machine_learning_RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib as plt\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter the data folder name and WCR information\n",
    "\n",
    "# select fold\n",
    "fold_idx = 1\n",
    "\n",
    "# Load the data folder path\n",
    "path = os.getcwd()\n",
    "train_path_upper = path + f'/data_mortar/FOLD_{fold_idx}/train'\n",
    "valid_path_upper = path + f'/data_mortar/FOLD_{fold_idx}/valid'\n",
    "test_path_upper = path + f'/data_mortar/FOLD_{fold_idx}/test'\n",
    "\n",
    "# WCR information\n",
    "WC_name_tag = [40, 42.5, 45, 47.5, 50, 52.5, 55, 57.5, 60]\n",
    "\n",
    "# Fit the random seed\n",
    "seed_data = 4885\n",
    "random.seed(seed_data)\n",
    "np.random.seed(seed_data)\n",
    "os.environ[\"PYTHONHASHSEED\"] = str(seed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data normalization\n",
    "\n",
    "# Normalize to the minimum and maximum sensor measurement values.\n",
    "def sense_min_max_Normalization(input_data, min_value, max_value):\n",
    "    data = (input_data - min_value) / (max_value - min_value) \n",
    "    \n",
    "    return data\n",
    "\n",
    "def data_set_normalization(data):\n",
    "    # sensor_min_value\n",
    "    vwc_min = 0\n",
    "    ec_min = 0\n",
    "    salinity_min = 0\n",
    "    tds_min = 0\n",
    "    epsilon_min = 0\n",
    "    temp_min = -40\n",
    "    \n",
    "    # sensor_max_value\n",
    "    vwc_max = 100\n",
    "    ec_max = 20000\n",
    "    salinity_max = 20000\n",
    "    tds_max = 20000\n",
    "    temp_max = 80\n",
    "    epsilon_max = 82\n",
    "    \n",
    "    # sensor_normalization \n",
    "    data['TEMP'] = sense_min_max_Normalization(data['TEMP'],temp_min,temp_max)\n",
    "    data['EC'] = sense_min_max_Normalization(data['EC'],ec_min,ec_max)\n",
    "    data['VWC'] = sense_min_max_Normalization(data['VWC'],vwc_min,vwc_max)\n",
    "    data['TDS'] = sense_min_max_Normalization(data['TDS'],tds_min,tds_max)\n",
    "    data['SALINITY'] = sense_min_max_Normalization(data['SALINITY'],salinity_min,salinity_max)\n",
    "    data['EPSILON'] = sense_min_max_Normalization(data['EPSILON'],epsilon_min,epsilon_max)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WC_ratio</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>EC</th>\n",
       "      <th>VWC</th>\n",
       "      <th>TDS</th>\n",
       "      <th>SALINITY</th>\n",
       "      <th>EPSILON</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.514417</td>\n",
       "      <td>0.19600</td>\n",
       "      <td>0.6549</td>\n",
       "      <td>0.09800</td>\n",
       "      <td>0.10780</td>\n",
       "      <td>0.740610</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.514250</td>\n",
       "      <td>0.19630</td>\n",
       "      <td>0.6568</td>\n",
       "      <td>0.09815</td>\n",
       "      <td>0.10795</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.513833</td>\n",
       "      <td>0.19660</td>\n",
       "      <td>0.6587</td>\n",
       "      <td>0.09830</td>\n",
       "      <td>0.10810</td>\n",
       "      <td>0.745244</td>\n",
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       "      <th>468</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.495333</td>\n",
       "      <td>0.28745</td>\n",
       "      <td>0.9264</td>\n",
       "      <td>0.14370</td>\n",
       "      <td>0.15805</td>\n",
       "      <td>0.955366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.495333</td>\n",
       "      <td>0.28750</td>\n",
       "      <td>0.9374</td>\n",
       "      <td>0.14375</td>\n",
       "      <td>0.15810</td>\n",
       "      <td>0.961341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.495333</td>\n",
       "      <td>0.28795</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.14395</td>\n",
       "      <td>0.15835</td>\n",
       "      <td>0.958415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.495333</td>\n",
       "      <td>0.28795</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.14395</td>\n",
       "      <td>0.15835</td>\n",
       "      <td>0.958415</td>\n",
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       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.495333</td>\n",
       "      <td>0.28750</td>\n",
       "      <td>0.9374</td>\n",
       "      <td>0.14375</td>\n",
       "      <td>0.15810</td>\n",
       "      <td>0.961341</td>\n",
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       "<p>13347 rows × 7 columns</p>\n",
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      "text/plain": [
       "     WC_ratio      TEMP       EC     VWC      TDS  SALINITY   EPSILON\n",
       "0         0.4  0.514917  0.19240  0.6391  0.09620   0.10580  0.720488\n",
       "1         0.4  0.514417  0.19580  0.6512  0.09790   0.10765  0.736098\n",
       "2         0.4  0.514417  0.19600  0.6549  0.09800   0.10780  0.740610\n",
       "3         0.4  0.514250  0.19630  0.6568  0.09815   0.10795  0.742927\n",
       "4         0.4  0.513833  0.19660  0.6587  0.09830   0.10810  0.745244\n",
       "..        ...       ...      ...     ...      ...       ...       ...\n",
       "468       0.6  0.495333  0.28745  0.9264  0.14370   0.15805  0.955366\n",
       "469       0.6  0.495333  0.28750  0.9374  0.14375   0.15810  0.961341\n",
       "470       0.6  0.495333  0.28795  0.9320  0.14395   0.15835  0.958415\n",
       "471       0.6  0.495333  0.28795  0.9320  0.14395   0.15835  0.958415\n",
       "472       0.6  0.495333  0.28750  0.9374  0.14375   0.15810  0.961341\n",
       "\n",
       "[13347 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Load FDR Data for Train\n",
    "\n",
    "path_list = []\n",
    "path_under=os.listdir(train_path_upper)\n",
    "\n",
    "# load data_path\n",
    "for i in range(len(path_under)): # collect under directory file path\n",
    "    path_sub=os.listdir(train_path_upper+'/'+path_under[i])\n",
    "    \n",
    "    for j in range(len(path_sub)):\n",
    "        path_list.append(train_path_upper+'/'+path_under[i]+'/'+path_sub[j])\n",
    "\n",
    "# make zero matrix\n",
    "data_list = [0]*len(path_list)\n",
    "rand_x_data_list = [0]*len(path_list)\n",
    "rand_y_data_list = [0]*len(path_list)\n",
    "\n",
    "# load data_value\n",
    "for i,j in enumerate(path_list):\n",
    "    data_list[i] =pd.read_csv(j)\n",
    "    data_list[i] = data_set_normalization(data_list[i])\n",
    "    \n",
    "train_data = pd.concat(data_list)\n",
    "\n",
    "# Print the data format\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>WC_ratio</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>EC</th>\n",
       "      <th>VWC</th>\n",
       "      <th>TDS</th>\n",
       "      <th>SALINITY</th>\n",
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       "      <td>0.6052</td>\n",
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       "      <td>0.10095</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.18405</td>\n",
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       "      <td>0.9488</td>\n",
       "      <td>0.14325</td>\n",
       "      <td>0.15755</td>\n",
       "      <td>0.967439</td>\n",
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       "      <th>456</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.492667</td>\n",
       "      <td>0.28650</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14325</td>\n",
       "      <td>0.15755</td>\n",
       "      <td>0.967439</td>\n",
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       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.492667</td>\n",
       "      <td>0.28650</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14325</td>\n",
       "      <td>0.15755</td>\n",
       "      <td>0.967439</td>\n",
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       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.492417</td>\n",
       "      <td>0.28655</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14325</td>\n",
       "      <td>0.15760</td>\n",
       "      <td>0.967439</td>\n",
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       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.492667</td>\n",
       "      <td>0.28650</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14325</td>\n",
       "      <td>0.15755</td>\n",
       "      <td>0.967439</td>\n",
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       "<p>4345 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "     WC_ratio      TEMP       EC     VWC      TDS  SALINITY   EPSILON\n",
       "0         0.4  0.491083  0.18165  0.6014  0.09080   0.09990  0.665610\n",
       "1         0.4  0.491083  0.18250  0.6014  0.09125   0.10035  0.665610\n",
       "2         0.4  0.490833  0.18360  0.6052  0.09180   0.10095  0.671707\n",
       "3         0.4  0.490833  0.18405  0.6052  0.09200   0.10120  0.671707\n",
       "4         0.4  0.490583  0.18455  0.6092  0.09225   0.10150  0.677927\n",
       "..        ...       ...      ...     ...      ...       ...       ...\n",
       "455       0.6  0.492417  0.28650  0.9488  0.14325   0.15755  0.967439\n",
       "456       0.6  0.492667  0.28650  0.9488  0.14325   0.15755  0.967439\n",
       "457       0.6  0.492667  0.28650  0.9488  0.14325   0.15755  0.967439\n",
       "458       0.6  0.492417  0.28655  0.9488  0.14325   0.15760  0.967439\n",
       "459       0.6  0.492667  0.28650  0.9488  0.14325   0.15755  0.967439\n",
       "\n",
       "[4345 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Load FDR Data for Valid\n",
    "\n",
    "valid_path_list = []\n",
    "valid_path_under=os.listdir(valid_path_upper)\n",
    "\n",
    "# load data_path\n",
    "for i in range(len(valid_path_under)): # collect under directory file path\n",
    "    valid_path_sub=os.listdir(valid_path_upper+'/'+valid_path_under[i])\n",
    "    \n",
    "    for j in range(len(valid_path_sub)):\n",
    "        valid_path_list.append(valid_path_upper+'/'+valid_path_under[i]+'/'+valid_path_sub[j])\n",
    "\n",
    "# make zero matrix\n",
    "valid_data_list = [0]*len(valid_path_list)\n",
    "valid_rand_x_data_list = [0]*len(valid_path_list)\n",
    "valid_rand_y_data_list = [0]*len(valid_path_list)\n",
    "\n",
    "# load data_value\n",
    "for i,j in enumerate(valid_path_list):\n",
    "    valid_data_list[i] =pd.read_csv(j)\n",
    "    valid_data_list[i] = data_set_normalization(valid_data_list[i])\n",
    "    \n",
    "valid_data = pd.concat(valid_data_list)\n",
    "\n",
    "# Print the data format\n",
    "valid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WC_ratio</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>EC</th>\n",
       "      <th>VWC</th>\n",
       "      <th>TDS</th>\n",
       "      <th>SALINITY</th>\n",
       "      <th>EPSILON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>0.491500</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.5588</td>\n",
       "      <td>0.07810</td>\n",
       "      <td>0.08590</td>\n",
       "      <td>0.589146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.491083</td>\n",
       "      <td>0.15690</td>\n",
       "      <td>0.5597</td>\n",
       "      <td>0.07845</td>\n",
       "      <td>0.08625</td>\n",
       "      <td>0.590976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.490833</td>\n",
       "      <td>0.15750</td>\n",
       "      <td>0.5597</td>\n",
       "      <td>0.07875</td>\n",
       "      <td>0.08660</td>\n",
       "      <td>0.590976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.490833</td>\n",
       "      <td>0.15805</td>\n",
       "      <td>0.5597</td>\n",
       "      <td>0.07900</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.590976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.487667</td>\n",
       "      <td>0.29310</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14655</td>\n",
       "      <td>0.16120</td>\n",
       "      <td>0.967439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.487667</td>\n",
       "      <td>0.29355</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14675</td>\n",
       "      <td>0.16145</td>\n",
       "      <td>0.967439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.487667</td>\n",
       "      <td>0.29400</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14700</td>\n",
       "      <td>0.16170</td>\n",
       "      <td>0.967439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.487667</td>\n",
       "      <td>0.29355</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14675</td>\n",
       "      <td>0.16145</td>\n",
       "      <td>0.967439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.487667</td>\n",
       "      <td>0.29355</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.14675</td>\n",
       "      <td>0.16145</td>\n",
       "      <td>0.967439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4392 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     WC_ratio      TEMP       EC     VWC      TDS  SALINITY   EPSILON\n",
       "0         0.4  0.491500  0.15550  0.5570  0.07775   0.08550  0.585610\n",
       "1         0.4  0.491500  0.15625  0.5588  0.07810   0.08590  0.589146\n",
       "2         0.4  0.491083  0.15690  0.5597  0.07845   0.08625  0.590976\n",
       "3         0.4  0.490833  0.15750  0.5597  0.07875   0.08660  0.590976\n",
       "4         0.4  0.490833  0.15805  0.5597  0.07900   0.08690  0.590976\n",
       "..        ...       ...      ...     ...      ...       ...       ...\n",
       "513       0.6  0.487667  0.29310  0.9488  0.14655   0.16120  0.967439\n",
       "514       0.6  0.487667  0.29355  0.9488  0.14675   0.16145  0.967439\n",
       "515       0.6  0.487667  0.29400  0.9488  0.14700   0.16170  0.967439\n",
       "516       0.6  0.487667  0.29355  0.9488  0.14675   0.16145  0.967439\n",
       "517       0.6  0.487667  0.29355  0.9488  0.14675   0.16145  0.967439\n",
       "\n",
       "[4392 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Load FDR Data for Test\n",
    "\n",
    "test_path_list = []\n",
    "test_path_under=os.listdir(test_path_upper)\n",
    "\n",
    "# load data_path\n",
    "for i in range(len(test_path_under)): # collect under directory file path\n",
    "    test_path_sub=os.listdir(test_path_upper+'/'+test_path_under[i])\n",
    "    \n",
    "    for j in range(len(test_path_sub)):\n",
    "        test_path_list.append(test_path_upper+'/'+test_path_under[i]+'/'+test_path_sub[j])\n",
    "\n",
    "# make zero matrix\n",
    "test_data_list = [0]*len(test_path_list)\n",
    "test_rand_x_data_list = [0]*len(test_path_list)\n",
    "test_rand_y_data_list = [0]*len(test_path_list)\n",
    "\n",
    "# load data_value\n",
    "for i,j in enumerate(test_path_list):\n",
    "    test_data_list[i] =pd.read_csv(j)\n",
    "    test_data_list[i] = data_set_normalization(test_data_list[i])\n",
    "    \n",
    "test_data = pd.concat(test_data_list)\n",
    "\n",
    "# Print the data format\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GT-label for Train (WCR)\n",
    "y_R = round(train_data['WC_ratio']*100, 1)\n",
    "y_train = y_R.to_numpy()\n",
    "\n",
    "# FDR sensor data for Train\n",
    "x_R = train_data[['TEMP','VWC','EPSILON','SALINITY','TDS','EC']]\n",
    "x_train = x_R.to_numpy()\n",
    "\n",
    "x_train_shuffled, y_train_shuffled = shuffle(x_train, y_train, random_state=seed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GT-label for Valid (WCR)\n",
    "y_R_valid = round(valid_data['WC_ratio']*100, 1)\n",
    "y_valid = y_R_valid.to_numpy()\n",
    "\n",
    "# FDR sensor data for Valid\n",
    "x_R_valid = valid_data[['TEMP','VWC','EPSILON','SALINITY','TDS','EC']]\n",
    "x_valid = x_R_valid.to_numpy()\n",
    "\n",
    "x_valid_shuffled, y_valid_shuffled = shuffle(x_valid, y_valid, random_state=seed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GT-label for Test (WCR)\n",
    "y_R_test = round(test_data['WC_ratio']*100, 1)\n",
    "y_test = y_R_test.to_numpy()\n",
    "\n",
    "# FDR sensor data for Test\n",
    "x_R_test = test_data[['TEMP','VWC','EPSILON','SALINITY','TDS','EC']]\n",
    "x_test = x_R_test.to_numpy()\n",
    "\n",
    "x_test_shuffled, y_test_shuffled = shuffle(x_test, y_test, random_state=seed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=20, random_state=4885)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=RandomForestRegressor(max_depth=20, random_state=seed_data)\n",
    "model.fit(x_train_shuffled, y_train_shuffled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': 20,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 4885,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print model's parameter\n",
    "\n",
    "params = model.get_params()\n",
    "\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.875, 57.5  , 42.175, ..., 43.8  , 59.075, 45.25 ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Print the prediction results\n",
    "\n",
    "p_result_0 = model.predict(x_test_shuffled)\n",
    "\n",
    "p_result_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 2.0835\n",
      "r2_score : 0.8999\n"
     ]
    }
   ],
   "source": [
    "### print RMSE and R2-score\n",
    "\n",
    "rmse_0 = round(mean_squared_error(y_test_shuffled, p_result_0, squared=False), 4)\n",
    "r2_0 = round(r2_score(y_test_shuffled, p_result_0), 4)\n",
    "\n",
    "print(f\"RMSE : {rmse_0}\")\n",
    "print(f\"r2_score : {r2_0}\")"
   ]
  }
 ],
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