examples/training_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## <center><a id=0 style='color:red'>Импорт библиотек</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\MTP\\test2\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from ds_nms import utils_io, model_train\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import (\n",
    "    LinearRegression,\n",
    "    Ridge,\n",
    "    Lasso,\n",
    "    PassiveAggressiveRegressor,\n",
    "    LassoLars,\n",
    "    BayesianRidge,\n",
    "    HuberRegressor,\n",
    "    QuantileRegressor,\n",
    "    RANSACRegressor,\n",
    "    TheilSenRegressor,\n",
    "    PoissonRegressor,\n",
    "    TweedieRegressor,\n",
    "    ARDRegression,\n",
    "    SGDRegressor,\n",
    "    ElasticNet,\n",
    ")\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "\n",
    "np.set_printoptions(suppress=True, precision=2)\n",
    "pd.set_option(\"display.max_rows\", 300)\n",
    "pd.set_option(\"display.max_columns\", 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## <center><a id=1 style='color:red'>Импорт датасета</a>\n",
    " [К описанию](#99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train/target_train_1.pkl успешно загружен.\n",
      "Файл data/train/target_train_2.pkl успешно загружен.\n",
      "Файл data/train/target_train_3.pkl успешно загружен.\n",
      "Файл data/train/X_train_1.pkl успешно загружен.\n",
      "Файл data/train/X_train_2.pkl успешно загружен.\n",
      "Файл data/train/X_train_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train'\n",
    "train_data = utils_io.load_data(sub_dir=DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test//target_test_1.pkl успешно загружен.\n",
      "Файл data/test//target_test_2.pkl успешно загружен.\n",
      "Файл data/test//target_test_3.pkl успешно загружен.\n",
      "Файл data/test//X_test_1.pkl успешно загружен.\n",
      "Файл data/test//X_test_2.pkl успешно загружен.\n",
      "Файл data/test//X_test_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test/'\n",
    "test_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train_std//target_train_1.pkl успешно загружен.\n",
      "Файл data/train_std//target_train_2.pkl успешно загружен.\n",
      "Файл data/train_std//target_train_3.pkl успешно загружен.\n",
      "Файл data/train_std//X_train_std_1.pkl успешно загружен.\n",
      "Файл data/train_std//X_train_std_2.pkl успешно загружен.\n",
      "Файл data/train_std//X_train_std_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train_std/'\n",
    "\n",
    "train_std_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test_std//target_test_1.pkl успешно загружен.\n",
      "Файл data/test_std//target_test_2.pkl успешно загружен.\n",
      "Файл data/test_std//target_test_3.pkl успешно загружен.\n",
      "Файл data/test_std//X_test_std_1.pkl успешно загружен.\n",
      "Файл data/test_std//X_test_std_2.pkl успешно загружен.\n",
      "Файл data/test_std//X_test_std_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test_std/'\n",
    "test_std_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train_corr//target_train_1.pkl успешно загружен.\n",
      "Файл data/train_corr//target_train_2.pkl успешно загружен.\n",
      "Файл data/train_corr//target_train_3.pkl успешно загружен.\n",
      "Файл data/train_corr//X_train_corr_1.pkl успешно загружен.\n",
      "Файл data/train_corr//X_train_corr_2.pkl успешно загружен.\n",
      "Файл data/train_corr//X_train_corr_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train_corr/'\n",
    "\n",
    "train_corr_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test_corr//target_test_1.pkl успешно загружен.\n",
      "Файл data/test_corr//target_test_2.pkl успешно загружен.\n",
      "Файл data/test_corr//target_test_3.pkl успешно загружен.\n",
      "Файл data/test_corr//X_test_corr_1.pkl успешно загружен.\n",
      "Файл data/test_corr//X_test_corr_2.pkl успешно загружен.\n",
      "Файл data/test_corr//X_test_corr_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test_corr/'\n",
    "\n",
    "test_corr_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train_mm//target_train_1.pkl успешно загружен.\n",
      "Файл data/train_mm//target_train_2.pkl успешно загружен.\n",
      "Файл data/train_mm//target_train_3.pkl успешно загружен.\n",
      "Файл data/train_mm//X_train_mm_1.pkl успешно загружен.\n",
      "Файл data/train_mm//X_train_mm_2.pkl успешно загружен.\n",
      "Файл data/train_mm//X_train_mm_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train_mm/'\n",
    "\n",
    "train_mm_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test_mm//target_test_1.pkl успешно загружен.\n",
      "Файл data/test_mm//target_test_2.pkl успешно загружен.\n",
      "Файл data/test_mm//target_test_3.pkl успешно загружен.\n",
      "Файл data/test_mm//X_test_mm_1.pkl успешно загружен.\n",
      "Файл data/test_mm//X_test_mm_2.pkl успешно загружен.\n",
      "Файл data/test_mm//X_test_mm_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test_mm/'\n",
    "\n",
    "test_mm_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train_poly/target_train_1.pkl успешно загружен.\n",
      "Файл data/train_poly/target_train_2.pkl успешно загружен.\n",
      "Файл data/train_poly/target_train_3.pkl успешно загружен.\n",
      "Файл data/train_poly/X_train_poly_1.pkl успешно загружен.\n",
      "Файл data/train_poly/X_train_poly_2.pkl успешно загружен.\n",
      "Файл data/train_poly/X_train_poly_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train_poly'\n",
    "\n",
    "train_poly_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test_poly//target_test_1.pkl успешно загружен.\n",
      "Файл data/test_poly//target_test_2.pkl успешно загружен.\n",
      "Файл data/test_poly//target_test_3.pkl успешно загружен.\n",
      "Файл data/test_poly//X_test_poly_1.pkl успешно загружен.\n",
      "Файл data/test_poly//X_test_poly_2.pkl успешно загружен.\n",
      "Файл data/test_poly//X_test_poly_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test_poly/'\n",
    "\n",
    "test_poly_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/train_poly_corr/target_train_1.pkl успешно загружен.\n",
      "Файл data/train_poly_corr/target_train_2.pkl успешно загружен.\n",
      "Файл data/train_poly_corr/target_train_3.pkl успешно загружен.\n",
      "Файл data/train_poly_corr/X_train_poly_corr_1.pkl успешно загружен.\n",
      "Файл data/train_poly_corr/X_train_poly_corr_2.pkl успешно загружен.\n",
      "Файл data/train_poly_corr/X_train_poly_corr_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'train_poly_corr'\n",
    "\n",
    "train_poly_corr_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл data/test_poly_corr//target_test_1.pkl успешно загружен.\n",
      "Файл data/test_poly_corr//target_test_2.pkl успешно загружен.\n",
      "Файл data/test_poly_corr//target_test_3.pkl успешно загружен.\n",
      "Файл data/test_poly_corr//X_test_poly_corr_1.pkl успешно загружен.\n",
      "Файл data/test_poly_corr//X_test_poly_corr_2.pkl успешно загружен.\n",
      "Файл data/test_poly_corr//X_test_poly_corr_3.pkl успешно загружен.\n"
     ]
    }
   ],
   "source": [
    "DIR = 'test_poly_corr/'\n",
    "\n",
    "test_poly_corr_data = utils_io.load_data( sub_dir=DIR )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### <a id=5>get_prediction</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>negative</th>\n",
       "      <th>RE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>NRMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression()</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585859</td>\n",
       "      <td>8262.828142</td>\n",
       "      <td>10829.754538</td>\n",
       "      <td>0.129021</td>\n",
       "      <td>0.647549</td>\n",
       "      <td>data_name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  negative        RE          MAE          RMSE  \\\n",
       "0  LinearRegression()         0  0.585859  8262.828142  10829.754538   \n",
       "\n",
       "      NRMSE        R2       data  \n",
       "0  0.129021  0.647549  data_name  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "trained_lr = lr.fit(train_data['X_train_1'], train_data['target_train_1'])\n",
    "\n",
    "result_df, y_pred = model_train.get_prediction(X=train_data['X_train_1'], y=train_data['target_train_1'],\n",
    "                                         model=trained_lr\n",
    "                                         )\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### <a id=5>train_cv</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>model</th>\n",
       "      <th>data_name</th>\n",
       "      <th>R2_train_macro</th>\n",
       "      <th>R2_train_std</th>\n",
       "      <th>R2_train_splits</th>\n",
       "      <th>RMSE_train_macro</th>\n",
       "      <th>RMSE_train_std</th>\n",
       "      <th>RMSE_train_splits</th>\n",
       "      <th>NRMSE_train_macro</th>\n",
       "      <th>NRMSE_train_std</th>\n",
       "      <th>NRMSE_train_splits</th>\n",
       "      <th>MAE_train_macro</th>\n",
       "      <th>MAE_train_std</th>\n",
       "      <th>MAE_train_splits</th>\n",
       "      <th>RE_train_macro</th>\n",
       "      <th>RE_train_std</th>\n",
       "      <th>RE_train_splits</th>\n",
       "      <th>negative_train_macro</th>\n",
       "      <th>negative_train_std</th>\n",
       "      <th>negative_train_splits</th>\n",
       "      <th>R2_val_macro</th>\n",
       "      <th>R2_val_std</th>\n",
       "      <th>R2_val_splits</th>\n",
       "      <th>RMSE_val_macro</th>\n",
       "      <th>RMSE_val_std</th>\n",
       "      <th>RMSE_val_splits</th>\n",
       "      <th>NRMSE_val_macro</th>\n",
       "      <th>NRMSE_val_std</th>\n",
       "      <th>NRMSE_val_splits</th>\n",
       "      <th>MAE_val_macro</th>\n",
       "      <th>MAE_val_std</th>\n",
       "      <th>MAE_val_splits</th>\n",
       "      <th>RE_val_macro</th>\n",
       "      <th>RE_val_std</th>\n",
       "      <th>RE_val_splits</th>\n",
       "      <th>negative_val_macro</th>\n",
       "      <th>negative_val_std</th>\n",
       "      <th>negative_val_splits</th>\n",
       "      <th>R2_val_micro</th>\n",
       "      <th>RMSE_val_micro</th>\n",
       "      <th>MAE_val_micro</th>\n",
       "      <th>RE_val_micro</th>\n",
       "      <th>Negative_micro</th>\n",
       "      <th>NRMSE_val_micro</th>\n",
       "      <th>R2_diff_rel</th>\n",
       "      <th>RMSE_diff_rel</th>\n",
       "      <th>MAE_diff_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso(alpha=1000)</td>\n",
       "      <td>orig</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.007</td>\n",
       "      <td>[[0.644], [0.616], [0.615], [0.616], [0.625], ...</td>\n",
       "      <td>11298.447</td>\n",
       "      <td>114.108</td>\n",
       "      <td>[[10481.148], [11356.79], [11353.249], [11319....</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.002</td>\n",
       "      <td>[[0.151], [0.135], [0.135], [0.135], [0.134], ...</td>\n",
       "      <td>8622.683</td>\n",
       "      <td>80.294</td>\n",
       "      <td>[[8273.36], [8703.924], [8689.455], [8621.569]...</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.007</td>\n",
       "      <td>[[0.643], [0.612], [0.612], [0.622], [0.622], ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
       "      <td>9150.902</td>\n",
       "      <td>7781.678</td>\n",
       "      <td>[[47433.396], [424.967], [2109.011], [9917.301...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
       "      <td>9150.902</td>\n",
       "      <td>7781.678</td>\n",
       "      <td>[[47433.396], [424.967], [2109.011], [9917.301...</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.491</td>\n",
       "      <td>[[0.0], [1.0], [1.0], [0.0], [0.0], [0.0], [1....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...</td>\n",
       "      <td>0.56638</td>\n",
       "      <td>12012.224171</td>\n",
       "      <td>9150.902172</td>\n",
       "      <td>0.59596</td>\n",
       "      <td>0</td>\n",
       "      <td>0.143108</td>\n",
       "      <td>0.080551</td>\n",
       "      <td>0.063175</td>\n",
       "      <td>0.061259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model data_name  R2_train_macro  R2_train_std  \\\n",
       "0  Lasso(alpha=1000)      orig           0.616         0.007   \n",
       "\n",
       "                                     R2_train_splits  RMSE_train_macro  \\\n",
       "0  [[0.644], [0.616], [0.615], [0.616], [0.625], ...         11298.447   \n",
       "\n",
       "   RMSE_train_std                                  RMSE_train_splits  \\\n",
       "0         114.108  [[10481.148], [11356.79], [11353.249], [11319....   \n",
       "\n",
       "   NRMSE_train_macro  NRMSE_train_std  \\\n",
       "0              0.135            0.002   \n",
       "\n",
       "                                  NRMSE_train_splits  MAE_train_macro  \\\n",
       "0  [[0.151], [0.135], [0.135], [0.135], [0.134], ...         8622.683   \n",
       "\n",
       "   MAE_train_std                                   MAE_train_splits  \\\n",
       "0         80.294  [[8273.36], [8703.924], [8689.455], [8621.569]...   \n",
       "\n",
       "   RE_train_macro  RE_train_std  \\\n",
       "0           0.615         0.007   \n",
       "\n",
       "                                     RE_train_splits  negative_train_macro  \\\n",
       "0  [[0.643], [0.612], [0.612], [0.622], [0.622], ...                   0.0   \n",
       "\n",
       "   negative_train_std                              negative_train_splits  \\\n",
       "0                 0.0  [[0], [0], [0], [0], [0], [0], [0], [0], [0], ...   \n",
       "\n",
       "   R2_val_macro  R2_val_std  \\\n",
       "0           NaN         NaN   \n",
       "\n",
       "                                       R2_val_splits  RMSE_val_macro  \\\n",
       "0  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...        9150.902   \n",
       "\n",
       "   RMSE_val_std                                    RMSE_val_splits  \\\n",
       "0      7781.678  [[47433.396], [424.967], [2109.011], [9917.301...   \n",
       "\n",
       "   NRMSE_val_macro  NRMSE_val_std  \\\n",
       "0              NaN            NaN   \n",
       "\n",
       "                                    NRMSE_val_splits  MAE_val_macro  \\\n",
       "0  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...       9150.902   \n",
       "\n",
       "   MAE_val_std                                     MAE_val_splits  \\\n",
       "0     7781.678  [[47433.396], [424.967], [2109.011], [9917.301...   \n",
       "\n",
       "   RE_val_macro  RE_val_std  \\\n",
       "0         0.596       0.491   \n",
       "\n",
       "                                       RE_val_splits  negative_val_macro  \\\n",
       "0  [[0.0], [1.0], [1.0], [0.0], [0.0], [0.0], [1....                 0.0   \n",
       "\n",
       "   negative_val_std                                negative_val_splits  \\\n",
       "0               0.0  [[0], [0], [0], [0], [0], [0], [0], [0], [0], ...   \n",
       "\n",
       "   R2_val_micro  RMSE_val_micro  MAE_val_micro  RE_val_micro  Negative_micro  \\\n",
       "0       0.56638    12012.224171    9150.902172       0.59596               0   \n",
       "\n",
       "   NRMSE_val_micro  R2_diff_rel  RMSE_diff_rel  MAE_diff_rel  \n",
       "0         0.143108     0.080551       0.063175      0.061259  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_model, result_dict = model_train.train_cv(X=train_std_data['X_train_std_1'], y=train_data['target_train_1'],\n",
    "                                                  model=Lasso(alpha=1000),\n",
    "                                                  cv_type='loo',\n",
    "                                                  metric_best='RMSE_val',\n",
    "                                                  data_name='orig')\n",
    "\n",
    "pd.DataFrame([result_dict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/std/lasso_std_1.pkl']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(value=trained_model,\n",
    "            filename=\"models/std/lasso_std_1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>data_name</th>\n",
       "      <th>R2_train_macro</th>\n",
       "      <th>R2_train_std</th>\n",
       "      <th>R2_train_splits</th>\n",
       "      <th>RMSE_train_macro</th>\n",
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       "      <th>RMSE_train_splits</th>\n",
       "      <th>NRMSE_train_macro</th>\n",
       "      <th>NRMSE_train_std</th>\n",
       "      <th>NRMSE_train_splits</th>\n",
       "      <th>MAE_train_macro</th>\n",
       "      <th>MAE_train_std</th>\n",
       "      <th>MAE_train_splits</th>\n",
       "      <th>RE_train_macro</th>\n",
       "      <th>RE_train_std</th>\n",
       "      <th>RE_train_splits</th>\n",
       "      <th>negative_train_macro</th>\n",
       "      <th>negative_train_std</th>\n",
       "      <th>negative_train_splits</th>\n",
       "      <th>R2_val_macro</th>\n",
       "      <th>R2_val_std</th>\n",
       "      <th>R2_val_splits</th>\n",
       "      <th>RMSE_val_macro</th>\n",
       "      <th>RMSE_val_std</th>\n",
       "      <th>RMSE_val_splits</th>\n",
       "      <th>NRMSE_val_macro</th>\n",
       "      <th>NRMSE_val_std</th>\n",
       "      <th>NRMSE_val_splits</th>\n",
       "      <th>MAE_val_macro</th>\n",
       "      <th>MAE_val_std</th>\n",
       "      <th>MAE_val_splits</th>\n",
       "      <th>RE_val_macro</th>\n",
       "      <th>RE_val_std</th>\n",
       "      <th>RE_val_splits</th>\n",
       "      <th>negative_val_macro</th>\n",
       "      <th>negative_val_std</th>\n",
       "      <th>negative_val_splits</th>\n",
       "      <th>R2_val_micro</th>\n",
       "      <th>RMSE_val_micro</th>\n",
       "      <th>MAE_val_micro</th>\n",
       "      <th>RE_val_micro</th>\n",
       "      <th>Negative_micro</th>\n",
       "      <th>NRMSE_val_micro</th>\n",
       "      <th>R2_diff_rel</th>\n",
       "      <th>RMSE_diff_rel</th>\n",
       "      <th>MAE_diff_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HuberRegressor(alpha=100, epsilon=5, max_iter=...</td>\n",
       "      <td>orig</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.015</td>\n",
       "      <td>[[0.607], [0.608], [0.643], [0.619], [0.643], ...</td>\n",
       "      <td>11227.236</td>\n",
       "      <td>240.998</td>\n",
       "      <td>[[11146.111], [11495.464], [10823.147], [11166...</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.009</td>\n",
       "      <td>[[0.16], [0.137], [0.129], [0.135], [0.13], [0...</td>\n",
       "      <td>8049.476</td>\n",
       "      <td>194.849</td>\n",
       "      <td>[[8130.431], [8206.299], [7741.758], [8145.403...</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.031</td>\n",
       "      <td>[[0.605], [0.547], [0.605], [0.632], [0.621], ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0]]</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.231</td>\n",
       "      <td>[[0.339], [0.735], [0.326], [0.658], [0.379], ...</td>\n",
       "      <td>13539.884</td>\n",
       "      <td>3258.928</td>\n",
       "      <td>[[16546.632], [8977.843], [15670.608], [11153....</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.04</td>\n",
       "      <td>[[0.211], [0.159], [0.264], [0.176], [0.215], ...</td>\n",
       "      <td>10355.955</td>\n",
       "      <td>1984.192</td>\n",
       "      <td>[[12165.39], [7687.463], [11399.86], [8195.069...</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.115</td>\n",
       "      <td>[[0.385], [0.692], [0.615], [0.5], [0.333], [0...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0]]</td>\n",
       "      <td>0.41661</td>\n",
       "      <td>13933.127713</td>\n",
       "      <td>10357.822063</td>\n",
       "      <td>0.505051</td>\n",
       "      <td>0</td>\n",
       "      <td>0.165993</td>\n",
       "      <td>0.328301</td>\n",
       "      <td>0.313665</td>\n",
       "      <td>0.321193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model data_name  \\\n",
       "0  HuberRegressor(alpha=100, epsilon=5, max_iter=...      orig   \n",
       "\n",
       "   R2_train_macro  R2_train_std  \\\n",
       "0           0.621         0.015   \n",
       "\n",
       "                                     R2_train_splits  RMSE_train_macro  \\\n",
       "0  [[0.607], [0.608], [0.643], [0.619], [0.643], ...         11227.236   \n",
       "\n",
       "   RMSE_train_std                                  RMSE_train_splits  \\\n",
       "0         240.998  [[11146.111], [11495.464], [10823.147], [11166...   \n",
       "\n",
       "   NRMSE_train_macro  NRMSE_train_std  \\\n",
       "0              0.137            0.009   \n",
       "\n",
       "                                  NRMSE_train_splits  MAE_train_macro  \\\n",
       "0  [[0.16], [0.137], [0.129], [0.135], [0.13], [0...         8049.476   \n",
       "\n",
       "   MAE_train_std                                   MAE_train_splits  \\\n",
       "0        194.849  [[8130.431], [8206.299], [7741.758], [8145.403...   \n",
       "\n",
       "   RE_train_macro  RE_train_std  \\\n",
       "0           0.612         0.031   \n",
       "\n",
       "                                     RE_train_splits  negative_train_macro  \\\n",
       "0  [[0.605], [0.547], [0.605], [0.632], [0.621], ...                   0.0   \n",
       "\n",
       "   negative_train_std                     negative_train_splits  R2_val_macro  \\\n",
       "0                 0.0  [[0], [0], [0], [0], [0], [0], [0], [0]]         0.417   \n",
       "\n",
       "   R2_val_std                                      R2_val_splits  \\\n",
       "0       0.231  [[0.339], [0.735], [0.326], [0.658], [0.379], ...   \n",
       "\n",
       "   RMSE_val_macro  RMSE_val_std  \\\n",
       "0       13539.884      3258.928   \n",
       "\n",
       "                                     RMSE_val_splits  NRMSE_val_macro  \\\n",
       "0  [[16546.632], [8977.843], [15670.608], [11153....            0.218   \n",
       "\n",
       "   NRMSE_val_std                                   NRMSE_val_splits  \\\n",
       "0           0.04  [[0.211], [0.159], [0.264], [0.176], [0.215], ...   \n",
       "\n",
       "   MAE_val_macro  MAE_val_std  \\\n",
       "0      10355.955     1984.192   \n",
       "\n",
       "                                      MAE_val_splits  RE_val_macro  \\\n",
       "0  [[12165.39], [7687.463], [11399.86], [8195.069...         0.503   \n",
       "\n",
       "   RE_val_std                                      RE_val_splits  \\\n",
       "0       0.115  [[0.385], [0.692], [0.615], [0.5], [0.333], [0...   \n",
       "\n",
       "   negative_val_macro  negative_val_std  \\\n",
       "0                 0.0               0.0   \n",
       "\n",
       "                        negative_val_splits  R2_val_micro  RMSE_val_micro  \\\n",
       "0  [[0], [0], [0], [0], [0], [0], [0], [0]]       0.41661    13933.127713   \n",
       "\n",
       "   MAE_val_micro  RE_val_micro  Negative_micro  NRMSE_val_micro  R2_diff_rel  \\\n",
       "0   10357.822063      0.505051               0         0.165993     0.328301   \n",
       "\n",
       "   RMSE_diff_rel  MAE_diff_rel  \n",
       "0       0.313665      0.321193  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_model, result_dict = model_train.train_cv(X=train_data['X_train_1'], y=train_data['target_train_1'],\n",
    "                                                  model=HuberRegressor(alpha=100, epsilon=5, max_iter=1000),\n",
    "                                                  cv_type='stratify',\n",
    "                                                  metric_best='RE_val',\n",
    "                                                  n_splits=8,\n",
    "                                                  data_name='orig')\n",
    "\n",
    "pd.DataFrame([result_dict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>data_name</th>\n",
       "      <th>R2_train_macro</th>\n",
       "      <th>R2_train_std</th>\n",
       "      <th>R2_train_splits</th>\n",
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       "      <th>MAE_train_macro</th>\n",
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       "      <th>MAE_train_splits</th>\n",
       "      <th>RE_train_macro</th>\n",
       "      <th>RE_train_std</th>\n",
       "      <th>RE_train_splits</th>\n",
       "      <th>negative_train_macro</th>\n",
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       "      <th>NRMSE_val_std</th>\n",
       "      <th>NRMSE_val_splits</th>\n",
       "      <th>MAE_val_macro</th>\n",
       "      <th>MAE_val_std</th>\n",
       "      <th>MAE_val_splits</th>\n",
       "      <th>RE_val_macro</th>\n",
       "      <th>RE_val_std</th>\n",
       "      <th>RE_val_splits</th>\n",
       "      <th>negative_val_macro</th>\n",
       "      <th>negative_val_std</th>\n",
       "      <th>negative_val_splits</th>\n",
       "      <th>R2_val_micro</th>\n",
       "      <th>RMSE_val_micro</th>\n",
       "      <th>MAE_val_micro</th>\n",
       "      <th>RE_val_micro</th>\n",
       "      <th>Negative_micro</th>\n",
       "      <th>NRMSE_val_micro</th>\n",
       "      <th>R2_diff_rel</th>\n",
       "      <th>RMSE_diff_rel</th>\n",
       "      <th>MAE_diff_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression()</td>\n",
       "      <td>orig</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.008</td>\n",
       "      <td>[[0.662], [0.673], [0.665], [0.65], [0.655]]</td>\n",
       "      <td>10606.16</td>\n",
       "      <td>288.174</td>\n",
       "      <td>[[10479.282], [10140.965], [10999.542], [10769...</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[[0.151], [0.121], [0.133], [0.128], [0.127]]</td>\n",
       "      <td>8108.269</td>\n",
       "      <td>314.34</td>\n",
       "      <td>[[8345.463], [7589.246], [8483.811], [8168.599...</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.037</td>\n",
       "      <td>[[0.557], [0.57], [0.532], [0.633], [0.612]]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0]]</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.233</td>\n",
       "      <td>[[0.562], [0.378], [-0.061], [0.521], [0.536]]</td>\n",
       "      <td>13553.056</td>\n",
       "      <td>1157.38</td>\n",
       "      <td>[[12410.162], [14728.876], [15176.812], [12723...</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.051</td>\n",
       "      <td>[[0.158], [0.274], [0.294], [0.187], [0.216]]</td>\n",
       "      <td>9996.753</td>\n",
       "      <td>1021.059</td>\n",
       "      <td>[[8122.58], [11120.123], [10624.72], [9923.189...</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.08</td>\n",
       "      <td>[[0.7], [0.55], [0.5], [0.6], [0.474]]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0]]</td>\n",
       "      <td>0.443277</td>\n",
       "      <td>13610.950278</td>\n",
       "      <td>9994.769337</td>\n",
       "      <td>0.565657</td>\n",
       "      <td>0</td>\n",
       "      <td>0.162155</td>\n",
       "      <td>0.414221</td>\n",
       "      <td>0.277847</td>\n",
       "      <td>0.243904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model data_name  R2_train_macro  R2_train_std  \\\n",
       "0  LinearRegression()      orig           0.661         0.008   \n",
       "\n",
       "                                R2_train_splits  RMSE_train_macro  \\\n",
       "0  [[0.662], [0.673], [0.665], [0.65], [0.655]]          10606.16   \n",
       "\n",
       "   RMSE_train_std                                  RMSE_train_splits  \\\n",
       "0         288.174  [[10479.282], [10140.965], [10999.542], [10769...   \n",
       "\n",
       "   NRMSE_train_macro  NRMSE_train_std  \\\n",
       "0              0.132             0.01   \n",
       "\n",
       "                              NRMSE_train_splits  MAE_train_macro  \\\n",
       "0  [[0.151], [0.121], [0.133], [0.128], [0.127]]         8108.269   \n",
       "\n",
       "   MAE_train_std                                   MAE_train_splits  \\\n",
       "0         314.34  [[8345.463], [7589.246], [8483.811], [8168.599...   \n",
       "\n",
       "   RE_train_macro  RE_train_std                               RE_train_splits  \\\n",
       "0           0.581         0.037  [[0.557], [0.57], [0.532], [0.633], [0.612]]   \n",
       "\n",
       "   negative_train_macro  negative_train_std      negative_train_splits  \\\n",
       "0                   0.0                 0.0  [[0], [0], [0], [0], [0]]   \n",
       "\n",
       "   R2_val_macro  R2_val_std                                   R2_val_splits  \\\n",
       "0         0.387       0.233  [[0.562], [0.378], [-0.061], [0.521], [0.536]]   \n",
       "\n",
       "   RMSE_val_macro  RMSE_val_std  \\\n",
       "0       13553.056       1157.38   \n",
       "\n",
       "                                     RMSE_val_splits  NRMSE_val_macro  \\\n",
       "0  [[12410.162], [14728.876], [15176.812], [12723...            0.226   \n",
       "\n",
       "   NRMSE_val_std                               NRMSE_val_splits  \\\n",
       "0          0.051  [[0.158], [0.274], [0.294], [0.187], [0.216]]   \n",
       "\n",
       "   MAE_val_macro  MAE_val_std  \\\n",
       "0       9996.753     1021.059   \n",
       "\n",
       "                                      MAE_val_splits  RE_val_macro  \\\n",
       "0  [[8122.58], [11120.123], [10624.72], [9923.189...         0.565   \n",
       "\n",
       "   RE_val_std                           RE_val_splits  negative_val_macro  \\\n",
       "0        0.08  [[0.7], [0.55], [0.5], [0.6], [0.474]]                 0.0   \n",
       "\n",
       "   negative_val_std        negative_val_splits  R2_val_micro  RMSE_val_micro  \\\n",
       "0               0.0  [[0], [0], [0], [0], [0]]      0.443277    13610.950278   \n",
       "\n",
       "   MAE_val_micro  RE_val_micro  Negative_micro  NRMSE_val_micro  R2_diff_rel  \\\n",
       "0    9994.769337      0.565657               0         0.162155     0.414221   \n",
       "\n",
       "   RMSE_diff_rel  MAE_diff_rel  \n",
       "0       0.277847      0.243904  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_model, result_dict = model_train.train_cv(X=train_data['X_train_1'], y=train_data['target_train_1'],\n",
    "                                                  model=LinearRegression(),\n",
    "                                                  cv_type='kf',\n",
    "                                                  metric_best='RE_val',\n",
    "                                                  n_splits=5,\n",
    "                                                  data_name='orig')\n",
    "\n",
    "pd.DataFrame([result_dict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>data_name</th>\n",
       "      <th>R2_train_macro</th>\n",
       "      <th>R2_train_std</th>\n",
       "      <th>R2_train_splits</th>\n",
       "      <th>RMSE_train_macro</th>\n",
       "      <th>RMSE_train_std</th>\n",
       "      <th>RMSE_train_splits</th>\n",
       "      <th>NRMSE_train_macro</th>\n",
       "      <th>NRMSE_train_std</th>\n",
       "      <th>NRMSE_train_splits</th>\n",
       "      <th>MAE_train_macro</th>\n",
       "      <th>MAE_train_std</th>\n",
       "      <th>MAE_train_splits</th>\n",
       "      <th>RE_train_macro</th>\n",
       "      <th>RE_train_std</th>\n",
       "      <th>RE_train_splits</th>\n",
       "      <th>negative_train_macro</th>\n",
       "      <th>negative_train_std</th>\n",
       "      <th>negative_train_splits</th>\n",
       "      <th>R2_val_macro</th>\n",
       "      <th>R2_val_std</th>\n",
       "      <th>R2_val_splits</th>\n",
       "      <th>RMSE_val_macro</th>\n",
       "      <th>RMSE_val_std</th>\n",
       "      <th>RMSE_val_splits</th>\n",
       "      <th>NRMSE_val_macro</th>\n",
       "      <th>NRMSE_val_std</th>\n",
       "      <th>NRMSE_val_splits</th>\n",
       "      <th>MAE_val_macro</th>\n",
       "      <th>MAE_val_std</th>\n",
       "      <th>MAE_val_splits</th>\n",
       "      <th>RE_val_macro</th>\n",
       "      <th>RE_val_std</th>\n",
       "      <th>RE_val_splits</th>\n",
       "      <th>negative_val_macro</th>\n",
       "      <th>negative_val_std</th>\n",
       "      <th>negative_val_splits</th>\n",
       "      <th>R2_val_micro</th>\n",
       "      <th>RMSE_val_micro</th>\n",
       "      <th>MAE_val_micro</th>\n",
       "      <th>RE_val_micro</th>\n",
       "      <th>Negative_micro</th>\n",
       "      <th>NRMSE_val_micro</th>\n",
       "      <th>R2_diff_rel</th>\n",
       "      <th>RMSE_diff_rel</th>\n",
       "      <th>MAE_diff_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression()</td>\n",
       "      <td>None</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.109</td>\n",
       "      <td>[[1.0], [0.959], [0.887], [0.744], [0.697], [0...</td>\n",
       "      <td>9464.257</td>\n",
       "      <td>2940.976</td>\n",
       "      <td>[[0.0], [3971.914], [6384.4], [9367.55], [9461...</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.035</td>\n",
       "      <td>[[0.0], [0.051], [0.081], [0.119], [0.121], [0...</td>\n",
       "      <td>7144.06</td>\n",
       "      <td>2223.332</td>\n",
       "      <td>[[0.0], [3118.859], [5026.559], [6706.188], [6...</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.121</td>\n",
       "      <td>[[1.0], [0.714], [0.947], [0.625], [0.69], [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>2.745</td>\n",
       "      <td>[[-4.39], [0.278], [-10.627], [-2.503], [0.007...</td>\n",
       "      <td>15408.948</td>\n",
       "      <td>12369.316</td>\n",
       "      <td>[[10196.874], [13281.877], [57286.811], [14708...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.304</td>\n",
       "      <td>[[0.906], [0.358], [1.388], [0.643], [0.446], ...</td>\n",
       "      <td>12813.19</td>\n",
       "      <td>10119.042</td>\n",
       "      <td>[[8903.77], [12128.406], [47789.55], [11648.87...</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.243</td>\n",
       "      <td>[[0.2], [0.4], [0.0], [0.2], [0.4], [0.6], [0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...</td>\n",
       "      <td>-0.235133</td>\n",
       "      <td>19759.444919</td>\n",
       "      <td>12813.189569</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.283941</td>\n",
       "      <td>2.056176</td>\n",
       "      <td>0.906963</td>\n",
       "      <td>1.047594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model data_name  R2_train_macro  R2_train_std  \\\n",
       "0  LinearRegression()      None           0.716         0.109   \n",
       "\n",
       "                                     R2_train_splits  RMSE_train_macro  \\\n",
       "0  [[1.0], [0.959], [0.887], [0.744], [0.697], [0...          9464.257   \n",
       "\n",
       "   RMSE_train_std                                  RMSE_train_splits  \\\n",
       "0        2940.976  [[0.0], [3971.914], [6384.4], [9367.55], [9461...   \n",
       "\n",
       "   NRMSE_train_macro  NRMSE_train_std  \\\n",
       "0              0.116            0.035   \n",
       "\n",
       "                                  NRMSE_train_splits  MAE_train_macro  \\\n",
       "0  [[0.0], [0.051], [0.081], [0.119], [0.121], [0...          7144.06   \n",
       "\n",
       "   MAE_train_std                                   MAE_train_splits  \\\n",
       "0       2223.332  [[0.0], [3118.859], [5026.559], [6706.188], [6...   \n",
       "\n",
       "   RE_train_macro  RE_train_std  \\\n",
       "0           0.654         0.121   \n",
       "\n",
       "                                     RE_train_splits  negative_train_macro  \\\n",
       "0  [[1.0], [0.714], [0.947], [0.625], [0.69], [0....                   0.0   \n",
       "\n",
       "   negative_train_std                              negative_train_splits  \\\n",
       "0                 0.0  [[0], [0], [0], [0], [0], [0], [0], [0], [0], ...   \n",
       "\n",
       "   R2_val_macro  R2_val_std  \\\n",
       "0        -0.757       2.745   \n",
       "\n",
       "                                       R2_val_splits  RMSE_val_macro  \\\n",
       "0  [[-4.39], [0.278], [-10.627], [-2.503], [0.007...       15408.948   \n",
       "\n",
       "   RMSE_val_std                                    RMSE_val_splits  \\\n",
       "0     12369.316  [[10196.874], [13281.877], [57286.811], [14708...   \n",
       "\n",
       "   NRMSE_val_macro  NRMSE_val_std  \\\n",
       "0             0.41          0.304   \n",
       "\n",
       "                                    NRMSE_val_splits  MAE_val_macro  \\\n",
       "0  [[0.906], [0.358], [1.388], [0.643], [0.446], ...       12813.19   \n",
       "\n",
       "   MAE_val_std                                     MAE_val_splits  \\\n",
       "0    10119.042  [[8903.77], [12128.406], [47789.55], [11648.87...   \n",
       "\n",
       "   RE_val_macro  RE_val_std  \\\n",
       "0         0.433       0.243   \n",
       "\n",
       "                                       RE_val_splits  negative_val_macro  \\\n",
       "0  [[0.2], [0.4], [0.0], [0.2], [0.4], [0.6], [0....                 0.0   \n",
       "\n",
       "   negative_val_std                                negative_val_splits  \\\n",
       "0               0.0  [[0], [0], [0], [0], [0], [0], [0], [0], [0], ...   \n",
       "\n",
       "   R2_val_micro  RMSE_val_micro  MAE_val_micro  RE_val_micro  Negative_micro  \\\n",
       "0     -0.235133    19759.444919   12813.189569      0.433333               0   \n",
       "\n",
       "   NRMSE_val_micro  R2_diff_rel  RMSE_diff_rel  MAE_diff_rel  \n",
       "0         0.283941     2.056176       0.906963      1.047594  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_model, result_dict = model_train.train_cv(X=train_data['X_train_1'], y=train_data['target_train_1'],\n",
    "                                                  model=LinearRegression(),\n",
    "                                                  cv_type='ts',\n",
    "                                                  metric_best='R2_val',\n",
    "                                                  train_size=5,\n",
    "                                                  val_size=5)\n",
    "\n",
    "pd.DataFrame([result_dict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>features</th>\n",
       "      <th>importances</th>\n",
       "      <th>importances_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gsm zone mcd workplaces</td>\n",
       "      <td>-2.284</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gsm workplaces</td>\n",
       "      <td>1.534</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cluster</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gsm zone mcd population</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zone area shopping malls</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>zone area hospital</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gsm population</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>area hospital</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>area shopping malls</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    features  importances  importances_rel\n",
       "0    gsm zone mcd workplaces       -2.284            -0.49\n",
       "1             gsm workplaces        1.534             0.33\n",
       "2                    cluster        0.321             0.07\n",
       "3    gsm zone mcd population        0.299             0.06\n",
       "4  zone area shopping malls         0.130             0.03\n",
       "5         zone area hospital        0.057             0.01\n",
       "6             gsm population        0.047             0.01\n",
       "7              area hospital       -0.029            -0.01\n",
       "8        area shopping malls        0.002             0.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train.get_feature_importance_df(X=train_data['X_train_1'], model=trained_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "___"
   ]
  }
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