0.088806   \n",
       "R2 on train       0.886552  0.005415  0.872204  0.005703  0.877017  0.005470   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.067791  0.001399  0.094877  0.001528  0.028991  0.001265  \n",
       "MAE on validate   0.087175  0.088141  0.104598  0.104146  0.097524  0.095089  \n",
       "MSE on train      0.009404  0.000413  0.017071  0.000427  0.001556  0.000122  \n",
       "MSE on validate   0.015247  0.035068  0.021618  0.051402  0.018412  0.038121  \n",
       "RMSE on train     0.096946  0.002268  0.130646  0.001651  0.039414  0.001705  \n",
       "RMSE on validate  0.087175  0.088141  0.104598  0.104146  0.097524  0.095089  \n",
       "R2 on train       0.872204  0.005703  0.767985  0.006302  0.978847  0.001718  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Результаты кросс-валидации\n",
    "res2[0] #.to_excel('15_val.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Графики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2_plot(res2, save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_plots(res2, save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>72  0.286 79  0.244 78  0.232 82  0.185 28  0....</td>\n",
       "      <td>79  0.263 78  0.244 72  0.218 28  0.180 82  0....</td>\n",
       "      <td>79  0.207 75  0.140 52  0.109 28  0.107 78  0....</td>\n",
       "      <td>78  0.254 79  0.209 75  0.166 28  0.136 33  0....</td>\n",
       "      <td>79  0.238 78  0.096 28  0.087 52  0.082 42  0....</td>\n",
       "      <td>79  0.241 78  0.092 28  0.088 52  0.084 42  0....</td>\n",
       "      <td>79  0.238 78  0.096 28  0.087 52  0.082 42  0....</td>\n",
       "      <td>79  0.378 83  0.117 42  0.095 52  0.080 78  0....</td>\n",
       "      <td>79  0.239 28  0.143 83  0.100 42  0.082 82  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>72  0.535 79  0.494 78  0.482 82  0.430 28  0....</td>\n",
       "      <td>79  0.512 78  0.493 72  0.467 28  0.425 82  0....</td>\n",
       "      <td>79  0.455 75  0.374 52  0.331 28  0.328 78  0....</td>\n",
       "      <td>78  0.504 79  0.457 75  0.407 28  0.368 33  0....</td>\n",
       "      <td>79  0.488 78  0.310 28  0.296 52  0.286 42  0....</td>\n",
       "      <td>79  0.491 78  0.304 28  0.296 52  0.291 42  0....</td>\n",
       "      <td>79  0.488 78  0.310 28  0.296 52  0.286 42  0....</td>\n",
       "      <td>79  0.614 83  0.343 42  0.309 52  0.284 78  0....</td>\n",
       "      <td>79  0.489 28  0.379 83  0.316 42  0.288 82  0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   72  0.286 79  0.244 78  0.232 82  0.185 28  0....   \n",
       "RMSE on validate  72  0.535 79  0.494 78  0.482 82  0.430 28  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.263 78  0.244 72  0.218 28  0.180 82  0....   \n",
       "RMSE on validate  79  0.512 78  0.493 72  0.467 28  0.425 82  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.207 75  0.140 52  0.109 28  0.107 78  0....   \n",
       "RMSE on validate  79  0.455 75  0.374 52  0.331 28  0.328 78  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.254 79  0.209 75  0.166 28  0.136 33  0....   \n",
       "RMSE on validate  78  0.504 79  0.457 75  0.407 28  0.368 33  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.238 78  0.096 28  0.087 52  0.082 42  0....   \n",
       "RMSE on validate  79  0.488 78  0.310 28  0.296 52  0.286 42  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.241 78  0.092 28  0.088 52  0.084 42  0....   \n",
       "RMSE on validate  79  0.491 78  0.304 28  0.296 52  0.291 42  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.238 78  0.096 28  0.087 52  0.082 42  0....   \n",
       "RMSE on validate  79  0.488 78  0.310 28  0.296 52  0.286 42  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.378 83  0.117 42  0.095 52  0.080 78  0....   \n",
       "RMSE on validate  79  0.614 83  0.343 42  0.309 52  0.284 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.239 28  0.143 83  0.100 42  0.082 82  0....  \n",
       "RMSE on validate  79  0.489 28  0.379 83  0.316 42  0.288 82  0....  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Объекты, на которых наибольшие ошибки\n",
    "find_biggest_error(res2[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.071005</td>\n",
       "      <td>0.084683</td>\n",
       "      <td>0.076244</td>\n",
       "      <td>0.081082</td>\n",
       "      <td>0.067928</td>\n",
       "      <td>0.066469</td>\n",
       "      <td>0.067928</td>\n",
       "      <td>0.094123</td>\n",
       "      <td>0.029618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.062001</td>\n",
       "      <td>0.066280</td>\n",
       "      <td>0.063042</td>\n",
       "      <td>0.075618</td>\n",
       "      <td>0.047260</td>\n",
       "      <td>0.047891</td>\n",
       "      <td>0.047260</td>\n",
       "      <td>0.098436</td>\n",
       "      <td>0.063977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.009482</td>\n",
       "      <td>0.011651</td>\n",
       "      <td>0.008991</td>\n",
       "      <td>0.008433</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.009085</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.017042</td>\n",
       "      <td>0.001640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.006813</td>\n",
       "      <td>0.005878</td>\n",
       "      <td>0.006894</td>\n",
       "      <td>0.007296</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.022751</td>\n",
       "      <td>0.009658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.097375</td>\n",
       "      <td>0.107941</td>\n",
       "      <td>0.094821</td>\n",
       "      <td>0.091833</td>\n",
       "      <td>0.097151</td>\n",
       "      <td>0.095318</td>\n",
       "      <td>0.097151</td>\n",
       "      <td>0.130546</td>\n",
       "      <td>0.040503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.082543</td>\n",
       "      <td>0.076666</td>\n",
       "      <td>0.083030</td>\n",
       "      <td>0.085419</td>\n",
       "      <td>0.064882</td>\n",
       "      <td>0.065487</td>\n",
       "      <td>0.064882</td>\n",
       "      <td>0.150834</td>\n",
       "      <td>0.098273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.871196</td>\n",
       "      <td>0.841727</td>\n",
       "      <td>0.877866</td>\n",
       "      <td>0.885440</td>\n",
       "      <td>0.871788</td>\n",
       "      <td>0.876581</td>\n",
       "      <td>0.871788</td>\n",
       "      <td>0.768496</td>\n",
       "      <td>0.977715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.903077</td>\n",
       "      <td>0.916389</td>\n",
       "      <td>0.901931</td>\n",
       "      <td>0.896206</td>\n",
       "      <td>0.940116</td>\n",
       "      <td>0.938993</td>\n",
       "      <td>0.940116</td>\n",
       "      <td>0.676361</td>\n",
       "      <td>0.862617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.071005  0.084683  0.076244  0.081082  0.067928  0.066469   \n",
       "MAE on test        0.062001  0.066280  0.063042  0.075618  0.047260  0.047891   \n",
       "MSE on all train   0.009482  0.011651  0.008991  0.008433  0.009438  0.009085   \n",
       "MSE on test        0.006813  0.005878  0.006894  0.007296  0.004210  0.004289   \n",
       "RMSE on all train  0.097375  0.107941  0.094821  0.091833  0.097151  0.095318   \n",
       "RMSE on test       0.082543  0.076666  0.083030  0.085419  0.064882  0.065487   \n",
       "R2 on all train    0.871196  0.841727  0.877866  0.885440  0.871788  0.876581   \n",
       "R2 on test         0.903077  0.916389  0.901931  0.896206  0.940116  0.938993   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.067928  0.094123  0.029618  \n",
       "MAE on test        0.047260  0.098436  0.063977  \n",
       "MSE on all train   0.009438  0.017042  0.001640  \n",
       "MSE on test        0.004210  0.022751  0.009658  \n",
       "RMSE on all train  0.097151  0.130546  0.040503  \n",
       "RMSE on test       0.064882  0.150834  0.098273  \n",
       "R2 on all train    0.871788  0.768496  0.977715  \n",
       "R2 on test         0.940116  0.676361  0.862617  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Предсказание на тесте\n",
    "res2[1] #.to_excel('15_test.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Оценка важности дескрипторов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "                         n_restarts_optimizer=10, normalize_y=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianProcessRegressor</label><div class=\"sk-toggleable__content\"><pre>GaussianProcessRegressor(alpha=0.2, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)</pre></div></div></div></div></div>"
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       "GaussianProcessRegressor(alpha=0.2, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Permutation explainer: 65it [00:15,  1.45it/s]                                                                         \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x655.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_0 = x_train[desc_15]\n",
    "regressor = GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True)\n",
    "regressor.fit(x_0, y_train)\n",
    "explainer = shap.explainers.Permutation(regressor.predict, x_0)\n",
    "shap_values = shap.explainers.Permutation(regressor.predict, x_0)\n",
    "shap_values = explainer(x_0)\n",
    "shap.summary_plot(shap_values, x_0, show=True, max_display=19)\n",
    "#plt.savefig(\"shap15.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vAmsy84ze/G8By7rJk2lPO+0MbJikvr8HVmfm9bNquSRJmpWaA85oyFgGXNU9Xt4vEBGPBz4PnJSZ7+kvy8wDx5TdAbizq2Pc+oBnAU+IiLd10w8EnhgRv5mZK2e6I5IkaXqqvpt4RPwL8BPgFcADgE8AK4F9aa+cuoD20vD3T7G+tbRjcF4I3AF8oHv8jMxc333PTt8ngEuAv8rMGyer27uJL0zeTVySFrxx7yZe+6f3S4D303653+hVVKM9Katpe1hOi4jTeus8JzMvmaC+1wLvBb4N/Iz2Kqrnjy7MzOv6hSPi58DPthZuJEnSYFXdg7M9sQdnYbIHR5IWvHF7cGq/TFySJA0hA44kSaqOAUeSJFXHgCNJkqrjCMoF4vwV6xgZGZnvZkiSVAV7cCRJUnUMOJIkqToGHEmSVB0DjiRJqo4BR5IkVceAI0mSqmPAkSRJ1THgSJKk6ng38QXCu4nPD+8WLknbPe8mLkmShoMBR5IkVceAI0mSqmPAkSRJ1THgSJKk6sxJwImIWyPiyXNR9zTacEhEbJ7PNnTtOCEiLprvdkiSNEzm5BrZzFw8m/UjYgPw5sz8yFTmz4WIWA5cDeyTmddtbb4kSVo4PEUlSZKqs9UenK7X5O+BZwJPpO29eClwIHASsCfwCeDozNzcrdMAKzPz0og4Engz8G7gDcCuwD8Af5KZW2ba8IjYBfgI8BvALsCVwBsz84szrXOG7TiYdt8OBHYAvgS8JjOv2pbtkCRJ95pqD87/Bv4EeBDwdeAfgWcABwOPAX4HOHyS9ZcBDwN+lTYkvQj4g5k1+R6LgE8DjwAeDHwM+FRE7DnLeqerAU4A9gaWA7fSBi9JkjRPpjoG58zMvAIgIj5K24PzpMy8DbgtItYDAZwzwfp3AMd3PTZXRsTFWykP8L6IOH3MvN1HH2Tm2CDxzoh4I22A+twU92sqvtX1SI26TyjMzG/0Jn8eEScCl0XELpl5+wDbIUmSpmiqAedHvce3A1sy8+Yx83abZP2bxpyOum0r5QGOmmCQ8ejjBwDvBH4beAhwd1fnoHtwDpxgkPHo9K927fj1bvujYWhP4JoBt0WSJE3B9jzI+C+Ap9GODXpgZu4B/JQJbro1h84AbgEOyszdgad087d1OyRJUmd7Dji7Az8HfgzsFBHHA3vMUztuAzZGxEOAt85DGyRJUs/2HHD+GtgI/BC4ivY02YaJCkfESyPi1t70yu4LCZd200u76ZXTbMfrgJXAz4BLgAumub4kSRqw0jTN1ktpzpU1m30i5kGzak6+61KStO2MOyRke+7BkSRJGpcBR5IkVceAI0mSqmPAkSRJ1XGE5QJx/op1jIyMzHczJEmqgj04kiSpOgYcSZJUHQOOJEmqjgFHkiRVx4AjSZKqY8CRJEnVMeBIkqTqGHAkSVJ1vJv4AuHdxAfDu4NL0tDxbuKSJGk4GHAkSVJ1DDiSJKk6BhxJklQdA44kSaqOAUeSJFVnKK+pjYj1wEWZubqbPhg4BXgc8DBgZWZeupU6TgCempmH9eY9CTgOCOD+wJXASZl53uD3QpIkTcQenNadwKeB582ynl8CPg4cCDwIOAn4WEQ8cZb1SpKkaRi6HpyIOB1YCTw5It4EXJ+ZK4AruuVTqeNw4BhgUUTc2s0+KDM/N6boeRHx9W57XxnQLkiSpK0YuoCTma+JiEfTO0U1gzo+HhH7M+YU1VgRsRdtb87XZ9ZaSZI0E56imiMRsSvwKeCzmXnxfLdHkqRhMnQ9ODMREetoTzMBfCQzj95K+d2AzwI3AUfMcfMkSdIYwxpw7p5O4cx8zlTriIgHA+uA7wMvy8zN02+eJEmajWENODcA+41OREQBdu4t3yki7g/clZlbJqljaUTslJl3dvXsBVwEfBV45STrSpKkOTSsAec0YG1EbASuB54LXN1bPjpm5hXA2RPU8QngcOCGiFhE+x06R9AOKt4XeGHviqyTM/PkAbZfkiRNojRNM99tEFDWbPaJGIBm1bBmdkkaWmW8mV5FJUmSqmPAkSRJ1THgSJKk6hhwJElSdRyRuUCcv2IdIyMj890MSZKqYA+OJEmqjgFHkiRVx4AjSZKqY8CRJEnVMeBIkqTqGHAkSVJ1DDiSJKk6BhxJklQd7ya+QHg38enxruGSpI53E5ckScPBgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOLMUEVdGxJHz3Q5JknSvobnWNiLWAxdl5upu+mDgFOBxwMOAlZl56Sy38STgOCCA+wNXAidl5nmzqVeSJE3PMPfg3Al8GnjeAOv8JeDjwIHAg4CTgI9FxBMHuA1JkrQVQ9GDExGnAyuBJ0fEm4DrM3MFcEW3fKr13A94B/Ay4G7gtP7yzPzcmFXOi4ivd9v+ymz2QZIkTd1Q9OBk5muAS2hPFy3uws1MvIm2x+c3gH2B5cCyiQpHxF60vTlfn+H2JEnSDAxFwBmgI4B3ZOaVmXkHsAoY9xYLEbEr8Cngs5l58TZsoyRJQ28oTlHNRESsoz21BPCRzDwaWAJsGC2TmbdFxE3jrLsb8FngJtpQJEmStqFhCjh3T6dwZj5nnNnX056WAu7ppdmzXyAiHgysA74PvCwzN0+7pZIkaVaGKeDcAOw3OhERBdi5t3yniLg/cFdmbpmgjg8Df9ldcv5D4FR6p/m6MTcXAV8FXjlJPZIkaQ4NU8A5DVgbERtpe2KeC1zdWz46TuYVwNkT1PF22kvBvwRs6eq8prf8KNpBxfsCL+xdnXVyZp486z2QJElTUppm3DGy2sbKms0+EdPQrBqmbC5JmkQZb6ZXUUmSpOoYcCRJUnUMOJIkqToOZFggzl+xjpGRkfluhiRJVbAHR5IkVceAI0mSqmPAkSRJ1THgSJKk6hhwJElSdQw4kiSpOgYcSZJUHQOOJEmqjjfbXCC82eb0eLNNSVLHm21KkqThYMCRJEnVMeBIkqTqGHAkSVJ1DDiSJKk6BhxJklQdA84sRcTmiDhkvtshSZLuNTRfJhIR64GLMnN1N30EcDSwP7AF+Arwhsy8bBbb+G1gFXAQsAPwTeCYzLxkdq2XJEnTMcw9OLsBbwGWAHsDXwMujIhdZlHng4D3APsBewIfBdZFxD6zbKskSZqGoejBiYjTgZXAkyPiTcD1mbliTJmTgGOAR9GGnfHq2Q04HRgBbgGO7y/PzHPGrPJ3EfEW4InAtQPYFUmSNAVDEXAy8zUR8Wh6p6jG8UzgduB7k1T1LuARwAHAHcBa2lNR44qIxwAPAWZ82kuSJE3fMJ+iukdEPJI2rLw+M2+ZoMwi4KXAcZl5Q2ZuAt44SZ0PBT4FrMnMyUKTJEkasKHowZlMRBwAfJE2iJzRm/8tYFk3eTJwFrAzsKG3+tUT1PkrXZ0XAv938K2WJEmTGaaAc/fYGRHxeODzwEmZ+Z7+ssw8cEzZHYA7geXAVd3s5ePUuRy4GPjHzFw1gHZLkqRpGqZTVDfQXt0EQEQ8hTaIHDs23IwnM7fQXhV1YkQ8LCJ2B07pl4mIRwGXAh8z3EiSNH+GqQfnNGBtRGwErgduAh4InBYRp/XKPWeS7615LfBe4NvAz2ivonp+b/kbaS85//OI+PPe/KPGucJKkiTNkdI0zXy3QUBZs9knYhqaVcOUzSVJkyjjzRymU1SSJGlIGHAkSVJ1DDiSJKk6DmRYIM5fsY6RkZH5boYkSVWwB0eSJFXHgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqo4321wgvNnm+LyppiRpK7zZpiRJGg4GHEmSVB0DjiRJqo4BR5IkVceAI0mSqmPAkSRJ1fEa3E5ELAeuBvbJzOumuM4S4Fpg38zcMHetkyRJ01FND05ErI+IN091/jZozzsioomIl23rbUuSNOyqCTgLSUT8GvAc4Efz3RZJkobRUJ2iioi1wGHAHrSnllZn5kensf5ewJnA04EbgVPHKbMzcBbwR8DHZt9qSZI0XcPWg3Mp8FjagPNW4OyIOGAa658DbAGWAk8DjhynzAnAP2fm/zeLdkqSpFmorQfn2IhYNWbeYuAigMw8qzf/3K7sIcDlW6s4IvYGDgX2y8xNwKaIOBG4sFcmgBfRhihJkjRPags4b8vM1f0ZEbG++72ItnflcGAvoAF2BfYcr6KIuLU3eRRwZff4mt78q3vldwLWAn+amf11JUnSNlZbwJnMi4FXAc8GLs/MuyMimeAupJm5uD/dXRIOsAy4qnu8vFfkV4ADgXPajhwAHgT8XUQ8JzNfOoidkCRJWzdMY3B2BzYDNwOLIuKVwMFTXbn7bpz1wKkRsXtEPAw4vlfkWtqxOY/t/fwQOAb4s1m3XpIkTdkw9eB8kHYMzZXA7cCHgUumWcdLgPfThpnRq6hWAmTmFuA+XxAYEVuAn2bmj2fVckmSNC2laZr5boOAsmazT8Q4mlXDlMElSTMw7lCTYTpFJUmShoQBR5IkVceAI0mSquMAhwXi/BXrGBkZme9mSJJUBXtwJElSdQw4kiSpOgYcSZJUHQOOJEmqjgFHkiRVx4AjSZKqY8CRJEnVMeBIkqTqeLPNBWJ7v9mmN8WUJM0Tb7YpSZKGgwFHkiRVx4AjSZKqY8CRJEnVMeBIkqTqGHAkSVJ1hura3ohYAlwL7JuZGwZU52bgsMxcP4j6JEnS7A1VwOmLiCOAo4H9gS3AV4A3ZOZls6hzJfA3wHJgB+AqYHVmfnrWDZYkSVM2zKeodgPeAiwB9ga+BlwYEbvMos7vAC8AHgzsAfw58JGI2H9WLZUkSdNSdQ9OROwFnAk8HbgROHV0WWa+d0zZk4BjgEfRhp3x6tsNOB0YAW4Bju8vz8ybemUXAXfThsj9gCtmvUOSJGlKqg44wDnAz4ClwAOAT05S9pnA7cD3JinzLuARwAHAHcBa2lNR9xERG4FdaY/vvwEXTrvlkiRpxqo9RRURewOHAqsyc1Nm3gCcOEHZR9KGlddn5i0TlFkEvBQ4LjNvyMxNwBvHK5uZewCLaU9XfQ7YPMvdkSRJ01BzD86S7vc1vXlXjy0UEQcAXwTWZOYZvfnfApZ1kycDZwE7Axsmq29UZv4cOC8iPgdsBN437T2QJEkzUnPAub77vYz2aiZor266R0Q8Hvg8cFJmvqe/LDMPHFN2B+DOro5x65vAjrSntSRJ0jZSmqaZ7zbMmYj4F+AnwCtox+B8AlgJ7Et75dQFtJeGv3+K9a2lDSsvpB2D84Hu8TMyc31EvBD4Lu2A4h2BlwN/BzwnM784Wd1lzebt+oloVtWclSVJC1gZb2a1Y3A6L6E9rXQtcAnwod6y1cADgdMi4tbez8pJ6nst7WmpbwOXAZ+h/Q6dUb8MfJr2lNQPgVcCL95auJEkSYNVdQ/O9sQeHEmSZmQoe3AkSdIQMuBIkqTqGHAkSVJ1HDixQJy/Yh0jIyPz3QxJkqpgD44kSaqOAUeSJFXHgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVB1vtrlAbG832/TmmpKkBcKbbUqSpOFgwJEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVJ2hCjgRsSQimohYPsA6r4yIIwdVnyRJmr2h/TKTiDgYOAV4HPAwYGVmXjrLOp8EHAcEcH/gSuCkzDxvdq2VJEnTMVQ9OGPcCXwaeN4A6/wl4OPAgcCDgJOAj0XEEwe4DUmStBVV9+BExF7AmcDTgRuBU0eXZeYVwBVduanWdz/gHcDLgLuB0/rLM/NzY1Y5LyK+DqwEvjKjnZAkSdNWew/OOcAWYCnwNODIWdb3Jtoen98A9gWWA8smKtwFrAOBr89yu5IkaRqqDTgRsTdwKLAqMzdl5g3AibOs9gjgHZl5ZWbeAawCxr2HVETsCnwK+GxmXjzL7UqSpGmo+RTVku73Nb15V0915YhYR3tqCeAjmXl0V+eG0TKZeVtE3DTOursBnwVuog1FkiRpG6o54Fzf/V4GXNU9Xj7VlTPzORPUeU8dXS/Nnv0CEfFgYB3wfeBlmbl5yi2WJEkDUW3AyczrImI9cGpEvAJ4AHD86PKIKMDOvVV2ioj7A3dl5pYJqv0w8JddvT+kHbR8z2m+bszNRcBXgVdOUo8kSZpD1Y7B6byENsRcC1wCfKi3bBlwR/cDcHH3+OWT1Pd24AvAl2hPd/2A+54CO4p2UPHvAZsi4tbu55jZ74okSZqq0jTjjpHVNlbWbN6unohmVbWdf5Kk7UsZb2btPTiSJGkIGXAkSVJ1DDiSJKk6DqRYIM5fsY6RkZH5boYkSVWwB0eSJFXHgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqo4321wgFtLNNr2RpiRpO+LNNiVJ0nAw4EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqo7XA3ciYjlwNbBPZl43xXWWANcC+2bmhrlrnSRJmo5qAk5ErAcuyszVU5k/R214EfAWYO9u1reAYzPzX+d625Ik6V6eohqsLwHPyswHAQ8G3g18LiL2mNdWSZI0ZKrpwZmKiFgLHAbsQXtqaXVmfnQa6+8FnAk8HbgROLW/PDOv7U0WYAuwC7APsHEWTZckSdMwVAEHuBRYRRs2XgR8KCL+KzMvn+L65wA/A5YCDwA+ObZARCwFvgHsRttDdm5mXjb7pkuSpKmqLeAcGxGrxsxbDFwEkJln9eaf25U9BNhqwImIvYFDgf0ycxOwKSJOBC7sl8vMHwB7RMSutCFq5xnuiyRJmqHaAs7bJhhkTEQsAk4ADgf2AhpgV2DP8SqKiFt7k0cBV3aPr+nNv3qihmTmbcDZEXF5RGzIzC9Ma08kSdKM1RZwJvNi4FXAs4HLM/PuiEgmuAtpZi7uT3eXhAMsA67qHi+fwnZ3BB4BGHAkSdpGhing7A5sBm4GFkXEkcDBwAVTWTkzr+t6g06NiFfQjsE5vl8mIo4A/h/wfdreodfRjtf558HsgiRJmophukz8g8CXaU81XQ8cAFwyzTpeQjum5tpu3Q+NWf5I4GLgFtqQ83TgudMYxCxJkgagNE0z320QUNZsXjBPRLNqmDr2JEnbuXGHmgxTD44kSRoSBhxJklQdA44kSaqOgy0WiPNXrGNkZGS+myFJUhXswZEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqo4BR5IkVceAI0mSquPNNheIbXWzTW+kKUmqjDfblCRJw8GAI0mSqmPAkSRJ1THgSJKk6hhwJElSdQw4kiSpOgacWYqIzRFxyHy3Q5Ik3WtovhQlItYDF2Xm6m76COBoYH9gC/AV4A2ZedkstrES+BtgObADcBWwOjM/PavGS5KkaRnmHpzdgLcAS4C9ga8BF0bELrOo8zvAC4AHA3sAfw58JCL2n1VLJUnStAxFD05EnA6sBJ4cEW8Crs/MFWPKnAQcAzyKNuyMV89uwOnACHALcHx/eWbe1Cu7CLibNkTuB1wxqP2RJEmTG4qAk5mviYhH0ztFNY5nArcD35ukqncBjwAOAO4A1tKeirqPiNgI7Ep7fP8NuHCmbZckSdM3zKeo7hERj6QNK6/PzFsmKLMIeClwXGbekJmbgDeOVzYz9wAW056u+hyweS7aLUmSxjcUPTiTiYgDgC8CazLzjN78bwHLusmTgbOAnYENvdWvnqjezPw5cF5EfA7YCLxvoA2XJEkTGqaAc/fYGRHxeODzwEmZ+Z7+ssw8cEzZHYA7aa+QuqqbvXwK292R9rSWJEnaRoYp4NxAO9gXgIh4CnAB7aXh79/aypm5JSI+CpwYEd+kHYNzSr9MRLwQ+C7tgOIdgZcDhwLvHNROSJKkrRumgHMasLYbAHw9cBPwQOC0iDitV+45mXnJBHW8Fngv8G3gZ7RXUT2/t/yXaUPPL9P29nwHeHFmfnFwuyFJkramNE0z320QUNZs3iZPRLNqmDKtJGkIlPFmehWVJEmqjgFHkiRVx4AjSZKq44CMBeL8FesYGRmZ72ZIklQFe3AkSVJ1DDiSJKk6BhxJklQdA44kSaqOAUeSJFXHgCNJkqpjwJEkSdUx4EiSpOp4s80FwpttSpI0I95sU5IkDQcDjiRJqo4BR5IkVceAI0mSqmPAkSRJ1THgSJKk6gxVwImIJRHRRMTyAdZ5ZUQcOaj6JEnS7A3tl6JExMHAKcDjgIcBKzPz0lnWuQL4IPAI4H7AdcC7MvPMWTZXkiRNw1D14IxxJ/Bp4HkDrPMG4OXAQzNzd+APgNUR8ewBbkOSJG1F1T04EbEXcCbwdOBG4NTRZZl5BXBFV26q9d0PeAfwMuBu4LT+8szcBGzqzWq6nxXAhTPcDUmSNE1VBxzgHOBnwFLgAcAnZ1nfm2h7fH4DuB74a2DZ2EIR8Q3aULMT8E3gY7PcriRJmoZqT1FFxN7AocCqzNyUmTcAJ86y2iOAd2TmlZl5B7CKtofmPjLzIGAxcBjtabDbZrldSZI0DdUGHGBJ9/ua3ryrp7pyRKyLiFu7nzN6dW4YLZOZtwE3jbd+Zt6VmRcDewLHT6fhkiRpdmo+RXV993sZcFX3ePlUV87M50xQ5z11RMSutAFmMjvSXlUlSZK2kWoDTmZeFxHrgVMj4hW0Y3Du6UmJiALs3Ftlp4i4P3BXZm6ZoNoPA3/Z1ftD2kHL9/SCRcRvAhuB/6Q9dfXbtAOS/89g9kqSJE1FzaeoAF5CG2KuBS4BPtRbtgy4o/sBuLh7/PJJ6ns78AXgS7Snu37AfU+B7Q58APgJcDNwAvD6zDxrlvshSZKmoTTNL4yR1TwoazZvkyeiWVVtp50kaTiV8WbW3oMjSZKGkAFHkiRVx4AjSZKq44CMBeL8FesYGRmZ72ZIklQFe3AkSVJ1DDiSJKk6BhxJklQdA44kSaqOAUeSJFXHgCNJkqpjwJEkSdUx4EiSpOp4s80FYi5utumNNSVJQ8CbbUqSpOFgwJEkSdUx4EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqs5WvyglIm7tTe7c/f756IzMXDzoRm1rEXEC8NTMPGy+2yJJkmZvqwGnH2Ai4u+BHTPzyLls1CBFxP0y865tsJ0dgCYz757rbUmSpMnN6qtuI2Ip8NfAU4EG+Azw+sy8pVveAP8HOBLYH/g68PvAi4C/AHYBzsjMY7vyhwAXAX8IvBXYDTgfeE1m3tqVeTBwKvBs4P7AvwD/JzNv7JZvAD4APAN4IvCqiLgCeDdwILAD8KWuzqsi4nDgGGBRr7fqIOBpwJszc7/e/p4NbM7MV0XEcuBq4FXA64FfBZZFxF2TtU+SJM29GY/BiYj7A/8MXA7sCxwALAH+ZkzRlwHPB/YE/qdb50G0geBQYFVEPKVXfgdghDZk7A88kjZEEREFOI82TD0aWAbcAnx0zDZfTRugdgP+qSt/ArA3sBy4FfgIQGZ+HDgZWJ+Zi7uf70/jULyk24/dgJun2D5JkjSHZtOD8zygZObx3fQdEXEc8P8i4tWZuaWb/1eZeR1ARHwSOAU4oTuV8/WI+DoQwL/36n5jZm4CNkXE8cAFEXE08HjgCcBhmfnzrs43AP8dEUtGtwO8PzP/c7RdwDd6df88Ik4ELouIXTLz9lkcA4ATM/OGri0xxfZJkqQ5NJuAsy+wNCI2jpnfAHsB13fTP+otux24acw4ldtpez/6ruk93kA7uPkh3TZ3Bm5ss8Q9/gdYClzXW+ceEfGrwDuBX++2NXpjyz3HbGsm+tuaavskSdIcmk3AuQb4bmYeOKjG9CwDruoeL6e9auu/u23eBvzSVgbzjl12BvBD4KDM/HFEPBq4jHvvQDpeXbcAu46Z9yvADybZ1lTbJ0mS5tBsvgfnAmCniDgmInaLiBIRe0fECwbQrrdHxO4R8VDasTMf7gJD0g5Ufnc32JiI2DMi/mAr9e1OGzw2RsRDaAcw991A2xu1U2/efwEPjYjnRcSibr+etpXtzLR9kiRpgGYccLqxK4fSDi7+NrAJuBh47CzbtAX4LG0Py3eA79MOGKYLOb9L2/Py1Yi4hfaKqEO2UufrgJXAz4BLaMNZ3yeAa4EbImJjROybmVcBrwXOBH4C/Bbwqck2Mov2SZKkASpN02y91DYyepl4Zs7q8vXtUVmzeeBPRLNq6A6jJGn4lPFmeqsGSZJUHQOOJEmqzoI6h5GZ61lgbZIkSdsfw8QCcf6KdYyMjMx3MyRJqoKnqCRJUnUMOJIkqToGHEmSVB0DjiRJqo4BR5IkVceAI0mSqmPAkSRJ1THgSJKk6iyom20Os0HdbNMbbEqShow325QkScPBgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJIkqTpDdU1xRCwBrgX2zcwNA6pzM3BYZq4fRH2SJGn2hirg9EXEi4C3AHt3s74FHJuZ/zqg+v8Y+FvguMxcPYg6JUnS1AzzKaovAc/KzAcBDwbeDXwuIvaYbcURsQx4PXDZbOuSJEnTV3UPTkTsBZwJPB24ETh1dFlmXtsrWoAtwC7APsDGCerbDTgdGAFuAY6fYNNnAccCfzyrHZAkSTNSdcABzgF+BiwFHgB8sr8wIpYC3wB2o+3NOjczJ+t1eRfwCOAA4A5gLbDDmDqPAm7LzI93p6kkSdI2Vm3AiYi9gUOB/TJzE7ApIk4ELhwtk5k/APaIiF2BFwE7T1LfIuClwHMz84Zu3huBF/TKLAXeDDxp8HskSZKmqtqAAyzpfl/Tm3f1eAUz8zbg7Ii4PCI2ZOYXIuJbwLKuyMm0p512BjZMUt/fA6sz8/rZNl6SJM1czQFnNGQsA67qHi/fyjo70p6C+kJmHthfEBE7AHd2dUxU37OAJ0TE27rpBwJPjIjfzMyV02y/JEmaoWoDTmZeFxHrgVMj4hW0Y3DuGRQcEUcA/w/4PrAr8DrasTr/PEF9WyLio8CJEfFN2jE4p4wpts+Y6U8AlwB/NesdkiRJU1ZtwOm8BHg/7Zf7jV5FNdqT8kjgJOAhwO20g42fm5mXT1Lfa4H3At+mHbx8PPD80YWZeV2/cET8HPhZZt44gH2RJElTVJqmme82CChrNg/kiWhW1Z5ZJUm6jzLezGH+oj9JklQpA44kSaqOAUeSJFXHARsLxPkr1jEyMjLfzZAkqQr24EiSpOoYcCRJUnUMOJIkqToGHEmSVB0DjiRJqo4BR5IkVceAI0mSqmPAkSRJ1THgSJKk6hhwJElSdQw4kiSpOgYcSZJUHQOOJEmqjgFHkiRVx4AjSZKqY8CRJEnVMeBIkqTqGHAkSVJ1DDiSJKk6pWma+W6DgJ133vmbd9555//MdzsWgh133PEhmzdv/u/5bsd88zjcy2PR8jjcy2PR8jgA8N9N0/zW2Jk7zkdL9Ise85jH/E9mxny3YyGIiPRYeBz6PBYtj8O9PBYtj8PEPEUlSZKqY8CRJEnVMeAsHGfOdwMWEI9Fy+NwL49Fy+NwL49Fy+MwAQcZS5Kk6tiDI0mSqmPAkSRJ1fEy8TkWEY8EPgg8GPgxcERmfm9MmR2AdwO/BTTAKZn591tbtj0ZwHF4NnAy8BjgPZm5ahs2f6AGcCyOA/4A2ALcBRyTmV/YdnswGAM4Dq8AXgfcDewAvD8z373t9mBwZnssemVWAP8J/O32+B4ZwGviBOBPgB92xf89M/9027R+sAbxmoiI3weOA0q3/LDMvHHb7MH8swdn7p0BvDczHwm8F3jfOGVeCuwHPAJ4MnBCRCyfwrLtyWyPw/eBVwHvnPumzrnZHov/AJ6YmQcBrwQ+HhEPmPNWD95sj8OngIMz87HAbwCvj4iD5rrRc2S2x2L0j937gPPmurFzaNbHAfhQZj62+9kuw01nVsciIgI4AXhWZj4aeCqwae6bvXAYcOZQRDwUeDzwsW7Wx4DHR8SeY4oeTvvf592ZeTPtB9SLprBsuzCI45CZV2bmfwGbt0mj58iAjsUXMvP2rtw3aP87e/Bct32QBnQcfpaZo1dJ7ALcj/a/1O3KgD4nAN4EXAB8d25bPDcGeBy2ewM6Fq8D1mTmDQCZuSkzh+rb8g04c2sf4PrM3ALQ/f5hN79vKXBNb/oHvTKTLdteDOI41GLQx+II4KrMvG4O2jqXBnIcIuJ3IuJbXZl3ZuZlc9rquTHrYxERBwO/CZw2562dO4N6b/xBRHwjIi6MiCfPZYPn0CCOxQHAwyPi3yLiaxHx5ogoc9zuBcWAI22nIuLpwEnAi+e7LfMlM8/PzAOBRwIv78agDJWIuB/td6EcPfoHcYidAezbnb59J/BPEbFd9W4O0A7AQcCzgKcDzwFePq8t2sYMOHPrWmDv7tz46DnyX+nm9/0AWNabXtorM9my7cUgjkMtBnIsuv9MPwI8PzO/M6ctnhsDfU1k5g9oxyY9b05aO7dmeyx+GfhV4HMRsQH4c+DVEbG9fQHcrF8TmXlDZt7VPf5iN//Rc9zuuTCovx2fzMyfZ+YtwD8BvzanrV5gDDhzKDNvAv6Le//DfjHwn9250r5P0H4gLerOsT4f+OQUlm0XBnQcqjCIYxERTwQ+DvxeZn5tW7R70AZ0HPYfLRQRDwGeAWx3p6hmeywy8weZ+ZDMXJ6Zy4F30Y7L+KNt0f5BGdBrYu/RQhHxWGA5sN39AzCgz8yPAs+OiNL18j0T+Ppct30h8TLxuXc08MGIOB74Ke2YCSLic8DxmZnAh4FfB0YvAXxrZl7dPZ5s2fZkVschIp4KnAvsDpSI+APgD7fHy6OZ/Wvib4EHAO9rL5QA4OXb4fiT2R6HP+q+PuAu2oHWp2fmhdtyBwZotseiFrM9DidHxBNov0LhTtr3xQ3bcgcGaLbH4lwggMtpv0rhC8BZ2675889bNUiSpOp4ikqSJFXHgCNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJozpZTfLKVc0ps+pJSyYR6btM2UUs4upQzsru+llOWllKY3vWcp5ZpSykOmsO7RpZQPD6ot24NSyspSysb5bscwKqW8bDrv80G/VzS5uXpvzOB5P6WUctKg29FnwNGcKKUU2vvivGUr5f64lPLNUsrPSik/LaVkKeXw3vINpZSXjbPeL8wvre92dS0es+yQUkpTSrm1+/lhKWVtKeWXZren86Npmptpv8hra8d3V+CttHcVHhpN01zSNM0e892OiZRSTiilXDTf7RgGc3WsSynrSylvHnS9c23se2MeX4vvAP60lLL3VkvOkAFHc+XZwE7Av0xUoJTyYto/0H8IPJD2q8hfR/ulVjPxDODhtF9qNd79mbY0TbO4aZrFwFOBJ9N+6+v26gPAK0opu09S5mXAZU3TXLWN2nQfpZQdSil+zki6j6ZpfgqsA46aq234wVOBrjfjzaWUf+l6Jy4rpRxUSnlxKeXKUsqmUsrfl1J27K2ztJTyyVLKDaWUH5VSziyl7NZbfnIp5ftdfVeVUv68t2x51xvy8lLK5aWUW0opF5ZSfrnXrOcDFzWTf5PkbwD/1jTNl5vWHd1/FzP9NtqjgM/TfrvnpG+apmm+D1wAPG7sslLKjt0xef6Y+WeXUtZ2j59ZSvly1+t0cynl3FLKQyfaXne8ntqbPqSUsnnMNo/peqA2llL+vZQS49d2zz58D/hv4LBJij0f+OKYtry2lPLt7nn7QSnl7aWUHbpl7yylnDem/CFd2V276UeXUr7Q7ffo+vfrlo2+Nv6wlHI5cDvw0FLKH5RSvt71rv2olPK+0fq69fYqpXyme61+t1u/KaUs75V5ddfbt6mU8p+llGdPtNPjHN+zSykfLqV8oDu+13fvj8eWUr7S7d+/lFJ+pbfOhlLK8aWUS7v3QZZSnthbPulroJRyv+45/U5X/1WllN8rbQ/lMcAh5d4exYdPsB9P77axqXvOjuotO6SUsrmUcnhX96ZSyj/038fj1DeTz4qDSin/3O3n97v1d+gt/7Xu2NxaSrmU9p+M/jZ3KaWsKaVcXUr5SSnl86WU/SZq4zhtfnAp5UOl/ay6oZTywdLreS1jenN7r8ElEx3rUsqR3f6+sXs93lRK+atxXsdLevUeWUq5snt8OrASOK6rc9zbQZS2d+TiUso7utfIj0spf1FKWdYd01tKKV8tpezfW2dW75Vy72v9/eXe1/ovvG66x5MenzH7cp9TiQN63r9I+xk1N5qm8Wc7/wE20H5V9/7A/WhvwngV7R2Gd6W9AdtNwEu78vcHrqQ9dfEA4EHA54AP9Op8GW2PSgEOBe4AfrNbthxoaAPCQ2hvn/DvwPt7638Z+LMx7TwE2NCbfhHwP8Bq2vuk7DHBvr1sa/OBPYGfA/+LNrQ0wBPGbHtzb3o/2nvUfGCCY3oqcF5vejFwK7Cym34q8ETa253sBfwb8LFe+bOBv+9NN8BTJ2nP27pj9nDauwD/IW14eVD/mI/Tzs8Aqyd5bdwI/M6YeS8E9u2e28d1ZY7qlh1A+xX3e/bKfxA4q3v8UODHtAFyJ2BvIIHjx7w2Lu6Oy07d/jwHOJD2n6r9aL8+/u29bVwMfKp7LT0UWN/Vs7xb/mra1+zBXR2/3T0f+02w32OP79m0r+Hndusf3a1/PrAE2AX4Z+77Gt4A/BB4QrcfbwJuBnaf4mvgHd1+HtQd6yXAQd2yE2j/AZjsfb1v1+Yju208CfgJ8KLePja0X7+/GHgY7efAsQP8rHhg9/o4Dti5W+/7wF/2lv+4OzY7dcfjBu77Pj+H9rPiYV2ZE4FvA/cb770yTps/T/s6f1D381ngs5N8FizvjsuSiY51d0zvAt5L+xn4q8B3gWPGq6O3zpW96fXAm7fyHJ7QbedV3Ps+2AJcNOY5+GJvndm+V86mfd38TlfH/+rasGyC98ZEx+fKMfPueZ4G8bx3ZZ5A2+O+02THcaY/A6/Qn23/073B/7I3/dvdC77/R+ofgNO6x78HXDWmjifQBoQdJtjGJ4FTu8ejb/4n9pb/KfCfvenvAkeOqeOQ/hugm/c84NO0H6JbaE9pPXrMvt0GbBzzczf3/VB7A+0H8+iH5teA943ZdtOt+1PgauAMxglVXfn9af/QP7SbfiXw3Umeg+cBN/Wm7/kw6KYnDDi0f/xuAZ42ps7LRveRiQPOOcDfTtKuO4FDtvL6WQP8Q2/6y8Druse7dcf/Kd30KuCfx6z/QroPw95r42lb2eZrgP/oHi/p1nl4b/kzue+H9jeBI8bU8Rkm+APD+AGn/0dxl67+F/Xm/Qn3fQ1vAE7qTRfaOzS/ZGuvga7srcBzJyh7AlsPOMcA/z5m3tuBL4x5Tfff5+8E/nGSOjcwvc+Kl9Denbr0lh8FfKd7/NLumPSXv43ufU77D1ADLO0tXwRsons/MEnAof0nqwEe0Zu3opv3y719mknA+TmwS2/eq+je42Pr6K0zk4DzrTHzbhrnOfjpAN8rZ9N7rXfzbgZ+d4L3xkTHZ7KAM+vnvZv3iK7cQyc7jjP98Wab9fhR7/HttONNbh4zb7Trel9gafnFkfQN7X+i15dS/oz2v+YltB/WD6Ad1DrRNm/r1Q9tiJhsbEi7waa5gDblU0p5FO2NJC8opezbdO8A2t6Fj/TXK73R+qWU0rX1I03T3NXNPgs4pZSyqmmaW7p5W5opDjxtmuaKUsrXaHuy/hp4BbC2t80nACfT9ijsQnuMFo9T1VQ8pFv3M6V3pRTtf3dLxl/lHrvThrWJ/MLzUNqxT39B21u0I+1/V1/qFVkL/DHtIPHfB65rmubfu2X7Ak8Z89optP+d9m0Ys81nAccDj6LtCdiB9oMe2l4gaD8wR10zpr59gfeWUt7dm7cjcB1Td8/rtWma29uXzS+8b8ae3tnQW6cppfyA7jnZymtgT9oeke9Oo31j7cMvPrdXAb/bmx77Ph/7PhzPdD4r9gGu6b0XR9uwT/d4yTjL+23et/v9je54j7pfr47JjJbp13lVb9mPmLmbmqa5vTe9ga2/32ZibBtvZ5LX3QDeK+Ntcyqvi+kY1PO+O/f+4zlwjsEZTtfQ/qeyx5if+zdNc30p5Sm03etHAQ/pQsFnaD/Ap+o/aU93TFnTNN+m/aO6jLYreqoOpe3KfeXoeXra7tDFtP+BztRa4MjuvPGTgA/1lp1L20v0yKZpdmf8Qc19t9L+wRv1K73H/037AXTYmOdj16ZpTtlKvY+mPdYTuc/zUErZh7ZLfDXtf8APpO2m7z+35wKPLKU8nvY/ubW9ZdfQ/rfXb+cDm3bgdt/dvW3uBJzX1bu0O15v7G3z+u730t76/cej233lmO0ubprmjyfZ90FYPvqgC9JLuTdUTfYauJn2D9cjJqj37gnm913b337n4d38beVaYFm571+pfhuuH2f58t7j0T++jxjz3O3SNM3Hprj9sXU+fMyyW5j4vQUTH+uHllJ2GdPu0ed29J+imdQ7YwN6r0zXePsx9pjCffd/UM/7o2l7uO6cYdsnZcAZThcAO5V2AORupbV3KeUF3fLdaU8X3Qw0pZTn0p4Xno7zaLtOJ1RKeWUp5UWl+y6XbkDf0cDlTdP8ZBrbOop2/MOjgMd2P4+m/cP8R9Nsd9+5tMHp3bTnyK/vLdudtrv1llLKUtpz0ZP5KvC/Syk7dYMB/2J0Qfdf0N8Aa0opjwAopSwu7fcIjf1QvUcXvPakPZ8/kfO47yDkxbTv+5uBu0opTwJe3l+haZqNwD/ShqAn0Y7BGfUhILrn7v6llEXdoMTfmqQNO9H+J/rTpmnuKKUcQNvtPrq962i7+0/pXo97AmMvvz0NOKG0g4JLKeUBpZSndr1+c+mVpZTHl3bw6V/S9tR8tls24Wuge07/Fji1tIOyS2kHvR7UFbmBthd1p0m2/THgCaWUI0o7CP3XaF/rZw10Dyf3Wdrn7pjutbuC9g/uaBsuoH1N/WVpB1U/nnb8GABN09xE2/P7t6W7HLiUskcp5QVlzFc5jKdpmh8CFwJ/1a33IOCvgHVN04z2UnwVeHH3ntmTdrxQ30THehHwju619HDa068f7Lb7Y7pQXdorAR9D20s8tt4pD5aeokG8V6ZrvOPzX7QB8Hnde/wFwNN6ywf1vD+L9jNqThhwhlDXLXso7X/236b9kL6YNhgAfIH2D9l/0PYu/B7tH7zp+AKwuZRyyCRlfkp7KuSKUspttGM/NtKOZZiS0l618nxgTdM0N/R/aHuhHle2cjXSRJqm2US738+hvSS7749oz9nfQjuG6BNbqe41tB+GP6Ed43D2mOVvAf4J+KdSys9oB4IezeTv0VcCZ3ftnMiHgYO7D3Caprmit62NtH+Ux/tPei3tfn+h94eE7rg+g/aYb6B9Dv+RMVdQ9DVNcyvt83xqKeVW2h6jsac7X0IbHq6jHbA+ejx/3tXxftqB32u7bf6A9g/Z/SbZ90E4kzbg/hQ4nHZMzejx3tpr4Fja5/q8rsx67v2D+AnaHogbSnuly75j1qVpmqtpx2e8hnZA54eB45qm+YcB7dtWdfv6bNqQfCP3fjb8dbd8I+3A7cNpj9G7gb8bU82raQf0ry+l3EI7tuxFtKcmpuJltMfvO7SfVxuBI3rL30z7D9mPaI/xuWPWn+hYX0P7erua9rPn87SvsVH/m/azaFO3v2OD5Wm0YX9jKeVbU9yXSQ3ivTIDv3B8mvZrJV5L+/r/CfBbtAObR9u5kVk+76WUPWhf32fMsN1bVe57Ck0anO6/+mOapnlaN30I7R/k5fPYrO1S1+tzddM0pZvek/bqpRgzfmK8dY+mHST88snKLSSllN+kDWEPaObpQ6q047zePHb8l7Z/pZQjaZ/bQffAbHML4b0yE6WUt9OO/5qzL0t0kLHmTNM0n6f9r0gD1oWaZVMsewZz+F/SIJRSHks7FuAy2gGKq4GPb08f2NK2UMt7pWma/zvX2/AUlbalDWzf3xw8nzbSDpyu1YNoT/PcClwKfIO2i1zSfflemSJPUUmSpOrYgyNJkqpjwJEkSdUx4EiSpOoYcCRJUnUMOJIkqTr/PzA0+2ultqCKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x655.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, x_0, plot_type=\"bar\", show=True, max_display=19)\n",
    "#plt.savefig(\"shap15_2.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Сохранение предобученной модели\n",
    "with open('model_1_for_19d.pickle', 'wb') as f:\n",
    "    pickle.dump((regressor, scaler), f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 3. Обучение на 2 геометрических признаках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In train set 67 In test set 12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50, 9, 14, 3, 3, 0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Подготовка данных\n",
    "x_train, x_test, y_train, y_test, scaler = train_test_split_and_normolize(df, drop=False, train_size=0.85, random_state=159, shuffle=True, stratify=df['X'])\n",
    "sum(x_train['I'] == 1), sum(x_test['I'] == 1), sum(x_train['Br'] == 1), sum(x_test['Br'] == 1), sum(x_train['Br'] == 0.5), sum(x_test['Br'] == 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "descriptors_sets3 = [['M', 'X', 'Temperature', 'min Hal...Hal', 'delta d', 'sigma^2'],\n",
    "                ['M', 'X', 'Temperature', 'aver Hal...Hal', 'delta d', 'sigma^2'],\n",
    "                ['M', 'X', 'Temperature', 'aver Hal...Hal', 'Number of Hal...Hal contacts', 'delta d', 'sigma^2'],\n",
    "                ['M', 'X', 'Temperature', 'N/aver-d', 'delta d', 'sigma^2'],\n",
    "                ['M', 'X', 'Temperature', 'min Hal...Hal', 'Number of Hal...Hal contacts', 'delta d', 'sigma^2'],\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Подбор гиперпараметров\n",
    "\n",
    "# a = [0.1, 0.15, 0.19, 0.2, 0.21, 0.25, 0.5, 0.75, 1]\n",
    "# gpr1 = {'GPR_rbf ' + str(x): GaussianProcessRegressor(kernel=RBF(), alpha=x, n_restarts_optimizer=10, normalize_y=True) for x in a}\n",
    "#          # 'GPR_mat': GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True),\n",
    "# res_gpr3 = pipline_true(x_train, x_test, y_train, y_test, descriptors_sets3[2], models=gpr1, metrics=metrics)\n",
    "# res_gpr3[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear\n",
      "\n",
      "SVR_lin\n",
      "\n",
      "SVR_poly\n",
      "\n",
      "SVR_rbf\n",
      "\n",
      "GPR_rbf\n",
      "\n",
      "GPR_mat\n",
      "\n",
      "GPR_rt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RF\n",
      "\n",
      "XGB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The optimal value found for dimension 0 of parameter alpha is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n"
     ]
    }
   ],
   "source": [
    "res3 = []\n",
    "for descriptors_set in descriptors_sets3:\n",
    "    res3.append(pipline_true(x_train, x_test, y_train, y_test, descriptors_set, models=models1, metrics=metrics))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.081356</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.087724</td>\n",
       "      <td>0.002161</td>\n",
       "      <td>0.089570</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.083256</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.079598</td>\n",
       "      <td>0.001635</td>\n",
       "      <td>0.078592</td>\n",
       "      <td>0.001646</td>\n",
       "      <td>0.079598</td>\n",
       "      <td>0.001635</td>\n",
       "      <td>0.109519</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>0.052018</td>\n",
       "      <td>0.001476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.092027</td>\n",
       "      <td>0.095720</td>\n",
       "      <td>0.097298</td>\n",
       "      <td>0.099928</td>\n",
       "      <td>0.105338</td>\n",
       "      <td>0.091551</td>\n",
       "      <td>0.108954</td>\n",
       "      <td>0.102021</td>\n",
       "      <td>0.095228</td>\n",
       "      <td>0.097671</td>\n",
       "      <td>0.095775</td>\n",
       "      <td>0.097377</td>\n",
       "      <td>0.095228</td>\n",
       "      <td>0.097671</td>\n",
       "      <td>0.113836</td>\n",
       "      <td>0.096586</td>\n",
       "      <td>0.087394</td>\n",
       "      <td>0.109539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.013809</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.014307</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.013224</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.011117</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.012984</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>0.012584</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.012984</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>0.020436</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.017494</td>\n",
       "      <td>0.038485</td>\n",
       "      <td>0.019303</td>\n",
       "      <td>0.043262</td>\n",
       "      <td>0.019353</td>\n",
       "      <td>0.035936</td>\n",
       "      <td>0.022124</td>\n",
       "      <td>0.046272</td>\n",
       "      <td>0.018466</td>\n",
       "      <td>0.040544</td>\n",
       "      <td>0.018514</td>\n",
       "      <td>0.040369</td>\n",
       "      <td>0.018466</td>\n",
       "      <td>0.040544</td>\n",
       "      <td>0.022148</td>\n",
       "      <td>0.051004</td>\n",
       "      <td>0.019457</td>\n",
       "      <td>0.055246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.117490</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.119588</td>\n",
       "      <td>0.002319</td>\n",
       "      <td>0.114975</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.105419</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.113927</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>0.112158</td>\n",
       "      <td>0.002184</td>\n",
       "      <td>0.113927</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>0.142926</td>\n",
       "      <td>0.002829</td>\n",
       "      <td>0.077124</td>\n",
       "      <td>0.002289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.092027</td>\n",
       "      <td>0.095720</td>\n",
       "      <td>0.097298</td>\n",
       "      <td>0.099928</td>\n",
       "      <td>0.105338</td>\n",
       "      <td>0.091551</td>\n",
       "      <td>0.108954</td>\n",
       "      <td>0.102021</td>\n",
       "      <td>0.095228</td>\n",
       "      <td>0.097671</td>\n",
       "      <td>0.095775</td>\n",
       "      <td>0.097377</td>\n",
       "      <td>0.095228</td>\n",
       "      <td>0.097671</td>\n",
       "      <td>0.113836</td>\n",
       "      <td>0.096586</td>\n",
       "      <td>0.087394</td>\n",
       "      <td>0.109539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.819528</td>\n",
       "      <td>0.006894</td>\n",
       "      <td>0.813027</td>\n",
       "      <td>0.007024</td>\n",
       "      <td>0.827160</td>\n",
       "      <td>0.006794</td>\n",
       "      <td>0.854689</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>0.830303</td>\n",
       "      <td>0.006528</td>\n",
       "      <td>0.835534</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.830303</td>\n",
       "      <td>0.006528</td>\n",
       "      <td>0.732929</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.922198</td>\n",
       "      <td>0.004335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.081356  0.001624  0.087724  0.002161  0.089570  0.001610   \n",
       "MAE on validate   0.092027  0.095720  0.097298  0.099928  0.105338  0.091551   \n",
       "MSE on train      0.013809  0.000523  0.014307  0.000533  0.013224  0.000480   \n",
       "MSE on validate   0.017494  0.038485  0.019303  0.043262  0.019353  0.035936   \n",
       "RMSE on train     0.117490  0.002317  0.119588  0.002319  0.114975  0.002163   \n",
       "RMSE on validate  0.092027  0.095720  0.097298  0.099928  0.105338  0.091551   \n",
       "R2 on train       0.819528  0.006894  0.813027  0.007024  0.827160  0.006794   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.083256  0.001713  0.079598  0.001635  0.078592  0.001646   \n",
       "MAE on validate   0.108954  0.102021  0.095228  0.097671  0.095775  0.097377   \n",
       "MSE on train      0.011117  0.000419  0.012984  0.000486  0.012584  0.000471   \n",
       "MSE on validate   0.022124  0.046272  0.018466  0.040544  0.018514  0.040369   \n",
       "RMSE on train     0.105419  0.002072  0.113927  0.002216  0.112158  0.002184   \n",
       "RMSE on validate  0.108954  0.102021  0.095228  0.097671  0.095775  0.097377   \n",
       "R2 on train       0.854689  0.005970  0.830303  0.006528  0.835534  0.006312   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.079598  0.001635  0.109519  0.001535  0.052018  0.001476  \n",
       "MAE on validate   0.095228  0.097671  0.113836  0.096586  0.087394  0.109539  \n",
       "MSE on train      0.012984  0.000486  0.020436  0.000761  0.005953  0.000331  \n",
       "MSE on validate   0.018466  0.040544  0.022148  0.051004  0.019457  0.055246  \n",
       "RMSE on train     0.113927  0.002216  0.142926  0.002829  0.077124  0.002289  \n",
       "RMSE on validate  0.095228  0.097671  0.113836  0.096586  0.087394  0.109539  \n",
       "R2 on train       0.830303  0.006528  0.732929  0.010037  0.922198  0.004335  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.080342</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>0.082647</td>\n",
       "      <td>0.001974</td>\n",
       "      <td>0.087641</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.078129</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.078724</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.077637</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.078724</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.109675</td>\n",
       "      <td>0.001527</td>\n",
       "      <td>0.053422</td>\n",
       "      <td>0.001376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.090384</td>\n",
       "      <td>0.096534</td>\n",
       "      <td>0.094148</td>\n",
       "      <td>0.102840</td>\n",
       "      <td>0.104518</td>\n",
       "      <td>0.095300</td>\n",
       "      <td>0.110067</td>\n",
       "      <td>0.111880</td>\n",
       "      <td>0.094437</td>\n",
       "      <td>0.098098</td>\n",
       "      <td>0.095021</td>\n",
       "      <td>0.097481</td>\n",
       "      <td>0.094437</td>\n",
       "      <td>0.098098</td>\n",
       "      <td>0.114247</td>\n",
       "      <td>0.097048</td>\n",
       "      <td>0.091795</td>\n",
       "      <td>0.105671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.013959</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.014303</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.013178</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.010518</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.012912</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.012426</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.012912</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.020490</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.000344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.017349</td>\n",
       "      <td>0.038631</td>\n",
       "      <td>0.019282</td>\n",
       "      <td>0.041085</td>\n",
       "      <td>0.019870</td>\n",
       "      <td>0.038284</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>0.046675</td>\n",
       "      <td>0.018398</td>\n",
       "      <td>0.040822</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>0.040533</td>\n",
       "      <td>0.018398</td>\n",
       "      <td>0.040822</td>\n",
       "      <td>0.022330</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>0.019426</td>\n",
       "      <td>0.054063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.118128</td>\n",
       "      <td>0.002331</td>\n",
       "      <td>0.119571</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.114775</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.102540</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.113609</td>\n",
       "      <td>0.002175</td>\n",
       "      <td>0.111453</td>\n",
       "      <td>0.002137</td>\n",
       "      <td>0.113609</td>\n",
       "      <td>0.002175</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.002806</td>\n",
       "      <td>0.078919</td>\n",
       "      <td>0.002317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.090384</td>\n",
       "      <td>0.096534</td>\n",
       "      <td>0.094148</td>\n",
       "      <td>0.102840</td>\n",
       "      <td>0.104518</td>\n",
       "      <td>0.095300</td>\n",
       "      <td>0.110067</td>\n",
       "      <td>0.111880</td>\n",
       "      <td>0.094437</td>\n",
       "      <td>0.098098</td>\n",
       "      <td>0.095021</td>\n",
       "      <td>0.097481</td>\n",
       "      <td>0.094437</td>\n",
       "      <td>0.098098</td>\n",
       "      <td>0.114247</td>\n",
       "      <td>0.097048</td>\n",
       "      <td>0.091795</td>\n",
       "      <td>0.105671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.817563</td>\n",
       "      <td>0.007012</td>\n",
       "      <td>0.813077</td>\n",
       "      <td>0.007214</td>\n",
       "      <td>0.827766</td>\n",
       "      <td>0.006529</td>\n",
       "      <td>0.862525</td>\n",
       "      <td>0.005364</td>\n",
       "      <td>0.831254</td>\n",
       "      <td>0.006352</td>\n",
       "      <td>0.837599</td>\n",
       "      <td>0.006099</td>\n",
       "      <td>0.831255</td>\n",
       "      <td>0.006352</td>\n",
       "      <td>0.732216</td>\n",
       "      <td>0.010088</td>\n",
       "      <td>0.918536</td>\n",
       "      <td>0.004510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.080342  0.001518  0.082647  0.001974  0.087641  0.001413   \n",
       "MAE on validate   0.090384  0.096534  0.094148  0.102840  0.104518  0.095300   \n",
       "MSE on train      0.013959  0.000529  0.014303  0.000538  0.013178  0.000461   \n",
       "MSE on validate   0.017349  0.038631  0.019282  0.041085  0.019870  0.038284   \n",
       "RMSE on train     0.118128  0.002331  0.119571  0.002333  0.114775  0.002080   \n",
       "RMSE on validate  0.090384  0.096534  0.094148  0.102840  0.104518  0.095300   \n",
       "R2 on train       0.817563  0.007012  0.813077  0.007214  0.827766  0.006529   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.078129  0.001452  0.078724  0.001578  0.077637  0.001569   \n",
       "MAE on validate   0.110067  0.111880  0.094437  0.098098  0.095021  0.097481   \n",
       "MSE on train      0.010518  0.000374  0.012912  0.000477  0.012426  0.000459   \n",
       "MSE on validate   0.024445  0.046675  0.018398  0.040822  0.018390  0.040533   \n",
       "RMSE on train     0.102540  0.001900  0.113609  0.002175  0.111453  0.002137   \n",
       "RMSE on validate  0.110067  0.111880  0.094437  0.098098  0.095021  0.097481   \n",
       "R2 on train       0.862525  0.005364  0.831254  0.006352  0.837599  0.006099   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.078724  0.001578  0.109675  0.001527  0.053422  0.001376  \n",
       "MAE on validate   0.094437  0.098098  0.114247  0.097048  0.091795  0.105671  \n",
       "MSE on train      0.012912  0.000477  0.020490  0.000757  0.006234  0.000344  \n",
       "MSE on validate   0.018398  0.040822  0.022330  0.051159  0.019426  0.054063  \n",
       "RMSE on train     0.113609  0.002175  0.143116  0.002806  0.078919  0.002317  \n",
       "RMSE on validate  0.094437  0.098098  0.114247  0.097048  0.091795  0.105671  \n",
       "R2 on train       0.831255  0.006352  0.732216  0.010088  0.918536  0.004510  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.080574</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.083979</td>\n",
       "      <td>0.002111</td>\n",
       "      <td>0.082369</td>\n",
       "      <td>0.001568</td>\n",
       "      <td>0.077367</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>0.076426</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.075401</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>0.076426</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.109733</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.054332</td>\n",
       "      <td>0.001457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.093378</td>\n",
       "      <td>0.099553</td>\n",
       "      <td>0.097155</td>\n",
       "      <td>0.105099</td>\n",
       "      <td>0.106594</td>\n",
       "      <td>0.114628</td>\n",
       "      <td>0.121266</td>\n",
       "      <td>0.120611</td>\n",
       "      <td>0.094221</td>\n",
       "      <td>0.101447</td>\n",
       "      <td>0.095001</td>\n",
       "      <td>0.101108</td>\n",
       "      <td>0.094221</td>\n",
       "      <td>0.101447</td>\n",
       "      <td>0.114556</td>\n",
       "      <td>0.096741</td>\n",
       "      <td>0.094257</td>\n",
       "      <td>0.104218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.013872</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.014317</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.012208</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.009195</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.012459</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.012007</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.012459</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.020513</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.006189</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.018482</td>\n",
       "      <td>0.040108</td>\n",
       "      <td>0.020320</td>\n",
       "      <td>0.042785</td>\n",
       "      <td>0.024306</td>\n",
       "      <td>0.054445</td>\n",
       "      <td>0.029036</td>\n",
       "      <td>0.053394</td>\n",
       "      <td>0.019015</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>0.019095</td>\n",
       "      <td>0.040449</td>\n",
       "      <td>0.019015</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>0.022342</td>\n",
       "      <td>0.051038</td>\n",
       "      <td>0.019584</td>\n",
       "      <td>0.052499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.117756</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>0.119631</td>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.110467</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.095882</td>\n",
       "      <td>0.001365</td>\n",
       "      <td>0.111600</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.109559</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.111600</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.143198</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.078640</td>\n",
       "      <td>0.002277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.093378</td>\n",
       "      <td>0.099553</td>\n",
       "      <td>0.097155</td>\n",
       "      <td>0.105099</td>\n",
       "      <td>0.106594</td>\n",
       "      <td>0.114628</td>\n",
       "      <td>0.121266</td>\n",
       "      <td>0.120611</td>\n",
       "      <td>0.094221</td>\n",
       "      <td>0.101447</td>\n",
       "      <td>0.095001</td>\n",
       "      <td>0.101108</td>\n",
       "      <td>0.094221</td>\n",
       "      <td>0.101447</td>\n",
       "      <td>0.114556</td>\n",
       "      <td>0.096741</td>\n",
       "      <td>0.094257</td>\n",
       "      <td>0.104218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.818710</td>\n",
       "      <td>0.006962</td>\n",
       "      <td>0.812890</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.840433</td>\n",
       "      <td>0.006786</td>\n",
       "      <td>0.879802</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>0.837167</td>\n",
       "      <td>0.006180</td>\n",
       "      <td>0.843069</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>0.837167</td>\n",
       "      <td>0.006180</td>\n",
       "      <td>0.731917</td>\n",
       "      <td>0.009830</td>\n",
       "      <td>0.919113</td>\n",
       "      <td>0.004418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.080574  0.001573  0.083979  0.002111  0.082369  0.001568   \n",
       "MAE on validate   0.093378  0.099553  0.097155  0.105099  0.106594  0.114628   \n",
       "MSE on train      0.013872  0.000529  0.014317  0.000517  0.012208  0.000479   \n",
       "MSE on validate   0.018482  0.040108  0.020320  0.042785  0.024306  0.054445   \n",
       "RMSE on train     0.117756  0.002335  0.119631  0.002241  0.110467  0.002239   \n",
       "RMSE on validate  0.093378  0.099553  0.097155  0.105099  0.106594  0.114628   \n",
       "R2 on train       0.818710  0.006962  0.812890  0.007000  0.840433  0.006786   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.077367  0.001305  0.076426  0.001563  0.075401  0.001583   \n",
       "MAE on validate   0.121266  0.120611  0.094221  0.101447  0.095001  0.101108   \n",
       "MSE on train      0.009195  0.000257  0.012459  0.000459  0.012007  0.000441   \n",
       "MSE on validate   0.029036  0.053394  0.019015  0.040674  0.019095  0.040449   \n",
       "RMSE on train     0.095882  0.001365  0.111600  0.002114  0.109559  0.002066   \n",
       "RMSE on validate  0.121266  0.120611  0.094221  0.101447  0.095001  0.101108   \n",
       "R2 on train       0.879802  0.004197  0.837167  0.006180  0.843069  0.005921   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.076426  0.001563  0.109733  0.001470  0.054332  0.001457  \n",
       "MAE on validate   0.094221  0.101447  0.114556  0.096741  0.094257  0.104218  \n",
       "MSE on train      0.012459  0.000459  0.020513  0.000743  0.006189  0.000338  \n",
       "MSE on validate   0.019015  0.040674  0.022342  0.051038  0.019584  0.052499  \n",
       "RMSE on train     0.111600  0.002114  0.143198  0.002748  0.078640  0.002277  \n",
       "RMSE on validate  0.094221  0.101447  0.114556  0.096741  0.094257  0.104218  \n",
       "R2 on train       0.837167  0.006180  0.731917  0.009830  0.919113  0.004418  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
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       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.087256</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>0.091107</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.078249</td>\n",
       "      <td>0.001447</td>\n",
       "      <td>0.081465</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.079633</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.078458</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.079622</td>\n",
       "      <td>0.001607</td>\n",
       "      <td>0.109489</td>\n",
       "      <td>0.001509</td>\n",
       "      <td>0.055273</td>\n",
       "      <td>0.001341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.100849</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>0.103105</td>\n",
       "      <td>0.107481</td>\n",
       "      <td>0.098157</td>\n",
       "      <td>0.107004</td>\n",
       "      <td>0.114282</td>\n",
       "      <td>0.116957</td>\n",
       "      <td>0.096553</td>\n",
       "      <td>0.102170</td>\n",
       "      <td>0.096626</td>\n",
       "      <td>0.101296</td>\n",
       "      <td>0.096615</td>\n",
       "      <td>0.102178</td>\n",
       "      <td>0.113989</td>\n",
       "      <td>0.096811</td>\n",
       "      <td>0.092149</td>\n",
       "      <td>0.107413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.015177</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>0.015530</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.011945</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.012716</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.013130</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.020428</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.006644</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.021041</td>\n",
       "      <td>0.046548</td>\n",
       "      <td>0.022010</td>\n",
       "      <td>0.050446</td>\n",
       "      <td>0.020914</td>\n",
       "      <td>0.047636</td>\n",
       "      <td>0.026535</td>\n",
       "      <td>0.052990</td>\n",
       "      <td>0.019605</td>\n",
       "      <td>0.042063</td>\n",
       "      <td>0.019444</td>\n",
       "      <td>0.041287</td>\n",
       "      <td>0.019619</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.022226</td>\n",
       "      <td>0.051183</td>\n",
       "      <td>0.019857</td>\n",
       "      <td>0.056749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.123172</td>\n",
       "      <td>0.002418</td>\n",
       "      <td>0.124602</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.109280</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>0.108267</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.114581</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.112744</td>\n",
       "      <td>0.002138</td>\n",
       "      <td>0.114567</td>\n",
       "      <td>0.002176</td>\n",
       "      <td>0.142901</td>\n",
       "      <td>0.002769</td>\n",
       "      <td>0.081476</td>\n",
       "      <td>0.002508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.100849</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>0.103105</td>\n",
       "      <td>0.107481</td>\n",
       "      <td>0.098157</td>\n",
       "      <td>0.107004</td>\n",
       "      <td>0.114282</td>\n",
       "      <td>0.116957</td>\n",
       "      <td>0.096553</td>\n",
       "      <td>0.102170</td>\n",
       "      <td>0.096626</td>\n",
       "      <td>0.101296</td>\n",
       "      <td>0.096615</td>\n",
       "      <td>0.102178</td>\n",
       "      <td>0.113989</td>\n",
       "      <td>0.096811</td>\n",
       "      <td>0.092149</td>\n",
       "      <td>0.107413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.801662</td>\n",
       "      <td>0.007245</td>\n",
       "      <td>0.797041</td>\n",
       "      <td>0.006794</td>\n",
       "      <td>0.843863</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.846705</td>\n",
       "      <td>0.007332</td>\n",
       "      <td>0.828352</td>\n",
       "      <td>0.006447</td>\n",
       "      <td>0.833814</td>\n",
       "      <td>0.006256</td>\n",
       "      <td>0.828397</td>\n",
       "      <td>0.006453</td>\n",
       "      <td>0.733020</td>\n",
       "      <td>0.009982</td>\n",
       "      <td>0.913172</td>\n",
       "      <td>0.004916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.087256  0.001693  0.091107  0.001681  0.078249  0.001447   \n",
       "MAE on validate   0.100849  0.105047  0.103105  0.107481  0.098157  0.107004   \n",
       "MSE on train      0.015177  0.000573  0.015530  0.000535  0.011945  0.000396   \n",
       "MSE on validate   0.021041  0.046548  0.022010  0.050446  0.020914  0.047636   \n",
       "RMSE on train     0.123172  0.002418  0.124602  0.002222  0.109280  0.001858   \n",
       "RMSE on validate  0.100849  0.105047  0.103105  0.107481  0.098157  0.107004   \n",
       "R2 on train       0.801662  0.007245  0.797041  0.006794  0.843863  0.005791   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.081465  0.002011  0.079633  0.001604  0.078458  0.001629   \n",
       "MAE on validate   0.114282  0.116957  0.096553  0.102170  0.096626  0.101296   \n",
       "MSE on train      0.011728  0.000519  0.013134  0.000484  0.012716  0.000468   \n",
       "MSE on validate   0.026535  0.052990  0.019605  0.042063  0.019444  0.041287   \n",
       "RMSE on train     0.108267  0.002482  0.114581  0.002174  0.112744  0.002138   \n",
       "RMSE on validate  0.114282  0.116957  0.096553  0.102170  0.096626  0.101296   \n",
       "R2 on train       0.846705  0.007332  0.828352  0.006447  0.833814  0.006256   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.079622  0.001607  0.109489  0.001509  0.055273  0.001341  \n",
       "MAE on validate   0.096615  0.102178  0.113989  0.096811  0.092149  0.107413  \n",
       "MSE on train      0.013130  0.000484  0.020428  0.000747  0.006644  0.000385  \n",
       "MSE on validate   0.019619  0.042062  0.022226  0.051183  0.019857  0.056749  \n",
       "RMSE on train     0.114567  0.002176  0.142901  0.002769  0.081476  0.002508  \n",
       "RMSE on validate  0.096615  0.102178  0.113989  0.096811  0.092149  0.107413  \n",
       "R2 on train       0.828397  0.006453  0.733020  0.009982  0.913172  0.004916  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Linear</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_lin</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_poly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SVR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rbf</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_mat</th>\n",
       "      <th colspan=\"2\" halign=\"left\">GPR_rt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">RF</th>\n",
       "      <th colspan=\"2\" halign=\"left\">XGB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on train</th>\n",
       "      <td>0.081299</td>\n",
       "      <td>0.001646</td>\n",
       "      <td>0.088157</td>\n",
       "      <td>0.001994</td>\n",
       "      <td>0.082957</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.081021</td>\n",
       "      <td>0.001909</td>\n",
       "      <td>0.077334</td>\n",
       "      <td>0.001607</td>\n",
       "      <td>0.075981</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>0.077334</td>\n",
       "      <td>0.001607</td>\n",
       "      <td>0.109512</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.053019</td>\n",
       "      <td>0.001636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on validate</th>\n",
       "      <td>0.094083</td>\n",
       "      <td>0.098713</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>0.099070</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.112662</td>\n",
       "      <td>0.122533</td>\n",
       "      <td>0.114100</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.100781</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>0.100929</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.100781</td>\n",
       "      <td>0.113819</td>\n",
       "      <td>0.096388</td>\n",
       "      <td>0.090523</td>\n",
       "      <td>0.108799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on train</th>\n",
       "      <td>0.013803</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.014336</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.012104</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.009710</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.012478</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.012036</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.012478</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.020434</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.005926</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>0.018451</td>\n",
       "      <td>0.040616</td>\n",
       "      <td>0.019343</td>\n",
       "      <td>0.041657</td>\n",
       "      <td>0.024001</td>\n",
       "      <td>0.052959</td>\n",
       "      <td>0.027839</td>\n",
       "      <td>0.048959</td>\n",
       "      <td>0.019097</td>\n",
       "      <td>0.040695</td>\n",
       "      <td>0.019184</td>\n",
       "      <td>0.040448</td>\n",
       "      <td>0.019097</td>\n",
       "      <td>0.040695</td>\n",
       "      <td>0.022107</td>\n",
       "      <td>0.050977</td>\n",
       "      <td>0.019855</td>\n",
       "      <td>0.055580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on train</th>\n",
       "      <td>0.117462</td>\n",
       "      <td>0.002371</td>\n",
       "      <td>0.119712</td>\n",
       "      <td>0.002341</td>\n",
       "      <td>0.109998</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.098525</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.111684</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>0.109690</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.111684</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>0.142922</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>0.076944</td>\n",
       "      <td>0.002280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>0.094083</td>\n",
       "      <td>0.098713</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>0.099070</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.112662</td>\n",
       "      <td>0.122533</td>\n",
       "      <td>0.114100</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.100781</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>0.100929</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.100781</td>\n",
       "      <td>0.113819</td>\n",
       "      <td>0.096388</td>\n",
       "      <td>0.090523</td>\n",
       "      <td>0.108799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on train</th>\n",
       "      <td>0.819611</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.812633</td>\n",
       "      <td>0.007244</td>\n",
       "      <td>0.841787</td>\n",
       "      <td>0.006719</td>\n",
       "      <td>0.873071</td>\n",
       "      <td>0.005129</td>\n",
       "      <td>0.836920</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.842694</td>\n",
       "      <td>0.005939</td>\n",
       "      <td>0.836920</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.732947</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>0.922560</td>\n",
       "      <td>0.004322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Linear             SVR_lin            SVR_poly            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.081299  0.001646  0.088157  0.001994  0.082957  0.001687   \n",
       "MAE on validate   0.094083  0.098713  0.098361  0.099070  0.107225  0.112662   \n",
       "MSE on train      0.013803  0.000534  0.014336  0.000538  0.012104  0.000473   \n",
       "MSE on validate   0.018451  0.040616  0.019343  0.041657  0.024001  0.052959   \n",
       "RMSE on train     0.117462  0.002371  0.119712  0.002341  0.109998  0.002211   \n",
       "RMSE on validate  0.094083  0.098713  0.098361  0.099070  0.107225  0.112662   \n",
       "R2 on train       0.819611  0.007018  0.812633  0.007244  0.841787  0.006719   \n",
       "\n",
       "                   SVR_rbf             GPR_rbf             GPR_mat            \\\n",
       "                      mean       std      mean       std      mean       std   \n",
       "MAE on train      0.081021  0.001909  0.077334  0.001607  0.075981  0.001582   \n",
       "MAE on validate   0.122533  0.114100  0.095351  0.100781  0.095652  0.100929   \n",
       "MSE on train      0.009710  0.000345  0.012478  0.000461  0.012036  0.000442   \n",
       "MSE on validate   0.027839  0.048959  0.019097  0.040695  0.019184  0.040448   \n",
       "RMSE on train     0.098525  0.001807  0.111684  0.002123  0.109690  0.002073   \n",
       "RMSE on validate  0.122533  0.114100  0.095351  0.100781  0.095652  0.100929   \n",
       "R2 on train       0.873071  0.005129  0.836920  0.006203  0.842694  0.005939   \n",
       "\n",
       "                    GPR_rt                  RF                 XGB            \n",
       "                      mean       std      mean       std      mean       std  \n",
       "MAE on train      0.077334  0.001607  0.109512  0.001458  0.053019  0.001636  \n",
       "MAE on validate   0.095351  0.100781  0.113819  0.096388  0.090523  0.108799  \n",
       "MSE on train      0.012478  0.000461  0.020434  0.000739  0.005926  0.000329  \n",
       "MSE on validate   0.019097  0.040695  0.022107  0.050977  0.019855  0.055580  \n",
       "RMSE on train     0.111684  0.002123  0.142922  0.002746  0.076944  0.002280  \n",
       "RMSE on validate  0.095351  0.100781  0.113819  0.096388  0.090523  0.108799  \n",
       "R2 on train       0.836920  0.006203  0.732947  0.009810  0.922560  0.004322  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Результаты кросс-валидации\n",
    "for r in res3:\n",
    "    r[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>79  0.244 78  0.131 52  0.122 82  0.077 28  0....</td>\n",
       "      <td>79  0.241 72  0.215 78  0.113 52  0.096 82  0....</td>\n",
       "      <td>79  0.201 47  0.156 78  0.116 42  0.076 83  0....</td>\n",
       "      <td>78  0.236 79  0.235 52  0.186 82  0.064 45  0....</td>\n",
       "      <td>79  0.253 78  0.135 52  0.125 28  0.085 82  0....</td>\n",
       "      <td>79  0.252 78  0.133 52  0.123 28  0.086 82  0....</td>\n",
       "      <td>79  0.253 78  0.135 52  0.125 28  0.085 82  0....</td>\n",
       "      <td>79  0.385 83  0.109 42  0.100 23  0.100 78  0....</td>\n",
       "      <td>79  0.384 78  0.184 52  0.126 83  0.097 28  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>79  0.494 78  0.362 52  0.350 82  0.277 28  0....</td>\n",
       "      <td>79  0.491 72  0.463 78  0.337 52  0.311 82  0....</td>\n",
       "      <td>79  0.448 47  0.395 78  0.340 42  0.276 83  0....</td>\n",
       "      <td>78  0.485 79  0.485 52  0.432 82  0.254 45  0....</td>\n",
       "      <td>79  0.503 78  0.368 52  0.353 28  0.293 82  0....</td>\n",
       "      <td>79  0.502 78  0.364 52  0.351 28  0.293 82  0....</td>\n",
       "      <td>79  0.503 78  0.368 52  0.353 28  0.293 82  0....</td>\n",
       "      <td>79  0.620 83  0.330 42  0.317 23  0.317 78  0....</td>\n",
       "      <td>79  0.619 78  0.429 52  0.355 83  0.312 28  0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   79  0.244 78  0.131 52  0.122 82  0.077 28  0....   \n",
       "RMSE on validate  79  0.494 78  0.362 52  0.350 82  0.277 28  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.241 72  0.215 78  0.113 52  0.096 82  0....   \n",
       "RMSE on validate  79  0.491 72  0.463 78  0.337 52  0.311 82  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.201 47  0.156 78  0.116 42  0.076 83  0....   \n",
       "RMSE on validate  79  0.448 47  0.395 78  0.340 42  0.276 83  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.236 79  0.235 52  0.186 82  0.064 45  0....   \n",
       "RMSE on validate  78  0.485 79  0.485 52  0.432 82  0.254 45  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.253 78  0.135 52  0.125 28  0.085 82  0....   \n",
       "RMSE on validate  79  0.503 78  0.368 52  0.353 28  0.293 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.252 78  0.133 52  0.123 28  0.086 82  0....   \n",
       "RMSE on validate  79  0.502 78  0.364 52  0.351 28  0.293 82  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.253 78  0.135 52  0.125 28  0.085 82  0....   \n",
       "RMSE on validate  79  0.503 78  0.368 52  0.353 28  0.293 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.385 83  0.109 42  0.100 23  0.100 78  0....   \n",
       "RMSE on validate  79  0.620 83  0.330 42  0.317 23  0.317 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.384 78  0.184 52  0.126 83  0.097 28  0....  \n",
       "RMSE on validate  79  0.619 78  0.429 52  0.355 83  0.312 28  0....  "
      ]
     },
     "execution_count": 41,
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       "      <th>MSE on validate</th>\n",
       "      <td>79  0.246 78  0.138 52  0.116 83  0.069 28  0....</td>\n",
       "      <td>79  0.238 78  0.152 72  0.135 52  0.108 83  0....</td>\n",
       "      <td>79  0.219 47  0.166 78  0.110 72  0.082 83  0....</td>\n",
       "      <td>79  0.238 78  0.192 52  0.169 47  0.145 45  0....</td>\n",
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       "      <td>79  0.254 78  0.132 52  0.120 28  0.091 83  0....</td>\n",
       "      <td>79  0.255 78  0.136 52  0.122 28  0.091 83  0....</td>\n",
       "      <td>79  0.384 83  0.110 42  0.105 23  0.103 78  0....</td>\n",
       "      <td>79  0.388 78  0.164 52  0.110 83  0.104 28  0....</td>\n",
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       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>79  0.496 78  0.372 52  0.341 83  0.262 28  0....</td>\n",
       "      <td>79  0.488 78  0.390 72  0.368 52  0.329 83  0....</td>\n",
       "      <td>79  0.468 47  0.407 78  0.332 72  0.286 83  0....</td>\n",
       "      <td>79  0.487 78  0.438 52  0.412 47  0.380 45  0....</td>\n",
       "      <td>79  0.504 78  0.369 52  0.349 28  0.303 83  0....</td>\n",
       "      <td>79  0.504 78  0.364 52  0.346 28  0.303 83  0....</td>\n",
       "      <td>79  0.504 78  0.369 52  0.349 28  0.303 83  0....</td>\n",
       "      <td>79  0.620 83  0.332 42  0.324 23  0.321 78  0....</td>\n",
       "      <td>79  0.623 78  0.405 52  0.332 83  0.323 28  0....</td>\n",
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       "                                                             Linear  \\\n",
       "MSE on validate   79  0.246 78  0.138 52  0.116 83  0.069 28  0....   \n",
       "RMSE on validate  79  0.496 78  0.372 52  0.341 83  0.262 28  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.238 78  0.152 72  0.135 52  0.108 83  0....   \n",
       "RMSE on validate  79  0.488 78  0.390 72  0.368 52  0.329 83  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.219 47  0.166 78  0.110 72  0.082 83  0....   \n",
       "RMSE on validate  79  0.468 47  0.407 78  0.332 72  0.286 83  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   79  0.238 78  0.192 52  0.169 47  0.145 45  0....   \n",
       "RMSE on validate  79  0.487 78  0.438 52  0.412 47  0.380 45  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.255 78  0.136 52  0.122 28  0.091 83  0....   \n",
       "RMSE on validate  79  0.504 78  0.369 52  0.349 28  0.303 83  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.254 78  0.132 52  0.120 28  0.091 83  0....   \n",
       "RMSE on validate  79  0.504 78  0.364 52  0.346 28  0.303 83  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.255 78  0.136 52  0.122 28  0.091 83  0....   \n",
       "RMSE on validate  79  0.504 78  0.369 52  0.349 28  0.303 83  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.384 83  0.110 42  0.105 23  0.103 78  0....   \n",
       "RMSE on validate  79  0.620 83  0.332 42  0.324 23  0.321 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.388 78  0.164 52  0.110 83  0.104 28  0....  \n",
       "RMSE on validate  79  0.623 78  0.405 52  0.332 83  0.323 28  0....  "
      ]
     },
     "execution_count": 41,
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       "      <th>MSE on validate</th>\n",
       "      <td>79  0.249 78  0.142 52  0.122 82  0.076 72  0....</td>\n",
       "      <td>79  0.234 72  0.172 78  0.152 52  0.112 83  0....</td>\n",
       "      <td>79  0.309 47  0.264 78  0.140 52  0.105 82  0....</td>\n",
       "      <td>52  0.221 79  0.216 78  0.215 47  0.198 82  0....</td>\n",
       "      <td>79  0.239 78  0.144 52  0.125 28  0.096 82  0....</td>\n",
       "      <td>79  0.237 78  0.142 52  0.123 28  0.098 82  0....</td>\n",
       "      <td>79  0.239 78  0.144 52  0.125 28  0.096 82  0....</td>\n",
       "      <td>79  0.384 83  0.109 42  0.106 23  0.101 78  0....</td>\n",
       "      <td>79  0.373 78  0.161 52  0.110 83  0.101 28  0....</td>\n",
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       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>79  0.499 78  0.377 52  0.350 82  0.277 72  0....</td>\n",
       "      <td>79  0.484 72  0.414 78  0.390 52  0.335 83  0....</td>\n",
       "      <td>79  0.556 47  0.514 78  0.375 52  0.325 82  0....</td>\n",
       "      <td>52  0.470 79  0.465 78  0.464 47  0.445 82  0....</td>\n",
       "      <td>79  0.488 78  0.380 52  0.353 28  0.310 82  0....</td>\n",
       "      <td>79  0.487 78  0.378 52  0.351 28  0.313 82  0....</td>\n",
       "      <td>79  0.488 78  0.380 52  0.353 28  0.310 82  0....</td>\n",
       "      <td>79  0.619 83  0.330 42  0.326 23  0.318 78  0....</td>\n",
       "      <td>79  0.611 78  0.401 52  0.332 83  0.319 28  0....</td>\n",
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       "                                                             Linear  \\\n",
       "MSE on validate   79  0.249 78  0.142 52  0.122 82  0.076 72  0....   \n",
       "RMSE on validate  79  0.499 78  0.377 52  0.350 82  0.277 72  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.234 72  0.172 78  0.152 52  0.112 83  0....   \n",
       "RMSE on validate  79  0.484 72  0.414 78  0.390 52  0.335 83  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.309 47  0.264 78  0.140 52  0.105 82  0....   \n",
       "RMSE on validate  79  0.556 47  0.514 78  0.375 52  0.325 82  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   52  0.221 79  0.216 78  0.215 47  0.198 82  0....   \n",
       "RMSE on validate  52  0.470 79  0.465 78  0.464 47  0.445 82  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.239 78  0.144 52  0.125 28  0.096 82  0....   \n",
       "RMSE on validate  79  0.488 78  0.380 52  0.353 28  0.310 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.237 78  0.142 52  0.123 28  0.098 82  0....   \n",
       "RMSE on validate  79  0.487 78  0.378 52  0.351 28  0.313 82  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.239 78  0.144 52  0.125 28  0.096 82  0....   \n",
       "RMSE on validate  79  0.488 78  0.380 52  0.353 28  0.310 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.384 83  0.109 42  0.106 23  0.101 78  0....   \n",
       "RMSE on validate  79  0.619 83  0.330 42  0.326 23  0.318 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.373 78  0.161 52  0.110 83  0.101 28  0....  \n",
       "RMSE on validate  79  0.611 78  0.401 52  0.332 83  0.319 28  0....  "
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     "execution_count": 41,
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       "    <tr>\n",
       "      <th>MSE on validate</th>\n",
       "      <td>79  0.268 72  0.177 78  0.157 52  0.127 83  0....</td>\n",
       "      <td>79  0.320 72  0.190 78  0.134 52  0.121 83  0....</td>\n",
       "      <td>79  0.332 78  0.130 52  0.097 82  0.096 83  0....</td>\n",
       "      <td>78  0.278 79  0.235 52  0.187 47  0.133 82  0....</td>\n",
       "      <td>79  0.244 78  0.154 52  0.125 28  0.096 72  0....</td>\n",
       "      <td>79  0.239 78  0.152 52  0.124 28  0.097 72  0....</td>\n",
       "      <td>79  0.244 78  0.154 52  0.125 28  0.096 72  0....</td>\n",
       "      <td>79  0.385 83  0.111 23  0.102 42  0.100 78  0....</td>\n",
       "      <td>79  0.392 78  0.201 28  0.112 52  0.110 83  0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>79  0.518 72  0.421 78  0.397 52  0.357 83  0....</td>\n",
       "      <td>79  0.566 72  0.436 78  0.366 52  0.348 83  0....</td>\n",
       "      <td>79  0.576 78  0.361 52  0.311 82  0.311 83  0....</td>\n",
       "      <td>78  0.527 79  0.485 52  0.432 47  0.364 82  0....</td>\n",
       "      <td>79  0.494 78  0.392 52  0.354 28  0.310 72  0....</td>\n",
       "      <td>79  0.489 78  0.389 52  0.352 28  0.311 72  0....</td>\n",
       "      <td>79  0.494 78  0.392 52  0.354 28  0.310 72  0....</td>\n",
       "      <td>79  0.621 83  0.333 23  0.320 42  0.317 78  0....</td>\n",
       "      <td>79  0.626 78  0.448 28  0.336 52  0.332 83  0....</td>\n",
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       "                                                             Linear  \\\n",
       "MSE on validate   79  0.268 72  0.177 78  0.157 52  0.127 83  0....   \n",
       "RMSE on validate  79  0.518 72  0.421 78  0.397 52  0.357 83  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.320 72  0.190 78  0.134 52  0.121 83  0....   \n",
       "RMSE on validate  79  0.566 72  0.436 78  0.366 52  0.348 83  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.332 78  0.130 52  0.097 82  0.096 83  0....   \n",
       "RMSE on validate  79  0.576 78  0.361 52  0.311 82  0.311 83  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   78  0.278 79  0.235 52  0.187 47  0.133 82  0....   \n",
       "RMSE on validate  78  0.527 79  0.485 52  0.432 47  0.364 82  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.244 78  0.154 52  0.125 28  0.096 72  0....   \n",
       "RMSE on validate  79  0.494 78  0.392 52  0.354 28  0.310 72  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.239 78  0.152 52  0.124 28  0.097 72  0....   \n",
       "RMSE on validate  79  0.489 78  0.389 52  0.352 28  0.311 72  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.244 78  0.154 52  0.125 28  0.096 72  0....   \n",
       "RMSE on validate  79  0.494 78  0.392 52  0.354 28  0.310 72  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.385 83  0.111 23  0.102 42  0.100 78  0....   \n",
       "RMSE on validate  79  0.621 83  0.333 23  0.320 42  0.317 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.392 78  0.201 28  0.112 52  0.110 83  0....  \n",
       "RMSE on validate  79  0.626 78  0.448 28  0.336 52  0.332 83  0....  "
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       "      <th>MSE on validate</th>\n",
       "      <td>79  0.259 78  0.133 52  0.124 82  0.086 72  0....</td>\n",
       "      <td>79  0.241 72  0.185 78  0.121 52  0.096 82  0....</td>\n",
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       "      <td>79  0.385 83  0.108 23  0.101 42  0.101 78  0....</td>\n",
       "      <td>79  0.389 78  0.182 52  0.126 83  0.096 28  0....</td>\n",
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       "    <tr>\n",
       "      <th>RMSE on validate</th>\n",
       "      <td>79  0.509 78  0.366 52  0.353 82  0.293 72  0....</td>\n",
       "      <td>79  0.491 72  0.430 78  0.348 52  0.310 82  0....</td>\n",
       "      <td>79  0.563 47  0.471 78  0.378 52  0.362 82  0....</td>\n",
       "      <td>79  0.467 78  0.456 52  0.422 47  0.404 82  0....</td>\n",
       "      <td>79  0.491 78  0.377 52  0.358 28  0.304 82  0....</td>\n",
       "      <td>79  0.488 78  0.375 52  0.356 28  0.307 82  0....</td>\n",
       "      <td>79  0.491 78  0.377 52  0.358 28  0.304 82  0....</td>\n",
       "      <td>79  0.620 83  0.328 23  0.318 42  0.317 78  0....</td>\n",
       "      <td>79  0.623 78  0.427 52  0.355 83  0.311 28  0....</td>\n",
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      "text/plain": [
       "                                                             Linear  \\\n",
       "MSE on validate   79  0.259 78  0.133 52  0.124 82  0.086 72  0....   \n",
       "RMSE on validate  79  0.509 78  0.366 52  0.353 82  0.293 72  0....   \n",
       "\n",
       "                                                            SVR_lin  \\\n",
       "MSE on validate   79  0.241 72  0.185 78  0.121 52  0.096 82  0....   \n",
       "RMSE on validate  79  0.491 72  0.430 78  0.348 52  0.310 82  0....   \n",
       "\n",
       "                                                           SVR_poly  \\\n",
       "MSE on validate   79  0.317 47  0.221 78  0.142 52  0.131 82  0....   \n",
       "RMSE on validate  79  0.563 47  0.471 78  0.378 52  0.362 82  0....   \n",
       "\n",
       "                                                            SVR_rbf  \\\n",
       "MSE on validate   79  0.218 78  0.208 52  0.178 47  0.163 82  0....   \n",
       "RMSE on validate  79  0.467 78  0.456 52  0.422 47  0.404 82  0....   \n",
       "\n",
       "                                                            GPR_rbf  \\\n",
       "MSE on validate   79  0.241 78  0.142 52  0.128 28  0.092 82  0....   \n",
       "RMSE on validate  79  0.491 78  0.377 52  0.358 28  0.304 82  0....   \n",
       "\n",
       "                                                            GPR_mat  \\\n",
       "MSE on validate   79  0.239 78  0.140 52  0.127 28  0.094 82  0....   \n",
       "RMSE on validate  79  0.488 78  0.375 52  0.356 28  0.307 82  0....   \n",
       "\n",
       "                                                             GPR_rt  \\\n",
       "MSE on validate   79  0.241 78  0.142 52  0.128 28  0.092 82  0....   \n",
       "RMSE on validate  79  0.491 78  0.377 52  0.358 28  0.304 82  0....   \n",
       "\n",
       "                                                                 RF  \\\n",
       "MSE on validate   79  0.385 83  0.108 23  0.101 42  0.101 78  0....   \n",
       "RMSE on validate  79  0.620 83  0.328 23  0.318 42  0.317 78  0....   \n",
       "\n",
       "                                                                XGB  \n",
       "MSE on validate   79  0.389 78  0.182 52  0.126 83  0.096 28  0....  \n",
       "RMSE on validate  79  0.623 78  0.427 52  0.355 83  0.311 28  0....  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Наибольшие ошибки\n",
    "for r in res3:\n",
    "    find_biggest_error(r[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.081376</td>\n",
       "      <td>0.087998</td>\n",
       "      <td>0.089764</td>\n",
       "      <td>0.083532</td>\n",
       "      <td>0.079635</td>\n",
       "      <td>0.078651</td>\n",
       "      <td>0.079635</td>\n",
       "      <td>0.109714</td>\n",
       "      <td>0.052536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.064542</td>\n",
       "      <td>0.063229</td>\n",
       "      <td>0.064166</td>\n",
       "      <td>0.079572</td>\n",
       "      <td>0.058847</td>\n",
       "      <td>0.059092</td>\n",
       "      <td>0.058847</td>\n",
       "      <td>0.090627</td>\n",
       "      <td>0.056140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.013833</td>\n",
       "      <td>0.014338</td>\n",
       "      <td>0.013274</td>\n",
       "      <td>0.011153</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.012618</td>\n",
       "      <td>0.013015</td>\n",
       "      <td>0.020480</td>\n",
       "      <td>0.006033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.005286</td>\n",
       "      <td>0.005253</td>\n",
       "      <td>0.006379</td>\n",
       "      <td>0.008089</td>\n",
       "      <td>0.004713</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.004713</td>\n",
       "      <td>0.011616</td>\n",
       "      <td>0.005619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.117612</td>\n",
       "      <td>0.119741</td>\n",
       "      <td>0.115213</td>\n",
       "      <td>0.105610</td>\n",
       "      <td>0.114085</td>\n",
       "      <td>0.112328</td>\n",
       "      <td>0.114085</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.077674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.072706</td>\n",
       "      <td>0.072475</td>\n",
       "      <td>0.079866</td>\n",
       "      <td>0.089940</td>\n",
       "      <td>0.068649</td>\n",
       "      <td>0.069650</td>\n",
       "      <td>0.068649</td>\n",
       "      <td>0.107777</td>\n",
       "      <td>0.074961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.819295</td>\n",
       "      <td>0.812694</td>\n",
       "      <td>0.826593</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>0.829971</td>\n",
       "      <td>0.835169</td>\n",
       "      <td>0.829971</td>\n",
       "      <td>0.732452</td>\n",
       "      <td>0.921184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.902510</td>\n",
       "      <td>0.903129</td>\n",
       "      <td>0.882362</td>\n",
       "      <td>0.850815</td>\n",
       "      <td>0.913085</td>\n",
       "      <td>0.910532</td>\n",
       "      <td>0.913085</td>\n",
       "      <td>0.785773</td>\n",
       "      <td>0.896367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.081376  0.087998  0.089764  0.083532  0.079635  0.078651   \n",
       "MAE on test        0.064542  0.063229  0.064166  0.079572  0.058847  0.059092   \n",
       "MSE on all train   0.013833  0.014338  0.013274  0.011153  0.013015  0.012618   \n",
       "MSE on test        0.005286  0.005253  0.006379  0.008089  0.004713  0.004851   \n",
       "RMSE on all train  0.117612  0.119741  0.115213  0.105610  0.114085  0.112328   \n",
       "RMSE on test       0.072706  0.072475  0.079866  0.089940  0.068649  0.069650   \n",
       "R2 on all train    0.819295  0.812694  0.826593  0.854296  0.829971  0.835169   \n",
       "R2 on test         0.902510  0.903129  0.882362  0.850815  0.913085  0.910532   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.079635  0.109714  0.052536  \n",
       "MAE on test        0.058847  0.090627  0.056140  \n",
       "MSE on all train   0.013015  0.020480  0.006033  \n",
       "MSE on test        0.004713  0.011616  0.005619  \n",
       "RMSE on all train  0.114085  0.143109  0.077674  \n",
       "RMSE on test       0.068649  0.107777  0.074961  \n",
       "R2 on all train    0.829971  0.732452  0.921184  \n",
       "R2 on test         0.913085  0.785773  0.896367  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.082782</td>\n",
       "      <td>0.087561</td>\n",
       "      <td>0.077922</td>\n",
       "      <td>0.078769</td>\n",
       "      <td>0.077688</td>\n",
       "      <td>0.078769</td>\n",
       "      <td>0.109646</td>\n",
       "      <td>0.053024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.059789</td>\n",
       "      <td>0.062768</td>\n",
       "      <td>0.060555</td>\n",
       "      <td>0.092887</td>\n",
       "      <td>0.058233</td>\n",
       "      <td>0.058615</td>\n",
       "      <td>0.058233</td>\n",
       "      <td>0.090115</td>\n",
       "      <td>0.060039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.013982</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>0.013193</td>\n",
       "      <td>0.010522</td>\n",
       "      <td>0.012942</td>\n",
       "      <td>0.012459</td>\n",
       "      <td>0.012942</td>\n",
       "      <td>0.020472</td>\n",
       "      <td>0.006296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.005442</td>\n",
       "      <td>0.005702</td>\n",
       "      <td>0.005517</td>\n",
       "      <td>0.012949</td>\n",
       "      <td>0.005064</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.005064</td>\n",
       "      <td>0.011506</td>\n",
       "      <td>0.005972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.118244</td>\n",
       "      <td>0.119562</td>\n",
       "      <td>0.114859</td>\n",
       "      <td>0.102576</td>\n",
       "      <td>0.113762</td>\n",
       "      <td>0.111619</td>\n",
       "      <td>0.113762</td>\n",
       "      <td>0.143079</td>\n",
       "      <td>0.079350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.073769</td>\n",
       "      <td>0.075514</td>\n",
       "      <td>0.074280</td>\n",
       "      <td>0.113793</td>\n",
       "      <td>0.071164</td>\n",
       "      <td>0.072148</td>\n",
       "      <td>0.071164</td>\n",
       "      <td>0.107267</td>\n",
       "      <td>0.077277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.817349</td>\n",
       "      <td>0.813254</td>\n",
       "      <td>0.827656</td>\n",
       "      <td>0.862546</td>\n",
       "      <td>0.830931</td>\n",
       "      <td>0.837242</td>\n",
       "      <td>0.830931</td>\n",
       "      <td>0.732564</td>\n",
       "      <td>0.917746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.899637</td>\n",
       "      <td>0.894834</td>\n",
       "      <td>0.898244</td>\n",
       "      <td>0.761190</td>\n",
       "      <td>0.906601</td>\n",
       "      <td>0.903999</td>\n",
       "      <td>0.906601</td>\n",
       "      <td>0.787796</td>\n",
       "      <td>0.889867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.080357  0.082782  0.087561  0.077922  0.078769  0.077688   \n",
       "MAE on test        0.059789  0.062768  0.060555  0.092887  0.058233  0.058615   \n",
       "MSE on all train   0.013982  0.014295  0.013193  0.010522  0.012942  0.012459   \n",
       "MSE on test        0.005442  0.005702  0.005517  0.012949  0.005064  0.005205   \n",
       "RMSE on all train  0.118244  0.119562  0.114859  0.102576  0.113762  0.111619   \n",
       "RMSE on test       0.073769  0.075514  0.074280  0.113793  0.071164  0.072148   \n",
       "R2 on all train    0.817349  0.813254  0.827656  0.862546  0.830931  0.837242   \n",
       "R2 on test         0.899637  0.894834  0.898244  0.761190  0.906601  0.903999   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.078769  0.109646  0.053024  \n",
       "MAE on test        0.058233  0.090115  0.060039  \n",
       "MSE on all train   0.012942  0.020472  0.006296  \n",
       "MSE on test        0.005064  0.011506  0.005972  \n",
       "RMSE on all train  0.113762  0.143079  0.079350  \n",
       "RMSE on test       0.071164  0.107267  0.077277  \n",
       "R2 on all train    0.830931  0.732564  0.917746  \n",
       "R2 on test         0.906601  0.787796  0.889867  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
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       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.080644</td>\n",
       "      <td>0.084150</td>\n",
       "      <td>0.082251</td>\n",
       "      <td>0.077086</td>\n",
       "      <td>0.076485</td>\n",
       "      <td>0.075495</td>\n",
       "      <td>0.076485</td>\n",
       "      <td>0.109885</td>\n",
       "      <td>0.053904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.059075</td>\n",
       "      <td>0.059857</td>\n",
       "      <td>0.078481</td>\n",
       "      <td>0.075371</td>\n",
       "      <td>0.057636</td>\n",
       "      <td>0.056316</td>\n",
       "      <td>0.057636</td>\n",
       "      <td>0.090418</td>\n",
       "      <td>0.063517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.013900</td>\n",
       "      <td>0.014321</td>\n",
       "      <td>0.012242</td>\n",
       "      <td>0.009166</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.012053</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.006238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.004824</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.008990</td>\n",
       "      <td>0.007237</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.004283</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.011546</td>\n",
       "      <td>0.006829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.117899</td>\n",
       "      <td>0.119671</td>\n",
       "      <td>0.110643</td>\n",
       "      <td>0.095739</td>\n",
       "      <td>0.111805</td>\n",
       "      <td>0.109784</td>\n",
       "      <td>0.111805</td>\n",
       "      <td>0.143275</td>\n",
       "      <td>0.078982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.069454</td>\n",
       "      <td>0.068720</td>\n",
       "      <td>0.094817</td>\n",
       "      <td>0.085073</td>\n",
       "      <td>0.065949</td>\n",
       "      <td>0.065446</td>\n",
       "      <td>0.065949</td>\n",
       "      <td>0.107452</td>\n",
       "      <td>0.082638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.818414</td>\n",
       "      <td>0.812913</td>\n",
       "      <td>0.840077</td>\n",
       "      <td>0.880259</td>\n",
       "      <td>0.836700</td>\n",
       "      <td>0.842549</td>\n",
       "      <td>0.836700</td>\n",
       "      <td>0.731833</td>\n",
       "      <td>0.918506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.911034</td>\n",
       "      <td>0.912907</td>\n",
       "      <td>0.834197</td>\n",
       "      <td>0.866523</td>\n",
       "      <td>0.919789</td>\n",
       "      <td>0.921008</td>\n",
       "      <td>0.919789</td>\n",
       "      <td>0.787064</td>\n",
       "      <td>0.874054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.080644  0.084150  0.082251  0.077086  0.076485  0.075495   \n",
       "MAE on test        0.059075  0.059857  0.078481  0.075371  0.057636  0.056316   \n",
       "MSE on all train   0.013900  0.014321  0.012242  0.009166  0.012500  0.012053   \n",
       "MSE on test        0.004824  0.004722  0.008990  0.007237  0.004349  0.004283   \n",
       "RMSE on all train  0.117899  0.119671  0.110643  0.095739  0.111805  0.109784   \n",
       "RMSE on test       0.069454  0.068720  0.094817  0.085073  0.065949  0.065446   \n",
       "R2 on all train    0.818414  0.812913  0.840077  0.880259  0.836700  0.842549   \n",
       "R2 on test         0.911034  0.912907  0.834197  0.866523  0.919789  0.921008   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.076485  0.109885  0.053904  \n",
       "MAE on test        0.057636  0.090418  0.063517  \n",
       "MSE on all train   0.012500  0.020528  0.006238  \n",
       "MSE on test        0.004349  0.011546  0.006829  \n",
       "RMSE on all train  0.111805  0.143275  0.078982  \n",
       "RMSE on test       0.065949  0.107452  0.082638  \n",
       "R2 on all train    0.836700  0.731833  0.918506  \n",
       "R2 on test         0.919789  0.787064  0.874054  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.087318</td>\n",
       "      <td>0.090961</td>\n",
       "      <td>0.078103</td>\n",
       "      <td>0.081611</td>\n",
       "      <td>0.079652</td>\n",
       "      <td>0.078519</td>\n",
       "      <td>0.079652</td>\n",
       "      <td>0.109881</td>\n",
       "      <td>0.055676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.069203</td>\n",
       "      <td>0.069171</td>\n",
       "      <td>0.073238</td>\n",
       "      <td>0.085939</td>\n",
       "      <td>0.058779</td>\n",
       "      <td>0.058190</td>\n",
       "      <td>0.058779</td>\n",
       "      <td>0.091116</td>\n",
       "      <td>0.068250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.015209</td>\n",
       "      <td>0.015530</td>\n",
       "      <td>0.011947</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.013172</td>\n",
       "      <td>0.012756</td>\n",
       "      <td>0.013172</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.006678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.006352</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0.009758</td>\n",
       "      <td>0.004923</td>\n",
       "      <td>0.004801</td>\n",
       "      <td>0.004923</td>\n",
       "      <td>0.011721</td>\n",
       "      <td>0.008002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.123326</td>\n",
       "      <td>0.124621</td>\n",
       "      <td>0.109301</td>\n",
       "      <td>0.108502</td>\n",
       "      <td>0.114770</td>\n",
       "      <td>0.112944</td>\n",
       "      <td>0.114770</td>\n",
       "      <td>0.143275</td>\n",
       "      <td>0.081720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.079701</td>\n",
       "      <td>0.078675</td>\n",
       "      <td>0.086445</td>\n",
       "      <td>0.098780</td>\n",
       "      <td>0.070164</td>\n",
       "      <td>0.069288</td>\n",
       "      <td>0.070164</td>\n",
       "      <td>0.108263</td>\n",
       "      <td>0.089456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.801311</td>\n",
       "      <td>0.797116</td>\n",
       "      <td>0.843933</td>\n",
       "      <td>0.846204</td>\n",
       "      <td>0.827922</td>\n",
       "      <td>0.833353</td>\n",
       "      <td>0.827922</td>\n",
       "      <td>0.731832</td>\n",
       "      <td>0.912758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.882847</td>\n",
       "      <td>0.885846</td>\n",
       "      <td>0.862183</td>\n",
       "      <td>0.820045</td>\n",
       "      <td>0.909207</td>\n",
       "      <td>0.911461</td>\n",
       "      <td>0.909207</td>\n",
       "      <td>0.783837</td>\n",
       "      <td>0.852414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.087318  0.090961  0.078103  0.081611  0.079652  0.078519   \n",
       "MAE on test        0.069203  0.069171  0.073238  0.085939  0.058779  0.058190   \n",
       "MSE on all train   0.015209  0.015530  0.011947  0.011773  0.013172  0.012756   \n",
       "MSE on test        0.006352  0.006190  0.007473  0.009758  0.004923  0.004801   \n",
       "RMSE on all train  0.123326  0.124621  0.109301  0.108502  0.114770  0.112944   \n",
       "RMSE on test       0.079701  0.078675  0.086445  0.098780  0.070164  0.069288   \n",
       "R2 on all train    0.801311  0.797116  0.843933  0.846204  0.827922  0.833353   \n",
       "R2 on test         0.882847  0.885846  0.862183  0.820045  0.909207  0.911461   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.079652  0.109881  0.055676  \n",
       "MAE on test        0.058779  0.091116  0.068250  \n",
       "MSE on all train   0.013172  0.020528  0.006678  \n",
       "MSE on test        0.004923  0.011721  0.008002  \n",
       "RMSE on all train  0.114770  0.143275  0.081720  \n",
       "RMSE on test       0.070164  0.108263  0.089456  \n",
       "R2 on all train    0.827922  0.731832  0.912758  \n",
       "R2 on test         0.909207  0.783837  0.852414  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Linear</th>\n",
       "      <th>SVR_lin</th>\n",
       "      <th>SVR_poly</th>\n",
       "      <th>SVR_rbf</th>\n",
       "      <th>GPR_rbf</th>\n",
       "      <th>GPR_mat</th>\n",
       "      <th>GPR_rt</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE on all train</th>\n",
       "      <td>0.081308</td>\n",
       "      <td>0.088245</td>\n",
       "      <td>0.083051</td>\n",
       "      <td>0.081280</td>\n",
       "      <td>0.077417</td>\n",
       "      <td>0.076060</td>\n",
       "      <td>0.077417</td>\n",
       "      <td>0.109452</td>\n",
       "      <td>0.053093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE on test</th>\n",
       "      <td>0.064493</td>\n",
       "      <td>0.062952</td>\n",
       "      <td>0.078799</td>\n",
       "      <td>0.076769</td>\n",
       "      <td>0.060357</td>\n",
       "      <td>0.059374</td>\n",
       "      <td>0.060357</td>\n",
       "      <td>0.090240</td>\n",
       "      <td>0.058855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on all train</th>\n",
       "      <td>0.013831</td>\n",
       "      <td>0.014355</td>\n",
       "      <td>0.012149</td>\n",
       "      <td>0.009750</td>\n",
       "      <td>0.012521</td>\n",
       "      <td>0.012083</td>\n",
       "      <td>0.012521</td>\n",
       "      <td>0.020430</td>\n",
       "      <td>0.006012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE on test</th>\n",
       "      <td>0.005228</td>\n",
       "      <td>0.005184</td>\n",
       "      <td>0.008916</td>\n",
       "      <td>0.007919</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>0.011592</td>\n",
       "      <td>0.006036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on all train</th>\n",
       "      <td>0.117607</td>\n",
       "      <td>0.119810</td>\n",
       "      <td>0.110224</td>\n",
       "      <td>0.098744</td>\n",
       "      <td>0.111896</td>\n",
       "      <td>0.109921</td>\n",
       "      <td>0.111896</td>\n",
       "      <td>0.142932</td>\n",
       "      <td>0.077538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE on test</th>\n",
       "      <td>0.072303</td>\n",
       "      <td>0.071998</td>\n",
       "      <td>0.094424</td>\n",
       "      <td>0.088989</td>\n",
       "      <td>0.067766</td>\n",
       "      <td>0.067294</td>\n",
       "      <td>0.067766</td>\n",
       "      <td>0.107665</td>\n",
       "      <td>0.077690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on all train</th>\n",
       "      <td>0.819310</td>\n",
       "      <td>0.812477</td>\n",
       "      <td>0.841285</td>\n",
       "      <td>0.872624</td>\n",
       "      <td>0.836432</td>\n",
       "      <td>0.842157</td>\n",
       "      <td>0.836432</td>\n",
       "      <td>0.733113</td>\n",
       "      <td>0.921459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2 on test</th>\n",
       "      <td>0.903586</td>\n",
       "      <td>0.904400</td>\n",
       "      <td>0.835567</td>\n",
       "      <td>0.853951</td>\n",
       "      <td>0.915308</td>\n",
       "      <td>0.916482</td>\n",
       "      <td>0.915308</td>\n",
       "      <td>0.786217</td>\n",
       "      <td>0.888684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Linear   SVR_lin  SVR_poly   SVR_rbf   GPR_rbf   GPR_mat  \\\n",
       "MAE on all train   0.081308  0.088245  0.083051  0.081280  0.077417  0.076060   \n",
       "MAE on test        0.064493  0.062952  0.078799  0.076769  0.060357  0.059374   \n",
       "MSE on all train   0.013831  0.014355  0.012149  0.009750  0.012521  0.012083   \n",
       "MSE on test        0.005228  0.005184  0.008916  0.007919  0.004592  0.004529   \n",
       "RMSE on all train  0.117607  0.119810  0.110224  0.098744  0.111896  0.109921   \n",
       "RMSE on test       0.072303  0.071998  0.094424  0.088989  0.067766  0.067294   \n",
       "R2 on all train    0.819310  0.812477  0.841285  0.872624  0.836432  0.842157   \n",
       "R2 on test         0.903586  0.904400  0.835567  0.853951  0.915308  0.916482   \n",
       "\n",
       "                     GPR_rt        RF       XGB  \n",
       "MAE on all train   0.077417  0.109452  0.053093  \n",
       "MAE on test        0.060357  0.090240  0.058855  \n",
       "MSE on all train   0.012521  0.020430  0.006012  \n",
       "MSE on test        0.004592  0.011592  0.006036  \n",
       "RMSE on all train  0.111896  0.142932  0.077538  \n",
       "RMSE on test       0.067766  0.107665  0.077690  \n",
       "R2 on all train    0.836432  0.733113  0.921459  \n",
       "R2 on test         0.915308  0.786217  0.888684  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Предсказание\n",
    "for r in res3:\n",
    "    r[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_plots(res3[2], save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2_plot(res3[2], save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Оценка важности признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianProcessRegressor(alpha=0.2, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianProcessRegressor</label><div class=\"sk-toggleable__content\"><pre>GaussianProcessRegressor(alpha=0.2, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianProcessRegressor(alpha=0.2, kernel=Matern(length_scale=1, nu=2.5),\n",
       "                         n_restarts_optimizer=10, normalize_y=True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Permutation explainer: 68it [00:15,  1.43it/s]                                                                         \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x280.8 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_0 = x_train[descriptors_sets3[2]]\n",
    "regressor2 = GaussianProcessRegressor(kernel=Matern(nu=2.5), alpha=0.2, n_restarts_optimizer=10, normalize_y=True)\n",
    "regressor2.fit(x_0, y_train)\n",
    "explainer = shap.explainers.Permutation(regressor2.predict, x_0)\n",
    "shap_values = shap.explainers.Permutation(regressor2.predict, x_0)\n",
    "shap_values = explainer(x_0)\n",
    "shap.summary_plot(shap_values, x_0, show=True, max_display=6)\n",
    "#plt.savefig(\"shap2.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x280.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, x_0, plot_type=\"bar\", show=True, max_display=6)\n",
    "#plt.savefig(\"shap2_gpr2.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Сохранение предобученной модели\n",
    "with open('model_2_for_7d.pickle', 'wb') as f:\n",
    "    pickle.dump((regressor2, scaler), f)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}